Extensively commented the 'save_result_in_csv_file' method that I debugged earlier.

View/acoustic_inversion_tab.py

@@ -552,6 +552,27 @@ class AcousticInversionTab(QWidget):
 
         stg.SSC_fine[data_id] = np.array([])
 
+        '''
+        stg.SSC_fine[data_id] = self.inv_hc.SSC_fine(
+            zeta=stg.zeta[0],
+            r2D=stg.depth_2D[data_id][stg.frequencies_for_calibration[0][1]],
+            VBI=stg.VBI_cross_section[data_id],
+            freq=stg.frequencies_for_calibration[0][0],
+            X=stg.X_exponent[0],
+            j_cross_section=stg.J_cross_section[data_id][0],
+            alpha_w=np.full(
+                shape=stg.depth_2D[data_id][
+                    stg.frequencies_for_calibration[0][1]
+                ].shape,
+                fill_value=stg.water_attenuation[data_id][
+                    stg.frequencies_for_calibration[0][1]
+                ]
+            )
+        ) #Inversion using the first frequency
+        '''
+
+
+        
         stg.SSC_fine[data_id] = self.inv_hc.SSC_fine(
             zeta=stg.zeta[1],
             r2D=stg.depth_2D[data_id][stg.frequency_for_inversion[1]],
@@ -567,19 +588,32 @@ class AcousticInversionTab(QWidget):
                     stg.frequency_for_inversion[1]
                 ]
             )
-        )
+        ) #Inversion using the second frequency
+        
 
     def compute_SSC_sand(self):
         data_id = self.combobox_acoustic_data_choice.currentIndex()
 
         stg.SSC_sand[data_id] = np.array([])
 
+        '''
+        stg.SSC_sand[data_id] = self.inv_hc.SSC_sand(
+            VBI=stg.VBI_cross_section[data_id],
+            freq=stg.frequencies_for_calibration[0][0],
+            X=stg.X_exponent,
+            ks=stg.ks[0]
+        ) #Inversion using the first frequency
+        '''
+
+        
         stg.SSC_sand[data_id] = self.inv_hc.SSC_sand(
             VBI=stg.VBI_cross_section[data_id],
             freq=stg.frequencies_for_calibration[1][0],
             X=stg.X_exponent,
             ks=stg.ks[1]
-        )
+        ) #Inversion using the second frequency
+        
+        
 
     def plot_SSC_fine(self):
         data_id = self.combobox_acoustic_data_choice.currentIndex()
@@ -694,7 +728,7 @@ class AcousticInversionTab(QWidget):
                 pcm_SSC_fine, ax=self.axis_SSC_fine, shrink=1,
                 location='right'
             )
-            cbar_SSC_fine.set_label(label='Fine SSC (g/L', rotation=270, labelpad=15)
+            cbar_SSC_fine.set_label(label='Fine SSC (g/L)', rotation=270, labelpad=15)
 
             self.figure_SSC_fine.supxlabel("Time (sec)", fontsize=10)
             self.figure_SSC_fine.supylabel("Depth (m)", fontsize=10)
@@ -1259,7 +1293,7 @@ class AcousticInversionTab(QWidget):
                 pcm_SSC_sand, ax=self.axis_SSC_sand, shrink=1,
                 location='right'
             )
-            cbar_SSC_sand.set_label(label='Sand SSC (g/L', rotation=270, labelpad=15)
+            cbar_SSC_sand.set_label(label='Sand SSC (g/L)', rotation=270, labelpad=15)
 
             self.figure_SSC_sand.supxlabel("Time (sec)", fontsize=10)
             self.figure_SSC_sand.supylabel("Depth (m)", fontsize=10)
@@ -1768,14 +1802,68 @@ class AcousticInversionTab(QWidget):
                 )
 
