Merge pull request #76 from cBLUE-dev-team/save_config

When cBLUE is run through the CLI the cblue_configuration.json file will no longer update by default. The config file will still update by default when run through the GUI. To update the config file when run through the CLI, add the --save_config flag to the command.

Added additional help text to water height field.

Moved subaqueous class list to cblue_configuration.json. Retrieve these values from the json in UserInput.py. Allows user to add or remove subaqueous class values by editing the cblue_configuration.json.

Added ability to handle LAS files that have ExtraBytes already added.

Add ability for user to select output as LAZ file. User can now use flags --las, --laz, and --csv to get output files of the respective type. No flags chosen will default to LAS file output. User can supply multiple output flags, for example: using --las and --csv will produce output files of both types. The GUI has also been modified to provide check boxes for LAS, LAZ, or CSV to match.

Author: kiefk

CBlueApp.py

@@ -1,148 +1,168 @@
-"""
-cBLUE (comprehensive Bathymetric Lidar Uncertainty Estimator)
-Copyright (C) 2019
-Oregon State University (OSU)
-Center for Coastal and Ocean Mapping/Joint Hydrographic Center, University of New Hampshire (CCOM/JHC, UNH)
-NOAA Remote Sensing Division (NOAA RSD)
-
-This library is free software; you can redistribute it and/or
-modify it under the terms of the GNU Lesser General Public
-License as published by the Free Software Foundation; either
-version 2.1 of the License, or (at your option) any later version.
-
-This library is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-Lesser General Public License for more details.
-
-You should have received a copy of the GNU Lesser General Public
-License along with this library; if not, write to the Free Software
-Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
-
-Contact:
-Christopher Parrish, PhD
-School of Construction and Civil Engineering
-101 Kearney Hall
-Oregon State University
-Corvallis, OR  97331
-(541) 737-5688
-[email protected]
-
-Last Edited By:
-Keana Kief (OSU)
-July 25th, 2023
-
-
-"""
-import os
-import pandas as pd
-import logging
-import numpy as np
-import numexpr as ne
-import laspy
-
-logger = logging.getLogger(__name__)
-
-"""
-This class provides the functionality to load las files into cBLUE.  One Las object
-is created for each loaded las file.
-"""
-
-
-class Las:
-    def __init__(self, las):
-        self.las = las
-        self.las_short_name = os.path.split(las)[-1]
-        self.las_base_name = self.las_short_name.replace(".las", "")
-        self.inFile = laspy.read(self.las)
-        self.points_to_process = self.inFile.points
-        self.unq_flight_lines = self.get_flight_line_ids()
-        self.num_file_points = self.points_to_process.array.shape[0]
-
-        """index list that would sort gps_time (to be used to
-        later when exporting las data and calculated tpu to a las
-        file
-        """
-        self.t_argsort = None
-
-    def get_bathy_points(self):
-        class_codes = {"BATHYMETRY": 26}
-        bathy_inds = self.inFile.raw_classification == class_codes["BATHYMETRY"]
-        return self.inFile.points.array[bathy_inds]["point"]
-
-    def get_flight_line_ids(self):
-        """generates a list of unique flight line ids
-
-        This method returns a list of unique flight line ids.
-
-        :return: ndarray
-        """
-
-        # pandas' unique faster than numpy's ?
-        return pd.unique(self.points_to_process["pt_src_id"])
-
-    def get_flight_line(self, sensor_type):
-        """retrieves the x, y, z, and timestamp data from the las data points
-
-        The x, y, and z values in the las file are stored as integers.  The
-        scale and offset values in the las file header are used to convert
-        the integer values to decimal values with centimeter precision.
-
-        :param ? fl:
-        :return: np.array, np.array, np.array, np.array
-        """
-
-        #xyz_to_coordinate converts the x, y, z integer values to coordinate values
-        x, y, z = self.xyz_to_coordinate()
-
-        t = self.points_to_process["gps_time"]
-
-        if "classification" in self.points_to_process.array.dtype.names:
-            c = self.points_to_process["classification"]
-        elif "classification_flags" in self.points_to_process.array.dtype.names:
-            c = self.points_to_process["classification_flags"]
-        else:
-            raise Exception("Unknown las version or missing classification attribute.")
-
-        self.t_argsort = t.argsort()
-
-        # Check if this is a multi beam sensor
-        if(sensor_type == "multi"):
-            #Get the fan angle and multiply it by 0.006 to convert to degrees
-            fan_angle = self.inFile.scan_angle*0.006
-            #Add xyztcf to an array together
-            xyztcf = np.vstack([x, y, z, t, c, fan_angle]).T
-            # logger.las(f"{sensor_type} Fan Angle: {fan_angle}")
-        else:
-            #Fan angle is not used by the other sensors
-            #Add xyztc to an array together
-            xyztcf = np.vstack([x, y, z, t, c]).T
-
-        flight_lines = self.points_to_process["pt_src_id"]
-
-        return xyztcf, self.t_argsort, flight_lines
-
-    
-    def xyz_to_coordinate(self):
-        """The x, y, and z values in the las file are stored as integers.  The
-        scale and offset values in the las file header are used to convert
-        the integer values to decimal values with centimeter precision."""
-
-        scale_x = np.asarray(self.inFile.header.scales[0])
-        scale_y = np.asarray(self.inFile.header.scales[1])
-        scale_z = np.asarray(self.inFile.header.scales[2])
-
