diff --git a/docs/source/_autosummary/pvdeg.geospatial.plot_sparse_analysis_land.rst b/docs/source/_autosummary/pvdeg.geospatial.plot_sparse_analysis_land.rst
new file mode 100644
index 0000000..16e8a98
--- /dev/null
+++ b/docs/source/_autosummary/pvdeg.geospatial.plot_sparse_analysis_land.rst
@@ -0,0 +1,6 @@
+pvdeg.geospatial.plot\_sparse\_analysis\_land
+=============================================
+
+.. currentmodule:: pvdeg.geospatial
+
+.. autofunction:: plot_sparse_analysis_land
\ No newline at end of file
diff --git a/docs/source/_autosummary/pvdeg.geospatial.rst b/docs/source/_autosummary/pvdeg.geospatial.rst
index 4524da2..159523f 100644
--- a/docs/source/_autosummary/pvdeg.geospatial.rst
+++ b/docs/source/_autosummary/pvdeg.geospatial.rst
@@ -34,6 +34,7 @@ pvdeg.geospatial
       pvdeg.geospatial.plot_Europe
       pvdeg.geospatial.plot_USA
       pvdeg.geospatial.plot_sparse_analysis
+      pvdeg.geospatial.plot_sparse_analysis_land
       pvdeg.geospatial.start_dask
       pvdeg.geospatial.template_parameters
       pvdeg.geospatial.zero_template
@@ -157,6 +158,13 @@ pvdeg.geospatial
    .. minigallery:: pvdeg.geospatial.plot_sparse_analysis
        :add-heading:
 
+   .. autofunction:: plot_sparse_analysis_land
+
+   .. _sphx_glr_backref_pvdeg.geospatial.plot_sparse_analysis_land:
+
+   .. minigallery:: pvdeg.geospatial.plot_sparse_analysis_land
+       :add-heading:
+
    .. autofunction:: start_dask
 
    .. _sphx_glr_backref_pvdeg.geospatial.start_dask:
diff --git a/docs/source/_autosummary/pvdeg.utilities.display_json.rst b/docs/source/_autosummary/pvdeg.utilities.display_json.rst
new file mode 100644
index 0000000..a59150c
--- /dev/null
+++ b/docs/source/_autosummary/pvdeg.utilities.display_json.rst
@@ -0,0 +1,6 @@
+pvdeg.utilities.display\_json
+=============================
+
+.. currentmodule:: pvdeg.utilities
+
+.. autofunction:: display_json
\ No newline at end of file
diff --git a/docs/source/_autosummary/pvdeg.utilities.linear_normalize.rst b/docs/source/_autosummary/pvdeg.utilities.linear_normalize.rst
new file mode 100644
index 0000000..8fae071
--- /dev/null
+++ b/docs/source/_autosummary/pvdeg.utilities.linear_normalize.rst
@@ -0,0 +1,6 @@
+pvdeg.utilities.linear\_normalize
+=================================
+
+.. currentmodule:: pvdeg.utilities
+
+.. autofunction:: linear_normalize
\ No newline at end of file
diff --git a/docs/source/_autosummary/pvdeg.utilities.merge_sparse.rst b/docs/source/_autosummary/pvdeg.utilities.merge_sparse.rst
new file mode 100644
index 0000000..9da49b5
--- /dev/null
+++ b/docs/source/_autosummary/pvdeg.utilities.merge_sparse.rst
@@ -0,0 +1,6 @@
+pvdeg.utilities.merge\_sparse
+=============================
+
+.. currentmodule:: pvdeg.utilities
+
+.. autofunction:: merge_sparse
\ No newline at end of file
diff --git a/docs/source/_autosummary/pvdeg.utilities.read_material.rst b/docs/source/_autosummary/pvdeg.utilities.read_material.rst
new file mode 100644
index 0000000..875bb77
--- /dev/null
+++ b/docs/source/_autosummary/pvdeg.utilities.read_material.rst
@@ -0,0 +1,6 @@
+pvdeg.utilities.read\_material
+==============================
+
+.. currentmodule:: pvdeg.utilities
+
+.. autofunction:: read_material
\ No newline at end of file
diff --git a/docs/source/_autosummary/pvdeg.utilities.rst b/docs/source/_autosummary/pvdeg.utilities.rst
index fe0b340..95e47e5 100644
--- a/docs/source/_autosummary/pvdeg.utilities.rst
+++ b/docs/source/_autosummary/pvdeg.utilities.rst
@@ -21,17 +21,22 @@ pvdeg.utilities
       
       pvdeg.utilities.compare_templates
       pvdeg.utilities.convert_tmy
+      pvdeg.utilities.display_json
       pvdeg.utilities.fix_metadata
       pvdeg.utilities.geospatial_from_csv
       pvdeg.utilities.get_kinetics
       pvdeg.utilities.get_state_bbox
       pvdeg.utilities.gid_downsampling
+      pvdeg.utilities.linear_normalize
+      pvdeg.utilities.merge_sparse
       pvdeg.utilities.meta_as_dict
       pvdeg.utilities.new_id
       pvdeg.utilities.nrel_kestrel_check
       pvdeg.utilities.quantile_df
+      pvdeg.utilities.read_material
       pvdeg.utilities.remove_scenario_filetrees
       pvdeg.utilities.restore_gids
+      pvdeg.utilities.search_json
       pvdeg.utilities.strip_normalize_tmy
       pvdeg.utilities.tilt_azimuth_scan
       pvdeg.utilities.ts_gid_df
@@ -65,6 +70,13 @@ pvdeg.utilities
    .. minigallery:: pvdeg.utilities.convert_tmy
        :add-heading:
 
+   .. autofunction:: display_json
+
+   .. _sphx_glr_backref_pvdeg.utilities.display_json:
+
+   .. minigallery:: pvdeg.utilities.display_json
+       :add-heading:
+
    .. autofunction:: fix_metadata
 
    .. _sphx_glr_backref_pvdeg.utilities.fix_metadata:
@@ -100,6 +112,20 @@ pvdeg.utilities
    .. minigallery:: pvdeg.utilities.gid_downsampling
        :add-heading:
 
+   .. autofunction:: linear_normalize
+
+   .. _sphx_glr_backref_pvdeg.utilities.linear_normalize:
+
+   .. minigallery:: pvdeg.utilities.linear_normalize
+       :add-heading:
+
+   .. autofunction:: merge_sparse
+
+   .. _sphx_glr_backref_pvdeg.utilities.merge_sparse:
+
+   .. minigallery:: pvdeg.utilities.merge_sparse
+       :add-heading:
+
    .. autofunction:: meta_as_dict
 
    .. _sphx_glr_backref_pvdeg.utilities.meta_as_dict:
@@ -128,6 +154,13 @@ pvdeg.utilities
    .. minigallery:: pvdeg.utilities.quantile_df
        :add-heading:
 
+   .. autofunction:: read_material
+
+   .. _sphx_glr_backref_pvdeg.utilities.read_material:
+
+   .. minigallery:: pvdeg.utilities.read_material
+       :add-heading:
+
    .. autofunction:: remove_scenario_filetrees
 
    .. _sphx_glr_backref_pvdeg.utilities.remove_scenario_filetrees:
@@ -142,6 +175,13 @@ pvdeg.utilities
    .. minigallery:: pvdeg.utilities.restore_gids
        :add-heading:
 
+   .. autofunction:: search_json
+
+   .. _sphx_glr_backref_pvdeg.utilities.search_json:
+
+   .. minigallery:: pvdeg.utilities.search_json
+       :add-heading:
+
    .. autofunction:: strip_normalize_tmy
 
    .. _sphx_glr_backref_pvdeg.utilities.strip_normalize_tmy:
diff --git a/docs/source/_autosummary/pvdeg.utilities.search_json.rst b/docs/source/_autosummary/pvdeg.utilities.search_json.rst
new file mode 100644
index 0000000..11f7324
--- /dev/null
+++ b/docs/source/_autosummary/pvdeg.utilities.search_json.rst
@@ -0,0 +1,6 @@
+pvdeg.utilities.search\_json
+============================
+
+.. currentmodule:: pvdeg.utilities
+
+.. autofunction:: search_json
\ No newline at end of file
diff --git a/docs/source/whatsnew/index.rst b/docs/source/whatsnew/index.rst
index 78b3536..8298a18 100644
--- a/docs/source/whatsnew/index.rst
+++ b/docs/source/whatsnew/index.rst
@@ -4,7 +4,8 @@ What's New
 ==========
 PVDegradationTools (pvdeg) change log:
 
-
+.. include:: releases/v0.4.3.rst
+.. include:: releases/v0.4.2.rst
 .. include:: releases/v0.4.1.rst
 .. include:: releases/v0.4.0.rst
 .. include:: releases/v0.3.3.rst
diff --git a/docs/source/whatsnew/releases/v0.4.2.rst b/docs/source/whatsnew/releases/v0.4.2.rst
index 597e551..d165d41 100644
--- a/docs/source/whatsnew/releases/v0.4.2.rst
+++ b/docs/source/whatsnew/releases/v0.4.2.rst
@@ -13,4 +13,4 @@ Tests
 Contributors
 ~~~~~~~~~~~~
 * Martin Springer (:ghuser:`martin-springer`)
-* Aidan Wesley (:ghuser:`AidanWesley``)
+* Aidan Wesley (:ghuser:`AidanWesley``)
\ No newline at end of file
diff --git a/docs/source/whatsnew/releases/v0.4.3.rst b/docs/source/whatsnew/releases/v0.4.3.rst
new file mode 100644
index 0000000..2b2691d
--- /dev/null
+++ b/docs/source/whatsnew/releases/v0.4.3.rst
@@ -0,0 +1,14 @@
+v0.4.3 (2024-10-10)
+===================
+
+Enhancements
+------------
+Suite of utility functions to facilitate accessing material parameter json files.
+
+* ``pvdeg.utilities.read_material`` creates a public api to replace the private ``pvdeg.untility._read_material`` function (to be deprecated soon)  
+* ``pvdeg.utilities.search_json`` to search jsons and identify keys for desired materials.  
+* ``pvdeg.utilities.display_json`` to view 2 level jsons in Jupyter Notebooks using HTML.  
+
+Contributors
+-----------
+* Tobin Ford (:ghuser:`tobin-ford`)
\ No newline at end of file
diff --git a/pvdeg/__init__.py b/pvdeg/__init__.py
index 6613597..0b770fc 100644
--- a/pvdeg/__init__.py
+++ b/pvdeg/__init__.py
@@ -12,7 +12,7 @@
 from . import humidity
 from . import letid
 from . import montecarlo
-from . import scenario
+from .scenario import Scenario, GeospatialScenario
 from . import spectral
 from . import symbolic
 from . import standards
diff --git a/pvdeg/data/H2Opermeation.json b/pvdeg/data/H2Opermeation.json
index 4f2502a..4e72e19 100644
--- a/pvdeg/data/H2Opermeation.json
+++ b/pvdeg/data/H2Opermeation.json
@@ -22,11 +22,11 @@
 		"source": "unpublished measurements",
 		"Fickian": true,
 		"Ead": 61.4781422330562,
-		"Do": 25790.6020262449,
+		"Do": 257.906020262449,
 		"Eas": 5.88752263485353,
-		"So": 0.00982242435416737,
-		"Eap": 67.3656648679097,
-		"Po": 5559396276.60964
+		"So": 0.0982242435416737,
+		"Eap": 66.9611315410624,
+		"Po": 189338932521.637
     },
     "W003": {
 		"name": "Coveme",
diff --git a/pvdeg/data/O2permeation.json b/pvdeg/data/O2permeation.json
index 9ecc24d..18eedd3 100644
--- a/pvdeg/data/O2permeation.json
+++ b/pvdeg/data/O2permeation.json
@@ -36,10 +36,10 @@
         "Fickian": true,
         "Ead": 29.43112031,
         "Do": 0.129061678,
-        "Eas": 32.3137806,
-        "So": 87.81142774,
-        "Eap": 61.7449009,
-        "Po": 97917899126
+        "Eas": 16.6314948252219,
+        "So": 0.136034525059804,
+        "Eap": 49.1083457348515,
+        "Po": 528718258.338532
     },
     "OX004": {
         "name": "AAA polyamide backsheet",
diff --git a/pvdeg/diffusion.py b/pvdeg/diffusion.py
index c299def..e40f26d 100644
--- a/pvdeg/diffusion.py
+++ b/pvdeg/diffusion.py
@@ -8,25 +8,24 @@
 from pvdeg import DATA_DIR
 from numba import jit
 import numpy as np
+from typing import Callable
 
 def esdiffusion(
     temperature, 
-    edge_seal=None, 
-    encapsulant=None, 
+    edge_seal="OX005", 
+    encapsulant="OX003", 
     edge_seal_width=1.5, 
     encapsulant_width=10, 
     seal_nodes=20, 
     encapsulant_nodes=50,
     press = 0.209,
     repeat = 1,
-    Dos=None,
-    Eads=None,
-    Sos=None,
-    Eass=None,
-    Doe=None,
-    Eade=None,
-    Soe=None,
-    Ease=None,
+    Dos=None, Eads=None, Sos=None,Eass=None, Doe=None, Eade=None, Soe=None, Ease=None,
+    react_func = None,
+    deg_func = None,
+    deg = None,
+    perm = None,
+    printout = True,
     **kwarg
     ):
 
@@ -58,40 +57,68 @@ def esdiffusion(
         This is the partial pressure of oxygen.
     repeat : integer, optional
         This is the number of times to do the calculation for the whole dataset. E.g. repeat the 1-y data for 10 years.
+    react_func : string, optional
+        This is the name of the function that will be calculating the consumption of oxygen.
+    deg_func :string, optional
+        This is the name of the function that will be calculating the degradation.
+    printout : Boolean
+        This allows you to suppress printing messages during code execution by setting it to false.
+    deg : Numpy Array
+        One can send in an array with predefined degradation data already in it if desired. 
+        I.e. you can have some pre degradation or areas that require more degradation.
+    perm : Numpy Array
+        One can send in an array with the permeant already in it if desired.
     kwargs : dict, optional
-        If es or enc are left at 'None' then the use parameters, Dos, Eads, Sos, Eass, Doe, Eade, Soe, Ease in units of
+        If edge_seal or encapsulant are set at 'None' then you can enter your own parameters for, Dos, Eads, Sos, Eass, Doe, Eade, Soe, Ease in units of
         [cm²/s], [g/cm³], or [kJ/mol] for diffusivity, solubility, or activation energy respectively. If specific parameters are provided,
-        then the JSON ones can be overridden.
+        then the JSON ones will be overridden.
+        Should also contain any key word arguments that need to be passed to the function calculating consumption of the permeant or degradation.
 
     Returns
     -------
     ingress_data : pandas.DataFrame
-        This will give the concentration profile as a function of temperature along with degradation parameters in futur iterations..
+        This will give the concentration profile as a function of time.
+        If there is a degradation function called, this data will also be inclueded on a node by node basis under a third index.
     """
 
     with open(os.path.join(DATA_DIR, "O2permeation.json")) as user_file:
         O2 = json.load(user_file)
         user_file.close()
-    # O2
-    if edge_seal == None:
-        esp = O2.get("OX005")  # This is the number for the edge seal in the json file
-    else:
-        esp = O2.get(edge_seal)
+    with open(os.path.join(DATA_DIR, "H2Opermeation.json")) as user_file:
+        H2O = json.load(user_file)
+        user_file.close()
 
-    if encapsulant == None:
-        encp = O2.get(
-            "OX003"
-        )  # This is the number for the encapsulant in the json file
+    if edge_seal[0:2]=="OX":
+        esp = O2.get(edge_seal)
+        if printout:
+            print("Oxygen ingress parameters loaded for the edge seal.")
     else:
-        encp = O2.get(encapsulant)
+        if edge_seal[0:1]=="W":
+            esp = H2O.get(edge_seal)
+            if printout:
+                print("Water ingress parameters loaded for the edge seal.")
+        else:
+            print("Edge seal material not found")
 
-    try:
-        print("The edge seal is", esp.get("name"), ".")
-        print("The encapsulant is", encp.get("name"), ".")
-    except:
-        print("")
+    if encapsulant[0:2]=="OX":
+        encp = O2.get(encapsulant)
+        if printout:
+            print("Oxygen ingress parameters loaded for the encapsulant.")
+    else:
+        if encapsulant[0:1]=="W":
+            encp = H2O.get(encapsulant)
+            if printout:
+                print("Water ingress parameters loaded for the eencapsulant.")
+        else:
+            print("Encapsulant material not found")
+    if printout:
+        try:
+            print("The edge seal is", esp.get("name"), ".")
+            print("The encapsulant is", encp.get("name"), ".")
+        except:
+            print("Unknown material selected.")
 
-    # These are the edge seal oxygen permeation parameters
+    # These are the edge seal oxygen or water permeation parameters
     if Dos == None:
         Dos = esp.get("Do")
     if Eads == None:
@@ -136,12 +163,14 @@ def esdiffusion(
 
     perm_mid = np.array(
         np.zeros((seal_nodes + encapsulant_nodes + 3)), dtype=np.float64
-    )  # This is the profile at a transition point between output points.
-    perm = np.array(
-        np.zeros(
-            (len(temperature) * repeat - repeat + 1, seal_nodes + encapsulant_nodes + 3), dtype=np.float64
-        )
-    )  # It adds in two nodes for the interface concentration for both materials and one for the hour column.
+        )  # This is the profile at a transition point between output points.
+    if perm == None:
+        perm = np.array(
+            np.zeros(
+                (len(temperature) * repeat - repeat + 1, seal_nodes + encapsulant_nodes + 3), dtype=np.float64
+            )
+        )  # It adds in two nodes for the interface concentration for both materials and one for the hour column.
+
     temperature = pd.DataFrame(
         temperature, columns=["module_temperature", "time", "time_step"]
     )  # This adds the number of time steps to be used as a subdivision between data points. [s]
@@ -163,7 +192,8 @@ def esdiffusion(
                 time_step[row] = np.trunc(fos / f_max) + 1
             else:
                 time_step[row] = np.trunc(foe / f_max) + 1
-
+    if deg_func != None and deg == None: # Sets up an array to do the degradation calculation.
+        deg=perm
     perm[0][1] = Sos * np.exp(-Eass / met_data[0][0])
     perm_mid = perm[0]
     for rp_num in range(repeat):
@@ -187,10 +217,9 @@ def esdiffusion(
                 )
                 # Cs edge seal/Ce encapsulant
                 r1 = so * np.exp(-eas / (met_data[row][0] + dtemp * mid_point))
-                r2 = (
-                    dod * np.exp(-ead / (met_data[row][0] + dtemp * mid_point))
-                    * r1 * encapsulant_width / edge_seal_width
-                )  # Ds/De*Cs/Ce*We/Ws
+                r2 = dod * np.exp(-ead / (met_data[row][0] + dtemp * mid_point)
+                                  )* r1 * encapsulant_width / edge_seal_width
+                  # Ds/De*Cs/Ce*We/Ws
                 # Calculates the edge seal nodes. Adjusted to not calculate ends and to have the first node be temperature.
                 for node in range(2, seal_nodes):
                     perm[row + 1 + rp_row][node] = perm_mid[node] + fos * (
@@ -201,36 +230,54 @@ def esdiffusion(
                     perm[row + 1 + rp_row][node] = perm_mid[node] + foe * (
                         perm_mid[node - 1] + perm_mid[node + 1] - 2 * perm_mid[node]
                     )
-                # Calculates the center encapsulant node. Accounts for temperature and two interfade nodes.
+                # Calculates the center encapsulant node. Accounts for temperature and two interface nodes.
                 perm[row + 1 + rp_row][encapsulant_nodes + seal_nodes + 2] = perm_mid[
-                    encapsulant_nodes + seal_nodes + 2
-                ] + 2 * foe * (perm_mid[encapsulant_nodes + seal_nodes + 1] - perm_mid[encapsulant_nodes + seal_nodes + 2])
+                    encapsulant_nodes + seal_nodes + 2] + 2 * foe * (
+                        perm_mid[encapsulant_nodes + seal_nodes + 1] - 
+                        perm_mid[encapsulant_nodes + seal_nodes + 2])
+                
                 # Calculated edge seal node adjacent to the first encapsulant node. Node numbers shifted.
                 perm[row + 1 + rp_row][seal_nodes] = perm_mid[seal_nodes] + fos * (
                     perm_mid[seal_nodes - 1]
                     + perm_mid[seal_nodes + 3] * r1 * 2 / (1 + r2)
-                    - perm_mid[seal_nodes] * (1 + 2 / (1 + r2))
-                )
+                    - perm_mid[seal_nodes] * (1 + 2 / (1 + r2)))
+  
                 # Calculated encapsulant node adjacent to the last edge seal node. Node numbers shifted.
                 perm[row + 1 + rp_row][seal_nodes + 3] = perm_mid[seal_nodes + 3] + foe * (
                     perm_mid[seal_nodes] / r1 * 2 / (1 + 1 / r2)
                     + perm_mid[seal_nodes + 4]
-                    - perm_mid[seal_nodes + 3] * (1 + 2 / (1 + 1 / r2))
-                )
+                    - perm_mid[seal_nodes + 3] * (1 + 2 / (1 + 1 / r2)))
+                
                 # sets the concentration at the edge seal to air interface.
                 perm[row + 1 + rp_row][1] = Sos * np.exp(
                     -Eass / (met_data[row + 1][0] + dtemp * mid_point)
                 )
+
+
+                # Runs the degradation calculation.            
+                if deg_func != None:
+                    print('oops')
+                # Runs the reaction with permeant function.
+                if react_func != None:
+                    print('oops')
+                    
                 perm_mid = perm[row + 1 + rp_row]
 
-            # calculate edge seal at interface to encapsulant.
-            perm[row + 1 + rp_row][seal_nodes + 1] = (
-                perm_mid[seal_nodes + 3] / r2 * r1 + perm_mid[seal_nodes]
-            ) / (1 / r2 + 1)
-            # calculate encapsulant at interface to the edge seal.
-            perm[row + 1 + rp_row][seal_nodes + 2] = perm[row + 1 + rp_row][seal_nodes + 1] / r1
+            # Calculate edge seal at interface to encapsulant.
+            # Blocked out code did weird things and was based on equal fluxes. Actually using a simple averaging. This looks better and is not used in the diffusion calculations.
+            #perm[row + 1 + rp_row][seal_nodes + 1] = (perm_mid[seal_nodes + 3]*r1  
+            #                                          + perm_mid[seal_nodes]*r2) / (1+r2)
+            perm[row + 1 + rp_row][seal_nodes + 1] = perm_mid[seal_nodes ]+(perm_mid[seal_nodes]-perm_mid[seal_nodes-1])/2
+
+            # Calculate encapsulant at interface to the edge seal.
+            #perm[row + 1 + rp_row][seal_nodes + 2] = perm[row + 1 + rp_row][seal_nodes + 1] / r1
+            perm[row + 1 + rp_row][seal_nodes + 2] = perm_mid[seal_nodes + 3]+(perm_mid[seal_nodes + 4]-perm_mid[seal_nodes+3])/2
+
+            # Puts in the time for the first column.
             perm[row + 1 + rp_row][0] = rp_time + met_data[row + 1][1]
 
+
+
         # Because it is cycling around, it needs to start with the last temperature.
         met_data[0][0] = met_data[met_data.shape[0] - 1][0]
 
diff --git a/pvdeg/geospatial.py b/pvdeg/geospatial.py
index d54877e..85455f6 100644
--- a/pvdeg/geospatial.py
+++ b/pvdeg/geospatial.py
@@ -85,7 +85,7 @@ def start_dask(hpc=None):
 
     client = Client(cluster)
     print("Dashboard:", client.dashboard_link)
-    client.wait_for_workers(n_workers=1)
+    # client.wait_for_workers(n_workers=1)
 
     return client
 
@@ -898,7 +898,7 @@ def elevation_stochastic_downselect(
         Options : `'mean'`, `'sum'`, `'median'`
     normalization : str, (default = 'linear')
         function to apply when normalizing weights. Logarithmic uses log_e/ln
-        options : `'linear'`, `'logarithmic'`, '`exponential'`
+        options : `'linear'`, `'log'`, '`exp'`, `'invert-linear'`
 
     Returns:
     --------
@@ -927,7 +927,10 @@ def elevation_stochastic_downselect(
 
 
 def interpolate_analysis(
-    result: xr.Dataset, data_var: str, method="nearest"
+    result: xr.Dataset,
+    data_var: str,
+    method="nearest",
+    resolution=100j,
 ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
     """
     Interpolate sparse spatial result data against DataArray coordinates.
@@ -935,6 +938,9 @@ def interpolate_analysis(
 
     Parameters:
     -----------
+    resolution: complex
+        Change the amount the input is interpolated.
+        For more interpolation set higher (200j is more than 100j)
 
     Result:
     -------
@@ -951,8 +957,8 @@ def interpolate_analysis(
     )  # probably a nicer way to do this
 
     grid_lat, grid_lon = np.mgrid[
-        df["latitude"].min() : df["latitude"].max() : 100j,
-        df["longitude"].min() : df["longitude"].max() : 100j,
+        df["latitude"].min() : df["latitude"].max() : resolution,
+        df["longitude"].min() : df["longitude"].max() : resolution,
     ]
 
     grid_z = griddata(data[:, 0:2], data[:, 2], xi=(grid_lat, grid_lon), method=method)
@@ -960,13 +966,46 @@ def interpolate_analysis(
     return grid_z, grid_lat, grid_lon
 
 
-def plot_sparse_analysis(result: xr.Dataset, data_var: str, method="nearest") -> None:
+# api could be updated to match that of plot_USA
+def plot_sparse_analysis(
+    result: xr.Dataset,
+    data_var: str,
+    method="nearest",
+    resolution: complex = 100j,
+    figsize: tuple = (10, 8),
+    show_plot: bool = False,
+) -> None:
+    """
+    Plot the output of a sparse geospatial analysis using interpolation.
+
+    Parameters
+    -----------
+    result: xr.Dataset
+        xarray dataset in memory containing coordinates['longitude', 'latitude'] and at least one datavariable.
+    data_var: str
+        name of datavariable to plot from result
+    method: str
+        interpolation method.
+        Options: `'nearest', 'linear', 'cubic'`
+        See [`scipy.interpolate.griddata`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html)
+    resolution: complex
+        Change the amount the input is interpolated.
+        For more interpolation set higher (200j is more than 100j)
+
+    Returns
+    -------
+    fig, ax: tuple
+        matplotlib figure and axes of plot
+    """
+
     grid_values, lat, lon = interpolate_analysis(
-        result=result, data_var=data_var, method=method
+        result=result, data_var=data_var, method=method, resolution=resolution
     )
 
-    fig = plt.figure()
-    ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False)
+    fig = plt.figure(figsize=figsize)
+    ax = fig.add_axes(
+        [0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False
+    )  # these should be the same ccrs
     ax.patch.set_visible(False)
 
     extent = [lon.min(), lon.max(), lat.min(), lat.max()]
@@ -976,8 +1015,8 @@ def plot_sparse_analysis(result: xr.Dataset, data_var: str, method="nearest") ->
         extent=extent,
         origin="lower",
         cmap="viridis",
-        transform=ccrs.PlateCarree(),
-    )  # should this be trnsposed
+        transform=ccrs.PlateCarree(),  # why are ccrs different
+    )
 
     shapename = "admin_1_states_provinces_lakes"
     states_shp = shpreader.natural_earth(
@@ -994,7 +1033,73 @@ def plot_sparse_analysis(result: xr.Dataset, data_var: str, method="nearest") ->
     cbar = plt.colorbar(img, ax=ax, orientation="vertical", fraction=0.02, pad=0.04)
     cbar.set_label("Value")
 
-    plt.title("Interpolated Heatmap")
+    plt.title(f"Interpolated Sparse Analysis, {data_var}")
     plt.xlabel("Longitude")
     plt.ylabel("Latitude")
-    plt.show()
+
+    if show_plot:
+        plt.show()
+
+    return fig, ax
+
+
+def plot_sparse_analysis_land(
+    result: xr.Dataset,
+    data_var: str,
+    method="nearest",
+    resolution: complex = 100j,
+    figsize: tuple = (10, 8),
+    show_plot: bool = False,
+    proj=ccrs.PlateCarree(),
+):
+    import matplotlib.path as mpath
+    from cartopy.mpl.patch import geos_to_path
+
+    grid_values, lat, lon = interpolate_analysis(
+        result=result, data_var=data_var, method=method, resolution=resolution
+    )
+
+    fig = plt.figure(figsize=figsize)
+    ax = fig.add_axes([0, 0, 1, 1], projection=proj, frameon=False)
+    ax.patch.set_visible(False)
+
+    extent = [lon.min(), lon.max(), lat.min(), lat.max()]
+    ax.set_extent(extent, crs=proj)
+
+    mesh = ax.pcolormesh(lon, lat, grid_values, transform=proj, cmap="viridis")
+
+    land_path = geos_to_path(list(cfeature.LAND.geometries()))
+    land_path = mpath.Path.make_compound_path(*land_path)
+    plate_carre_data_transform = proj._as_mpl_transform(ax)
+    mesh.set_clip_path(land_path, plate_carre_data_transform)
+
+    shapename = "admin_1_states_provinces_lakes"
+    states_shp = shpreader.natural_earth(
+        resolution="110m", category="cultural", name=shapename
+    )
+
+    ax.add_geometries(
+        shpreader.Reader(states_shp).geometries(),
+        proj,
+        facecolor="none",
+        edgecolor="black",
+        linestyle=":",
+    )
+
+    cbar = plt.colorbar(mesh, ax=ax, orientation="vertical", fraction=0.02, pad=0.04)
+    cbar.set_label("Value")
+
+    utilities._add_cartopy_features(
+        ax=ax,
+        features=[
+            cfeature.BORDERS,
+            cfeature.COASTLINE,
+            cfeature.LAND,
+            cfeature.OCEAN,
+        ],
+    )
+
+    if show_plot:
+        plt.show()
+
+    return fig, ax
diff --git a/pvdeg/scenario.py b/pvdeg/scenario.py
index 1e4d1e7..aab8afa 100644
--- a/pvdeg/scenario.py
+++ b/pvdeg/scenario.py
@@ -1,5 +1,7 @@
 """Class to define an analysis scenario."""
 
+import matplotlib.axes
+import matplotlib.figure
 import pvdeg
 from pvdeg import utilities
 
@@ -16,16 +18,13 @@
 import matplotlib.pyplot as plt
 from collections import OrderedDict
 from copy import deepcopy
-from typing import List, Union, Optional, Tuple, Callable
+from typing import List, Union, Optional, Tuple, Callable, overload
 from functools import partial
 import pprint
 from IPython.display import display, HTML
 import cartopy.crs as ccrs
 import cartopy.feature as cfeature
 
-### premade scenario with locations of interest. Ask Mike?
-# TODO: geospatial reset weather and addLocation from gids.
-
 
 class Scenario:
     """
@@ -792,7 +791,7 @@ def plot(
         start_time: Optional[dt] = None,
         end_time: Optional[dt] = None,
         title: str = "",
-    ) -> None:
+    ) -> tuple:
         """
         Plot scenario results along an axis using `Scenario.extract`
 
@@ -844,7 +843,8 @@ def plot(
 
         Returns:
         -------
-        None
+        fig, ax: tuple
+            matplotlib figure and axis objects
 
         See Also:
         ---------
@@ -866,6 +866,8 @@ def plot(
         ax.set_title(f"{self.name} : {title}")
         plt.show()
 
+        return fig, ax
+
     def _ipython_display_(self):
         file_url = "no file provided" 
         if self.path:
@@ -1106,7 +1108,12 @@ def __init__(
         self.hpc = hpc
 
     def __eq__(self, other):
-        raise NotImplementedError("cannot directly compare geospatial scenario objects")
+        raise NotImplementedError("""
+            Cannot directly compare pvdeg.GeospatialScenario objects
+            due to larger than memory/out of memory datasets stored in 
+            GeospatialScenario.weather_data attribute.
+            """)
+                                 
 
     # add restoring from gids functionality from nsrdb
     def addLocation(
@@ -1181,7 +1188,6 @@ def addLocation(
             "attributes": nsrdb_attributes,
         }
 
-        # nsrdb_fp = r"/datasets/NSRDB" # kestrel directory
         geo_weather, geo_meta = pvdeg.weather.get(
             weather_db, geospatial=True, **weather_arg
         )
@@ -1193,27 +1199,38 @@ def addLocation(
             bbox_gids = pvdeg.geospatial.apply_bounding_box(geo_meta, **bbox_kwarg)
             geo_meta = geo_meta.loc[bbox_gids]
 
+
+        #                Downselect by Region
+        # ======================================================
+
         # string to list whole word list or keep list
         toList = lambda s: s if isinstance(s, list) else [s]
 
         if country:
             countries = toList(country)
+            self._check_set(countries, set(geo_meta["country"]))
             geo_meta = geo_meta[geo_meta["country"].isin(countries)]
+
+
         if state:
             states = toList(state)
             states = [
                 pvdeg.utilities._get_state(entry) if len(entry) == 2 else entry
                 for entry in states
             ]
+
+            self._check_set(states, set(geo_meta["state"]))
             geo_meta = geo_meta[geo_meta["state"].isin(states)]
+
+
         if county:
             if isinstance(county, str):
                 county = toList(county)
 
+            self._check_set(county, set(geo_meta["county"]))
             geo_meta = geo_meta[geo_meta["county"].isin(county)]
+        # ======================================================
 
-        # we don't downsample weather data until this runs
-        # because on NSRDB we are storing weather OUT of MEMORY with dask
         geo_meta, geo_gids = pvdeg.utilities.gid_downsampling(
             geo_meta, downsample_factor
         )
@@ -1451,8 +1468,8 @@ def downselect_elevation_stochastic(
             method to calculate elevation weights for each point.
             Options : `'mean'`, `'sum'`, `'median'`
         normalization : str, (default = 'linear')
-            function to apply when normalizing weights. Logarithmic uses log_e/ln
-            options : `'linear'`, `'logarithmic'`, '`exponential'`
+            function to apply when normalizing weights. Logarithmic uses $log_e$, $ln$
+            options : `'linear'`, `'log'`, '`exp'`, `'invert-linear'`
 
         Returns:
         --------
@@ -1533,7 +1550,7 @@ def coords_tonumpy(self) -> np.array:
 
         return coords
 
-    def geospatial_data(self) -> tuple[xr.Dataset, pd.DataFrame]:
+    def get_geospatial_data(self) -> tuple[xr.Dataset, pd.DataFrame]:
         """
         Extract the geospatial weather dataset and metadata dataframe from the scenario object
 
@@ -1548,7 +1565,7 @@ def geospatial_data(self) -> tuple[xr.Dataset, pd.DataFrame]:
 
         Returns:
         --------
-        (weather_data, meta_data): (xr.Dataset, pd.DataFrame)
+        (weather_data, meta_data): tuple[xr.Dataset, pd.DataFrame]
             A tuple of weather data as an `xarray.Dataset` and the corresponding meta data as a dataframe.
         """
         # downsample here, not done already happens at pipeline runtime
@@ -1557,6 +1574,26 @@ def geospatial_data(self) -> tuple[xr.Dataset, pd.DataFrame]:
         )
         return geo_weather_sub, self.meta_data
 
+    # @dispatch(xr.Dataset, pd.DataFrame)
+    def set_geospatial_data(self, weather_ds: xr.Dataset, meta_df: pd.DataFrame ) -> None:
+        """
+        Parameters:
+        -----------
+        weather_ds : xarray.Dataset
+            Dataset containing weather data for a block of gids.
+        meta_df : pandas.DataFrame
+            DataFrame containing meta data for a block of gids.
+
+        Modifies:
+        ----------
+        self.weather_data
+            sets to weather_ds
+        self.meta_data
+            sets to meta_df
+        """
+        self.weather_data, self.meta_data = weather_ds, meta_df
+
+
     def addJob(
         self,
         func: Callable = None,
@@ -1580,7 +1617,6 @@ def addJob(
             set flag to get a userWarning notifying the user of the job added
            to the pipeline in method call. ``default = False``
         """
-        # check if we can do geospatial analyis on desired function
         try:
             pvdeg.geospatial.template_parameters(func)
         except ValueError:
@@ -1590,10 +1626,6 @@ def addJob(
 
         geo_job_dict = {"geospatial_job": {"job": func, "params": func_params}}
 
-        # # UNTESTED
-        # if func_params:
-        #     geo_job_dict.update(func_params)
-
         self.pipeline = geo_job_dict
 
         if see_added:
@@ -1740,7 +1772,8 @@ def getValidRegions(
                 f"self.geospatial should be True. Current value = {self.geospatial}"
             )
 
-        discard_weather, meta_df = Scenario._get_geospatial_data(year=2022)
+        # discard_weather, meta_df = Scenario._get_geospatial_data(year=2022)
+        discard_weather, meta_df = self._get_geospatial_data(year=2022)
 
         if country:
             meta_df = meta_df[meta_df["country"] == country]
@@ -1761,13 +1794,15 @@ def plot(self):
             "The 'plot' method is not accessible in GeospatialScenario, only in Scenario"
         )
 
+    import matplotlib
+
     def plot_coords(
         self,
         coord_1: Optional[tuple[float]] = None,
         coord_2: Optional[tuple[float]] = None,
         coords: Optional[np.ndarray[float]] = None,
         size: Union[int, float] = 1,
-    ) -> None:
+    ) -> tuple[matplotlib.figure, matplotlib.axes]:
         """
         Plot lat-long coordinate pairs on blank map. Quickly view
         geospatial datapoints before your analysis.
@@ -1789,6 +1824,11 @@ def plot_coords(
         size : float
             matplotlib scatter point size. Without any downsampling NSRDB
             points will siginficantly overlap.
+
+        Returns:
+        --------
+        fig, ax
+            matplotlib figure and axis
         """
         fig = plt.figure(figsize=(15, 10))
         ax = plt.axes(projection=ccrs.PlateCarree())
@@ -1811,6 +1851,9 @@ def plot_coords(
         plt.title(f"Coordinate Pairs from '{self.name}' Meta Data")
         plt.show()
 
+        return fig, ax
+
+
     def plot_meta_classification(
         self,
         col_name: str = None,
@@ -1818,7 +1861,7 @@ def plot_meta_classification(
         coord_2: Optional[tuple[float]] = None,
         coords: Optional[np.ndarray[float]] = None,
         size: Union[int, float] = 1,
-    ):
+    ) -> tuple[matplotlib.figure, matplotlib.axes]:
         """
         Plot classified lat-long coordinate pairs on map. Quicly view
         geospatial datapoints with binary classification in a meta_data
@@ -1844,6 +1887,11 @@ def plot_meta_classification(
         size : float
             matplotlib scatter point size. Without any downsampling NSRDB
             points will siginficantly overlap.
+
+        Returns:
+        --------
+        fig, ax
+            matplotlib figure and axis
         """
         if not col_name:
             raise ValueError("col_name cannot be none")
@@ -1892,11 +1940,13 @@ def plot_meta_classification(
         plt.legend()
         plt.show()
 
+        return fig, ax
+
     def plot_world(
         self,
         data_variable: str,
         cmap: str = "viridis",
-    ):
+    ) -> tuple[matplotlib.figure, matplotlib.axes]:
         da = (self.results)[data_variable]
 
         fig, ax = plt.subplots(
@@ -1915,6 +1965,8 @@ def plot_world(
         ax.add_feature(cfeature.LAKES, edgecolor="black")
         plt.show()
 
+        return fig, ax
+
     # test this
     def plot_USA(
         self,
@@ -1923,7 +1975,7 @@ def plot_USA(
         cmap: str = "viridis",
         vmin: Union[int, float] = 0,
         vmax: Optional[Union[int, float]] = None,
-    ):
+    ) -> tuple[matplotlib.figure, matplotlib.axes]:
         """
         Plot a vizualization of the geospatial scenario result.
         Only works on geospatial scenarios.
@@ -1957,6 +2009,19 @@ def plot_USA(
         fpath if fpath else [f"os.getcwd/{self.name}-{self.results[data_from_result]}"]
         fig.savefig()
 
+        return fig, ax
+
+
+    def _check_set(self, iterable, to_check: set):
+        """Check if iterable is a subset of to_check"""
+        if not isinstance(iterable, set):
+            iterable = set(iterable)
+
+        if not iterable.issubset(to_check):
+            raise ValueError(f"All of iterable: {iterable} does not exist in {to_check}")
+
+    
+
     def format_pipeline(self):
         pipeline_html = "<div>"
         if "geospatial_job" in self.pipeline:
@@ -2005,11 +2070,11 @@ def _ipython_display_(self):
             </div>
             <div>
                 <h3>Weather Dataset</h3>
-                {self.format_weather()}
+                {self.format_geo_weather()}
             </div>
             <div>
                 <h3>Meta Dataframe</h3>
-                {self.format_meta()}
+                {self.format_geo_meta()}
             </div>
         </div>
         <script>
@@ -2049,35 +2114,10 @@ def format_results(self):
         results_html += "</div>"
         return results_html
 
-    def format_meta(self):
+    def format_geo_meta(self):
         meta_data_html = ""
-        if isinstance(self.meta_data, pd.DataFrame):
-            if len(self.meta_data) > 10:
-                first_five = self.meta_data.head(5)
-                last_five = self.meta_data.tail(5)
-                ellipsis_row = pd.DataFrame(
-                    [["..."] * len(self.meta_data.columns)],
-                    columns=self.meta_data.columns,
-                )
 
-                # Create the custom index with ellipses
-                custom_index = np.concatenate(
-                    [
-                        np.arange(0, 5, dtype=object).astype(str),
-                        ["..."],
-                        np.arange(
-                            len(self.meta_data) - 5, len(self.meta_data), dtype=object
-                        ).astype(str),
-                    ]
-                )
-
-                # Concatenate the DataFrames
-                display_data = pd.concat(
-                    [first_five, ellipsis_row, last_five], ignore_index=True
-                )
-                display_data.index = custom_index
-            else:
-                display_data = self.meta_data
+        if self.meta_data is not None:
 
             meta_data_html = f"""
             <div id="meta_data" onclick="toggleVisibility('content_meta_data')" style="cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;">
@@ -2086,8 +2126,29 @@ def format_meta(self):
                     Meta Data
                 </h4>
             </div>
-            <div id="content_meta_data" style="display:none; margin-left: 20px; padding: 5px; background-color: #f0f0f0; color: #000;">
-                {display_data.to_html()}
+            <div id="content_meta_data" style="display:none; margin-left: 20px; padding: 5px;">
+                {self.meta_data._repr_html_()}
             </div>
             """
+
         return meta_data_html
+
+    def format_geo_weather(self):
+        weather_data_html = ""
+
+        if self.weather_data is not None:
+
+            weather_data_html = f"""
+            <div id="weather_data" onclick="toggleVisibility('content_weather_data')" style="cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;">
+                <h4 style="font-family: monospace; margin: 0;">
+                    <span id="arrow_content_weather_data" style="color: #b676c2;">►</span>
+                    Weather Data
+                </h4>
+            </div>
+            <div>
+            <div id="content_weather_data" style="display:none; margin-left: 20px; padding: 5px>
+                {self.weather_data._repr_html_()}
+            </div>
+            """
+
+        return weather_data_html
\ No newline at end of file
diff --git a/pvdeg/spectral.py b/pvdeg/spectral.py
index b66cb63..1e87fbe 100644
--- a/pvdeg/spectral.py
+++ b/pvdeg/spectral.py
@@ -4,6 +4,8 @@
 
 import pvlib
 import pandas as pd
+import inspect
+import warnings
 from pvdeg.decorators import geospatial_quick_shape
 
 
@@ -67,14 +69,19 @@ def solar_position(weather_df: pd.DataFrame, meta: dict) -> pd.DataFrame:
 def poa_irradiance(
     weather_df: pd.DataFrame,
     meta: dict,
+    module_mount="fixed",
     sol_position=None,
-    tilt=None,
-    azimuth=None,
+    dni_extra=None,
+    airmass=None,
+    albedo=0.25,
+    surface_type=None,
     sky_model="isotropic",
+    model_perez="allsitescomposite1990",
+    **kwargs_mount,
 ) -> pd.DataFrame:
     """
-    Calculate plane-of-array (POA) irradiance using pvlib based on weather data from the
-    National Solar Radiation Database (NSRDB) for a given location (gid).
+    Calculate plane-of-array (POA) irradiance using `pvlib.irradiance.get_total_irradiance` for different module mounts
+     as fixed tilt systems or tracked systems.
 
     Parameters
     ----------
@@ -82,56 +89,343 @@ def poa_irradiance(
         The file path to the NSRDB file.
     meta : dict
         The geographical location ID in the NSRDB file.
+    module_mount: string
+        Module mounting configuration. Can either be `fixed` for fixed tilt systems or
+        `single_axis` for single-axis tracker systems.
     sol_position : pd.DataFrame, optional
         pvlib.solarposition.get_solarposition Dataframe. If none is given, it will be calculated.
-    tilt : float, optional
-        The tilt angle of the PV panels in degrees, if None, the latitude of the
-        location is used.
-    azimuth : float, optional
-        The azimuth angle of the PV panels in degrees. Equatorial facing by default.
+    dni_extra : pd.Series, optional
+        Extra-terrestrial direct normal irradiance. If None, it will be calculated.
+    airmass : pd.Series, optional
+        Airmass values. If None, it will be calculated.
+    albedo : float, optional
+        Ground reflectance. Default is 0.25.
+    surface_type : str, optional
+        Type of surface for albedo calculation. If None, a default value will be used.
     sky_model : str, optional
-        The pvlib sky model to use, 'isotropic' by default.
+        Sky diffuse model to use. Default is `isotropic`.
+        Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'.
+    model_perez : str, optional
+        Perez model to use for diffuse irradiance. Default is `allsitescomposite1990`.
+        Only used when sky_model is 'perez'.
+    **kwargs_mount : dict
+        Additional keyword arguments for the POA model based on mounting configuration. See
+        `poa_irradiance_fixed` or `poa_irradiance_tracker` for details.
+
+        - For `module_mount='fixed'`:
+            - surface_tilt : float
+                Tilt angle of the modules (degrees).
+            - surface_azimuth : float
+                Azimuth angle of the modules (degrees).
+
+        - For `module_mount='single_axis'`:
+            - axis_tilt : float
+                Tilt angle of the tracker axis (degrees).
+            - axis_azimuth : float
+                Azimuth angle of the tracker axis (degrees).
+            - max_angle : float
+                Maximum rotation angle of the tracker (degrees).
+            - backtrack : bool
+                Whether to enable backtracking for single-axis trackers.
+            - gcr : float
+                Ground coverage ratio of the tracker system.
+
 
     Returns
     -------
     poa : pandas.DataFrame
          Contains keys/columns 'poa_global', 'poa_direct', 'poa_diffuse',
          'poa_sky_diffuse', 'poa_ground_diffuse'. [W/m2]
+
+    Notes
+    -----
+    This function uses pvlib to calculate the plane-of-array irradiance based on the provided weather data and
+    mounting configuration. See the pvlib documentation for further information on the parameters.
+    """
+
+    # Allow legacy keys for tilt and azimuth
+    if "tilt" in kwargs_mount:
+        kwargs_mount["surface_tilt"] = kwargs_mount.pop("tilt")
+    if "azimuth" in kwargs_mount:
+        kwargs_mount["surface_azimuth"] = kwargs_mount.pop("azimuth")
+
+    def check_keys_in_list(dictionary, key_list):
+        extra_keys = [key for key in dictionary.keys() if key not in key_list]
+        for key in extra_keys:
+            dictionary.pop(key)
+            warnings.warn(
+                f"Key '{extra_keys}' cannot be used for the selected `module_mount` and are ignored."
+            )
+
+    if sol_position is None:
+        sol_position = solar_position(weather_df, meta)
+
+    if module_mount == "fixed":
+        arg_names = ["surface_tilt", "surface_azimuth"]
+        check_keys_in_list(kwargs_mount, arg_names)
+
+        poa = poa_irradiance_fixed(
+            weather_df=weather_df,
+            meta=meta,
+            sol_position=sol_position,
+            dni_extra=dni_extra,
+            airmass=airmass,
+            albedo=albedo,
+            surface_type=surface_type,
+            model=sky_model,
+            model_perez=model_perez,
+            **kwargs_mount,
+        )
+    elif module_mount == "single_axis":
+        args = inspect.signature(pvlib.tracking.singleaxis).parameters
+        arg_names = [param.name for param in args.values()]
+        check_keys_in_list(kwargs_mount, arg_names)
+
+        poa = poa_irradiance_tracker(
+            weather_df=weather_df,
+            meta=meta,
+            sol_position=sol_position,
+            dni_extra=dni_extra,
+            airmass=airmass,
+            albedo=albedo,
+            surface_type=surface_type,
+            model=sky_model,
+            model_perez=model_perez,
+            **kwargs_mount,
+        )
+    else:
+        raise NotImplementedError(
+            f"The input module_mount '{module_mount}' is not implemented"
+        )
+
+    return poa
+
+
+@geospatial_quick_shape(
+    1,
+    [
+        "poa_global",
+        "poa_direct",
+        "poa_diffuse",
+        "poa_sky_diffuse",
+        "poa_ground_diffuse",
+    ],
+)
+def poa_irradiance_fixed(
+    weather_df: pd.DataFrame,
+    meta: dict,
+    sol_position=None,
+    surface_tilt=None,
+    surface_azimuth=None,
+    dni_extra=None,
+    airmass=None,
+    albedo=0.25,
+    surface_type=None,
+    model="isotropic",
+    model_perez="allsitescomposite1990",
+) -> pd.DataFrame:
     """
+    Calculate plane-of-array (POA) irradiance using `pvlib.irradiance.get_total_irradiance` for a fixed tilt system.
 
-    # TODO: change for handling HSAT tracking passed or requested
-    if tilt is None:
+    Parameters
+    ----------
+    weather_df : pd.DataFrame
+        The file path to the NSRDB file.
+    meta : dict
+        The geographical location ID in the NSRDB file.
+    sol_position : pd.DataFrame, optional
+        pvlib.solarposition.get_solarposition Dataframe. If none is given, it will be calculated.
+    dni_extra : pd.Series, optional
+        Extra-terrestrial direct normal irradiance. If None, it will be calculated.
+    airmass : pd.Series, optional
+        Airmass values. If None, it will be calculated.
+    albedo : float, optional
+        Ground reflectance. Default is 0.25.
+    surface_type : str, optional
+        Type of surface for albedo calculation. If None, a default value will be used.
+    sky_model : str, optional
+        Sky diffuse model to use. Default is `isotropic`.
+        Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'.
+    model_perez : str, optional
+        Perez model to use for diffuse irradiance. Default is `allsitescomposite1990`.
+        Only used when sky_model is 'perez'.
+    surface_tilt : float, optional
+        Tilt angle of the modules (degrees).
+    surface_azimuth : float, optional
+        Azimuth angle of the modules (degrees).
+
+    Returns
+    -------
+    poa : pd.DataFrame
+        DataFrame containing the calculated plane-of-array irradiance components with columns:
+        'poa_global', 'poa_direct', 'poa_diffuse', 'poa_sky_diffuse', 'poa_ground_diffuse'. [W/m2]
+
+
+    Notes
+    -----
+    This function uses pvlib to calculate the plane-of-array irradiance based on the provided weather data and
+    mounting configuration for fixed tilt systems. See the pvlib documentation for further information on the parameters.
+    """
+
+    if surface_tilt is None:
         try:
-            tilt = float(meta["tilt"])
+            surface_tilt = float(meta["tilt"])
         except:
-            tilt = float(meta["latitude"])
+            surface_tilt = float(abs(meta["latitude"]))
             print(
-                f"The array tilt angle was not provided, therefore the latitude tilt of {tilt:.1f} was used."
+                f"The array surface_tilt angle was not provided, therefore the latitude tilt of {surface_tilt:.1f} was used."
             )
-    if azimuth is None:  # Sets the default orientation to equator facing.
+
+    if surface_azimuth is None:  # Sets the default orientation to equator facing.
         try:
-            azimuth = float(meta["azimuth"])
+            surface_azimuth = float(meta["azimuth"])
         except:
             if float(meta["latitude"]) < 0:
-                azimuth = 0
+                surface_azimuth = 0
             else:
-                azimuth = 180
+                surface_azimuth = 180
                 print(
-                    f"The array azimuth was not provided, therefore an azimuth of {azimuth:.1f} was used."
+                    f"The array azimuth was not provided, therefore an azimuth of {surface_azimuth:.1f} was used."
                 )
 
     if sol_position is None:
         sol_position = solar_position(weather_df, meta)
 
     poa = pvlib.irradiance.get_total_irradiance(
-        surface_tilt=tilt,
-        surface_azimuth=azimuth,
+        surface_tilt=surface_tilt,
+        surface_azimuth=surface_azimuth,
         dni=weather_df["dni"],
         ghi=weather_df["ghi"],
         dhi=weather_df["dhi"],
         solar_zenith=sol_position["apparent_zenith"],
         solar_azimuth=sol_position["azimuth"],
-        model=sky_model,
+        dni_extra=dni_extra,
+        airmass=airmass,
+        albedo=albedo,
+        surface_type=surface_type,
+        model=model,
+        model_perez=model_perez,
     )
 
     return poa
+
+
+@geospatial_quick_shape(
+    1,
+    [
+        "poa_global",
+        "poa_direct",
+        "poa_diffuse",
+        "poa_sky_diffuse",
+        "poa_ground_diffuse",
+    ],
+)
+def poa_irradiance_tracker(
+    weather_df: pd.DataFrame,
+    meta: dict,
+    sol_position=None,
+    axis_tilt=0,
+    axis_azimuth=None,
+    max_angle=90,
+    backtrack=True,
+    gcr=0.2857142857142857,
+    cross_axis_tilt=0,
+    dni_extra=None,
+    airmass=None,
+    albedo=0.25,
+    surface_type=None,
+    model="isotropic",
+    model_perez="allsitescomposite1990",
+) -> pd.DataFrame:
+    """
+    Calculate plane-of-array (POA) irradiance using pvlib based on weather data from the
+    National Solar Radiation Database (NSRDB) for a given location (gid).
+
+    Parameters
+    ----------
+    weather_df : pd.DataFrame
+        The file path to the NSRDB file.
+    meta : dict
+        The geographical location ID in the NSRDB file.
+    sol_position : pd.DataFrame, optional
+        pvlib.solarposition.get_solarposition Dataframe. If none is given, it will be calculated.
+    dni_extra : pd.Series, optional
+        Extra-terrestrial direct normal irradiance. If None, it will be calculated.
+    airmass : pd.Series, optional
+        Airmass values. If None, it will be calculated.
+    albedo : float, optional
+        Ground reflectance. Default is 0.25.
+    surface_type : str, optional
+        Type of surface for albedo calculation. If None, a default value will be used.
+    sky_model : str, optional
+        Sky diffuse model to use. Default is `isotropic`.
+        Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'.
+    model_perez : str, optional
+        Perez model to use for diffuse irradiance. Default is `allsitescomposite1990`.
+        Only used when sky_model is 'perez'.
+    axis_tilt : float, optional
+        Tilt angle of the tracker axis (degrees). Default is 0.0.
+    axis_azimuth : float, optional
+        Azimuth angle of the tracker axis (degrees). Default is 0.0.
+    max_angle : float, optional
+        Maximum rotation angle of the tracker (degrees). Default is 45.0.
+    backtrack : bool, optional
+        Whether to enable backtracking for single-axis trackers. Default is True.
+    gcr : float, optional
+        Ground coverage ratio of the tracker system. Default is 0.3.
+
+    Returns
+    -------
+    tracker_poa : pandas.DataFrame
+         Contains keys/columns 'poa_global', 'poa_direct', 'poa_diffuse',
+         'poa_sky_diffuse', 'poa_ground_diffuse'. [W/m2]
+
+    Notes
+    -----
+    This function uses pvlib to calculate the plane-of-array irradiance based on the provided weather data and
+    mounting configuration for single-axis tracker systems. See the pvlib documentation for further information on the parameters.
+    """
+
+    if axis_azimuth is None:  # Sets the default orientation to north-south.
+        try:
+            axis_azimuth = float(meta["axis_azimuth"])
+        except:
+            if float(meta["latitude"]) < 0:
+                axis_azimuth = 0
+            else:
+                axis_azimuth = 180
+                print(
+                    f"The array axis_azimuth was not provided, therefore an azimuth of {axis_azimuth:.1f} was used."
+                )
+
+    if sol_position is None:
+        sol_position = solar_position(weather_df, meta)
+
+    tracker_data = pvlib.tracking.singleaxis(
+        sol_position["apparent_zenith"],
+        sol_position["azimuth"],
+        axis_tilt=axis_tilt,
+        axis_azimuth=axis_azimuth,
+        max_angle=max_angle,
+        backtrack=backtrack,
+        gcr=gcr,
+        cross_axis_tilt=cross_axis_tilt,
+    )
+
+    tracker_poa = pvlib.irradiance.get_total_irradiance(
+        surface_tilt=tracker_data["surface_tilt"],
+        surface_azimuth=tracker_data["surface_azimuth"],
+        dni=weather_df["dni"],
+        ghi=weather_df["ghi"],
+        dhi=weather_df["dhi"],
+        solar_zenith=sol_position["apparent_zenith"],
+        solar_azimuth=sol_position["azimuth"],
+        dni_extra=dni_extra,
+        airmass=airmass,
+        albedo=albedo,
+        surface_type=surface_type,
+        model=model,
+        model_perez=model_perez,
+    )
+
+    return tracker_poa
diff --git a/pvdeg/standards.py b/pvdeg/standards.py
index acdb176..e0d5181 100644
--- a/pvdeg/standards.py
+++ b/pvdeg/standards.py
@@ -208,7 +208,7 @@ def standoff(
     weather_df: pd.DataFrame = None,
     meta: dict = None,
     weather_kwarg: dict = None,
-    tilt: Union[float, int] = None,
+    tilt: Union[float, int, str] = None,
     azimuth: Union[float, int] = None,
     sky_model: str = "isotropic",
     temp_model: str = "sapm",
@@ -220,6 +220,7 @@ def standoff(
     x_0: float = 6.5,  # [cm]
     wind_factor: float = 0.33,
     irradiance_kwarg={},
+    tracker_irradiance_kwarg={},
     model_kwarg={},
 ) -> pd.DataFrame:
     """
@@ -239,7 +240,8 @@ def standoff(
     weather_kwarg : dict
         other variables needed to access a particular weather dataset.
     tilt : float, optional
-        Tilt angle of PV system relative to horizontal. [°]
+        Tilt angle of rack mounted PV system relative to horizontal. [°]
+        If single-axis tracker mounted, specify keyword 'single_axis'
     azimuth : float, optional
         Azimuth angle of PV system relative to north. [°]
     sky_model : str, optional
@@ -316,16 +318,29 @@ def standoff(
 
     solar_position = spectral.solar_position(weather_df, meta)
 
-    irradiance_dict = {
-        "sol_position": solar_position,
-        "tilt": tilt,
-        "azimuth": azimuth,
-        "sky_model": sky_model,
-    }
+    if tilt == "single_axis":
+        irradiance_dict = {
+            "sol_position": solar_position,
+            "axis_azimuth": azimuth,
+            "sky_model": sky_model,
+        }
+        poa = spectral.poa_irradiance_tracker(
+            weather_df=weather_df,
+            meta=meta,
+            **irradiance_dict | tracker_irradiance_kwarg,
+        )
 
-    poa = spectral.poa_irradiance(
-        weather_df=weather_df, meta=meta, **irradiance_dict | irradiance_kwarg
-    )
+    else:
+        irradiance_dict = {
+            "sol_position": solar_position,
+            "tilt": tilt,
+            "azimuth": azimuth,
+            "sky_model": sky_model,
+        }
+
+        poa = spectral.poa_irradiance(
+            weather_df=weather_df, meta=meta, **irradiance_dict | irradiance_kwarg
+        )
 
     T_0 = temperature.temperature(
         cell_or_mod="cell",
@@ -484,8 +499,8 @@ def T98_estimate(
 ):
     """
     Estimate the 98ᵗʰ percential temperature for the module at the given tilt, azimuth, and x_eff.
-    If any of these factors are supplied, it default to latitide tilt, equatorial facing, and
-    open rack mounted, respectively.
+    If any of these factors are not supplied, it default to latitide tilt, equatorial facing, and
+    open rack mounted as needed.
 
     Parameters
     ----------
@@ -633,3 +648,122 @@ def standoff_x(
     ).x[0]
 
     return temp_df
+
+
+def x_eff_temperature_estimate(
+    weather_df=None,
+    meta=None,
+    weather_kwarg=None,
+    sky_model="isotropic",
+    temp_model="sapm",
+    conf_0="insulated_back_glass_polymer",
+    conf_inf="open_rack_glass_polymer",
+    wind_factor=0.33,
+    module_mount=None,
+    tilt=None,
+    azimuth=None,
+    x_eff=None,
+    x_0=6.5,
+    model_kwarg={},
+):
+    """
+    Estimate the temperature for the module at the given tilt, azimuth, and x_eff.
+    If any of these factors are not supplied, it default to latitide tilt, equatorial facing, and
+    open rack mounted, respectively.
+
+    Parameters
+    ----------
+    x_eff : float
+        This is the effective module standoff distance according to the model. [cm]
+    x_0 : float, optional
+        Thermal decay constant. [cm]
+    weather_df : pd.DataFrame
+        Weather data for a single location.
+    meta : pd.DataFrame
+        Meta data for a single location.
+    weather_kwarg : dict
+        other variables needed to access a particular weather dataset.
+    tilt : float,
+        Tilt angle of PV system relative to horizontal. [°]
+    azimuth : float, optional
+        Azimuth angle of PV system relative to north. [°]
+    sky_model : str, optional
+        Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'.
+    temp_model : str, optional
+        Options: 'sapm'.  'pvsyst' and 'faiman' will be added later.
+        Performs the calculations for the cell temperature.
+    conf_0 : str, optional
+        Model for the high temperature module on the exponential decay curve.
+        Default: 'insulated_back_glass_polymer'
+    conf_inf : str, optional
+        Model for the lowest temperature module on the exponential decay curve.
+        Default: 'open_rack_glass_polymer'
+    wind_factor : float, optional
+        Wind speed correction exponent to account for different wind speed measurement heights
+        between weather database (e.g. NSRDB) and the tempeature model (e.g. SAPM)
+        The NSRDB provides calculations at 2 m (i.e module height) but SAPM uses a 10 m height.
+        It is recommended that a power-law relationship between height and wind speed of 0.33
+        be used*. This results in a wind speed that is 1.7 times higher. It is acknowledged that
+        this can vary significantly.
+    model_kwarg : dict, optional
+        keyword argument dictionary to provide other arguments to the temperature model.
+        See temperature.temperature for more information.
+
+    R. Rabbani, M. Zeeshan, "Exploring the suitability of MERRA-2 reanalysis data for wind energy
+        estimation, analysis of wind characteristics and energy potential assessment for selected
+        sites in Pakistan", Renewable Energy 154 (2020) 1240-1251.
+
+    Returns
+    -------
+    T_x_eff: Pandas Dataframe
+        This is the estimate for the module temperature at the given tilt, azimuth, and x_eff.
+
+    """
+
+    parameters = ["temp_air", "wind_speed", "dhi", "ghi", "dni"]
+
+    if isinstance(weather_df, dd.DataFrame):
+        weather_df = weather_df[parameters].compute()
+        weather_df.set_index("time", inplace=True)
+    elif isinstance(weather_df, pd.DataFrame):
+        weather_df = weather_df[parameters]
+    elif weather_df is None:
+        weather_df, meta = weather.get(**weather_kwarg)
+
+    solar_position = spectral.solar_position(weather_df, meta)
+    poa = spectral.poa_irradiance(
+        weather_df=weather_df,
+        meta=meta,
+        sol_position=solar_position,
+        module_mount=module_mount,
+        tilt=tilt,
+        azimuth=azimuth,
+        sky_model=sky_model,
+    )
+    T_inf = temperature.temperature(
+        cell_or_mod="cell",
+        weather_df=weather_df,
+        meta=meta,
+        poa=poa,
+        temp_model=temp_model,
+        conf=conf_inf,
+        wind_factor=wind_factor,
+        model_kwarg=model_kwarg,
+    )
+
+    if x_eff == None:
+        return T_inf
+    else:
+        T_0 = temperature.temperature(
+            cell_or_mod="cell",
+            weather_df=weather_df,
+            meta=meta,
+            poa=poa,
+            temp_model=temp_model,
+            conf=conf_0,
+            wind_factor=wind_factor,
+            model_kwarg=model_kwarg,
+        )
+        T_x_eff = T_0 - (T_0 - T_inf) * (1 - np.exp(-x_eff / x_0))
+
+        return T_x_eff
diff --git a/pvdeg/temperature.py b/pvdeg/temperature.py
index ba50047..0035268 100644
--- a/pvdeg/temperature.py
+++ b/pvdeg/temperature.py
@@ -196,7 +196,7 @@ def cell(
     Calculate the PV cell temperature using PVLIB
     Currently this only supports the SAPM temperature model.