     def save_result_in_csv_file(self, dirname, filename):
-        d_id = []
-        t = []
-        r = []
-        ssc_fine = []
-        ssc_sand = []
+
+        '''
+        Comments by Bjarne VINCENT
+        
+        This function exports the acoustic inversion results to a separate .csv
+        file. In AcouSed, the SSC fields computed for each dataset are stored
+        as 2D arrays of size M*P, where 'M' is the number of vertical positions and
+        'P' is the number of time stamps. The idea is to flatten these arrays (i.e.,
+        convert them to 1D arrays) in order to export the data as a table.
+
+        '''
+
+        # Define & initialize the lists of 1D arrays to be written to the .csv
+        # file. Each of these lists shall form one column in the written .csv
+        # file. Each list is of length N, where 'N' stands for the number of
+        # datasets loaded into AcouSed:
+        
+        d_id = [] #List of the datasets' ID (integers ranging from 0 to N-1)
+        t = [] #List of time stamps (s)
+        r = [] #List of vertical positions (m)
+        ssc_fine = [] #List of flattened fine SSC arrays (g/L)
+        ssc_sand = [] #List of flattened sand SSC arrays (g/L)
+
+
+        # Loop over all datasets loaded into AcouSed:
         
         for k in range(self.combobox_acoustic_data_choice.count()):
 
+            '''
+            Comments by Bjarne VINCENT
+
+            Array-flattening convention: the index spanning the number of vertical
+            positions runs the fastest, followed by the index spanning the number of
+            time stamps, followed by the index spanning the number of datasets.
+
+            Example of file output:
+
+            data_id   |   time (s)   |  Depth(m)
+               0           0.0            0.1
+               0           0.0            0.2
+               .            .              .
+               .            .              .
+               .            .              .
+               0           0.0            1.0
+               0           0.1            0.1
+               0           0.1            0.2
+               .            .              .
+               .            .              .
+               .            .              .
+               0           5.0            1.0
+               1           0.0            0.1
+               1           0.0            0.2
+               .            .              .
+               .            .              .
+               .            .              .
+
+            etc.
+            
+            '''
+
+            # Retrieve the time stamps and vertical positions:
+            
             if stg.time_cross_section[k].shape != (0,):
                 time_data = stg.time_cross_section
             else:
@@ -1787,19 +1875,25 @@ class AcousticInversionTab(QWidget):
                 depth_data = stg.depth
 
 
+            # Form the 1D arrays of time stamps and vertical positions
+            # for the k-th dataset:
+            
             time_shape = time_data[k].shape[1]
             depth_shape = depth_data[k].shape[1]
 
-            d_id += np.repeat(k, depth_shape * time_shape).tolist()
+            d_id += np.repeat(k, depth_shape * time_shape).tolist() #Flattened array of the current dataset's index
             
             tmp_t = np.repeat(
                 time_data[k][stg.frequency_for_inversion[1]],
                 depth_shape
-            )
+            ) #Flattened array of time stamps
 
             tmp_r = np.zeros(
                 depth_shape * time_shape
-            )
+            ) #Initialise the flattened array of vertical positions
+
+
+            # Fill the flattened array of vertical positions:
 
             for i in range(time_shape):
                 for j in range(depth_shape):
@@ -1811,18 +1905,31 @@ class AcousticInversionTab(QWidget):
                         ]
                     )
 
+
+            # Add the flattened arrays of time stamps and vertical positions to their respective
+            # lists:
+            
             t += tmp_t.tolist()
             r += tmp_r.tolist()
 
+
+            # If no fine or sand SSC concentrations have been computed, create empty 2D arrays:
+            
             if stg.SSC_fine[k].shape == (0,):
                 stg.SSC_fine[k] = np.zeros(tmp_r.shape[0])
             if stg.SSC_sand[k].shape == (0,):
                 stg.SSC_sand[k] = np.zeros(tmp_r.shape[0])
  
 
+            # Flatten the fine and sand SSC arrays in a column-major order (i.e., "the first index
+            # runs the fastest"):
+                
             ssc_fine += np.reshape( stg.SSC_fine[k], tmp_r.shape[0], 'F' ).tolist()
             ssc_sand += np.reshape( stg.SSC_sand[k], tmp_r.shape[0], 'F' ).tolist()
 
+
+        # Finally, create a Pandas 'DataFrame' with all lists, and write that DataFrame to a .csv file:
+            
         results = pd.DataFrame(
             {
                 'acoustic data': d_id,