-        offset_x = np.asarray(self.inFile.header.offsets[0])
-        offset_y = np.asarray(self.inFile.header.offsets[1])
-        offset_z = np.asarray(self.inFile.header.offsets[2])
-
-        X = self.points_to_process["X"]
-        Y = self.points_to_process["Y"]
-        Z = self.points_to_process["Z"]
-
-
-        x = ne.evaluate("X * scale_x + offset_x")
-        y = ne.evaluate("Y * scale_y + offset_y")
-        z = ne.evaluate("Z * scale_z + offset_z")
-
-        return x, y, z
+"""
+cBLUE (comprehensive Bathymetric Lidar Uncertainty Estimator)
+Copyright (C) 2019
+Oregon State University (OSU)
+Center for Coastal and Ocean Mapping/Joint Hydrographic Center, University of New Hampshire (CCOM/JHC, UNH)
+NOAA Remote Sensing Division (NOAA RSD)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
+
+Contact:
+Christopher Parrish, PhD
+School of Construction and Civil Engineering
+101 Kearney Hall
+Oregon State University
+Corvallis, OR  97331
+(541) 737-5688
+[email protected]
+
+Last Edited By:
+Keana Kief (OSU)
+September 20th, 2024 
+
+
+"""
+import os
+import pandas as pd
+import logging
+import numpy as np
+import numexpr as ne
+import laspy
+
+logger = logging.getLogger(__name__)
+
+"""
+This class provides the functionality to load las files into cBLUE.  One Las object
+is created for each loaded las file.
+"""
+
+
+class Las:
+    def __init__(self, las):
+        self.las = las
+        self.las_short_name = os.path.split(las)[-1]
+        if ".las" in self.las_short_name:
+            self.las_base_name = self.las_short_name.replace(".las", "")
+        else:
+            self.las_base_name = self.las_short_name.replace(".laz", "")
+        self.inFile = laspy.read(self.las)
+        self.points_to_process = self.inFile.points
+        self.unq_flight_lines = self.get_flight_line_ids()
+        self.num_file_points = self.points_to_process.array.shape[0]
+
+        """index list that would sort gps_time (to be used to
+        later when exporting las data and calculated tpu to a las
+        file
+        """
+        self.t_argsort = None
+
+    def get_bathy_points(self):
+        class_codes = {"BATHYMETRY": 26}
+        bathy_inds = self.inFile.raw_classification == class_codes["BATHYMETRY"]
+        return self.inFile.points.array[bathy_inds]["point"]
+
+    def get_flight_line_ids(self):
+        """generates a list of unique flight line ids
+
+        This method returns a list of unique flight line ids.
+
+        :return: ndarray
+        """
+
+        # pandas' unique faster than numpy's ?
+        return pd.unique(self.points_to_process["pt_src_id"])
+
+    def get_flight_line(self, sensor_type):
+        """retrieves the x, y, z, and timestamp data from the las data points
+
+        The x, y, and z values in the las file are stored as integers.  The
+        scale and offset values in the las file header are used to convert
+        the integer values to decimal values with centimeter precision.
+
+        :param ? fl:
+        :return: np.array, np.array, np.array, np.array
+        """
+
+        #xyz_to_coordinate converts the x, y, z integer values to coordinate values
+        x, y, z = self.xyz_to_coordinate()
+
+        t = self.points_to_process["gps_time"]
+
+        if "classification" in self.points_to_process.array.dtype.names:
+            c = self.points_to_process["classification"]
+        elif "classification_flags" in self.points_to_process.array.dtype.names:
+            c = self.points_to_process["classification_flags"]
+        else:
+            raise Exception("Unknown las version or missing classification attribute.")
+
+        self.t_argsort = t.argsort()
+
+        # Check if this is a multi beam sensor
+        if(sensor_type == "multi"):
+            #Get the fan angle and multiply it by 0.006 to convert to degrees
+            fan_angle = self.inFile.scan_angle*0.006
+            #Add xyztcf to an array together
+            xyztcf = np.vstack([x, y, z, t, c, fan_angle]).T
+            # logger.las(f"{sensor_type} Fan Angle: {fan_angle}")
+        else:
+            #Fan angle is not used by the other sensors
+            #Add xyztc to an array together
+            xyztcf = np.vstack([x, y, z, t, c]).T
+
+        flight_lines = self.points_to_process["pt_src_id"]
+
+        return xyztcf, self.t_argsort, flight_lines
+
+    
+    def xyz_to_coordinate(self):
+        """The x, y, and z values in the las file are stored as integers.  The
+        scale and offset values in the las file header are used to convert
+        the integer values to decimal values with centimeter precision."""
+
+        scale_x = np.asarray(self.inFile.header.scales[0])
+        scale_y = np.asarray(self.inFile.header.scales[1])
+        scale_z = np.asarray(self.inFile.header.scales[2])
+        # print(f"\nLAS File: {self.las_short_name}")
+        # print(f"\nScale X: {scale_x}")
+        # print(f"Scale Y: {scale_y}")
+        # print(f"Scale Z: {scale_z}")
+
+        offset_x = np.asarray(self.inFile.header.offsets[0])
+        offset_y = np.asarray(self.inFile.header.offsets[1])
+        offset_z = np.asarray(self.inFile.header.offsets[2])
+
+        # print(f"\nOffset X: {offset_x}")
+        # print(f"Offset Y: {offset_y}")
+        # print(f"Offset Z: {offset_z}")
+
+
+        X = self.points_to_process["X"]
+        Y = self.points_to_process["Y"]
+        Z = self.points_to_process["Z"]
+
+        # print(f"\nX: {X}")
+        # print(f"Y: {Y}")
+        # print(f"Z: {Z}")
+
+
+        x = ne.evaluate("X * scale_x + offset_x")
+        y = ne.evaluate("Y * scale_y + offset_y")
+        z = ne.evaluate("Z * scale_z + offset_z")
+
+        # print(f"\nx: {x}")
+        # print(f"y: {y}")
+        # print(f"z: {z}")
+
+        return x, y, z