 
-    Parameters:
+    Parameters
     -----------
     weather_df : (pd.dataframe)
         Data Frame with minimum requirements of 'temp_air' and 'wind_speed'
@@ -304,7 +304,7 @@ def temperature(
     Calculate the PV cell or module temperature using PVLIB
     Current supports the following temperature models:
 
-    Parameters:
+    Parameters
     -----------
     cell_or_mod : (str)
         choose to calculate the cell or module temperature. Use
@@ -347,12 +347,12 @@ def temperature(
         keyword argument dictionary used for the pvlib temperature model calculation.
         See https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/temperature.html for more.
 
-    Return:
+    Return
     -------
     temp_cell : pandas.DataFrame
         This is the temperature of the cell in a module at every time step.[°C]
 
-    References:
+    References
     -----------
     R. Rabbani, M. Zeeshan, "Exploring the suitability of MERRA-2 reanalysis data for wind energy
         estimation, analysis of wind characteristics and energy potential assessment for selected
diff --git a/pvdeg/utilities.py b/pvdeg/utilities.py
index 2374df9..c4413a5 100644
--- a/pvdeg/utilities.py
+++ b/pvdeg/utilities.py
@@ -11,6 +11,15 @@
 from collections import OrderedDict
 import xarray as xr
 from subprocess import run
+import cartopy.feature as cfeature
+
+
+# A mapping to simplify access to files stored in `pvdeg/data`
+pvdeg_datafiles = { 
+    "AApermeation": os.path.join(DATA_DIR, "AApermeation.json"),
+    "H2Opermeation": os.path.join(DATA_DIR, "H2Opermeation.json"),
+    "O2permeation": os.path.join(DATA_DIR, "O2permeation.json"),
+}
 
 
 def gid_downsampling(meta, n):
@@ -492,8 +501,7 @@ def convert_tmy(file_in, file_out="h5_from_tmy.h5"):
         )
 
 
-# previously: fname="materials.json"
-# add control over what parameters (O2, H2, AA)?
+### DEPRECATE ###
 def _read_material(name, fname="O2permeation.json"):
     """
     read a material from materials.json and return the parameter dictionary
@@ -515,10 +523,21 @@ def _read_material(name, fname="O2permeation.json"):
     fpath = os.path.join(DATA_DIR, fname)
     with open(fpath) as f:
         data = json.load(f)
+    f.close()
 
     if name is None:
-        material_list = data.keys()
-        return [*material_list]
+        return list(data.keys())
+
+        # Mike Added
+        # broke test
+        # =========== 
+        # material_list = ''
+        # print('working')
+        # for key in data:
+        #     if 'name' in data[key].keys():
+        #         material_list = material_list + key + "=" + data[key]['name'] + '\n'
+        # material_list = material_list[0:len(material_list)-1]
+        # return [*material_list]
 
     mat_dict = data[name]
     return mat_dict
@@ -1012,7 +1031,7 @@ def _find_bbox_corners(coord_1=None, coord_2=None, coords=None):
         min and max latitude and longitudes. Minimum latitude at lats[0].
         Maximum latitude at lats[1]. Same pattern for longs.
     """
-    if coord_1 and coord_2:
+    if coord_1 is not None and coord_2 is not None:
         lats = [coord_1[0], coord_2[0]]
         longs = [coord_1[1], coord_2[1]]
     elif coords.any():
@@ -1043,20 +1062,21 @@ def _plot_bbox_corners(ax, coord_1=None, coord_2=None, coords=None):
     return
 
 
-def _add_cartopy_features(ax):
+def _add_cartopy_features(
+        ax, 
+        features = [
+            cfeature.BORDERS,
+            cfeature.COASTLINE,
+            cfeature.LAND,
+            cfeature.OCEAN,
+            cfeature.LAKES,
+            cfeature.RIVERS,
+        ],
+    ):
     """
     Add cartopy features to an existing matplotlib.pyplot axis.
     """
-    import cartopy.feature as cfeature
-
-    features = [
-        cfeature.BORDERS,
-        cfeature.COASTLINE,
-        cfeature.LAND,
-        cfeature.OCEAN,
-        cfeature.LAKES,
-        cfeature.RIVERS,
-    ]
+   
 
     for i in features:
         if i == cfeature.BORDERS:
@@ -1064,6 +1084,16 @@ def _add_cartopy_features(ax):
         else:
             ax.add_feature(i)
 
+def linear_normalize(array: np.ndarray)->np.ndarray:
+    """
+    Normalize a non-negative input array.
+    """
+
+    return np.divide(
+        np.subtract(array, np.min(array)),
+        np.subtract(np.max(array), np.min(array)),
+    )
+
 
 def _calc_elevation_weights(
     elevations: np.array,
@@ -1090,7 +1120,7 @@ def _calc_elevation_weights(
         Options : `'mean'`, `'sum'`, `'median'`
     normalization : str, (default = 'linear')
         function to apply when normalizing weights. Logarithmic uses log_e/ln
-        options : `'linear'`, `'logarithmic'`, '`exponential'`
+        options : `'linear'`, `'log'`, '`exp'`, `'invert-linear'`
     kdtree : sklearn.neighbors.KDTree or str
         kdtree containing latitude-longitude pairs for quick lookups
         Generate using ``pvdeg.geospatial.meta_KDTree``. Can take a pickled
@@ -1118,19 +1148,27 @@ def _calc_elevation_weights(
             delta = np.median(delta_elevation)
         weights[i] = delta
 
+    linear_weights = linear_normalize(weights)
+
     if normalization == "linear":
-        pass  # do nothing
-    elif normalization == "exponential":
-        weights = np.exp(weights)
-    elif normalization == "logarithmic":
-        weights = np.log(weights)
-
-    normalized_weights = np.divide(
-        np.subtract(weights, np.min(weights)),
-        np.subtract(np.max(weights), np.min(weights)),
-    )
+        return linear_weights
+
+    if normalization == "invert-linear":
+        return 1 - linear_weights
 
-    return normalized_weights
+    elif normalization == "exp":
+        return linear_normalize(np.exp(linear_weights))
+
+    elif normalization == "log":
+        # add 1 to shift the domain right so results of log will be positive
+        # there may be a better way to do this, the value wont be properly normalized between 0 and 1
+        return linear_normalize(np.log(linear_weights + 1)) 
+
+    raise ValueError(f"""
+        normalization method: {normalization} does not exist.
+        must be: "linear", "exp", "log"
+        """
+    )
 
 
 def fix_metadata(meta):
@@ -1262,3 +1300,183 @@ def compare_templates(
             return False
 
     return True
+
+def merge_sparse(files: list[str])->xr.Dataset:
+    """
+    Merge an arbitrary number of geospatial analysis results. 
+    Creates monotonically increasing indicies.
+
+    Uses `engine='h5netcdf'` for reliability, use h5netcdf to save your results to netcdf files.
+
+    Parameters
+    -----------
+    files: list[str]
+        A list of strings representing filepaths to netcdf (.nc) files.
+        Each file must have the same coordinates, `['latitude','longitude']` and identical datavariables.
+
+    Returns
+    -------
+    merged_ds: xr.Dataset
+        Dataset (in memory) with `coordinates = ['latitude','longitude']` and datavariables matching files in 
+        filepaths list.
+    """
+
+    datasets = [xr.open_dataset(fp, engine='h5netcdf').compute() for fp in files]
+
+    latitudes = np.concatenate([ds.latitude.values for ds in datasets])
+    longitudes = np.concatenate([ds.longitude.values for ds in datasets])
+    unique_latitudes = np.sort(np.unique(latitudes))
+    unique_longitudes = np.sort(np.unique(longitudes))
+
+    data_vars = datasets[0].data_vars
+
+    merged_ds = xr.Dataset(
+        {var: (['latitude', 'longitude'], np.full((len(unique_latitudes), len(unique_longitudes)), np.nan)) for var in data_vars},
+        coords={
+            'latitude': unique_latitudes,
+            'longitude': unique_longitudes
+        }
+    )
+
+    for ds in datasets:
+        lat_inds = np.searchsorted(unique_latitudes, ds.latitude.values)
+        lon_inds = np.searchsorted(unique_longitudes, ds.longitude.values)
+
+        for var in ds.data_vars:
+            merged_ds[var].values[np.ix_(lat_inds, lon_inds)] = ds[var].values
+
+    return merged_ds
+
+def display_json(
+    pvdeg_file: str = None, 
+    fp: str = None, 
+    ) -> None:
+    """
+    Interactively view a 2 level JSON file in a JupyterNotebook
+
+    Parameters:
+    ------------
+    pvdeg_file: str
+        keyword for material json file in `pvdeg/data`. Options:
+        >>> "AApermeation", "H2Opermeation", "O2permeation"
+    fp: str
+        file path to material parameters json with same schema as material parameters json files in `pvdeg/data`.  `pvdeg_file` will override `fp` if both are provided.
+    """
+    from IPython.display import display, HTML
+
+    if pvdeg_file:
+        try:
+            fp = pvdeg_datafiles[pvdeg_file]
+        except KeyError:
+            raise KeyError(f"{pvdeg_file} does not exist in pvdeg/data. Options are {pvdeg_datafiles.keys()}")
+
+    with open(fp, 'r') as file:
+        data = json.load(file)
+
+    def json_to_html(data):
+        json_str = json.dumps(data, indent=2)
+        for key in data.keys():
+            json_str = json_str.replace(f'"{key}":', f'<span style="color: plum;">"{key}":</span>')
+        
+        indented_html = '<br>'.join([' ' * 4 + line for line in json_str.splitlines()])
+        return f'<pre style="color: white; background-color: black; padding: 10px; border-radius: 5px;">{indented_html}</pre>'
+
+    html = f'<h2 style="color: white;">JSON Output at fp: {fp}</h2><div>'
+    for key, value in data.items():
+        html += (
+            f'<div>'
+            f'<strong style="color: white;">{key}:</strong> '
+            f'<span onclick="this.nextElementSibling.style.display = this.nextElementSibling.style.display === \'none\' ? \'block\' : \'none\'" style="cursor: pointer; color: white;">&#9660;</span>'
+            f'<div style="display: none;">{json_to_html(value)}</div>'
+            f'</div>'
+        )
+    html += '</div>'
+
+    # Display the HTML
+    display(HTML(html))
+
+
+def search_json(
+    pvdeg_file: str = None, 
+    fp: str = None, 
+    name_or_alias: str = None,
+    )-> str:
+    """
+    Search through a 2 level JSON with arbitrary key names for subkeys with matching attributes of name or alias.
+
+    Parameters
+    ------------
+    pvdeg_file: str
+        keyword for material json file in `pvdeg/data`. Options:
+        >>> "AApermeation", "H2Opermeation", "O2permeation"
+    fp: str
+        file path to material parameters json with same schema as material parameters json files in `pvdeg/data`. `pvdeg_file` will override `fp` if both are provided.
+    name_or_alias: str
+        searches for matching subkey value in either `name` or `alias` attributes. exits on the first matching instance.
+
+    Returns
+    ---------
+    jsonkey: str
+        arbitrary key from json that owns the matching subattribute of `name` or `alias`.
+    """
+
+    if pvdeg_file:
+        try:
+            fp = pvdeg_datafiles[pvdeg_file]
+        except KeyError:
+            raise KeyError(rf"{pvdeg_file} does not exist in pvdeg/data. Options are {pvdeg_datafiles.keys()}")
+
+    with open(fp, "r") as file:
+        data = json.load(file)
+
+    for key, subdict in data.items():
+        if "name" in subdict and "alias" in subdict:
+            if (subdict["name"] == name_or_alias or subdict["alias"] == name_or_alias):
+                return key
+
+    raise ValueError(rf"name_or_alias: {name_or_alias} not in JSON at {os.path(fp)}")
+
+def read_material(
+    pvdeg_file: str = None, 
+    fp: str = None, 
+    key: str = None,
+    parameters: list[str] = None,
+)-> dict:
+    """
+    Read material parameters from a `pvdeg/data` file or JSON file path.
+
+    Parameters
+    -----------
+    pvdeg_file: str
+        keyword for material json file in `pvdeg/data`. Options:
+        >>> "AApermeation", "H2Opermeation", "O2permeation"
+    fp: str
+        file path to material parameters json with same schema as material parameters json files in `pvdeg/data`. `pvdeg_file` will override `fp` if both are provided.
+    key: str
+        key corresponding to specific material in the file. In the pvdeg files these have arbitrary names. Inspect the files or use `display_json` or `search_json` to identify the key for desired material.
+    parameters: list[str]
+        parameters to grab from the file at index key. If none, will grab all items at index key. the elements in parameters must match the keys in the json exactly or the output value for the specific key/parameter in the retunred dict will be `None`.
+
+    Returns
+    --------
+    material: dict
+        dictionary of material parameters from the seleted file at the index key.
+    """
+
+    # these live in the `pvdeg/data` folder
+    if pvdeg_file:
+        try:
+            fp = pvdeg_datafiles[pvdeg_file]
+        except KeyError:
+            raise KeyError(f"{pvdeg_file} does not exist in pvdeg/data. Options are {pvdeg_datafiles.keys()}")
+
+    with open(fp, "r") as file:
+        data = json.load(file)
+
+    # take subdict from file
+    material_dict = data[key] 
+    
+    if parameters:
+        material_dict = {k: material_dict.get(k, None) for k in parameters} 
+
+    return material_dict
\ No newline at end of file
diff --git a/pvdeg/weather.py b/pvdeg/weather.py
index dbe06f7..3af59fa 100644
--- a/pvdeg/weather.py
+++ b/pvdeg/weather.py
@@ -16,9 +16,10 @@
 import h5py
 import dask.dataframe as dd
 import xarray as xr
+from geopy.geocoders import Nominatim
 
 
-def get(database, id=None, geospatial=False, **kwargs):
+def get(database, id=None, geospatial=False, find_meta: bool=None , **kwargs):
     """
     Load weather data directly from  NSRDB or through any other PVLIB i/o
     tools function
@@ -35,6 +36,9 @@ def get(database, id=None, geospatial=False, **kwargs):
         dask dataframe. This is useful for large scale geospatial analyses on
         distributed compute systems. Geospaital analyses are only supported for
         NSRDB data and locally stored h5 files that follow pvlib conventions.
+    find_meta : (bool)
+        if true, this instructs the code to look up additional meta data. 
+        The default is True if geospatial is False.
     **kwargs :
         Additional keyword arguments to pass to the get_weather function
         (see pvlib.iotools.get_psm3 for PVGIS, and get_NSRDB for NSRDB)
@@ -47,6 +51,8 @@ def get(database, id=None, geospatial=False, **kwargs):
         Dictionary of metadata for the weather data
     """
 
+    if find_meta == None:
+        find_meta = not(geospatial)
     META_MAP = {"elevation": "altitude", "Local Time Zone": "tz"}
 
     if type(id) is tuple:
@@ -97,6 +103,8 @@ def get(database, id=None, geospatial=False, **kwargs):
         # switch weather data headers and metadata to pvlib standard
         map_weather(weather_df)
         map_meta(meta)
+        if find_meta:
+            meta=find_metadata(meta)
 
         if "relative_humidity" not in weather_df.columns:
             print('\r','Column "relative_humidity" not found in DataFrame. Calculating...', end='')
@@ -121,7 +129,7 @@ def get(database, id=None, geospatial=False, **kwargs):
         return weather_ds, meta_df
 
 
-def read(file_in, file_type, map_variables=True, **kwargs):
+def read(file_in, file_type, map_variables=True, find_meta=True, **kwargs):
     """
     Read a locally stored weather file of any PVLIB compatible type
 
@@ -163,6 +171,8 @@ def read(file_in, file_type, map_variables=True, **kwargs):
     if map_variables == True:
         map_weather(weather_df)
         map_meta(meta)
+    if find_meta:
+        meta=find_metadata(meta)
 
     if weather_df.index.tzinfo is None:
         tz = "Etc/GMT%+d" % -meta["tz"]
@@ -265,12 +275,24 @@ def map_meta(meta):
         "Dew Point": "dew_point",
         "Longitude": "longitude",
         "Latitude": "latitude",
+        "state": "State",
+        "county": "County",
+        "country": "Country",
+        "Neighborhood": "neighbourhood" ,
+        "country_code": "Country Code" ,
+        "postcode": "Zipcode",
+        "road": "Street",
+        "village": "City",
+        "city": "City",
+        "town": "City",
     }
 
     # map meta-names as needed
     for key in [*meta.keys()]:
         if key in META_MAP.keys():
             meta[META_MAP[key]] = meta.pop(key)
+    if "Country Code" in meta.keys():
+        meta["Country Code"]=meta["Country Code"].upper()
 
     return meta
 
@@ -930,4 +952,34 @@ def get_anywhere(database = "PSM3", id=None, **kwargs):
                 meta = {'result': 'This location was not found in either the NSRDB or PVGIS'}
                 weather_db = {'result': 'NA'}
 
-    return weather_db, meta 
\ No newline at end of file
+    return weather_db, meta 
+
+def find_metadata(meta):
+    """
+    Fills in missing meta data for a geographic location. 
+    The meta dictionary must have longitude and latitude information.
+    Make sure meta_map has been run first to eliminate the creation of duplicate entries with different names.
+    It will only replace empty keys and those with one character of length.
+
+    Parameters:
+    -----------
+    meta : (dict)
+        Dictionary of metadata for the weather data
+
+    Returns:
+    --------
+    meta : (dict)
+        Dictionary of metadata for the weather data
+    """
+    geolocator = Nominatim(user_agent="geoapiexercises")
+    location = geolocator.reverse(str(meta['latitude']) + ',' + str(meta['longitude'])).raw['address']
+    map_meta(location)
+
+    for key in [*location.keys()]:
+        if key in meta.keys():
+            if len(meta[key])<2:
+                meta[key] = location[key]
+        else:
+            meta[key] = location[key]
+
+    return meta
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 768ae44..0cfef44 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,7 +28,7 @@ keywords = [
     "PV",
 ]
 dependencies = [
-    "aiohttp==3.9.5",
+    "aiohttp==3.10.2",
     "numpy==1.26.4",
     "pvlib==0.10.3",
     "scipy<1.14.0",
@@ -67,6 +67,7 @@ docs = [
     "nbsphinx",
     "sphinx_toggleprompt",
     "pydata_sphinx_theme",
+    "mistune==3.0.0",
 ]
 test = [
     "pytest",
diff --git a/tests/data/test-scenario.json b/tests/data/test-scenario.json
index d641306..387888f 100644
--- a/tests/data/test-scenario.json
+++ b/tests/data/test-scenario.json
@@ -14,10 +14,10 @@
                 "Fickian": true,
                 "Ead": 29.43112031,
                 "Do": 0.129061678,
-                "Eas": 32.3137806,
-                "So": 87.81142774,
-                "Eap": 61.7449009,
-                "Po": 97917899126
+                "Eas": 16.6314948252219,
+                "So": 0.136034525059804,
+                "Eap": 49.1083457348515,
+                "Po": 528718258.338532
 
             },
             "temp_model": "sapm",
diff --git a/tests/sandbox.ipynb b/tests/sandbox.ipynb
index 0ec2d66..8a9ed66 100644
--- a/tests/sandbox.ipynb
+++ b/tests/sandbox.ipynb
@@ -419,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -590,6 +590,7 @@
     "from pvdeg.scenario import Scenario\n",
     "from pvdeg.standards import standoff\n",
     "from pvdeg import TEST_DATA_DIR\n",
+    "import pvdeg\n",
     "import json\n",
     "import pandas as pd\n",
     "import pytest\n",
@@ -624,11 +625,304 @@
     "\n",
     "    assert a == restored"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pvdeg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvdeg.utilities.pvdeg_datafiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test_read_material_special():\n",
+    "\n",
+    "    template_material = pvdeg.utilities.read_material(pvdeg_file=\"AApermeation\", key=\"AA000\")\n",
+    "\n",
+    "    assert len(template_material) == 1\n",
+    "    assert \"comment\" in template_material\n",
+    "\n",
+    "test_read_material_special()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template_material = pvdeg.utilities.read_material(pvdeg_file=\"AApermeation\", key=\"AA000\")\n",
+    "\n",
+    "[type(x) for x in template_material.values()][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test_read_material_normal():\n",
+    "\n",
+    "    res = {\n",
+    "        'name': 'ST504', \n",
+    "        'alias': 'PET1', \n",
+    "        'contributor': 'Michael Kempe', \n",
+    "        'source': 'unpublished measurements', \n",
+    "        'Fickian': True,\n",
+    "        'Ead': 47.603, \n",
+    "        'Do': 0.554153, \n",
+    "        'Eas': -11.5918, \n",
+    "        'So': 9.554366e-07, \n",
+    "        'Eap': 34.2011, \n",
+    "        'Po': 2128.8937\n",
+    "    }\n",
+    "\n",
+    "    template_material = pvdeg.utilities.read_material(pvdeg_file=\"O2permeation\", key=\"OX002\")\n",
+    "\n",
+    "    assert template_material == res\n",
+    "\n",
+    "test_read_material_normal()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test_read_material_fewer_params():\n",
+    "\n",
+    "    res = {\n",
+    "        'name': 'ST504', \n",
+    "        'Fickian': True,\n",
+    "    }\n",
+    "\n",
+    "    template_material = pvdeg.utilities.read_material(pvdeg_file=\"O2permeation\", key=\"OX002\", parameters=[\"name\", \"Fickian\"])\n",
+    "\n",
+    "    assert template_material == res\n",
+    "\n",
+    "test_read_material_fewer_params()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test_read_material_extra_params():\n",
+    "\n",
+    "    res = {\n",
+    "        'namenotindict1': None,\n",
+    "        'namenotindict2': None,\n",
+    "    }\n",
+    "\n",
+    "    template_material = pvdeg.utilities.read_material(pvdeg_file=\"O2permeation\", key=\"OX002\", parameters=[\"namenotindict1\", \"namenotindict2\"])\n",
+    "\n",
+    "    assert template_material == res\n",
+    "\n",
+    "\n",
+    "test_read_material_extra_params()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test_search_json():\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pvdeg_file should override fp if both are provided\n",
+    "def test_read_material_fp_override():\n",
+    "\n",
+    "    res = {\n",
+    "        'name': 'ST504', \n",
+    "        'alias': 'PET1', \n",
+    "        'contributor': 'Michael Kempe', \n",
+    "        'source': 'unpublished measurements', \n",
+    "        'Fickian': True,\n",
+    "        'Ead': 47.603, \n",
+    "        'Do': 0.554153, \n",
+    "        'Eas': -11.5918, \n",
+    "        'So': 9.554366e-07, \n",
+    "        'Eap': 34.2011, \n",
+    "        'Po': 2128.8937\n",
+    "    }\n",
+    "\n",
+    "    from pvdeg import DATA_DIR\n",
+    "\n",
+    "    # pass pvdeg file and it gets overridden by the file path\n",
+    "    template_material = pvdeg.utilities.read_material(\n",
+    "        pvdeg_file=\"O2permeation\", \n",
+    "        fp=os.path.join(DATA_DIR, \"AApermeation.json\"), \n",
+    "        key=\"OX002\",\n",
+    "    )\n",
+    "\n",
+    "    assert template_material == res\n",
+    "\n",
+    "test_read_material_fp_override()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test_search_json():\n",
+    "\n",
+    "    name_res = pvdeg.utilities.search_json(pvdeg_file=\"H2Opermeation\", name_or_alias=\"Ethylene Vinyl Acetate\")\n",
+    "    alias_res = pvdeg.utilities.search_json(pvdeg_file=\"H2Opermeation\", name_or_alias=\"EVA\")\n",
+    "\n",
+    "    assert name_res == \"W001\"\n",
+    "    assert alias_res == \"W001\"\n",
+    "\n",
+    "test_search_json()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "Failed",
+     "evalue": "Invalid regex pattern provided to 'match': incomplete escape \\U at position 46",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFailed\u001b[0m                                    Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[3], line 10\u001b[0m\n\u001b[0;32m      4\u001b[0m invalid_name_or_alias \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnamenotindict\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      5\u001b[0m expected_error_message \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m      6\u001b[0m     \u001b[38;5;124mrf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname_or_alias: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00minvalid_name_or_alias\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in JSON at \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      7\u001b[0m     \u001b[38;5;124mrf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mos\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(DATA_DIR,\u001b[38;5;250m \u001b[39mpvdeg\u001b[38;5;241m.\u001b[39mutilities\u001b[38;5;241m.\u001b[39mpvdeg_datafiles[pvdeg_file])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      8\u001b[0m )\n\u001b[1;32m---> 10\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mpytest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraises\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;167;43;01mValueError\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexpected_error_message\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m     11\u001b[0m     pvdeg\u001b[38;5;241m.\u001b[39mutilities\u001b[38;5;241m.\u001b[39msearch_json(pvdeg_file\u001b[38;5;241m=\u001b[39mpvdeg_file, name_or_alias\u001b[38;5;241m=\u001b[39minvalid_name_or_alias)\n",
+      "    \u001b[1;31m[... skipping hidden 1 frame]\u001b[0m\n",
+      "File \u001b[1;32mc:\\Users\\tford\\AppData\\Local\\miniconda3\\envs\\deg\\lib\\site-packages\\_pytest\\python_api.py:997\u001b[0m, in \u001b[0;36mRaisesContext.__init__\u001b[1;34m(self, expected_exception, message, match_expr)\u001b[0m\n\u001b[0;32m    995\u001b[0m     re_error \u001b[38;5;241m=\u001b[39m e\n\u001b[0;32m    996\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m re_error \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 997\u001b[0m     \u001b[43mfail\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mInvalid regex pattern provided to \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatch\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m: \u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mre_error\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\tford\\AppData\\Local\\miniconda3\\envs\\deg\\lib\\site-packages\\_pytest\\outcomes.py:178\u001b[0m, in \u001b[0;36mfail\u001b[1;34m(reason, pytrace)\u001b[0m\n\u001b[0;32m    165\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Explicitly fail an executing test with the given message.\u001b[39;00m\n\u001b[0;32m    166\u001b[0m \n\u001b[0;32m    167\u001b[0m \u001b[38;5;124;03m:param reason:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    175\u001b[0m \u001b[38;5;124;03m    The exception that is raised.\u001b[39;00m\n\u001b[0;32m    176\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    177\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 178\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m Failed(msg\u001b[38;5;241m=\u001b[39mreason, pytrace\u001b[38;5;241m=\u001b[39mpytrace)\n",
+      "\u001b[1;31mFailed\u001b[0m: Invalid regex pattern provided to 'match': incomplete escape \\U at position 46"
+     ]
+    }
+   ],
+   "source": [
+    "from pvdeg import DATA_DIR\n",
+    "\n",
+    "pvdeg_file = \"H2Opermeation\"\n",
+    "invalid_name_or_alias = \"namenotindict\"\n",
+    "expected_error_message = (\n",
+    "    rf\"name_or_alias: {invalid_name_or_alias} not in JSON at \"\n",
+    "    rf\"{os.path.join(DATA_DIR, pvdeg.utilities.pvdeg_datafiles[pvdeg_file])}\"\n",
+    ")\n",
+    "\n",
+    "with pytest.raises(ValueError, match=expected_error_message):\n",
+    "    pvdeg.utilities.search_json(pvdeg_file=pvdeg_file, name_or_alias=invalid_name_or_alias)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'invalid_name_or_alias' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m expected_error_message \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m----> 2\u001b[0m     \u001b[38;5;124mrf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname_or_alias: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00minvalid_name_or_alias\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in JSON at \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      3\u001b[0m     \u001b[38;5;124mrf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mos\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(DATA_DIR,\u001b[38;5;250m \u001b[39mpvdeg\u001b[38;5;241m.\u001b[39mutilities\u001b[38;5;241m.\u001b[39mpvdeg_datafiles[pvdeg_file])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      4\u001b[0m )\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'invalid_name_or_alias' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "expected_error_message = (\n",
+    "    rf\"name_or_alias: {invalid_name_or_alias} not in JSON at \"\n",
+    "    rf\"{os.path.join(DATA_DIR, pvdeg.utilities.pvdeg_datafiles[pvdeg_file])}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'name_or_alias: namenotindict not in JSON at C:\\\\Users\\\\tford\\\\dev\\\\PVDegradationTools\\\\pvdeg\\\\data\\\\H2Opermeation.json'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expected_error_message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'module' object is not callable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpvdeg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mutilities\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearch_json\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpvdeg_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpvdeg_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname_or_alias\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minvalid_name_or_alias\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\dev\\PVDegradationTools\\pvdeg\\utilities.py:1437\u001b[0m, in \u001b[0;36msearch_json\u001b[1;34m(pvdeg_file, fp, name_or_alias)\u001b[0m\n\u001b[0;32m   1434\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m (subdict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m name_or_alias \u001b[38;5;129;01mor\u001b[39;00m subdict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124malias\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m name_or_alias):\n\u001b[0;32m   1435\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m key\n\u001b[1;32m-> 1437\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mrf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname_or_alias: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname_or_alias\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in JSON at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mos\u001b[38;5;241m.\u001b[39mpath(fp)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[1;31mTypeError\u001b[0m: 'module' object is not callable"
+     ]
+    }
+   ],
+   "source": [
+    "pvdeg.utilities.search_json(pvdeg_file=pvdeg_file, name_or_alias=invalid_name_or_alias)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "deg",
    "language": "python",
    "name": "python3"
   },
@@ -642,7 +936,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.9.19"
   },
   "orig_nbformat": 4
  },
diff --git a/tests/test_spectral.py b/tests/test_spectral.py
index f1f8e50..f6497fc 100644
--- a/tests/test_spectral.py
+++ b/tests/test_spectral.py
@@ -58,7 +58,12 @@ def test_poa_irradiance():
     weather dataframe, meta dictionary, and solar_position dataframe
     """
     result = pvdeg.spectral.poa_irradiance(
-        WEATHER, META, solpos_expected, tilt=None, azimuth=180, sky_model="isotropic"
+        WEATHER,
+        META,
+        sol_position=solpos_expected,
+        tilt=None,
+        azimuth=180,
+        sky_model="isotropic",
     )
 
     pd.testing.assert_frame_equal(result, poa_expected, check_dtype=False)
diff --git a/tests/test_utilities.py b/tests/test_utilities.py
index cd80552..2ef9bb3 100644
--- a/tests/test_utilities.py
+++ b/tests/test_utilities.py
@@ -80,6 +80,7 @@ def test_get_kinetics_bad():
     assert res == desired_output
 
 
+### DEPRECATE WITH THE OLD FUNCTION _read_material, replaced by read_material
 def test_read_material_bad():
     # no name case
     fpath = os.path.join(DATA_DIR, "O2permeation.json")
@@ -136,3 +137,98 @@ def test_nrel_kestrel_check_bad():
 
     with pytest.raises(ConnectionError):
         pvdeg.utilities.nrel_kestrel_check()
+
+
+# NEW MATERIAL UTIL FUNCTIONS
+# These tests will likely fail if the associated materials are changed
+# ===========================
+def test_read_material_special():
+
+    template_material = pvdeg.utilities.read_material(pvdeg_file="AApermeation", key="AA000")
+
+    assert len(template_material) == 1
+    assert "comment" in template_material
+
+def test_read_material_normal():
+
+    res = {
+        'name': 'ST504', 
+        'alias': 'PET1', 
+        'contributor': 'Michael Kempe', 
+        'source': 'unpublished measurements', 
+        'Fickian': True,
+        'Ead': 47.603, 
+        'Do': 0.554153, 
+        'Eas': -11.5918, 
+        'So': 9.554366e-07, 
+        'Eap': 34.2011, 
+        'Po': 2128.8937
+    }
+
+    template_material = pvdeg.utilities.read_material(pvdeg_file="O2permeation", key="OX002")
+
+    assert template_material == res
+
+def test_read_material_fewer_params():
+
+    res = {
+        'name': 'ST504', 
+        'Fickian': True,
+    }
+
+    template_material = pvdeg.utilities.read_material(pvdeg_file="O2permeation", key="OX002", parameters=["name", "Fickian"])
+
+    assert template_material == res
+
+
+
+def test_read_material_extra_params():
+
+    res = {
+        'namenotindict1': None,
+        'namenotindict2': None,
+    }
+
+    template_material = pvdeg.utilities.read_material(pvdeg_file="O2permeation", key="OX002", parameters=["namenotindict1", "namenotindict2"])
+
+    assert template_material == res
+
+# pvdeg_file should override fp if both are provided
+def test_read_material_fp_override():
+
+    res = {
+        'name': 'ST504', 
+        'alias': 'PET1', 
+        'contributor': 'Michael Kempe', 
+        'source': 'unpublished measurements', 
+        'Fickian': True,
+        'Ead': 47.603, 
+        'Do': 0.554153, 
+        'Eas': -11.5918, 
+        'So': 9.554366e-07, 
+        'Eap': 34.2011, 
+        'Po': 2128.8937
+    }
+
+    from pvdeg import DATA_DIR
+
+    # fp gets overridden by pvdeg_file
+    template_material = pvdeg.utilities.read_material(
+        pvdeg_file="O2permeation", 
+        fp=os.path.join(DATA_DIR, "AApermeation.json"), 
+        key="OX002",
+    )
+
+    assert template_material == res
+
+
+def test_search_json():
+    name_res = pvdeg.utilities.search_json(pvdeg_file="H2Opermeation", name_or_alias="Ethylene Vinyl Acetate")
+    alias_res = pvdeg.utilities.search_json(pvdeg_file="H2Opermeation", name_or_alias="EVA")
+
+    assert name_res == "W001"
+    assert alias_res == "W001"
+
+
+# def test_search_json_bad():
+#     ...
\ No newline at end of file
diff --git a/tutorials_and_tools/tutorials_and_tools/Scenario - Geospatial.ipynb b/tutorials_and_tools/tutorials_and_tools/Scenario - Geospatial.ipynb
index 9b47deb..805dcdc 100644
--- a/tutorials_and_tools/tutorials_and_tools/Scenario - Geospatial.ipynb	
+++ b/tutorials_and_tools/tutorials_and_tools/Scenario - Geospatial.ipynb	
@@ -14,7 +14,8 @@
    "metadata": {},
    "source": [
     "## Define Geospatial Scenario Object\n",
-    "Scenario is a general class that can be used to replace the legacy functional pvdeg analysis approach with an object orented one. ``Scenario`` can preform single location or geospatial analysis. The scenario constructor takes many arguments but the only required one for the following use cases is ``geospatial``. Choose ``geospatial = True`` to preform geospatial analysis or ``geospatial = False`` to preform a single location scenario. The ``name`` attribute is visible in when we display the entire scenario and is present in the file of saved information about the scenario."
+    "\n",
+    "To preform geospatial analysis we can create a `GeospatialScenario` object. Alternatively, to preform single location analysis use `Scenario`. Scenario and GeospatialScenario are generalized classes that can be used to replace the legacy functional pvdeg analysis approach with an object orented one."
    ]
   },
   {
@@ -23,9 +24,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "geospatial_standoff_scenario = pvdeg.scenario.Geospatial_Scenario(\n",
+    "geospatial_standoff_scenario = pvdeg.GeospatialScenario(\n",
     "    name='standoff geospatial',\n",
-    "    geospatial = True,\n",
     ")"
    ]
   },
@@ -46,19 +46,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/tford/.conda-envs/pvdeg_env/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"phony_dim_1\" starting at index 500. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "/home/tford/.conda-envs/rpp/lib/python3.10/site-packages/xarray/core/dataset.py:274: UserWarning: The specified chunks separate the stored chunks along dimension \"phony_dim_1\" starting at index 500. This could degrade performance. Instead, consider rechunking after loading.\n",
       "  warnings.warn(\n",
-      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:1155: UserWarning: Gids Added - [449211 449212 450161 450162 450163 450164 451112 451113 451114 451115\n",
-      " 452064 452065 452066 452067 452068 453015 453016 453017 453018 453019\n",
-      " 453020 453965 453966 453967 453968 453969 453973 453974 453975 454915\n",
-      " 454916 454917 454918 454919 454920 454922 454923 454924 454925 454926\n",
-      " 455867 455868 455869 455870 455871 455872 455873 455874 455875 455876\n",
-      " 455877 456818 456819 456820 456821 456822 456823 456824 456825 456826\n",
-      " 456827 456828 457768 457769 457770 457771 457772 457773 457774 457775\n",
-      " 457776 457777 458719 458720 458721 458722 458723 458724 458725 458726\n",
-      " 459670 459671 459672 459673 459674 459675 459676 460610 460611 460612\n",
-      " 460613 460614 460615 461552 461553 461554 461555 461556 462496 462497\n",
-      " 462498 462499 463455 464426]\n",
+      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:1237: UserWarning: Gids Added - [449212 454915 460611]\n",
       "  warnings.warn(message, UserWarning)\n"
      ]
     }
@@ -90,8 +80,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:1183: UserWarning: standoff added to pipeline as \n",
-      " {'geospatial_job': <function standoff at 0x7f15d5386a70>}\n",
+      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:1626: UserWarning: standoff added to pipeline as \n",
+      " {'geospatial_job': {'job': <function standoff at 0x7faea33f0790>, 'params': {}}}\n",
       "  warnings.warn(message, UserWarning)\n"
      ]
     }
@@ -113,37 +103,25 @@
      "output_type": "stream",
      "text": [
       "Name : standoff geospatial\n",
-      "\u001b[1;32mGEOSPATIAL = True\u001b[0m\n",
       "Pipeline : \n",
-      "                          geospatial_job\n",
-      "0  <function standoff at 0x7f15d5386a70>\n",
+      "                      geospatial_job.job\n",
+      "0  <function standoff at 0x7faea33f0790>\n",
       "Results : Pipeline results : \n",
       "Pipeline has not been run\n",
-      "('gids : [449211 449212 450161 450162 450163 450164 451112 451113 451114 '\n",
-      " '451115\\n'\n",
-      " ' 452064 452065 452066 452067 452068 453015 453016 453017 453018 453019\\n'\n",
-      " ' 453020 453965 453966 453967 453968 453969 453973 453974 453975 454915\\n'\n",
-      " ' 454916 454917 454918 454919 454920 454922 454923 454924 454925 454926\\n'\n",
-      " ' 455867 455868 455869 455870 455871 455872 455873 455874 455875 455876\\n'\n",
-      " ' 455877 456818 456819 456820 456821 456822 456823 456824 456825 456826\\n'\n",
-      " ' 456827 456828 457768 457769 457770 457771 457772 457773 457774 457775\\n'\n",
-      " ' 457776 457777 458719 458720 458721 458722 458723 458724 458725 458726\\n'\n",
-      " ' 459670 459671 459672 459673 459674 459675 459676 460610 460611 460612\\n'\n",
-      " ' 460613 460614 460615 461552 461553 461554 461555 461556 462496 462497\\n'\n",
-      " ' 462498 462499 463455 464426]')\n",
+      "'gids : [449212 454915 460611]'\n",
       "'test modules :'\n",
-      "scenario weather : <xarray.Dataset> Size: 44MB\n",
-      "Dimensions:            (time: 17520, gid: 104)\n",
+      "scenario weather : <xarray.Dataset> Size: 2TB\n",
+      "Dimensions:            (time: 17520, gid: 2018267)\n",
       "Coordinates:\n",
-      "  * gid                (gid) int64 832B 449211 449212 450161 ... 463455 464426\n",
+      "  * gid                (gid) int64 16MB 0 1 2 3 ... 2018264 2018265 2018266\n",
       "  * time               (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31T...\n",
       "Data variables:\n",
-      "    temp_air           (time, gid) float32 7MB dask.array<chunksize=(17520, 2), meta=np.ndarray>\n",
-      "    wind_speed         (time, gid) float32 7MB dask.array<chunksize=(17520, 2), meta=np.ndarray>\n",
-      "    dhi                (time, gid) float32 7MB dask.array<chunksize=(17520, 2), meta=np.ndarray>\n",
-      "    ghi                (time, gid) float32 7MB dask.array<chunksize=(17520, 2), meta=np.ndarray>\n",
-      "    dni                (time, gid) float32 7MB dask.array<chunksize=(17520, 2), meta=np.ndarray>\n",
-      "    relative_humidity  (time, gid) float32 7MB dask.array<chunksize=(17520, 2), meta=np.ndarray>\n",
+      "    temp_air           (time, gid) float64 283GB dask.array<chunksize=(17520, 500), meta=np.ndarray>\n",
+      "    wind_speed         (time, gid) float64 283GB dask.array<chunksize=(17520, 500), meta=np.ndarray>\n",
+      "    dhi                (time, gid) float64 283GB dask.array<chunksize=(17520, 500), meta=np.ndarray>\n",
+      "    ghi                (time, gid) float64 283GB dask.array<chunksize=(17520, 500), meta=np.ndarray>\n",
+      "    dni                (time, gid) float64 283GB dask.array<chunksize=(17520, 500), meta=np.ndarray>\n",
+      "    relative_humidity  (time, gid) float64 283GB dask.array<chunksize=(17520, 500), meta=np.ndarray>\n",
       "Attributes:\n",
       "    full_version_record:  {\"rex\": \"0.2.80\", \"pandas\": \"2.0.0\", \"numpy\": \"1.23...\n",
       "    package:              rex\n",
@@ -169,218 +147,26 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/tford/.conda-envs/rpp/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n",
+      "Perhaps you already have a cluster running?\n",
+      "Hosting the HTTP server on port 37481 instead\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dashboard: http://127.0.0.1:8787/status\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "Dashboard: http://127.0.0.1:37481/status\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
       "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
       "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
       "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n"
      ]
     }
@@ -769,162 +555,31 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 6kB\n",
-       "Dimensions:    (latitude: 14, longitude: 17)\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 164B\n",
+       "Dimensions:    (latitude: 2, longitude: 3)\n",
        "Coordinates:\n",
-       "  * latitude   (latitude) float32 56B 39.37 39.41 39.45 ... 39.81 39.85 39.89\n",
-       "  * longitude  (longitude) float32 68B -106.4 -106.4 -106.3 ... -105.8 -105.8\n",
+       "  * latitude   (latitude) float32 8B 39.61 39.85\n",
+       "  * longitude  (longitude) float32 12B -106.4 -106.2 -105.9\n",
        "Data variables:\n",
-       "    T98_inf    (latitude, longitude) float64 2kB nan nan nan nan ... nan nan nan\n",
-       "    x          (latitude, longitude) float64 2kB nan nan nan nan ... nan nan nan\n",
-       "    T98_0      (latitude, longitude) float64 2kB nan nan nan nan ... nan nan nan\n",
+       "    x          (latitude, longitude) float64 48B nan nan 0.5751 1.432 1.421 nan\n",
+       "    T98_0      (latitude, longitude) float64 48B nan nan 72.15 75.22 75.25 nan\n",
+       "    T98_inf    (latitude, longitude) float64 48B nan nan 46.77 48.81 48.51 nan\n",
        "Attributes:\n",
-       "    long_name:  Standoff dataset</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c7316c52-9b26-4582-ba03-1b1b014647f0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c7316c52-9b26-4582-ba03-1b1b014647f0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 14</li><li><span class='xr-has-index'>longitude</span>: 17</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d54fe3cb-3849-40a3-8c67-4a1c89f89a9d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d54fe3cb-3849-40a3-8c67-4a1c89f89a9d' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>39.37 39.41 39.45 ... 39.85 39.89</div><input id='attrs-2dbd9a05-6481-44e2-aec2-bf29d590cdf7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2dbd9a05-6481-44e2-aec2-bf29d590cdf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b65e45f-f74e-4a2b-9aa7-3efbbf0f4ea7' class='xr-var-data-in' type='checkbox'><label for='data-9b65e45f-f74e-4a2b-9aa7-3efbbf0f4ea7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([39.37, 39.41, 39.45, 39.49, 39.53, 39.57, 39.61, 39.65, 39.69, 39.73,\n",
-       "       39.77, 39.81, 39.85, 39.89], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-106.4 -106.4 ... -105.8 -105.8</div><input id='attrs-3f770120-56c4-476e-8baf-ce2b26148fca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f770120-56c4-476e-8baf-ce2b26148fca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e2fe75e-ba0d-42fc-9c69-4dae9c231001' class='xr-var-data-in' type='checkbox'><label for='data-9e2fe75e-ba0d-42fc-9c69-4dae9c231001' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-106.42, -106.38, -106.34, -106.3 , -106.26, -106.22, -106.18, -106.14,\n",
-       "       -106.1 , -106.06, -106.02, -105.98, -105.94, -105.9 , -105.86, -105.82,\n",
-       "       -105.78], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e1e9a6b2-20c5-4ceb-8100-d4b9f3b33cf1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e1e9a6b2-20c5-4ceb-8100-d4b9f3b33cf1' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>T98_inf</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-568d5692-7f15-436f-96bd-64852d302fe7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-568d5692-7f15-436f-96bd-64852d302fe7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-165d650b-acda-4da8-a7bf-7e16e8a3b7bf' class='xr-var-data-in' type='checkbox'><label for='data-165d650b-acda-4da8-a7bf-7e16e8a3b7bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>98th percential temperature of a theoretical rack mounted module</dd><dt><span>units :</span></dt><dd>Celsius</dd></dl></div><div class='xr-var-data'><pre>array([[        nan,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan,         nan,         nan, 45.35322484,\n",
-       "        43.89937604,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan,         nan,         nan,         nan,         nan,\n",
-       "                nan, 46.98650807, 44.31112328, 44.61857221, 45.56447351,\n",
-       "        44.35847009, 45.71251047,         nan,         nan,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan,         nan,         nan,         nan,         nan,\n",
-       "        45.56057541, 45.23266111, 46.4749555 , 45.45866643, 46.83209903,\n",
-       "        47.53475671, 46.21043791, 44.97454313,         nan,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan,         nan,         nan,         nan,         nan,\n",
-       "        46.2408306 , 47.70973162, 48.30700967, 47.53154377, 48.81785937,\n",
-       "        47.65733536, 46.68088972, 47.0857014 , 46.35772207,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan,         nan,         nan,         nan,         nan,\n",
-       "        47.55100402, 45.23275417, 48.80965997, 49.08378759, 49.58939307,\n",
-       "        48.32212707, 48.23412481, 47.22990295, 45.81461932, 43.75751638,\n",
-       "                nan,         nan],\n",
-       "...\n",
-       "       [        nan,         nan,         nan, 44.88069015, 46.39475897,\n",
-       "        46.44447161, 50.11742163, 51.96570951, 50.32797192, 47.11341201,\n",
-       "                nan,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan, 46.33443188, 45.50806442, 49.05191767, 49.90184082,\n",
-       "        50.84454158, 52.69066389, 53.18600001, 48.24824607,         nan,\n",
-       "                nan,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan, 48.21716814, 49.46232219, 50.54736076, 52.00476748,\n",
-       "        52.89231671, 53.93599492, 51.70862262, 48.77753987,         nan,\n",
-       "                nan,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan],\n",
-       "       [48.81362929, 50.29714338, 51.16092959, 53.69803177, 54.62206591,\n",
-       "        53.61957774, 48.50935854,         nan,         nan,         nan,\n",
-       "                nan,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan],\n",
-       "       [50.69564991, 53.01139587, 54.07169789, 54.99251546, 54.22057367,\n",
-       "                nan,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-d882a89a-c7e0-4452-a2c6-319e19b5eb0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d882a89a-c7e0-4452-a2c6-319e19b5eb0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce3a1e3b-049b-47c7-bb21-3763d22ee5db' class='xr-var-data-in' type='checkbox'><label for='data-ce3a1e3b-049b-47c7-bb21-3763d22ee5db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Standoff distance</dd><dt><span>units :</span></dt><dd>cm</dd></dl></div><div class='xr-var-data'><pre>array([[       nan,        nan,        nan,        nan,        nan,\n",
-       "               nan,        nan,        nan,        nan, 0.38174937,\n",
-       "        0.13659464,        nan,        nan,        nan,        nan,\n",
-       "               nan,        nan],\n",
-       "       [       nan,        nan,        nan,        nan,        nan,\n",
-       "               nan, 0.9130456 , 0.50536888, 0.3667705 , 0.42310054,\n",
-       "        0.06835816, 0.54953583,        nan,        nan,        nan,\n",
-       "               nan,        nan],\n",
-       "       [       nan,        nan,        nan,        nan,        nan,\n",
-       "        0.56023646, 0.45104524, 0.70785479, 0.4749976 , 0.78740578,\n",
-       "        0.96547941, 0.73029192, 0.42816909,        nan,        nan,\n",
-       "               nan,        nan],\n",
-       "       [       nan,        nan,        nan,        nan,        nan,\n",
-       "        0.73907568, 1.11088712, 1.26272601, 1.15979828, 1.27547234,\n",
-       "        1.02971509, 0.73545644, 0.72544465, 0.62266267,        nan,\n",
-       "               nan,        nan],\n",
-       "       [       nan,        nan,        nan,        nan,        nan,\n",
-       "        1.01519859, 0.47589118, 1.35760827, 1.62037989, 1.55823714,\n",
-       "        1.01313484, 1.07268246, 0.74378345, 0.47957896, 0.        ,\n",
-       "               nan,        nan],\n",
-       "...\n",
-       "       [       nan,        nan,        nan, 0.37415299, 0.97772076,\n",
-       "        0.9206585 , 1.96365567, 2.55442995, 1.98162568, 1.00761762,\n",
-       "               nan,        nan,        nan,        nan,        nan,\n",
-       "               nan,        nan],\n",
-       "       [       nan, 0.55791939, 0.42296545, 1.70265453, 2.09715226,\n",
-       "        2.26759819, 2.8711312 , 2.87617753, 1.33642089,        nan,\n",
-       "               nan,        nan,        nan,        nan,        nan,\n",
-       "               nan,        nan],\n",
-       "       [       nan, 1.20988196, 1.6802628 , 2.13183147, 2.55030884,\n",
-       "        2.97268427, 3.31105303, 2.42166861, 1.3194013 ,        nan,\n",
-       "               nan,        nan,        nan,        nan,        nan,\n",
-       "               nan,        nan],\n",
-       "       [1.43243746, 1.96503997, 2.25850429, 3.25529759, 3.62285751,\n",
-       "        3.28747475, 1.42132977,        nan,        nan,        nan,\n",
-       "               nan,        nan,        nan,        nan,        nan,\n",
-       "               nan,        nan],\n",
-       "       [2.15902184, 3.03008154, 3.3627609 , 3.70965497, 3.42559455,\n",
-       "               nan,        nan,        nan,        nan,        nan,\n",
-       "               nan,        nan,        nan,        nan,        nan,\n",
-       "               nan,        nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T98_0</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-b8b9e061-a354-46f3-9245-8b673f55730a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8b9e061-a354-46f3-9245-8b673f55730a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cc8b482-c679-4fb4-9603-d88c8e2aa29d' class='xr-var-data-in' type='checkbox'><label for='data-3cc8b482-c679-4fb4-9603-d88c8e2aa29d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>98th percential temperature of a theoretical module with no standoff</dd><dt><span>units :</span></dt><dd>Celsius</dd></dl></div><div class='xr-var-data'><pre>array([[        nan,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan,         nan,         nan, 71.49087316,\n",
-       "        70.55429687,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan,         nan,         nan,         nan,         nan,\n",
-       "                nan, 73.47073124, 72.0769815 , 71.47335535, 71.64347548,\n",
-       "        70.27108569, 72.14265925,         nan,         nan,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan,         nan,         nan,         nan,         nan,\n",
-       "        72.19988195, 71.77967791, 72.70659545, 71.86054945, 72.98361322,\n",
-       "        73.5974435 , 72.82875312, 71.70398765,         nan,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan,         nan,         nan,         nan,         nan,\n",
-       "        72.86108811, 74.15443528, 74.65138317, 74.38898397, 74.59234099,\n",
-       "        73.83524128, 72.7935523 , 72.70556514, 72.37682001,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan,         nan,         nan,         nan,         nan,\n",
-       "        73.79481762, 71.88133973, 74.92200598, 75.92165319, 75.52932367,\n",
-       "        73.65642072, 73.90536883, 72.76046804, 71.85190888, 69.77808463,\n",
-       "                nan,         nan],\n",
-       "...\n",
-       "       [        nan,         nan,         nan, 71.48834272, 73.83161838,\n",
-       "        73.58424525, 77.01253907, 78.68175116, 77.01194756, 73.83760228,\n",
-       "                nan,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan, 72.12103048, 71.64672617, 76.27305986, 77.65270655,\n",
-       "        77.99649437, 79.61305995, 79.35827707, 74.96518041,         nan,\n",
-       "                nan,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan, 74.4564558 , 76.05838286, 77.55059991, 78.64605219,\n",
-       "        79.920056  , 80.67100199, 78.25767803, 74.77619672,         nan,\n",
-       "                nan,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan],\n",
-       "       [75.22337446, 76.95482827, 77.82708371, 80.59734793, 81.47288496,\n",
-       "        80.78248131, 75.2526506 ,         nan,         nan,         nan,\n",
-       "                nan,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan],\n",
-       "       [77.60537288, 80.08905941, 80.79258011, 81.54876731, 80.94883566,\n",
-       "                nan,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan,         nan,         nan,         nan,\n",
-       "                nan,         nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4eb42477-8cd2-4cee-a084-ea63a0a934b0' class='xr-section-summary-in' type='checkbox'  ><label for='section-4eb42477-8cd2-4cee-a084-ea63a0a934b0' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ac2070bd-6896-4fef-97e8-496ea8b42da5' class='xr-index-data-in' type='checkbox'/><label for='index-ac2070bd-6896-4fef-97e8-496ea8b42da5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([39.369998931884766,  39.40999984741211,  39.45000076293945,\n",
-       "         39.4900016784668, 39.529998779296875,  39.56999969482422,\n",
-       "        39.61000061035156, 39.650001525878906, 39.689998626708984,\n",
-       "        39.72999954223633,  39.77000045776367, 39.810001373291016,\n",
-       "       39.849998474121094,  39.88999938964844],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-37363bec-e51a-4e07-8054-eb7ccb891d8c' class='xr-index-data-in' type='checkbox'/><label for='index-37363bec-e51a-4e07-8054-eb7ccb891d8c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-106.41999816894531, -106.37999725341797, -106.33999633789062,\n",
-       "       -106.30000305175781, -106.26000213623047, -106.22000122070312,\n",
-       "       -106.18000030517578, -106.13999938964844,  -106.0999984741211,\n",
-       "       -106.05999755859375,  -106.0199966430664,  -105.9800033569336,\n",
-       "       -105.94000244140625,  -105.9000015258789, -105.86000061035156,\n",
-       "       -105.81999969482422, -105.77999877929688],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-31bf9e73-56b0-48bb-8dc6-965ab2c7cd34' class='xr-section-summary-in' type='checkbox'  checked><label for='section-31bf9e73-56b0-48bb-8dc6-965ab2c7cd34' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Standoff dataset</dd></dl></div></li></ul></div></div>"
+       "    long_name:  Standoff dataset</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fdfbc439-732a-46c9-b6c1-95bb5221c767' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fdfbc439-732a-46c9-b6c1-95bb5221c767' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 2</li><li><span class='xr-has-index'>longitude</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d06624ca-8e0d-460e-aef3-17d367539050' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d06624ca-8e0d-460e-aef3-17d367539050' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>39.61 39.85</div><input id='attrs-02413e04-b4fb-4dc3-84a1-ea8c8308f383' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02413e04-b4fb-4dc3-84a1-ea8c8308f383' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5261c829-1c46-48ac-98e1-6b0692ff07ba' class='xr-var-data-in' type='checkbox'><label for='data-5261c829-1c46-48ac-98e1-6b0692ff07ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([39.61, 39.85], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-106.4 -106.2 -105.9</div><input id='attrs-2c227c56-c1fe-4994-adfd-2e026b4afbff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c227c56-c1fe-4994-adfd-2e026b4afbff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23bb59cc-9ad9-418f-aa4c-485c6788251b' class='xr-var-data-in' type='checkbox'><label for='data-23bb59cc-9ad9-418f-aa4c-485c6788251b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-106.42, -106.18, -105.94], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5d8e5dab-c695-4e9d-85ef-23b48c6f29d3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d8e5dab-c695-4e9d-85ef-23b48c6f29d3' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan 0.5751 1.432 1.421 nan</div><input id='attrs-7164ab65-0d6f-4b8a-bb01-b22883bbd1ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7164ab65-0d6f-4b8a-bb01-b22883bbd1ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-baf22aac-17e8-418e-9950-c72381dc30f0' class='xr-var-data-in' type='checkbox'><label for='data-baf22aac-17e8-418e-9950-c72381dc30f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Standoff distance</dd><dt><span>units :</span></dt><dd>cm</dd></dl></div><div class='xr-var-data'><pre>array([[       nan,        nan, 0.57509802],\n",
+       "       [1.43243647, 1.42132958,        nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T98_0</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan 72.15 75.22 75.25 nan</div><input id='attrs-08a06c39-91a7-4d72-aaed-4897eff5b7d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08a06c39-91a7-4d72-aaed-4897eff5b7d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ae4ea6c-902d-40ee-a321-e5f79562bf88' class='xr-var-data-in' type='checkbox'><label for='data-5ae4ea6c-902d-40ee-a321-e5f79562bf88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>98th percential temperature of a theoretical module with no standoff</dd><dt><span>units :</span></dt><dd>Celsius</dd></dl></div><div class='xr-var-data'><pre>array([[        nan,         nan, 72.14898719],\n",
+       "       [75.22337147, 75.25265046,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T98_inf</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan 46.77 48.81 48.51 nan</div><input id='attrs-06e63488-565a-44a6-a9b3-d820b68f651f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06e63488-565a-44a6-a9b3-d820b68f651f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a90b42e-d7b0-4224-8e08-04afe26cf32a' class='xr-var-data-in' type='checkbox'><label for='data-4a90b42e-d7b0-4224-8e08-04afe26cf32a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>98th percential temperature of a theoretical rack mounted module</dd><dt><span>units :</span></dt><dd>Celsius</dd></dl></div><div class='xr-var-data'><pre>array([[        nan,         nan, 46.76989297],\n",
+       "       [48.81362521, 48.50935599,         nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6179ccf4-4869-43f2-bc87-efa518375487' class='xr-section-summary-in' type='checkbox'  ><label for='section-6179ccf4-4869-43f2-bc87-efa518375487' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fc14279d-1b26-450a-baa6-cad447ae1b0a' class='xr-index-data-in' type='checkbox'/><label for='index-fc14279d-1b26-450a-baa6-cad447ae1b0a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([39.61, 39.85], dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-396aeb0c-1d00-482e-a28a-5d5d84c842ac' class='xr-index-data-in' type='checkbox'/><label for='index-396aeb0c-1d00-482e-a28a-5d5d84c842ac' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-106.41999816894531, -106.18000030517578, -105.94000244140625], dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-47f842da-27c3-4f04-aa55-bd8fdee8fc58' class='xr-section-summary-in' type='checkbox'  checked><label for='section-47f842da-27c3-4f04-aa55-bd8fdee8fc58' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Standoff dataset</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset> Size: 6kB\n",
-       "Dimensions:    (latitude: 14, longitude: 17)\n",
+       "<xarray.Dataset> Size: 164B\n",
+       "Dimensions:    (latitude: 2, longitude: 3)\n",
        "Coordinates:\n",
-       "  * latitude   (latitude) float32 56B 39.37 39.41 39.45 ... 39.81 39.85 39.89\n",
-       "  * longitude  (longitude) float32 68B -106.4 -106.4 -106.3 ... -105.8 -105.8\n",
+       "  * latitude   (latitude) float32 8B 39.61 39.85\n",
+       "  * longitude  (longitude) float32 12B -106.4 -106.2 -105.9\n",
        "Data variables:\n",
-       "    T98_inf    (latitude, longitude) float64 2kB nan nan nan nan ... nan nan nan\n",
-       "    x          (latitude, longitude) float64 2kB nan nan nan nan ... nan nan nan\n",
-       "    T98_0      (latitude, longitude) float64 2kB nan nan nan nan ... nan nan nan\n",
+       "    x          (latitude, longitude) float64 48B nan nan 0.5751 1.432 1.421 nan\n",
+       "    T98_0      (latitude, longitude) float64 48B nan nan 72.15 75.22 75.25 nan\n",
+       "    T98_inf    (latitude, longitude) float64 48B nan nan 46.77 48.81 48.51 nan\n",
        "Attributes:\n",
        "    long_name:  Standoff dataset"
       ]
@@ -949,7 +604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -960,25 +615,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Limited Geospatial Functionality\n",
-    "Only the following functions are currently supported for pvdeg geospatial analysis\n",
+    "## Example Geospatial Functionality\n",
+    "Many functions are supported for geospatial analysis, here are a few.\n",
     "- ``pvdeg.standards.standoff`` \n",
     "- ``pvdeg.humidity.module`` \n",
-    "- ``pvdeg.letid.calc_letid_outdoors``"
+    "- ``pvdeg.letid.calc_letid_outdoors``\n",
+    "\n",
+    "See the Geospatial Templates tutorial for an example on this."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/tford/.conda-envs/pvdeg_env/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"phony_dim_1\" starting at index 500. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "/home/tford/.conda-envs/rpp/lib/python3.10/site-packages/xarray/core/dataset.py:274: UserWarning: The specified chunks separate the stored chunks along dimension \"phony_dim_1\" starting at index 500. This could degrade performance. Instead, consider rechunking after loading.\n",
       "  warnings.warn(\n",
-      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:1155: UserWarning: Gids Added - [474405 474406 474407 474408 474409 474410 474411 474412 474413 474414\n",
+      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:1237: UserWarning: Gids Added - [474405 474406 474407 474408 474409 474410 474411 474412 474413 474414\n",
       " 474415 474416 474417 474418 474419 474420 474421 474422 474423 475420\n",
       " 475421 475422 475423 475424 475425 475426 475427 475428 475429 475430\n",
       " 475431 475432 475433 475434 475435 475436 475437 475438 476435 476436\n",
@@ -993,14 +650,14 @@
       " 481539 481540 481542 481543 481544 482557 482558 482559 482560 482561\n",
       " 482562 482564 482565]\n",
       "  warnings.warn(message, UserWarning)\n",
-      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:1183: UserWarning: module added to pipeline as \n",
-      " {'geospatial_job': <function module at 0x7f15ecb44d30>}\n",
+      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:1626: UserWarning: module added to pipeline as \n",
+      " {'geospatial_job': {'job': <function module at 0x7fb8d85a9900>, 'params': {}}}\n",
       "  warnings.warn(message, UserWarning)\n"
      ]
     }
    ],
    "source": [
-    "geospatial_humidity_scenario = pvdeg.scenario.Geospatial_Scenario(\n",
+    "geospatial_humidity_scenario = pvdeg.GeospatialScenario(\n",
     "    name = 'humidity scenario',\n",
     "    geospatial = True\n",
     ")\n",
@@ -1019,16 +676,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/tford/.conda-envs/pvdeg_env/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n",
+      "/home/tford/.conda-envs/rpp/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n",
       "Perhaps you already have a cluster running?\n",
-      "Hosting the HTTP server on port 37307 instead\n",
+      "Hosting the HTTP server on port 40703 instead\n",
       "  warnings.warn(\n"
      ]
     },
@@ -1036,406 +693,140 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dashboard: http://127.0.0.1:37307/status\n",
+      "Dashboard: http://127.0.0.1:40703/status\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n"
+      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n"
      ]
     }
    ],
@@ -1445,7 +836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1821,176 +1212,176 @@
        "  * longitude           (longitude) float32 36B -105.4 -105.3 ... -105.1 -105.1\n",
        "  * time                (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31...\n",
        "Data variables:\n",
+       "    RH_surface_outside  (time, latitude, longitude) float64 24MB 76.96 ... 58.81\n",
        "    RH_back_encap       (time, latitude, longitude) float64 24MB 76.96 ... 44.67\n",
-       "    RH_front_encap      (time, latitude, longitude) float64 24MB 42.21 ... 31.69\n",
        "    RH_backsheet        (time, latitude, longitude) float64 24MB 76.96 ... 51.74\n",
-       "    RH_surface_outside  (time, latitude, longitude) float64 24MB 76.96 ... 58.81</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-67a55e38-21ab-416c-a351-3eed12e953da' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-67a55e38-21ab-416c-a351-3eed12e953da' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 19</li><li><span class='xr-has-index'>longitude</span>: 9</li><li><span class='xr-has-index'>time</span>: 17520</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-616df235-b2bd-48a0-bfd7-21085d551a6d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-616df235-b2bd-48a0-bfd7-21085d551a6d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>39.17 39.21 39.25 ... 39.85 39.89</div><input id='attrs-42ed5b3a-77cb-4cfa-9cc7-025c6723fc62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42ed5b3a-77cb-4cfa-9cc7-025c6723fc62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd6054c2-dccf-411a-aa3f-65233968a8ed' class='xr-var-data-in' type='checkbox'><label for='data-dd6054c2-dccf-411a-aa3f-65233968a8ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([39.17, 39.21, 39.25, 39.29, 39.33, 39.37, 39.41, 39.45, 39.49, 39.53,\n",
+       "    RH_front_encap      (time, latitude, longitude) float64 24MB 42.21 ... 31.69</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2c96a5b9-7596-4cc5-b6d5-4658bf7a6532' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2c96a5b9-7596-4cc5-b6d5-4658bf7a6532' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 19</li><li><span class='xr-has-index'>longitude</span>: 9</li><li><span class='xr-has-index'>time</span>: 17520</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fd49e260-dd68-4ffc-9b6e-775c546cc5cb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd49e260-dd68-4ffc-9b6e-775c546cc5cb' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>39.17 39.21 39.25 ... 39.85 39.89</div><input id='attrs-852d4118-3a6a-4b1f-9af9-0fc1741c9f81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-852d4118-3a6a-4b1f-9af9-0fc1741c9f81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bd81dec-dc08-42b6-a8c3-e69bc4d3ab64' class='xr-var-data-in' type='checkbox'><label for='data-7bd81dec-dc08-42b6-a8c3-e69bc4d3ab64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([39.17, 39.21, 39.25, 39.29, 39.33, 39.37, 39.41, 39.45, 39.49, 39.53,\n",
        "       39.57, 39.61, 39.65, 39.69, 39.73, 39.77, 39.81, 39.85, 39.89],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-105.4 -105.3 ... -105.1 -105.1</div><input id='attrs-be650135-1610-42aa-b3f7-3ebcee6b411e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be650135-1610-42aa-b3f7-3ebcee6b411e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd78b974-e36c-4d2d-8e99-4eb127951ce6' class='xr-var-data-in' type='checkbox'><label for='data-cd78b974-e36c-4d2d-8e99-4eb127951ce6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-105.38, -105.34, -105.3 , -105.26, -105.22, -105.18, -105.14, -105.1 ,\n",
-       "       -105.06], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-12-31T23:30:00</div><input id='attrs-e49fc412-10c0-473d-8053-840382e53fa3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e49fc412-10c0-473d-8053-840382e53fa3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4773122d-426f-4f1e-b584-116c9d8bac59' class='xr-var-data-in' type='checkbox'><label for='data-4773122d-426f-4f1e-b584-116c9d8bac59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-01T00:30:00.000000000&#x27;,\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-105.4 -105.3 ... -105.1 -105.1</div><input id='attrs-16dff014-ea84-4217-b42e-4d491298a18e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-16dff014-ea84-4217-b42e-4d491298a18e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ca5b5b0-f5c3-47cd-8b68-f0fee36d18fc' class='xr-var-data-in' type='checkbox'><label for='data-4ca5b5b0-f5c3-47cd-8b68-f0fee36d18fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-105.38, -105.34, -105.3 , -105.26, -105.22, -105.18, -105.14, -105.1 ,\n",
+       "       -105.06], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01 ... 2022-12-31T23:30:00</div><input id='attrs-ab1f6c1a-301c-4a41-88ed-296d38be9db4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab1f6c1a-301c-4a41-88ed-296d38be9db4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4187675-c5e2-40d7-aeff-00c57459941b' class='xr-var-data-in' type='checkbox'><label for='data-d4187675-c5e2-40d7-aeff-00c57459941b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-01T00:30:00.000000000&#x27;,\n",
        "       &#x27;2022-01-01T01:00:00.000000000&#x27;, ..., &#x27;2022-12-31T22:30:00.000000000&#x27;,\n",
        "       &#x27;2022-12-31T23:00:00.000000000&#x27;, &#x27;2022-12-31T23:30:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-492170aa-c101-45e3-ae2a-d305b63f9f0b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-492170aa-c101-45e3-ae2a-d305b63f9f0b' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>RH_back_encap</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>76.96 72.91 66.56 ... 53.04 44.67</div><input id='attrs-a7c79401-b46c-4fe1-aed8-7da2d6740385' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7c79401-b46c-4fe1-aed8-7da2d6740385' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cd79c67-b604-43f7-a679-53dc542ebb10' class='xr-var-data-in' type='checkbox'><label for='data-6cd79c67-b604-43f7-a679-53dc542ebb10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 76.95999816,  72.90998813,  66.55999019, ...,          nan,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fa413b99-b712-4a35-98ef-452e57f31be9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fa413b99-b712-4a35-98ef-452e57f31be9' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>RH_surface_outside</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>76.96 72.91 66.56 ... 65.54 58.81</div><input id='attrs-f1f382b8-3a69-4bdd-96f0-de8439299b88' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1f382b8-3a69-4bdd-96f0-de8439299b88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97dfbdee-0c6b-43f3-8e02-66fef56cadc5' class='xr-var-data-in' type='checkbox'><label for='data-97dfbdee-0c6b-43f3-8e02-66fef56cadc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 76.96      ,  72.91      ,  66.56      , ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 83.23999445,  74.00999079,  68.03999327, ...,          nan,\n",
+       "        [ 83.24      ,  74.01      ,  68.04      , ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 86.22999226,  85.34000105,  70.75999725, ...,          nan,\n",
+       "        [ 86.23      ,  85.34      ,  70.76      , ...,          nan,\n",
        "                  nan,          nan],\n",
        "        ...,\n",
-       "        [ 99.99999976,  99.99998417,  88.82998403, ...,  78.70999481,\n",
-       "          79.89999871,  79.71999235],\n",
-       "        [ 94.26999692,  90.53999238,  87.27999488, ...,  80.68998706,\n",
-       "          81.44998912,  85.05998887],\n",
-       "        [ 95.96999436,  96.95999215,  88.04999401, ...,          nan,\n",
-       "          82.66999362,  85.13999069]],\n",
-       "\n",
-       "       [[ 78.28697501,  73.94967328,  67.68803042, ...,          nan,\n",
+       "        [100.        , 100.        ,  88.83      , ...,  78.71      ,\n",
+       "          79.9       ,  79.72      ],\n",
+       "        [ 94.27      ,  90.54      ,  87.28      , ...,  80.69      ,\n",
+       "          81.45      ,  85.06      ],\n",
+       "        [ 95.97      ,  96.96      ,  88.05      , ...,          nan,\n",
+       "          82.67      ,  85.14      ]],\n",
+       "\n",
+       "       [[ 80.62      ,  75.77      ,  69.56      , ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 84.68623598,  75.28075436,  69.19586543, ...,          nan,\n",
+       "        [ 87.13      ,  77.51      ,  71.12      , ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 87.73623352,  86.82883315,  71.96833239, ...,          nan,\n",
+       "        [ 90.41      ,  89.47      ,  74.07      , ...,          nan,\n",
        "                  nan,          nan],\n",
        "...\n",
-       "        [ 50.88439484,  51.40681192,  46.10186298, ...,  35.90589376,\n",
-       "          34.06232635,  32.2255579 ],\n",
-       "        [ 47.48018827,  44.35144403,  45.6199294 , ...,  48.78475104,\n",
-       "          41.83926705,  39.11277403],\n",
-       "        [ 48.33548504,  47.94072103,  44.74681487, ...,          nan,\n",
-       "          48.40090686,  40.44408641]],\n",
-       "\n",
-       "       [[ 41.8817194 ,  38.70865126,  35.61625861, ...,          nan,\n",
+       "        [ 48.29684427,  48.63877046,  47.30323217, ...,  42.97424484,\n",
+       "          39.50804956,  34.00664582],\n",
+       "        [ 47.43691135,  43.6120267 ,  52.72160402, ...,  51.67492832,\n",
+       "          40.62357079,  41.40364629],\n",
+       "        [ 46.51148038,  43.8259391 ,  46.93663194, ...,          nan,\n",
+       "          50.96723416,  45.09428596]],\n",
+       "\n",
+       "       [[ 61.31477627,  59.58381693,  53.73629418, ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 44.43430208,  39.563639  ,  34.57449508, ...,          nan,\n",
+       "        [ 66.26356971,  60.4095116 ,  53.18439525, ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 43.95612608,  43.18834928,  36.57350468, ...,          nan,\n",
+       "        [ 67.6248845 ,  66.49543541,  54.46856356, ...,          nan,\n",
        "                  nan,          nan],\n",
        "        ...,\n",
-       "        [ 59.25856005,  59.53473813,  51.49974679, ...,  39.44505937,\n",
-       "          38.86237429,  38.23333952],\n",
-       "        [ 53.83253637,  51.0841646 ,  50.27111782, ...,  53.29519028,\n",
-       "          48.10693275,  45.34753518],\n",
-       "        [ 55.30388832,  56.22437991,  50.61009581, ...,          nan,\n",
-       "          53.042118  ,  44.66817252]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RH_front_encap</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>42.21 40.38 37.98 ... 32.78 31.69</div><input id='attrs-ae2bd62a-dd75-4583-b008-8cb232a4661f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae2bd62a-dd75-4583-b008-8cb232a4661f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-600c8e93-f6b9-4312-b530-0d7e41eec910' class='xr-var-data-in' type='checkbox'><label for='data-600c8e93-f6b9-4312-b530-0d7e41eec910' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[42.21067   , 40.37601643, 37.98324369, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [44.52176444, 40.71783433, 38.26589782, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [45.12717146, 44.39353788, 38.93504777, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        ...,\n",
-       "        [49.76043466, 50.18874593, 45.10002094, ..., 40.05120833,\n",
-       "         40.46379864, 40.21168976],\n",
-       "        [47.68965172, 46.07159017, 44.44327416, ..., 41.04024475,\n",
-       "         40.9328183 , 41.79215422],\n",
-       "        [48.32376537, 48.48231899, 44.83114684, ...,         nan,\n",
-       "         41.17109941, 41.70459695]],\n",
-       "\n",
-       "       [[42.92159129, 40.93748593, 38.61020367, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [45.27954715, 41.40002638, 38.89940362, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [45.89932995, 45.15199533, 39.58300029, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "...\n",
-       "        [32.99609551, 33.28880812, 31.23675619, ..., 28.16316046,\n",
-       "         27.51018697, 25.97077736],\n",
-       "        [32.02219422, 30.51302182, 32.06930029, ..., 30.37528682,\n",
-       "         27.85914258, 28.17521338],\n",
-       "        [31.95051924, 31.36665265, 30.8780504 , ...,         nan,\n",
-       "         30.07190974, 28.91849874]],\n",
-       "\n",
-       "       [[35.57628397, 34.32254831, 32.1848749 , ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [37.47853221, 34.55622531, 32.02763601, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [37.65022422, 36.90757951, 32.33583117, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        ...,\n",
-       "        [38.25378958, 38.38917252, 34.71317989, ..., 30.63236135,\n",
-       "         31.04144048, 30.48618467],\n",
-       "        [36.12765875, 34.95273502, 35.08214694, ..., 33.01207264,\n",
-       "         31.84762686, 32.38900731],\n",
-       "        [36.39514449, 36.6283083 , 34.70506483, ...,         nan,\n",
-       "         32.78349709, 31.69381967]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RH_backsheet</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>76.96 72.91 66.56 ... 59.29 51.74</div><input id='attrs-a5dbb01b-ac0d-4e6e-b6ef-1e95e0f50f44' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a5dbb01b-ac0d-4e6e-b6ef-1e95e0f50f44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cce2de8e-b77f-482f-9deb-d71998da64cf' class='xr-var-data-in' type='checkbox'><label for='data-cce2de8e-b77f-482f-9deb-d71998da64cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 76.95999816,  72.90998813,  66.55999019, ...,          nan,\n",
+       "        [ 74.58260936,  73.95202277,  64.37381224, ...,  54.7933037 ,\n",
+       "          55.98946564,  53.8994496 ],\n",
+       "        [ 67.53097457,  64.82170173,  68.58065805, ...,  65.86956744,\n",
+       "          59.82781391,  61.99228078],\n",
+       "        [ 68.10683602,  68.9773368 ,  66.01276209, ...,          nan,\n",
+       "          65.54264791,  58.8102974 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RH_back_encap</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>76.96 72.91 66.56 ... 53.04 44.67</div><input id='attrs-6c481c67-3d3c-4c86-93e6-0cdab9657a85' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c481c67-3d3c-4c86-93e6-0cdab9657a85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e84d7f3e-11a0-4028-9c19-d1549ee01394' class='xr-var-data-in' type='checkbox'><label for='data-e84d7f3e-11a0-4028-9c19-d1549ee01394' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 76.96      ,  72.91      ,  66.56      , ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 83.23999445,  74.00999079,  68.03999327, ...,          nan,\n",
+       "        [ 83.24      ,  74.01      ,  68.04      , ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 86.22999226,  85.34000105,  70.75999725, ...,          nan,\n",
+       "        [ 86.23      ,  85.34      ,  70.76      , ...,          nan,\n",
        "                  nan,          nan],\n",
        "        ...,\n",
-       "        [ 99.99999976,  99.99998417,  88.82998403, ...,  78.70999481,\n",
-       "          79.89999871,  79.71999235],\n",
-       "        [ 94.26999692,  90.53999238,  87.27999488, ...,  80.68998706,\n",
-       "          81.44998912,  85.05998887],\n",
-       "        [ 95.96999436,  96.95999215,  88.04999401, ...,          nan,\n",
-       "          82.66999362,  85.13999069]],\n",
-       "\n",
-       "       [[ 79.45348119,  74.85983483,  68.62401135, ...,          nan,\n",
+       "        [100.        , 100.        ,  88.83      , ...,  78.71      ,\n",
+       "          79.9       ,  79.72      ],\n",
+       "        [ 94.27      ,  90.54      ,  87.28      , ...,  80.69      ,\n",
+       "          81.45      ,  85.06      ],\n",
+       "        [ 95.97      ,  96.96      ,  88.05      , ...,          nan,\n",
+       "          82.67      ,  85.14      ]],\n",
+       "\n",
+       "       [[ 78.28697722,  73.9496852 ,  67.68804054, ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 85.90811426,  76.39537054,  70.15792842, ...,          nan,\n",
+       "        [ 84.68624187,  75.28076349,  69.19587248, ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 89.07311275,  88.1494146 ,  73.01916203, ...,          nan,\n",
+       "        [ 87.73624046,  86.82883237,  71.96833546, ...,          nan,\n",
        "                  nan,          nan],\n",
        "...\n",
-       "        [ 49.59061924,  50.02278892,  46.70254771, ...,  39.44007032,\n",
-       "          36.78518739,  33.11609958],\n",
-       "        [ 47.45854531,  43.98173414,  49.17076805, ...,  50.22983898,\n",
-       "          41.2314185 ,  40.25821133],\n",
-       "        [ 47.42348113,  45.88333004,  45.84172267, ...,          nan,\n",
-       "          49.68406964,  42.76918615]],\n",
-       "\n",
-       "       [[ 51.59824778,  49.14623136,  44.6762761 , ...,          nan,\n",
+       "        [ 50.88439573,  51.40681294,  46.10186458, ...,  35.90589484,\n",
+       "          34.06232731,  32.22555914],\n",
+       "        [ 47.48019026,  44.35144509,  45.61993068, ...,  48.7847531 ,\n",
+       "          41.83926857,  39.11277564],\n",
+       "        [ 48.33548743,  47.94072184,  44.7468164 , ...,          nan,\n",
+       "          48.40090851,  40.44408805]],\n",
+       "\n",
+       "       [[ 41.88172089,  38.70865218,  35.6162601 , ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 55.34893435,  49.98657441,  43.879444  , ...,          nan,\n",
+       "        [ 44.43430352,  39.56364003,  34.57449572, ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 55.79050145,  54.84189383,  45.5210337 , ...,          nan,\n",
+       "        [ 43.95612825,  43.18835048,  36.57350631, ...,          nan,\n",
        "                  nan,          nan],\n",
        "        ...,\n",
-       "        [ 66.92058254,  66.7433754 ,  57.93677872, ...,  47.11918214,\n",
-       "          47.42591895,  46.06639074],\n",
-       "        [ 60.68174941,  57.95293   ,  59.42588884, ...,  59.58237713,\n",
-       "          53.9673724 ,  53.6699094 ],\n",
-       "        [ 61.70536026,  62.60085661,  58.31142679, ...,          nan,\n",
-       "          59.29238189,  51.73923473]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RH_surface_outside</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>76.96 72.91 66.56 ... 65.54 58.81</div><input id='attrs-f2c7485d-f060-471d-958c-da4e5f4ceb03' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2c7485d-f060-471d-958c-da4e5f4ceb03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d43d902-46ce-4492-ba88-a67162fe1b5b' class='xr-var-data-in' type='checkbox'><label for='data-1d43d902-46ce-4492-ba88-a67162fe1b5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 76.95999816,  72.90998813,  66.55999019, ...,          nan,\n",
+       "        [ 59.25856205,  59.53474033,  51.49974911, ...,  39.44506083,\n",
+       "          38.86237549,  38.23334124],\n",
+       "        [ 53.8325386 ,  51.08416665,  50.27111957, ...,  53.295193  ,\n",
+       "          48.10693466,  45.34753707],\n",
+       "        [ 55.30389085,  56.22438186,  50.61009816, ...,          nan,\n",
+       "          53.04211979,  44.66817438]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RH_backsheet</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>76.96 72.91 66.56 ... 59.29 51.74</div><input id='attrs-325b4d4c-eecc-4903-9570-f5cfb0789bbb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-325b4d4c-eecc-4903-9570-f5cfb0789bbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb8bf502-c902-4e25-b842-90867cfa891d' class='xr-var-data-in' type='checkbox'><label for='data-cb8bf502-c902-4e25-b842-90867cfa891d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 76.96      ,  72.91      ,  66.56      , ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 83.23999445,  74.00999079,  68.03999327, ...,          nan,\n",
+       "        [ 83.24      ,  74.01      ,  68.04      , ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 86.22999226,  85.34000105,  70.75999725, ...,          nan,\n",
+       "        [ 86.23      ,  85.34      ,  70.76      , ...,          nan,\n",
        "                  nan,          nan],\n",
        "        ...,\n",
-       "        [ 99.99999976,  99.99998417,  88.82998403, ...,  78.70999481,\n",
-       "          79.89999871,  79.71999235],\n",
-       "        [ 94.26999692,  90.53999238,  87.27999488, ...,  80.68998706,\n",
-       "          81.44998912,  85.05998887],\n",
-       "        [ 95.96999436,  96.95999215,  88.04999401, ...,          nan,\n",
-       "          82.66999362,  85.13999069]],\n",
-       "\n",
-       "       [[ 80.61998737,  75.76999639,  69.55999228, ...,          nan,\n",
+       "        [100.        , 100.        ,  88.83      , ...,  78.71      ,\n",
+       "          79.9       ,  79.72      ],\n",
+       "        [ 94.27      ,  90.54      ,  87.28      , ...,  80.69      ,\n",
+       "          81.45      ,  85.06      ],\n",
+       "        [ 95.97      ,  96.96      ,  88.05      , ...,          nan,\n",
+       "          82.67      ,  85.14      ]],\n",
+       "\n",
+       "       [[ 79.45348861,  74.8598426 ,  68.62402027, ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 87.12999253,  77.50998672,  71.11999141, ...,          nan,\n",
+       "        [ 85.90812093,  76.39538174,  70.15793624, ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 90.40999199,  89.46999604,  74.06999167, ...,          nan,\n",
+       "        [ 89.07312023,  88.14941618,  73.01916773, ...,          nan,\n",
        "                  nan,          nan],\n",
        "...\n",
-       "        [ 48.29684364,  48.63876591,  47.30323245, ...,  42.97424687,\n",
-       "          39.50804843,  34.00664127],\n",
-       "        [ 47.43690235,  43.61202426,  52.7216067 , ...,  51.67492693,\n",
-       "          40.62356994,  41.40364862],\n",
-       "        [ 46.51147722,  43.82593906,  46.93663047, ...,          nan,\n",
-       "          50.96723242,  45.09428589]],\n",
-       "\n",
-       "       [[ 61.31477617,  59.58381146,  53.73629359, ...,          nan,\n",
+       "        [ 49.59062   ,  50.0227917 ,  46.70254838, ...,  39.44006984,\n",
+       "          36.78518844,  33.11610248],\n",
+       "        [ 47.4585508 ,  43.9817359 ,  49.17076735, ...,  50.22984071,\n",
+       "          41.23141968,  40.25821096],\n",
+       "        [ 47.4234839 ,  45.88333047,  45.84172417, ...,          nan,\n",
+       "          49.68407134,  42.76918701]],\n",
+       "\n",
+       "       [[ 51.59824858,  49.14623455,  44.67627714, ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 66.26356661,  60.40950981,  53.18439293, ...,          nan,\n",
+       "        [ 55.34893661,  49.98657581,  43.87944548, ...,          nan,\n",
        "                  nan,          nan],\n",
-       "        [ 67.62487683,  66.49543837,  54.46856273, ...,          nan,\n",
+       "        [ 55.79050637,  54.84189295,  45.52103494, ...,          nan,\n",
        "                  nan,          nan],\n",
        "        ...,\n",
-       "        [ 74.58260503,  73.95201267,  64.37381065, ...,  54.79330491,\n",
-       "          55.9894636 ,  53.89944195],\n",
-       "        [ 67.53096246,  64.8216954 ,  68.58065986, ...,  65.86956398,\n",
-       "          59.82781205,  61.99228362],\n",
-       "        [ 68.10683219,  68.97733331,  66.01275778, ...,          nan,\n",
-       "          65.54264578,  58.81029694]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-99411fb1-b856-4b5e-b1ed-64eb1914f1fc' class='xr-section-summary-in' type='checkbox'  ><label for='section-99411fb1-b856-4b5e-b1ed-64eb1914f1fc' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7042c2f1-750a-4672-8275-4e4dcb230a70' class='xr-index-data-in' type='checkbox'/><label for='index-7042c2f1-750a-4672-8275-4e4dcb230a70' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01 00:00:00&#x27;, &#x27;2022-01-01 00:30:00&#x27;,\n",
+       "        [ 66.9205857 ,  66.74338155,  57.93678068, ...,  47.11918227,\n",
+       "          47.42592057,  46.06639542],\n",
+       "        [ 60.68175659,  57.95293419,  59.42588881, ...,  59.58238022,\n",
+       "          53.96737428,  53.66990893],\n",
+       "        [ 61.70536343,  62.60085933,  58.31143013, ...,          nan,\n",
+       "          59.29238385,  51.73923589]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RH_front_encap</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>42.21 40.38 37.98 ... 32.78 31.69</div><input id='attrs-787e4c70-9579-4ca0-8c48-79ecc93783b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-787e4c70-9579-4ca0-8c48-79ecc93783b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf887ea6-a987-49b0-922f-82c6c47b2b7b' class='xr-var-data-in' type='checkbox'><label for='data-cf887ea6-a987-49b0-922f-82c6c47b2b7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[42.21067079, 40.37601718, 37.98324422, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [44.52176509, 40.71783507, 38.2658983 , ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [45.1271723 , 44.39353853, 38.9350482 , ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        ...,\n",
+       "        [49.76043607, 50.18874685, 45.10002181, ..., 40.05120903,\n",
+       "         40.46379893, 40.21168996],\n",
+       "        [47.68965229, 46.07159092, 44.44327462, ..., 41.04024539,\n",
+       "         40.9328189 , 41.79215484],\n",
+       "        [48.32376651, 48.48232011, 44.83114753, ...,         nan,\n",
+       "         41.17109978, 41.70459763]],\n",
+       "\n",
+       "       [[42.9215922 , 40.9374867 , 38.61020431, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [45.27954794, 41.40002697, 38.89940421, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [45.89933032, 45.15199612, 39.58300083, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "...\n",
+       "        [32.99609542, 33.28880815, 31.23675624, ..., 28.16316043,\n",
+       "         27.51018727, 25.97077769],\n",
+       "        [32.02219511, 30.51302173, 32.0693005 , ..., 30.37528683,\n",
+       "         27.85914277, 28.1752137 ],\n",
+       "        [31.95052006, 31.36665254, 30.87805043, ...,         nan,\n",
+       "         30.07191015, 28.91849909]],\n",
+       "\n",
+       "       [[35.57628458, 34.32254893, 32.18487548, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [37.47853286, 34.55622571, 32.02763626, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [37.6502249 , 36.90758002, 32.33583162, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        ...,\n",
+       "        [38.25379008, 38.38917312, 34.71318033, ..., 30.63236159,\n",
+       "         31.04144089, 30.48618514],\n",
+       "        [36.12765962, 34.95273543, 35.08214746, ..., 33.01207294,\n",
+       "         31.84762718, 32.3890078 ],\n",
+       "        [36.39514528, 36.62830881, 34.70506527, ...,         nan,\n",
+       "         32.78349752, 31.69382013]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f91a869-03ef-4c3f-8809-1b68bd7f689c' class='xr-section-summary-in' type='checkbox'  ><label for='section-0f91a869-03ef-4c3f-8809-1b68bd7f689c' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f10f06b9-0af0-45e8-b9f2-c8be1d991728' class='xr-index-data-in' type='checkbox'/><label for='index-f10f06b9-0af0-45e8-b9f2-c8be1d991728' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01 00:00:00&#x27;, &#x27;2022-01-01 00:30:00&#x27;,\n",
        "               &#x27;2022-01-01 01:00:00&#x27;, &#x27;2022-01-01 01:30:00&#x27;,\n",
        "               &#x27;2022-01-01 02:00:00&#x27;, &#x27;2022-01-01 02:30:00&#x27;,\n",
        "               &#x27;2022-01-01 03:00:00&#x27;, &#x27;2022-01-01 03:30:00&#x27;,\n",
@@ -2001,17 +1392,17 @@
        "               &#x27;2022-12-31 21:00:00&#x27;, &#x27;2022-12-31 21:30:00&#x27;,\n",
        "               &#x27;2022-12-31 22:00:00&#x27;, &#x27;2022-12-31 22:30:00&#x27;,\n",
        "               &#x27;2022-12-31 23:00:00&#x27;, &#x27;2022-12-31 23:30:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=17520, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b6902407-1963-4c93-955a-0ffdc07500ad' class='xr-index-data-in' type='checkbox'/><label for='index-b6902407-1963-4c93-955a-0ffdc07500ad' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 39.16999816894531, 39.209999084472656,              39.25,\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=17520, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e8c374c-b460-4044-a4df-69fcd30f799a' class='xr-index-data-in' type='checkbox'/><label for='index-8e8c374c-b460-4044-a4df-69fcd30f799a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 39.16999816894531, 39.209999084472656,              39.25,\n",
        "       39.290000915527344,  39.33000183105469, 39.369998931884766,\n",
        "        39.40999984741211,  39.45000076293945,   39.4900016784668,\n",
        "       39.529998779296875,  39.56999969482422,  39.61000061035156,\n",
        "       39.650001525878906, 39.689998626708984,  39.72999954223633,\n",
        "        39.77000045776367, 39.810001373291016, 39.849998474121094,\n",
        "        39.88999938964844],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a8c1f89d-dd25-4a04-ae0c-5d1c5e07cf80' class='xr-index-data-in' type='checkbox'/><label for='index-a8c1f89d-dd25-4a04-ae0c-5d1c5e07cf80' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-105.37999725341797, -105.33999633789062, -105.30000305175781,\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-afac0bf3-db66-411d-a896-bee1fbf457e4' class='xr-index-data-in' type='checkbox'/><label for='index-afac0bf3-db66-411d-a896-bee1fbf457e4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-105.37999725341797, -105.33999633789062, -105.30000305175781,\n",
        "       -105.26000213623047, -105.22000122070312, -105.18000030517578,\n",
        "       -105.13999938964844,  -105.0999984741211, -105.05999755859375],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4004b5e2-42b4-4e3c-9157-956ab668d671' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4004b5e2-42b4-4e3c-9157-956ab668d671' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d571fd3-29ec-433f-a907-2be1913a443e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6d571fd3-29ec-433f-a907-2be1913a443e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 96MB\n",
@@ -2021,13 +1412,13 @@
        "  * longitude           (longitude) float32 36B -105.4 -105.3 ... -105.1 -105.1\n",
        "  * time                (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31...\n",
        "Data variables:\n",
+       "    RH_surface_outside  (time, latitude, longitude) float64 24MB 76.96 ... 58.81\n",
        "    RH_back_encap       (time, latitude, longitude) float64 24MB 76.96 ... 44.67\n",
-       "    RH_front_encap      (time, latitude, longitude) float64 24MB 42.21 ... 31.69\n",
        "    RH_backsheet        (time, latitude, longitude) float64 24MB 76.96 ... 51.74\n",
-       "    RH_surface_outside  (time, latitude, longitude) float64 24MB 76.96 ... 58.81"
+       "    RH_front_encap      (time, latitude, longitude) float64 24MB 42.21 ... 31.69"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2039,9 +1430,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pvdeg_env",
+   "display_name": "rpp",
    "language": "python",
-   "name": "python3"
+   "name": "rpp"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2057,5 +1448,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/tutorials_and_tools/tutorials_and_tools/Scenario - Non-uniform Mountain Prefferential Downselect.ipynb b/tutorials_and_tools/tutorials_and_tools/Scenario - Non-uniform Mountain Prefferential Downselect.ipynb
index 1dcf4a8..aa289f1 100644
--- a/tutorials_and_tools/tutorials_and_tools/Scenario - Non-uniform Mountain Prefferential Downselect.ipynb	
+++ b/tutorials_and_tools/tutorials_and_tools/Scenario - Non-uniform Mountain Prefferential Downselect.ipynb	
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -33,7 +33,7 @@
     }
    ],
    "source": [
-    "dynamic_points = pvdeg.scenario.GeospatialScenario(\n",
+    "dynamic_points = pvdeg.GeospatialScenario(\n",
     "    name='dynamic-selection'\n",
     ")\n",
     "\n",
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -66,6 +66,17 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 1500x1000 with 1 Axes>,\n",
+       " <GeoAxes: title={'center': \"Coordinate Pairs from 'dynamic-selection' Meta Data\"}>)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -76,18 +87,9 @@
     ")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Downselecting\n",
-    "\n",
-    "Using weighted random choices based on elevation we will create a sparse grid from the full metadata for fast calculations. This requires sklearn to be installed but this is not in the `pvdeg` dependency list to you will have to install it seperately."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -123,21 +125,21 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>407393</th>\n",
-       "      <td>38.529999</td>\n",
-       "      <td>-108.260002</td>\n",
-       "      <td>2203</td>\n",
+       "      <th>282901</th>\n",
+       "      <td>41.889999</td>\n",
+       "      <td>-114.019997</td>\n",
+       "      <td>1704</td>\n",
        "      <td>-7</td>\n",
        "      <td>United States</td>\n",
-       "      <td>Colorado</td>\n",
-       "      <td>Montrose</td>\n",
+       "      <td>Utah</td>\n",
+       "      <td>Box Elder</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>295746</th>\n",
-       "      <td>41.889999</td>\n",
-       "      <td>-113.379997</td>\n",
-       "      <td>2537</td>\n",
+       "      <th>282903</th>\n",
+       "      <td>41.810001</td>\n",
+       "      <td>-114.019997</td>\n",
+       "      <td>2093</td>\n",
        "      <td>-7</td>\n",
        "      <td>United States</td>\n",
        "      <td>Utah</td>\n",
@@ -145,36 +147,36 @@
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>462552</th>\n",
-       "      <td>37.410000</td>\n",
-       "      <td>-105.860001</td>\n",
-       "      <td>2297</td>\n",
+       "      <th>282905</th>\n",
+       "      <td>41.730000</td>\n",
+       "      <td>-114.019997</td>\n",
+       "      <td>1880</td>\n",
        "      <td>-7</td>\n",
        "      <td>United States</td>\n",
-       "      <td>Colorado</td>\n",
-       "      <td>Alamosa</td>\n",
+       "      <td>Utah</td>\n",
+       "      <td>Box Elder</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>416313</th>\n",
-       "      <td>37.889999</td>\n",
-       "      <td>-107.860001</td>\n",
-       "      <td>3061</td>\n",
+       "      <th>282907</th>\n",
+       "      <td>41.650002</td>\n",
+       "      <td>-114.019997</td>\n",
+       "      <td>1903</td>\n",
        "      <td>-7</td>\n",
        "      <td>United States</td>\n",
-       "      <td>Colorado</td>\n",
-       "      <td>San Miguel</td>\n",
+       "      <td>Utah</td>\n",
+       "      <td>Box Elder</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>377412</th>\n",
-       "      <td>37.250000</td>\n",
-       "      <td>-109.620003</td>\n",
-       "      <td>1352</td>\n",
+       "      <th>282909</th>\n",
+       "      <td>41.570000</td>\n",
+       "      <td>-114.019997</td>\n",
+       "      <td>1936</td>\n",
        "      <td>-7</td>\n",
        "      <td>United States</td>\n",
        "      <td>Utah</td>\n",
-       "      <td>San Juan</td>\n",
+       "      <td>Box Elder</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -189,96 +191,96 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>368545</th>\n",
-       "      <td>38.209999</td>\n",
-       "      <td>-110.019997</td>\n",
-       "      <td>1527</td>\n",
+       "      <th>559787</th>\n",
+       "      <td>37.330002</td>\n",
+       "      <td>-102.099998</td>\n",
+       "      <td>1139</td>\n",
        "      <td>-7</td>\n",
        "      <td>United States</td>\n",
-       "      <td>Utah</td>\n",
-       "      <td>Wayne</td>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>Baca</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>310605</th>\n",
-       "      <td>41.810001</td>\n",
-       "      <td>-112.660004</td>\n",
-       "      <td>1496</td>\n",
+       "      <th>559789</th>\n",
+       "      <td>37.250000</td>\n",
+       "      <td>-102.099998</td>\n",
+       "      <td>1141</td>\n",
        "      <td>-7</td>\n",
        "      <td>United States</td>\n",
-       "      <td>Utah</td>\n",
-       "      <td>Box Elder</td>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>Baca</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>343651</th>\n",
-       "      <td>40.529999</td>\n",
-       "      <td>-111.139999</td>\n",
-       "      <td>2394</td>\n",
+       "      <th>559791</th>\n",
+       "      <td>37.169998</td>\n",
+       "      <td>-102.099998</td>\n",
+       "      <td>1149</td>\n",
        "      <td>-7</td>\n",
        "      <td>United States</td>\n",
-       "      <td>Utah</td>\n",
-       "      <td>Wasatch</td>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>Baca</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>456842</th>\n",
-       "      <td>38.849998</td>\n",
-       "      <td>-106.099998</td>\n",
-       "      <td>2682</td>\n",
+       "      <th>559793</th>\n",
+       "      <td>37.090000</td>\n",
+       "      <td>-102.099998</td>\n",
+       "      <td>1096</td>\n",
        "      <td>-7</td>\n",
        "      <td>United States</td>\n",
        "      <td>Colorado</td>\n",
-       "      <td>Chaffee</td>\n",
+       "      <td>Baca</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>389841</th>\n",
-       "      <td>39.889999</td>\n",
-       "      <td>-109.059998</td>\n",
-       "      <td>1921</td>\n",
+       "      <th>559795</th>\n",
+       "      <td>37.009998</td>\n",
+       "      <td>-102.099998</td>\n",
+       "      <td>1122</td>\n",
        "      <td>-7</td>\n",
        "      <td>United States</td>\n",
-       "      <td>Utah</td>\n",
-       "      <td>Uintah</td>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>Baca</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>795 rows × 8 columns</p>\n",
+       "<p>7956 rows × 8 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "         latitude   longitude  altitude  tz        country     state  \\\n",
-       "407393  38.529999 -108.260002      2203  -7  United States  Colorado   \n",
-       "295746  41.889999 -113.379997      2537  -7  United States      Utah   \n",
-       "462552  37.410000 -105.860001      2297  -7  United States  Colorado   \n",
-       "416313  37.889999 -107.860001      3061  -7  United States  Colorado   \n",
-       "377412  37.250000 -109.620003      1352  -7  United States      Utah   \n",
+       "282901  41.889999 -114.019997      1704  -7  United States      Utah   \n",
+       "282903  41.810001 -114.019997      2093  -7  United States      Utah   \n",
+       "282905  41.730000 -114.019997      1880  -7  United States      Utah   \n",
+       "282907  41.650002 -114.019997      1903  -7  United States      Utah   \n",
+       "282909  41.570000 -114.019997      1936  -7  United States      Utah   \n",
        "...           ...         ...       ...  ..            ...       ...   \n",
-       "368545  38.209999 -110.019997      1527  -7  United States      Utah   \n",
-       "310605  41.810001 -112.660004      1496  -7  United States      Utah   \n",
-       "343651  40.529999 -111.139999      2394  -7  United States      Utah   \n",
-       "456842  38.849998 -106.099998      2682  -7  United States  Colorado   \n",
-       "389841  39.889999 -109.059998      1921  -7  United States      Utah   \n",
-       "\n",
-       "            county  wind_height  \n",
-       "407393    Montrose            2  \n",
-       "295746   Box Elder            2  \n",
-       "462552     Alamosa            2  \n",
-       "416313  San Miguel            2  \n",
-       "377412    San Juan            2  \n",
-       "...            ...          ...  \n",
-       "368545       Wayne            2  \n",
-       "310605   Box Elder            2  \n",
-       "343651     Wasatch            2  \n",
-       "456842     Chaffee            2  \n",
-       "389841      Uintah            2  \n",
-       "\n",
-       "[795 rows x 8 columns]"
+       "559787  37.330002 -102.099998      1139  -7  United States  Colorado   \n",
+       "559789  37.250000 -102.099998      1141  -7  United States  Colorado   \n",
+       "559791  37.169998 -102.099998      1149  -7  United States  Colorado   \n",
+       "559793  37.090000 -102.099998      1096  -7  United States  Colorado   \n",
+       "559795  37.009998 -102.099998      1122  -7  United States  Colorado   \n",
+       "\n",
+       "           county  wind_height  \n",
+       "282901  Box Elder            2  \n",
+       "282903  Box Elder            2  \n",
+       "282905  Box Elder            2  \n",
+       "282907  Box Elder            2  \n",
+       "282909  Box Elder            2  \n",
+       "...           ...          ...  \n",
+       "559787       Baca            2  \n",
+       "559789       Baca            2  \n",
+       "559791       Baca            2  \n",
+       "559793       Baca            2  \n",
+       "559795       Baca            2  \n",
+       "\n",
+       "[7956 rows x 8 columns]"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -287,9 +289,26 @@
     "dynamic_points.meta_data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Downselecting\n",
+    "\n",
+    "Using weighted random choices based on elevation we will create a sparse grid from the full metadata for fast calculations. This requires sklearn to be installed but this is not in the `pvdeg` dependency list to you will have to install it seperately.\n",
+    "\n",
+    "### Normalization\n",
+    "\n",
+    "At each metadata point in our dataset we will calculate a weight based on its changes in elevation compared to its neighbors. The higher the weight, the greater the change in elevation from a point's immediate neighbors. The downselection methods and functions use these weights to randomly select a subset of the datapoints, prefferentially selecting those with higher weights. \n",
+    "\n",
+    "We have some control over which points get selected because all points' weights must be normalized (mapped from 0 to 1) before downselecting. We can apply a function such as $e^x$ or $\\log x$ to the weights during normalization. This could help change the distribution of weights that are chosen. This could remove points from the mountains and add them to areas with fewer changes in elevation, or vice versa.\n",
+    "\n",
+    "*Note: `pvdeg`'s downselection functions use `numpy.random`, the random seed is not fixed so the result will change between runs.*"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,23 +317,35 @@
     "dynamic_points.downselect_elevation_stochastic(\n",
     "    kdtree=west_tree,\n",
     "    downselect_prop=0.1,\n",
+    "    normalization=\"linear\",\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJ4AAAIWCAYAAAAWIEdlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAAD8GElEQVR4nOz9d3wdV534/7/OzNyr3rtsWbLkIjfFju102+mdYBbYTQIhwM+sKQsfh93lA7v8gAC7WZZlCVsIm10c8skSlg2EQBJSSSE9cdx7UbHcVK1e7p2Z8/1jIsWyZOtK1tVt7+fj4YftW8+cKXfmPe/zPkprrRFCCCGEEEIIIYQQYooZkW6AEEIIIYQQQgghhIhPEngSQgghhBBCCCGEEGEhgSchhBBCCCGEEEIIERYSeBJCCCGEEEIIIYQQYSGBJyGEEEIIIYQQQggRFhJ4EkIIIYQQQgghhBBhIYEnIYQQQgghhBBCCBEWEngSQgghhBBCCCGEEGEhgSchhBBCCCGEEEIIERYSeBJCCBFR9fX1KKX42c9+NvzYt771LZRSkWtUFLn88su5/PLLw/49Bw4c4NprryUrKwulFI899ljYv3M6TFf/TYef/exnKKWor6+PdFNGiVQ/v/TSSyileOmll6b9u4UQQggRGgk8CSFEnDp06BDr16+nsrKS5ORkMjMzufTSS/nRj35Ef39/pJsXcbt37+Zb3/rWlF/ED10ID/3x+XxUVlbyiU98gtra2in9rql05513smPHDv7u7/6Ohx56iBUrVkS6SWc01MfRGIAR4fHjH/94RHB6up0eHB/LUBBdKcV3v/vdMV/zsY99DKUU6enpk2rH73//e771rW9N6r1n88lPfnLEcSs9PZ3Kyko+8pGP8Otf/xrXdSf92Q8//DD33nvv1DVWCCFEzLEi3QAhhBBT78knn+SjH/0oSUlJfOITn2Dx4sUEAgFeffVV/vqv/5pdu3Zx//33R7qZZ/T1r3+dr371q2H9jt27d3P33Xdz+eWXU1FRMeWf/6UvfYmVK1cSDAbZvHkz999/P08++SQ7duygtLQ05M959tlnp7xtp+vv7+eNN97gb//2b/mLv/iLsH+fmJw77riDW2+9laSkpEg3Zdr9+Mc/Jj8/n09+8pMjHl+9ejX9/f34/f7INGwMycnJ/OIXv+DrX//6iMd7e3v57W9/S3Jy8qQ/+/e//z3//u//HpbgU1JSEv/1X/8FeMeEhoYGHn/8cT7ykY9w+eWX89vf/pbMzMwJf+7DDz/Mzp072bBhwxS3WAghRKyQwJMQQsSZuro6br31VsrLy3nhhRcoKSkZfu4LX/gCBw8e5Mknn5zWNvX19ZGamhry6y3LwrJi+ydq1apVfOQjHwHgU5/6FPPmzeNLX/oSDz74IF/72tdC/pxQLqgHBgbw+/0YxuQSmVtaWgDIzs4e97W9vb2kpaVN6nvEuTFNE9M0I92MqGIYxjkFcsLhxhtv5NFHH2Xbtm2cd955w4//9re/JRAIcP311/PCCy9EsIVjsyyLj3/84yMe++53v8s//MM/8LWvfY3PfOYz/PKXv4xQ64QQQsQyGWonhBBx5h//8R/p6enhpz/96Yig05A5c+bwf/7P/xn+v23bfOc736GqqoqkpCQqKir4m7/5GwYHB0e998c//jGLFi0iKSmJ0tJSvvCFL9DR0THiNZdffjmLFy/m3XffZfXq1aSmpvI3f/M3AHR0dPDJT36SrKwssrOzufPOO0e9H8au8aSU4i/+4i947LHHWLx4MUlJSSxatIinn356xOsaGhr4/Oc/z/z580lJSSEvL4+PfvSjI4Zl/exnP+OjH/0oAFdcccXw8JJT68Q89dRTrFq1irS0NDIyMrjpppvYtWvXmH0eiiuvvBLwAoMADzzwAFdeeSWFhYUkJSWxcOFC7rvvvlHvO712ztAws//5n//h61//OjNmzCA1NZWuri6CwSB33303c+fOJTk5mby8PC677DKee+65M7brW9/6FuXl5QD89V//NUqp4QywofWwe/dubr/9dnJycrjsssuA0LebiooKbr75Zl566SVWrFhBSkoKS5YsGe7rRx99lCVLlpCcnMzy5cvZsmXLpPoX4P7776eqqoqUlBQuuOACXnnllRHP9/T0kJaWNmL7H3LkyBFM0+See+4B3q+n9Nprr/HlL3+ZgoIC0tLS+NCHPjQcqBvy29/+lptuuonS0lKSkpKoqqriO9/5Do7jjHjd0L6xfft21qxZQ2pqKnPmzOFXv/oVAC+//DIXXnghKSkpzJ8/n+eff37E+89U4+mpp55izZo1ZGRkkJmZycqVK3n44YfH7a9NmzZx3XXXkZ+fT0pKCrNnz+bTn/70iNe4rsu9997LokWLSE5OpqioiPXr13Py5MlxP39wcJBvfvObzJkzh6SkJMrKyvjKV74y5rHlv//7v7ngggtITU0lJyeH1atXD2f7VVRUsGvXLl5++eXhfXVonzhTjadHHnmE5cuXk5KSQn5+Ph//+Mc5evToiNd88pOfJD09naNHj7J27VrS09MpKCjgr/7qr0atu4m4+OKLmT179qh18POf/5zrr7+e3NzcMd833jHnk5/8JP/+7/8OMGJY3JB/+qd/4pJLLiEvL4+UlBSWL18+vG2di69+9atce+21PPLII+zfv3/48VC2+8svv5wnn3yShoaG4fYOHV8CgQDf+MY3WL58OVlZWaSlpbFq1SpefPHFc26zEEKI6BLbt5OFEEKM8vjjj1NZWckll1wS0uvXrVvHgw8+yEc+8hH+8i//krfeeot77rmHPXv28Jvf/Gb4dd/61re4++67ufrqq/nc5z7Hvn37uO+++3jnnXd47bXX8Pl8w69ta2vjhhtu4NZbb+XjH/84RUVFaK354Ac/yKuvvspnP/tZFixYwG9+8xvuvPPOkJft1Vdf5dFHH+Xzn/88GRkZ/Mu//Asf/vCHOXz4MHl5eQC88847vP7669x6663MnDmT+vp67rvvPi6//HJ2795Namoqq1ev5ktf+hL/8i//wt/8zd+wYMECgOG/H3roIe68806uu+46vve979HX18d9993HZZddxpYtWyY1NO/QoUMAw+287777WLRoEbfccguWZfH444/z+c9/Htd1+cIXvjDu533nO9/B7/fzV3/1VwwODuL3+/nWt77FPffcw7p167jgggvo6upi06ZNbN68mWuuuWbMz/mTP/kTsrOzueuuu7jtttu48cYbR9Wf+ehHP8rcuXP5+7//e7TWQOjbDcDBgwe5/fbbWb9+PR//+Mf5p3/6Jz7wgQ/wk5/8hL/5m7/h85//PAD33HMPf/qnf8q+ffsmnL3105/+lPXr13PJJZewYcMGamtrueWWW8jNzaWsrAyA9PR0PvShD/HLX/6Sf/7nfx6RPfSLX/wCrTUf+9jHRnzuF7/4RXJycvjmN79JfX099957L3/xF38xIvPjZz/7Genp6Xz5y18mPT2dF154gW984xt0dXXx/e9/f8TnnTx5kptvvplbb72Vj370o9x3333ceuut/PznP2fDhg189rOf5fbbb+f73/8+H/nIR2hsbCQjI+OMy/2zn/2MT3/60yxatIivfe1rZGdns2XLFp5++mluv/32M76vubmZa6+9loKCAr761a+SnZ1NfX09jz766IjXrV+/np/97Gd86lOf4ktf+hJ1dXX827/9G1u2bBm135/KdV1uueUWXn31Vf78z/+cBQsWsGPHDn74wx+yf//+EcXr7777br71rW9xySWX8O1vfxu/389bb73FCy+8wLXXXsu9997LF7/4RdLT0/nbv/1bAIqKis7aJ5/61KdYuXIl99xzD01NTfzoRz/itddeY8uWLSMy+xzH4brrruPCCy/kn/7pn3j++ef5wQ9+QFVVFZ/73OfO+B3jue222/jv//5v/uEf/gGlFK2trTz77LM89NBDo4LlENoxZ/369Rw7doznnnuOhx56aNRn/OhHP+KWW27hYx/7GIFAgP/5n//hox/9KE888QQ33XTTpJcFvGGezz77LM899xzz5s0DQtvu//Zv/5bOzk6OHDnCD3/4Q4Dh40tXVxf/9V//xW233cZnPvMZuru7+elPf8p1113H22+/zdKlS8+pzUIIIaKIFkIIETc6Ozs1oD/4wQ+G9PqtW7dqQK9bt27E43/1V3+lAf3CCy9orbVubm7Wfr9fX3vttdpxnOHX/du//ZsG9MaNG4cfW7NmjQb0T37ykxGf+dhjj2lA/+M//uPwY7Zt61WrVmlAP/DAA8OPf/Ob39Sn/0QB2u/364MHDw4/tm3bNg3of/3Xfx1+rK+vb9RyvvHGGxrQ/+///b/hxx555BEN6BdffHHEa7u7u3V2drb+zGc+M+LxEydO6KysrFGPn+7FF18c7pOWlhZ97Ngx/eSTT+qKigqtlNLvvPPOGdt53XXX6crKyhGPrVmzRq9Zs2bU51dWVo76jPPOO0/fdNNNZ23fWOrq6jSgv//97494fGg93HbbbSMeD3W70Vrr8vJyDejXX399+LFnnnlGAzolJUU3NDQMP/4f//EfY66T8QQCAV1YWKiXLl2qBwcHhx+///77NTCi/4a++6mnnhrxGTU1NSNe98ADD2hAX3311dp13eHH77rrLm2apu7o6Bh+bKx1uX79ep2amqoHBgaGHxvaNx5++OHhx/bu3asBbRiGfvPNN0e189T9YqhNdXV1WmutOzo6dEZGhr7wwgt1f3//iO8/tc1j+c1vfqOB4e1xLK+88ooG9M9//vMRjz/99NOjHj99O33ooYe0YRj6lVdeGfHen/zkJxrQr732mtZa6wMHDmjDMPSHPvShEceW05dh0aJFIz5/yND+MLTNDG0LixcvHtEnTzzxhAb0N77xjeHH7rzzTg3ob3/72yM+c9myZXr58uVn7JczOXU/2rlzpwaGl//f//3fdXp6uu7t7dV33nmnTktLG37fRI45X/jCF0YdG4ecvh0GAgG9ePFifeWVV47b9tPbdLotW7ZoQN91111n/D6tx97ub7rpJl1eXj7qtbZtj9hftdb65MmTuqioSH/6058et81CCCFihwy1E0KIONLV1QVw1gyJU/3+978H4Mtf/vKIx//yL/8SYLgW1PPPP08gEGDDhg0jMlE+85nPkJmZOapmVFJSEp/61KdGfZdlWSOyCEzT5Itf/GJIbQW4+uqrqaqqGv5/TU0NmZmZI2aLS0lJGf53MBikra2NOXPmkJ2dzebNm8f9jueee46Ojg5uu+02Wltbh/+YpsmFF14Y8jCQT3/60xQUFFBaWspNN91Eb28vDz744PBscae2s7Ozk9bWVtasWUNtbS2dnZ3jfv6dd9454jPAq9G0a9cuDhw4EFIbQ/XZz352xP9D3W6GLFy4kIsvvnj4/xdeeCHgDT+cNWvWqMcnOvvfpk2baG5u5rOf/eyImlhDwzpPdfXVV1NaWsrPf/7z4cd27tzJ9u3bR9W3AfjzP//zEcOZVq1aheM4NDQ0DD926nro7u6mtbWVVatW0dfXx969e0d8Xnp6Orfeeuvw/+fPn092djYLFiwYXn4IrS+ee+45uru7+epXvzqqztHpQ1VPN5T188QTTxAMBsd8zSOPPEJWVhbXXHPNiH1h+fLlpKenn3VfeOSRR1iwYAHV1dUj3js05HTovY899hiu6/KNb3xjVJbbeMswlqFt4fOf//yIPrnpppuorq4es77d6dv3qlWrznkGykWLFlFTU8MvfvELwCuw/cEPfnDMWndTdcw5dTs8efIknZ2drFq1KqTj3niGspS6u7vH/L7xtvuxmKY5vL+6rkt7ezu2bbNixYopabMQQojoIUPthBAijgzNOHTqxcHZNDQ0YBgGc+bMGfF4cXEx2dnZwxfXQ3/Pnz9/xOv8fj+VlZUjLsIBZsyYMaoodkNDAyUlJaOGcZ3+mWdzapBiSE5Ozoh6M/39/dxzzz088MADHD16dHhoGBBSQGcoaDN0gXy6UGd1+sY3vsGqVaswTZP8/HwWLFgwomD6a6+9xje/+U3eeOMN+vr6Rry3s7NzVMDkdLNnzx712Le//W0++MEPMm/ePBYvXsz111/PHXfcQU1NTUhtDvW7Qt1uhpy+3oaWbWgI3OmPh1I/6PT2AMydO3fE4z6fj8rKyhGPGYbBxz72Me67777hovc///nPSU5OHq77dba25+TkjGrjrl27+PrXv84LL7wwHPwdcvo2N3PmzFEBlaysrEn1xdDwzcWLF5/xNT09PfT09Az/3zRNCgoKWLNmDR/+8Ie5++67+eEPf8jll1/O2rVruf3224dnzTtw4ACdnZ0UFhaO+dnNzc1n/N4DBw6wZ88eCgoKzvreQ4cOYRgGCxcuPONnTcSZjlUA1dXVvPrqqyMeS05OHtXG048pk3X77bfzgx/8gLvuuovXX399uNbd6abqmPPEE0/w3e9+l61bt46oozWZAN7phrahU29qTGS7P5MHH3yQH/zgB+zdu3dEAHSs45sQQojYJYEnIYSII5mZmZSWlrJz584JvW8qLkxOdXomzlQ504xepwaXvvjFL/LAAw+wYcMGLr74YrKyslBKceutt+K67rjfMfSahx56iOLi4lHPhzrb3pIlS7j66qvHfO7QoUNcddVVVFdX88///M+UlZXh9/v5/e9/zw9/+MOQ2jlWH69evZpDhw7x29/+lmeffZb/+q//4oc//CE/+clPWLduXUjtDvW7IPTt5kzrLZT1GQ6f+MQn+P73v89jjz3GbbfdxsMPP8zNN988ZrBvvDZ2dHSwZs0aMjMz+fa3v01VVRXJycls3ryZ//t//++odTndffFP//RP3H333cP/Ly8vp76+HqUUv/rVr3jzzTd5/PHHeeaZZ/j0pz/ND37wA958803S09NxXZfCwsIR2WGnOlNQCbz9aMmSJfzzP//zmM+fHmiLlHDOEnjbbbcNzwaXl5fHtddeO+brpuKY88orr3DLLbewevVqfvzjH1NSUoLP5+OBBx4IqdD8eIZ+U4aCzRPd7sfy3//933zyk59k7dq1/PVf/zWFhYXDBf6HgqpCCCHigwSehBAiztx8883cf//9vPHGGyOGN42lvLwc13U5cODAcGFtgKamJjo6OoZnOxv6e9++fSMySAKBAHV1dWcMsJz+XX/4wx/o6ekZkfW0b9++CS3feH71q19x55138oMf/GD4sYGBgVGz550paDI0lK+wsDCk5ZqMxx9/nMHBQX73u9+NyKiZitmccnNz+dSnPsWnPvUpenp6WL16Nd/61rfOKfB0ulC3m+ky9H0HDhwYkTUSDAapq6sbMaU9eBlCy5Yt4+c//zkzZ87k8OHD/Ou//uukvvull16ira2NRx99lNWrVw8/PjR7YTgNbas7d+4clX025BOf+MTwTIQwOoh40UUXcdFFF/F3f/d3PPzww3zsYx/jf/7nf1i3bh1VVVU8//zzXHrppRMOJldVVbFt2zauuuqqswYoq6qqcF2X3bt3n7WYdKhBzlOPVadnEO3bt29at81Zs2Zx6aWX8tJLL/G5z33ujAGkiRxzztQPv/71r0lOTuaZZ54ZzlgDb/bMqfDQQw+hlBqepGAi2/2Z2vyrX/2KyspKHn300RGv+eY3vzklbRZCCBE9pMaTEELEma985SukpaWxbt06mpqaRj1/6NAhfvSjHwFw4403AnDvvfeOeM1QlsLQTEhXX301fr+ff/mXfxmRgfHTn/6Uzs7OkGZMuvHGG7Ftm/vuu2/4McdxJn3BfyamaY7KEvnXf/3XUdOjp6WlAYwKSF133XVkZmby93//92PWvmlpaZmSNgKjhgGe60ViW1vbiP+np6czZ86cMaevPxehbjfTZcWKFRQUFPCTn/yEQCAw/PjPfvazUet3yNAsXffeey95eXnccMMNk/rusdZlIBDgxz/+8aQ+byKuvfZaMjIyuOeeexgYGBjx3FB7Kisrufrqq4f/XHrppYA3hO/0/WQo8DO0vfzpn/4pjuPwne98Z9R327Z9xr4deu/Ro0f5z//8z1HP9ff309vbC8DatWsxDINvf/vbo7JkTm1fWlraWb9vyIoVKygsLOQnP/nJiO3+qaeeYs+ePdO+bX73u9/lm9/85llr2U3kmHOm45ZpmiilRhzn6uvrR8weOFn/8A//wLPPPsuf/dmfDQ9nnch2n5aWNubQu7E+46233uKNN9445zYLIYSILpLxJIQQcaaqqoqHH36YP/uzP2PBggV84hOfYPHixQQCAV5//XUeeeQRPvnJTwJw3nnnceedd3L//fcPD514++23efDBB1m7di1XXHEF4A2p+drXvsbdd9/N9ddfzy233MK+ffv48Y9/zMqVK8csyny6D3zgA1x66aV89atfpb6+noULF/Loo4+GXAskVDfffDMPPfQQWVlZLFy4kDfeeIPnn3+evLy8Ea9bunQppmnyve99j87OTpKSkrjyyispLCzkvvvu44477uD888/n1ltvpaCggMOHD/Pkk09y6aWX8m//9m/n1MZrr70Wv9/PBz7wAdavX09PTw//+Z//SWFhIcePH5/05y5cuJDLL7+c5cuXk5uby6ZNm/jVr37FX/zFX5xTe08X6nYzXXw+H9/97ndZv349V155JX/2Z39GXV0dDzzwwKgaT0Nuv/12vvKVr/Cb3/yGz33uc/h8vkl99yWXXEJOTg533nknX/rSl1BK8dBDD4V9uCB4Q2t/+MMfsm7dOlauXMntt99OTk4O27Zto6+vjwcffPCM733wwQf58Y9/zIc+9CGqqqro7u7mP//zP8nMzBwOLK5Zs4b169dzzz33sHXrVq699lp8Ph8HDhzgkUce4Uc/+hEf+chHxvz8O+64g//93//ls5/9LC+++CKXXnopjuOwd+9e/vd//5dnnnmGFStWMGfOHP72b/+W73znO6xatYo/+ZM/ISkpiXfeeYfS0lLuueceAJYvX859993Hd7/7XebMmUNhYeGYNZF8Ph/f+973+NSnPsWaNWu47bbbaGpq4kc/+hEVFRXcddddU9DzoVuzZg1r1qw562syMzNDPuYsX74cgC996Utcd911mKbJrbfeyk033cQ///M/c/3113P77bfT3NzMv//7vzNnzhy2b98eUltt2+a///u/AS9LtKGhgd/97nds376dK664gvvvv3/4tRPZ7pcvX84vf/lLvvzlL7Ny5UrS09P5wAc+wM0338yjjz7Khz70IW666Sbq6ur4yU9+wsKFC0fUJRNCCBEHpn8iPSGEENNh//79+jOf+YyuqKjQfr9fZ2Rk6EsvvVT/67/+64iproPBoL777rv17Nmztc/n02VlZfprX/vaiNcM+bd/+zddXV2tfT6fLioq0p/73Of0yZMnR7xmzZo1etGiRWO2qa2tTd9xxx06MzNTZ2Vl6TvuuGN4mu5Tp43/5je/OWrKcEB/4QtfGPWZ5eXl+s477xz+/8mTJ/WnPvUpnZ+fr9PT0/V1112n9+7dO+p1Wmv9n//5n7qyslKbpjliSnatvWnar7vuOp2VlaWTk5N1VVWV/uQnP6k3bdo05rKd+j5AP/LII2d93e9+9ztdU1Ojk5OTdUVFhf7e976nN27cqAFdV1c3/LrTp6k/2+d/97vf1RdccIHOzs7WKSkpurq6Wv/d3/2dDgQCZ23LqdPAn2poPbS0tIx6T6jbTXl5ub7ppptGvX+s9XmmdoTqxz/+sZ49e7ZOSkrSK1as0H/84x9H9d+pbrzxRg3o119/fdRzDzzwgAb0O++8M+Lxof4/dVt57bXX9EUXXaRTUlJ0aWmp/spXvqKfeeaZUa87074Rah8NtenU7UNrb1u65JJLdEpKis7MzNQXXHCB/sUvfjHmMg/ZvHmzvu222/SsWbN0UlKSLiws1DfffPOY2/f999+vly9frlNSUnRGRoZesmSJ/spXvqKPHTs2YtlO7+dAIKC/973v6UWLFumkpCSdk5Ojly9fru+++27d2dk54rUbN27Uy5YtG37dmjVr9HPPPTf8/IkTJ/RNN92kMzIyNDD8XWOtD621/uUvfzn8ebm5ufpjH/uYPnLkyIjX3HnnnTotLW3U8o51/AlFqNvvmb43lGOObdv6i1/8oi4oKNBKqRHt/OlPf6rnzp2rk5KSdHV1tX7ggQdCXpY777xTA8N/UlNTdUVFhf7whz+sf/WrX2nHcUa9J9TtvqenR99+++06OztbA7q8vFxrrbXruvrv//7vdXl5uU5KStLLli3TTzzxhL7zzjuHXyOEECI+KK2n4ZacEEIIIUSU+dCHPsSOHTs4ePBgpJsihBBCCBG3pMaTEEIIIRLO8ePHefLJJ7njjjsi3RQhhBBCiLgmNZ6EEEIIkTDq6up47bXX+K//+i98Ph/r16+PdJOEEEIIIeKaZDwJIYQQImG8/PLL3HHHHdTV1fHggw9SXFwc6SYJIYQQQsQ1qfEkhBBCCCGEEEIIIcJCMp6EEEIIIYQQQgghRFhI4EkIIYQQQgghhBBChEXIxcUHBgYIBALhbIsQQgghhBBCCCGEiBF+v5/k5OSzviakwNPAwAAVFbNoamqZkoYJIYQQQgghhBBCiNhWXFxMXV3dWYNPIQWeAoEATU0t7Nz5RzIy0qesgUKIyDpwwmDXEZNblgcxVKRbMzHP77To7B9/tPDcIoeaWc40tMgzaMMTW/xcPCdIaY7M3TARjW0Gb9dafGBZAH/I+bhCCBF+B08YbGsc+8CUmexSXeoyM9dFxdhvqRDxqLNPcfSkQXuPor1HEXQVSaZmRq7LrHyX3DQt+6oQU6S7u4fFi1cTCATOPfA0JCMjnczMjHNunBAiOvg7TbKyDLKzgpFuyoT0DUJA+UlJHf+1A7hkZtrhb9R7XA2paT4c0yEz0522740HgXaT/ByD/NzY2h6FEPHP6jBJSTVHPT6/xGFpuSMXsUJEkcxMKCv2/q01nOxVHG41aGgzON6gsAxNTpqmPN+lvMDFN3rXFkJMMbmnLEQCc1ywYnCKgWMdoTd6IDC9VwOGgqpCl73HTMryXDJTpvXrY9pAEGwHWrsVuek65rLwhBDxqWfAy3gaS2mOZDkJEc2Ugtx0TW66Q025Q2u3oq1b0dJt8G6dybbDJtWlDtWlLmYMnhMLESsk8CREAnMcMI3YGw527GToZwZGBJavZpZDc5fBH/f4uGZJkCTftDchJp03y6G7X/H8Th+moclO1cwtdinPlws7IURk2A68us8i4Ix9EHp1n8WMXJfSbE1xtivDhIWIYoaCwkxNYaZmAS69g7D/uMnOIya1zSbZqRrL1GSmaLl5KMQUk59HIRKY7RJzd3dcDU2doUchIpE147dgdXWQZ3f42NFosqJy+mpMxbLUJLhqsU1Ll6KzT3Gi0+DNgxZ7jrqsqHQoyIy9IKkQIrbZDiyc4dI76NIzqKhvMXDc939Ygo6ivsWkvgUUmqIszXnlDjlpcrwSItqlJcGyCofZBS4Hmgz6BhW9g4pjJw12NFoUZbnMK3YozZGaUEKcKwk8CZHAHBesGBvX3jfIiJP+8UQqsJaeDHOLXfYeMzh/tiPDxkJkGlCcrSnO1swvdWntVmyuM3l1n8WNSyV7TAgxvZL9MCv//Xp91aUOf9xr0T3G5BYaxYlOxYntispCl5pZDslyzBIi6mWnaVaecpPQceFwm8HBEwav7PNRlOWyotIm4+yzxQshziLGch2EEFPJdlXMZTz1DE4sghPJgE9RpovjKrr6Jeo0WfkZmtXVNlrD7qMxFiUVQsSdjGRYWDrexBGK2maTJ7f42HvMwJF5JoSIKaYBswtcrllis6Y6SM+A4umtPg6codabEGJ8kvEkRAJzXEiyYms4QEvnxH70fRE8yg0F9XRsdXHUSfZ7AajuAQngCSEirzRnZCQpK8UlP0OTmarxmdAfgKPtBu29BlsbLA41aZaW2zJcR4gYVJKjuSEzyLYGk3frLHoHHc6bJTNZCjFREngSIoENBiE9KdKtmJjewYm9PpJ1NoLvZW37TIk8nSvDQLIGhBBRIckHlqGxXcWsPIeL5o4eTr1ople4+Gi7wZF2g1f3WRRmac6vcMhKld8EIWKJZcLySof0FM2WepP+AFxQ5cTcqAEhIkkCT0IkqIEgdPV7dShiSXaahtbQXz8zN3LL1zOoUGip8TEFknya4x0Gro7s8EkhhABIS9J09ntFiHsHnTFrv6QlwbwSl3klLoNBON5hcLJXkZ6s5YJViBg0v8Qlxa9584BFf0Bx2XwbQ0HPgCI1ScuslkKcheweQiSo/cdNDAXl+bEVeJqZ67K1IbTXpiVpsiN4Z/lEh0Feho65Au7RqLLA5VCTyZZ6k/NmOdKnQoiIykzVdPaDz4SUEG4uJPmgoiC2fm+FEKPNytMk+2xe2WvxxBYftgOuVliG5vzZTszd0BViukjgSYgEVddsMLvQjblZwtKTISM5tHo/ZbluxMbguy40dSrmlzjjv1iMKy9Ds3y2zeY6k4YWgxm5LnkZmhS/JjdNk+KPdAuFEIkkJ1XT2Babs8MKIc5NYabm6sVBDjWZpCdrctI0tc0Gbx+y6AvYLJoRufNPIaKVBJ6ESEC2A/1BFdFsoHPh1Uwa/xd9Zl7k7jodbjMIOoqZebHZx9FobrFLSbbLwSaTEx2K+hYD/d52UJLtsrLSJjXGapYJIWJTZop3bA84ioCNDLERIsFkpcL5s9+/uZif4ZCerNnRaNE74LBw5thDcIVIVPIzKUQCskxIT9J09sXm7RgjhNoYyT5NXnpkgj5aw95jBiXZbswG96JVejIsLXeg3MsqGwjCiU6DHYdNnt7uY0WlTVmuzBwlhAivjlN+Pzv7FAWZcqwXIpEp5U0qkOK32VJvUtdiUpTlUlXoMiPXlbpuIuFJ4EmIBFWa43Ko2aCyKPaCI6H8eGemRC74cKJT0dFnsKwiGJkGJAjDgNQkqCx0mZHj8k6txev7faT4NCU5LgtKHTJSIt1KIUQ80Rr2HjfYecQbX1ec7XqTXgghBN45yaw8l8Z2g0NNBq8fsEiyNBUFLuX5LgNBaO9VtPcYDAS9G8HpyZCerElP0mSkSPkAEZ8k8CREgqqZ5dDUpXhxt8Wq+Tb5GbFz4jwrz6Wp8+zRpxR/5JZnz1GT3DSXQrkDPm2SfHDpPJvmLm+WqcY2g4YWr46ZZXrDSw0DZuS4FGXJehFChG4wCM1diqPtBic6DQaCCtDMLnBYWeXITJtCiBEsE2YXuMwucOnsg9pmk7oWg33HvYC139TkpmuyUjQ9g4qWbkV/4P0DSVaKS2mOdxMtP0PLMUbEBQk8CZGgLBOuWGjz2j6LF3ZZrKxymB0jM+6U57tsa9AEnDP/Eocyy1A4NHcpmrsMLpkXlOFe00wpKMrSFGU5LClz2HXU5Gi7gdZgmZqArdh/3KQg02XJTIdCCUAJIcZgO3Ck3aCuxaCtW2G73sHcUJr0ZE11qUNVkYtPiooLIcaRlQrLKhxqZjm0ditSkzTpSYw6R3Rc6BmAzj6DYx2K2haDPcdM/KamONulqsi7oSnnliJWSeBJiASW7IPLF9psqjV566BFkhWkNCf6L8Yt00tl3nv8zGf9KUnTvxwnexWv7rPIz3CZmRv9/RjPLBPOm+Vw3qz3C39qDcdOKnYeMXlht49FMx0Wz3TkJE4IgauhuVNR32pwpN3AdhQFGS7zih0yUyE/wyVtjItFIYQIhWlw1oxr0/CCVFmpLrPyQWuH9h7FsQ6DI22KF3f7yE1zWTDDYUauZEGJ2COBJyESnGnABVUO/QHF5jqL0pzYqEtUlnf2wFPONNfcsB14aY9FepJm1XxbTgiikFIwI1dTmmOz55jB9sMWQdu7EykXk0IkHq29WisNrQaHW70hdBnJmuoSh4oCl3SZkUoIESFKQV6GJi/DYfFMONGh2HPM5LX9XgDq8oW2zKYpYopsrkIIlPIyiF4/YDEQ9DKhol1OuibJ0gzaoyMGmSnutNes6h2EwaDikrk2STHQf4lMKVg4w8Vn2rxbZ2K+lx0lhEgM3f3Q0GrQ0GrSPaBI9mlm5bmUF7jkpslQFiFEdFEKSnI0JTk2LV2KV/ZZvLzH4vKFtgz5FTFDJnZMABs3PkxNzRo2bnx41HPr1t1Ffn4169bdNaWfK2JP0AHQMfMDZiiYkTu6JpVCs6Jy+ou9au19oVywxI65xS7nzXLYc9TkcKv8HAoR71wNL+yyeHKrn73HTPIyXNYsCHLL8iDnz3bIS5egkxAiuhVkai5fYNPZr3hlr4Ut981EjJAz7QRw773/QWPjMe699z9GPffYY0/hOA6PPfbUlH6uiD0t3YrsVI0ZQ0eFhTMcLOP9zKahoNN0zyY3EIR3ar0CkNmpUtspllSXupTnO7x1yKS9R644hYhnCugeUBRluaxdEeSiOQ4l2VIrRQgRW3LTNaurbdp6FC/ssugbjHSLhBhfDF1iisnasGE9ZWWlbNiwftRza9fegGmarF17w5R+rph+WkNts8FLuy1+/baPp7dZ7DpihHQnxHbgaLsRE4XFT5WeDGsW2BRkupRke+Pdq4qmd2a+jl7Fs9t99A4q1iyQ8faxRilYWemQlaJ5dZ831FQIEZ+GhpW3dCmauyTaJISIXYWZmqsW2fQHFU9s8fHCLovth03qWww6ehU6tk7pRQJQWo+/WXZ1dZGVlUVDw2YyMzOmo11CiAnac9Rg22GLoiyXgkyX7n5FY5tBih9WVtoUZ595V29oNXjjgMVNSwNkpExjo2Pc0XbFGwcs0pM1q6pt0pIi3SIxWX2D8Mx2HyXZLhfNlbx1IeKV48Lr+y2OdyiWljvMKXYl40kIEbMGgnC41eBEp0F7j2Ig6B3QUnya0hyXsnyXokwZRizCp6urm/Ly8+ns7CQzM/OMr5N780LEga5+2NFosqDU4bzy9y+aF8102FRr8dIeH7PyHBbNdMhKHf3+umaD/AxXgk4T0NKleHW/xYwczUVzbKwYqY0lxjZUEF4KwwsR30wDLp1ns6XBZHO9RX2rywWVDtnTPBOqEEJMhWQfzCtxmVfiZfwHbDjZqzh20uBou8GhZpOcNJcFMxxm5srQYhE5EngSIsZpDZtqLVL8sLhsZKZGZgpcsdCmvsVge6PJU9tMZuS4VJc65KZ7Pz67j3p3SS6osiO0BLEnYMNr+y3yMzSXzLUxZNByzDvSbjBoK6qKJNtJiHhnGLB8tsOsPJd3ak2e2WFRXeKyaKYjNxGEEDHNb0FRlqYoy2FpuUNTp2LPMZPX9/tIT9YsKHWoKHBjqqariA8SeBIixh1qMmjuMlizIDjmj4hSMLvQZVa+S0OrwZ6jJn/Y5UOh8ftgMKhYNNNhdsH01kaKZbuOmNgOXDxHgk7xoqNXYRqxVVxfCHFuCjI119XY7D1msOuISWObwaIyh7x0l4xkmaVUCBHblILibE1xtleIfM9Rk3dqTXY0mswvcZhT5OKTaICYJnKKHSM2bnyYmpo1bNz4cKSbErMi0Yfh+s6hz/3ZL55jS4NJZaE3M8/ZmIZXVPXGpUGuXBRkRaVDZaE3lfSSMidiJ9jh7qOp/tz+AOw/YbBghkOq1HSKG9WlDkkWPLfDx45Gk/5ApFskhJgOpgGLZrpcf16Q1CTNWwctfr/Vz++3+mSmKCFE3MhL11w23+bGpUFKc1x2NJr8brOP7YdNmVhFTAspLh4jamrW0Nh4jLKyUrZvfznSzYlJkejDcH1nTc0abJXGX//gUWYVp7NmQezWGApnH4Xjc/ccNdjZaPLBFUGZwS7O9AzAnqMmDa3ePZlFMx3mlUg6uhCJZDAI7T2Ktw5ZFGa6XDJPht8KIeJP3yDsO25yqMlAa290RHWpQ3pypFsmYk2oxcXldDpGbNiwnrKyUjZsWB/ppsSsSPRhuL5zw19tYMM9D5Nk2qyqjt2gE4Sxj8L0uY1tBqW5rgSd4lB6MqyscrhleZDKQpfth01e2m3JlMRCJJAkH5TkaBaXORxuMznZK+PthBDxJzUJllU4fOD8IAtnOjS2GTy5xccbB0xaupSc+4gpJxlPQsSgd+u8rIwbzguS4o90axKH1vDrd3wsmuGwYIbUxIp3JzoUL+3xsbo6SGmOnIEJkUj6A/Dbd/2smh9kRq7s/0KI+GY7UNtssO+4Se+gIj1JU1HgFSKXLChxNqFmPMk9eyFiUEOrQVWRK0GnaRawwXYU6clyEZIIirI0fktzsldJ4EmIBJPkA4WmP6gA2f+FEPHNMmFeicvcYpfmLkV9i8HeYyY7j1gUZLrMLnApy3PxxfAoCxFZEngSIsZo7QVAUnxyIjzdega8IRdy5ycxKAVZKZq2HgOQDDchEomhINkP/YMy1E4IkTiU8m68FWU5LJ/t0NhuUNds8PYhk021JnnpmsJMTWGWS166julyH2J6SeBJiBijgVQ/nOg0mFciF8PTqfu9wFNakgT9EkVFgcs7tRZ1zQazC2V/EyKR5KRpGloNqksdmXJcCJFwLBNmF3jZTr2DcLTdoLnL4ECTwa6jJobS5KVrZuR6r0nyRbrFIprJz6gQMcZQUDPL4c2DFm3dirwMCYJMh4Eg7DpikpsmhcUTSWWhS3uPw1uHTFDeCZgQIjEsK7d5doePV/ZZrFlgywyXQoiElZbkDcWbV+KiNXT2K5o7FU2dBtsPm+xoNCnP94bq5aTJtYkYTX5Cp9jGjQ9TU7OGjRsfjnRTxAScy3qLxDqfle+S4tccbkvsXXi6+r6+xeD3W3wM2nDRXDus3yWii1KwotKhstDl7YMm9S2Jvc8JkUgyUmB1tU1bj+LtQzKeRAghwDs3yk7VzCtxWVVtc8v5QRbOcDjeYfDMdh/P77RoaDVw5F6dOIXMajfFamrW0Nh4jLKyUrZvfznSzREhOpf1Fql1/sIuiySf5tJ5zrR9Z7QJd9+7GrY2mOw/blKe77CswiFZ0ogTktbw9iEv8HTRXIfyfDmbEiJR1LUYvHXQ4vIFQYqz5U6+EEKMxdVwtF1x4IRJc5eBaWhy0zX56Zr8DJe8DC3n0XEo1Fnt5NbtFNuwYT1lZaVs2LA+0k0RE3Au6y0S61xr6AuohD94h7vvt9abHDhucH6FzUVzJOiUyJSCC6ocygtc3jxgcrhVfj6FSBQV+S45aS4HTkjWkxBCnImhoCxPc+UimxvOC7CkzCHJ8kYOvLLPx2Ob/Dyx2ccbB0x2HzXYc8qfvccMjp1UBGVgQdySjCchYlBbt+K5nT7WVAcpkWnew6KpU/Hibh/LKmzmSxF38R5Xw9sHTRpaDVZWeUPwhBDxzXHhme0WWamJnWUshBCT4d0wh9Zug7ZuRWuPGp4peojrgu0qDKUpydbMzHXJSddkJmsMudcX1ULNeJISuULEIJ/lBZsGbYU3z52YatsPm+RnuMwrlsCCeJ+h4II5DqYBbx+y6OxzWFruoGTGdSHi1o7DJj0DiovmSNBJCCEmSimvOHlakkt5/tiv0Rp6BuDoSYPGNoO3DnlhiqGZ8+aVOMzM1XK+FcMk8CRiiu3AoWaDjl5Ff0DRFwBXK/IzvFkU8tITIwiT4gdTaQaCkW5JfHI1dPQqaiSgIMZgvFdwPCtVs7neomdAcV65TVoSMuuVEHHmeIdi73GTpeU2uQlyjiGEENNNKW9Ch+oUl+pSl4ANHX2Kjl5FY7vBa/t9ZCRrqksdKgpcOd+KQRJ4EjGlodVgS/3ozbZnwKS+xSsAvbTcIcUfgcZNoyPtBo5WlOVJNk44BG1wtCLVLxcZYmxKedMKn+h0OXrS4OhJP35Tc/5s74RICBH7ugfgrYMWxVmuDLkWQohp5LegMFNTmOnNntfardhz1OSdWosdjZr5JQ5zilx8Es2IGRIrTADTNeV8OGkNtc0Ge4+dvbBnQ6vJmwetuJ++s77FoCDDJS1paj83HraVqeC3vNTe/oCkO4mzu2y+zc3LAlyxMEhJjsubBy0a22S7ESKWaQ37jxs8tdWHUnDhHFuyX4UQIoLyMzSrqm1uXBqgJNtlR6PJY5t8vLLXorbZYFBGgUQ9KS6eAMI95fx02HPUYNvh0EPa2akuF8/1hsLEm4ANv3nHx/JKL9I/leJhW5kqj2/2UZbnsrRcanqI0GgNr+6zaO1W3LA0KLMgChGjjrYrXtnnY16xQ80sB0smsxNCiKjSNwiH2wyOtBu0disUUJDpFSWfkTv1N+fFmYVaXFwynhJAuKecP1d9g3DspOJkrzeF5mAQWroUB5sMNteZPLfDmlDQCaCjz+DlvfGZe+m4oFGkhGEYWLRvK9MpLUnTNxjpVohYohSsrPLmAX47ATIvhYhXJ3sVST5v6KwEnYQQIvqkJkF1qcvVi20+uDzI8kpv4petDSaPb/bzzHaLXUcMOvsU46fZiOkgGU8iIhwXjrYbHGwyaO4KT/wzJ83luho7LJ8dSUEHfv22n5pZNgtnyJVtuLx5wKRnUHH14vjbhkR4HTupeHWfRVoSLJjh1XwyZJiOEDHjzYMm3f2Ka5bI8V8IIWJJ0IZjHQZH2hXHTxrYriIjWTMj12VmrjcRlQydnlqhZjzFZ0qIiDq1zQb7jhn0Dio03pAUV4d3ry/Jjs/wts+EOUUO2w9bdPU7nF/h4Jc9ecqlJkFzl/wyiYkrzdFcs8RmZ6PJ24csaptd1lTbUgBTiBiR7PMyr4UQQsQWnwXl+S7l+eC4Dk2diiPtBnXv1QrOTXNZXOZQki0BqOkmp8FiWuw6YtI7OL17d3F2/GYDLZ/tkJuu2Vxn4rpwyTypQzTVUv2a/gC4GslWEROWk+YVwWzpUry81+LtWpNL5jpykiNEDMhM0fQOesP/JWAshBCxyTS8m4GlOQ5upReE2nXE5I97fRKAigCp8SSmxeIyhyRrejOQ4jlYMBD0ZrZzXAg6cbygEZSapNEo+gORbomIZQWZmguqbBrbTA42yU+uELGgKMvFUJq9x6XAkxBCxANDeaNhrlpkc/nCIIYBf9zr47kdFsdOSh2o6SBnwWLSNm58mJqaNWzc+PC4r51d4PLBFUGuXBRkRk74M5FS/ZqM5NCPIBNZlql872Qd7/BqYy2Y4XJB1cgaFJFoTzxK9Xt/9wcksCfOzaw8zdxihy31JsdPyvYkRLRLS4L5JS67jxi8tt/kcKtBUBKLhRAi5ikFxVljB6AONRn0B5AgVJhIcXExaTU1a2hsPEZZWSnbt78c8vuGphw/ejI8cc/8DJfL5tkk+0N/z2SX5VzfO1mNbYrX9vv44PIAKactZyTaE48CNjz6jp8LqmwqC+N32KaYHq4Lr+63ONGhWFbhMKfIldRuIaKY68LBJoO6FoOTvQZKaTJTNHnpmpoyZ0LnGEIIIaKT1tDUpdh9xKSlS6FRGEqT7INkn/d3XobLrHyXjORItzY6hVpcXDKexKRt2LCesrJSNmxYP6H3KQXdA+G44tJcMtfmqkUTCzrB5JflXN87WUnv1ZwY6w5sJNoTj/wWFGS4HDhh0Dt45tf1DsKJDknRFWdnGHDpPJuqIpd36yxe3WeddbsSQkSWYcC8Em923JuXBVhe4ZCfrjnSbvBOrRR+EkKIeDCUAXXlIpu1K4JcOi/I0nJvRuLsNI0G9hw1eXKLn7cOmgxICY5Jk4wnERFvHjSpb5na2gk5ad4JYjzT2rsDW9vs3YFduyJAsi/SrYpfzV2KV/dZBGzITtUUZGrSkjR+Czr7FCc6FJ39Xvx+dXWQ0hyJPonxHWlTvFtnEXBg0UyH+SUuptwGEiImHDhhsLne5OZlQdKSIt0aIYQQ4WY7UNdisKPRRGtYOMPLXJfJJzyhZjxJ4ElMq6MnFbVNJsc7FK6e2qwnn6m5aWkwJtPftSakYTcNrQZvHLAoynKpLHQpz5chYOEWdKCxzaC5S9HWbTAQ9Aq6p/g0xdkuJTkue4+aWCZcuSi+A59i6gQd2Nlosv+4QUYKLJ9tU5QlgUshol3Qhie3+kiyNIvLHIqzNT6pQS6EEHFvMOidux1sMjANqChwmVvskJUa6ZZFlgSeRFQ51OQV5mzuMjgWptpO4A2NumKRHVMz2nX1w8t7fCyc4VBZeOa6L66GP+z0QuvXLJEARyQ5rjc7xtC62nPUYNcRk49cGIxsw0TM6ehVbKozae02KM93WFrujKrbJoSILp19ircOmbT3GBjKy4adU+QwM1em5RZCiHjXNwiHmk0ONRkMBBWFmV4Aakaujqlr0KkSauBJEsTEtOjoVRxoCv8twZZugxMdKmaGPB07qXin1qI/4P19stfhvFnOmKmb+48btPcorpCsmog7fViUIcOkxCRlp3kzq9S1GGxrMPn9SYMlZQ5zit2EPHkRIhZkpWquXWLTMwDHThocaTd4bb+PZRU280skE1kIIeJZahIsKXNYOMPhSLvBgRPeb0BWistFcx1y0mLjOnS6yeWSmBazp3FWsJ1HTHoGpu3rJs1xvek7+wPvX10ebDJ5apuPzr6Rrx0Mwu6jJpWFLoWZcjCLNgqQtSImSymoLHS5cWmQWfkum+tNntth0dYjkScholl6sleA/MpFNnOLHXYclsKzQgiRKEwDyvNdrl5sc+2SIErBczssdjYadPQqBoLeiBXhkcBTFNm48WFqatawcePDE3ouFuSma/Izpif41N5j8MQWP5tqzSmZaSxcfW+PMSMdQF9A8ebBkSlPO494y7K47AxvmoBo25bGa0+42jvVnyuz2olzleSDlZUO1yz2shpf2GnR2i3BJyFiweKZDkrBmwctgpKYLIQQCSU3XXPNEpt5JS67jpo8vd3HY5v8PPKmj5d2W3THQFJEuEmNpyhSU7OGxsZjlJWVsn37yyE/FysOtyle3z+9U7CdX+EdAM5FuPq+PwBPbvFhu6MvLH2m5sMXePWCOvvgme0+Fpc5LJxx7sG7aNuWxmtPuNo7lZ974ITBlnqTP71IajyJqeG48NJui84+xc3nB/HLwHghot7xDsXr+y1S/LBqfpCMlEi3SAghxHQLOl6ZmcGgl1Cw/4RJIAiXzLMpzo6/O9Wh1niSjKcosmHDesrKStmwYf2EnosVM3M1qf7p3dm2Npi0n+NwlXD0/WAQ/rjXIjVJU11i88HlAT64PMDyyiDJPo0Cfvuujyc2+3h2h4/0ZM284qnJGIu2bWm89oSrvVP9ufH3MyIiyTRgXolDwFE4UjJGiJhQkq25ZkkQreHZHT6Od0jGohBCJBqfCQWZmpl5mnklLtcuCZKbrnl5j8U7h0xqm72axF39Zx4BE48k40lMqz1HDbYdnt5b9yk+zapqm9z08IYGgjbUtRh09ik6+xWZKZqVlV7qfX8A9h83KcpyyUzRvLTHYtBWXLHAJvu0AnSuC83dipZOA0dDsk9TUeCSPL3JYmICDjYZvFtr8mcXS8aTmBp9g/DKPouArfjA+bJdCRFLAja8ecDieIfivHKH+SVnnrFWCCFE/HO1N1HU3mMmA8GRPwipSZrZBS4LSh2s8M/FNeVkVjsRleYWu9S3uHT2T1+yXX9Q8dwOi3klLkvKwrdD72g02X/i/Q9v7YaOPkVBhuZIu0HvoGLPMZPsVBet4apFQTLHSMM3DCjO0hRnJVAIPMZ5xcXlqkJMnVf2WQwEFavmS7EYIWKN34LLqm12NJpsbbDo6HW4YI4jM1UKIUSCMhRUl7pUl7o4LvQFoH9Q0RdQtHYr9h4zaGg1uGiOTX5GfI6jkMCTmFaWCRfPc3huu8LR03cGplHsO27S2GawcKbDrDx3RM2Ujl7FnmMGaUngtzRFWXrCU2E2d41envYeg/aekY919BkkWxp/DEa0xdlpjdzVFucs6MDJXoML54Q/U1MIER6GgvNmOWSnat48aGIYcEGV3FASQohEZxqQkQwZyRrQVBR45RXeOmjxh50WF811KM+PvzoLEngS0y47VbO4zJn2IXfgFXjbVGuxuU5TnKUpynLxWV5x6JO9I7OwSrJdzq+wQyoOajvQ2R9axEGhCTjQ1GXE5UElEUmwSZwrV0PvADR3ecN1AXLT5PggRKwrz3exHXin1qvruGiGDLsTQggxUmYKXLXY5s0DJptqTQozXVL8kW7V1JLi4uKMwjWNPcC8EnfaC42fytWKYx0GWxos3j5kjQo6ARzvMHh+ly+kwr69gwodYgbXikqHP70oOKGg00DQy6hq61F09Cm6+70aMINBL+g1fqW2+BTObXQyEnQ1iCnw+n6LJ7f6eafW4lCzwZwih6zUSLdKCDEVKgtdFs+02dlo8W6dpDsLIYQYzVBw/mznvUmm/Pxmk49ntlu8ccCksU0RjPGkWSkuLs4oXNPYD6lvMXjzYPQn3V21KEhB5tl3E9eFNw6aNLaNf0LptzQfOD+IL8Rzz/3HDbbUm2etIXR+hc28ksTLjgj3NhqquhaDtw5afPTCAKaE88Uk7DpisKPR4tolQXLStGRECBGHDpwweLfO4prFQfLitIaHEEKIc9M7CE2dxnAdKC/xwMBQXjmYGbkuM3KiJyMq1OLicokkzihc09gPKc93yYmBoSSt3eNfARoGXDrPYdX88WefCtiK+hZv19Pay1o6NfzruN5jfYPwbp3J5npr3MLVOxrNmI+CT0a4t9FQDRWMdaN/cxZRqrrUJT1Js/2wZEMIEa+qirwfiY4+iSwLIYQYW1rSUKasy8oqh+vPs7l5WYDzyh1sF96tNfntuz6e22Gx56hBV3+kWxwayXgSEXX8pOLlvb5IN+Os5hQ5rKgMLaoTtOHX74wfflZ4xcsHgt5sBlkpLsXZmq5+RXOXwnEnflJ66bwgZXlyBzUSWroUf9jl4/rzgmSnyjoQkzOUBXrzsgDpyZFujRBiqnX2wVPb/KypDlKSI78VQgghJm4wCMc6DI62Gxzv8K4bM5I1M3NdZuS65KVPb+Z8qBlP0T/OScS1gkyNUjrk+kiRMKFMohAXQ6No733/xZ39Bp3nEK32WxOfhU9MnfRkr+97ByBb6vKICRoMwluHLJo7FQo9YsZNIUT8ONFhYCo97vB9IYQQ4kySfDC7wGV2gTd5RVOn4ki7QW2zwZ5jJpahyUjR+ExI8WvyMzSzC1ysCCfVy+mtiCjLhKwUHdVp5wPB0NoWsOGl3dO/S+WkuVw23yYtadq/Wrwn2QeG0vQOKqTEuJiobYdNWroUi2Y6FGVJ4EmIeNXRp8hM1RE/+RdCCBEfLBNm5Gpm5Dq42qGtW9HaregeUNgO9AwoGtsMdh8xuWiuTVFW5K5T5PRWRFxuuqajL9KtOLOTPQpXv1/H50waWg3ax5gd72ySfJrcNE3Q8Wans13FYBCCzugvWzzTpqHVpHvg/efK8x1WVjpyEhthSnlZT6dmsQkRiuYuRW2zyYpKmzlFUiRMiHiW6odjHQqtkQkEhBBCTClDeaOJTs+q7RmAtw9ZvLzH4uK5dsRKsyRscfFom4Y9keWmR3eGSMBRvHnApHOc4FhFgUt5fujj8oqyXK6vCbJmgc3Vi+33CscFWbsiyAVVNpkp7/dLTprLwpkuq6qDnF9hU1XocH6FzUVzJOgULcrzXRpbDQbHry8vEpjjeicArvb+vanWIi/dpapQgk5CxLucNJfBoKJnINItEUIIkSjSk2HNApuZuS6v77fYfjgyk1IlbHHxaJmGPdF09cP2wxYDQUhL0qQmQVOHorNfMTPX5USHwaAdvbcBl5TZLJp55gtEV8MTm330Bc68DArN4jKHBTPcs2ZRae1lQ7T1KMpyXTJSzqXlItwGgvC7d30seW/dCnG6w60Gm+tNBoIKQ2ly0zVtPYrrlthkS402IeKe7cDvNvuYXeCyrCIBp6IVQggRMa6GXY0me44Z+C1YMMOhMFOTmaIxzyEdSYqLj2PDhvXce+9/RHwa9kTianhtn0Vnv7dlt3aDZWjmFrusXmCT7IOmTpeXdlvoUKt0T7MTHcZZA0+GgtIcl4NNZ05DuqzaZkYIs9koBUVZOqJjcUXokn1e1tOORpNBWzGnyJGZycQwx4VNdSa5aZp5JTbtPYq9x0wWlLoSdBIiQVgmVBW5HDhhMLdYfiOEEEJMH0PBklkOlUUO2w+bbK030Xg3Q7NSvYmqhv5kj1GPcDDo3Wjv7PNmZdfaKxujB0O7bk/YjCcx/epaDN466MU6ldIsnOEyr9ghyTfydYeaDN6pjc6Y6LJym/mlZ89mOXZS8ce9vjGfm1fscP5sucsZrxwXdh4xOXjCwHFh+WyHKqnbk/AGgvCHnRaGgkvm2WS9N/Oh43onAVLrRYjEEbDhme0+fKbm8oXeTTchhBBiugUd6OhVnDzlT2e/QmtvluWMFG+IeEGG5sAJYzh55HT9fd18+SOLJeNJRAetYWfj+2HTtCRYUjZ2AKaqyCUzJci7dSYdfdFVhqwyhCBCUZYm1a9HDbfLTNHUzJKgUzwzDThvlsOiGQ6bak22NJjMyHXlwiKBdfQpXtxlMWgrKguc4aATcE5pzUKI2OS34LL5Ni/ssvjduz6Ks7VXesDvlR/w/tak+Mef1EQIIYSYLJ85uhi543oZTacGowzl1WSeV+LVIPZbGld7z7X3KBqbQrvJLoEnMS2CDvQNvv//ngFFVz9knqFuUUGm5toam9omg+2NJoEoqPtkKI0vhELepgHVpQ6b69/fvZTSXDTHlkLgCcIyYVmFw/EOg9f3W6xZYEuQIQG1dile2G3haoWpNOdXSuBZCAE5aZqblwWpbTZo6jRo6lT0BQzsU2a0VXjDHZbMcijJluG4Qgghws80vCDT+JN/eb9RlYUwL98O6bMl8CSmhd+CqxfbHO9QdPQZdPfDYFBBypk3akPBnGKXsjyX7YdNDjVHNmrjakXQIaTgU2WhS2u3w+E2A1CU57lkpcqJYyJJ8sGl821e2m3xx70WK2bbUiA+gbT3wIvvBZ18puaqRUEsCT4KId6T5IMFM9wRk1EEbOgLKPoGvb8bWgxe2Wtx5SKb/Aw5hxBCCBG7pMaTiBlvHzKpjXDw6fwKm3klodfsefOgSX2L12ZTaXLSNf5Twr2FmS7V49SMErHtRIfizYMWg0GoKHBZPtuRzLc45bqw/4RBY5tBW4+XuVBV6LB8toshQSchxAQ5rhfA7h5QXLM4KMXIhRBCRJ1QZ7VL2FPhjRsfpqZmDRs3PhzppkRULPXDopkOSkX2jl9dy8R2mVMHCDpa0dptcOzk+38Ot0bXLhiu7SGWtrOpVpztDalYVuFwuNVg22GJOsWruhaDrQ0WbT1epmOq38tokKCTEGIyTANWzbexDHhlr0UwtNEMQgghRNRJ2NPhe+/9Dxobj3Hvvf8R6aZEVCz1Q1oSVORHNjtoIOhNHTlVOvu84XvRIlzbQyxtZ+FgmTCvxOW8cocDJ0zauiNfs0yEX19A8fxOHx19sr6FEJOT5IM1C4L0BRSvH7BwZcSdEEKIGJSwgacNG9ZTVlbKhg3rI92UiIq1fqiZ5ZCWFLmzrv6Aorb57LuNq6GtW7HziFc09GwcrXh9vzcMKxqEa3uIte0sXOYWu6QlaQ42JeyhN67NzHXxmSOPTwNBxVsHzSkNWAshEktmClw6z+ZEh2JLvWTNCiGEiD1S40nEnKANm+tN6loid/I1u8BhablDku/9xxrbFA2tJk2diqAzsQyHZJ9mZZXNjBy5Oo13O48Y7Dlq8uGVQRmCFYe6B6C+xWT3EQN9ymDby+YHmZkr+7cQYvIONhlsqrVYPttmbrHUhxRCCBF5odZ4klntRMzxWXDhHIcZuS5vH7II2NM/jKWuxeToSYN5xQ7ZaZrjHQaHmiYfCBsIKl7Z66Oy0GFZuYNP9sy4VZSp2dmo6OxX5KRJICLeZCTDkjIH14U9x94/JrT1GMzMjaJxtUKImDOnyKW9x2H7YZOCTE2S5f2GJPm8mYCFEEKIaCWXtyJmzczVZKUEeWaHD3uCGUZTIWArdh6Z2l2ottmkpcvgykVBUvxT+tEiSiT7vAuFQSkSG9eKstwRgaeegQg2RggRNxbOcGhsM3h62/sp15ahWVzmML/ERUkASgghRBSSwJOIaRkpkJOqaYmSYs1KaSryXfIzNOnJms4+xdYGE1eH3r7uAcXuIybLKyU7Ih51D3jbQqpfsp3iWUbKyPXrutFxjBJCxLb0ZPjg8iBtPQrHBa3hRKc3o6ZSNvNLZAieEEKI6CMVRkRM6+6H1igJOgHkpGmKsjSZKZrCTM28EpcrFtoTLoh+fJyi5OGycePD1NSsYePGh+P+c8PVpvEcbjVIT9ZkJE/r14ppdvqwF1euBYUQU8QyoShLU5qjmZGrWT7bYV6Jw7YGM6rOiYQQQoghUlxcxLR9xw221Edn4l5locP5FQ6WCSd7Fc/tsCaU+fShFYERxcunQ03NGhobj1FWVsr27S/H9eeGq01n09UPT231sbTCkbvSca67H57c+v542RSfywdXyPhKIUR4OC68tNviZK9iVbVNUZZk1QohhAi/UIuLS8aTiGlOFF+71zabPPauj/3HDbJTNeX5E2vs/hPTP2vfhg3rKSsrZcOG9XH/ueFq09lsP2yRmuQViBXxLS0ZVsy2KcpyAU1/UEkmghAibEwDVi+wyc/QvLzHorFNjjdCCCGih2Q8iZh2qMngndrozHg6VWWhg1JMeOa7hTMcFsxw8E1/DEpMseYuxQu7fFw0x6aiQAJPieSxTT4Ggor0ZM11NUHZn4UQYeO48NZBk8Y2gwvnOPJ7I4QQIqxCzXiK/it2Ic6iKMvFUHpCQ9giobZ5cleau4+aHGwyWFDqDc0yJEcxJrkaNteZ5Ka7E858E7Gtqx8Gg96/ewYUDa2GZLwJIcLGNODiuQ6mAW8fMslM0eSmy7A7IYQQkSWXsSKmpSfD/NL4vogL2Ipthy021UmaRKyqbTbo6FMsn+3IVNcJxHHhjQMWmvdX+lAQSgghwkUpWFHpkJ2qeW2/FdVlCYQQQiQGCTyJmFeSlRhnVLXNJt0DkW6FmChXw96jJrPyXPLkrnNCefuQycne939mLUMzW4a9CCGmgWnAyiqH3kFFU6fc8RBCCBFZEngaQyxO0Z4oxurfrv7EOaE6fvLcd1nZRqfXsZOKnkFFdZxn5onR0pNG/n/RTIfUpLFfK4QQUy07VZORrGlsk9N9IYQQ58Z2oLPPu7bZf9xgS73JWwdN3jwYWvUmKS4+hliboj2RjNW/tgPP77To6Iv/E6uiLJcrFp7blOyyjU6vbQ0mDa0GtyyXMVaJxnbg0Xd8uFqRlqS5cWkQM/4PU0KIKLL9sMnBEwZrVwSlTqQQQogzcjX0B7yapL2D6r2/3///QPD9ZA9DaVKTIMnS2IPd3H5NjRQXn4wNG9Zz773/Mekp2if7XjG+sfrXMqE836XjcGyeUWWluvhMaO0ev/1NnQYHm86tOLFso9OrdxDSkmSIXSJpbFM0dxk0d6rhiQ/mlTgSdBJCTLtZ+S67j5rsOmKyZJYT6eYIIYSIIlpDS7fiwHGToyfVKRN2aVL83jVMRoqmOFuTnqxJS9KkJ3nPDdWt7eoKLSlCMp5EXNh91GD74diMo15XEyQ7VVPfarDvmBFS5tbq6iClORLMiAVbG0xqm7yMJ0vqwyeEoaLiR9qH9mXN2hVBkn0RbZYQIkENnSNdUGVTWSjDvoUQItHZDhxuM9h/3Lv2zEjWVBZ6k1KkJWvSkgj5hmlXVzfl5edLxpNIDJkpsReEKc1xKcl2SUvSKAWzC1xmF7gcPGGwqe7su+abBywWlzlkp2myUjRJckEbteYUOew7ZgzfbTYSpyRZwjINqJllc6TdD4DfRIJOQoiIWVDq0jPg8E6tSarfu3MthBAi8fQOwsEmk0NNBgFbUZrtcl55kOIsHfaZtyXwJGLaYBCe2OLDjsHs8VS/Zk6RO2onrypyaWx3aeo8c5g54Cg217+/+yb7NCXZLmV5LkVZWob0RJH0ZK+o9M4jJi3diguqbDJTIt0qEW6+U7LbDMNLZQ73D7oQQoxFKVgx26FvUPHafosblwZJ8Ue6VUIIIaZLR59iV6PJkXaFZXoJD3OLHTKm8ZpEAk8ipiX54PrzgpzsVQwGFUdPGhw7x5nfslNdZua6JPng3XEyj87FwSaT2maD7DTN6mp7OCNCKSjLO3vg6XQDQUVdi0ldi4nP1JTmuFQWuhRmhj96Lca3uMylMEvz9iGLZ7b5WDLLYV6JK9lPcaw/8P7KHQgq2noU+RmSZSCEiAzDgEvm2Ty51cfmepNL58XgHTshhBATVt9i8E6tSYofzp/tUFHgjrhBOl0kL0LEvLQkmJmrKctz6R04989bPtthcZnLnCKX/Izw1kJwtaK9Z3SwrLt/8hGJoKNoaDV5cbePP+616Bs811aKqVCYqbm+JkhVscvWBpMXdll09Ue6VeJcBR3o6B29v9Y2j9yn3z5kEZTrPCFEBPktWDrLobHNlN8fIYSIc44L79aZvHnQoizP5fqaIHOLIxN0Agk8iTDZuPFhamrWsHHjw9P2nZtqTTr7z32THnhv1vuufjUtF4pDGVanykufmsyI4x0Gz+zw0dod/tSaSKzzWGOZcH6Fw1WLbAaCime2+ahvkcNwLGvrUTy93cfbh0wC703q0dimONg0cr129Sv2HJXq8kKIyJqZ56LQNHfJb48QQsSr/gC8uNviUJPB8tk2F1Y5EZ/kSH51RFjce+9/0Nh4jHvv/Y9p+86ctKkJ1rxTa9EzAC/ssugMYYa5czW/1MV/2oi+mXle4fGpMBhUvLDLoqkzvMGnSKzzWFXwXvZTWb7LWwe98dYiNhVkaPIzXGqbTX73ro/ndli8tt8HjF6n6cky1E4IEVk+E5L9I4cDCyGEiA9aw7GTime2++gdUFy5yGZu8eiawpEggScRFhs2rKesrJQNG9ZP23cumOFy5aIg2annFrAJ2IoXdvkYtKdnD3XGyKoylFeLISdtaoJPrlZsqrVwwjhyMBLrPJZZJlxQ5TAzz+X1/TIkMlaZBly50ObCKpu0JE1bz+if1YIM79gk05gLIaKBZRDW8wEhhBDTx9VeHadX91k8vtnHH/f6yErVXFcTjKr6okprPW5rurq6yMrKoqFhM5mZGdPRLiEmzdVefZX9x026zqFW0nS5fEHwjFMbt3Urnts5dfOwl+c7rKyMfKqleF/Qht++66O61KstJmKTq2H7YZO9xwxAkZHsUlHgMjNXk5kiRf6FENHj6W0WBRma5ZVSeE4IIWKVq6GxzWDXEe+aNz/DJT9DU5TlUpw1feeeXV3dlJefT2dnJ5mZmWd8ncxqJ+KOoWBOkUtVoUtzl+LdOpOuKaj9FC79QQWMHXjKTdf4LU1girKvGlpN2roNLp1vT9nQRHFufBYUZWlaug1AAk+xaCAAT2+3GAga+C3NVYsCZKVGulVCCDE20wBbfm6EECLq9AxAXYvJ8Q5Fsg+yUjRZqd6fjGSNZcJgEBrbDfYfN+jqNyjJdrlwjj1lNYLDRQJPIm4p5V3QX7XI5sXdFh3TUK9pMg4cNyjPczHGaJ5SkJmip7Q4eF8AbLnJGVXSkjUnOqJz+xTj23bYZCCoKMpyWVZuk5kS6RYJIcSZmTLUTgghosZAANp6FUfaDOpbDEwTSrNdAraivtUYUZPPUNpLV9BQkq25sCpIXhQNpzsbCTyJuJfkg1XVNk9v8xF0Jh/AKc1xOXZy6oMD7b0Gm+tNVoyR8u5q6OqbuqBTUZbLebMccqM8Ip5oUv2a3kGvIKAMyYo9jW0GlgFrFtgYIaw/x4XWbkVzl0HA9gLBjgu2q0iyNEVZmuJsl+SpG2UrhBDDkn1aiosLIUQEBGxo71G09yraewzaetTw8TjFp1lW4VBZ6I4oixKwvdmRuwcUQRsMA2bkuKT4I7QQkyS32EVCSEuCm5cFuWJhkLUrApTnTyzlZ0aOy6r5NmW54blFeLDJ5FDT6N2xrVsROIdg2ZCZuS7XLAlyxUI76oJOGzc+TE3NGjZufDjSTRk23W1KS9I4riJgT8vXiSlmmRrbVexsNOgPjHzuyiv/hJycuXzkti+z95jBS7stHn3Hx4u7fRw4YdDcqejqVwwEvf28vVfx5kGLxzb5eXqbxdYGkxMdakSAavzKjOEx3n4RjfvyZIVrWc72ubHWf7HW3mgUqT7MSIHuAQk8CSFEONkOtHQp9h0zeH2/yRNbfDz6jp+X9vjYVucSsKE83+XSeUE+cH6AW5YHmVfijqrF67cgP0Mzu8BlXonLnKLYCzqBFBcXCShow+NbfCHXTcpM0Vy5MEiyH46fVLy8NzxpCIbSXLHQpiDz/V2yucubYW8yFJpZ+S4LZ7hkpUZXsOlUNTVraGw8RllZKdu3vxzp5gDT36ahIvLX1QSl9lYMGgjAjkaTxnaDgK1IS9IUZLpkp2ru/9kTVC+9lMycAkylKcj0spmKsjTZqWMXfuwPQFOnwYkOxYlOYzgoNZL3XgMvS+7UP6c+ZigwDE16kncsy0jxvjcnbeJFJ8fbL6JxX56scC3L2T431vov1tobjSLVh/UtBm8etPiTlQH8MvZBCCGmxFCx7xMdivYe78aiRmEa3nlXbprm+3//Hba88TJ+c5Dt216KdJOnhBQXF+IMWrtVyEGnwkyXVdU2vvciz0VZmmSfPsOF4LlxteK1/RbXLgmSmuQ9luKbeBDCNLyIeHWpQ3ryFDcyDDZsWM+99/4HGzasj3RThk13m1KTvPXcNwg5adPylWIKJfthZZXD4jKH1m5FS5dBS7eiodVg9rzFvPHcIwS7j/Lg/d/EDCHPOMUPFQUuFQWgtUNnv6KrT+FqbxoCrb2TG33qH0Y+5mo1/LjtQM+A156+99K5i7JcLp5jkzyBO2bj7RfRuC9PVriW5WyfG2v9F2vtjUaR6sPMFO83p7tfxUxtECGEiFauC/WtBruPmvQMKHLSXPIzNfNKXHLTvcLgQ6UYrrm4gl1vPcOG/5N4v52S8SQSju3Aszt8dPWPHzy6Zklw1AwBh5oM3qkNX8w2L93lqsVerZihu5JnU5brUl7gkJWi8VvgMxmzULmIXlrDI2/5WFbhMLdYKr7Gi2is2WU70NSpePuQRUGm5rL5Mr5TiETT0ad4epuPyxcGKc6SwJMQQkyE1tDVD63dBq3diqZO78bejByXRTMTr5auZDwJcQaWCdcuCXKo2WDPUfOM2Uum0mMOe5pd6HLghBu2WfLaegxqmw3mFLlj1n0akpqkuaDKlpPGOKCUl+XSOxhlUQpxTqIt6ATe8W9Grma5tnl9v48jbYqZeXIMESJRaA3v1pok+bxhH0IIIc7OdrwanK3dajjYFLAVCi+baUaOS2WRK+UyxiGBJ5GQLBPmv1ec7d06k9pmc9RrstL0mDNUGcoLPm2pD19a0dZ6cziD6XQ5aS6zC9xRMx6I2Oa4hDQMS4ipUJarKc1xebfOojArKHVehEgQJzoVLd0Gly+Q/V4IIcYyEPSKgg8FmU72KlytsAxNXoZmbrFLQYZLXrrGJ8fRkElXiYRmGrCi0qG9R43KYDpb1DrcWUa2q3hpj+UVaDlFdYnDeeVOVGZSiMnr7IOBoCI7VYbZiemhFCyfbfPUNh9/2Glx4RyH7DSN44LjgO1CahJjBt+FELGrrtkgM8Wb4EAIIRKR1t4N31M5rjepU12LyfGTXlHwVL8mP8OlPN+lIHNkrSYxcXJ/XcSUcEw9bChYWuGMerzgLAU3s1L1cHHOcHFchaPfP7qZhmZRmQSd4lFdi4m2B7jlmqsSfmpymaI9NGfrp/H6cOj5X/78Ya5e7NV4enaHj/9908+v3/bz2Lt+ntji57FNPt46aHKkXWGPPkSKBBCJ/VGOAaGZTD8FbTh60qCiwE2IcwnZloQQQwaD0NBq8NZBk9+96+NXb/tH/PnNJj+v7ffRH4DzZzvccn6AW5YHuWSew7wSbxhdKEEnOe6cmRQXFzElnFMP/3GvxbGTXiy2NMflsvn2WQ8wL+22ONE5fbHb7FSX68+TQsDxxtXwu3d9vPrsr/jp9/8q4acmlynaQ3O2fhqvD09/3nGhuVPRH1SYBliGRilo6TY42m7Q1e/VMUhP9mbDysvwhvumTGBGPBGbIrE/yjEgNJPpp9pmg7cPmXzg/CBpSWFuYBSQbUmIxOZqOH5Ssf+ESVOnAhRZKS7F2aPr+BoKctJdMs5xRvBEPO6EWlxcMp5ETNmwYT1lZaVhmXp4WYVNil/jMzUrK88edAJvKMp08kk9p7h0skcxGIRFFUlh27ZjSTj38Xhytn4arw9Pf940oCRHU1nopZPPyNWU5mjOm+Vw49IgNy4NsKLSoSTHxXFh1xGT32328cYBk97BsC6miLBI7I9yDAjNZPqpvsWgMFMnRNAJZFsSIlEFbNh7zODJLT5e2efDdmBlpcMHlwe4YanNsgqHigJ3xJ9Z+ecedAI57pyNZDwJcQrbgaBDSHfyn9pq0dk/fbHbgkyXqxZJxlM8GghCkhWds6AJcbqADXUt3qygQQcWznCoLnWlOL4QUax3EB7f7OOCKofKQqknKISIP519cOCESV2LgdZQlucyr9gl7yzlU8S5CzXjSYqLC3EKyyTkmeJcPb1RgpxUOWjGq2RfpFsgROj8ljcraGWBy66jJruOeDODXjLXlpM7IaJUY5uBqaAsV4JOQoj40t6j2HnE5NhJg2SfZkGpQ1WRlASINhJ4EmKSKgsdth2ejl1Is6zcK2wnhBDRwmfB0nKHykKHtw5avLDL4tL5NqU5EnwSItq0dRvkZsjU30KI+HFqwCkjWXPhHJtZeZKBHa3k50eISaoudVHKZluDiSY82U+G0lw816YsTy7khBDRKTMFrlxk8/p+i9f2W1y1yCY3XY5ZQkST9l7FTMl2EkLEgdMDThfNsZmV74Y065yIHAk8CTFJSnnBp6xUzev7LYLO1B7tfKZm1Xybwiy5gBNCRDfTgIvn2ryw2+KPey2uWZIYs2YJEQs6ehW9g4q8DAk8CSFilwScYpskoomos3Hjw9TUrGHjxocj3ZSQlGRrrlg4tUW/i7Ncrl4sQadzEWvbUTRat+4u8vOrWbfurkg35ZyFa1nibTs72/KMt6yWCavn25gG/GGXj86+cLdWxKpz2c4SxVT2w/ZGk/QkzcwwDoO98so/ISdnLlde+SdjPi/r3BNPyxquZYmnPhJTo71H8ce9Fs/u8NHdr7hojs0NS4NUFEjQKZbIrHYi6tTUrKGx8RhlZaVs3/5ypJsTEq3h1+/4sM8x66kgw2XJLIfCTAk4natY3I6iTX5+NY7jYJomra17I92ccxKuZYm37exsyxPqsvYOwu+3+piV53LhHCfcTRYxaCq2s3g3Vf3Q2q14fqePi+bYVBSEL+MpJ2fu8L9Pnjww6nlZ5554WtZwLUs89ZGYnP4AtHQrWroMWroUHX1ehtOimY5kOEWhUGe1k4wnEXU2bFhPWVkpGzasj3RTQqYUmOdwEMxNc7l8QZArF9kSdJoisbgdRZu1a2/ANE3Wrr0h0k05Z+Falnjbzs62PKEua++gwnEVs/JlWI8Y21RsZ/Fuqvph7zGTrBSX8jDvj8uWLRnx9+lknXviaVnDtSzx1EdiYrSG1/eb/PZdP6/v93H8pEFOmuaSuZLhFA8k40mIKfLbTT76gxM9GmoWzXRZNNORA6kQIi68us+iq19xw3lBlBzXhIiop7ZaFGZpls+W7EMhRHQ7eMJgU53F+RU2ZXkuKf5It0iEQjKehJhm1TMmdlKX4vdqQy0pk6CTECI+9A7C0XbF3GJHgk5CRIH+gCLFL5nUQojo5rqwo9FkdoHDvBIJOsUjmdVOiClSWeiypX7816X6NQtmOFQWupgS+hVCxJEdh018FmGtJSOECE1/AAKOkhkmhRBRr6lTMWgr5pXI+UO8ksCTEFNk1xHzrM9nJHsBp4p8F0MCTkKIONPUqahvNbmgysZ39sOhECLMXA2bai0sQ1OcJRdyQojo1tDmFRDPTpUMzXgll79CTJGFMxyWVdjkpY88wctOdYeL4lUWTk3QKZGmmo22ZY229oipEa71um7dXeTnV7Nu3V1T+rnRxnG9i9yCDJfZku0kIiQaj8+RaJPW8PYhk2Mdikvn2ST5pu2rp1w0rtNoFGv9FGvtFeHluHCk3WBWvivD9OOYFBcXIgz6A9A9oEiyNJkpTPlBNJGmmo22ZY229oipEa71mp9fjeM4mKZJa+veKfvcaLOz0WDXUZPra2yy5G6liJBoPD5Pd5u0hq0NJvuOG1w81wn7bHbhFo3rNBrFWj/FWntF+GgN79Sa1DUb3LA0SGZKpFskJkqKiwsRQSl+KMzUZKVOfdAJEmuq2Whb1mhrj5ga4Vqva9fegGmarF17w5R+bjTp6ofdR02qS10JOomIisbj83S3qa1Hse+4ycIZbswHnSA612k0irV+irX2ivBwXC87s7bZ5II5jgSd4pxkPAkhhBBiUrSGF3db9A4qbjgviCW1nYSIqP4APLXVh2nCykqb0hwJBgshok/PALy236KrT7GiypFh+jFMMp6EEEIIEVYNrQbNXQYrZtsSdBIiCqT44frzgmSnav6418ebB02CTqRbJYQQ72vqVDyz3UfAVly9xJagU4KQWe2EEEIIMWGDQdhSb1KW51AiWRVCRI3UJFhdbVPXYrC53mQwqFhVbWNI0V4hRIR5NZ0sslM1q6pt/BKNSBiS8SSEEEKICdt22MTVcH6FpFMIEW2UgspCl0vn2RzvUBw8Iaf8QojIa+pU9AwolsxyJOiUYORXSEyaTIUqpoJsR9ErntbNeMsy2WWNxT6aija3dClqm01qZjmk+L3H1q27i/z8atatu2uKWirE1AnXMSDalWRrZhe47DlqMn5VVyGEmJxQj6EHm0wyU1wKMuSAlGikuLiYNJkKVUwF2Y6iVzytm/GWZbLLGot9NBVtfna7d5vy6iXvD9/Jz6/GcRxM06S1de9UNVeIKRGuY0AsOHZS8ce9Pm5eFiA9OdKtEULEo/GOoVrDzkaTXUdNVlbaVBVJXad4IcXFRdjJVKhiKsh2FL3iad2MtyyTXdZY7KNzbXPAhvZeg7nF7oiaMWvX3oBpmqxde8MUtVSIqROuY0AsUFLbSQgRZmc7hgZseOOAF3Q6b5YEnRKVZDwJIYQQImRNnYoXd/u44bwAWamRbo0QYjw7Gk32HTf4k5VBKTAuhJhWJzoVbx20sB1YUelQni9Bp3gTasaTlPQSQgghRMjaexSWoclIiXRLhBDj0RoaWgzKcl0JOgkhpo3WsP2wyZ5jJoWZLhfOsUlLinSrRCRJ4EkIIYRIQFpDzwA4rkK/93+tQQM+U5OeDKYx+j1H2g1y07VcxAoRA9p6FD2DipUFkmUghJgeWsOWBpP9x72hddWlrgz5FRJ4EkIIIRKJq+FQk8GeYyZ9g2c+E1RostM0JdmarFSNUpr9x03aegyuXBicxhYLISarvsUgxa8pzJQZpIQQ02NHoxd0Wj7bZm6xBL2FRwJPQgghRILQGt7Yb9HYblCR71A+28VvecWHh+5GKuUVAu3qV7R0KQ41GQza3pM5aS6XLwhSmCUXsUJEO8eFw20GlYWSbSCEmB67jhjsPmqytFyCTmIkmdVOCBGSdevuIj+/mnXr7hrz+Y0bH6amZg0bNz48Zc9FSjS2SYRXoqzzhlaDxnaDS+cFuWiuQ0mOJi9Dk5uuyUnz/mSnetkRc4pcLp7rsHZFkA9fEKD/wP/y1x+/lN8/+vNIL8awRFlv5yJcfTTeb0K0OZd+CNeyhvu38USHImArKqSY77SJtf1CiKniathcZ7Kj0WLxTG94Xaii8bc8GtsU62RWOyFESPLzq3EcB9M0aW3dO+r5mpo1NDYeo6yslO3bX56S5yIlGtskwisR1rnW8OwOiyQLLl9oT/j90dhH0dimaBOuPhrvNyHanEs/hGtZw/nb6Ljw7HYLy4Rrlkx8fxeTE2v7hRDnSmvYd9ygttmge0BxfoXDnKKJZVlG4295NLYpWoU6q51kPAkhQrJ27Q2YpsnatTeM+fyGDespKytlw4b1U/ZcpERjm0R4JcI6r281ONlrML/EmdT7o7GPorFN0SZcfTTeb0K0OZd+CNeyhuu30XZgc71J14BiReXk9ncxObG2XwhxLlwX3jpksrXBIjMFrl7sDa+b6NDeaPwtj8Y2xTrJeBJCCCHikKuhq0/R2qM42m5wvMNgVp7DxXMdqfciRBzSGo62KzY3WAwEYFmFIzVWhBBhYTvw+gGL4x2Ki+Y4lMuQ3oQVasaTFBcXQggh4kh/AN6ptWjuVNiuGp6d7tJ5QWbmagk6CRGnDjUbbKq1KMl2uWKBTUZKpFskhIhHPQPw5kGLk72K1fNtSnJkwhExPgk8CSGEEHGibxCe3+lDA4tmOuSle4XDLTPSLRNChFt7jyInzWV1tS0BZiHElHJcONhkUN/iDdtPsjRXLLTJz5CgkwiNBJ6EEEKIONEfUPQFFJfOC1KWJyeDQiSSgK3wW0jQSQgxpYIO/HGvRWuXYmauZn6JzcxcV25qiQmR4uIibkTjtJfR2KZoI1MPe8brh7NtS5HoQ9m2o1NuusZvabr65cpTiGgwncfK9GTZ94UQUytow8t7vGF1Vy6yuXS+TUWBBJ3ExElxcRE3onHay2hsU7SRqYc94/XD2balSPShbNuRd6RdcbjVwDJhSZlDit97/OltFvkZWmazEiIKTOex8ki74tV9Pm5aGpD6TkKIcxZ4L+jU1a9Ys0CG1YmxhVpcXDKeRNyIxmkvo7FN0UamHvaM1w9n25Yi0YeybUfW0fcuMLsHvBnrntzqY89Rg4DtzWzlyOQyQkSF6TxWFmdpknyaAyckFUFEn6Ptiud2WPx+q48Xd1tsrjOpbzEIyj2SqBSw4aXdFt39Smo5iSkhGU9CCCFEDHFceGKzj+w0zepqm6ADOw6bHGo2MBTYruKyeUFmSo0nIRJOe48iM0UmFBDRpXsAntrqIz9Dk52q6Qsouvq9P6ahmZXnsrjMIS0p0i0VPQNw7KTBwSaTgSBcsdAmJ03OJ8SZhZrxJMXFhRBCiBjS3KXoDyrWzPJmrvJbsLzSYeFMh4MnTDr6lExtLESCyk2Pz31faymaHoscFxrbDLY2mCT7YHW1PSIo2jsIDa0G+4+bNLQazCtxWTjDwS9XqNNuMOgVEG/rMTCUpjBTc8lch2wJOokpIru1EEIIEUOOdxik+DVZqSNPBlP8sGSWjFkQQsSX3kF4eY+PS+bachEcA1q7FbXNBu09XkaTqxWlOS4rK+1RmXhpSbBwhsvcYpe9x0z2HjOoazYozXHJStVkpXi/dSl+CTyG2/7jJp19ikvm2pRku/gkSiCmmGxSQgghRAw5ftKgJNuVk3AhREKobzHoHYS0JAk6RbP2HsWORpPjHQYZyZqCTJeqIpfCTJes1LO/1/feJBlVhQ57jpm0dSsOtxk4rvdDl5akmV3gUFnokirD8aac1nC8U1GcrZmVL0UiRXhIcXGRMKJtOvrxTHebegdh/3EvHXpLvcmhJoOOXsX4VeCEENOlewC6BxR73n152qZoj7TpnI5eCBEeZ9uPx3vu8ee2MtjZOOEMjMl+Z6REY5tC0dGreGWvxbM7fPQOehkzNy4NckGVw9zi8YNOp0pNguWzHa6tsfnIBUFuXhbgsvlBCjO9jKgntvjY1mASsMO3PPEg1G2pqx/qmg1eP2DS3mMwu0CypkX4SHFxkTCibTr68Uxnm453eCcNrh6dQpHs0xRnuxRneX8n+8LaFCHEWQwFh//xi1dz6ODBaZmiPdKmczp6IUR4nG0/Hu+56+/4/5OTm8/X/n9Lp+U7IyUa23QmvYNeAepjJw2OdyjSkmBxmUN5vosRpmzcoA17j3vD8SwDFs10mFPkYkQojSJgQ9+gwnHBdr16Vn4L8tJ1xDOSz7Ytae3NMLjziElHn9d5mSku80tcKgslm1pMXKjFxSXjSSSMaJuOfjzT2aacVE2yf+znBoKK+haTNw9a/O5dH7uPGpIFJUQEaA11LQYFmZrPf+6OaZuiPdKmczp6IUR4nG0/Hu+5nvajVMxfijPBEUCT/c5IicY2na67H57fafH4Zj+b600cF1ZWOty0NMjsgvAFnQB8ljcc7+ZlQWbkumyuN3lht0V/IHzfOZbeQXi3zuS3m3w8vd3Hczt9vLjbxx/3+nh+p48nt/jYdcSgb3B623WqsbYlreFEh+K5HRav7veR5INV84P8ycoANy61qSqSoJMIL8l4EkIA0Nnn/RjZ7vi/OpfOC1ImU7ULMa1qmw3ePmRx5aIghZmy/wkhEsPJXsUz231csTBIUZYc+yIhYI+cnW5JmUNxthvR2edauxWv7rNQClbNt8M+o2NnH+w55s2+5zNhXrFDcbbGMsA0NKYBPQOK2haDxjYD14WZuZqacpuM5LA2bVyt3Yrth02auwzy0l1qZjmyL4kpE2rGkxQXF0IAkJWqWTLLYUv9+IeF1m6DsjwZBy7EVBm6BXSmu43d/bC1wWRWniNBJyFEQslK1Sil6epXcrE8zfoDsKXe5Ei7gasVZXkOKyudiAachuRnaK6rCfLqPovnd1osKXOYV+JiTtF4Hq297KaOPi/z/0i7IsUPS2c5VBa5+MzR70lN0hRmOSyvcGhoM9h9xOSprT7mFbssnDn9/dbRq9jeaHLspEF2qsuq6iCl2ZEfCigSUxQcNoQQ0WJusUtdszs85vtMegflF0uIqdDUqTjYZNDcaRBwIMmCJJ8myfLqqyX5vGBUfYtBkuUVXRVCiERiKEj1e/V0xPTp6FW8vNfC1XBeuUNZbvTNKJfihysX2Ww/bLL9sMnBJpPiLJfsNC8DSSlQnPq3F7gc+r9hQG6a91sLYDtw9KRBfYtBa7ci6HjbXHqyZmWlQ0VBaIEtnwVzilwq8l32HjfYc9SkrsVgySxvZr5wDkkE72bVziNedlZ6Mlw812ZWngylE5ElgSchxDBDeWP1n9up8H6Sx9bSpQg6jHm3RwgRmuYuxct7LDJTNFVFLil+zWAQBoOKAdurr9bZB45WzMx1OW+WM3xyLIQQiSTZpxkIRroViUFrONRssKXeJDNFs7raJuUMdUCjgWnAsgqH2QUu+094AaNDzQZnO489XUayxjQ03QMKx1XkZ7gsmOGQnarJTtWk+M+ckXw2lgmLZ7pUFrhsbzTZVGtx4ITLsnJvmN5U6xuEXUdMapsNkv3eOf3sgsgVYBfiVBJ4EkKMkJehmZXncrjtzFGlQdubBe+iOXbU3f0SItpoPfqEVWt466BFXobm8gX2lA0NEEKIeORq5Dg5DVwX3q41qW8xqSx0WFbhxMxNxuw0zQVVXlaw1qB5fxj7qf8/9bGgA609Bid7FY4DFQUuM3JcMlKmtm2pSXDRHIe5xS5b6k1e2uOjNMelqtChMEufUx939XszDB5t94JuPsvLUJtT5GLFyLoTiSFhD+EbNz5MTc0aNm58ONJNEdMk2tb5ubQn3MsSyl2Y5i6Dp7f56OwLSxNCEm3rFKKzTbEknvrPduChJw/yi9cMfv5cK81d70efWrsVvYOKJWVOzFxMjbdu1q27i/z8atatu2tav1eIcAvXti1C57qEfXiSgC0NJodbDS6aY3NBVewEnU6nlLe9mIb3xzK9LH2/BUk+70+yHzJSYHaBy/kVDiurHKpLpz7odKq8dM1Vi2wumWvT3a94ZZ+P37zj4w+7LHYe8Yb4nehQdPQqBoJeUff+APQMeMXNW7sVdS0G2w+bvLrP4onNPn6/1c+OwyY+C1ZUOnxgWZDqUgk6ieiTsLPa1dSsobHxGGVlpWzf/nKkmyOmQbSt83NpT7iXZfthk91Hx//FUmiuq7HJTotMsc9oW6cQnW2KJfHSf4NB+MMuH22dAV783QMsWbmGGRXVrK62Kc7WbKr1in1+4PxgzNRcGG/d5OdX4zgOpmnS2rp32r5XiHAL17YtQvfkFi9DZFmF1LkLl/4APL7Zx6KZDotmupFuTtzT2gsoneg0ONFh0Nz1fk2p8aT4NZkp3p/iLJeiLC2BJhExoc5qFyP3Wafehg3rKSsrZcOG9ZFuipgm0bbOz6U94VwWraGhNbRDQ3G2jljQCaJvnUJ0timWxEv/1TYb9AyA3fgUm557kKz+dyjK0ry81+K1/V6R0fL82Cr0Od66Wbv2BkzTZO3aG6b1e4UIt3Bt2yJ0yX5NXyDSrYhvvYMKVytKc2TmwOmglJdxNbfYZVW1zYcvCPKRCwLcvCzA1YuDXDYvyCXzgqyaH+TyBUGuWhTkupogH74gwAeXB7lioc3y2Q4zciXoJGJDwmY8CSHG1timeG1/aBWM/ZZm7YqgpL8LcZqnt3lFwy+Z9/7deceFvccMaptNynJdFpXF7jAGIYSYTjsOm+w7YbB2eVAussOkPwC/fdfPJXNtZuVLxpMQIjSS8SSEmDBXw/bDoc85ELAV/XIHUogRXBc6+xUFmSPv65gGLJrp8oHzgyyNoYKtQggRaZVFDo4L+47LpUu4JPsgL91lS4Mp53ZCiCknR28hxLC6ZoPugYmlL+06YvLmQZOX91i8ddDkUJOBK1naIoH1DILWiswU2RGEEGIqpCXBvGKX3UdNegcj3Zr4pBRcNt8G4JW9FraU0xJCTCEJPAkhAOgdhO2NE0/BqG32pt093mFQ12LyTq3F4RBrRAkRj4K2F7z1h548KIQQYhyLZnqZotsaJF00XFL8sLraprNfsVX6WQgxheTqcIrJtM+xKdHXW8CGl/f4GAxOTbGm1KTQMz3C1feJvk5F5CT5vO1/MHhunyPbcHhJ/47vbH0Ujf23bt1d5OdXs27dXdP2ndHYD9HYpslwXUZk3fgtqJnlcLjNpKN3/POVc9l+I9GH0bLectI0C0od6lsNnAiUeoqWfhBCTC0pLj7FZNrn2JTI681x4aU9Fi1dE49DG0qT4oe+QchK1ZRka/IzXWZMYEaUcPV9Iq9TEVmOC4++46MgQ3PJPHvSmU+yDYeX9O/4ztZH0dh/+fnVOI6DaZq0tu6dlu+Mxn6IxjZNxmv7LFq6FYvLHKoKvVlAHRd+846PBTMcFs08e1TkXLbfSPRhNK23jl7F09t9XLEwSFHW9A4bj6Z+EEKMT4qLR4hM+xybEnW9aQ1vHTQnFXQCuHS+zQfOD/LRi4Jcf57NeeXOhIJOEL6+T9R1KiLPNGDVfJu2HsXjm320dU8uk1C24fCS/h3f2fooGvtv7dobME2TtWtvmLbvjMZ+iMY2TUbPIAwEFZtqLbY2mGjtHV8zUzWdfeMfV89l+41EH0bTekvxe+dyAXv6vzua+kEIMXUk40mIBLatwWTPsbOP4VdoUpLAVO8XTQaoKHC4sMpBTc3oPCHizqEmg3dqLS5fEKQ4WwqNCyHEROw6YrCj8f2U0axUl4o8h+1HLM6vcJhbHIFxYAki6MCv3/ZzyVybWfnSz0KIMws140lKnwqRoJo61bhBp/QkzcVzbfIyvItm1x0KPkFmChJ0EuI0ARsOnDBo6zE43qGYkeNSOM3DFIQQIh7MK3Hp6HVobPfOVTr7DLb1GZhKkz6BWpJi4oz3zu8c6WYhxBSRoXZCJKiegbNHjbJTXa5ZEhwOOgEYhhdwykqVoJMQY9naYLKj0SLowLJyh0vm2cMn8EIIIc7MdaG7H072KrQGy4Cgo8hNd5mR45L83sQNjlb8ca9FS5ccXMPFUOAzNf2BSLdECBEvJONJiAQ1u9Cls89h/wkDGH3ytny2Q5Jv+tslRKxyNZzoNJhT5LCi0hn/DUIIkeC0huYuxd5jJic6FPq985G8dBe/BSc6FaurbUpzNFpDe49iU53JyV6DnUdMrlgYgSJECUApb3a71m4DkKF2QohzJxlPQnBuU7fG6rSvhoLzZzuUjlEMPCvVJT9j6vKrY7WPhAiVq2FTrUnfoKKyMLSTdNkvxERE2/YSbe0Jp0gsayL0b++gN6vui7t99A3CsgqHyxcGWVUdRCno6lesmm8Pn6coBXkZmmuW2CwttynOir2ASCyt19IclxMdioFgpFsihIgHUlxcCM5t6tZYn/b16W0WHX0jY9DnV9jMK5m6E7pY7yMhzsZ24PUDFsc7FCsrnZADT7JfiImItu0l2toTTpFY1njrX9eFgOPVwRsIKI53GBxsMrBMWDHbCy4lwhD+WFqvg0H47bs+lpY7U3pOKISIL6EWF5eMJyE4t6lbY33a15m5o08mslKntppkrPeREGcyEIQXdls0vzccJNSgE8h+ISYm2raXaGtPOEViWWO5f4MO7D9u8OwOi8c3+/j12z7+9y0/j23y8/utfl7Y7eNQs0FFvssN5wWZkZsYQSeIrfU6EFS4WuGXwixCiCkgGU9CCI6dVLx1yGIw6J35SY0aIcbX3Q8v7/Fhu7C62iY3Xab/EUIkrv4AHDhhcvCEQdCBmbmajBSN39L4LYb/TrIgI1ljyO3vqLb7qMGeoyZrVwQxZV0JIc4g1IwniWELISjN0VQVuuw+apKf4bJopgSdhDibtm5vViW/BVcvDpKeHOkWCSHE5NmOl6kUsCFgK2zXm1XOb3mzm/ks7/+nZyZpDZ39in3HDBpaDQwFVUUu80oc0pIisyxiavQNKlKTtASdhBBTQgJPQiQ4reHlPRYnOg0sU3P5AhvLjHSrhIg+roaOXsWJTsWuIyY5aZpV822Z/VEIEZUcF072Ktp7FINBNVxjKWArAjYEHfXe/8HV4491U3gBKL8JPssLSHT3KwZtRYpPs6TMoarIlaFZccJ5L/gohBBTQX4ahEhwvYPeFPAAeelagk5CnEJr2HfcoLbZpGfAuzgzlKay0GVpuSP7ixAiqgwE4VCTQXOXQWu3wnG9Y1aS772hbqaXxTQ8BM4cGgb3/lA4n+mdC3hZUIqgDYHhvyFoe0Es24GiLE1uuktxlmTGxBvT8IJPQggxFSTwJESCsx3FzFyX4iyXWflyhiHEkKADbx6wOHpSUVHgMqdIk5OmyU2XCywhRHTRGnYdMdh7zIuGF2Z5GUgFGd5xa/L1lPRpf4tE4QWeEqTquxAi7OTUWcSUjRsfpqZmDRs3Pjyt741n2Wmay+bbzCmOTHp8vK2XWFueWGvvdOkdhD/stGjqVKyqtrlojjeddEHm9AWd1q27i/z8atatu2t6vjDOyLYtosF0bIeOC28cMNl1xGROscsHzg+yutqmutQlL0OKeI9Fjg/jM5Q3xFyIqSD7nJBZ7URMqalZQ2PjMcrKStm+/eVpe68In3hbL7G2PLHW3unQ0qV4dZ+FZcKqapvs1MiceefnV+M4DqZp0tq6NyJtiGWybYtoEO7t0Hbgtf1ekPziuTZleRIpCIUcH8b3xgGT3kHF1YvtSDdFxAHZ5+JXqLPayT0QEVM2bFhPWVkpGzasn9b3ivCJt/USa8sTa+0Nt9pmgxd3W2SmaK5dEoxY0Alg7dobME2TtWtviFgbYpls2yIahHM7DNjw0h6Lli7F6moJOk2EHB/G19GryEyRbUpMDdnnhGQ8CSGESHiuhu0NJnuPm1QWOiyf7UgdJyFE1OoPeDPS9gUUa6pt8jIkQCCmzkAAHnvXz0VzbCoKpP6nEOLMQs14kuLiQgghElrQhjcOWBzvUCyrsJlX7KKknqoQIkqd6FS8ddA7hb9qUZCs1Ag3SMSdpi7vzktRlgSdhBBTQwJPQgghElbPALyy18saWL3ApiRbsgaEENGpZwB2NJo0tJoUZrpcNMcmNSnSrRLx6ESHIivFJcUf6ZYIIeKFBJ6EEEIkJK29TCfbVVyzJEhmSqRbJIQQow0GYddRk4MnDPwWrKi0qSqUzEwRHm3disNtBvNLJNtJCDF1pILFJMh0kIklkdZ3uJY1kfowEmS9TU5/ANp6DM6b5UjQSYgYEe/HpdM1time2OKjttlg0UyHm5cFmVMUvqDT2fo3En0fjes71vphIu/t7IOX91rkpGkWznAm01QhhBiTFBefBJkOMrEk0voO17ImUh9Ggqy3yWnqVLy428eNSwMSeBIiRsT7celU+48bbK63mJnrsqLSJtkX/u88W/9Gou+jcX3HWj+E+l7HhSe3+vCZmqsW2fhlXIwQIgShFheXjKdJkOkgE0sire9wLWsi9WEkyHqbOFdDfYuBz9SkSY0UIWJGPB+XTtXeo9jaYDK32OHSedMTdIKz928k+j4a13es9UOo723rVvQNKmrKHAk6CSGmnGQ8CSGESBiDQfjjXouTvQpXK5bPtplbLHUshBBTy3bgcJuBoSAtSZObrjFDvN0bdOCZ7V7mydWL7ZDfJ8S5cFz41Vs+5hS7LJ8tw+yEEKEJNeNJ4tlCCCESxo5Gk85+xeIyh6JMTV6GzGInhJg6jgu1zQa7jpgMBN8vxJSRrLl4rk1u+vjHnE21JgMBWFMjQScxfQ42GWgURZlyM0YIMfUk8CSEECJhtPUoZuW5LJwhJ9ZCiHOntZehNBCEEx0G+46b9A5CRYHL4pkOST7o7FO8W2fy3E6LlZUOlYVnPv7UtRg0tJpcNMcmQ2rPiWly9KRiS71JdanDzDy5ISOEmHoSeBJCCJEwMpI1J3tlDnIhxMTYDpzsVbT3Ktp7FB29ioGgImCDxjumKDQzcjWrqx2yUt+/eM/P8IbMvVtn8s4hk2SfpjRn9MV9Vz+8W2tSUeBQUSDBcTE9TvYq3thvMSNHc94sGWInhAgPSeCdhGic2lUIER3k+BBe59K/rgbHVQTt2Ag8xdqU3eESbdO7xxvpwzMbDMLWBpOntlr8+m0ff9jlY1uDSfeAoiBTM6/EYfD4m/zm/q8z0PAMH1oZ5LL59oig0xDTgBWVDqU5mtf2WxxuU5xaZbV3EF7fb5HiJ6T6OuvW3UV+fjXr1t016rlEWqeJtKzh0B+AV/ZaZKR4Q0FVbPw8ChFRctyZHCkuPgnROLWrECI6yPEhvCbTv47rDV/Zd8ykZwAunBMb2QSxNmV3uETb9O7xRvpwbPZ7Bb4HglCW55KX7hUIz0rRGKfctp1o/9kOvHHA4uhJg4IMl2UVDq3diu2HTSwT1iywyUkbf6hTfn41juNgmiatrXtHPJdI6zSRlnWq9Qx4QaeArbhmSZBUmeFViJDIcWekUIuLS8bTJETj1K5CiOggx4fwmmj/Oi68uNtiU61JVqrmmiV2TASdIPam7A6XaJvePd5IH47tULNBzyBcsyTIBVUOVUUuOWkjg04w8f6zTFhVbbNmQZBBG57d4WNzvUlFgcuNS4MhBZ0A1q69AdM0Wbv2hlHPJdI6TaRlnSpaw6Emg6e3+7BdxeULJegkxETIcWdyJONJCCFE3GrqVLy420dVkcOK2Y4MIxBCjEtreHqbRWaq5tJ54at542pobDNIT5IZNsX02XnEYGejxewCh2UVDn6p+CuEOAehZjzJoUYIIUTcykrVlGS7HGoy6e5XXL7QxpDgkxDiLNp7FJ39BueVB8P6PYaC8vzYyMAU8aFvEHYdMVk4w6FGCokLIaaRDLUTQggRt5J9Xs2U1dVBmrsMjp/8/9r789i6znTP9/u+a63NeR5EihQ1D9REyZLLQ7lsuVzlqtKpU6fUp/t0B+lMuNCNA9wgkROgg5v81egEQW6AoIMgCC6Cq4ugc42+6dOnz6lT8+ShymW7bNma55kSJXEUZ3Kv4c0fS5YsWyYpkptrrb1/H4CmLIp7v+td7xr2s973eRR1EpGvF0Zw7LpLXaWlvUGzkKS4XLzr4jmwvVNBJxFZWQo8iYhIUZv1YWImDjh5bsKNEZHU8kN4/6LH6KTh+U2aHSnFZXTKcOmOw+a2iJyuhSKywrTUTkREipIfwKlel8v3HCJraKmNaFEeFRF5gqlZeO+8x8SM4aVtgXIuSVGZzsP7F12qK2DnGs12EpGVpxlPsmhHj75FT88Bjh59K+mmLEjW2ivFZyljMGvjd7HtXa7tnJyFX5zIcaXfYeeakB/ty/OdnQGurnqyRFk7FtOoUH041+vO9bPhCcNvTuXIB4bv7groaEx/0KmYxuHhw2/S0tLN4cNvLuvrFlMfLcX9ScOvT+XwA8NLW33N/BWRRKiqnSxaT88Benv76Orq4OTJd5Nuzryy1l4pPksZg1kbv4tt73Jt5x/OewxPGr6z06emYtEvI/IVWTsW06hQfTjX637dz24NGT647FFfZXl5W0Bl2bI1p6CKaRy2tHQThiGu6zI4eH7ZXreY+mix+kYMf7roUVNheaU7oKo86RaJSLFZaFU7PfuVRTty5A26ujo4cuSNpJuyIFlrrxSfpYzBrI3fxbZ3ObZzahZujzjsWhMq6CTLLmvHYhoVqg/net0n/Wxk0vD+JY+OxojXdmQn6ATFNQ4PHTqI67ocOnRwWV+3mPpoMS7ddfjDeY9V9Zbv7FLQSUSSpRlPIiJSVC7fczh21eXQsz7luaRbIyJp9fZZj5k8fK9Hy3CleEQWjt9wuXjHZWt7yN71oRLli0jBLHTGk5KLi4hIUbkz4tBSaxV0EpGvNTpluDfq8OIWBZ2kePghfHjJo2/EsH9DwJb2KOkmiYgACjyJiEgRCUK4O2rYpao9IvI1rI0rXlbkLGua9MFcikMYwdtnPMamDS93ZyNJvoiUDgWeREQk09495zE4bqgut4SRIYygUx8mReQJ7k8aTvW63B5xeGmrr9lOUjQu3HEYmYorMzbXKOgkIoVnLQyMLWwtry63i5BEGeA0SrpcuqRLocohSzoldR788s8nZ+HO/XhpXXOtpakm4sUtIXWVK9NeWZjFlrnPoqxtT6Gu5Wnrh6NH3+LIv/5bfnkyx8hkXFa+q7nwH84Xe21MW//JI6+99tc0Nm7htdf+OummPObOiMPqBqugk4gUnLVw577hd2c83ruwsNwWSi6+CEmUAU6jpMulS7oUqhyypFNS58Ev//z6gMOHlz3+ybP5OXM66byTrMWUuc+qrG1Poa7laeuHb736L/hf/p//E+///L/l//6v/yXOCj16Xey1MW39J480Nm55+OeRkUsJtuRxH152GZ82vL47SLopIlKk/BDu3jecu+0yPOnQVBOxrv4+L+x5Zt7k4prxtAhJlAFOoyTLpUv6FKocsqRTUufBL/98cNxQVzl/InGdd5L1tGXusyxr21Ooa3na+uF/fuT/gJ+f5ZlNuRULOsHir41p6z955Jlndj/2PS1qyi0TsypfJyLLx1q4N2o4ds3lVyc9/u7POd6/mMN14NXtPq/vCljdsLBZlprxJCIimfXRZZfRacP39IRXRObwq5MeNeXw0jadK6Q49Q4Z3r+Y40f78lSXJ90aEcm64Qnz4D7boarcsqouorXW0loXPZbSYmxsnHXr9s0740nJxUVEJLPKPPADPeEVka83MmkYmXTY0ekn3RSRgmmrtzjGcmPQYUenCmyIyOJEFs7ddjh9y6WhyvLaTp/WWotZ4u22Ak8iIpJZZZ4lrwkMIvI1wiieGVlXaVVeXopamQcbV0Wcu+3S0WhpqNJ4F5H5WQvjMzA47jA4bugbcZj1YXtnxM414bJVf1XgSUREMssx8QdLEZEnOXMrXo77+q5g2W6eRdJqd1fI9QGHc7cdXtwSJt0cEUkhP4ShCcPQuGFw3GFo3JAPDWCpr7J0NUdsWhXRUL28wWtdgkUKSOWQRQpreNIs+4VRnmy+85nOd4VVTP27EtsShHD8usvZ2w671oQ0FVmJ+ayNh6y1N6vGpg1BZFjfqicy8mQ6FkuDtZAPYGwaBsYM1wYcPrnq8ssTcYLwd87mON/nArB1dcir233+6Td8Du4J2L8hLMi9tZKLixSQyiGLFMaMDx9f8bg94rC7K2DnGt1kF9p85zOd7wqrmPq30NvSP2r4+KrH5Gw8A2RbR4RTZKngsjYestberPrkqsvtEYe/2ucvOR+LFCcdi8UlCKF32OHufcOMb5j1YdY3zAYQ2cdPAnWVluaaiJZaS0ttnCB8Oc4TC00urhlPIgWkcsgiy2/Gh9+fyTE4YfjmlkBJVFfIfOczne8Kq5j6txDbMjYNp3sdfn7c4/dnc5TnLD/Y47O9s/iCTpC98ZC19mZRZKF3yGFtc6Sgk3wtHYvZZy30j8UV5/7+kxwfXfYYnzaUudBSa9nYFvHM+pBvbgn49g6fg3t8/vobef5ir8/zm0M2tUXUVy1P0OlpaMaTiIhkymfXXa72O3xvt09t5fz/XkSK0/g03Bxy6B1yuD/l4LmWzsaItc0RqxttUQacRL7O3VHDO2dzfG+3X3RLS0UEJmfh2oDDtX6XyVlDdbllQ2vI+taImork2rXQGU9KLi4iIpkx68OVew5bV0cKOomUoImZeFbHzSGHkUkHz7F0NEbs6vJZ3WCVQFxK1sCYQ7lnaVTeQ5Gi8flSumv9Dv1j8TWvqzliQ2tEa53N1OxGBZ5ERCQzrg84RBa2rVa1HpFSMTn7INg06DA86eA+CDbt6IyDTZ6bdAtFkjcwZmiuzdYHURH5KmthYNxwrT+e0RtEhlV1Ec9vCljTHJHL6DVPgScREcmM2yMO7fWW8lzSLRHJjjCiYDOBIhsveRudNkznDX4AfmgIIyj3oDxnqSizVHz+5xyUefPnlpiajZ/y3hx0GJpwcI1ldaOluyOgozFSsEnkC/pGDP1jDs9vDpJuiogs0QeXXG4OuVSXW7o7kl9Kt1wUeBIRkUzIB/ET3f0bNdtJZC5RFAdtrtxzGJ0yzAaGyjJLU7WlsTqiqSYOAHmuxXPAc+Ov+XIiRRbGpgxDE/HX8IRhbNo8rJzjGkvOg5wLrmOZDeIKO1+urGPM44Goilw8S2M6b5jOw1Te4IcGx1hWN1he2BzQ2ZTdp7wihTQ2DR9c8ljdELG+RcU2RLJuc3vE5rbsLaWbj1bCL8LRo2/R03OAo0ffSropS1ZM2yLF6fDhN2lp6ebw4TeTbookrG/EwWL4/ssvazxIQc113knqurnQ970/afjFiRwfXIqfLW5ZHfLcpoD1LRFBBBfvuLx3PsevT+X4+fEyfvJpGX/3cRl/+1GOX5zweP+iy6lel2sD8Wyj6wMOJ264/P6Mx9/9OccvT+b45KrL8IShqcayd13It3f4/JNn8/zNCz6HnvX54TM+P9gT8OP9Pn/z/OfVdPK8ttPnpa0++9aHbGyLSzp7LkzMxgEsz4VV9ZbtnXE1nkPP+rzcHbC+tfBBp7n6d7E/S4qum6Vj1of3zuWoKrO8uCUoqg+pIqVqVZ1lVX1xBZ1AVe0WpafnAL29fXR1dXDy5LtJN2dJimlbpDi1tHQThiGu6zI4eD7p5kiCPrrs8oc/X+T/9F/8QONBCmqu805S182FvO/d+4Y/XPCorbA8vzl8YpJha2EqD/nA4IcQhhBE8WyjselHXzP+ozveipylucbSUhvPlmqqsUU3+2iu/l3sz5Ki62ZpCCN456zH2LTh9d1+USzFEZHsWWhVO814WoQjR96gq6uDI0feSLopS1ZM2yLF6dChg7iuy6FDB5NuiiTMGKitqdR4kIKb67yT1HVzvvedycMHlz1aai3f3RV8bWUrY6C6HBqrLavq4rxJXc2Wrasjnt0Y8trOeKbRP3suzz/9Rp5/+lyeH++PZx5t74xoqy++oBPM3b+L/VlSdN0sDcevuwxNGF7uDhR0EpHU04wnERHJhBM3XG4OOfxon590U0RSJYzgvfMeo1OGH+zxqVDyfZGiNj4NPz+eY8+6kO4O5XUSkeQsdMaTkouLiEgmlOcss4o5iWBtnFB4aMJhaMLQN+yQD+CV7YGCTiIl4PaIg2NgS7uCTiKSDQo8iYhIJlSXW4LIMD4NtZVJt0Zk5czkeVBJznlYTc4PDWCpq4TOpoitq0PqdFyIFL3hCcO1foeGaourpCkikhEKPImISCasbrDkXMu1AZeetWHSzREpqMjCnRHDxbsu90bjT5efJ/nu7ghprrU0VVvKdCcnUhKCED6+6nJj0KW2wrJvfZB0k0REFkxxcllxaSw9XEoK1f9JlJvWWEpOEvvUc2Ftc8T1AYf5sxPKYqTxmEqiTUmes6IILvQ5/PSzHH+4kCMI4blNAT/a9yjJ9841Ee31CjrJky1l/KbxHLBYWduWudobhPDuOY/bww7PbQo4uNenuVYXwlKWtfEtouTisuLSWHq4lBSq/5MoN62xlJyk9unguOG3p3O8ut2nvUE33cstjcdUEm1KanyPTBo+uuwyOmVY1xqxpT2iuUbjXJ7OUsZvGs8Bi5W1bZmrvTcHHf50yeO1nT6r6nROkOyNbyleC00urhlPsuLSWHq4lBSq/5MoN62xlJyk9mlzjaW2wnJtQJevQkjjMZVEm1Z6fIcRnLzp8uuTHhZ4fXfAC5tDBZ1kUZYyftN4DlisrG3LXO3NB2CwtGqWkzyQtfEtohlPIiKSKWdvO5y55fLj/b6WGknmDY0bPrriMTEDOzpDtndGShgsIo/pHzP8/kyOb23zWdOk4JOIpIdmPImISFFa3xIRRdA7pEuYZFcQwmfXXX572sNzLN/bHbCrS0EnEfmqllpLuWcZHNcJQkSySc+KRUQkU6rKob0hXm63qS1KujkiT61/1PDnKx5TeehZG7KtI8IxSbdKRNLqzC2XfABdTbrmiUg2KfAkIiKZ01wTcfmem3QzRJ6KH8KJGy6X77m01Ea8sj2grjLpVolIml25Fy8v37UmUCU7EckszdcUEfmSuUrUzle+VuVtV4brxGXni9lix1KxjcFCbc9K99Od+4b/8H6eszdmyd/9kO/sfDzodPjwm7S0dHP48JvL+r5J9V+xjcO0KdR4kXTpHTJ8fNVlS1vIzjVFftGT1Cul83opbetKUXJxEZEvmatE7Xzla1XedmVcvONw4qbL3zzvJ92UglnsWCq2MVio7VmpfsoHcS6nawMu1859zH/zX71JVVn4lfdsaekmDENc12Vw8PyyvX9S/Vds4zBtCjVeJD2shX84lqOl1vLS1gCj5biSsFI6r5fSti6VkouLiCzSXCVq5ytfq/K2K8Nx4hL08z86ya7FjqViG4OF2p6V6Kdbw4afH89xa9jhGxsD2jhDVVn4xPc8dOggruty6NDBZW1DUv1XbOMwbQo1XiQ98gHM+Ia1LaGCTpIKpXReL6VtXSma8SQiIplz+pbDxT6Xf/INXzfkkjozPnx6zeXmkEtHY8SzGwKqypNulYhkycCY4Xdncnxvt09TTRE/ZRGRTFvojCclFxcRkcwZnnBorLEKOkmqWAs3hxyOXYsT37+wOWBdS6RxKiJPrW/EodyzNFQr6CQi2afAk4iIZIofQP+YoXt1mHRTRB6azsMnVz1ujzh0NYfs3xBSkUu6VSKSRdGDIHZnU4SjwLWIFAEFnkREJFOuDTiEIWxYpQo/kg4jk4a3z3o4Br611WdNs2YoiMji9Q0bJmcNm9p0nROR4qDAk4iIZMbkLJzudVnbElGtnDmSAvenDO+c9agut7y6PaBcs5xEZInO33FprY1oVm4nESkSqmq3zI4efYuengMcPfrWiv6uyOfmG0dpG2eFam9S25nE+6ZtnxZKPoA/XfRwXdi/QcvssqRYr42jU/D2WY/KMgWd0iCJsXL48Ju0tHRz+PCbK/ae80nzMSPzGxw3DI47bOtYnuucxoOIpIGq2i2znp4D9Pb20dXVwcmT767Y74p8br5xlLZxVqj2JrWdSbxv2vZpIUzn4Z2zHtO+4UB3QHOtngJnSTFeG8em4fdncpR7ltd2KuiUBkmMlZaWbsIwxHVdBgfPr8h7zietx4wszPsXPO5PGf5i7/JUbdV4EJFCWmhVO814WmZHjrxBV1cHR468saK/K/K5+cZR2sZZodqb1HYm8b5p26eF8MlVj9nA8J2dvoJOGVRs18bxGXj7bI4yD76toFNqJDFWDh06iOu6HDp0cMXecz5pPGZkYSZm4NawYdvqcNmqYWo8iEgaaMaTiIik2vg0/Ox4Gc9vDtjQqkSrkqwwgp8fz+EYeG2nT2VZ0i0SkWLx6TWX64MOf7XPx3OTbo2IyPw040lERIpCZOPHvrUVmukkybt812FqFl7uVtBJRJbP+DRc7XfY0hYp6CQiRUeBJxERSbWcGwec8kHCDREB7o06tNVb6iqTbomIFIvpPLxzLkdlGcuWVFxEJE0UeBIRkVSrLAPXsYxPL1PCC5ElcHXnJCLLKB/Au+c8Iguv7vAp85JukYjI8tPtU0aoFGrxmW+fLnafF9tYKbbtKRXLud+MgZpyy8RM9gJPhRq/aSzhnjWL3TeuawlSlmosifPkUt4za8dFGq9DaWzTXLLW3pUShPCH8x5Ts4ZXt/tUlyfdIpHF0TEu81Fy8YxQKdTiM98+Xew+L7axUmzbUyqWe7/98oRHS63l2Y3ZWoJQqPGbxhLuWbPYffPJVZehCcP3e9Kz9jOJ8+RS3jNrx0Uar0NpbNNc/vn/7F9z7IN3qC6PMtHelRBZeP+Cx91Rw7d3BLSoYqtkWNbOSbJ8lFy8yKgUavGZb58udp8X21gptu0pFcu938LI4GTwilWo8ZvGEu5Zs5h9E0XQP+akblZCEufJpbxn1o6LNF6H0timrxNFcOg/+y/5N//Nu/yr/8u/Y3Qqe7NXl5u1cRC7b8Tw0lYFnST7snROkmRoxpOIiKTajA9//0kZL2wOWN+asjVOJW5sGq72uzgGmqojVjfaos2BZC18fNXl2oDDd3cFNNfog6JkQ9+Iocwj0eBGEMKVfofzfS7TecP61pD9G0JyJVq97cRNl3O3XZ7fHLBB1zURybCFznhS+joREUm16wMOBsuqOt2cp0nvkOGjyx6uEyfcPnvbpcyzrG+J2LAqoqHKYopoYsPZ2w5X+12e36Sgk2TLmVsuORde3ZHc8lDPhW2rIza3RVwbcDh+3WV4wuGVbp+aisSalYgLdxzO3XbZu05BJxEpHQo8iYhIKuUDuHLP4VSvy+a2iKqULW8qVZGFkzddzve5dDWHPLcpnrUwOmW4NuBwfcDh4l2XhqqIDa0Rq+otdZXZngl1Y9DhVK/HrjUBG1bpg6Jky6a2iD9fcZmYIfEgj+vA5raIVXURvz+T43yfm7ncfUtxfcDhs+se3R0h3R06l4hI6VDgSUREUuf6gMOxay5BBFvaIvasK50PJmk248OfLnoMjBmeWRewdXX0cFZTfZVl77qQnrUhd+8brvW7nLjpElmDwZJzwXWhIhcHohqrLasbIuoqSfXMqMnZOBfLupaQnWv0QVGyp6s54tPrLtf6XXavTce5tK4S1rdGXOt32LchxEnxOWC5jE/Dn6+4rG8N2ZOS/SAislIy/PwxOSoXGVM/ZFPaym7P1x6Ns/kVqo+Wst+WIh/EuXTa6i1/tc9n34Yw07Nlvk4S+20phicMvzqZY3Q6rsC0rSN6YsDIMdDRaHlpW8A/+YbPd3f5PLsxZHtnyMbWeAne5Kzh1E2XX5wo4623R/n+j/4XqTzGrYWPLnt4LuzfEC4oQKZz1tIkdU3I2n57mvbmXFjfEnHhrsPo1Ao07gme1N61zRGzgeHe6MpHnVZ6f8c54jwqy+DZBZ5Lvihr41NE5MuUXHwRVC4ypn7IprSV3Z6vPRpn8ytUHy1lvy3F6JThFydyfHeXX9SVfpLYb4uVD+AXx3NUllm+tS1YlmWPQQj3Rg1/+9trtHZu5vgf/oF/87/9IeW5pb/2crl01+HYNY9Xt/u0NyxsLOqctTRJXROytt+etr1+AL897RFGhtd3+yt+nD2pvdbCz4/naKmNeH7zys4AWun9fbXf4c9Xnu5c8kVZG58iUjoWmly8CJ8hF57KRcbUD9mUtrLb87VH42x+heqjpey3pQgfrGYq9qUXSey3pxGE0D9m+Oy6y08/yxFELFvQCeJkw51NltqJD/jt/+/f8sy3fsjPj+e42u8w/yOxwhufgeM3XDa3hU/1QVHnrKVJ6pqQtf32tO3NefByd0A+hD9d8ohW+Bh7UnuNgfWtITcGHQbGVvaEv5L7O7Jw4ka8XHcxQSfI3vgUEfkyzXgSEZFUuT1s+MOFHD/en6eyLOnWlBY/gPN3XO7eN4xMGiJrKPMsG1ojtrSHBU1MPJ2PAz03Bl1aaiP2bwhprE4uAvXhZZd7ow5/sdcv2ZLvUnzujRreOeexuS0+xpIWRvDOWY/xmXgmVnURFpEYGjf85nSO7+z0aa1LQVRdRGQZLXTGk5KLi4hIqoxMGnKupSJFS65Kwb1RwweXPIIQOhoj1rVEtNZZ6qvsisw+qyyDF7eEbFwVceyay69Pemxpj9jVFVK2gncrfhiXO78+4PDM+lBBJykqbfWWfetDjl3zaKiybGpLNmG+68BL2wJ+cyrHb0/nONAd0JBgwLkQ7o0ZPMfSXFNc2yUi8jQUeBIRkVS5c9+hrd6mutJZsblz3/DH8x4tdZbnNgWJzjpoq7d8vyfg4h2HM7dcbg457F0Xsq7lycnMl8vEDFy863Kt3yEIYUdnxNZ2VbGT4rOlPWJ0KuSTay61lZZVCc/CqcjBd3f5vHvO43dnPJ7bFNDVXDxBmrv3HVbVWRwlOBGREqbAk4iIpMasH1dPe3Zj8ktASsXnQae2+rgSXRoqCLoObO+MWNsScfy6x4eXPa72R+zfEFBftXzvY22cx+riHZfbI4YyDza3R2xpC5ctl5VIGu1bHzI2bXj/gsfBvX7iM0wry+A7OwM+uuLx/sUcXU0Ru9cG1FUm266lCkIYHDfsWadrmoiUNgWeREQkNe6NOlgMqxs006TQrIUr/Q6fXnNpT1HQ6Yuqy+NlOHfuG45d8/jlyRxrmyPWt0a01S9+CWAQwo1Bh4t3HUanHOorI76xMZ5V5WlpnZQAx4Fvbg34+fEcx6+7vLAl+cBIzoOXtgbcGHQ4cdPlF8dzbFgVsXNNmNncT4Pjca68NuV2EpESl7JbTEmTo0ffoqfnAEePvpV0U0qS+r/4FNM+LdS29N031FdFmZltUsh9WsjXnpiBP1zw+OSqx6ZVUSqDTl+0usFycI9PT1fI8ITDu+dy/ORYjk+vu9wYdLg/aR5WQ/w6fhjPpjt50+Unn+b4+KpLdTm8usPnB3sCNrUtLOg0337J2nGeRHvnes+k+jdrr7scKnKwd13I9UGXO/fnj+KuxLbEle4i/vIZn73rQm4PO/zssxyfXnOZyRfsbQvm3qhDeS7OlSciUspU1U6+Vk/PAXp7++jq6uDkyXeTbk7JUf8Xn2Lap4XYFmvhH47lWN8asTcjyxIKuU8L1cdnbzucveVSnoN96wPWZCyXirVxAOn6oMOtYYfpfPyB2RhLbQWUe3EuFdcB17HkA8PYtHn47zzXsvFBlb7aRSzjmW+/ZO04T6K9c71nUv2btdddLtbC22c9JmcNP9zrz5mHKIlt8UO4eMfhfJ9LZGHr6ojtHStbcGApfn3So6bC8s2t2bimiYg8rYVWtUvx801J2pEjb9DV1cGRI28k3ZSSpP4vPsW0TwuxLfenDDN+tpbZFXKfLvdrByG8f9HjVK/L1tURf7HXz1zQCeIZEc21lv0bQn683+evv5HnOzt99q8PaauPqC63lHkWA/iBIefChtaI5zcHvL7L59B+n30bFhd0gvn3S9aO8yTaO9d7JtW/WXvd5WIMPLM+ZHLWcHd07llPSWxLzoWda+IZUFvbIy7ecfjHT3OcueXgpzyWkw9geNLQ3pC986yIyHLTjCcREUmFy/ccjl11+WfP+6le9pVFM3l474LH6JThm1sCOpv0QUhEYtbCL07kaKyOeDEFuZ7mMp2Hs7ddrtxzyHmwozNkc1uUymtG75Dh/Ys5frQvn9kcVSIi81nojKeMTFQVEZFiN+NDWY5UfoDIKmt5mJg7jOKqUU01CjqJyCPGwNrmkPN9LkEYpjrBfmUZ7N8Q0t0RcrrX5fh1l/N9LrvWhGxojeZcKrjS7o061FRYBZ1ERNBSOxERSYlZ31DhKSiyHD4POP32tMd753NUlVle3+0r6CQiT7SuJSKIDFf7s/HRoLocnt8ccnCvT2ttxMdXPX5+IseNQYf513IUXnwOdmirz87ScRGRQtKMJxERSYXZBzOeZGmGxg3Hb7gMjDs010S8ut2nrd5i5i9aJSIlqrYSNreFHL/hEkbQ2RRRt8g8aCuprhK+uTVk+2TEqV6XDy55nL0dsb0jYk3TwipVFkLvsGFy1rC+RYEnERHQjKfMmKuE7eHDb9LS0s3hw28m0DIplFIs7byS1A/pE0YGV8GRJbl8z+G3Zzz8EF7p9vnuroD2huSDTks53rJ2rM7V3sX+bCnvKQtTKn041z3jM+tD8vev8cnlgJ8fL+Ojyy75IIFGPoXP99t/+u//O17pDvjuLp+KHHx42eMfjuX45KrL0IRZ0VlQ1sKZXpf2+ojWuhRMvyoypXKsihQbJRfPiLlK2La0dBOGIa7rMjh4PqEWynIr1dLOK0X9kD6fXXe5Nezwo31+0k3JpLujhnfOemxui9i3PkxVrpOlHG9ZO1bnau9if7aU95SFKZU+nO+esafnAHfvDfGDf/af8eP/6f+OMg9+sMcnl9K8T1+338an4dqAy7V+h2nfUF8VsXFVxLqWiIoCz6y9Oejwp0se393l01KrwNNyK5VjVSQrFppcPEW3pTKXuUrYHjp0ENd1OXToYAItk0Ip1dLOK0X9kD71VZapWVL/hD2tzvS6tNRa9m9IV9AJlna8Ze1Ynau9i/3ZUt5TFqZU+nC+e8YjR96gva2ZV/e3873dPtN5ON+X0qgTX7/faiuhZ23Ij/b7vNLtU1cBJ264/ORYjj9e8OgbMUQFiAnlAzjZ67K6IVLQqUBK5VgVKTaa8SQiIqkwMQM//ayMl7b6dDXrhv1pRBb+9qMce9aGbOtQThERWR4nbrhcvOvww70+VRmvzjbrw/VBh6v3HEanHSpzlg2rIja0htQuQz6rKIJ3z3uMTBi+u9vPRI4sEZGlWuiMJyUXFxGRVKipgOpyy+C4Q1dzmHRzMsUArgOh4nUisox2dIZc6Xe4eMdl7/psn5fLc7BtdcTW9oiRybiC36W7Dmdvu7TWxkvxupoXl5DcWvjkmsvAmOHA9kBBJxGRL1HgSUREUqMiZ7XUbhGMiZcq3p9UdnYRWT45D9a1RFwfdOhZF+IUwSnGGGiqsTTVhOxdF3Jr2OFqv8NHVzyOXbesbY7Y3BbRVLOwSH4YwfEbLlf7XZ7fHNBWrycAIiJfpsCTiIikQhDC5KyheYE3+/K4hirLwFgRfCoUkVTZ0Bpx6a7L3fuGjsbiOj97LqxvjVjfGjEx8ygh+dV+l87GiN1rQxqqvn6bJ2fhTxc9RiYN+zcEbGjVUmcRkSdJWfpRWYw0lhVVWejSU6j9pvFQOi7fc5j1YcvqbC/nSEp9lWVsxhAV2eeeuc4Bi/1ZKVE/LF0SfTjfexaqTYcPv0lLSzeHD7/58O8aqy31VRHXBr7+Y0MxjLOaCtjdFfKX+3xe2Bxwf8rwyxMeH1xymc4//m+thd4hw69O5pjOG76zM2BLe5GdfCmO/Soi6aDk4kUgjWVFVRa69BRqv2k8FD8/gPN3XM7ectjUFvHsRgWeFuNqv8Ofr3j88+fzqatqtxRznQMW+7NSon5YuiT6cL73LFSbWlq6CcMQ13UZHDz/8O/P9zmcvOly6FmfsieslyjGcRZGcK3f4dQtF2th3/qQdS1xfqjPrrsMjDt0NEY8vymgPJd0awujGPeriCyvhSYXL6Jb09KVxrKiKgtdegq13zQeiltk4Z1zHuduO2zvjNi3QUGnxbAWbg871JTbogo6wdzngMX+rJSoH5YuiT6c7z0L1aZDhw7iui6HDh187O/XtURYCzcGn3yCKcZx5jqwuT3iL/b4tNdHfHjZ41cnPX59Kkc+gAPdPq90F2/QCYpzv4pIMjTjSUREEjM6ZfjFiRwvd/t0FlnukJUSRvDxVZfrAy7f3BKwtqX4lnuISPLeO+8x48P3dpdmBYjeIcOluy5rW+IKeMWQaF1EZKkWOuNJycVFRCQx4YMYSVVZsu3Iso+vutwcdHhhs4JOIlI4G1pD3r+YY3QK6quSbs3K62q2dDWXZtBNRGSpimxCvoiIZEmZF89ymsrP8w/liaZm4fqAyzPrQ9armpKIFFBHo6XMtVwfcJNuioiIZIwCTyIikpjqcqjIWe7d1+VoMaIHqxNrK7VMUUQKy3XiXE/XB5yH5x4REZGF0J2+iIgkxhjYuCriSr/zlXLV8hT0IVBEVsCGVRHTvuHefSU4EhGRhVPg6QmOHn2Lnp4DHD36ViZeN63vK6UlbeMsbe1JShb6YdvqkDIX3r/oPcz5VArm2zcL2XdmEZ/9sjAmkjZXH6n/YuqH0tRYbamvjLg28PhHiMOH36SlpZvDh9984u/pmEqO+jebtN+k2Kiq3RP09Bygt7ePrq4OTp58N/Wvm9b3ldKStnGWtvYkJSv9MDhu+P0Zj42rIp7dGCbdnBUx375ZyL6bnIV//LSMA9t9VjcsbNpTVsZEkubqI/VfTP1Qus7ddjjV63LoWZ+yB2WKWlq6CcMQ13UZHDz/ld/RMZUc9W82ab9JViy0qp1mPD3BkSNv0NXVwZEjb2TiddP6vlJa0jbO0taepGSlH1pqLfs3hFy+53LudmlcmubbN3P9PIxgbBruPMiN5T7FzKesjIkkzdVH6r+Y+qF0rW+NsBZuDj06Vx86dBDXdTl06OATf0fHVHLUv9mk/SbFRjOeREQkNU71upy55bJnbcD2zhJad/cU7k8afnfGww/jaFNLbcRrOwMcpVwRkRXyzlkPC3x7R5B0U0REJEELnfHkrWCbRERE5rRrTbzM7sRND1Dw6Uk+ve5SVWbZtyGgtsJSWba4XE8iIovVUGW5NVwas1NFRGTpFHgSEZHUMEbBp/nM+tDWYGmrVyk7EUmGgt0iIvI0FHgSEZFU+Tz4ZG0cfGqq8RVk+YLQGhzUHyKSHAug4JOIiCyQ5siWAJXjLKys9W8S7VUZ5VihtrUY+9AY2N0V0lob8clVj/mzEZaGyMaV7KorstUhaRyjaWzTXNLW3qW0J23bUmxW4ppbVWaZmoW8Ujylno43STuN0dKg5OIlQOU4Cytr/ZtEe1VGOVaobS3mPjx+3eX8HYe/ed7H1aMSLt91+OSax3d3+bTUZif4lMYxmsY2zSVt7V1Ke9K2LcVmJa65M3n4x09zrG+N+MamcKlNlgLS8SZppzGabQtNLq7b+BKgcpyFlbX+TaK9KqMcK9S2FmMfxsvsXM7fcdnRGZV80CmycLXf4dPrLpvbwkwFnSCdYzSNbZpL2tq7lPakbVuKzUpccyvKYN+GkCv9Lqd7Hc1KTTEdb5J2GqOlQTOeREQkde6NGt4+m2PP2tJOLm4t9A4bTvd6jE0buppDXtgclnwgTkTS4cwth1O9HlvbQ55ZHyrpuIhIiVnojCclFxcRkdQZnjB4jqW7o3SDTiOThj9fcRmZdFjdEPHC5oCmGk0rEJH02LkmoswLOHbNZXLW8PzmgDJ9uhARkS/RpUFERFJnbNpQUZZ0K5JhLVy+5/DZdZe6Sst3dvq01ingJCLptKU9oqrc8tElj9+f8fjurgDPTbpVIiKSJpqsLyIiqdPVHDExEy/jKBXWws1Bh1+e9Dh2zWNTW8TruwMFnUQk9TobLd/eGTA6bbg2UDrnbRERWRhdGVaQSkWml/ZNNhXTfiumbVkOHY2W3V0hp2953BpKb9KQpey3MIqXFPaPGnqHDG+f9fjTJY+qMnhtp8/+DYvL5VSosZTUGNWxkU5L2S9Z26dztffw4Tdpaenm8OE3E2jZyppvvzVWW9rrLb1DT3fiytp4KBT1g4gUMyUXX0EqFZle2jfZVEz7rZi2ZblYC3+65HJnxOG7uwMaqtI382ex++3WkOHDKx5B+Cio1lAV0bM2pKNxadtZqLGU1BjVsZFOS9kvWdunc7W3paWbMAxxXZfBwfMJtXBlLGS/Xbnn8MlVl0PP+pTnlu91S4H6QUSyaKHJxTXjaQWpVGR6ad9kUzHtt2LaluViDDy/KaSmwvKH8x6zftIt+qrF7LcZH/581aO11vL6Lp8f7s3z4/15vt8TLDnotNg2Jfm6aX1fmdtS9kvW9ulc7T106CCu63Lo0MEEWrayFrLfOhsjLHB7ZOEfMbI2HgpF/SAixUwznkREJNUmZuDXp3K01Vle2hYk3Zwlu3TX4dPrLj/e71OxwBkBIiJZ8dvTHmUevNKd/fO1iIjMTTOeRESkKNRUQM/akFvDhsnZpFuzfBR0EpFi1FJrGZ1Kb24+ERFZeQo8iYhI6q1riXAduNqf7RrdYQQjk4YKL+mWiIgUhudYwijpVoiISJro1ldERFIv58bBp2v9DrvWhJgMPUwfGjfcHTX0jzkMjhvCyNDdESbdLBGRghifMVSXp68YhIiIJEeBJxERyYQ1zRFX+l0u3XWoqbDUVVpqKpJu1dzO3nY4edPDcy2rai27u0JW1Vkaq/WhTESKTxRB/6hDV7OmPImIyCNaapciR4++RU/PAY4efSvppixZMW2LLMzhw2/S0tLN4cNvrth7apyVllV1llV1EZ9e93jvfI6fflbGiRvpXXp35V4cdNq5JuSvv+HzyvaA7o6Iphq75BlbGvtSqrI29pO4Ns6l0P3XO+ww7Rs2rFLgSRYna8e4LIz2q6iqXYr09Bygt7ePrq4OTp58N+nmLEkxbYssTEtLN2EY4roug4PnV+Q9Nc5KSxjBvVHDjUGH3iGHyBrWtYS8uCV9y9b8EH76aY7VDRHPb17+pYEa+1Kqsjb2k7g2zqWQ/Tedh1+cyNFaa3lZFe1kkbJ2jMvCaL8WL1W1y6AjR96gq6uDI0feSLopS1ZM2yILc+jQQVzX5dChgyv2nhpnpaNvxPAfPirjvfM5BscdNq2KeKXb5/nNyQedhicMfSOGLz7GuXTXwQ9h99rC5KPS2JdSlbWxn8S1cS6F7L879x3ygeG5TQo6yeJl7RiXhdF+Fc14EhGR1Pvwssv1gXhZXV1l9GDZnaWtPqI8l1y7Prrscu1Bu9Y0Rby4JSCK4B8/zbG2JeLZjckHxkREVsKluw6fXnP5/p6AhirlsRMRKQULnfGk5OIiIpJ6L2wO6ekK6R9z6B8z3Bt1uHzP4DmWnrUhW1evfD4RP4RrAw47OkMaqiP+dDHHzSGHkQlDGMGOTgWdRKR0dDZGXL7r8JuTHnvWhWxuj3AyVIFUREQKR4EnERHJhKpyWN8asb4VIGRqFk72unx2w2V9a0TZCl/RxqcNYOhsjGiutZyvjvjoctyIfesDqspXtj0iIkmqKofXdwccv+Hy6XWPy/csPV0BnU1LL6ggIiLZphxPIiKSSVXlsHFVhLWGu/dX/nL28IPUg++fz7pqr4/Y0q6KTiJSejwXnt0Y8r3dPpVllj9ezPHb0x79o4o8iYiUMgWeRGRZzFUmVSVUZTndnzKc7nV495zHO2c9qsst9fPkEynEGHSd+D3DBzGm9a0Rr+30eWlboKf7yyht54+0tSeLCtWH2jfp0VRj+faOgFe3+0QWfn82x7vnPEYmn+7kON8+LaZ9XkzbIiLyZUouLiLLYq4yqSqhKstlcNzw29M5PNfSWmtZVRfPLvLcuX+vEGNwchb+8dMyDnT7rG5UIt1CSdv5I23tyaJC9aH2TTpZC73DhlM3PcZnDGubQ3avDamtmP9359unxbTPi2lbRKR0LDS5uGY8iciymKtMqkqoynK5ORhftn70jM+B7QHbO+cPOkFhx6AW1RVW2s4faWtPFhWqD7Vv0skYWNtsObjH59mNAQNjDj8/nuPYVZfp/Ny/O98+LaZ9XkzbIiLyZZrxJCIimTE8YXj7rEd9leXA9oDcAoJOhXJ9wOHDyx4/3p+nsiy5doiIZEkQwqW7Dmdvu0QWtq2O6O4IV7xAhIiILJ1mPImISNFpqrG8uj1gdMrw3nmPKMEVbjcGHZqqIwWdRESegufC9s6IH+3z2doecaHP4aef5Tjf5zzMmSciIsVFzxZERCRTmmstL3cH/P6Mx40Bhw2rVv6TysQM3LlveG5TuOLvLSJSDMo82LMuZEt7yJlbLiduuFy847KrK2R9a4STwiIN1kL/mKF3yGF02uAHcSAt54LngOdacm78kKSrOcLVI34REUCBJxERyaBVdZb6SsvghGHDqpV//1O9LuUerG3W43kRkaWoKodvbArZ1hFyqtflz1c8zvdZetYGdDba1FQJnZiBY9c87tx3qC63NNdE1FdCEEEQGvwQpn3DrG+4eNdw4qZl15qQDa0RjgJQIlLiFHgSEZHMsTa+wa8sW/nAz61hw41Bl+c2BQtKbC4iIvOrq4SXtoYMTUScvOnyxws5mmsi9qwNWVW/Muuqx6dhdMpQWQaN1RbHgTCCi3ccTt+KHzh8a5s/b0BsfDp+QPHxVY9zfZbdXSFrm6PUBNFERFaa4u8iCTl69C16eg5w9OhbSTcl1dRP8iSj04Z8YGiqLtyHkTCC9857vPXuLP+vnwzw1ruz/OOnOf54IUdnY8SG1mzOdprrmErieFvKeyb1u4WQtvaIJKW5xvLtHQGvbveJLPz+bI53z3mMTC5f1OaLx5u10Dtk+PnxHD87XsYfL+b4zekc//HjHD8/nuPv/pzj5E2XzW0RB/f6rGmafxZWbSV8c2vI93t86iotH1zyHlbyu9rvcHvYcHfUcGPQ4exth0+uurx3zuMXJzx+ftzjxA2X4QnD/CWgvro9IrJ0OqaWn6raiSSkp+cAvb19dHV1cPLku0k3J7XUT/Ikp3pdLt5xOPSsX7AcGv1jht+fyXH6o1/Se+0ydXU1/Of/+f+Y2grL+tbsPrme65hK4nhbynsm9buFkLb2iKSBtdA7bDh102N8xrCuJWR3V0hNxdJe9/PjbWv3dv6vR39O332H1Q0Rm9pCmmssU7OGgXHD1KyhusLSXh9RX7X49xscN1y55zA47jA+8/jFI+daqsstVeVQXWYJIrg94pAPzMMlfZ4LrvPoK+da6qssTdWW8pzOHyLLTcfUwi20qp2W2okk5MiRN/i3//a/5siRN5JuSqqpn+RJeocMnY2FTdwaPZjQtKl5gp8c/e84cuQNetZmP5n4XMdUEsfbUt4zqd8thLS1RyQNjIG1zZY1jT5XBxxO97rcHHLYvCpi55qQikVWFT1y5A3+9h8/4H/05v+NoQnDy9t8OpsePYuvLLM01y7fjNqWWktLbQiEBCH4YZwbqsKD3BM+jUVR+DCJ+diMIZyNZ+GGkSF8+PtxAKumwvKv/qv/L2//8ie8/PxWwgglNRdZIl2Tl59mPImISKbM+vCfPinjhc0B6wu43O3OfcO753L8aF+e6vKCvY2IiCxQEMKluw5nb7sYA9/YGNDV/HQBoslZuNDncumuQ0ut5cUtAVUZO8dbGyc7H5pwGJ4wDE0YRiYNkTUYY2mosjTXxF+dTRFlmmogIgWiGU8iIlKUPn+SW+ilbp8/lkljSW8RkVLkubC9M2LjqohPrnq8fzHH9s6Qnq5w3mvC2DScu+1yfdAh58CurpDtnVEmz/HGxHmkaisj1rfGfxdGcWL0oQnD8IShf8xw+Z6De81lS3vE7q5QM6FEJDEKPImISKa4DlTkLHfvG9a1FO59ogeBp6zmchIRKVblOfjm1oALdxyO3/AII3hm3ZODT0MThnO3XW4NGypzsGdtyKa2iFyRVSV1HWiqsTTVPJoBNp2Hy3ddzvU53LlveHFLSEPVylQIFBH5IgWeREQkU4yBHZ0hn113aau3rGspTKLvh4Gn5X9pERFZImOguyPCdQKOXfOYmjU8uyGgoiyesXpv1HD2tkv/mENtheUbG0PWtxY2N2DaVJbB7rUha5ojPrzk8ttTHge2B7TWKfgkIiurhE692ZbGko6FalMat1Ukq4r1eNrUFrGmyfLhZY9//CzH8Rsu+WD5Xj8I4daQQ2XOKjfGAiUx1uZ6z6yN/aTae/jwm7S0dHP48Jsr+r5ZkrWxNJc0bstSjuPDh9/kxV1bOfm7/zf3Rg3/cCzHL457/OOnOd45l8MP4aWtPgf3+mxqK62g0xc1Vlte3x3QVGN595zH4LgeqYjIylJy8YxIY0nHQrUpjdsqklXFfjwNjBluDDpcH3RwHXhlW7CkSkSTs/DBJY/hCYMF9q8P2dxeuATmxSSJsTbXe2Zt7CfV3paWbsIwxHVdBgfPr9j7ZknWxtJc0rgtSzmOvzh+b985z61hh5FJg+dCe31EW73VcukvCEJ495zHyJTh1e0BLctYuU9EStNCk4uXaNw/e44ceYOuro5UlXQsVJvSuK0iWVXsx1NrneXZjSE/3OtTXRbPgArCxb+eY2Bs2uA6cHCPr6DTU0hirM31nlkb+0m199Chg7iuy6FDB1f0fbMka2NpLmnclqUcx18cv+W5eDbssxtD9q4LaW9Q0OnLPBde2R7QWGV5RzOfRGQFacaTiIgUhbFp+MXxHPs3hmxuW3zA6NqAw0eXPf76G3ktsxMRkaLjP5j5NPpg5tNSZgqLSGnTjCcRESkpdZXQ3mC5MbC0S1s+AIOelIuISHHKuXBge0D9g5lPQxO64IlIYSnwJCIiRWNdS8TAuMPEzOJ+f8aHC3dcOpts0ZXaFhER+VzOhQPdAXWVlrfPely+6zD/OhgRkcVR4ElERIrGmqYIz7Fc6X/6qJEfwnvnPKII9q5bxhJ5IiIiKZTz4NXtAWubIz655vGrkx43hwyh0huKyDJT4CkjllL+drG/m8aSu0uhfkin+Up5p61EuyRrvn3jubClPeJin8PY9MJfN4rgTxc9xmYMB7YH1FQsU4OLSLEdF1nbnvnOlbI0WRsPi5XG7SxUm9K4rYtVyG3JefDcppDv7PQpz8GfLub4u49zvHPW489XXE7ddOkfy/5SvCQ+SyX1uiJppOTiGbGU8reL/d00ltxdCvVDOs1XyjttJdolWQvZN34IvznlEYSG7+zyqS6f/3U/veZy+Z7DK90B7Q1aa/AkxXZcZG175jtXytJkbTwsVhq3s1BtSuO2LtZKbsvoFPSNOAyOO0z7MDlrmPUN61pCntsU4mZ02kISn6WSel2RlaTk4kVmKeVvF/u7aSy5uxTqh3Sar5R32kq0S7IWsm9yLnx7R4Ax8PbZHNP5uV/z/pTh0l2HnrWhgk5zKLbjImvbM9+5UpYma+NhsdK4nYVqUxq3dbFWclvqq2B7Z8TL3QHf2x1waL/PC5sDbg05/OG8hx8W5n2thSCE6XxcpXZo3HDnvuHmkOHKPYdLdx1uDhnujRpGpwwzeYie4pKdxGeppF5XJI0040lERIrSxAz89nSOhirLge3BE6vUWQvvnPOYmjX8YI+f2Se5IiIihXRv1PCH8x6OgcYaS12Fpao8rgD7+adJy+N/xoLFPPgeB5b8EPzQkA8e/DkwD/4OIvv1S/oMNn6tLynzLOUelOfi719uT861dDRaOhsjPBUNEVl2C53x5K1gm0RERFZMTQU8uzHgjxdy3Bs1T5zNdOe+4d6ow7e2KegkIiLyddrqLQf3+ly55zI2begfM0zlH104zYP/PPYdHj70McR5GHOupcyDipyltgJynn34d7kHP889+HOZF1eY9dz49/0QZn2YDQwzPsz6htngwfcHf499vD1j04Ybgy6VZZZvbQ1ortXMZpEkKPAkIiJFq7PRUpmz9I85tDc8vj4gCOGz6x6r6iI6G3UjKiIiMpfqcuhZW6C1dgtQ5sVftZ9Hl1jYtXt8Gj687PG7Mx771odsaoueOAtaRApHz3dFRKRoGQNV5Zbx6cfvMMMovgmdysO+DaFuQEVERIpUbSW8tjNg06qIT655fHTFJUgufiZSkhR4EhGRotU3YhiacGhviB7+3YwP75z16BsxvLgloKFKs51ERESKmevA/o0hz28O6B1y+O1pj7HppFslUjoUeFpBR4++RU/PAY4efSsTryuyEnRcyNN4mv2aD+CPFzw6GiM2tMaBp5FJw69P5RifMby2M2BNU3qDThrD80uij7RfFiZt/ZS29kiysjYestbeNNvQGvHdXQFBaPj58Rx/OO/RP2qeqkKeiDw9VbVbQT09B+jt7aOrq4OTJ99N/euKrAQdF/I0nma/5gP4+09yNFZb9m8IGRw3nLjpUldp+da2gOryFWr0ImkMzy+JPtJ+WZi09VPa2iPJytp4yFp7syAI4cagw4U7cbJ017E0VVuaay3NNRHNNZaqlN8niKTBQqvaacbTCjpy5A26ujo4cuSNTLyuyErQcSFP42n2a5kHr24PmJw1/O60x2fXXda1RHxnZ/qDTqAxvBBJ9JH2y8KkrZ/S1h5JVtbGQ9bamwWeC5vaIg7u8fnOTp/dXSHluTgY9f7FHD/5tIx/OJbjjxc8zvc59I8Z5YUSWQLNeBIRkaLkh/DBJY++EYeOhog96wLqq5JulYiIiKTZ1CwMTcQ5IofGDcOThjAyGCz1VZbG6kdfDdWWnJt0i0WSs9AZT94KtklERGRFzOThvQseY1OGl7t9OhuVvEFERETmV1UeV8Ttao6nOEUWRqfMwyDUyKThxqBDZA1gqa2AxuroYSCqsdpSkUt2G0TSRoEnEREpKiOThj9c8IiiuHxyU42CTiIiIrI4juHhDKfPhRGMTcdBqM+/+kYcgsgAUFn2YFZUlaWq3FKRs5TnePjdc8CYpLZIZOUp8CQiIkUhH8DpXpdLdx3qqyyv7AyUGFRERESWnet8NRhlLUzMwMik8zAYdaXfYcYHeDzK5JrHA1HluXiWVMWD7801ETUVCk5J8VBy8YwopjKqhdqWYuojKL7tESmUyMLlew4//SzH1X6HnrUhr+9W0Emk1Oi6mZyl9H0p3Rcm0aY07ptiZQzUVsLalog960Je3RFw6Fmff/6Cz6H9eX6wx+fbO3xe3BKwZ13I+tZ4eZ5jYGLGcGvY4cwtlw8ve/zseBk/+TTHh5dcrj4MXkmS5sqMbS1M5+HeqOHSXYdPr7l8fMXldK/DlXsOfSNxIHLWj/+ttfGsuSCMH5zO+PHvT87C+DSMTsH9ScPwRLzEc2DMcG/UMDhu8DOa5F7JxTOimMqoFmpbiqmPoPi2R6QQ+scMn15zuT/lsL41ZM/akMqypFslIknQdTM5S+n7UrovTKJNadw3Mrd8AAPjhv7RuJreyKTBMdDVHLG5LaKl1mom1DIKI/ADCKK4InLOfTTTLAjhzC2XawMO+QCqy+N8XvVVFmthfMYwNm0YnzEEYfxLxlhqysFzLdN584QZb5Yvz4B7WrUV9mFesbUtUaLVmpVcvMgcOfIG//bf/tdFUUa1UNtSTH0Exbc9IstpxodPr7ncHHJpqol4fZdPc61yOYmUMl03k7OUvi+l+8Ik2pTGfSNzK/Ogs9HS2RhPbZnx4fqAw+W7LjcGXeqrInq6Qjqb0n3fE4RxheGK3ONLBsMIZn2oKIvzZxXa2DTcHnYYmnCYDQAL7Q1x0OZqv8vtYYP9QiDI+cIyyKlZQxDC5vaI6nLLxIxhaMJwbzReOFZbGS+3XNscUVdpqa2Mg07OF9aVRRFM+zCdN0znYTYwGOJtNwYcJ5719vD/v/D18P+dONgYhIb7X8ordvKmS1dzxLbVUarvhTXjSUREMmVsGt49lyMIYe+Dqep68iciIiLFzNp4Kde5Ppd7ow5dzSH714dUJDjT29oHy8NmDOPTj2b/jE0bpvPxzVlFztLRGFFZBoMPlo1F1uAYS12lpaPR0lobUVEWVw9crmWFM34cmBmbNriOpaU2TvIeRoa79w1BZKitsGxuD6kpt7gO5IP4/Wf9+HtZDjatCqmpWJ42LTc/hGv9DhfvuEzMGlpqI7atjoOSKxHUg4XPeFLgSUREMmNw3PDeeY+KnOXA9iDRqcUiIiIiK81auDHo8Ol1F4Bn1oesb1mZh3CzfvzeA2MOYzMwMW0IbfzGjrHUVlhqK4ln/1RYcq5lYNzh7n3DbGBoqIqDUNUV8WyioXFD332HfPCo8Tl3eWbteE48s6mzKaK93uK5j372eVXC+qqVC9AUUmShb8Rw4Y7LwJhDVbllY2scgGqoKuzSTAWeRESkqIQR/OyzHJVlcdCpTIvFRUREpETN+PDZ9Xj5XXt9xLMbg4LNzJn14cRNl+sD8Rqyllr7cGlZXUX8vap8cUvnPp81NeMb6iqt7u+WaHjCcPGuw61hhyA01FRYntsUsKquMMvwlONJRESKysU7DtN5eHWHgk4iIiJS2ipy8OKWkHUtEZ9c9fjliRy714ZsaY+WbRbP/SnD9QGHa/0OkYXdXSEbVkVU5Jbn9SHOY1RTATUV6c1PlCVNNZYXNoeEUcjAmOH0LZffn/HYuOpBYvov/fvynKWmwlJdDq7zxJdcFgV86exS6dCY+kHSoJTKLMvXm/Xh7G2XzW0RdZXL//qHD79JS0s3hw+/ufwvXiR0zBSnufZr1kq/F4rODyKSZh2NloN7fTa0Rnx23eV3pz1GpxYfebI2Tmb+yxNxMOtqv8Pa5oi/2OuzvXN5g05SOK4D7Q2W13YG7Fkbcm/U4c9XPD760td753P8/HgZ/+GjHD85luP3Zzz+fMXl3G2H3qE4ibkfLL09Wmr3BCodGlM/SBqUUpll+XqfXnO5OuDwl8/4BbnhaWnpJgxDXNdlcPD88r9BEdAxU5zm2q9ZK/1eKDo/iEhWDIwZPr7qMTEDW9ojNreF1D7FA7vRKfjwssfIpENHQ8TGtpDVDbagM2Fk5UTR4/9viZdsTsyYR1+zj/7fDx8FMMu9eGZU/AWr6uIZVJMTC1tqpyH0BEeOvEFXV0fJlw5VP0gaFGocanxnx/gMXL7nsL0jLNhTtkOHDuK6LocOHSzMGxQBHTPFaa79msQ+T+M40/lBRLKitc7y/Z54ZtK1foefHS/j7bMeF+849I8Z8l+auWJtHHi4N2r45KrLr07mCCPDd3f5vLI9YE2Tgk7FxHEe/3IdqC6HtnrLpraIPetCXtoa8v2egL/+hs8/eTbP67t8XtgcsKU9pLbSMjlruHTX4e2zOf7Txzk+uLSw/Bea8SQiIqkVRfD2OY+JGcMP9/qPVSQRERERkScLQugddrhyz2F4whA9qD7nuRZsHHSKAPvg7yvLLJvbQratjnS/JXOyNs7/dee+4cqtSf4nP+hRcnEREcmGsWkYmnBwDLhOXC3l8l2XwXHDt3cEugkSERERWSDPhQ2tERtaI6IIxmYM9ycNM36c0NsQf6/IWWorob7SYpYpKbkUN2OgsdrSWG1ZU7uwBFAKPImISKK+mE/gSfZvKFwJWBEREZFi5zjQUGVpqNL9lCRDgScREUnUyZse+cDw0laf9oa4zKsfwti0IbLQXq+bJBERERGRrFLgSUREEjM2DbdHDM9tCulqfhRg8tw414CIiIiIiGSbctSvoKNH36Kn5wBHj76VdFNEnloax28a27RY821LMW0rQGTh7n3Dh5c8KnKwriWa/5fIXj9krb0SK9R+S2I8aAwWJ+3XwlL/iogsL1W1W0E9PQfo7e2jq6uDkyffTbo5Ik8ljeM3jW1arPm2pZi2dXIW/nDe4/6UQ31VxP4N4YJzOGWtH7LWXokVar8lMR40BouT9mthqX9FRBZmbGycdev2zVvVTjOeVtCRI2/Q1dXBkSNvJN0UkaeWxvGbxjYt1nzbUkzbeuaWy4xv+M5Onx/0PF3i8Kz1Q9baK7FC7bckxoPGYHHSfi0s9a+IyPLSjCcREVlRP/00R0dTxL71YdJNERERERGRRdKMJxERSaXpPNSUK3G4iIiIiEgpUFU7ERH5WtaCH8KMD35gaKyxOGbxr5cPILQGz12+NoqIiIiISHop8CQiIk90f8rw2XWXe6OPJsfmXMv6lojynMVxwDEPvhxwH3x3jH34d44B98H38Zn49TzH0lyjGU8iIiIiIqVAS+0WQSVW5YtKZTyUynYuVTGVYR+bMoxNPT69KQih777DlXsuF/pcztxyOX7D5ZOrHh9d8fjgksf7F3P84UKOd8/lePtsjt+ezvHrUzk+uOTRXGM5uNenvmpxgafF9kMWx28W27zS1EexrPXDUto71++msR8K1abDh9+kpaWbw4ffXNbXLRXz7ZfXXvtrGhu38Nprf73CLVucUro2inyRxnB2KLn4IqjEqnxRqYyHUtnOpSqmMuwAF/ocPrsRT471HMsP9/pUln/131kLFogiiGz8PbRf+H9rcAzUVVrMEpbqLbYfsjh+s9jmlaY+imWtH5bS3rl+N439UKg2tbR0E4YhrusyOHh+2V63VMy3Xxobtzz888jIpZVs2qKU0rVR5Is0hpOn5OIFpBKr8kWlMh5KZTuXqpjKsAOMTX8eJbJ8e0fwxKATgHmw5M5zocyDijKoLofaSqivgsZqS33V0oJOsPh+yOL4zWKbV5r6KJa1flhKe+f63TT2Q6HadOjQQVzX5dChg8v6uqVivv3yzDO7H/uedqV0bRT5Io3h7NCMJxEReaLxafjFyRxRZOhoCHlle5h0k0REREREJCUWOuNJycVFROQrhicMf7roEUXxFKUyXS1EpIRYy5JnaIqIiEhMHyVERASIP2jdHjZcvOvSP/b4SuzxGX0CE5HiNzxhOH7DZXgiPudVlkFlmaWqzFJdDm0NEa21S182LCIiUkoUeBIREQA+uORyc8h94s+GJwz5QDOfRKR43blveO+8R32lZeeaEGNgKm+YzsPErOHOfcOZ2y7NNRE714SsblAASkREZCGUXFwyRSUzC0v9u3RJ9OFylCb/b//dP35t0AnAYugfW/gnrLT1QxpLj+t4i83XD4Xqp8W+rsZSYS1lPCzlHBBG8PFVj1V1ltd3B2zvjOjuiNi3PuTCH/8d/5v/4UuMnPr/8Eq3D8B753P8+pRH75AhmiNbatrGryxdGvu+UNe/NG6rpFMpjZU03gdkgZKLS6aoZGZhqX+XLok+XI7S5H/1L/8LDv7Lf/Xw7xurI7atjhgajwNO4zOGHWtCdq2JCt6mxZrrPdNYelzHW2y+fihUPy32dTWWCmsp42Ep54C+EcN753N8v8ensfrxW+Mvv6610D9mOHMrXpZc5llWN0SsbrB0NkXk3K//3eVSTPs8a9LY94W6/qVxWyWdSmmspPE+IEkLTS6uGU+SKSqZWVjq36VLog+XozT5q89vJOc++rA1MukwPm14ZkPIwb0Bf/O8z/aOhQWdltqmxZrrPdNYelzHW2y+fihUPy32dTWWCmsp42Ep54CbQw61FZaGqq8+j/3y6xoDbfWW13YGfG+3z+a2iLFpw4eXPf7hWI5PrrpMzCxsexarmPZ51qSx7wt1/Uvjtko6ldJYSeN9QBZoxpOIiAAwOG5495yHHz5aUre7K2DnAmc5iYg8ibUQhJBLaY64MIK//yTHlvaInrXhol9nchau3HO52u+QD+CFLQFrm+e9zRYREckszXgSEZGn0lJr+cEen5baR4GmW8O6TIjI0lztd/j5iRx376czE/fd+wY/NKxtXlqQvbocetaG/OUzPmuaIj667DE5u0yNFBERybCUPnsSEZEkVJfD5raIwfE44DQ1m84PiiKSHe0NETcGHd45l2NDa8i6lgjPjWdCWQuWr/+z50JlGVTmLGUeD6vIzfowNGEYHHcYHDeMTRv8EFwHWmstrXURrXWWxmqLM8dpbNaHY9c8Wmoj6p+wzG4xPBe+sSmk7xOH6wOOZo2KiEjJU+BJREQe88VZCRVlWiYiIktTXQ7f3hFw6a7DuT6XawNfX0FzLq5jqcjFS+Nm/Pg8VZ6ztNRYNreFlHkwGxgGxgynel3CyOA5lpYHgaimmjgQVe5BZOHeqOF0r0sQwYtbgodBreWQc2FNc8Tley6b2yLKc8v32iIiIlmjwJOIiDx0+a7D9cFHHwqryhJsjIgUDWNg6+qILe0RY9NgMRgsxsQ/M4Dz+Z8f/L8x4IcwnTdM52Eqb5jJGzzXUl0OLbUR1eU8MWAURjAyGQehBsYczve5j+Wv+1xTdcTL2wKqy5d/m3etCblz3+E3p3M8tzFgVb0C+SIiUpqUvCMjjh59i56eAxw9+taK/m4hpK09kiyNh3SwFj677vDJtcefR1Su8IwnjQf5ssWOCY2ldDIG/uO/f4tXXniF//jv36KuEmoroKYCqsrjZXUVOSjPQZnHgwCTpavZsm11xP/j3/yvOLB3K//H//J/TU3Fk4NOEC+5a6m1bO+MeGV7wF9/w+eHz+R5aavPNzYGPL854Lu7fF7fHdBaV5jzXE0FvL7Lp8yz/P5sjj+c9xibLshbPUZjXyT7dBxLsVFVu4zo6TlAb28fXV0dnDz57or9biGkrT2SLI2HdDhx0+Xc7a8uf+lZG7Cjc+Xyk2g8yJctdkxoLKXXUvZNS0s3YRjiui6Dg+cL1MLlZS3cHHI4cdNlejbOo7ezK6SiQMvvNPZFsk/HsWSFqtoVmSNH3qCrq4MjR95Y0d8thLS1R5Kl8ZC80SkeCzo1VUdsaQ95YXPAlvaVTYqr8SBfttgxobGUXkvZN4cOHcR1XQ4dOliAlhWGMbCuJeKHe3161oZcH3T42Wc5zt12CAtwitXYF8k+HcdSbDTjSUSkxJ3udTh9K15it6E15PnNYcItEhEpXrM+nLnlcumeQ2UOetbGlf6WM7n5XPwwzqfl6vGziIgs0UJnPCm5uIhIibs3+ujTR9+Iw6wfqgKTiEiBlOdg34aQLe0hJ256fHjZ4+KdiO2dIVVlMDlr8EOwxMv0ePD9y//vOFCZi6uPVuYslWXgfWHFdBDC/SnD8IRhaMIwMukwNQtBFCd2r62EukpLXaWlvT6itc6uWPBLRERKiwJPIiIlLLJx5afPzQaG07dc9m/QrCcRkUKqrYRvbQvoHzMcv+7y/sUvR/ztw+p+BuAL1f4MceW+0D4eKcq5ljIP/ADyD6r4OcbSUG1pq4uoqbCU5yCIYGzKMDptuNrvcPa2S31VxNb2iHUt0WMBLBERkaVS4ElEpISdveUQRI9/cJnxE2qMiEgJWlVneX13wFQe8oGhutySc7++Yt/nrI2XzU3nYcY3TOcN0w9eo8yLA0wNVZb6Kjvnsjpr4d6Y4dIdl4+vupy44dLdGbJtdaTleCIisix0OREpMSrPGivlfpj14Xq/w69Oepy+9fljbUtjdcTW1SF712m2k2RfKR/jX6R+SK8v7htjoLocGqvjGUsLWfJmDJR5UF8FbfWW9a0R2zsj9qwL2d4ZsXFVRFPN3EGnz1+nvd7ycnfAXz7js6414tRNl1+eyHHnfmmuvVvscaPjTdJOYzSmflh5Si4uUmJUnjVWiv1w977hxA2XkSnDg4UbVOQs+zcEtDfET9hFikUpHuNPon5IrzTvm/tThmPXXAbGHNY0RTyzPqC6POlWrZzF7ps071MR0Bj9nPph+Sw0ubhmPImUGJVnjZVSPwQhfHjZ5Z1zOUamHMDgGMvutQE/3u/T1aygkxSfUjrG56J+SK8075uGKstrOwJe3BIwNG74+fEcZ245hFHSLVsZi903ad6nIqAx+jn1w8rTjCcRkSI2k4d3z3uMTD56zlBfGXFge0BVCT29FhGRxfFDOHPL5cIdh6oy2NEZsr5V+Z9ERGThM56UXFxEpEiNTcM7Z3NM5R/l6NjYGrJ/Y6gPDCIisiA5F/auC9nQGnKq1+Pjqy6nel22rQ7Z1BZRpk8TIiIyD10qRESK0KwPvzudYzZ4FHTq7gjZszZcUNJaERGRL6qvgm9tCxibhvN9cfDpVK9LY7WludbSUhvRUmM1m1ZERL5CgScRkSL08VX3saDTrjUBO9dECjqJiMiS1FXCc5tCdneF9A45DE0Ybg87XLwTJwusKnsQhKq1tNRaGqosjmbZioiUNF0GRDIoiRKghw+/SUtLN4cPv7li7wkqd7oYNwcdbg0/yha+d13Arq6nDzqp74vTXMey9nnxSercvVjzjcFCjdEkxv5c75mFY7GyDLaujnhxS8iP9vn8eH+el7b6dDVHTOUNx2+4/PpUjv/4cY7fnfE4ccPl9rBh1l/4e2ShH76omManiMhyUnJxkQxKogRoS0s3YRjiui6Dg+dX5D1B5U5n8vD2WY9Xti+slPV0Hn76WY4wiqNM+zcEbGlfXBmiUu/7YjXXsax9XnySOncv1nxjsFBjNImxP9d7FsOxGEYwMmkYHDcMjjsMjhtm/PjaVFthaa6NqC6HMs9SmYOmmvj/v/iQJGv9UEzjU0RkIRaaXFwznkQyKIkSoIcOHcR1XQ4dOrhi7wkqdzobwOi0w7FrHvM9JrAW3r/oPQg6WZ7buPigE6jvi9Vcx7L2efFJ6ty9WPONwUKN0STG/lzvWQzHoutAS62luyPiW9sCfrzf5y+fyfPC5oD2hoixKcPVfoeTN13+dMnjp5+V8ffHcly44zy83mWtH4ppfIqILCfNeBIRSbEghL8/liMIDTs7Q3avDb/233542eX6gAtYnt8csKF13tO7iIhI4mZ8GJ4w3Bp2uNrv0lIb8dymgLrKpFsmIiJz0YwnEZEi4LmwaVU8a+nMbZcr95582j7Z63B9wMUxlle6FXQSEZHsqMhBR6PluU0hr+30mckbfnE8x7vnPG4Nm3ln/IqISLop8CQiklIfXnLpHzPs6AzJufFd9ydXXe6MfDVL+JW7cdDpR8/4dDTqDl1ERLJpVZ3lB3t89m8MmQ3gjxdynOp15/9FERFJLQWeRERSqrnW8vYZj/N9Lmua4llPFsP7Fz2GJx4Fn+6NGmaDOMdTsPiUTiIiIqngubC5LeJ7uwP2rgs4e9vl8tfM+BURkfTzkm6AiIg82ea2iL4Rh3N9jz/pDSLDr0/lKPMs1WWW+9MGMFji5XgvbP76PFAiIiJZsm11xORsyLGrLpVllk7N6hURyRw9OlhmR4++RU/PAY4efSvppoisKI395WcMPLcpoMx78k12PjCMTDlYG89+qiyzbFu9MlOeim1/Z217stbeNCpUHyaxb5bynov93WLqvyTfV+ZnDDyzPqSzyfL+BY+bQ19dbi6Lc/jwm7S0dHP48JtJN0WkJJTytUZV7ZZZT88Benv76Orq4OTJd5NujsiK0dgvnN4hw/sXc4/9nWssrXURq+osleVQV2lpqraYFbofL7b9nbXtyVp706hQfZjEvlnKey72d4up/5J8X1m4MII/X3G5Meiwb33I1hV60FLMWlq6CcMQ13UZHDyfdHNEil4xXmtU1S4hR468QVdXB0eOvJF0U0RWlMZ+4XQ1W3atCR77u9pKyze3huxYE7GhNaK5ZuWCTlB8+ztr25O19qZRofowiX2zlPdc7O8WU/8l+b6ycK4DL2wO2bY64tPrHidvuqp2t0SHDh3EdV0OHTqYdFNESkIpX2s040lEJCMu33P45KoLxBGmukrLsxsDVtXpzltERErH+T6H4zc8ujtC9q5TXkMRkaRoxpOISJHZ3BbxrW3BgzTiMDZt+P2ZHB9ccpnOJ9w4ERGRFdLdEfHM+oDzfap2JyKSBTpTi4hkyJomy/4Njz/dvTHocvme+zW/ISIihWAtjM/AzUGHi3cc7txX0uuVtG11xNrmkPN9WnInIpJ2XtINEBGRpxN94Qa7qtzy/KaAtnrddYuIrIRZP176fKXfZWo2DjY5xrK5LWJ1g5Z9raT1rRE3h1zuTxkaq3UdFBFJK814KnFLKek4VwnWJEpFzlcSNm3lK9PWnkIqpW0tJGvh3G2HT6/Hs5vWt4Qc7PEVdFoAjUH5siTGRBrH4VxtKtQ9QpZdG3D42fEcZ2+7tNdHvNLt80+ezfM3z/vs2/DVoFOh+iGNY6kQ5tvOtnpLTYXl4ysuYQkXucvaeMhae5OgPpJio+TiJW4pJR3nKsGaRKnI+UrCpq18ZdraU0iltK2F9NFll2sDcdCpZ23Ajs4Svst+ShqD8mVJjIk0jsO52lSoe4QsCkI4di0+B69riRNaV5bN/3uF6oc0jqVCWMh2Dk8YfnvaY1Nb9JWl6KUia+Mha+1NgvpIskLJxWVBllLSca4SrEmUipyvJGzaylemrT2FVErbWigz+fhJO0BdZaSg01PSGJQvS2JMpHEcztWmQt0jZM34DPz2tMfNIYfnNwW8uGVhQScoXD+kcSwVwkK2s6nG0t0RcX3AeWwpeinJ2njIWnuToD6SYqMZTyIiGTA8Yfj1qRwAO9eE7O4qzae6IiIraXjC8M45j3IPXtoa0KA8Qql0e8Twh/M5/mpfnqrypFsjIlI6NONJRKSIlHmPPuw0VGm2k4hIoQ2OG94+61FbYXl9t6+gU4rVV8b7ZmBcH21ERNJIZ2cRkQz4vHISQF1lgg0RESkB/WOGd856NFRZXt0RUKY60KlWUwGttRFX7umjjYhIGukyKiKSkJFJw7FrcSWeukpLbaWlvtJSVwk1FRb3wf3z6CS8fzE+XVeVWeoq9dRdRKRQJmfhjxc8mmosr3QHeG7SLZKF2NQW8eFlj/EZqK1IujUiIvJFeiywzJZS+rKYSjurBKikXRrGaJlnGZk0jEw63Bh0Od3r8f7FHL84keM/fJTjv/8gx7//IMcvTpYxGxhcY/nWtgBj5n/tNJrOx7MIbo8Ybgw6XL7ncPe+Yf5Mg5IFaTim5KsOH36TlpZuDh9+M+mmLIvFjrOF/l4UwZ8uerhOnNMprUEn3b991ZqmiJxrudqf3E7TfhEReTIlF19mSyl9WUylnVUCVNIuqTE6OG4o9yy1D5bLzfpwfdDhdK+LH35dRMmyqs7y/OaA6hQlTR2bht4hB8+BdS0RFU+o8hRFcdLXq/0ud+4b4Kvb2Fgdsb0jYk1zhJPRoJrovJ9WLS3dhGGI67oMDp5PujlLtthxttDf++y6y8W7Dt/ZGdBSm96ouO7fnuzYVZfeYYe/2ufjJPB4XftFREqNkosnZCmlL4uptLNKgEraJTVG6yotvz+T448XvDgIlYMt7REVucf/XVWZZevqkBe3BPx4v89rO9MTdAojONXr8osTOc71uZy46fKrUzlGpx5Fjcam4fgNl3/4NMf7F3PMBvCNjSF/sTfPj/fn+afP5fkXL+R5dYdPmQd/uuTx65PxEoliEUYwNGHoHzWMTYM/RyHC+1OGP5z3+MmnOf500eXSXYf7k9maDabzfjodOnQQ13U5dOhg0k1ZFosdZwv5vVtDhgt3XPauC1MddALdv32djW0RM76h734yTzG0X0REnkwznkREVtjQhOH3ZzzCyNBUE1GZg9sj8XOAipzl+U0B7Q020SV11sZlxG+NOMzkwT74u8jCwJjDbAA7OiN2dIbkA3j3nMfErKGlxjI+Y5icNZS5lvWtERtXRfNWgxoaN3xw2SMfwLe2BqyqT/eHvrnkAzhzy+Vqv/OVWWw511JZFu/nnAtBFAekhicM1eXQ2RgxNGEYnjRYG/dhS52ltTaitc7SWP0o95eILJ/xGfj1yRxt9ZaXtmZ3SbPAr096VJTBK91B0k0RESl6C53xpOTiIiIrrLnG8sz6kE+uegxPPIoi5FzL93t8Kp+wZG0l5QP44JLHnfsO5Z6lpiIOghkAA2ubIza2RTRUxcGhyjJ4bWfA2dsu49OGrqaIphpLZ1O04CBJc63l9V0+71/0ePucx7MbQja1RQXbxkLpHTIcu+YRRrC5LV4+mHMt03nDdN4w4/PgzxCEhjLPUlUGG1sjNqx61F9BGAcoB8YMA+MOp2+5hJHBdSzNNZbWB8Gollqb2hw0IlkRPsjrVJ6D5zYp6JR1q+rsw4c5IiKSDgo8iYgkYNOqiIt3LGPTjz7hfHNLsOJBJ2vh8j2Hi3fi6EVFzjI2Ey/x+tY2n45Gu6C8S2Ue7F03x1qyBSjPwavbAz697vLxVY+x6ZA968JM5H2azsOxax63hh06GyP2bwio+sLSyLgS4cJncXkutNVb2uotEBFFcRXEgXHDwJjDpbsOZ265GOIg1K41YaZniYkk6Wp/vLT1ez0BZbozzrzsPbIQESl+uryKiCTAGGiuiRibjgM+TdUR7Q0rGzgII/jzFZcbgy7rWkIqcjDjQ1NNnF8qiZxSjgP7N4TUVVo+u+4yNm3Ytz54mIw9bWZ96B12OHHDxXHgm1t9upqWf5mk48SzwpprLd0dEdbC2HQ8I+pKv8Pvz+Zor4/oWRvSVKMAlMhCWQuX7rqsaYqXskq2zfhwfcCho1HhJxGRNFHgSUQkIZOzj6ITu7vCJQcrZv34yf3krKG51rK2+euXun1eMvzufcOLWwLWtaTnJt0Y2Lo6orbS8uFlj58dz9HRaNm2OmRVXbK5ryZn4w81/WMOEw9yWQGsbw15Zl1IeW6eF1gmxkB9laW+yrKpLeLWsOFUr8evT+VY0xSxuyugvmpl2iKSZaNThrFpw951ygeUddbCJ1fjjzZLnYErIiLLSwugM+Lo0bfo6TnA0aNvPdXPCvWehZTU+8rcsrZfDh9+k5aWbg4ffnNZX3e+flhoP/WNGPrHHkVQlhpMmZqFX53KcfqWy8CY4aPLLr877TGd/+q/DUL44LLLnfuGl7alK+j0RasbLH+1z+e5TSGTM/D22Ry/Oulxrd8hXMEmW/tgKd1Vl59+muPcbRfXga6miNnb7/H//N//NWff+3crFnT6MmOgq9nygz0+z28KGJk0/OJEjg8vuQyNp78yXrFdw9ImqX4o1Dl4ud0eMXju58taJcuuDTjcGnZ4bmPwlUqxpWwpnyF0HhWR5aKqdhnR03OA3t4+uro6OHny3QX/rFDvWUhJva/MLWv7paWlmzAMcV2XwcHzy/a68/XDQvppdMrwq1MeURRHm+orI17dsbT8Tu9fdBkcd/juLp/q8jgx9R/Oe7gOvLrDp7YiXlo3Mmn49JrL6LThxc0Ba5qz8WHLWrg3Gpc6v3PfoSJneW5TQEdjYds/OQvvnssxNh0n9t7dFSc9zz1I6J3G4yKM4plvZ2+7TOcN1eWWTW0h2zuiVCZNLrZrWNok1Q+FOgcvt1+f9KiusLy0VTNksu7PV1xGJg3f79HstS9aymcInUdFZD4LrWqnGU8ZceTIG3R1dXDkyBtP9bNCvWchJfW+Mres7ZdDhw7iui6HDh1c1tedrx/m+7m18KeL7mNBp9d2Li3oFEUw4xs2rXqUl6m5xvL6bh8D/Opkjn//QRl/++ccvz2dY8Y3fGdndoJOEM/qaW+wHNgecHBPnoYqyweXPIYnli+SEoRwutfhvfMevUPx614bcJiYiRO//9U+n+6OR0EnSOdx4TqwpT3iR/t8vr3DZ1VdxMmbHjcG03nJL7ZrWNok1Q+FOgcvp8lZGJ506CxwAFtWRhiBl87TXKKW8hlC51ERWS6a8SQisoJ6hwzvX4zXADjG8sNn/CUn8b43anj7bI5tqwOeWf/4GrTpPJzvc7k56NDZFLGhNaKh2n5t7qesyAfw9lmP+1OGHZ0R2ztCPHf+35uYgbujDvWVliCCWd+QD+LAXe+Qw1Qe6istw5MOXc0h/aMOzTWWV7Zn+wn6Hy94jE4ZfviMn3RTRFLjg0sud+87/OUzPjllPc28EzdcrvTH+1PVCUVEVsZCZzzptCwiskKshTO3HkVHGqsto1OGcs8uKGjyJPkAProcn8qHJxy+XEi6sgyeWR/yzPriWkZS5sHruwLO3HY5e8vhyj2H7o6QzW3Rw76c8WFkwmCBllpLzo3zRH0xqTvEAcByL67m93J3QG0FnOtzuD7g0t4QV4rLuvWtIX+8kGNsGupSWiFQZCXdHHS4Mejy3KZAQacisaU95MIdh1+dzHFwj7/o66qIiCw/XWpFRFaAtfHT9ftTj6YaDU04vHfeocyzbGkLWd8aUVnGYzfL1sbBpXwIQWgIQpgNYGLGMDRhuDXkYIkDKQ0lVgrcceJqgOtbQ87ddjlx0+XcbZcda0I2ror4xfEcs8GDvqmK+MGegNa6iMkBl11dARtaI8q8eGnGl3Mf7eiM2NGZzqTri9FebynzLOduuzy3aekVFEWybHQKPrrisq4lZENr8Rznpa6qHNrqLYPjBkfnOBGRVFHgSURKXmRheMLQXGML9oH8Dxdc+kae/Pg1HxjO3PY4czv+f8fYh+2Kzd+oltqIPUUwM2cxaivguU0hOzrjANRn1z0u37XMBoYD3T5/uhQvMzt21WV8Ou5LPzBLXuKYJZ4Lu9aEfHrdY2LWsLsrZFVdaQUqRT53+pZLRQ6+sVFB2GIy48Od+4Zn1oU4GV9OLiJSbHRaXkFLKVm6lHKmhXrdxVJp1qVL2z5NoyCEu6OG/jHD5CxzlpR3DLx/weM3pzyuDziEy/wA/MPLj4JODZURe9YGfL/H5wc9Pt/d6fPyNp9nN/rsWhOwpS1kTVNER2P8tbrh0VdbXURLTURzTURrbciquoiNrSHf2ubz6vag5JcV1FTANzaF7N8QMDkL61tC2hviSnRV5TAwbijz4EC3z9512Q7SLeY437o64tXtPnkffn8mx+9Oe9y5r0/dUhhpvRbN+HBrKF6auxznzMOH36SlpZvDh99c+ovJkoxOGcCwulGz2ERE0kbJxVfQUkqWLqWcaaFed7FUmnXp0rZP02pownDihkv/mENFzrK9M2Rr+5NLyp+77XDiZjwJtCJn2dIesqktoiK3tDZcuOPw2fX4082etSHdKS1pX2ys/eryuWKylOPcWugbMZy57TI84dCzNiiqZYWSDmm9Ft0cdPjTJY8f788vqZro51paugnDENd1GRw8v/QXlEW7dNfh0+suf/OcrxlPIiIrZKHJxXVaXkFLKVm6lHKmhXrdxVJp1qVL2z5Nq+Yay7d3BLy8zScfwGfXPf50yX3ijKZtqyPqK+MfzPiGU70ePzmWe/AEdXGu3DMPg04vbgnY3qmg00op9n5eynFuDHQ2WV7fFbBzTcjJm5r5JMsvrdeiu6OGuspoWYJOAIcOHcR1XQ4dOrg8LyiLNj5tqK1AQScRkRTSjCcRKQnHrrpcuhcHgfauC+ju+Gr06dJdh2PXHk9995fP5KmpeLr3CiM4ds3lan989/vshoDN7cqnI+mTD+DvPi7j2Y0Bm9s060mKm7Xw089ydDZG7NuQ7eW28lXvnPVwHXi5O0i6KSIiJWOhM56UXFxESkJF2aPAz8U7LttWf3X20d37X31M+uWldtbGgaUgivNIhVFcac4PYXTacH3AYWQyzjNhsLy0NWBNs4JOkk69Q/GYX92goJMUv+uDDpOzhjXNGu/FaGzasK5F+1ZEJI0UeBKRohNGMDRuqKuyVOTi6nB3Rx8FlabyhpFJQ1PNo4DQzUGH2yNfDTz95FgOY+JAUxSBXUCFOddYulpC9qwNl205h0ghfJ5c+U8XPb69QwnqpXgNTxg+veaytlkVHYtREMbX9rpK7VsRkTRS4ElEUi0I46eYXwwSzcXaz5e5xZ+gyz1LmQfjM48HjG4POzTVxEstZnz45OqTP3Hnwy8HmuLQU9ya+E+eC7UVlpZay7qWkOaa4s8xJMVhXUtEVZnP22c9Ltxx2LlGswWk+AxPGN4561FbaXl2o5bYFaOx6fiiq8CTiEg6Kf2eyDyWUhI6reWks8Rz4Wq/wx/Oe+QXkLZhKs/DoBPAbGC+EnQCuDnkEEbxErl3z3lPCDCB51iqHizRy7mWg3vy/A9e9PkXL/r8ixd8/vnz8f//s+d8vt8TsH9DSEutgk6SLa11cXB2cFy3BFJ8RibjoFNNpeXV7QFlJf7Idb77krl+nuZ7ms8DT7UpDzyluQ+zQn0okk1KLi4yj6WUhE5rOemsmfXhZ8dzVJVZDmwP5ly+NjULP/l0Yevb6iojpmYNQRTfsLbWRmxqi6itsNRUWMpz8fK6U7dcZn14bpOelEtx+s0pj8lZw/d2+1SVJ90akeURRvDz4znKPcurOxR0gvnvS+b6eZrvaU7ddLnS73DoWT/ppswpzX2YFepDkXRZaHJxPd4UmcdSSkKntZx01pTnoGdtyP0ph9+dyTEx8/X/NueBYxb2xHNs2nkYdALY1RWyvjWiuTYOOkFclnnP2lBBJylqL2wOcAzzHl8iWTE8YfjtaY/pPLywRUGnz813XzLXz9N6TxPZeBZz8wKX5CcprX2YJepDkWzSjCcRyYTIws8+yzE5a6gss7y63ae+6sn/9uRNl7O3nz5L8qs7fNrr03/jKlIIk7Pwztkcfgjf3vH1x5dImgUhnLnlcr7Pob7K8tymcME5AiWbBscNvz2d47WdvhLHi4isMM14EpFMiCKYmIHpfJwY/Os4BroelMCezht+dybH8MSTkylt7wypyD39zefFOyrpJaWruhy+s9OnPGf5/RzHl0ha9Y8afnUyx4U7Dru6Qr63O1DQqQR8nv+x3NO+FhFJK008FpHEWAsfXXG5MRgHfMo8S0NV/FX/he+eG894ir5wT5kPDL8/6/HytoC2L81Syrmwuyvk46uPTnHmQfU5/0tJxBuqIvKBYSpv6BtxuHzPYXObKntJaaoog9d2BLx33uPtsx6vdAe0agaBFEgYxbVBnad8DGptnEzaD+NggzFw7rbLlX6XltqIl7sD6ioL0mRJoVV1Fs+xnOtzeX5TqAIfIiIppMCTiCTGGHh+U4gfxkGffGDoHzP0j33xX1lqKuIZT59XrflcEMbVil7YErCuJf5wHEbQO2Q4e/vxTzLPbQoZmzac63s0q2lnZ8iurjh308W7DiduuHxy1SMIA7o7FHyS0lSeg1d3BPzhvMc75+LgbnuDgk+yPKyF3mHDxTsuQ+MGi8ExlnIPqsotVeWW6rL4z9XllqpyqCqLKy8GEVwfcLh4x/1KtVLPsezfELC5LVLgocR4LuzfGPLRZZdZ3/DC5uBhnkYREUkH5XgSkcQFIbx33qN/bPGrfx1jcUz8wSR+hv7ImqaIl7YG3L1vePf8o7vRf/pcntwXVteNTBo+vOQyOu3w/OaADa0KPknpCkJ4/6LHvVHDN7cGrGlS8EmW5u59w4mbLiOTDm31EV1NEa4DfgizgWFqFiZn4xmoU7MQ2UfnctexhJHBYFnTHLFpVURFDmaD+IFDU7WlYmEFTaVI3blv+OCSh+fAd3b5VKtCp4hIwSnHk0jGHT36Fj09Bzh69K2km1Jwngsvdwc0VS8u0FPuxU/Ly7w4T01TdcSappAtbSEvbfX55tYAY6C9wbKu5VF1OudLT8Ubqy0/2BPww7151rekK+h0+PCbtLR0c/jwm0k3pSiV0vG2UJ4L39oW0NkU8f5Fj8FxTSORR57mmAkjOHbN5Z1zOVwHXtvp8+0dAZvbIzasiti6OmJ3V8jzm0Ne2xnwl8/4/M3zPof253l9l89LW316ukKe2xTww2d8Xtoa0t5gaai2tNVbOhoVdBJY3WD5fo+PH8KFvnTmbEzbtSZt7SmkUtrWUqL9mh2a8SSSUj09B+jt7aOrq4OTJ99NujkrYtaPy7l/eUndkxgsW9ojNq6KaKhe+EwM+6Ds8tlbDs9uDDOTv6alpZswDHFdl8HB80k3p+iU4vG2UFEEvz3tgYHv7Q6Sbo6kxEKPmRkf3j3nMTpl2LsuZEu7lsJJYf3ssxwdjRHPrA/n/8crLG3XmrS1p5BKaVtLifZr8jTjSSTjjhx5g66uDo4ceSPppqyY8hy8uCXAMH8wyGIYnDDM+E/3HsbAupaIg3uzVe3o0KGDuK7LoUMHk25KUSrF422hHAcsUKUZJfIFCz1mjl1zmZo1vL47YOtqBZ2k8PJhXKwkjdJ2rUlbewqplLa1lGi/ZodmPIlI6py66XLm9sKmya+qi3htp2ZhiBTKjA9//0kZa5oiXtwS4OqRlSzQjA//8EmOfRvimU4ihWYt/IePcjyzXmNORGQlaMaTiGTWjjUhlbmFPa1sq9eNpUghVeTgGxsD+kYMvznlcfe+Yf5HViJwezi+zexq1nlaVkYYxUnpc+lM8SQiUrK8pBsgIvJlrgOb2kJO3/rqKaqpJmJ1vaWm0tJYZZ8qv5OILM6mtoimGsvHV+ME0S21Ec+sC2mu1fEnX29y1lBZHgcvRVZC/sEE6LQutRMRKVUKPIlIKm1qizh9K75xXN1gWdMU0dEYUak8MyKJaKy2vL4r4M59w6lel7fPery2M1u50mRl+QGUuRofsnLyYZxErEyfcEREUkVL7UQklfrHDOU52Ls25MD2gE1tCjqJJM0Y6Gi0fGdnQH2V5d1zHmPTSbdK0iofoiVPsqJyDwKds09ZeERERApLgScRSZ0bgw4fXvJY3RCxrUO5QUTSxnPhle6Aipzl7TM5Tvc6DE0o95M8cn/ScOe+Q3W5BoWsnKoyqMhZBsb0EUdEJE00EVVEUuXKPYePr7qsb414blOo0tsiKVWegwPbAz674XLhjsvpW4aca6mvstRXxt/rqiwNlZbyHDqWS0Q+gAt9LhfuONRWWp5ZHybdJCkhxsDa5ojL9xy2rg6pKk+6RSIiAgo8iUhKBCEcv+Fy+Z7L5raQ/RsUdBJJu6pyeGlrSBSFDE0Y+scMY9OGoQnDtQGHyH6ebyUORjVUWTqaItrqLY6O76Lih3DxjsP5PpfIwpa2iB1rQuXakRW3qyvk5pDDZ9c9XtoWJN0cERFBgScRSdjQhOHGoEPvoEM+gP0bAja3RQo6iWSI40BrnaW17tGyqsjCxAyMThlGpw2jU/HSq0v3XGorLHvWBqxp1jKsrAtCuHTX4VyfSxDGhSF2dIbKySeJKfNg77qQDy973BkxrG7UeUZEJGkKPIlIYo7fcDnf51KRs3Q1R2xuC6mvSrpVIrIcHAN1lVBXaeki/uBnbTwz6nSvyx8v5ljfGs9uVALq7IkiuDrgcLrXZTaAjavigFO1ljZJCqxribg2EPHJNY+DdT6ezjEiIolS4ElEEtE/Zjjf57K+NeS5TaGW3YiUAGOgpdby6o6AawMOx666DI07fHNrQGO1ZiVkgbVwa9hw8qbH+Ez8AX93V0hNRdItE3nEGNi3PuAXJ8q4PeKwrkWFSkREkqTAk4ismOk8XO13uNrv4j4oOBNFKOgkUoI2tEY010T86aLHb055vLQ1oLNJwac06x8znLjhMjTh0F4f8c2toQKGklr1VdBQFdE3YljXknRrRERKmwJPIlJw1sKpXpdzfQ6OgfZ6y3Q+Djh9MSeMiJSWukp4fXfAu+c8ztxy6WxSIuC0sRaGJw1nbrn0jTg0Vke8usOnvV7nbkm/xmrL0IQDqLqiiEiSFHgSkYKyFk7cjHM57VwTsm31oypH1qrEukipcx1orrHcGHKSbkpJmpqFW8MOA+OGIDSEUZwwPIwgiAz5APzQUF1ueXFLwNpmFX+QbIgs3Lnv0NWsZXYiIklT4ElECurCg/La+9YHbF39+M2fPryICEBdlWWqzzA0YWiu0UyalRCEcPqWy4U+B2Pi4F+ZB2VlFtcBzwHXteRcS3ONpaXWPlwiLZIFwxOGGd+wpkmBJxGRpCnwJCIFM+vD6V6Xre3hV4JOIiKf62iIqK2w/OZUDoAyz9JWH7FvfUhlWcKNK0L3Rg1/vuIxnYedXSFb26OHM1FFikVthaXMs1ztd2ir11I7EZEk6TZDRArm7G0XDOxcoxs+Efl65Tk4uNenb8Qw6xum83D5nss75wwbWiNuDDpEEWxYFbFxlYIkS3H5rsOxay6tdZYD2wPqKpNukUhhlOdgd1fIsWse+9aHlOeSbpGISOnSrZuIFMTULFy667CjUzd7IjI/x8CaJgvES+06myy/OeVx8qZLZ6PFGMvJmy4X+lxe2BLQpuTWTyUfxEUeLt2NZ6HuXR+qoqgUvbb6eLb18KRhdYPOGSIiSVHgSUQK4swtF8+FbVpiJyKL0Fht+cEen4ocD2c4Tc2GfHjZ4+2zHp2NlrUtEa6xzAZxEuzZwDDrQz4wzAbxd9ex1Fda6qssDVWWxhpLRQkEw2fycHPI4e6ow/1Jw1Te4Br7xHx7IsWqtiL+PjVr+DyoLSIiK++pAk/j4xOFaoeIFJGJGThzPcfurpDpqYjppBskIpk148PMF/5/3xq4NuBwY8Dh8u1H2a5zjqXcg1wu/l7mWmo9CCK4N2S41GsIbPzhs7nG0tkY0dEYUV2+4ptUUJGNA/+X78Z901xraa62bGiytNZGVJXB2FjCjRRZIdbC9FQZk5MBY2MKuIqILLeFxogWFHgqKyujvb2dXbteWVKjRERERERERESkOLS3t1NWNnc1GGOtXdC805mZGfL5/LI0TEREREREREREsq2srIyKioo5/82CA08iIiIiIiIiIiJPw5n/n4iIiIiIiIiIiDw9BZ5ERERERERERKQgFHgSEREREREREZGCUOBJREREREREREQKQoEnEREREREREREpCAWeRERERERERESkIBR4EhERERERERGRgvj/AwscADJ/0xZKAAAAAElFTkSuQmCC",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1500x1000 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 1500x1000 with 1 Axes>,\n",
+       " <GeoAxes: title={'center': \"Coordinate Pairs from 'dynamic-selection' Meta Data\"}>)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -332,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -354,1478 +385,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.1 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "conflicting sizes for dimension 'gid': length 795 on 'gid' and length 723 on {'gid': 'x'}",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[28], line 9\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# geospatial analysis now\u001b[39;00m\n\u001b[1;32m      3\u001b[0m geo \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunc\u001b[39m\u001b[38;5;124m\"\u001b[39m: pvdeg\u001b[38;5;241m.\u001b[39mstandards\u001b[38;5;241m.\u001b[39mstandoff,\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweather_ds\u001b[39m\u001b[38;5;124m\"\u001b[39m: sub_weather,\n\u001b[1;32m      6\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmeta_df\u001b[39m\u001b[38;5;124m\"\u001b[39m: dynamic_points\u001b[38;5;241m.\u001b[39mmeta_data,\n\u001b[1;32m      7\u001b[0m }\n\u001b[0;32m----> 9\u001b[0m analysis_result \u001b[38;5;241m=\u001b[39m \u001b[43mpvdeg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgeospatial\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manalysis\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mgeo\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/dev/PVDegradationTools/pvdeg/geospatial.py:206\u001b[0m, in \u001b[0;36manalysis\u001b[0;34m(weather_ds, meta_df, func, template, **func_kwargs)\u001b[0m\n\u001b[1;32m    202\u001b[0m mindex_obj \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mMultiIndex\u001b[38;5;241m.\u001b[39mfrom_arrays(\n\u001b[1;32m    203\u001b[0m     [meta_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlatitude\u001b[39m\u001b[38;5;124m\"\u001b[39m], meta_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlongitude\u001b[39m\u001b[38;5;124m\"\u001b[39m]], names\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlatitude\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlongitude\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    204\u001b[0m )\n\u001b[1;32m    205\u001b[0m mindex_coords \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mCoordinates\u001b[38;5;241m.\u001b[39mfrom_pandas_multiindex(mindex_obj, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgid\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 206\u001b[0m stacked \u001b[38;5;241m=\u001b[39m \u001b[43mstacked\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massign_coords\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmindex_coords\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    208\u001b[0m stacked \u001b[38;5;241m=\u001b[39m stacked\u001b[38;5;241m.\u001b[39mdrop_duplicates(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgid\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    209\u001b[0m res \u001b[38;5;241m=\u001b[39m stacked\u001b[38;5;241m.\u001b[39munstack(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgid\u001b[39m\u001b[38;5;124m\"\u001b[39m)  \u001b[38;5;66;03m# , sparse=True\u001b[39;00m\n",
-      "File \u001b[0;32m~/.conda-envs/rpp/lib/python3.10/site-packages/xarray/core/common.py:644\u001b[0m, in \u001b[0;36mDataWithCoords.assign_coords\u001b[0;34m(self, coords, **coords_kwargs)\u001b[0m\n\u001b[1;32m    641\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    642\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_calc_assign_results(coords_combined)\n\u001b[0;32m--> 644\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresults\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    645\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n",
-      "File \u001b[0;32m~/.conda-envs/rpp/lib/python3.10/site-packages/xarray/core/coordinates.py:566\u001b[0m, in \u001b[0;36mCoordinates.update\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    560\u001b[0m \u001b[38;5;66;03m# special case for PandasMultiIndex: updating only its dimension coordinate\u001b[39;00m\n\u001b[1;32m    561\u001b[0m \u001b[38;5;66;03m# is still allowed but depreciated.\u001b[39;00m\n\u001b[1;32m    562\u001b[0m \u001b[38;5;66;03m# It is the only case where we need to actually drop coordinates here (multi-index levels)\u001b[39;00m\n\u001b[1;32m    563\u001b[0m \u001b[38;5;66;03m# TODO: remove when removing PandasMultiIndex's dimension coordinate.\u001b[39;00m\n\u001b[1;32m    564\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_drop_coords(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_names \u001b[38;5;241m-\u001b[39m coords_to_align\u001b[38;5;241m.\u001b[39m_names)\n\u001b[0;32m--> 566\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_coords\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexes\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.conda-envs/rpp/lib/python3.10/site-packages/xarray/core/coordinates.py:751\u001b[0m, in \u001b[0;36mDatasetCoordinates._update_coords\u001b[0;34m(self, coords, indexes)\u001b[0m\n\u001b[1;32m    748\u001b[0m variables\u001b[38;5;241m.\u001b[39mupdate(coords)\n\u001b[1;32m    750\u001b[0m \u001b[38;5;66;03m# check for inconsistent state *before* modifying anything in-place\u001b[39;00m\n\u001b[0;32m--> 751\u001b[0m dims \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_dimensions\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    752\u001b[0m new_coord_names \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(coords)\n\u001b[1;32m    753\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dim, size \u001b[38;5;129;01min\u001b[39;00m dims\u001b[38;5;241m.\u001b[39mitems():\n",
-      "File \u001b[0;32m~/.conda-envs/rpp/lib/python3.10/site-packages/xarray/core/variable.py:3008\u001b[0m, in \u001b[0;36mcalculate_dimensions\u001b[0;34m(variables)\u001b[0m\n\u001b[1;32m   3006\u001b[0m             last_used[dim] \u001b[38;5;241m=\u001b[39m k\n\u001b[1;32m   3007\u001b[0m         \u001b[38;5;28;01melif\u001b[39;00m dims[dim] \u001b[38;5;241m!=\u001b[39m size:\n\u001b[0;32m-> 3008\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   3009\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconflicting sizes for dimension \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdim\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3010\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlength \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msize\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m and length \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdims[dim]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlast_used\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3011\u001b[0m             )\n\u001b[1;32m   3012\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dims\n",
-      "\u001b[0;31mValueError\u001b[0m: conflicting sizes for dimension 'gid': length 795 on 'gid' and length 723 on {'gid': 'x'}"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# geospatial analysis now\n",
     "\n",
@@ -1849,483 +411,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "</symbol>\n",
-       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 61kB\n",
-       "Dimensions:    (latitude: 42, longitude: 60)\n",
-       "Coordinates:\n",
-       "  * latitude   (latitude) float32 168B 37.01 37.09 37.17 ... 41.49 41.65 41.89\n",
-       "  * longitude  (longitude) float32 240B -113.9 -113.6 -113.4 ... -102.7 -102.6\n",
-       "Data variables:\n",
-       "    x          (latitude, longitude) float64 20kB nan nan nan ... nan nan nan\n",
-       "    T98_0      (latitude, longitude) float64 20kB nan nan nan ... nan nan nan\n",
-       "    T98_inf    (latitude, longitude) float64 20kB nan nan nan ... nan nan nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-53f4de66-b4d1-4d82-b72f-8a15edd58bef' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-53f4de66-b4d1-4d82-b72f-8a15edd58bef' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 42</li><li><span class='xr-has-index'>longitude</span>: 60</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7e07188c-0cc2-40df-ba5f-5d8a42ee0348' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e07188c-0cc2-40df-ba5f-5d8a42ee0348' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>37.01 37.09 37.17 ... 41.65 41.89</div><input id='attrs-2a25b9aa-f9af-410c-b181-1e29c5ce2ecd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a25b9aa-f9af-410c-b181-1e29c5ce2ecd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ae8ff1f-e525-423e-8475-3fa015528682' class='xr-var-data-in' type='checkbox'><label for='data-2ae8ff1f-e525-423e-8475-3fa015528682' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([37.01, 37.09, 37.17, 37.33, 37.41, 37.49, 37.57, 37.65, 37.73, 37.81,\n",
-       "       37.89, 38.21, 38.45, 38.53, 38.69, 38.77, 38.85, 38.93, 39.09, 39.33,\n",
-       "       39.41, 39.49, 39.65, 39.73, 39.81, 39.89, 40.05, 40.13, 40.21, 40.29,\n",
-       "       40.37, 40.45, 40.61, 40.69, 40.85, 40.93, 41.09, 41.17, 41.25, 41.49,\n",
-       "       41.65, 41.89], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-113.9 -113.6 ... -102.7 -102.6</div><input id='attrs-c25276e5-48e6-4e9a-894b-ddc6a9929bdb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c25276e5-48e6-4e9a-894b-ddc6a9929bdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d806d3e7-eb57-4838-9f2a-d05665d32cfb' class='xr-var-data-in' type='checkbox'><label for='data-d806d3e7-eb57-4838-9f2a-d05665d32cfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-113.86, -113.62, -113.38, -113.3 , -112.98, -112.9 , -112.74, -112.42,\n",
-       "       -112.34, -112.26, -112.02, -111.94, -111.86, -111.7 , -111.62, -111.54,\n",
-       "       -111.3 , -110.98, -110.74, -110.66, -110.34, -110.26, -110.02, -109.94,\n",
-       "       -109.7 , -109.62, -109.46, -109.38, -109.06, -108.82, -108.42, -108.34,\n",
-       "       -108.02, -107.78, -107.62, -107.54, -107.38, -107.3 , -107.06, -106.9 ,\n",
-       "       -106.82, -106.66, -106.5 , -106.42, -106.34, -106.26, -106.18, -106.02,\n",
-       "       -105.7 , -105.62, -105.54, -105.46, -105.22, -104.9 , -104.02, -103.94,\n",
-       "       -103.46, -103.3 , -102.74, -102.58], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-be3f1866-7dc5-40fd-9909-32a2da27fb93' class='xr-section-summary-in' type='checkbox'  checked><label for='section-be3f1866-7dc5-40fd-9909-32a2da27fb93' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-ea67e225-d3a9-4d2e-a2ce-89447c13f02a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea67e225-d3a9-4d2e-a2ce-89447c13f02a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6447aa4-d7ef-4689-8910-8d3870b8cc2b' class='xr-var-data-in' type='checkbox'><label for='data-b6447aa4-d7ef-4689-8910-8d3870b8cc2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[       nan,        nan,        nan, ...,        nan,        nan,\n",
-       "               nan],\n",
-       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
-       "               nan],\n",
-       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
-       "               nan],\n",
-       "       ...,\n",
-       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
-       "               nan],\n",
-       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
-       "               nan],\n",
-       "       [       nan, 1.64025639,        nan, ...,        nan,        nan,\n",
-       "               nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T98_0</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-5213ffe9-318d-476f-a5ff-f977a67cdd8b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5213ffe9-318d-476f-a5ff-f977a67cdd8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-312d1da7-4d29-49db-87e3-c1abf245d46a' class='xr-var-data-in' type='checkbox'><label for='data-312d1da7-4d29-49db-87e3-c1abf245d46a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[        nan,         nan,         nan, ...,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan,         nan,         nan, ...,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan,         nan,         nan, ...,         nan,\n",
-       "                nan,         nan],\n",
-       "       ...,\n",
-       "       [        nan,         nan,         nan, ...,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan,         nan,         nan, ...,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan, 75.58484362,         nan, ...,         nan,\n",
-       "                nan,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T98_inf</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-5b5f8b14-437b-4cac-b350-cc3dc5436749' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b5f8b14-437b-4cac-b350-cc3dc5436749' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e1326c9-e614-4bae-a9ab-b74a4c55ed89' class='xr-var-data-in' type='checkbox'><label for='data-5e1326c9-e614-4bae-a9ab-b74a4c55ed89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[        nan,         nan,         nan, ...,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan,         nan,         nan, ...,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan,         nan,         nan, ...,         nan,\n",
-       "                nan,         nan],\n",
-       "       ...,\n",
-       "       [        nan,         nan,         nan, ...,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan,         nan,         nan, ...,         nan,\n",
-       "                nan,         nan],\n",
-       "       [        nan, 50.54351198,         nan, ...,         nan,\n",
-       "                nan,         nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e8081ad6-56ba-47fa-9885-1a4072384026' class='xr-section-summary-in' type='checkbox'  ><label for='section-e8081ad6-56ba-47fa-9885-1a4072384026' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c1d919cd-d11d-46de-8220-f1657c1d3822' class='xr-index-data-in' type='checkbox'/><label for='index-c1d919cd-d11d-46de-8220-f1657c1d3822' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  37.0099983215332,  37.09000015258789,  37.16999816894531,\n",
-       "        37.33000183105469,  37.40999984741211,   37.4900016784668,\n",
-       "        37.56999969482422, 37.650001525878906,  37.72999954223633,\n",
-       "       37.810001373291016,  37.88999938964844, 38.209999084472656,\n",
-       "        38.45000076293945, 38.529998779296875, 38.689998626708984,\n",
-       "        38.77000045776367, 38.849998474121094,  38.93000030517578,\n",
-       "        39.09000015258789,  39.33000183105469,  39.40999984741211,\n",
-       "         39.4900016784668, 39.650001525878906,  39.72999954223633,\n",
-       "       39.810001373291016,  39.88999938964844,  40.04999923706055,\n",
-       "       40.130001068115234, 40.209999084472656, 40.290000915527344,\n",
-       "       40.369998931884766,  40.45000076293945,  40.61000061035156,\n",
-       "       40.689998626708984, 40.849998474121094,  40.93000030517578,\n",
-       "        41.09000015258789,  41.16999816894531,              41.25,\n",
-       "         41.4900016784668, 41.650001525878906,  41.88999938964844],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db68f082-cad6-46db-8913-5c45d3785a7c' class='xr-index-data-in' type='checkbox'/><label for='index-db68f082-cad6-46db-8913-5c45d3785a7c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-113.86000061035156, -113.62000274658203, -113.37999725341797,\n",
-       "       -113.30000305175781,  -112.9800033569336,  -112.9000015258789,\n",
-       "       -112.73999786376953, -112.41999816894531, -112.33999633789062,\n",
-       "       -112.26000213623047,  -112.0199966430664, -111.94000244140625,\n",
-       "       -111.86000061035156, -111.69999694824219, -111.62000274658203,\n",
-       "       -111.54000091552734, -111.30000305175781,  -110.9800033569336,\n",
-       "       -110.73999786376953, -110.66000366210938, -110.33999633789062,\n",
-       "       -110.26000213623047,  -110.0199966430664, -109.94000244140625,\n",
-       "       -109.69999694824219, -109.62000274658203, -109.45999908447266,\n",
-       "       -109.37999725341797, -109.05999755859375, -108.81999969482422,\n",
-       "       -108.41999816894531, -108.33999633789062,  -108.0199966430664,\n",
-       "       -107.77999877929688, -107.62000274658203, -107.54000091552734,\n",
-       "       -107.37999725341797, -107.30000305175781, -107.05999755859375,\n",
-       "        -106.9000015258789, -106.81999969482422, -106.66000366210938,\n",
-       "                    -106.5, -106.41999816894531, -106.33999633789062,\n",
-       "       -106.26000213623047, -106.18000030517578,  -106.0199966430664,\n",
-       "       -105.69999694824219, -105.62000274658203, -105.54000091552734,\n",
-       "       -105.45999908447266, -105.22000122070312,  -104.9000015258789,\n",
-       "        -104.0199966430664, -103.94000244140625, -103.45999908447266,\n",
-       "       -103.30000305175781, -102.73999786376953, -102.58000183105469],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;longitude&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f9901271-1c5a-4fd5-a87d-4db961535b0f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f9901271-1c5a-4fd5-a87d-4db961535b0f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 61kB\n",
-       "Dimensions:    (latitude: 42, longitude: 60)\n",
-       "Coordinates:\n",
-       "  * latitude   (latitude) float32 168B 37.01 37.09 37.17 ... 41.49 41.65 41.89\n",
-       "  * longitude  (longitude) float32 240B -113.9 -113.6 -113.4 ... -102.7 -102.6\n",
-       "Data variables:\n",
-       "    x          (latitude, longitude) float64 20kB nan nan nan ... nan nan nan\n",
-       "    T98_0      (latitude, longitude) float64 20kB nan nan nan ... nan nan nan\n",
-       "    T98_inf    (latitude, longitude) float64 20kB nan nan nan ... nan nan nan"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "analysis_result"
    ]
@@ -2341,31 +429,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 640x480 with 2 Axes>,\n",
-       " <GeoAxes: xlabel='longitude', ylabel='latitude'>)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pvdeg.geospatial.plot_USA(analysis_result['x'])"
    ]
@@ -2381,20 +447,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pvdeg.geospatial.plot_sparse_analysis(analysis_result, data_var='x', method='linear')"
    ]
diff --git a/tutorials_and_tools/tutorials_and_tools/Scenario - Single Location.ipynb b/tutorials_and_tools/tutorials_and_tools/Scenario - Single Location.ipynb
index 65cf9f3..64de766 100644
--- a/tutorials_and_tools/tutorials_and_tools/Scenario - Single Location.ipynb	
+++ b/tutorials_and_tools/tutorials_and_tools/Scenario - Single Location.ipynb	
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -44,8 +44,8 @@
      "output_type": "stream",
      "text": [
       "Working on a  Linux 4.18.0-477.10.1.el8_8.x86_64\n",
-      "Python version  3.10.9 | packaged by conda-forge | (main, Feb  2 2023, 20:20:04) [GCC 11.3.0]\n",
-      "pvdeg version  0.3.1.dev220+gaf1083c.d20240608\n"
+      "Python version  3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0]\n",
+      "pvdeg version  0.3.1.dev223+g03f3e6a.d20240708\n"
      ]
     }
    ],
@@ -73,11 +73,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "simple_scenario = pvdeg.scenario.Scenario(\n",
+    "simple_scenario = pvdeg.Scenario(\n",
     "    name=\"Point Minimum Standoff\",\n",
     "    email='user@mail.com',\n",
     "    api_key='DEMO_KEY'\n",
@@ -96,21 +96,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Column \"relative_humidity\" not found in DataFrame. Calculating...\n"
+      "                                                                     \r"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:212: UserWarning: Gids Added - [1060699]\n",
+      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:230: UserWarning: Gids Added - [1060699]\n",
       "  warnings.warn(message, UserWarning)\n"
      ]
     }
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -412,7 +412,7 @@
        "[8760 rows x 15 columns]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -443,15 +443,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:375: UserWarning: standoff added to pipeline as \n",
-      " {'job': <function standoff at 0x7f78403acf70>, 'params': {}}\n",
+      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:396: UserWarning: standoff added to pipeline as \n",
+      " {'job': <function standoff at 0x7f63ace76b90>, 'params': {}}\n",
       "  warnings.warn(message, UserWarning)\n"
      ]
     }
@@ -474,15 +474,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:375: UserWarning: solder_fatigue added to pipeline as \n",
-      " {'job': <function solder_fatigue at 0x7f78403aca60>, 'params': {'wind_factor': 0.33}}\n",
+      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:396: UserWarning: solder_fatigue added to pipeline as \n",
+      " {'job': <function solder_fatigue at 0x7f63ace76680>, 'params': {'wind_factor': 0.33}}\n",
       "  warnings.warn(message, UserWarning)\n"
      ]
     }
@@ -511,15 +511,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:375: UserWarning: standoff added to pipeline as \n",
-      " {'job': <function standoff at 0x7f78403acf70>, 'params': {'weather_df':                            Year  Month  Day  Hour  Minute  dni  dhi  ghi  \\\n",
+      "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:396: UserWarning: standoff added to pipeline as \n",
+      " {'job': <function standoff at 0x7f63ace76b90>, 'params': {'weather_df':                            Year  Month  Day  Hour  Minute  dni  dhi  ghi  \\\n",
       "1999-01-01 00:30:00-07:00  1999      1    1     0      30  0.0  0.0  0.0   \n",
       "1999-01-01 01:30:00-07:00  1999      1    1     1      30  0.0  0.0  0.0   \n",
       "1999-01-01 02:30:00-07:00  1999      1    1     2      30  0.0  0.0  0.0   \n",
@@ -577,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -586,34 +586,8 @@
      "text": [
       "Name : Point Minimum Standoff\n",
       "Pipeline : \n",
-      "                               BLHZH.job                                    WCBOQ.job  WCBOQ.params.wind_factor                              PUKRE.job                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     PUKRE.params.weather_df PUKRE.params.meta.Source PUKRE.params.meta.Location ID PUKRE.params.meta.City PUKRE.params.meta.State PUKRE.params.meta.Country PUKRE.params.meta.Clearsky DHI Units PUKRE.params.meta.Clearsky DNI Units PUKRE.params.meta.Clearsky GHI Units PUKRE.params.meta.Dew Point Units PUKRE.params.meta.DHI Units PUKRE.params.meta.DNI Units PUKRE.params.meta.GHI Units PUKRE.params.meta.Solar Zenith Angle Units PUKRE.params.meta.Temperature Units PUKRE.params.meta.Pressure Units PUKRE.params.meta.Relative Humidity Units PUKRE.params.meta.Precipitable Water Units PUKRE.params.meta.Wind Direction Units PUKRE.params.meta.Wind Speed Units PUKRE.params.meta.Cloud Type -15 PUKRE.params.meta.Cloud Type 0 PUKRE.params.meta.Cloud Type 1 PUKRE.params.meta.Cloud Type 2 PUKRE.params.meta.Cloud Type 3 PUKRE.params.meta.Cloud Type 4 PUKRE.params.meta.Cloud Type 5 PUKRE.params.meta.Cloud Type 6 PUKRE.params.meta.Cloud Type 7 PUKRE.params.meta.Cloud Type 8 PUKRE.params.meta.Cloud Type 9 PUKRE.params.meta.Cloud Type 10 PUKRE.params.meta.Cloud Type 11 PUKRE.params.meta.Cloud Type 12 PUKRE.params.meta.Fill Flag 0 PUKRE.params.meta.Fill Flag 1 PUKRE.params.meta.Fill Flag 2 PUKRE.params.meta.Fill Flag 3 PUKRE.params.meta.Fill Flag 4 PUKRE.params.meta.Fill Flag 5 PUKRE.params.meta.Surface Albedo Units PUKRE.params.meta.Version  PUKRE.params.meta.latitude  PUKRE.params.meta.longitude  PUKRE.params.meta.altitude  PUKRE.params.meta.tz                              MUDMB.job                                    FOYEQ.job  FOYEQ.params.wind_factor                              UJIKB.job                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     UJIKB.params.weather_df UJIKB.params.meta.Source UJIKB.params.meta.Location ID UJIKB.params.meta.City UJIKB.params.meta.State UJIKB.params.meta.Country UJIKB.params.meta.Clearsky DHI Units UJIKB.params.meta.Clearsky DNI Units UJIKB.params.meta.Clearsky GHI Units UJIKB.params.meta.Dew Point Units UJIKB.params.meta.DHI Units UJIKB.params.meta.DNI Units UJIKB.params.meta.GHI Units UJIKB.params.meta.Solar Zenith Angle Units UJIKB.params.meta.Temperature Units UJIKB.params.meta.Pressure Units UJIKB.params.meta.Relative Humidity Units UJIKB.params.meta.Precipitable Water Units UJIKB.params.meta.Wind Direction Units UJIKB.params.meta.Wind Speed Units UJIKB.params.meta.Cloud Type -15 UJIKB.params.meta.Cloud Type 0 UJIKB.params.meta.Cloud Type 1 UJIKB.params.meta.Cloud Type 2 UJIKB.params.meta.Cloud Type 3 UJIKB.params.meta.Cloud Type 4 UJIKB.params.meta.Cloud Type 5 UJIKB.params.meta.Cloud Type 6 UJIKB.params.meta.Cloud Type 7 UJIKB.params.meta.Cloud Type 8 UJIKB.params.meta.Cloud Type 9 UJIKB.params.meta.Cloud Type 10 UJIKB.params.meta.Cloud Type 11 UJIKB.params.meta.Cloud Type 12 UJIKB.params.meta.Fill Flag 0 UJIKB.params.meta.Fill Flag 1 UJIKB.params.meta.Fill Flag 2 UJIKB.params.meta.Fill Flag 3 UJIKB.params.meta.Fill Flag 4 UJIKB.params.meta.Fill Flag 5 UJIKB.params.meta.Surface Albedo Units UJIKB.params.meta.Version  UJIKB.params.meta.latitude  UJIKB.params.meta.longitude  UJIKB.params.meta.altitude  UJIKB.params.meta.tz\n",
-      "0  <function standoff at 0x7f78403acf70>  <function solder_fatigue at 0x7f78403aca60>                      0.33  <function standoff at 0x7f78403acf70>                             Year  Month  Day  Hour  Minute  dni  dhi  ghi  \\\n",
-      "1999-01-01 00:30:00-07:00  1999      1    1     0      30  0.0  0.0  0.0   \n",
-      "1999-01-01 01:30:00-07:00  1999      1    1     1      30  0.0  0.0  0.0   \n",
-      "1999-01-01 02:30:00-07:00  1999      1    1     2      30  0.0  0.0  0.0   \n",
-      "1999-01-01 03:30:00-07:00  1999      1    1     3      30  0.0  0.0  0.0   \n",
-      "1999-01-01 04:30:00-07:00  1999      1    1     4      30  0.0  0.0  0.0   \n",
-      "...                         ...    ...  ...   ...     ...  ...  ...  ...   \n",
-      "1999-12-31 19:30:00-07:00  1999     12   31    19      30  0.0  0.0  0.0   \n",
-      "1999-12-31 20:30:00-07:00  1999     12   31    20      30  0.0  0.0  0.0   \n",
-      "1999-12-31 21:30:00-07:00  1999     12   31    21      30  0.0  0.0  0.0   \n",
-      "1999-12-31 22:30:00-07:00  1999     12   31    22      30  0.0  0.0  0.0   \n",
-      "1999-12-31 23:30:00-07:00  1999     12   31    23      30  0.0  0.0  0.0   \n",
-      "\n",
-      "                           temp_air  dew_point  wind_speed  relative_humidity  \n",
-      "1999-01-01 00:30:00-07:00       0.0       -5.0         1.8              79.39  \n",
-      "1999-01-01 01:30:00-07:00       0.0       -4.0         1.7              80.84  \n",
-      "1999-01-01 02:30:00-07:00       0.0       -4.0         1.5              82.98  \n",
-      "1999-01-01 03:30:00-07:00       0.0       -4.0         1.3              85.01  \n",
-      "1999-01-01 04:30:00-07:00       0.0       -4.0         1.3              85.81  \n",
-      "...                             ...        ...         ...                ...  \n",
-      "1999-12-31 19:30:00-07:00       0.0       -3.0         0.9              83.63  \n",
-      "1999-12-31 20:30:00-07:00       0.0       -3.0         1.2              86.82  \n",
-      "1999-12-31 21:30:00-07:00       0.0       -4.0         1.6              83.78  \n",
-      "1999-12-31 22:30:00-07:00       0.0       -4.0         1.7              81.22  \n",
-      "1999-12-31 23:30:00-07:00       0.0       -5.0         1.8              79.43  \n",
-      "\n",
-      "[8760 rows x 12 columns]                    NSRDB                        145809                      -                       -                         -                                 w/m2                                 w/m2                                 w/m2                                 c                        w/m2                        w/m2                        w/m2                                     Degree                                   c                             mbar                                         %                                         cm                                Degrees                                m/s                              N/A                          Clear                 Probably Clear                            Fog                          Water             Super-Cooled Water                          Mixed                     Opaque Ice                         Cirrus                    Overlapping                   Overshooting                         Unknown                            Dust                           Smoke                           N/A                 Missing Image                Low Irradiance              Exceeds Clearsky      Missing CLoud Properties            Rayleigh Violation                                    N/A                     3.0.6                       39.73                      -105.18                        1820                    -7  <function standoff at 0x7f78403acf70>  <function solder_fatigue at 0x7f78403aca60>                      0.33  <function standoff at 0x7f78403acf70>                             Year  Month  Day  Hour  Minute  dni  dhi  ghi  \\\n",
+      "                               KWRFI.job                                    MRFGY.job  MRFGY.params.wind_factor                              CXKHX.job                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     CXKHX.params.weather_df CXKHX.params.meta.Source CXKHX.params.meta.Location ID CXKHX.params.meta.City CXKHX.params.meta.State CXKHX.params.meta.Country CXKHX.params.meta.Clearsky DHI Units CXKHX.params.meta.Clearsky DNI Units CXKHX.params.meta.Clearsky GHI Units CXKHX.params.meta.Dew Point Units CXKHX.params.meta.DHI Units CXKHX.params.meta.DNI Units CXKHX.params.meta.GHI Units CXKHX.params.meta.Solar Zenith Angle Units CXKHX.params.meta.Temperature Units CXKHX.params.meta.Pressure Units CXKHX.params.meta.Relative Humidity Units CXKHX.params.meta.Precipitable Water Units CXKHX.params.meta.Wind Direction Units CXKHX.params.meta.Wind Speed Units CXKHX.params.meta.Cloud Type -15 CXKHX.params.meta.Cloud Type 0 CXKHX.params.meta.Cloud Type 1 CXKHX.params.meta.Cloud Type 2 CXKHX.params.meta.Cloud Type 3 CXKHX.params.meta.Cloud Type 4 CXKHX.params.meta.Cloud Type 5 CXKHX.params.meta.Cloud Type 6 CXKHX.params.meta.Cloud Type 7 CXKHX.params.meta.Cloud Type 8 CXKHX.params.meta.Cloud Type 9 CXKHX.params.meta.Cloud Type 10 CXKHX.params.meta.Cloud Type 11 CXKHX.params.meta.Cloud Type 12 CXKHX.params.meta.Fill Flag 0 CXKHX.params.meta.Fill Flag 1 CXKHX.params.meta.Fill Flag 2 CXKHX.params.meta.Fill Flag 3 CXKHX.params.meta.Fill Flag 4 CXKHX.params.meta.Fill Flag 5 CXKHX.params.meta.Surface Albedo Units CXKHX.params.meta.Version  CXKHX.params.meta.latitude  CXKHX.params.meta.longitude  CXKHX.params.meta.altitude  CXKHX.params.meta.tz\n",
+      "0  <function standoff at 0x7f63ace76b90>  <function solder_fatigue at 0x7f63ace76680>                      0.33  <function standoff at 0x7f63ace76b90>                             Year  Month  Day  Hour  Minute  dni  dhi  ghi  \\\n",
       "1999-01-01 00:30:00-07:00  1999      1    1     0      30  0.0  0.0  0.0   \n",
       "1999-01-01 01:30:00-07:00  1999      1    1     1      30  0.0  0.0  0.0   \n",
       "1999-01-01 02:30:00-07:00  1999      1    1     2      30  0.0  0.0  0.0   \n",
@@ -708,7 +682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -728,57 +702,13 @@
        "            <div>\n",
        "                <h3>Pipeline</h3>\n",
        "                <div>\n",
-       "            <div id=\"BLHZH\" onclick=\"toggleVisibility('pipeline_BLHZH')\" style=\"cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;\">\n",
-       "                <h4 style=\"font-family: monospace; margin: 0;\">\n",
-       "                    <span id=\"arrow_pipeline_BLHZH\" style=\"color: #b676c2;\">►</span> \n",
-       "                    standoff, <span style=\"color: #b676c2;\">#BLHZH</span>\n",
-       "                </h4>\n",
-       "            </div>\n",
-       "            <div id=\"pipeline_BLHZH\" style=\"display:none; margin-left: 20px; padding: 5px; background-color: #f0f0f0; color: #000;\">\n",
-       "                <p>Job: standoff</p>\n",
-       "                <p>Parameters:</p>\n",
-       "                <div style=\"margin-left: 20px;\">\n",
-       "                    <pre>{}</pre>\n",
-       "                </div>\n",
-       "            </div>\n",
-       "            \n",
-       "            <div id=\"WCBOQ\" onclick=\"toggleVisibility('pipeline_WCBOQ')\" style=\"cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;\">\n",
-       "                <h4 style=\"font-family: monospace; margin: 0;\">\n",
-       "                    <span id=\"arrow_pipeline_WCBOQ\" style=\"color: #b676c2;\">►</span> \n",
-       "                    solder_fatigue, <span style=\"color: #b676c2;\">#WCBOQ</span>\n",
-       "                </h4>\n",
-       "            </div>\n",
-       "            <div id=\"pipeline_WCBOQ\" style=\"display:none; margin-left: 20px; padding: 5px; background-color: #f0f0f0; color: #000;\">\n",
-       "                <p>Job: solder_fatigue</p>\n",
-       "                <p>Parameters:</p>\n",
-       "                <div style=\"margin-left: 20px;\">\n",
-       "                    <pre>{\n",
-       "  \"wind_factor\": 0.33\n",
-       "}</pre>\n",
-       "                </div>\n",
-       "            </div>\n",
-       "            \n",
-       "            <div id=\"PUKRE\" onclick=\"toggleVisibility('pipeline_PUKRE')\" style=\"cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;\">\n",
-       "                <h4 style=\"font-family: monospace; margin: 0;\">\n",
-       "                    <span id=\"arrow_pipeline_PUKRE\" style=\"color: #b676c2;\">►</span> \n",
-       "                    standoff, <span style=\"color: #b676c2;\">#PUKRE</span>\n",
-       "                </h4>\n",
-       "            </div>\n",
-       "            <div id=\"pipeline_PUKRE\" style=\"display:none; margin-left: 20px; padding: 5px; background-color: #f0f0f0; color: #000;\">\n",
-       "                <p>Job: standoff</p>\n",
-       "                <p>Parameters:</p>\n",
-       "                <div style=\"margin-left: 20px;\">\n",
-       "                    <pre>Unserializable data type</pre>\n",
-       "                </div>\n",
-       "            </div>\n",
-       "            \n",
-       "            <div id=\"MUDMB\" onclick=\"toggleVisibility('pipeline_MUDMB')\" style=\"cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;\">\n",
+       "            <div id=\"KWRFI\" onclick=\"toggleVisibility('pipeline_KWRFI')\" style=\"cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;\">\n",
        "                <h4 style=\"font-family: monospace; margin: 0;\">\n",
-       "                    <span id=\"arrow_pipeline_MUDMB\" style=\"color: #b676c2;\">►</span> \n",
-       "                    standoff, <span style=\"color: #b676c2;\">#MUDMB</span>\n",
+       "                    <span id=\"arrow_pipeline_KWRFI\" style=\"color: #b676c2;\">►</span>\n",
+       "                    standoff, <span style=\"color: #b676c2;\">#KWRFI</span>\n",
        "                </h4>\n",
        "            </div>\n",
-       "            <div id=\"pipeline_MUDMB\" style=\"display:none; margin-left: 20px; padding: 5px; background-color: #f0f0f0; color: #000;\">\n",
+       "            <div id=\"pipeline_KWRFI\" style=\"display:none; margin-left: 20px; padding: 5px; background-color: #f0f0f0; color: #000;\">\n",
        "                <p>Job: standoff</p>\n",
        "                <p>Parameters:</p>\n",
        "                <div style=\"margin-left: 20px;\">\n",
@@ -786,13 +716,13 @@
        "                </div>\n",
        "            </div>\n",
        "            \n",
-       "            <div id=\"FOYEQ\" onclick=\"toggleVisibility('pipeline_FOYEQ')\" style=\"cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;\">\n",
+       "            <div id=\"MRFGY\" onclick=\"toggleVisibility('pipeline_MRFGY')\" style=\"cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;\">\n",
        "                <h4 style=\"font-family: monospace; margin: 0;\">\n",
-       "                    <span id=\"arrow_pipeline_FOYEQ\" style=\"color: #b676c2;\">►</span> \n",
-       "                    solder_fatigue, <span style=\"color: #b676c2;\">#FOYEQ</span>\n",
+       "                    <span id=\"arrow_pipeline_MRFGY\" style=\"color: #b676c2;\">►</span>\n",
+       "                    solder_fatigue, <span style=\"color: #b676c2;\">#MRFGY</span>\n",
        "                </h4>\n",
        "            </div>\n",
-       "            <div id=\"pipeline_FOYEQ\" style=\"display:none; margin-left: 20px; padding: 5px; background-color: #f0f0f0; color: #000;\">\n",
+       "            <div id=\"pipeline_MRFGY\" style=\"display:none; margin-left: 20px; padding: 5px; background-color: #f0f0f0; color: #000;\">\n",
        "                <p>Job: solder_fatigue</p>\n",
        "                <p>Parameters:</p>\n",
        "                <div style=\"margin-left: 20px;\">\n",
@@ -802,13 +732,13 @@
        "                </div>\n",
        "            </div>\n",
        "            \n",
-       "            <div id=\"UJIKB\" onclick=\"toggleVisibility('pipeline_UJIKB')\" style=\"cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;\">\n",
+       "            <div id=\"CXKHX\" onclick=\"toggleVisibility('pipeline_CXKHX')\" style=\"cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;\">\n",
        "                <h4 style=\"font-family: monospace; margin: 0;\">\n",
-       "                    <span id=\"arrow_pipeline_UJIKB\" style=\"color: #b676c2;\">►</span> \n",
-       "                    standoff, <span style=\"color: #b676c2;\">#UJIKB</span>\n",
+       "                    <span id=\"arrow_pipeline_CXKHX\" style=\"color: #b676c2;\">►</span>\n",
+       "                    standoff, <span style=\"color: #b676c2;\">#CXKHX</span>\n",
        "                </h4>\n",
        "            </div>\n",
-       "            <div id=\"pipeline_UJIKB\" style=\"display:none; margin-left: 20px; padding: 5px; background-color: #f0f0f0; color: #000;\">\n",
+       "            <div id=\"pipeline_CXKHX\" style=\"display:none; margin-left: 20px; padding: 5px; background-color: #f0f0f0; color: #000;\">\n",
        "                <p>Job: standoff</p>\n",
        "                <p>Parameters:</p>\n",
        "                <div style=\"margin-left: 20px;\">\n",
@@ -826,7 +756,7 @@
        "                \n",
        "            <div id=\"weather_data\" onclick=\"toggleVisibility('content_weather_data')\" style=\"cursor: pointer; background-color: #000000; color: #FFFFFF; padding: 5px; border-radius: 3px; margin-bottom: 1px;\">\n",
        "                <h4 style=\"font-family: monospace; margin: 0;\">\n",
-       "                    <span id=\"arrow_content_weather_data\" style=\"color: #E6E6FA;\">►</span> \n",
+       "                    <span id=\"arrow_content_weather_data\" style=\"color: #E6E6FA;\">►</span>\n",
        "                    Weather Data\n",
        "                </h4>\n",
        "            </div>\n",
@@ -1098,25 +1028,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 25.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 25.8 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 25.8 was used.\n",
       "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
       "The array tilt angle was not provided, therefore the latitude tilt of 25.8 was used.\n",
@@ -1140,183 +1058,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "BLHZH             x     T98_0    T98_inf\n",
+      "KWRFI             x     T98_0    T98_inf\n",
       "0  1.20347  74...\n",
-      "WCBOQ                                    WCBOQ\n",
+      "MRFGY                                    MRFGY\n",
       "0  4.562892\n",
-      "PUKRE              x      T98_0    T98_inf\n",
-      "0  2.008636 ...\n",
-      "MUDMB             x     T98_0    T98_inf\n",
-      "0  1.20347  74...\n",
-      "FOYEQ                                    FOYEQ\n",
-      "0  4.562892\n",
-      "UJIKB              x      T98_0    T98_inf\n",
+      "CXKHX              x      T98_0    T98_inf\n",
       "0  2.008636 ...\n",
       "dtype: object\n",
       "We can't see out data in here so we need to do another step\n",
       "\n",
-      "this is the list of all available frames in results : Index(['BLHZH', 'WCBOQ', 'PUKRE', 'MUDMB', 'FOYEQ', 'UJIKB'], dtype='object')\n",
+      "this is the list of all available frames in results : Index(['KWRFI', 'MRFGY', 'CXKHX'], dtype='object')\n",
       "\n",
-      "BLHZH\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>T98_0</th>\n",
-       "      <th>T98_inf</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.20347</td>\n",
-       "      <td>74.00243</td>\n",
-       "      <td>50.322188</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         x     T98_0    T98_inf\n",
-       "0  1.20347  74.00243  50.322188"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WCBOQ\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>WCBOQ</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4.562892</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      WCBOQ\n",
-       "0  4.562892"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PUKRE\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>T98_0</th>\n",
-       "      <th>T98_inf</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2.008636</td>\n",
-       "      <td>77.038644</td>\n",
-       "      <td>50.561112</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          x      T98_0    T98_inf\n",
-       "0  2.008636  77.038644  50.561112"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MUDMB\n"
+      "KWRFI\n"
      ]
     },
     {
@@ -1368,7 +1128,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "FOYEQ\n"
+      "MRFGY\n"
      ]
     },
     {
@@ -1392,7 +1152,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>FOYEQ</th>\n",
+       "      <th>MRFGY</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1405,7 +1165,7 @@
        "</div>"
       ],
       "text/plain": [
-       "      FOYEQ\n",
+       "      MRFGY\n",
        "0  4.562892"
       ]
      },
@@ -1416,7 +1176,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "UJIKB\n"
+      "CXKHX\n"
      ]
     },
     {
@@ -1489,7 +1249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1499,9 +1259,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "rd-tools",
+   "display_name": "rpp",
    "language": "python",
-   "name": "python3"
+   "name": "rpp"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1513,9 +1273,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/tutorials_and_tools/tutorials_and_tools/Scenario - Temperatrure.ipynb b/tutorials_and_tools/tutorials_and_tools/Scenario - Temperatrure.ipynb
index 71737e5..3816328 100644
--- a/tutorials_and_tools/tutorials_and_tools/Scenario - Temperatrure.ipynb	
+++ b/tutorials_and_tools/tutorials_and_tools/Scenario - Temperatrure.ipynb	
@@ -81,7 +81,7 @@
     }
    ],
    "source": [
-    "scene_temp = pvdeg.scenario.Scenario(\n",
+    "scene_temp = pvdeg.Scenario(\n",
     "    name= 'temperature and degradation',\n",
     "    api_key='DEMO_KEY',\n",
     "    email='user@mail.com',\n",
@@ -2361,9 +2361,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "rd-tools",
+   "display_name": "rpp",
    "language": "python",
-   "name": "python3"
+   "name": "rpp"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2379,5 +2379,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb b/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb
index a88a81a..7112684 100644
--- a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb	
+++ b/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb	
@@ -80,9 +80,9 @@
       "Working on a  Windows 10\n",
       "Python version  3.9.18 (main, Sep 11 2023, 14:09:26) [MSC v.1916 64 bit (AMD64)]\n",
       "Pandas version  2.1.4\n",
-      "pvdeg version  0.2.0+11.g7f12df3.dirty\n",
+      "pvdeg version  0.4.3.dev14+g08d739a\n",
       "dask version 2023.11.0\n",
-      "c:\\users\\mkempe\\documents\\github\\pvdegradationtools\\pvdeg\\data\n"
+      "C:\\Users\\mkempe\\Documents\\GitHub\\new\\PVDegradationTools\\pvdeg\\data\n"
      ]
     }
    ],
@@ -112,14 +112,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'Source': 'NSRDB', 'Location ID': 145809.0, 'City': '-', 'State': '-', 'Country': '-', 'Clearsky DHI Units': 'w/m2', 'Clearsky DNI Units': 'w/m2', 'Clearsky GHI Units': 'w/m2', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Solar Zenith Angle Units': 'Degree', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Relative Humidity Units': '%', 'Precipitable Water Units': 'cm', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Cloud Type -15': 'N/A', 'Cloud Type 0': 'Clear', 'Cloud Type 1': 'Probably Clear', 'Cloud Type 2': 'Fog', 'Cloud Type 3': 'Water', 'Cloud Type 4': 'Super-Cooled Water', 'Cloud Type 5': 'Mixed', 'Cloud Type 6': 'Opaque Ice', 'Cloud Type 7': 'Cirrus', 'Cloud Type 8': 'Overlapping', 'Cloud Type 9': 'Overshooting', 'Cloud Type 10': 'Unknown', 'Cloud Type 11': 'Dust', 'Cloud Type 12': 'Smoke', 'Fill Flag 0': 'N/A', 'Fill Flag 1': 'Missing Image', 'Fill Flag 2': 'Low Irradiance', 'Fill Flag 3': 'Exceeds Clearsky', 'Fill Flag 4': 'Missing CLoud Properties', 'Fill Flag 5': 'Rayleigh Violation', 'Surface Albedo Units': 'N/A', 'Version': '3.0.6', 'latitude': 39.73, 'longitude': -105.18, 'tz': -7.0, 'altitude': 1820.0}\n"
+      "{'Source': 'NSRDB', 'Location ID': 145809.0, 'City': 'West Pleasant View', 'State': 'Colorado', 'Country': 'United States', 'Clearsky DHI Units': 'w/m2', 'Clearsky DNI Units': 'w/m2', 'Clearsky GHI Units': 'w/m2', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Solar Zenith Angle Units': 'Degree', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Relative Humidity Units': '%', 'Precipitable Water Units': 'cm', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Cloud Type -15': 'N/A', 'Cloud Type 0': 'Clear', 'Cloud Type 1': 'Probably Clear', 'Cloud Type 2': 'Fog', 'Cloud Type 3': 'Water', 'Cloud Type 4': 'Super-Cooled Water', 'Cloud Type 5': 'Mixed', 'Cloud Type 6': 'Opaque Ice', 'Cloud Type 7': 'Cirrus', 'Cloud Type 8': 'Overlapping', 'Cloud Type 9': 'Overshooting', 'Cloud Type 10': 'Unknown', 'Cloud Type 11': 'Dust', 'Cloud Type 12': 'Smoke', 'Fill Flag 0': 'N/A', 'Fill Flag 1': 'Missing Image', 'Fill Flag 2': 'Low Irradiance', 'Fill Flag 3': 'Exceeds Clearsky', 'Fill Flag 4': 'Missing CLoud Properties', 'Fill Flag 5': 'Rayleigh Violation', 'Surface Albedo Units': 'N/A', 'Version': '3.0.6', 'latitude': 39.73, 'longitude': -105.18, 'tz': -7.0, 'altitude': 1820.0, 'ISO3166-2-lvl4': 'US-CO', 'Street': 'West 8th Avenue', 'County': 'Jefferson County', 'Zipcode': '80419', 'Country Code': 'US'}\n"
      ]
     }
    ],
@@ -132,14 +132,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'latitude': -43.52646, 'longitude': 172.62165, 'altitude': 4.0, 'wind_height': 10, 'Source': 'PVGIS'}\n"
+      "{'Source': 'NSRDB', 'Location ID': '79584', 'City': 'Phoenix', 'State': 'Arizona', 'Country': 'United States', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Surface Albedo Units': 'N/A', 'Version': '3.2.0', 'latitude': 33.61, 'longitude': -112.14, 'altitude': 388, 'tz': -7, 'wind_height': 2, 'house_number': '3730', 'neighbourhood': 'Sunray Manor', 'ISO3166-2-lvl4': 'US-AZ', 'Street': 'West Rue De Lamour Avenue', 'County': 'Maricopa County', 'Zipcode': '85029', 'Country Code': 'US'}\n"
      ]
     }
    ],
@@ -189,20 +189,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The array tilt angle was not provided, therefore the latitude tilt of 24.7 was used.\n",
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The estimated T₉₈ of an insulated-back module is 89.6°C. \n",
-      "The estimated T₉₈ of an open-rack module is 63.8°C. \n",
-      "Level 0 certification is valid for a standoff greather than 9.3 cm. \n",
-      "Level 1 certification is required for a standoff between than 9.3 cm, and 3.0 cm. \n",
-      "Level 2 certification is required for a standoff less than 3.0 cm.\n"
+      "The array tilt angle was not provided, therefore the latitude tilt of 43.5 was used.\n",
+      "The estimated T₉₈ of an insulated-back module is 69.4°C. \n",
+      "The estimated T₉₈ of an open-rack module is 44.0°C. \n",
+      "Level 0 certification is valid for a standoff greather than 0.0 cm. \n",
+      "\n"
      ]
     }
    ],
@@ -220,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -228,14 +226,14 @@
      "output_type": "stream",
      "text": [
       "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "First calculation standoff =  9.3  cm.\n",
+      "First calculation standoff =  7.0  cm.\n",
       "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "Second calculation standoff =  3.0  cm.\n",
-      "The estimated T₉₈ of an insulated-back module is 89.6°C. \n",
-      "The estimated T₉₈ of an open-rack module is 63.8°C. \n",
-      "Level 0 certification is valid for a standoff greather than 9.3 cm. \n",
-      "Level 1 certification is required for a standoff between than 9.3 cm, and 3.0 cm. \n",
-      "Level 2 certification is required for a standoff less than 3.0 cm.\n"
+      "Second calculation standoff =  1.9  cm.\n",
+      "The estimated T₉₈ of an insulated-back module is 86.4°C. \n",
+      "The estimated T₉₈ of an open-rack module is 61.6°C. \n",
+      "Level 0 certification is valid for a standoff greather than 7.0 cm. \n",
+      "Level 1 certification is required for a standoff between than 7.0 cm, and 1.9 cm. \n",
+      "Level 2 certification is required for a standoff less than 1.9 cm.\n"
      ]
     }
    ],
@@ -317,15 +315,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
-      "The 98ᵗʰ percential temperature is estimated to be 89.6 °C.\n"
+      "The 98ᵗʰ percential temperature is estimated to be 49.5 °C.\n"
      ]
     }
    ],
@@ -334,12 +331,43 @@
     "T_98 = pvdeg.standards.T98_estimate(\n",
     "    weather_df=WEATHER_df,\n",
     "    meta=META,\n",
-    "    tilt=META['latitude'],\n",
+    "    tilt=-META['latitude'],\n",
     "    azimuth=None,\n",
-    "    x_eff=0,)\n",
+    "    x_eff=10)\n",
     "print ('The 98ᵗʰ percential temperature is estimated to be' , '%.1f' % T_98 , '°C.')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The array axis_azimuth was not provided, therefore an azimuth of 180.0 was used.\n",
+      "The 98ᵗʰ percential temperature is estimated to be 64.4 °C.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This code will calculate the temperature for an arbitrary x_eff distance. \n",
+    "# This produces a slightly different value because it is set for a \"1_axis\" tracker (instead of a default of \"fixed\") \n",
+    "# and x_eff=None indicating open rack.\n",
+    "irradiance_kwarg ={\n",
+    "    \"tilt\": None,\n",
+    "    \"azimuth\": None,\n",
+    "    \"x_eff\": None,\n",
+    "    \"module_mount\": '1_axis'}\n",
+    "\n",
+    "T_xeff = pvdeg.standards.x_eff_temperature_estimate(\n",
+    "    weather_df=WEATHER_df,\n",
+    "    meta=META,\n",
+    "    **irradiance_kwarg)\n",
+    "print ('The 98ᵗʰ percential temperature is estimated to be' , '%.1f' % (T_xeff.quantile(q=0.98, interpolation=\"linear\")) , '°C.')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -353,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -399,25 +427,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[WinError 183] Cannot create a file when that file already exists: 'c:\\\\Users\\\\mkempe\\\\Documents\\\\GitHub\\\\PVDegradationTools\\\\TEMP\\\\results'\n"
+     "ename": "NameError",
+     "evalue": "name 'pd' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m standoff_series_df\u001b[38;5;241m=\u001b[39m\u001b[43mpd\u001b[49m\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTilt\u001b[39m\u001b[38;5;124m'\u001b[39m: standoff_series[:, \u001b[38;5;241m0\u001b[39m],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAzimuth\u001b[39m\u001b[38;5;124m'\u001b[39m: standoff_series[:, \u001b[38;5;241m1\u001b[39m],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mXₘᵢₙ\u001b[39m\u001b[38;5;124m'\u001b[39m: standoff_series[:, \u001b[38;5;241m2\u001b[39m]})\n\u001b[0;32m      2\u001b[0m x_fig \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m16\u001b[39m,\u001b[38;5;241m4\u001b[39m))\n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPlot of $\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mit\u001b[39m\u001b[38;5;132;01m{Xₘᵢₙ}\u001b[39;00m\u001b[38;5;124m$ for all orientations for $\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mit\u001b[39m\u001b[38;5;132;01m{T₉₈}\u001b[39;00m\u001b[38;5;124m$=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%.0f\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m kwarg_x[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mT98\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m°C.\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m15\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.08\u001b[39m)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
      ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1600x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -469,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -514,7 +536,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABK4AAAHBCAYAAABAAPNXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB1BUlEQVR4nO3deVyU5f7/8fcMy7CIuLOkIhqmZqVmmWiBlpbllm1qpmaWuVTm6VjmOUkbmpVZefJ865RZpi2ntKzcyqRMLfdMzTDR3FBTckEEZa7fH/6Y4wQoMDM3A76ePe5Hct/33O/rumdh5sN1X2MzxhgBAAAAAAAAfsZe3g0AAAAAAAAAikLhCgAAAAAAAH6JwhUAAAAAAAD8EoUrAAAAAAAA+CUKVwAAAAAAAPBLFK4AAAAAAADglyhcAQAAAAAAwC9RuAIAAAAAAIBfonAFAAAAAAAAv0ThCgAAAAAAAH6JwhUAAAAAAAD8EoUrAPADNpvNbbHb7apWrZquvvpq/ec//5ExptD+DRo0KJ/GeuiVV17RxRdfLIfDIZvNpuTk5LPu36BBg0Ln52xLWFiY8vPz/a4f/qS4x4+/PK78pR1Wqix99pfnxcmTJ1W3bl3Z7XZt2rSpyH3WrFmjsLAwBQUFafHixRa3sHSSk5PP+ro3f/788m4iAAA+E1jeDQAA/M+AAQMkSfn5+frtt9/0/fffa+nSpfr66681a9Ysr+dt375d8fHxSkpK0pIlS7x+/L/65JNP9NBDD6l69erq3r27wsPD1aRJk2L3z83NVYcOHQoV7ubNm6f9+/fr2muvVd26dd221a9fXwEBAT5pf4HS9gMVh7efE1Y/x8qTPz0vgoKC9MADD+ixxx7TSy+9pDfeeMNt+/79+9WzZ0/l5OTolVdeUceOHculnaV1yy23qEqVKoXWX3DBBeXQGgAArEHhCgD8yNtvv+3286JFi3TjjTfq/fff15133qmuXbuWT8O8ZM6cOZKk//73vyX6oOhwODRt2rRC6ws+pL388su6+OKLvdrGkihtP1A6mzdvVlBQUHk3w1KVoc/+9ry4//779eyzz+rdd9/Vs88+qzp16kiS8vLy1KtXL+3cuVODBw/WAw88UM4tLbkXXnihUozMAwCgNLhUEAD8WKdOnXTXXXdJ+t+Hwops165dkqSGDRuW+RgHDx7Unj175HA4dNFFF3mraaXijX6geE2aNFGjRo3KuxmWqgx99rfnRWRkpO655x7l5ubqX//6l2v98OHD9f3336t9+/Zu6wEAgH+icAUAfq5ly5aSpJ07d5Zo/+XLl6tHjx6qXbu2HA6HGjRooGHDhmnPnj1u+6WkpCg+Pl6SlJaW5jZfysCBA72eZbPZ9M0330iS4uPjXVmlvXxq/fr1kqSmTZsqMNDagcMl7UdJz4t0+lKygrmAjhw5or/97W+Kj49XUFCQRo4cec42ffHFFxo0aJCaNm2qqlWrKjw8XJdddplSU1OVm5vrra4Xyxd9LW6+p+3bt2vIkCFq0KCBHA6HateurVtvvVU//fTTWbNycnL02GOPKS4uTg6HQxdeeKGee+65QpeglvQ5UdJzXprnWHF9Luv5LWmfpdOjve666y41atRIISEhql27tlq0aKGRI0dq7969hfb/K398XhQYOXKkAgMD9dprryknJ0dTpkzRf/7zH9WvX18ff/yxgoODS3ysyub333/XiBEjlJCQoJCQENWsWVNXXnmlUlNTlZOTI8n9fsjOztaoUaNUr149hYaGqlWrVpo7d67reB999JGuvPJKhYeHKyoqSg8++KDrOAAAeIJLBQHAzx09elTS6cvmzmXGjBkaOHCgnE6nEhMTVa9ePa1Zs0ZTp07VJ598oiVLlrjmnGnRooVuueUWffzxx4qKitINN9zgOk779u29njVgwADNnz9f+/btc5unJTo6ulTno6BIcdlll5Xqdt5Qkn6U5rycKScnR0lJSdqxY4eSkpLUqlUrVa9e/Zxtuueee5Sdna2LL75Yl1xyiY4cOaIff/xRY8eO1ddff62FCxf6bM4vK/u6dOlS3XTTTTpy5Iguvvhide/eXbt379Ynn3yiL7/8Ul988YU6dOhQ6HZ5eXnq3LmzNm7cqCuvvFJNmzZVWlqaHnvsMR09elTPPPOMa9+SPidKes6tfI6Vtc9r1qxR+/btdeLECV155ZW68sordfToUW3btk0vv/yyevbsqZiYmLO20x+fFwXi4uJ066236v3339f999+vmTNnKiwsTJ9++qnr0sGK5M0339TBgwdlt9vVuHFj9ezZU/Xr1y/1cb799lt1795dhw8fVsOGDdWjRw9lZ2dr06ZNGjt2rPr27etWSM3Ly9O1116r3377TVdddZWOHTumb7/9VjfffLPmz5+vDRs2aPTo0briiivUuXNnfffdd3r11Vd18OBBvffee148AwCA85IBAJQ7Saaol2Sn02natm1rJJmxY8e67R8XF+e27++//25CQ0NNYGCgmTt3rmt9fn6+GTlypJFkrrjiCrfbZGRkGEkmKSmpVO0tS5YxxiQlJRlJJiMjo1R5Zxo4cKCRZF588cUyH8NTxfXDk/tAkmnbtq3JysoqVVtmz55tjh075rbuyJEjpmvXrkaSmT59eqHbFPX4Odv6oviyr39tx+HDh010dLQJCgoyH330kdu+ixYtMsHBweaCCy4wubm5RWZdffXV5sCBA65tK1euNIGBgSYsLMwcPXq0yDae7TlRmnNe0ufYX/vs6fktaZ8HDBhgJJmPP/64UJs2bdpk9uzZc9Z2n8mfnhdnWrVqletYNput0GPIlwrOSWmWb775psTHCQoKMk899VSp2nTo0CFTu3ZtI8m89NJLxul0um1PS0szf/75pzHG/X5ITk42hw4dcu03bdo0I8lceOGFpkaNGubbb791bdu9e7epU6eOkWR+++23UrUPAIC/onAFAH7gr4WrU6dOmV9//dVVpHE4HGbr1q1u+/+1wPDEE08YSeauu+4qdPwTJ06Y2NhYI8ksX77ctb6shauyZBnjncJVq1atjCSzaNGiYvc5ePCgueeee0xMTIypUqWKufbaa82mTZtc2w8cOGDuuusuExUVZSIiIsxll11Wqg+zxfXDk/tAklm5cmWJ23Au6enpRpLp1atXoW3eKFz5sq9/bcdLL71kJJkxY8YUuX9B8ePM4ktBlt1uN1u2bCl0m27duhVZJCjrc8KYos95WQtXnpzf0vS5S5cuRpJHhaEC/vq8OHLkiKlevbqRZIYOHerRsUpr/PjxZsCAAaVaNm/eXOg4//znP827775rfvvtN3P8+HGzZcsW8+yzz5rQ0FAjyUyePLnEbXruueeMJNO1a9dz7ltwPwQEBJj09HS3bfn5+a4C2BNPPFHotg8//LCRZKZNm1bitgEAUBQuFQQAP2Kz2Qqti4iI0PTp0885cfN3330nSbrzzjsLbXM4HLrtttv08ssv67vvvtNVV13lUTutzDpTfn6+Nm3aJEm69NJLi92vf//+2rt3r3744QfVqVNHY8eOVadOnbRlyxaFh4dr+PDh2rVrl9auXauoqCjNmjVLffr0UfPmzYu8XKmkPDkvMTExat26dZly09PT9eWXX2rr1q3Kzs6W0+l0zWWUnp5epmOei5V9XbRokSSpZ8+eRW5v3769Jk+erJUrV6pXr15u2xo0aKDGjRsXuk3BupLM4VQUX59zT85vafp8+eWXa968eerfv7/+8Y9/qHXr1rLbvTsFank9LyTJ6XSqb9++ysrKkiStXbu2zMc609y5c/X4448rPT1dMTExysjIKHLdY4895pW8p556yu3nxo0b6/HHH1fr1q11/fXXa9y4cbrvvvsUGhp6zmN99dVXkqQhQ4aUOL9Bgwa68MIL3dbZ7XbFxcXpwIED6tSpU6HbFPzOKutzDACAAhSuAMCPDBgwQNLpDwRVq1bVJZdcol69epVoTpeCCY6L+6r0gvVFTYRcWlZmnWnLli06ceKEoqKiip2fJjs7W19++aU+/PBD1atXT5I0fvx4vfLKK5o9e7b69eunrVu36u6773bN3XPnnXdq1KhR+vnnnz0qXHlyXsoyT40xRo888oheeumlIifdlv43R5q3WdnX7du3S5LatGlz1v3++OOPQuvq1q1b5L4FczCVdgJ7q865J+e3NH3++9//rqVLl2ru3LmaO3euIiMj1aZNG3Xt2lUDBw5URESEB72QWxutel6cacyYMfr888/VokUL5eTkaMWKFfr+++/Vrl27Mh8zKytLd9xxh5544gndddddCg0NLXKdFTp37qzWrVtr1apVWrFiRZHzvP1VwRd9lOZbLC+44IIi14eHhxe7vWCbFV8SAQCo3ChcAYAfefvttz0+RlGjtkqz3V+zpP99o2BJJmY/s6hgTl8ar7Vr16pfv3567LHH9K9//Us9e/ZUdHS03nvvPTmdTl199dVeaWdZzktISEipcz744ANNmjRJdevW1eTJk9W2bVvVrl1bQUFBysvLk8PhKLa44i1W9DU/P1+SdNtttyksLKzY/YoqbHn7MWj1OS/L+S1Nn6tWrarFixfr+++/19y5c7VkyRLXBPPjx4/Xd999V6oCR2nbeq7tZXleFJgxY4YmTpyoOnXq6NNPP9WCBQt033336YUXXvCocDVv3jwFBATokUcecX2z6cyZMwutk6QJEybol19+KdXxH3vssVIV0BMSErRq1apSj2wqzePE6td6AADOROEKACqJ2NhYbdmyRRkZGUVeJrRjxw5JOuc3hPlb1pkKvlHwbJcJhoeHq3PnzpowYYLatGmj2rVra9y4cTp16pSOHDkiSbriiisUFhamevXqKSAgQKGhoXrvvfcUFRXlUfusPi+zZ8+WJE2dOlVdu3Z127Zt2zavZBTHyr7WrVtXW7Zs0T/+8Y+z3vdWsOqcW3l+bTab2rdv7/qmwwMHDuihhx7SrFmz9Pjjj+uDDz7w6Pjl8Xrx448/6t5771VwcLA++eQT1a9fX/3799c///lPffbZZ9q6dWuhS98k6dChQ3rqqaf04Ycf6s8//9Qll1yil156SYmJiZKkVq1auS43DAoKUq1atVSvXr1C6w4cOCBJmj9/vtLS0krV9oEDB5aqcFVwGWTBiLpzqVevnn755Rdt3brVoxGmAABYxbuTGAAAyk3BaKGivno8Ly9PH330kdt+khQcHCxJOnXqlM+zvKFgxNW5ihfvvvuuLr30UiUmJqphw4bKz89X06ZNVatWLRlj1KlTJ9ntdu3du1cnTpzQjBkz1Lt3by1YsMCj9ll9Xgo+sBZcEnmmDz/80CsZxbGyr9ddd50kac6cOR4f61zO9Zwo7TmvaM8xSapdu7ZSUlIkSRs2bPD4eFb3Zffu3erZs6dOnDihf//7367RVQ6HQyNGjJDT6dSkSZMK3S4zM1OtWrXS4cOH9fnnn+unn35S27Zt1bVrV9f9vmDBAiUmJmrw4MHau3evfvnllyLXFViyZIlrxGdJl+Tk5BL39cCBA645xFq1alWi2xQ8n15//fUS5wAAUJ4oXAFAJXHPPfcoNDRUs2bN0hdffOFa73Q69fjjj2v37t264oor3CY/rlWrloKCgvTbb7+5LsfyVZY3lGTElXT6g/e0adO0a9cu7d27V2PGjFFGRoaSk5N16NAhbd26VUOGDFF0dLQCAwPVo0cPNW3aVJ9//rlH7bP6vBSMXnn99dfdLk/77rvv9Pzzz3slozhW9nXIkCGqXbu2UlNTNW3atEKX4mVnZ+udd97Rrl27PM4613OitOfc359j//73v5WRkVFo/bx58yR5PseUZO1jJScnRz179tTevXv18MMP6+6773bbPmzYMIWHh+vtt98uNCfaAw88oGuuuUbTpk1Tq1atdOGFF2rSpEnKzs7WqlWrJJ1+bdm8ebMSExMVHR2tmjVrFrnOm1asWKFvvvmm0ON++/btuvnmm5Wdna3u3bsXO7fZXw0ePFi1atXS3LlzNWXKlELH/e6773T48GGvtf+v+vfvryZNmrhGLwIAcE6WfHchAOCs9P+/9r00+8fFxRVa/+6775qAgABjs9lM+/btTZ8+fcxFF11kJJmoqKgiv2a9W7duRpK5+OKLzV133WXuuece89Zbb52zDWXJSkpKMpJMRkZGifta4ODBg0aSCQoKMrm5uWfdNz093ezdu9cYY8y2bdtMly5dzHXXXWecTqcxxphmzZqZHj16mD/++MPk5+ebL7/80jgcDjN9+vQSteVs/SjteSn4uvmkpKQSZZ9py5YtJjw83EgyzZo1M7179zZXX321sdls5pFHHin2cVLa9cXxVV+LasfSpUtNjRo1XNtuuukm06tXL9O6dWvXOVi7dm2Js8aNG2ckmWnTphXadrbnRFnOeUmeY0Xdztvnt6g+X3bZZa6+3HLLLeaOO+4wLVq0MJJMaGioWbZsWZHHKoo/PC969+5tJJnrr7/enDp1qsh9RowYYSSZJ5980rVu3759xm63m5CQEBMeHu62SDKLFy82xhjz22+/FXqsFbXOm6ZNm2YkmZiYGJOUlGTuuOMO065dOxMSEuJ6XO3bt69Ux1y8eLGJiIgwkkyjRo3M7bffbrp27Wri4+Pd7sNz3Q9nu88L2j1u3Lgib1PUcw8AgKJQuAIAP+CtwpUxxnz//femW7dupmbNmiYoKMjUr1/fDB061OzatavI/fft22fuuusuEx0dbQICAowkM2DAgBK1o7RZnhSuFi9ebCSZ5s2bn3Pf999/39StW9eEhoaa2NhYM2rUKJOdne3anp6ebnr16mXq1KljIiIizEUXXWReeOGFErflXP0ozXnxpHBljDGbNm0y3bp1M3Xq1DFhYWGmZcuW5vXXXzfGlL5AVdrClTG+6Wtx7di9e7f529/+Zpo0aWJCQ0NNlSpVTOPGjc0dd9xhPvjgA7eCpieFq3M9J0p7zkvyHCuuz948v0X1+bPPPjODBg0yF198salWrZoJCwszjRs3Nvfdd59JT08v8jjFKe/nxTPPPGMkmcaNG5usrKxi99u2bZsJCAgwderUMTk5OcYYYxYsWGAkmV9++cWkp6cXWgoeWx999JFxOBwmLy/Pdbyi1nnTpk2bzNChQ02rVq1M7dq1TWBgoImMjDRXXXWVefHFF83x48fLdNzffvvN3HfffSYuLs4EBwebWrVqmTZt2pjx48e7zguFKwCAP7AZ4+OvGwIAAAD82JIlS9ShQwft2LHjrJdHPv7441q4cKHr0sHi1gEAAO9hjisAAACc11q3bq3Y2Fj1799fy5cv1/bt2/X999/riSee0Jo1a1z7rV69utAk6EWtAwAA3kPhCgAAAOe1KlWqaMGCBQoPD1e3bt3UrFkzDRgwQHv27FGjRo1c+61du7ZQkaqodQAAwHu4VBAAAAAAAAB+iRFXAAAAAAAA8EsUrgAAAAAAAOCXKFwBAAAAAADAL1G4AgAAAAAAgF+icAUAAAAAAAC/ROEKAAAAAAAAfonCFQAAAAAAAPwShSsAAAAAAAD4JQpXAAAAAAAA8EsUrgAAAAAAAOCXKFwBAAAAAADAL1G4AgAAAAAAgF+icAUAAAAAAAC/ROEKAAAAAAAAfonCFQAAAAAAAPwShSsAAAAAAAD4JQpXAAAAAAAA8EsUrgAAAAAAAOCXKFwBAAAAAADAL1G4AgAAAAAAgF+icAUAAAAAAAC/ROEKAAAAAAAAfonCFQAAAAAAAPwShSsAAAAAAAD4JQpXAAAAAAAA8EsUrgAAAAAAAOCXKFwBAAAAAADAL1G4AgAAAAAAgF+icAUAAAAAAAC/FFjeDShP/W59z9K8gFNOS/OO1AixLOtwrTDLsiQpr16QpXmNLvrT2rxap6zNizCW5tUJzbcsq0qQtc+7QLu15/KU02Zp3vFT1uYdOmHdr6msPMuiJEl/WpyXbfF957T2qaegAGvzajqse67Xse7XuSSpdqi1v4NqhFibV9NhbZ4jwLo7MNAebFmWJNlk7etKvrH2vjvpPGFp3pE8a99D7Mux7v30jqPWvnf/5U9rH5ubdoRamrdze1VL86rvzLYsq/buo5ZlSdKM/97p84xp9mvLfNu7nV97sSUV13lduAIAAAAAAPAVu93aQmplROEKAAAAAADAB+wWjwqvjChcAQAAAAAA+AAjrjzH5OwAAAAAAADwS4y4AgAAAAAA8IEALhX0GIUrAAAAAAAAH+BSQc9RuAIAAAAAAPABOxM0eYzCFQAAAAAAgA/YAxhx5Sm/rP0dPXpUI0eOVFxcnEJDQ5WYmKiVK1e6thtjlJKSotjYWIWGhio5OVkbN24sxxYDAAAAAAC4s9vLvuA0vzwVgwcP1qJFi/Tuu+9qw4YN6ty5s6677jrt3r1bkjRx4kRNmjRJU6ZM0cqVKxUdHa1OnTrp6NGj5dxyAAAAAAAAeIvfFa5ycnL08ccfa+LEibrmmmt04YUXKiUlRfHx8Zo6daqMMZo8ebLGjh2rXr16qXnz5po+fbqOHz+umTNnlnfzAQAAAAAAJJ2+VLCsC07zu8LVqVOnlJ+fr5CQELf1oaGhWrp0qTIyMpSZmanOnTu7tjkcDiUlJWnZsmVWNxcAAAAAAKBIAfayLzjN705FRESE2rZtq6efflp79uxRfn6+ZsyYoR9++EF79+5VZmamJCkqKsrtdlFRUa5tAAAAAAAA5c1ut5V5KY0GDRrIZrMVWoYPHy5JGjhwYKFtV111lS+67HV++a2C7777rgYNGqQLLrhAAQEBatWqlfr27as1a9a49rHZ3O9EY0yhdWfKzc1Vbm6u27r8/JMKCAjybuMBAAAAAAAk2QOsyVm5cqXy8/NdP//888/q1KmTbrvtNte6G264QdOmTXP9HBwcbE3jPOR3I64kqVGjRkpLS9OxY8e0c+dO/fjjjzp58qTi4+MVHR0tSYVGV+3fv7/QKKwzjR8/XpGRkW7Lxi2f+bQfAAAAAADg/GXViKvatWsrOjratXz++edq1KiRkpKSXPs4HA63fWrUqOHt7vqEXxauCoSHhysmJkZZWVlasGCBevTo4SpeLVq0yLVfXl6e0tLSlJiYWOyxxowZo8OHD7stF1/U3YpuAAAAAAAAlEpubq6OHDnitvz1SrKi5OXlacaMGRo0aJDblWlLlixRnTp11LhxY917773av3+/L5vvNX5ZuFqwYIHmz5+vjIwMLVq0SB06dNBFF12ku+++WzabTSNHjlRqaqpmz56tn3/+WQMHDlRYWJj69u1b7DEdDoeqVq3qtnCZIAAAAAAA8BW7vexLUVeOjR8//pyZc+bM0Z9//qmBAwe61nXp0kXvvfeeFi9erBdffFErV65Ux44dS1QIK29+OcfV4cOHNWbMGO3atUs1atTQLbfcomeffVZBQacLTaNHj1ZOTo6GDRumrKwstWnTRgsXLlREREQ5txwAAAAAAOA0e0DpLvk705gxYzRq1Ci3dQ6H45y3e/PNN9WlSxfFxsa61t1xxx2ufzdv3lytW7dWXFycvvjiC/Xq1avMbbSCXxaubr/9dt1+++3FbrfZbEpJSVFKSop1jQIAAAAAACiFAA+uc3M4HCUqVJ1px44d+uqrr/TJJ5+cdb+YmBjFxcUpPT297A20iF8WrgAAAAAAACo6T0ZclcW0adNUp04d3XTTTWfd7+DBg9q5c6diYmIsalnZ+eUcVwAAAAAAABWdJ3NclZbT6dS0adM0YMAABQb+b5zSsWPH9Mgjj2j58uXavn27lixZom7duqlWrVq6+eabvdhb32DEFQAAAAAAQAX31Vdf6ffff9egQYPc1gcEBGjDhg1655139OeffyomJkYdOnTQBx98UCHmCqdwBQAAAAAA4AN2u3WXCnbu3FnGmELrQ0NDtWDBAsva4W0UrgAAAAAAAHzAHlDeLaj4KFwBAAAAAAD4gJUjriorClcAAAAAAAA+wIgrz1G4AgAAAAAA8IEARlx5rAxfsAgAAAAAAAD4HiOuAAAAAAAAfMDOcCGPUbgCAAAAAADwAXsAlwp66rwuXG1Pqm1p3okca0931Wq5lmVdUP+YZVmS1Cz6pKV5F1dzWpqXEHnC0rxaodbOGBgWEGlZliMg3LIsSbLbrD2XRsbSvHyntc+9PGdOpcySpLx8a/OMrH0dc1r70JTV00cE20OtywqwLkuSQgJqWpoXZHNYmqcTR6zNy7Pud7o5Zd17v/JgC7T4sRJq7XOhdkiQpXnRYVmWZcVHHLIsS5IaVrX2sdI40tpfej/VtfY9xNbfwyzL2r7P2s/oVmDElefO68IVAAAAAACAr9jsFv81rxKicAUAAAAAAOADNkZceYxTCAAAAAAAAL/EiCsAAAAAAAAfsNm4VNBTFK4AAAAAAAB8gEsFPUfhCgAAAAAAwAfsTM7uMQpXAAAAAAAAPsCIK89RuAIAAAAAAPABGyOuPOaXtb9Tp07pH//4h+Lj4xUaGqqGDRvqqaeektPpdO1jjFFKSopiY2MVGhqq5ORkbdy4sRxbDQAAAAAAAG/yy8LVc889p3//+9+aMmWKNm/erIkTJ+r555/Xq6++6tpn4sSJmjRpkqZMmaKVK1cqOjpanTp10tGjR8ux5QAAAAAAAKfZ7GVfcJpfXiq4fPly9ejRQzfddJMkqUGDBpo1a5ZWrVol6fRoq8mTJ2vs2LHq1auXJGn69OmKiorSzJkzNWTIkHJrOwAAAAAAgCTZKUB5zC9PYfv27fX111/r119/lSStX79eS5cu1Y033ihJysjIUGZmpjp37uy6jcPhUFJSkpYtW1YubQYAAAAAADiTzWbKvOA0vxxx9eijj+rw4cNq0qSJAgIClJ+fr2effVZ9+vSRJGVmZkqSoqKi3G4XFRWlHTt2WN5eAAAAAACAv+KSP8/5ZeHqgw8+0IwZMzRz5kxdfPHFWrdunUaOHKnY2FgNGDDAtZ/NZnO7nTGm0LoCubm5ys3NdVvnPJkne1Cw9zsAAAAAAADOe3yroOf8svb397//XY899ph69+6tSy65RHfddZcefvhhjR8/XpIUHR0t6X8jrwrs37+/0CisAuPHj1dkZKTbsmvRB77tCAAAAAAAAMrMLwtXx48fl/0vM5gFBATI6XRKkuLj4xUdHa1Fixa5tufl5SktLU2JiYlFHnPMmDE6fPiw21K30x2+6wQAAAAAADiv8a2CnvPLSwW7deumZ599VvXr19fFF1+stWvXatKkSRo0aJCk05cIjhw5UqmpqUpISFBCQoJSU1MVFhamvn37FnlMh8Mhh8Phto7LBAEAAAAAgK/YuVTQY35ZuHr11Vf1z3/+U8OGDdP+/fsVGxurIUOG6IknnnDtM3r0aOXk5GjYsGHKyspSmzZttHDhQkVERJRjywEAAAAAAE5j5JTn/LJwFRERocmTJ2vy5MnF7mOz2ZSSkqKUlBTL2gUAAAAAAFBSNhsjrjzll4UrAAAAAACAio4RV57jFAIAAAAAAMAvMeIKAAAAAADAB2xMzu4xClcAAAAAAAA+YOc6N49RuAIAAAAAAPABRlx5jsIVAAAAAACADzA5u+c4hQAAAAAAAPBLjLgCAAAAAADwAZuNSwU9ReEKAAAAAADAB5ic3XMUrgAAAAAAAHyAydk9d14Xrvpde8DSvBP5lsYpzMJ7t26407owSY2q5lqaFxNW1dK8KkHRlubZj2VZmmeyMq0LO3HMuixJyjtpaZzN4j/h2C3OCwp2WJYV7gizLEuSbI4IS/Mq/cygxtrfQ+ZkjnVhefusy5KknG2WxpnjFp5LSTp+wtI4k5tnaZ6lAgOszQsKsjYvLMTSOFtEFUvzqlSpZVlWRGRDy7Ikqboj29K8+AhrP1c2rmbtx/jfa1tXeDmYa+19Z4XK/hbMCud14QoAAAAAAMBnAmzl3YIKj9ofAAAAAAAA/BIjrgAAAAAAAHzAZmfElacoXAEAAAAAAPhCABe6eYrCFQAAAAAAgC8w4spjFK4AAAAAAAB8wMbk7B6jcAUAAAAAAOALjLjyGBdbAgAAAAAAwC8x4goAAAAAAMAXuFTQY3454qpBgway2WyFluHDh0uSjDFKSUlRbGysQkNDlZycrI0bN5ZzqwEAAAAAAP7HZreVecFpflm4Wrlypfbu3etaFi1aJEm67bbbJEkTJ07UpEmTNGXKFK1cuVLR0dHq1KmTjh49Wp7NBgAAAAAA+J8Ae9kXSPLTwlXt2rUVHR3tWj7//HM1atRISUlJMsZo8uTJGjt2rHr16qXmzZtr+vTpOn78uGbOnFneTQcAAAAAAJBk3Yirynzlml8Wrs6Ul5enGTNmaNCgQbLZbMrIyFBmZqY6d+7s2sfhcCgpKUnLli0rx5YCAAAAAACcIcBW9qUUKvOVa35fuJozZ47+/PNPDRw4UJKUmZkpSYqKinLbLyoqyrUNAAAAAADgfFGZr1zz+8LVm2++qS5duig2NtZtvc3mXn00xhRad6bc3FwdOXLEbTmZd9InbQYAAAAAAPBkxFVRdYzc3NxzRla2K9f8unC1Y8cOffXVVxo8eLBrXXR0tCQVGl21f//+QqOwzjR+/HhFRka6LQve+Mw3DQcAAAAAAOc9T+a4KqqOMX78+HNmVrYr1/y6cDVt2jTVqVNHN910k2tdfHy8oqOjXddrSqeriWlpaUpMTCz2WGPGjNHhw4fdluvv7e7T9gMAAAAAgPOYByOuiqpjjBkz5pyR3rpyzV8ElncDiuN0OjVt2jQNGDBAgYH/a6bNZtPIkSOVmpqqhIQEJSQkKDU1VWFhYerbt2+xx3M4HHI4HG7rgoKDfNZ+AAAAAABwnrOXfbxQUXWMcym4cu2TTz5xrTvzyrWYmBjX+nNdueYv/LZw9dVXX+n333/XoEGDCm0bPXq0cnJyNGzYMGVlZalNmzZauHChIiIiyqGlAAAAAAAAhdlK+e2AnjrXlWstW7aU9L8r15577jlL21cWflu46ty5s4wxRW6z2WxKSUlRSkqKtY0CAAAAAADwQ96+cs1f+G3hCgAAAAAAoEKzWzfiqrJeuUbhCgAAAAAAwBcsvFSwsl65RuEKAAAAAADAB2wWjriqrChcAQAAAAAA+EJA2b9VEKdRuAIAAAAAAPAFi79VsDKi9AcAAAAAAAC/xIgrAAAAAAAAH2COK89RuAIAAAAAAPAFLhX0GIUrAAAAAAAAX2DElccoXAEAAAAAAPiAjRFXHjuvC1e94nMtzTNyWpoXaAu2LCs0sKplWZIUFlDX0jxzeLelecpaY2mcc99BS/P05xHrso6fsC5LkjmRb23eSWtfV6xmC7Pu15TNYe2vRBPisDRPgQHW5jmNtXmnTlmbl3fSsiiTY12WJJlsa/Ocxy2+75zWvm5a/XvBUhZ/GLNXse69rSTZa4RYmmeLCLU0T9X2WZdV09r30qHVoyzNC4u09rNJ9ZrWvo5dVM269+55+TmWZVnGznfieYozCAAAAAAAAL90Xo+4AgAAAAAA8BnmuPIYhSsAAAAAAABf4FJBj1G4AgAAAAAA8AUKVx6jcAUAAAAAAOALXCroMQpXAAAAAAAAvsCIK49xBgEAAAAAAOCXGHEFAAAAAADgC4y48hiFKwAAAAAAAF9gjiuP+W3pb/fu3erXr59q1qypsLAwtWjRQqtXr3ZtN8YoJSVFsbGxCg0NVXJysjZu3FiOLQYAAAAAADiD3V72BZL8tHCVlZWldu3aKSgoSPPmzdOmTZv04osvqlq1aq59Jk6cqEmTJmnKlClauXKloqOj1alTJx09erT8Gg4AAAAAAFCAwpXH/PJSweeee0716tXTtGnTXOsaNGjg+rcxRpMnT9bYsWPVq1cvSdL06dMVFRWlmTNnasiQIVY3GQAAAAAAwB2XCnrML0t4n332mVq3bq3bbrtNderUUcuWLfXGG2+4tmdkZCgzM1OdO3d2rXM4HEpKStKyZcvKo8kAAAAAAADwMr8sXG3btk1Tp05VQkKCFixYoPvvv18PPvig3nnnHUlSZmamJCkqKsrtdlFRUa5tAAAAAAAA5YpLBT3ml5cKOp1OtW7dWqmpqZKkli1bauPGjZo6dar69+/v2s9mcx9yZ4wptK5Abm6ucnNz3dc5T8rhCPJy6wEAAAAAAEQBygv88gzGxMSoWbNmbuuaNm2q33//XZIUHR0tSYVGV+3fv7/QKKwC48ePV2RkpNvyyvNf+qD1AAAAAAAApwfclHXBaX5ZuGrXrp22bNnitu7XX39VXFycJCk+Pl7R0dFatGiRa3teXp7S0tKUmJhY5DHHjBmjw4cPuy0P/v1G33UCAAAAAACc37hU0GN+eangww8/rMTERKWmpur222/Xjz/+qNdff12vv/66pNMVy5EjRyo1NVUJCQlKSEhQamqqwsLC1Ldv3yKP6XA45HA43NadyOEyQQAAAAAA4CMUoDzml4WrK664QrNnz9aYMWP01FNPKT4+XpMnT9add97p2mf06NHKycnRsGHDlJWVpTZt2mjhwoWKiIgox5YDAAAAAADAW/yycCVJXbt2VdeuXYvdbrPZlJKSopSUFOsaBQAAAAAAUFJ25qrylN8WrgAAAAAAACo0LhX0GIUrAAAAAAAAX2DElccoXAEAAAAAAPgCI648RuEKAAAAAADAFyhceYzCFQAAAAAAAIr0zjvveO1Y/fv3L/VtKFwBAAAAAAD4QiWY42rgwIGy2bzTDwpXAAAAAAAA/qKSXCp42WWXqUePHmW+/Zw5c/TTTz+V6bYUrgAAAAAAAHyhkhSuWrRooXHjxpX59tu3b6dwBQAAAAAA4FcqwaWCVatWVVhYmEfHCA0NVdWqVct0WwpXAAAAAAAAvlAJRlz9+eefHh/jtdde02uvvVam257Xhas6eaHl3QSfMidzrAvL/d26LEnGC0+cUuUdtDZPf2RZGndqT7alec5D1j02ncdOWpYlSc7jpyzNyz9l7V9wrP69a7Mb67KCrD2Xtkrw1zd/YpzWPVYkSfnW5Z3MtfaJ57T4dcXq1zFnvrV5xmldls3i1+ggh4WdkxQQdNTSvKCqAZbm2SMdluYF1LLu/WZA1B+WZUmSahywNq+6tZ+FgqtVszYvpIplWbbgcMuyJEmVuyRQaZSocDVo0CCvhNlsNr355pteORYAAAAAAIBfs/qvCpVQiQpXb7/9tmw2m4zx7K+LFK4AAAAAAMB5o5IUrhYvXqxdu3apdevWatas2Vn33bRpk1atWqV69eqpQ4cOHmeX+FLB66+/Xo8++miZgyZMmKCFCxeW+fYAAAAAAAAVSiWY42rnzp266aabVK9ePa1evfqc+9erV08333yzdu3apfT0dMXGxnqUX+LCVXR0tJKSksoc9Pbbb5f5tgAAAAAAABVOJRhx9Z///Ed5eXmaOHGiIiIizrl/RESEnn/+efXs2VNvvvmm/vnPf3qUX6Iz2KNHD7Vq1cqjoFatWql79+4eHQMAAAAAAKDCsNnLvviJRYsWqXbt2urZs2eJb9O9e3dFRUVp3rx5HueXaMTV7NmzPQ564IEH9MADD3h8HAAAAAAAAFjjl19+Ubt27Up9u9atW2vZsmUe55f4UkEAAAAAAACUgh+NnCqr7OxsRUZGlvp2kZGROnbsmMf5FK4AAAAAAAB8oRJMzl69enXt27ev1Lfbt2+fqlev7nF+qc5gbm6u1q9fr8zMTI+DAQAAAAAAKrVKMMdVs2bNtGLFCuXk5JT4NsePH9fy5cvVrFkzj/NLfCbeeecdRUdHq2XLlrrgggvUu3dvHT9+3OMGFCUlJUU2m81tiY6Odm03xiglJUWxsbEKDQ1VcnKyNm7c6JO2AAAAAAAAlImFhavdu3erX79+qlmzpsLCwtSiRQutXr3atX3gwIGFai1XXXXVOY/brVs3ZWdn65lnnilxW5555hnl5OSoW7dupe7HX5XoTKxcuVKDBw9Wz549lZWVpU8//VSffPKJRo8e7XEDinPxxRdr7969rmXDhg2ubRMnTtSkSZM0ZcoUrVy5UtHR0erUqZOOHj3qs/YAAAAAAAD4o6ysLLVr105BQUGaN2+eNm3apBdffFHVqlVz2++GG25wq7V8+eWX5zz2kCFDFB0drQkTJuiZZ56R0+ksdl+n06mnn35aEyZMUFRUlIYMGeJp10o2x9V7772n/Px8vfjii4qMjFTXrl11/fXX67///a+mTJnicSOKbFhgoNsoqwLGGE2ePFljx45Vr169JEnTp09XVFSUZs6c6ZWTAgAAAAAA4DGLLvl77rnnVK9ePU2bNs21rkGDBoX2czgcRdZaziYsLEyffPKJrr32Wo0bN05vvPGGbrvtNrVq1Uq1a9eWJB04cEBr1qzRRx99pF27dikkJEQff/yxwsLCPOqXVMLCVUREhCTpxIkTrnUnTpxQYKDv5nZPT09XbGysHA6H2rRpo9TUVDVs2FAZGRnKzMxU586dXfs6HA4lJSVp2bJlFK4AAAAAAIB/8KBwlZubq9zcXLd1DodDDoej0L6fffaZrr/+et12221KS0vTBRdcoGHDhunee+9122/JkiWqU6eOqlWrpqSkJD377LOqU6fOOdty1VVXafny5erXr59+/vlnvfTSS4X2McZIOn0F3YwZM3TZZZeVprvFKtEZ7N27t4KDg3Xfffdpw4YNeu2117R48WL17dvXK434qzZt2uidd97RggUL9MYbbygzM1OJiYk6ePCga2L4qKgot9tERUWdddL43NxcHTlyxG3JzT3pk/YDAAAAAADIbi/zMn78eEVGRrot48ePLzJm27Ztmjp1qhISErRgwQLdf//9evDBB/XOO++49unSpYvee+89LV68WC+++KJWrlypjh07FiqOFefSSy/VTz/9pHnz5mn48OFKTEzURRddpMaNGysxMVHDhw/Xl19+qQ0bNnitaCVJNlNQEjuHefPmacSIEcrIyJDD4dDgwYP14osvKjg42GuNKU52drYaNWqk0aNH66qrrlK7du20Z88excTEuPa59957tXPnTs2fP7/IY6SkpOjJJ590Wzfu0V5KGXOrT9tenszJks/477HcY9ZlSdKff1oaZw5am6c/siyNO7Un29I85yHrHpvOY9YWqJ3HT1mal3/KZmme1d/ma7OX6FeUd7KCrD2XNru1eZWdcVr3WJEk5VuXdzLX2iee0+LXFatfx5z51uaZ4qcB8Tqrv4AqyGFh5yQFBFmbF1Q1wNI8e2ThERS+FFAr1LqsqHDLsiRJNSItjbNVr2ppnv4yZ5HPhVSxLMoWbPFjJbKP7zMOzyrzTXNDepV4xFVwcLBat26tZcuWudY9+OCDWrlypZYvX17k8ffu3au4uDi9//77rqmY/FGJr/Xr0qWLfvvtN2VlZSkyMlJ2Cz+9hIeH65JLLlF6erp69uwpScrMzHQrXO3fv7/QKKwzjRkzRqNGjXJb5zgxxxfNBQAAAAAA8OivCsUVqYoSExOjZs2aua1r2rSpPv7447PeJi4uTunp6WVuoxVKfQarV69uadFKOn2Z3+bNmxUTE6P4+HhFR0dr0aJFru15eXlKS0tTYmJiscdwOByqWrWq2+JwBFnRfAAAAAAAAJ9p166dtmzZ4rbu119/VVxcXLG3OXjwoHbu3Ok2KOhsdu7cqU8//VQrVqxQCS/e8wqLBxSXzCOPPKK0tDRlZGTohx9+0K233qojR45owIABstlsGjlypFJTUzV79mz9/PPPGjhwoMLCwnw25xYAAAAAAECp2exlX0rh4Ycf1ooVK5SamqqtW7dq5syZev311zV8+HBJ0rFjx/TII49o+fLl2r59u5YsWaJu3bqpVq1auvnmm895/GeeeUaNGjXS3Xffrfbt26t9+/b6448/ynRKSssvC1e7du1Snz59dNFFF6lXr14KDg7WihUrXJXC0aNHa+TIkRo2bJhat26t3bt3a+HCha5vPwQAAAAAACh3FhWurrjiCs2ePVuzZs1S8+bN9fTTT2vy5Mm68847JUkBAQHasGGDevToocaNG2vAgAFq3Lixli9ffs5ayldffaVx48bpkUce0aFDh7RmzRqtX79eDz30UJlPS2mUaHL2Vq1aqXv37kpJSSlz0Lhx4/T5559r9erVZT6G13kwSVpFwOTs3sPk7N7F5Ozew+TsXsxicvYKjcnZvYfJ2b2Lydm9h8nZvYvJ2b2Hydm9p1JOzn58dtlvG3bukVBWGDFihKZOnao///zTVeS65ZZblJaWZsmoqxL9elu3bp1+//13j4J+//13rVu3zqNjAAAAAAAAVBgWjbjypYIvwsvMzHSty8zMVNWq1hRtS/ytglu3btU777xT5qCtW7eW+bYAAAAAAAAVjh8VoMpqwIABmjRpku68807985//1Hfffafly5drwoQJluSXuHD1/fff6/vvvy9zkDFGNhuXRQAAAAAAAFQU9evX1+LFi/W3v/1N/fr1U506dfT8889r1KhRluSXqHA1btw4X7cDAAAAAACgcqkEI64kqWXLllq8eHG5ZFO4AgAAAAAA8AWrv92oEirxpYIAAAAAAAAohUoy4qo8UbgCAAAAAADwhUpQuDpy5IiCg4MVEhJS5mOcOHFCeXl5Zfomwop/BgEAAAAAAPyRzV72xU9Ur15dw4cP9+gYQ4cOVY0aNcp0W/85EwAAAAAAAPArxhgZY7xynLLgUkEAAAAAAABf8KORU55YunSpBg0a5NHty8pmvFE2q6CcXz1Q3k3wrbyTlkWZ7BOWZUmSOZpnaV7+HznW5u3LtjQv75ilccrNDrAsK++4tb8oTuZam5d/ymZpnnFaGldZfs/DAs58a58LVrL6eWf168qpk5X7raiVXyYVGGTtfRcQZO2DMzDY2sdKkMPa/jnC8y3NC42wLi+gqrXjJQJqlH0enrKwW5xndf8UHGRdVojDuixJ9utetSDlGw9u28FrrfCE3Uu/zGw2m/LzS//aw4grAAAAAAAAH/BkrJDNT/4u9803nhTfPFemwtWgQYPUvn37cw4Te/vtt/Xtt9/qrbfeKlPjAAAAAAAAKiqjso/e9JO6lZKSkso1v0zjvd5+++0SXZ/4/fffa/r06WWJAAAAAAAAqNCMB//hNJ9edZ+Xl6eAAOvmsgEAAAAAAEDl4bM5rowxWrNmjWrXru2rCAAAAAAAAL9lPPmWFX+5VrCclbhw1bFjR7ef58+fX2hdgVOnTum3335TZmam7rrrLs9aCAAAAAAAUAFxyZ/nSly4WrJkievfNptNmZmZyszMLHb/oKAgde3aVS+88IJHDQQAAAAAAKiIPBpxBUmlKFxlZGRIOn0JYMOGDXXrrbfq+eefL3Lf4OBg1apVS0FBQd5pJQAAAAAAQAXDiCvPlXhy9ri4OMXFxalBgwYaN26c+vXr51r31yUmJsZrRavx48fLZrNp5MiRrnXGGKWkpCg2NlahoaFKTk7Wxo0bvZIHAAAAAADgDUbOMi84rUzfKjhu3Dh1797d220pZOXKlXr99dd16aWXuq2fOHGiJk2apClTpmjlypWKjo5Wp06ddPToUZ+3CQAAAAAAAKf9+uuv+uSTT3TgwAFJ0qJFi/TQQw/pvffe88rxy1S4ssKxY8d055136o033lD16tVd640xmjx5ssaOHatevXqpefPmmj59uo4fP66ZM2eWY4sBAAAAAAD+xxhnmZeK4OOPP1bz5s01bNgwNW/eXJ9//rl69uyp77//Xvfee68mT57scUaJClcBAQFlXgIDSzyNlpvhw4frpptu0nXXXee2PiMjQ5mZmercubNrncPhUFJSkpYtW1amLAAAAAAAAG8zHvxXETz77LN6++23lZmZqYkTJ+rOO+/UrFmztGrVKn388cd6/fXXPc4oUVWpXr16stlsHoeV1Pvvv681a9Zo5cqVhbYVfJNhVFSU2/qoqCjt2LHDkvYBAAAAAACcS0UZOVVW27ZtU9++fSVJ/fr10+DBg9W1a1dJ0g033KA+ffp4nFGiwtX27ds9DiqpnTt36qGHHtLChQsVEhJS7H5/LaQZY85aXMvNzVVubq7buqC8fDmCAzxrMAAAAAAAQBEqysipsgoNDdX+/ftVp04dZWZmKj8/XwcPHlTt2rWVlZV11rpOSfndHFerV6/W/v37dfnllyswMFCBgYFKS0vTK6+8osDAQNdIq4KRVwX2799faBTWmcaPH6/IyEi3ZcL7q3zaFwAAAAAAcP6q7N8q2KlTJ91yyy167bXX1K9fP3Xp0kX33HOPvvjiCw0ePFhXX321xxl+V7i69tprtWHDBq1bt861tG7dWnfeeafWrVunhg0bKjo6WosWLXLdJi8vT2lpaUpMTCz2uGPGjNHhw4fdlsd6t7aiSwAAAAAAAJXOSy+9pNjYWL311lsaPny4XnvtNW3evFndunVTenq6nnvuOY8zSnSp4O+//y5JuuCCCxQQEOD6uaTq169f4n0jIiLUvHlzt3Xh4eGqWbOma/3IkSOVmpqqhIQEJSQkKDU1VWFhYa7rKovicDjkcDjc1jm5TBAAAAAAAPiIMZX7UsGaNWvqgw8+cFuXnp6uQ4cOqUaNGl7JKFHhqkGDBrLb7dq0aZMaN26sBg0alHiydpvNplOnTnnUyL8aPXq0cnJyNGzYMGVlZalNmzZauHChIiIivJoDAAAAAABQVhXlkj9v81bRSiph4eqaa66RzWZTWFiY289WWbJkidvPNptNKSkpSklJsawNAAAAAAAApVHZJ2eXpJycHM2ePVtr167VkSNHVKtWLXXo0EHXXXedV45fosLVXwtHf/0ZAAAAAAAA7oyp3COufvnlF3Xq1En79++XMUb5+flq0qSJXnrpJV1zzTWaM2eOx98sWKLJ2Rs2bKhHH33UoyAAAAAAAIDzifHgv4rgwQcfVO/evZWdna1jx47pb3/7m2688Ubt3LlTAQEBeuKJJzzOKFHhavv27Tpw4IDHYQAAAAAAAKgcfvjhB6WmpiowMFDBwcF68sknNXPmTNWsWVNTpkzRrFmzPM4oUeEKAAAAAAAApWOMs8xLRVClShW3gU5//PGH7PbTpab4+HhlZWV5nFGiOa4AAAAAAABQOhXlkr+zMcYU+wV9Xbp0UY8ePfT444/L6XRqwoQJ6ty5syRp3759Xvl2QUZcAQAAAAAA+ICRs8yLv4iIiNCTTz5Z5Lbnn39eUVFRuvXWW3X77berVq1amjhxoiTp2LFjeuGFFzzOL/GIq3Xr1umpp54qU4g3JuMCAAAAAACoSIyp+COujh8/rh07dhS5rXr16vr88891/PhxSVJYWJhrW6NGjdSoUSOP80tcuFq/fr3Wr19fqoMXDCejcAUAAAAAAM43/jRyypfOLFh5W4kLV40aNVK7du181hAAAAAAAADgTCUuXLVv315vvfWWL9sCAAAAAABQaVSGSwXLG5OzAwAAAAAA+EBlmJxdknbt2qVffvlFTqf17SrxiKvK6MTC9PJugm/lW/eAOpltbRU5/6S1NdeTudbm5R0PsjYvx9r+ncgOsCwr57i1L6x5udbmOZ2V+y84J09W3v45863tWzm8x6jUrHzuOfMtizqdV8lfV+z2or/O22d51v3KU2CgtX1zhFj7/iHYYW3/QkOt/agU5LA2zxFu3YtLkMPa1xVHeI6lecGhxyzNCwi09nzaHRY+1wOsfV0Ju873GZXl1+rXX3+tiy++WA6HQ02bNtVll12mSy+91LXUqlXLZ9nndeEKAAAAAADAV/KNtUV3X6ldu7YCAwO1Z88erV27VmvXrpXN9r++RUVFFSpmNW3aVIGBnpedKFwBAAAAAAD4QGUZcXXjjTfqrbfe0qFDh7R+/Xr99NNPrv9v2rRJmZmZyszM1IIFC1wFraCgIDVp0kTr1q3zKLtEhavyuIYRAAAAAAAA/qNGjRrq0KGDOnTo4FrndDq1ZcuWQgWt3bt3a8OGDR5nMuIKAAAAAADAB5yV5FLBs7Hb7WratKmaNm2q3r17u9ZnZWVp/fr1Hh+fwhUAAAAAAIAPWPx9PH6levXqSk5O9vg4FK4AAAAAAAB8oDKMuKpfv77bROxWo3AFAAAAAADgA5Vhcvbt27crLy+v3PLt5ZYMAAAAAABQieUbW5mX0tq9e7f69eunmjVrKiwsTC1atNDq1atd240xSklJUWxsrEJDQ5WcnKyNGzeW6NjBwcGlbo+3+GXhaurUqbr00ktVtWpVVa1aVW3bttW8efNc2z052QAAAAAAAJVJVlaW2rVrp6CgIM2bN0+bNm3Siy++qGrVqrn2mThxoiZNmqQpU6Zo5cqVio6OVqdOnXT06NHya3gJ+GXhqm7dupowYYJWrVqlVatWqWPHjurRo4erOFVRTzYAAAAAADh/OD1YSuO5555TvXr1NG3aNF155ZVq0KCBrr32WjVq1EjS6QFAkydP1tixY9WrVy81b95c06dP1/HjxzVz5kxvdNVn/LJw1a1bN914441q3LixGjdurGeffVZVqlTRihUrKvTJBgAAAAAA5w+nsZV5KY3PPvtMrVu31m233aY6deqoZcuWeuONN1zbMzIylJmZqc6dO7vWORwOJSUladmyZV7rry/4ZeHqTPn5+Xr//feVnZ2ttm3bVuiTDQAAAAAAzh9OU/YlNzdXR44ccVtyc3OLzNm2bZumTp2qhIQELViwQPfff78efPBBvfPOO5KkzMxMSVJUVJTb7aKiolzb/JXfFq42bNigKlWqyOFw6P7779fs2bPVrFmzCn2yAQAAAADA+cOTydnHjx+vyMhIt2X8+PFF5jidTrVq1Uqpqalq2bKlhgwZonvvvVdTp051289mcx/JZYwptM7fBJZ3A4pz0UUXad26dfrzzz/18ccfa8CAAUpLS3NtL+3Jzs3NLVSZzD/llCPQb2t3AAAAAACgAnOast92zJgxGjVqlNs6h8NR5L4xMTFq1qyZ27qmTZvq448/liRFR0dLOj3yKiYmxrXP/v37Cw0M8jd+W7UJDg7WhRdeqNatW2v8+PG67LLL9PLLL7ud7DOd62QXVal84YdtPu0DAAAAAABAWTgcDlWtWtVtKa5w1a5dO23ZssVt3a+//qq4uDhJUnx8vKKjo7Vo0SLX9ry8PKWlpSkxMdF3nfACvy1c/ZUxRrm5uWU+2WPGjNHhw4fdlkfaNLSi6QAAAAAA4Dxk1eTsDz/8sFasWKHU1FRt3bpVM2fO1Ouvv67hw4dLOn3V2siRI5WamqrZs2fr559/1sCBAxUWFqa+ffv6oute45eXCj7++OPq0qWL6tWrp6NHj+r999/XkiVLNH/+fLeTnZCQoISEBKWmpp7zZDscjkKVyeNcJggAAAAAAHwk34NLBUvjiiuu0OzZszVmzBg99dRTio+P1+TJk3XnnXe69hk9erRycnI0bNgwZWVlqU2bNlq4cKEiIiKsaWQZ+WXhat++fbrrrru0d+9eRUZG6tJLL9X8+fPVqVMnSRX3ZAMAAAAAgPNHaUdOeaJr167q2rVrsdttNptSUlKUkpJiWZu8wS8LV2+++eZZt1fUkw0AAAAAAM4fnkzOjtP8snAFAAAAAABQ0Vl1qWBlxiRPAAAAAAAA8EuMuAIAAAAAAPABK+e4qqwoXAEAAAAAAPgAc1x5jsIVAAAAAACAD+Qz4spjFK4AAAAAAAB8gBFXnmNydgAAAAAAAPglRlwBAAAAAAD4AJOze47CFQAAAAAAgA/kc6mgxyhcAQAAAAAA+AAjrjx3Xheudq8PtjTPbre21Op0WvcEceZb+2R0nrI279RJa++73Fxr807kOC3Nyz2RZ1mW09quKS/X2sCTFj82rT6fzkr8J6pTp6ztW36+pXGA3woKsvY9hN3CGWXtAdb2zeGw9pdCoMX33dFAa/OsfmyGhFn34HQ4AizLkqTAoCBL86x8nktSQJC1zz1bJZ4ZO8GCDCZn99x5XbgCAAAAAADwlUr8d1jLVOLaKQAAAAAAACoyRlwBAAAAAAD4ACOuPEfhCgAAAAAAwAeY48pzFK4AAAAAAAB8gBFXnqNwBQAAAAAA4AMUrjxH4QoAAAAAAMAHKFx5jm8VBAAAAAAAgF9ixBUAAAAAAIAPMDm75/xyxNX48eN1xRVXKCIiQnXq1FHPnj21ZcsWt32MMUpJSVFsbKxCQ0OVnJysjRs3llOLAQAAAAAA3OWbsi84zS8LV2lpaRo+fLhWrFihRYsW6dSpU+rcubOys7Nd+0ycOFGTJk3SlClTtHLlSkVHR6tTp046evRoObYcAAAAAADgNApXnvPLSwXnz5/v9vO0adNUp04drV69Wtdcc42MMZo8ebLGjh2rXr16SZKmT5+uqKgozZw5U0OGDCmPZgMAAAAAALhwqaDn/HLE1V8dPnxYklSjRg1JUkZGhjIzM9W5c2fXPg6HQ0lJSVq2bFm5tBEAAAAAAOBMjLjynN8XrowxGjVqlNq3b6/mzZtLkjIzMyVJUVFRbvtGRUW5tgEAAAAAAKBi88tLBc80YsQI/fTTT1q6dGmhbTabze1nY0yhdQVyc3OVm5vrti7Pma9ge4D3GgsAAAAAAPD/MXLKc3494uqBBx7QZ599pm+++UZ169Z1rY+OjpakQqOr9u/fX2gUVoHx48crMjLSbfn3tq2+azwAAAAAADiv5RtbmRec5peFK2OMRowYoU8++USLFy9WfHy82/b4+HhFR0dr0aJFrnV5eXlKS0tTYmJikcccM2aMDh8+7Lbc3/BCn/YDAAAAAACcv5ym7AtO88tLBYcPH66ZM2fq008/VUREhGtkVWRkpEJDQ2Wz2TRy5EilpqYqISFBCQkJSk1NVVhYmPr27VvkMR0OhxwOh9s6LhMEAAAAAAC+wqWCnvPLwtXUqVMlScnJyW7rp02bpoEDB0qSRo8erZycHA0bNkxZWVlq06aNFi5cqIiICItbCwAAAAAAUBiFK8/5ZeHKmHPfszabTSkpKUpJSfF9gwAAAAAAAGA5vyxcAQAAAAAAVHROZ3m3oOKjcAUAAAAAAOADXCroOQpXAAAAAAAAPkDhynMUrgAAAAAAAHzASeHKYxSuAAAAAAAAfIARV56zl3cDAAAAAAAAgKIw4goAAAAAAMAHGHHlOQpXAAAAAAAAPkDhynMUrgAAAAAAAHyAydk9R+EKAAAAAADABxhx5bnzunD1+9ZTlubZA2yW5jl5hniN02lt3qmT1t53J09Zm2flY9Pq+87q553V/Ttp8WPTSk6L/xzmzLc0zvL+wXvsdmvfP1jNHmBtXm6utc8FK++/gABr+3bqpLWPTbvFXysVGGRx/yz+rBBwxOJfRBay+nXT6tcxqwUGVt7fQwkWZDidlff8WYVvFQQAAAAAAIBfOq9HXAEAAAAAAPiKM58RV56icAUAAAAAAOADXCroOQpXAAAAAAAAPkDhynMUrgAAAAAAAHyAwpXnKFwBAAAAAAD4AHNceY5vFQQAAAAAAIBfonAFAAAAAADgA06nrcxLaaSkpMhms7kt0dHRru0DBw4stP2qq67ydnd9gksFAQAAAAAAfMDKOa4uvvhiffXVV66fAwIC3LbfcMMNmjZtmuvn4OBgy9rmCb8ccfXtt9+qW7duio2Nlc1m05w5c9y2G2OUkpKi2NhYhYaGKjk5WRs3biyfxgIAAAAAABTB6Sz7UlqBgYGKjo52LbVr13bb7nA43LbXqFHDS730Lb8sXGVnZ+uyyy7TlClTitw+ceJETZo0SVOmTNHKlSsVHR2tTp066ejRoxa3FAAAAAAAoGjOfFuZl9zcXB05csRtyc3NLTYrPT1dsbGxio+PV+/evbVt2za37UuWLFGdOnXUuHFj3Xvvvdq/f7+vu+8Vflm46tKli5555hn16tWr0DZjjCZPnqyxY8eqV69eat68uaZPn67jx49r5syZ5dBaAAAAAACAwjyZ42r8+PGKjIx0W8aPH19kTps2bfTOO+9owYIFeuONN5SZmanExEQdPHhQ0uk6y3vvvafFixfrxRdf1MqVK9WxY8ezFsL8hc0YY8q7EWdjs9k0e/Zs9ezZU5K0bds2NWrUSGvWrFHLli1d+/Xo0UPVqlXT9OnTS3zsrxvd6O3mnpU9wNqvwXTm+/VdW6GUZZimJ06dtPa+O3nK2jwrH5tW33dWP++s7t9Jix+bVnI6rX4eWBpnef/gPXZ75f4abXvAufepyKy8/wIsPpdWPzbtFv/JPTDI4v5Z/FkhwC+HMHiH5Y/NSv46FhhYeX8PXfvblz7PuPnDOWW+7fs9uhQqLDkcDjkcjnPeNjs7W40aNdLo0aM1atSoQtv37t2ruLg4vf/++0UOGvInFW5y9szMTElSVFSU2/qoqCjt2LGjPJoEAAAAAABQiCeTs5e0SFWU8PBwXXLJJUpPTy9ye0xMjOLi4ord7k8qXOGqgM3mfucbYwqtO1Nubm6hSmWeyVewrZKXxwEAAAAAQLnIzy+fEWu5ubnavHmzrr766iK3Hzx4UDt37lRMTIzFLSu9CjdANDo6WtL/Rl4V2L9/f6FRWGcq6trQWVnbit0fAAAAAADAE57McVUajzzyiNLS0pSRkaEffvhBt956q44cOaIBAwbo2LFjeuSRR7R8+XJt375dS5YsUbdu3VSrVi3dfPPNPuq591S4wlV8fLyio6O1aNEi17q8vDylpaUpMTGx2NuNGTNGhw8fdlv6VG9oRZMBAAAAAMB5yKrC1a5du9SnTx9ddNFF6tWrl4KDg7VixQrFxcUpICBAGzZsUI8ePdS4cWMNGDBAjRs31vLlyxUREeGjnnuPX14qeOzYMW3dutX1c0ZGhtatW6caNWqofv36GjlypFJTU5WQkKCEhASlpqYqLCxMffv2LfaYRV0bymWCAAAAAADAVzyZ46o03n///WK3hYaGasGCBZa0wxf8snC1atUqdejQwfVzwQz4AwYM0Ntvv63Ro0crJydHw4YNU1ZWltq0aaOFCxdWiEohAAAAAAAASsYvC1fJyckypviv7bbZbEpJSVFKSop1jQIAAAAAACgFZzlNzl6Z+GXhCgAAAAAAoKKz6lLByozCFQAAAAAAgA9QuPIchSsAAAAAAAAfoHDlOQpXAAAAAAAAPsAcV56zl3cDAAAAAAAAgKIw4goAAAAAAMAHuFTQcxSuAAAAAAAAfIDClecoXAEAAAAAAPgAc1x5jsIVAAAAAACADzDiynMUrgAAAAAAAHzBacq7BRXeeV24OrDvZHk3AQAqlICA8m6B7wQEWPzXsCBr4yT+2gfAu5wWfxhzOi2N06lTfNgEAH9wXheuAAAAAAAAfMXOiCuPUbgCAAAAAADwAVs+hStPUbgCAAAAAADwAUZceY7CFQAAAAAAgA9QuPIchSsAAAAAAAAfsFG48pi9vBsAAAAAAAAAFIURVwAAAAAAAD5gZ3J2j1G4AgAAAAAA8AHmuPIchSsAAAAAAAAfoHDluQo9x9Vrr72m+Ph4hYSE6PLLL9d3331X3k0CAAAAAACQdHpy9rIuOK3CFq4++OADjRw5UmPHjtXatWt19dVXq0uXLvr999/Lu2kAAAAAAADwggpbuJo0aZLuueceDR48WE2bNtXkyZNVr149TZ06tbybBgAAAAAAILvTlHnBaRWycJWXl6fVq1erc+fObus7d+6sZcuWlVOrAAAAAAAA/seeb8q84LQKOTn7H3/8ofz8fEVFRbmtj4qKUmZmZpG3yc3NVW5urtu6k8apIFuFrN0BAAAAAAA/x8gpz1Xoqo3NZnP72RhTaF2B8ePHKzIy0m359GSGFc0EAAAAAADnISZn91yFLFzVqlVLAQEBhUZX7d+/v9AorAJjxozR4cOH3ZYeQfFWNBcAAAAAAJyHmOPKcxWycBUcHKzLL79cixYtclu/aNEiJSYmFnkbh8OhqlWrui1cJggAAAAAAOC/KuQcV5I0atQo3XXXXWrdurXatm2r119/Xb///rvuv//+8m4aAAAAAAAAk6x7QYUtXN1xxx06ePCgnnrqKe3du1fNmzfXl19+qbi4uPJuGgAAAAAAAHNVeUGFLVxJ0rBhwzRs2LDybgYAAAAAAEAhdqezvJtQ4VXowhUAAAAAAIC/YpJ1z1G4AgAAAAAA8AHmuPIcX6sHAAAAAAAAv8SIKwAAAAAAAB9gcnbPUbgCAAAAAADwAea48hyFKwAAAAAAAB+gcOU5ClcAAAAAAAA+wOTsnqNwBQAAAAAA4APMceU5vlUQAAAAAAAAfokRVwAAAAAAAD7AHFdeYFAqJ06cMOPGjTMnTpwgrwJlkVex8ypz38ir2HmVuW/kVdws8sjz1yzyKnZeZe4beRU7z+q+wXo2Ywzlv1I4cuSIIiMjdfjwYVWtWpW8CpJFXsXOq8x9I69i51XmvpFXcbPII89fs8ir2HmVuW/kVew8q/sG6zHHFQAAAAAAAPwShSsAAAAAAAD4JQpXAAAAAAAA8EsUrkrJ4XBo3Lhxcjgc5FWgLPIqdl5l7ht5FTuvMveNvIqbRR55/ppFXsXOq8x9I69i51ndN1iPydkBAAAAAADglxhxBQAAAAAAAL9E4QoAAAAAAAB+icIVAAAAAAAA/BKFKwAAAAAAAPglClcAAAAAAADwS4Hl3QB/t2vXLk2dOlXLli1TZmambDaboqKilJiYqPvvv1/16tUr7yZ6JDs7WzNnzizUv3bt2qlPnz4KDw8v7yaWWWXum1S5+2d13yrzuQRwfjDG6Kuvvirydezaa6+VzWYjjzzLs8ir+HkA4A9sxhhT3o3wV0uXLlWXLl1Ur149de7cWVFRUTLGaP/+/Vq0aJF27typefPmqV27dl7LtPID9KZNm9SpUycdP35cSUlJbv1LS0tTeHi4Fi5cqGbNmnklrzL3TaJ/3uqf1X0rj3Mp8SGFPPKszqrMebt371bXrl21YcMGNW/e3O117Oeff9Zll12mzz77TBdccAF553leZe4bed7Pq6yvmeRV/DwKuOchg2K1bt3ajBw5stjtI0eONK1bt/Za3saNG01sbKypVq2a6dGjh7nvvvvMvffea3r06GGqVatmLrjgArNx40av5SUnJ5vevXub3NzcQttyc3NNnz59THJysleyKnPfjKF/3uyf1X2zOs8YY3bt2mVatGhhAgICzGWXXWY6d+5sOnXqZC677DITEBBgWrVqZXbt2lXhssgjz1+zKnte9+7dTceOHc2ePXsKbduzZ4/p2LGj6dGjh1eyyKvYeZW5b+R5N68yv2aSV7HzrO4b/AOFq7MICQkxv/zyS7HbN2/ebEJCQryWZ/UH6NDQ0LMWGzZs2GBCQ0O9klWZ+2YM/fNm/6zum9V5xvAhhTzyrM6q7Hnh4eFm3bp1xW5fs2aNCQ8P90oWeRU7rzL3jTzv5lXm10zyKnae1X2Df6BwdRbx8fHmrbfeKnb7W2+9ZeLj472WZ/UH6NjYWDNnzpxit8+ePdvExsZ6Jasy980Y+ufN/lndN6vzjOFDCnnkWZ1V2fNq1aplFi9eXOz2r7/+2tSqVcsrWeRV7LzK3DfyvJtXmV8zyavYeVb3Df6BbxU8i0ceeUT333+/RowYoU8//VQrVqzQDz/8oE8//VQjRozQ0KFDNXr0aK/lVa9eXenp6cVu37p1q6pXr+61vHvvvVcDBgzQCy+8oPXr1yszM1P79u3T+vXr9cILL2jQoEEaMmSIV7Iqc98k+ufN/lndN6vzJCk0NFSHDh0qdntWVpZCQ0MrXBZ55PlrVmXP6927twYMGKD//ve/Onz4sGv94cOH9d///ld33323+vbt65Us8ip2XmXuG3nezavMr5nkVew8q/sGP1HelTN/9/7775s2bdqYwMBAY7PZjM1mM4GBgaZNmzbmgw8+8GrWuHHjTGRkpHn++efNunXrzN69e01mZqZZt26def7550316tXNk08+6dXMCRMmmJiYGGOz2Yzdbjd2u93YbDYTExNjnnvuOa/lVOa+GUP/vN0/K/tWHnkjRoww9erVMx999JH5888/Xev//PNP89FHH5n69eubBx98sMJlkUeev2ZV9rzc3Fxz//33m+DgYGO3201ISIgJCQkxdrvdBAcHm6FDhxZ5qTd5519eZe4bed7Nq8yvmeRV7Dyr+wb/QOGqhPLy8syePXvMnj17TF5ens9yrP4AXWDbtm1m2bJlZtmyZWbbtm0+yfCHvv32228+y/GH/vnqvjOmfPpnVd+szuNDCnnkVe6+lUeeMcYcPnzYfP3112bmzJlm5syZZvHixebw4cNezfhr3uLFi8mrgHmVuW/nS56vn+uV/TWTvIqbVx6/X1H+bMYYU96jvlBYRkaGMjMzJUnR0dGKj48v5xZ5T3n2LTg4WOvXr1fTpk19llGZ7zupcvZv7969mjp1qpYuXaq9e/cqICBA8fHx6tmzpwYOHKiAgACf5B45ckSrVq3Svn37JJ0+n5dffrmqVq3qk6zVq1e73Xe+yiKPPH/NKq88q57nAOBtVr+GnQ+/E8ireFkofxSuKpCdO3dq3Lhxeuutt7x2zJycHK1evVo1atRQs2bN3LadOHFCH374ofr37++VrM2bN2vFihVKTEzURRddpF9++UUvv/yycnNz1a9fP3Xs2NErOZI0atSoIte//PLL6tevn2rWrClJmjRpktcyz5SVlaXp06crPT1dsbGx6t+/v+rVq+e1469du1bVqlVzFY1mzJihqVOn6vfff1dcXJxGjBih3r17ey3vgQce0O23366rr77aa8c8m1dffVWrVq3STTfdpNtvv13vvvuuxo8fL6fTqV69eumpp55SYGCgV7JWrVql6667TvHx8QoNDdUPP/ygO++8U3l5eVqwYIGaNm2qBQsWKCIiwit5AOAL2dnZmjlzppYtW6bMzEzZbDZFRUWpXbt26tOnj8LDwy1ry759+/R///d/euKJJ7x63F27dqlatWqqUqWK2/qTJ09q+fLluuaaa7yWdfDgQf3000+67LLLVKNGDf3xxx968803lZubq9tuu82nfwAr0LBhQy1YsEAJCQk+yzh58qS++OILpaenKyYmRjfffLNXHyu7du1SSEiIatWqJUn67rvv9O9//9v1fmX48OFq27at1/JefPFF3XrrrYqLi/PaMc9l7ty5WrVqlW644Qa1bdtWixcv1gsvvOB6z3Lfffd5NS8nJ0ezZs0q8o9t1157rVezAMBvlO+AL5TGunXrjN1u99rxtmzZYuLi4lyXfiUlJbl9rWhmZqbX8ubNm2eCg4NNjRo1TEhIiJk3b56pXbu2ue6668y1115rAgMDzddff+2VLGOMsdlspkWLFiY5Odltsdls5oorrjDJycmmQ4cOXsuLiYkxf/zxhzHm9OVmMTExJjo62nTq1MnUrVvXREZGms2bN3str2XLlq5vlXnjjTdMaGioefDBB83UqVPNyJEjTZUqVcybb77ptbyCx0hCQoKZMGGC2bt3r9eO/VdPPfWUiYiIMLfccouJjo42EyZMMDVr1jTPPPOMSU1NNbVr1zZPPPGE1/LatWtnUlJSXD+/++67pk2bNsYYYw4dOmRatGjhk+vkjx07Zl5//XUzcOBAc8MNN5guXbqYgQMHmjfeeMMcO3bM63nFyczM9Pr8a8YYs3PnTnP06NFC6/Py8kxaWppXs/744w+zePFic/DgQWOMMQcOHDATJkwwTz75pNm0aZNXs4oTHx9vfv31V5/n5OXlmdmzZ5uJEyead9991+uPlZ07d5oDBw64fv72229N3759Tfv27c2dd95pli1b5rWsF154wWzfvt1rxyuJzz77zDzxxBOufnz99demS5cu5vrrrzf/93//5/W848ePmzfffNPcfffd5oYbbjA33XSTGTFihPnqq6+8mrNx40YTGxtrqlWrZnr06GHuu+8+c++995oePXqYatWqmQsuuOCs3wzrbd5+v7Jnzx5zxRVXGLvdbgICAkz//v3dXl+8+X7FGGN++OEHExkZaWw2m6levbpZtWqViY+PNwkJCebCCy80oaGhZvXq1V7Le/nll4tcAgICzJgxY1w/e0Pbtm1NVlaWMcaY/fv3m+bNm5vg4GCTkJBgQkJCTP369c2uXbu8klWQ9+WXXxpjjJkzZ46x2+2me/fu5tFHHzU333yzCQoKMnPnzvVans1mMwEBAea6664z77//vs8vF5o6daoJDAw0l19+ualataqZMWOGiYiIMIMHDzZDhgwxoaGhZvLkyV7LS09PN3FxcaZmzZqu6Rtuuukm06ZNGxMQEGBuu+02c/LkSa/l+ct7FWN4v+ItleH9ipXvVeA/KFz5kU8//fSsy0svveTVN2Y9e/Y0Xbt2NQcOHDDp6emmW7duJj4+3uzYscMY4903gm3btjVjx441xhgza9YsU716dfP444+7tj/++OOmU6dOXskyxpjU1FQTHx9fqBgWGBjokzfvNpvN7Nu3zxhjTO/evU1ycrLJzs42xhhz4sQJ07VrV3Prrbd6LS8sLMx1P7Vs2bLQB6733nvPNGvWzGt5NpvNfPXVV+ahhx4ytWrVMkFBQaZ79+5m7ty5Jj8/32s5xhjTsGFD8/HHHxtjTn/4CQgIMDNmzHBt/+STT8yFF17otbzQ0FC3uc/y8/NNUFCQyczMNMYYs3DhQhMbG+u1PGP860MmHzBLx8oPmMZU7g+ZfMD03gfM5ORk07t37yLPYW5urunTp49JTk72SpYxxqxfv/6sywcffODV53n//v3NVVddZVauXGkWLVpkWrdubS6//HJz6NAhY8zp1xWbzea1vOuuu84MHjzYHDlyxDz//POmbt26ZvDgwa7t99xzj+nZs6fX8mw2m6lbt65p0KCB22Kz2cwFF1xgGjRoYOLj472WVfB+5d577zUtWrRw/THqjz/+MImJiWbQoEFeyTLGmIiICJORkWGMMaZNmzZmwoQJbttfffVV07JlS6/l2Ww2M23aNNOjRw8TFBRkatasaR566CGzYcMGr2WcqWnTpub11183xhizePFiExISYv71r3+5tk+bNs00bdrUa3ldunQxQ4YMcb33Gj9+vOnSpYsxxphff/3VNGjQwIwbN84rWf70XsUY3q+UVmV+v2J1QRz+gcKVHykY1VLw7YVFLd58Aa1Tp4756aef3NYNGzbM1K9f3/z2229efcGuWrWqSU9PN8acLgwEBga6vThv2LDBREVFeSWrwI8//mgaN25s/va3v7km1LeicFVUwWzFihWmbt26XsurWbOmWbVqlTHm9P24bt06t+1bt241oaGhXss7s395eXnmgw8+MNdff70JCAgwsbGx5vHHH3fdv54KDQ11FeWMMSYoKMj8/PPPrp+3b99uwsLCvJJljDFxcXFm6dKlrp/37NljbDabOX78uDHGmIyMDBMSEuK1PGOs/ZDJB8yK+wGzIK+yfsjkA6b3PmCGhoae9Xfbhg0bvP47obj3KwXrvfm6Ehsba3744QfXzydOnDA9evQwLVq0MAcPHvT6B8zq1au7Rj/k5eUZu93ulr9mzRpzwQUXeC3vvvvuMy1atCg04sIX71nOfE1p3Lix+fzzz922f/PNN6ZBgwZey4uMjDTr1683xpx+v1Lw7wJbt2716u/0M/u3b98+89xzz5kmTZoYu91urrjiCvP666+bI0eOeC2vqPcsZ76GZWRkeLV/YWFhbqNlcnNzTVBQkGvU/5w5c7x2/1EQ5/1KafOser9idUEc/oHClR+JjY01s2fPLnb72rVrvfqCHRERUeSw1BEjRpi6deuab7/91ieFK2OMqVKlitsol+3bt3u9OGCMMUePHjX9+/c3l156qfnpp59MUFCQzwpX+/fvN8acvh/PLLQYc/qNi8Ph8Fpev379zD333GOMMea2224z//jHP9y2p6ammksuucRreWf+MjrTjh07zLhx40xcXJzXHivx8fFm3rx5xpjTH+7sdrv58MMPXdu/+OILr76pfuihh0zz5s3NvHnzzOLFi02HDh3c3ojNnz/fNGrUyGt5xlj7IZMPmBX3A6YxlftDJh8wvfcBMzY21syZM6fY7bNnz/bqyNFatWqZN99802zfvr3I5YsvvvDq8zw8PLzQpS0nT540PXv2dP1+93ZewYciYwq/Z9mxY4fX37PMnj3b1KtXz7z66quudb4qXBW8X6lTp06h42/fvt2r71e6d+9uHnvsMWOMMddff32hER5vvPGGSUhI8Fpece9Xvv32WzNgwAATHh5uwsPDvZZX8H7ZGGN2795tbDab+eKLL1zblyxZ4tU/XMbGxrr94TcrK8vYbDbXa+W2bdu8dv9REOf9SmlY+X7F6oI4/AOFKz/SrVs3889//rPY7evWrfNq5f+KK64w77zzTpHbhg8fbqpVq+a1F+xLL73UVYww5vQvuzMvkfjuu++8WvX/q1mzZpmoqChjt9t99mJ9ySWXmJYtW5oqVaqYTz75xG17WlqaV38Z7d692zRo0MBcc801ZtSoUSY0NNS0b9/e3Hvvveaaa64xwcHBbm+cPFXcG8ECTqfTLFy40CtZY8eONbVr1zaDBw828fHxZsyYMaZ+/fpm6tSp5t///repV6+eefjhh72SZczp4ubtt99uAgMDjc1mM4mJiWbbtm2u7QsWLHArnHmDlR8y+YBZcT9gGlO5P2TyAdN7HzDHjRtnIiMjzfPPP2/WrVtn9u7dazIzM826devM888/b6pXr+7VuWGuv/568/TTTxe73dvvVy655BLz3//+t9D6gteW+vXre/V1pUmTJm4jpz///HPXKFxjvD+KusCuXbtMx44dzQ033GD27t3rs8LVjTfeaG6++WZTvXp11+U2BZYvX+7VEfCbNm0yNWvWNP379zdPP/20qVKliunXr5959tlnTf/+/Y3D4TDTpk3zWp7dbj/r+5XDhw+7Rl56w/Dhw01CQoJ55plnzJVXXmkGDBhgmjRpYubNm2fmz59vLrnkEq+Oih0wYIBJSkoymzdvNtu2bTN33HGH28iSJUuWmHr16nkli4I471dKw8r3K1YXxOEfKFz5kW+//datuPNXx44dM0uWLPFaXmpqquuyhaIMHTrUa288p06dWqjyfqbHH3/cNYLIV3bu3GnmzJnjk8kkU1JS3Jb58+e7bX/kkUdM7969vZqZlZVlHn30UdOsWTMTEhJigoODTVxcnOnbt69ZuXKlV7MaNGjgGiXga6dOnTLPPPOM6dq1q2vo76xZs0y9evVMzZo1zcCBA31yH+bk5BQ5OacvWPkhkw+YFfcDpjGV+0MmHzC99wHTGGMmTJjgmkvLbre7RijExMSY5557zms5xpyea/Ddd98tdvuhQ4fM22+/7bW80aNHm86dOxe57eTJk6Z79+5efV1JSUkxs2bNKnb7448/bnr16uW1vDM5nU6TmppqoqOjTUBAgNdfVwYOHOi2/PUPM4888oi5/vrrvZq5detW07t3bxMREeEaQRMUFGQSExPPeqVBWZzrD23eduzYMTN48GDTvHlzc//995u8vDzz/PPPm+DgYGOz2UxycrJX27Nv3z5z1VVXuZ7nDRo0MGvWrHFt/+ijj8wrr7zilSwK4rxfKQ0r369YXRCHf6BwBQDlwKoPmf74AdObbzwr8wdMYyr3h0w+YHrvA+aZtm3bZpYtW2aWLVvmNnq0Ijt58qQ5fPhwsdtPnTpl6TdUZmdnmxMnTvg0Y9WqVWby5Mmu+XascuzYMZOTk+OTYzudTpOZmWn27Nnjmnu0ssrJyfHqpc5/9euvvxa6esEXKIhTEC8pq9+vbN261dxxxx2WFMThH2zGGCMAQLnIyMhQZmamJCk6Olrx8fHl3CLPnDp1SsePH1fVqlWL3J6fn69du3YpLi7OkvYcP35cAQEBcjgcPstYvXq1li5dqv79+6t69eo+yylKdna2AgICFBIS4vVjG2O0f/9+OZ1O1apVS0FBQV7P8AcnTpzQyZMnFRER4ZPjp6enKzc3V02aNFFgYKBPMgDAlyrbexWJ9yuV5f3K+fJeBZK9vBsAAOez+Ph4tW3bVm3btnW9Edy5c6cGDRpkSb63swIDA4t9EyhJe/bs0ZNPPum1vHM5ePCghg4d6tOMyy+/XA899JCqV69u6X0nSYcOHdKwYcN8cmybzaaoqCjFxMS43ghW5MdmcUJCQhQREeGzvISEBDVv3rxQ0crbeTk5OVq6dKk2bdpUaNuJEyf0zjvveC2LvIqdV5n7Rp538zZv3qxp06YpLy9Pbdu2VfXq1TVx4kQNGjRIixcv9lrOX/N++eUXSdIvv/yioUOH+iQvMDBQu3fvLjYvLS3N60Wrs/VvxYoVXi9a/TUvPDxcv/zyi/72t7/59P7bsmWLJPf+/fDDD14tWhVk/frrr4qKitLhw4f14IMP+uyxCT9QvgO+AAB/tW7dOq8OT/eXLPLI89esip63ZcsWExcX57qcJykpyezZs8e13dvfjkVexc2rzH0jz7t58+bNM8HBwaZGjRomJCTEzJs3z9SuXdtcd9115tprrzWBgYFuczaRR55VeVb3Df6BMesAYLHPPvvsrNu3bdtWIbPII89fsyp73qOPPqpLLrlEq1at0p9//qlRo0apXbt2WrJkierXr++1HPIqfl5l7ht53s176qmn9Pe//13PPPOM3n//ffXt21dDhw7Vs88+K0kaO3asJkyYoI4dO5JHnqV5VvcNfqK8K2cAcL4p+EtpwWSSRS3e+ouplVnkkeevWZU9r06dOuann35yWzds2DBTv35989tvv3l91Ad5FTevMveNPO/mVa1a1aSnpxtjjMnPzzeBgYFm9erVru0bNmzw6rfakkeeP2bBfzDHFQBYLCYmRh9//LGcTmeRy5o1aypkFnnk+WtWZc/LyckpNIfWv/71L3Xv3l1JSUn69ddfvZZFXsXOq8x9I8/7eQXsdrtCQkJUrVo117qIiAgdPnyYPPLKNc/qvqH8ULgCAItdfvnlZ/3QarPZZLz0ha9WZpFHnr9mVfa8Jk2aaNWqVYXWv/rqq+rRo4e6d+/ulRzyKn5eZe4bed7Na9CggbZu3er6efny5W6XI+7cuVMxMTHkkWd5ntV9g3+gcAUAFvv73/+uxMTEYrdfeOGF+uabbypcFnnk+WtWZc+7+eabNWvWrCK3TZkyRX369PFqUY68iptXmftGnnfzhg4dqvz8fNfPf/121Hnz5nl1DiHyyPPHLPgPm/HmqykAAAAAAADgJYy4AgAAAAAAgF+icAUAAAAAAAC/ROEKAAAAAAAAfonCFQAAAAAAAPwShSsAAAAAAAD4JQpXAAAAAAAA8EsUrgAAAAAAAOCXKFwBAAAAAADAL1G4AgAAAAAAgF+icAUAAAAAAAC/ROEKAAAAAAAAfonCFQAAAAAAAPwShSsAAHBWP/zwg2w2m2w2m8aPH++14zZo0EA2m81rx7PSwIEDZbPZtGTJEq8cr+D8Fixz5swptM+pU6f05ptvavjw4XrppZd04sSJQvusW7eu0LG2b9/ulTYCAACUh8DybgAAAPBv7777rtu/x4wZU46tsUZycrLS0tKUkZGhBg0aWJIZHh6uW2+9VZJUv359t235+fnq3LmzvvnmG9e6adOmafny5QoPD3etq1GjhgYMGCBJmj9/vvbt22dBywEAAHyHEVcAAKBYJ0+e1AcffCCbzabo6Ght3rxZa9as8cqxv/76a23evNkrx6oMatWqpbfffltvv/22WrVq5bbtvffe0/fff68PP/xQx44d09dff61du3Zp8uTJbvvVr1/fdYwmTZpY2HoAAADfoHAFAACKNW/ePP3xxx+65pprdN9990lyH4HliUaNGlFcKaFVq1bpxhtv1G233abw8HB17NhR/fv3148//ljeTQMAAPApClcAAKBYBUWqfv36qV+/fpKkWbNmKT8/v9C+BfM+nW05c06ooua42r59u2w2m5KTk5Wdna1Ro0apXr16Cg0NVatWrTR37lzXvh999JGuvPJKhYeHKyoqSg8++KBycnIKtctmsxV7ud/bb78tm82mlJQUt/y0tDRJUnx8vFv7i/Ltt9+qY8eOioiIUNWqVXXTTTdp06ZNRZ/QMqpTp47Wr1+vo0ePSjo9Eu6HH35QVFSUV3MAAAD8DXNcAQCAIh0+fFiff/65HA6Hbr31VlWrVk1XXnmlfvzxRy1atEg33HCD2/7t27cv8jj5+fmuYldAQECJsvPy8nTttdfqt99+01VXXaVjx47p22+/1c0336z58+drw4YNGj16tK644gp17txZ3333nV599VUdPHhQ7733Xpn7XKVKFQ0YMMA1P9Qtt9yiKlWqFLv/3Llz9fLLL6t58+a6/vrrtWHDBn355Zf64Ycf9PPPPys6OrrMbTnTXXfdpQkTJqhp06ZKTEzU2rVrtX37dk2dOtUrxwcAAPBXFK4AAECRPvzwQ504cUK33HKLqlWrJun0yKsff/xRM2bMKFS4Gjx4sAYPHlzoOA899JDy8/PVtWtXtWvXrkTZy5cvV3Jysn799VdVr15d0unRUXfffbeGDh2qQ4cOafHixbr66qslSXv27FHLli01c+ZMPf3002rYsGGZ+lwwz1RycrL27dunF1544ayTs0+ePFkzZsxQnz59JJ0u0t1xxx36+OOP9dprr+mpp54qUzv+Ki4uTosWLdKoUaM0b948JSQkaN68eWrRooVXjg8AAOCvuFQQAAAU6czLBAv07t1bgYGBmj17to4dO3bOY7z55pt65ZVX1KxZM82cOVN2e8neegQEBOiNN95wFa0kqX///qpdu7a2bt2qESNGuIpWkhQbG6s777xT0ulL96zSt29fV9GqoN2PP/64T9rRtm1bLV++XEePHtWaNWt03XXXefX4AAAA/ojCFQAAKGT79u1aunSpatSooRtvvNG1vnbt2rr++ut1/PhxzZ49+6zH+O677zRs2DDVrFlTc+fOVURERInzGzRooAsvvNBtnd1uV1xcnCSpU6dOhW7TqFEjSdLevXtLnOOpzp07F1rXuHFjy9sBAABQWVG4AgAAhcyYMUPGGN1+++0KDg5221YwAuts3y64Y8cO3XLLLXI6nfroo49KfeneBRdcUOT68PDwYrcXbMvNzS1Vlifq1q1baF3BnFhWtgMAAKCyYo4rAABQyIwZMyRJX3/9daFJ1wsKMl9//bX27t2rmJgYt+3Z2dnq3r27Dhw4oNdee00dOnQodX5x3+BX0u0l5XQ6Pbq9t9oBAACAolG4AgAAbn788Udt2bJFkpSenq709PQi93M6nZo5c6b+9re/udYZY3TXXXfpp59+0tChQzV06FBL2nw2QUFBxc7HtXPnTotbAwAAgNLgUkEAAOCm4BLAv//97zLGFLksXLhQ0v9GZhV44oknNHv2bHXo0EGvvPKK5W0vSkxMjA4ePKhDhw4V2lbQj78quDzy1KlTPm0bAAAAzo7CFQAAcDl16pQ++OADSXL7try/6tixo+rUqaN169bp559/liR9+OGHeuaZZ9SwYUN99NFHCgz0j4HdSUlJkqSnn37atc4Yo/Hjx2vZsmVF3iY2NlaSXCPPAAAAUD784x0lAADwC/PmzdOBAwd00UUXqWXLlsXuFxAQoFtvvVWvvfaaZsyYoQkTJmjMmDGSThd9zrx88EyPPfaYmjRp4pO2F+fRRx/Vf//7X02ePFlLlixRo0aNtGHDBu3cuVPDhg3Ta6+9Vug23bt31/Tp09W3b1917txZkZGRkqT//Oc/lrYdAADgfEfhCgAAuBRcJti7d+9z7tunTx+99tpreu+995Samqr8/HxJ0tKlS7V06dIibzNw4EDLC1cXX3yxFi9erDFjxujHH3/Utm3b1K5dO3344Ydau3Ztkbfp1auXXnrpJb3xxhuaO3eua0J6ClcAAADWshljTHk3AgAA4Hxms9kUFxen7du3e+2YycnJSktLU0ZGhho0aOC14wIAAFiJEVcAAAB+4I8//tDAgQMlSQ8++KBatWpV6mP8/vvveuKJJyRJv/zyizebBwAAUC4YcQUAAFDObDab28+zZ89Wz549S32cdevWFZqbjBFXAACgIqNwBQAAAAAAAL9kL+8GAAAAAAAAAEWhcAUAAAAAAAC/ROEKAAAAAAAAfonCFQAAAAAAAPwShSsAAAAAAAD4JQpXAAAAAAAA8EsUrgAAAAAAAOCXKFwBAAAAAADAL1G4AgAAAAAAgF+icAUAAAAAAAC/9P8At0FsiYAxYHcAAAAASUVORK5CYII=",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x400 with 2 Axes>"
       ]
@@ -573,9 +595,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'pvdeg.scenario' has no attribute 'Geospatial_Scenario'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[15], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m geospatial_standoff_scenario \u001b[38;5;241m=\u001b[39m \u001b[43mpvdeg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscenario\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGeospatial_Scenario\u001b[49m(\n\u001b[0;32m      2\u001b[0m     name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstandoff geospatial\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      3\u001b[0m     geospatial \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m      4\u001b[0m )\n",
+      "\u001b[1;31mAttributeError\u001b[0m: module 'pvdeg.scenario' has no attribute 'Geospatial_Scenario'"
+     ]
+    }
+   ],
    "source": [
     "geospatial_standoff_scenario = pvdeg.scenario.Geospatial_Scenario(\n",
     "    name='standoff geospatial',\n",
@@ -677,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
@@ -705,7 +739,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
diff --git a/tutorials_and_tools/tutorials_and_tools/Tools-Edge Seal Oxygen Ingress.ipynb b/tutorials_and_tools/tutorials_and_tools/Tools-Edge Seal Oxygen Ingress.ipynb
index 555c9d4..db21127 100644
--- a/tutorials_and_tools/tutorials_and_tools/Tools-Edge Seal Oxygen Ingress.ipynb	
+++ b/tutorials_and_tools/tutorials_and_tools/Tools-Edge Seal Oxygen Ingress.ipynb	
@@ -26,28 +26,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n",
-    "# !pip install pvdeg==0.3.3"
+    "# !pip install pvdeg==0.4.2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
-    "from pathlib import Path\n",
     "import pvdeg\n",
     "import pandas as pd\n",
     "from pvdeg import DATA_DIR\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import seaborn as sns\n",
     "import json"
    ]
   },
@@ -66,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -86,14 +84,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'latitude': 33.4152, 'longitude': -111.8315, 'altitude': 381.0, 'wind_height': 10, 'Source': 'PVGIS'}\n"
+      "{'Source': 'NSRDB', 'Location ID': '77855', 'City': '-', 'State': '-', 'Country': '-', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Surface Albedo Units': 'N/A', 'Version': '3.2.0', 'latitude': 33.41, 'longitude': -111.82, 'altitude': 381, 'tz': -7, 'wind_height': 2}\n"
      ]
     }
    ],
@@ -111,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -152,9 +150,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['OX000', 'OX001', 'OX002', 'OX003', 'OX004', 'OX005', 'OX006', 'OX007', 'OX008', 'OX009', 'OX010', 'OX011', 'OX012', 'OX013']\n",
+      "['W000', 'W001', 'W002', 'W003', 'W004', 'W005', 'W006', 'W007', 'W008', 'W009', 'W010', 'W011', 'W012', 'W013', 'W014', 'W015', 'W016', 'W017', 'W018', 'W019', 'W020', 'W021', 'W022', 'W023', 'W024', 'W025', 'W026', 'W027', 'W028', 'W029', 'W030', 'W031', 'W032', 'W033', 'W034', 'W035', 'W036', 'W037', 'W038', 'W039', 'W040']\n"
+     ]
+    }
+   ],
    "source": [
     "es = 'OX005'    #This is the number for the edge seal in the json file\n",
     "enc = 'OX003'   #This is the number for the encapsulant in the json file\n",
@@ -162,7 +169,9 @@
     "encw = 10   #This is the encapsulant width in [cm]\n",
     "sn = 20     #This is the number of edge seal nodes to use\n",
     "en = 50     #This is the number of encapsulant nodes to use\n",
-    "pressure = 0.2109 * (1 - 0.0065 * meta.get('altitude') / 288.15) ** 5.25588"
+    "pressure = 0.2109 * (1 - 0.0065 * meta.get('altitude') / 288.15) ** 5.25588\n",
+    "print(pvdeg.utilities._read_material(name=None, fname=\"O2permeation.json\"))\n",
+    "print(pvdeg.utilities._read_material(name=None, fname=\"H2Opermeation.json\"))"
    ]
   },
   {
@@ -171,20 +180,22 @@
    "source": [
     "## 3. Run the Calculations\n",
     "\n",
-    "This runs the calculations for diffusion using a simple 1-D finite difference calculation. The first set of calculations is just for diffusion, then the next two include some consumption of oxygen. In typical PV applications, it is common for oxygen ingress distance to be limited by its consumption rate in the encapsulant."
+    "This runs the calculations for diffusion using a simple 1-D finite difference calculation. The first set of calculations is just for diffusion, then the next two (when written) will include some consumption of oxygen. In typical PV applications, it is common for oxygen ingress distance to be limited by its consumption rate in the encapsulant."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The edge seal is None .\n",
-      "The encapsulant is None .\n"
+      "Oxygen ingress parameters loaded for the edge seal.\n",
+      "Oxygen ingress parameters loaded for the encapsulant.\n",
+      "The edge seal is Helioseal_101_dry .\n",
+      "The encapsulant is EVA .\n"
      ]
     }
    ],
@@ -204,12 +215,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[WinError 183] Cannot create a file when that file already exists: 'c:\\\\Users\\\\mkempe\\\\Documents\\\\GitHub\\\\new\\\\PVDegradationTools\\\\TEMP\\\\results'\n"
+     ]
+    }
+   ],
+   "source": [
+    "#This sets up an a variable with the output folder information.\n",
+    "output_folder = os.path.join(os.path.dirname(os.path.dirname(os.getcwd())), 'TEMP', 'results')\n",
+    "try:\n",
+    "    os.makedirs(output_folder)\n",
+    "except OSError as error:\n",
+    "    print(error)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -220,88 +253,99 @@
    ],
    "source": [
     "n_lines=10\n",
-    "for index in range(1,n_lines):\n",
-    "    plt.plot(oxygen_profile.iloc[int(np.trunc(index*(len(oxygen_profile)-1)/n_lines))], label=np.round(index*((len(oxygen_profile)-1)/n_lines)/8760,3))\n",
+    "times=oxygen_profile.index.tolist()\n",
+    "for index in range(n_lines):\n",
+    "    plt.plot(oxygen_profile.iloc[int(np.trunc((index+1)*(len(oxygen_profile)-1)/n_lines))], \n",
+    "        label=np.round(times[int(np.trunc((index+1)*((len(oxygen_profile)-1)/n_lines)))]/365.25/24,2)) \n",
     "plt.legend(title='Time [year]')\n",
     "plt.ylabel('Oxygen Concentration [g/cm³]')\n",
     "plt.xlabel('Distance From Edge [cm]')\n",
     "plt.ticklabel_format(axis='y', style='plain')\n",
+    "\n",
+    "plt.savefig(os.path.join(output_folder, 'Edge_Seal_O2_ingress.png'), bbox_inches='tight')  #Creates an image file of the standoff plot\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Save data outputs.\n",
+    "\n",
+    "This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation. First check that the output folder exists."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "         0.000000      0.076923      0.153846      0.230769      0.307692   \\\n",
-      "0.0       0.000048  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
-      "1.0       0.000048  4.025938e-07  0.000000e+00  0.000000e+00  0.000000e+00   \n",
-      "2.0       0.000048  7.914468e-07  3.295784e-09  0.000000e+00  0.000000e+00   \n",
-      "3.0       0.000048  1.165328e-06  9.571569e-09  2.635343e-11  0.000000e+00   \n",
-      "4.0       0.000049  1.525385e-06  1.852325e-08  1.006938e-10  2.058149e-13   \n",
-      "...            ...           ...           ...           ...           ...   \n",
-      "17514.0   0.000042  4.267309e-05  4.035455e-05  3.816304e-05  3.614315e-05   \n",
-      "17515.0   0.000043  4.255340e-05  4.035980e-05  3.817013e-05  3.614761e-05   \n",
-      "17516.0   0.000043  4.247354e-05  4.035994e-05  3.817617e-05  3.615158e-05   \n",
-      "17517.0   0.000045  4.244049e-05  4.035799e-05  3.818062e-05  3.615468e-05   \n",
-      "17518.0   0.000047  4.244476e-05  4.035617e-05  3.818351e-05  3.615683e-05   \n",
-      "\n",
-      "         0.384615   0.461538   0.538462   0.615385   0.692308   ...  \\\n",
-      "0.0       0.000000   0.000000   0.000000   0.000000   0.000000  ...   \n",
-      "1.0       0.000000   0.000000   0.000000   0.000000   0.000000  ...   \n",
-      "2.0       0.000000   0.000000   0.000000   0.000000   0.000000  ...   \n",
-      "3.0       0.000000   0.000000   0.000000   0.000000   0.000000  ...   \n",
-      "4.0       0.000000   0.000000   0.000000   0.000000   0.000000  ...   \n",
-      "...            ...        ...        ...        ...        ...  ...   \n",
-      "17514.0   0.000034   0.000032   0.000031   0.000029   0.000027  ...   \n",
-      "17515.0   0.000034   0.000032   0.000031   0.000029   0.000027  ...   \n",
-      "17516.0   0.000034   0.000032   0.000031   0.000029   0.000027  ...   \n",
-      "17517.0   0.000034   0.000032   0.000031   0.000029   0.000027  ...   \n",
-      "17518.0   0.000034   0.000032   0.000031   0.000029   0.000027  ...   \n",
-      "\n",
-      "         9.681818   9.883838   10.085859  10.287879  10.489899  10.691919  \\\n",
-      "0.0       0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
-      "1.0       0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
-      "2.0       0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
-      "3.0       0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
-      "4.0       0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
-      "...            ...        ...        ...        ...        ...        ...   \n",
-      "17514.0   0.000009   0.000009   0.000009   0.000009   0.000009   0.000009   \n",
-      "17515.0   0.000009   0.000009   0.000009   0.000009   0.000009   0.000009   \n",
-      "17516.0   0.000009   0.000009   0.000009   0.000009   0.000009   0.000009   \n",
-      "17517.0   0.000009   0.000009   0.000009   0.000009   0.000009   0.000009   \n",
-      "17518.0   0.000009   0.000009   0.000009   0.000009   0.000009   0.000009   \n",
-      "\n",
-      "         10.893939  11.095960  11.297980  11.500000  \n",
-      "0.0       0.000000   0.000000   0.000000   0.000000  \n",
-      "1.0       0.000000   0.000000   0.000000   0.000000  \n",
-      "2.0       0.000000   0.000000   0.000000   0.000000  \n",
-      "3.0       0.000000   0.000000   0.000000   0.000000  \n",
-      "4.0       0.000000   0.000000   0.000000   0.000000  \n",
-      "...            ...        ...        ...        ...  \n",
-      "17514.0   0.000009   0.000009   0.000009   0.000009  \n",
-      "17515.0   0.000009   0.000009   0.000009   0.000009  \n",
-      "17516.0   0.000009   0.000009   0.000009   0.000009  \n",
-      "17517.0   0.000009   0.000009   0.000009   0.000009  \n",
-      "17518.0   0.000009   0.000009   0.000009   0.000009  \n",
-      "\n",
-      "[17519 rows x 72 columns]\n"
+      "OX001=Tedlar\n",
+      "OX002=ST504\n",
+      "OX003=EVA\n",
+      "OX004=AAA polyamide backsheet\n",
+      "OX005=Helioseal_101_dry\n",
+      "OX006=Helioseal_101_wet\n",
+      "OX007=LPO3_dry\n",
+      "OX008=LPO3_wet\n",
+      "OX009=Nagase_dry\n",
+      "OX010=Nagase_wet\n",
+      "OX011=Oppanol_B_200\n",
+      "OX012=EVA\n",
+      "OX013=polyolefin elastomer (POE)\n"
      ]
     }
    ],
    "source": [
-    "print(oxygen_profile)"
+    "fpath = os.path.join(DATA_DIR, \"O2permeation.json\")\n",
+    "with open(fpath) as f:\n",
+    "    data = json.load(f)\n",
+    "f.close()\n",
+    "\n",
+    "material_list = ''\n",
+    "for key in data:\n",
+    "    if 'name' in data[key].keys():\n",
+    "        material_list = material_list + key + \"=\" + data[key]['name'] + '\\n'\n",
+    "material_list = material_list[0:len(material_list)-1]\n",
+    "print(material_list)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your results will be stored in c:\\Users\\mkempe\\Documents\\GitHub\\new\\PVDegradationTools\\TEMP\\results\n",
+      "The folder must already exist or the file will not be created\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (\"Your results will be stored in %s\" % output_folder)\n",
+    "print ('The folder must already exist or the file will not be created')\n",
+    "\n",
+    "#Writes the meterological data to an *.csv file.\n",
+    "pvdeg.weather.write(data_df=weather_df, metadata=meta, savefile=os.path.join(output_folder, 'WeatherFile.csv')) \n",
+    "\n",
+    "#Writes a file with the edge seal oxygen profile calculations.\n",
+    "pd.DataFrame(oxygen_profile).to_csv(os.path.join(output_folder, 'ES_Oxygen_profile.csv')) \n",
+    "\n",
+    "#Writes a file with temperature data used in the model calculations.\n",
+    "pd.DataFrame(temperature).to_csv(os.path.join(output_folder, 'ES_Temperature_profile.csv')) \n"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },