Acoustic data: Fix temperature value at study open.

Model/read_table_for_open.py

@@ -498,6 +498,7 @@ class ReadTableForOpen:
             stg.SNR_filter_value.append(x[12])
             stg.Nb_cells_to_average_BS_signal.append(x[13])
 
+            logger.debug(f"stg.temperature: {stg.temperature}")
             logger.debug(f"stg.tmin: {stg.tmin}")
             logger.debug(f"stg.tmin: {stg.tmax}")
             logger.debug(f"stg.SNR_filter_value: {stg.SNR_filter_value}")

View/acoustic_data_tab.py

@@ -737,8 +737,11 @@ class AcousticDataTab(QWidget):
         logger.debug(f"{__name__}: Update")
         self.blockSignals(True)
         self.fileListWidget.blockSignals(True)
+        self.combobox_ABS_system_choice.blockSignals(True)
 
         self.combobox_ABS_system_choice.setCurrentText(stg.ABS_name[0])
+        self.ABS_system_choice()
+
         self.fileListWidget.addFilenames(stg.filename_BS_raw_data)
 
         self.fill_measurements_information_groupbox()
@@ -751,6 +754,7 @@ class AcousticDataTab(QWidget):
         self.compute_rmin_rmax()
         self.set_range_for_spinboxes_bathymetry()
 
+        self.combobox_ABS_system_choice.blockSignals(False)
         self.fileListWidget.blockSignals(False)
         self.blockSignals(False)
 
@@ -974,7 +978,7 @@ class AcousticDataTab(QWidget):
         self.gridLayout_groupbox_info.addWidget(self.lineEdit_temperature, 3, 1, 1, 1, Qt.AlignLeft)
         self.label_temperature_unit.show()
         self.gridLayout_groupbox_info.addWidget(self.label_temperature_unit, 3, 2, 1, 1, Qt.AlignLeft)
-        self.temperature_value()
+        self.setup_temperature_value()
 
         self.label_speed_of_sound.show()
         self.gridLayout_groupbox_info.addWidget(self.label_speed_of_sound, 4, 0, 1, 1, Qt.AlignLeft)
@@ -1227,25 +1231,31 @@ class AcousticDataTab(QWidget):
         self.update_plot_backscattered_acoustic_signal_recording()
         self.update_plot_profile()
 
-    def temperature_value(self):
+    def setup_temperature_value(self):
+        self.water_velocity()
+        self.water_attenuation()
 
+    def temperature_value(self):
         if findall(r",", self.lineEdit_temperature.text()):
             stg.temperature = float(self.lineEdit_temperature.text().replace(',', '.'))
             self.lineEdit_temperature.setText(self.lineEdit_temperature.text().replace(',', '.'))
         else:
             stg.temperature = float(self.lineEdit_temperature.text())
             self.lineEdit_temperature.setText(self.lineEdit_temperature.text())
+
         self.water_velocity()
         self.water_attenuation()
 
     def water_velocity(self):
         """Computing sond speed from Bilaniuk and Wong 1993"""
+        temp = float(self.lineEdit_temperature.text())
+
         C = (1.40238744 * 1e3 +
-             5.03836171 * float(self.lineEdit_temperature.text()) -
+             5.03836171 * temp -
-             5.81172916 * 1e-2 * float(self.lineEdit_temperature.text()) ** 2 +
+             5.81172916 * 1e-2 * temp ** 2 +
-             3.34638117 * 1e-4 * float(self.lineEdit_temperature.text()) ** 3 -
+             3.34638117 * 1e-4 * temp ** 3 -
-             1.48259672 * 1e-6 * float(self.lineEdit_temperature.text()) ** 4 +
+             1.48259672 * 1e-6 * temp ** 4 +
-             3.16585020 * 1e-9 * float(self.lineEdit_temperature.text()) ** 5)
+             3.16585020 * 1e-9 * temp ** 5)
         stg.water_velocity = C
 
         self.lineEdit_speed_of_sound.setText(str(round(stg.water_velocity, 2)))
@@ -1297,26 +1307,29 @@ class AcousticDataTab(QWidget):
     # -------- Computing water attenuation coefficient ----------- #
     def water_attenuation(self):
         """Computing attenuation from François and Garrison 1982"""
+        temp = float(self.lineEdit_temperature.text())
+        file_id = self.fileListWidget.currentRow()
+
         if self.fileListWidget.count() > 0:
-            stg.water_attenuation[self.fileListWidget.currentRow()].clear()
+            stg.water_attenuation[file_id].clear()
-            for f in stg.freq[self.fileListWidget.currentRow()]:
+            for f in stg.freq[file_id]:
-                if float(self.lineEdit_temperature.text()) > 20:
+                if temp > 20:
                     alpha = ((3.964 * 1e-4 -
-                             1.146 * 1e-5 * float(self.lineEdit_temperature.text()) +
+                             1.146 * 1e-5 * temp +
-                             1.45 * 1e-7 * float(self.lineEdit_temperature.text()) ** 2 -
+                             1.45 * 1e-7 * temp ** 2 -
-                             6.5 * 1e-10 * float(self.lineEdit_temperature.text()) ** 3) *
+                             6.5 * 1e-10 * temp ** 3) *
                              1e-3 * (np.log(10) / 20) * (f * 1e-3) ** 2)
                 else:
                     alpha = ((4.937 * 1e-4 -
-                             2.59 * 1e-5 * float(self.lineEdit_temperature.text()) +
+                             2.59 * 1e-5 * temp +
-                             9.11 * 1e-7 * float(self.lineEdit_temperature.text()) ** 2 -
+                             9.11 * 1e-7 * temp ** 2 -
-                             1.5 * 1e-8 * float(self.lineEdit_temperature.text()) ** 3) *
+                             1.5 * 1e-8 * temp ** 3) *
                              1e-3 * (np.log(10) / 20) * (f * 1e-3) ** 2)
 
-                stg.water_attenuation[self.fileListWidget.currentRow()].append(alpha)
+                stg.water_attenuation[file_id].append(alpha)
 
             self.lineEdit_sound_attenuation.setText(
-                str("%.6f" % stg.water_attenuation[self.fileListWidget.currentRow()][
+                str("%.6f" % stg.water_attenuation[file_id][
                               self.combobox_frequency_information.currentIndex()]))
 
     def open_dialog_box(self):
@@ -1752,6 +1765,7 @@ class AcousticDataTab(QWidget):
             .currentIndexChanged\
             .connect(self.combobox_frequency_information_update)
 
+        logger.debug(f"Set temperature = {stg.temperature}")
         self.lineEdit_temperature.setText(str(stg.temperature))
 
         self.label_profiles_value.setText(

View/mainwindow.py

@@ -21,6 +21,13 @@
 # -*- coding: utf-8 -*-
 
 import os
+import time
+import pickle
+import logging
+
+import numpy as np
+import pandas as pd
+from subprocess import Popen
 
 # Form implementation generated from reading ui file 'mainwindow.ui'
 #
@@ -38,12 +45,7 @@ from Model.calibration_constant_kt import CalibrationConstantKt
 from View.about_window import AboutWindow
 import settings as stg
 
-import numpy as np
+logger = logging.getLogger("acoused")
-import pandas as pd
-from subprocess import Popen
-
-import time
-
 
 class Ui_MainWindow(object):
     def setupUi(self, MainWindow):

View/note_tab.py

@@ -21,6 +21,9 @@ class NoteTab(QWidget):
 
         path_icon = "./icons/"
 
+        # FIXME: The note are disabled because there are never saved
+        widget_tab.setEnabled(False)
+
         self.verticalLayout_main_note_tab = QVBoxLayout(widget_tab)
 
         self.horizontalLayout_toolbar = QHBoxLayout()

View/sample_data_tab.py

@@ -284,6 +284,10 @@ class SampleDataTab(QWidget):
         self.lineEdit_sand_sediment.setToolTip(stg.path_sand)
         self.fill_table_sand()
 
+        #self.plot_sample_position_on_transect()
+        self.plot_total_concentration()
+        self.plot_PSD_fine_and_sand_sediments()
+
         self.blockSignals(False)
 
     def last_opened_file_path(self, priority="sand"):
@@ -331,6 +335,10 @@ class SampleDataTab(QWidget):
             self.lineEdit_fine_sediment.setToolTip(stg.path_fine)
             self.fill_table_fine()
 
+            self.plot_sample_position_on_transect()
+            self.plot_total_concentration()
+            self.plot_PSD_fine_and_sand_sediments()
+
     def open_dialog_box_sand_sediment(self):
         filename_sand_sediment = QFileDialog.getOpenFileName(
             self, "Sand sediment file",
@@ -356,6 +364,10 @@ class SampleDataTab(QWidget):
             self.lineEdit_sand_sediment.setToolTip(stg.path_sand)
             self.fill_table_sand()
 
+            self.plot_sample_position_on_transect()
+            self.plot_total_concentration()
+            self.plot_PSD_fine_and_sand_sediments()
+
     def load_fine_sediment_data(self):
         fine_granulo_data = GranuloLoader(
             os.path.join(stg.path_fine, stg.filename_fine)
@@ -486,10 +498,6 @@ class SampleDataTab(QWidget):
             self.combobox_x_axis.currentIndexChanged.connect(self.plot_total_concentration)
             self.combobox_y_axis.currentIndexChanged.connect(self.plot_total_concentration)
 
-            self.plot_sample_position_on_transect()
-            self.plot_total_concentration()
-            self.plot_PSD_fine_and_sand_sediments()
-
             self.tableWidget_fine.blockSignals(False)
         else:
             msgBox = QMessageBox()
@@ -588,10 +596,6 @@ class SampleDataTab(QWidget):
             self.combobox_y_axis.currentIndexChanged\
                                 .connect(self.plot_total_concentration)
 
-            self.plot_sample_position_on_transect()
-            self.plot_total_concentration()
-            self.plot_PSD_fine_and_sand_sediments()
-
             self.tableWidget_sand.blockSignals(False)
 
     # --- Function to extract position of sample from table checkboxes to update plots ---
@@ -752,13 +756,19 @@ class SampleDataTab(QWidget):
         self.combobox_acoustic_data.clear()
         for n, m in enumerate(stg.noise_method):
             if stg.noise_method[n] == 0:
-                self.combobox_acoustic_data.addItem(stg.filename_BS_raw_data[n])
+                self.combobox_acoustic_data\
-            elif stg.noise_method[n]!=0:
+                    .addItem(stg.filename_BS_raw_data[n])
-                self.combobox_acoustic_data.addItem(stg.data_preprocessed[n])
+            elif stg.noise_method[n] != 0:
+                self.combobox_acoustic_data\
+                    .addItem(stg.data_preprocessed[n])
 
         self.plot_sample_position_on_transect()
-        self.combobox_acoustic_data.currentIndexChanged.connect(self.update_plot_sample_position_on_transect)
+        self.combobox_acoustic_data\
-        self.combobox_frequencies.currentIndexChanged.connect(self.update_plot_sample_position_on_transect)
+            .currentIndexChanged\
+            .connect(self.update_plot_sample_position_on_transect)
+        self.combobox_frequencies\
+            .currentIndexChanged\
+            .connect(self.update_plot_sample_position_on_transect)
 
     def plot_sample_position_on_transect(self):
         self.verticalLayout_groupbox_plot_transect\

View/signal_processing_tab.py

@@ -515,7 +515,7 @@ class SignalProcessingTab(QWidget):
         self.combobox_acoustic_data_choice.blockSignals(True)
 
         self.full_update_fill_text()
-        self.update_SignalPreprocessingTab(recompute=True)
+        self.update_SignalPreprocessingTab()
 
         self.combobox_acoustic_data_choice.blockSignals(False)
         self.blockSignals(False)
@@ -535,7 +535,7 @@ class SignalProcessingTab(QWidget):
             str(stg.Nb_cells_to_average_BS_signal[data_id])
         )
 
-    def update_SignalPreprocessingTab(self, recompute=True):
+    def update_SignalPreprocessingTab(self):
 
         """ The tab is updated in two cases :
                 - the user remove a file (in the list widget) in the first tab (Acoustic data), so that the combobox
@@ -573,9 +573,7 @@ class SignalProcessingTab(QWidget):
                 stg.freq_text[data_id]
             )
 
-            if recompute:
             self.recompute()
-
             self.replot()
 
             logger.debug("Update the Signal preprocessing tab... Done")
@@ -583,6 +581,33 @@ class SignalProcessingTab(QWidget):
             self.combobox_freq_noise_from_profile_tail.blockSignals(False)
             self.combobox_acoustic_data_choice.blockSignals(False)
 
+    def _is_correct_shape(self, data):
+        data_id = self.combobox_acoustic_data_choice.currentIndex()
+
+        if stg.time_cross_section[data_id].shape != (0,):
+            x_time = stg.time_cross_section[data_id]
+        else:
+            x_time = stg.time[data_id]
+
+        if stg.depth_cross_section[data_id].shape != (0,):
+            y_depth = stg.depth_cross_section[data_id]
+        else:
+            y_depth = stg.depth[data_id]
+
+        time_shape, = x_time[data_id].shape
+        depth_shape, = y_depth[data_id].shape
+
+        logger.debug(f"_is_correct_shape: time shape: {time_shape}")
+        logger.debug(f"_is_correct_shape: depth shape: {depth_shape}")
+        logger.debug(f"_is_correct_shape: data shape: {data[data_id].shape}")
+
+        if data[data_id].shape == (0,):
+            return False
+
+        _, y, z = data[data_id].shape
+
+        return (y == depth_shape and z == time_shape)
+
     def recompute(self):
         data_id = self.combobox_acoustic_data_choice.currentIndex()
 
@@ -835,28 +860,29 @@ class SignalProcessingTab(QWidget):
         if len(stg.filename_BS_raw_data) == 0:
             pass
         else:
+            data_id = self.combobox_acoustic_data_choice.currentIndex()
 
-            stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.BS_noise_raw_data[data_id] = np.array([])
-            stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.BS_noise_averaged_data[data_id] = np.array([])
-            stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.SNR_raw_data[data_id] = np.array([])
-            stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.SNR_cross_section[data_id] = np.array([])
-            stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.SNR_stream_bed[data_id] = np.array([])
-            stg.time_noise[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.time_noise[data_id] = np.array([])
-            stg.SNR_filter_value[self.combobox_acoustic_data_choice.currentIndex()] = 0
+            stg.SNR_filter_value[data_id] = 0
 
-            stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.BS_raw_data_pre_process_SNR[data_id] = np.array([])
-            stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.BS_raw_data_pre_process_average[data_id] = np.array([])
 
-            stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.BS_cross_section_pre_process_SNR[data_id] = np.array([])
-            stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.BS_cross_section_pre_process_average[data_id] = np.array([])
 
-            stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.BS_stream_bed_pre_process_SNR[data_id] = np.array([])
-            stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.BS_stream_bed_pre_process_average[data_id] = np.array([])
-            print("stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()]", stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()])
+            print("stg.noise_method[data_id]", stg.noise_method[data_id])
-            if stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] == 0:
+            if stg.noise_method[data_id] == 0:
                 self.lineEdit_noise_file.clear()
 
-            elif stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] == 1:
+            elif stg.noise_method[data_id] == 1:
                 self.lineEdit_val1.clear()
                 self.lineEdit_val1.setText("0.00")
 
@@ -973,136 +999,140 @@ class SignalProcessingTab(QWidget):
 
 
     def load_noise_data_and_compute_SNR(self):
+        data_id = self.combobox_acoustic_data_choice.currentIndex()
 
-        stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] = 0
+        stg.noise_method[data_id] = 0
 
-        noise_data = AcousticDataLoader(stg.path_BS_noise_data[self.combobox_acoustic_data_choice.currentIndex()] +
+        noise_data = AcousticDataLoader(stg.path_BS_noise_data[data_id] +
                                         "/" +
-                                        stg.filename_BS_noise_data[self.combobox_acoustic_data_choice.currentIndex()])
+                                        stg.filename_BS_noise_data[data_id])
-        stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = noise_data._BS_raw_data
+        stg.BS_noise_raw_data[data_id] = noise_data._BS_raw_data
 
-        stg.time_noise[self.combobox_acoustic_data_choice.currentIndex()] = noise_data._time
+        stg.time_noise[data_id] = noise_data._time
-        stg.depth_noise[self.combobox_acoustic_data_choice.currentIndex()] = noise_data._r
+        stg.depth_noise[data_id] = noise_data._r
 
-        if stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+        if stg.BS_stream_bed[data_id].shape != (0,):
 
-            noise = np.zeros(stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape)
+            noise = np.zeros(stg.BS_stream_bed[data_id].shape)
             for f, _ in enumerate(noise_data._freq):
                 noise[f, :, :] = np.mean(
-                    stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :], axis=(0, 1))
+                    stg.BS_noise_raw_data[data_id][f, :, :], axis=(0, 1))
-            stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = noise
+            stg.BS_noise_averaged_data[data_id] = noise
-            stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()] = (
+            stg.SNR_stream_bed[data_id] = (
-                np.divide((stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()] -
+                np.divide((stg.BS_stream_bed[data_id] -
-                           stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
+                           stg.BS_noise_averaged_data[data_id]) ** 2,
-                          stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
+                          stg.BS_noise_averaged_data[data_id] ** 2))
 
-        elif stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+        elif stg.BS_cross_section[data_id].shape != (0,):
 
-            noise = np.zeros(stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape)
+            noise = np.zeros(stg.BS_cross_section[data_id].shape)
             for f, _ in enumerate(noise_data._freq):
                 noise[f, :, :] = np.mean(
-                    stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :], axis=(0, 1))
+                    stg.BS_noise_raw_data[data_id][f, :, :], axis=(0, 1))
-            stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = noise
+            stg.BS_noise_averaged_data[data_id] = noise
-            stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()] = (
+            stg.SNR_cross_section[data_id] = (
-                np.divide((stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()] -
+                np.divide((stg.BS_cross_section[data_id] -
-                           stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
+                           stg.BS_noise_averaged_data[data_id]) ** 2,
-                          stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
+                          stg.BS_noise_averaged_data[data_id] ** 2))
             # stg.SNR_reshape = np.reshape(stg.SNR_cross_section, (stg.r.shape[1] * stg.t.shape[1], stg.freq.shape[0]), order="F")
 
         else:
 
-            noise = np.zeros(stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()].shape)
+            noise = np.zeros(stg.BS_raw_data[data_id].shape)
             for f, _ in enumerate(noise_data._freq):
                 noise[f, :, :] = np.mean(
-                    stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :], axis=(0, 1))
+                    stg.BS_noise_raw_data[data_id][f, :, :], axis=(0, 1))
-            stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = noise
+            stg.BS_noise_averaged_data[data_id] = noise
-            stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
+            stg.SNR_raw_data[data_id] = (
-                np.divide((stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()] -
+                np.divide((stg.BS_raw_data[data_id] -
-                           stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
+                           stg.BS_noise_averaged_data[data_id]) ** 2,
-                          stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
+                          stg.BS_noise_averaged_data[data_id] ** 2))
 
     def open_plot_noise_window(self):
         pnw = PlotNoiseWindow()
         pnw.exec()
 
     def compute_noise_from_profile_tail_value(self):
+        data_id = self.combobox_acoustic_data_choice.currentIndex()
 
-        stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] = 1
+        stg.noise_method[data_id] = 1
+        stg.noise_value[data_id] = (
+            float(self.lineEdit_profile_tail_value.text().replace(",", "."))
+        )
 
-        stg.noise_value[self.combobox_acoustic_data_choice.currentIndex()] = (
+        if stg.time_cross_section[data_id].shape != (0,):
-            float(self.lineEdit_profile_tail_value.text().replace(",", ".")))
+            stg.time_noise[data_id] = (
-
+                stg.time_cross_section[data_id]
-        if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+            )
-            stg.time_noise[self.combobox_acoustic_data_choice.currentIndex()] = (
-                stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()])
         else:
-            stg.time_noise[self.combobox_acoustic_data_choice.currentIndex()] = (
+            stg.time_noise[data_id] = (
-                stg.time[self.combobox_acoustic_data_choice.currentIndex()])
+                stg.time[data_id]
-        if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+            )
-            stg.depth_noise[self.combobox_acoustic_data_choice.currentIndex()] = (
+        if stg.depth_cross_section[data_id].shape != (0,):
-                stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()])
+            stg.depth_noise[data_id] = (
+                stg.depth_cross_section[data_id]
+            )
         else:
-            stg.depth_noise[self.combobox_acoustic_data_choice.currentIndex()] = (
+            stg.depth_noise[data_id] = (
-                stg.depth[self.combobox_acoustic_data_choice.currentIndex()])
+                stg.depth[data_id]
+            )
 
         # --- Compute noise from value and compute SNR ---
-
+        if self._is_correct_shape(stg.BS_stream_bed):
-        if stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+            stg.BS_noise_raw_data[data_id] = np.array([])
-            stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.BS_noise_raw_data[data_id] = (
-            stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
+                np.full(stg.BS_stream_bed[data_id].shape,
-                np.full(stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape,
                         float(self.lineEdit_profile_tail_value.text().replace(",", "."))))
 
-            stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = (
+            stg.BS_noise_averaged_data[data_id] = (
-                stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][:, :,
+                stg.BS_noise_raw_data[data_id][:, :,
-                :stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape[2]])
+                :stg.BS_stream_bed[data_id].shape[2]])
-            stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()] = (
+            stg.SNR_stream_bed[data_id] = (
-                np.divide((stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()]
+                np.divide((stg.BS_stream_bed[data_id]
-                           - stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
+                           - stg.BS_noise_raw_data[data_id]) ** 2,
-                          stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
+                          stg.BS_noise_raw_data[data_id] ** 2))
 
-        elif stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+        elif self._is_correct_shape(stg.BS_cross_section):
-
+            stg.BS_noise_raw_data[data_id] = (
-            stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
+                np.full(stg.BS_cross_section[data_id].shape,
-                np.full(stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape,
                         float(self.lineEdit_profile_tail_value.text().replace(",", "."))))
 
-            stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = (
+            stg.BS_noise_averaged_data[data_id] = (
-                stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][:, :,
+                stg.BS_noise_raw_data[data_id][:, :,
-                :stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape[2]])
+                :stg.BS_cross_section[data_id].shape[2]])
-            stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()] = (
+            stg.SNR_cross_section[data_id] = (
-                np.divide((stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()]
+                np.divide((stg.BS_cross_section[data_id]
-                           - stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
+                           - stg.BS_noise_raw_data[data_id]) ** 2,
-                          stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2)) #
+                          stg.BS_noise_raw_data[data_id] ** 2)) #
 
         else:
 
-            stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
+            stg.BS_noise_raw_data[data_id] = (
-                np.full(stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()].shape,
+                np.full(stg.BS_raw_data[data_id].shape,
                         float(self.lineEdit_profile_tail_value.text().replace(",", "."))))
 
-            stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = (
+            stg.BS_noise_averaged_data[data_id] = (
-                stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()])
+                stg.BS_noise_raw_data[data_id])
-            stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
+            stg.SNR_raw_data[data_id] = (
-                np.divide((stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()]
+                np.divide((stg.BS_raw_data[data_id]
-                           - stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
+                           - stg.BS_noise_raw_data[data_id]) ** 2,
-                          stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
+                          stg.BS_noise_raw_data[data_id] ** 2))
 
         self.combobox_frequency_profile.clear()
         self.combobox_frequency_profile.addItems(
-            [f for f in stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()]])
+            [f for f in stg.freq_text[data_id]])
 
         # --- Trigger graphic widgets ---
 
-        if stg.SNR_filter_value[self.combobox_acoustic_data_choice.currentIndex()] == 0:
+        if stg.SNR_filter_value[data_id] == 0:
             self.lineEdit_SNR_criterion.setText("0.00")
         else:
-            self.lineEdit_SNR_criterion.setText(str(stg.SNR_filter_value[self.combobox_acoustic_data_choice.currentIndex()]))
+            self.lineEdit_SNR_criterion.setText(str(stg.SNR_filter_value[data_id]))
 
-        if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+        if stg.time_cross_section[data_id].shape != (0,):
-            self.slider.setMaximum(stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape[1])
+            self.slider.setMaximum(stg.time_cross_section[data_id].shape[1])
         else:
-            self.slider.setMaximum(stg.time[self.combobox_acoustic_data_choice.currentIndex()].shape[1])
+            self.slider.setMaximum(stg.time[data_id].shape[1])
 
         # self.activate_list_of_pre_processed_data()
 
@@ -1150,14 +1180,17 @@ class SignalProcessingTab(QWidget):
 
         # elif self.canvas_SNR == None:
         else:
+            data_id = self.combobox_acoustic_data_choice.currentIndex()
 
-            if ((self.combobox_acoustic_data_choice.currentIndex() != -1)
+            if ((data_id != -1)
-                    and (stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,))):
+                    and (stg.BS_noise_raw_data[data_id].shape != (0,))):
-
                 self.verticalLayout_groupbox_plot_SNR.removeWidget(self.toolbar_SNR)
                 self.verticalLayout_groupbox_plot_SNR.removeWidget(self.scroll_SNR)
 
-                self.fig_SNR, self.axis_SNR = plt.subplots(nrows=stg.freq[self.combobox_acoustic_data_choice.currentIndex()].shape[0], ncols=1, sharex=True, sharey=False, layout='constrained')
+                self.fig_SNR, self.axis_SNR = plt.subplots(
+                    nrows=stg.freq[data_id].shape[0], ncols=1,
+                    sharex=True, sharey=False, layout='constrained'
+                )
                 self.canvas_SNR = FigureCanvas(self.fig_SNR)
                 self.toolbar_SNR = NavigationToolBar(self.canvas_SNR, self)
 
@@ -1166,158 +1199,82 @@ class SignalProcessingTab(QWidget):
                 self.verticalLayout_groupbox_plot_SNR.addWidget(self.toolbar_SNR)
                 self.verticalLayout_groupbox_plot_SNR.addWidget(self.scroll_SNR)
 
-                for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
+                for f, _ in enumerate(stg.freq[data_id]):
-
+                    if stg.SNR_stream_bed[data_id].shape != (0,):
-                    if stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+                        SNR_data = stg.SNR_stream_bed
-
+                    elif stg.SNR_cross_section[data_id].shape != (0,):
-                        if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+                        SNR_data = stg.SNR_cross_section
-
-                            if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
-
-                                x, y = np.meshgrid(
-                                    stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :],
-                                    stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :])
-
-                            elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
-
-                                x, y = np.meshgrid(
-                                    stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :],
-                                    stg.depth[self.combobox_acoustic_data_choice.currentIndex()][f, :])
 
+                    if stg.time_cross_section[data_id].shape != (0,):
+                        time_data = stg.time_cross_section
                     else:
+                        time_data = stg.time
 
-                            if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+                    if stg.depth_cross_section[data_id].shape != (0,):
+                        depth_data = stg.depth_cross_section
+                    elif stg.depth[data_id].shape != (0,):
+                        depth_data = stg.depth
 
                     x, y = np.meshgrid(
-                                    stg.time[self.combobox_acoustic_data_choice.currentIndex()][f, :],
+                        time_data[data_id][f, :],
-                                    stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :])
+                        depth_data[data_id][f, :]
+                    )
 
-                            elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+                    val_min = np.nanmin(SNR_data[data_id][f, :, :])
+                    val_max = np.nanmax(SNR_data[data_id][f, :, :])
 
-                                x, y = np.meshgrid(
-                                    stg.time[self.combobox_acoustic_data_choice.currentIndex()][f, :],
-                                    stg.depth[self.combobox_acoustic_data_choice.currentIndex()][f, :])
-
-                        val_min = np.nanmin(stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
-                        val_max = np.nanmax(stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
-                        if val_min == val_max:
                     levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
                     bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
-                            norm = BoundaryNorm(boundaries=bounds, ncolors=300)
+
-                        else:
+                    if val_min != val_max:
                         if val_min == 0:
                             val_min = 1e-5
-                            if val_max > 1000:
-                                levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
-                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
-                                norm = BoundaryNorm(boundaries=bounds, ncolors=300)
                         else:
-                                levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1])
+                            levels = np.array(
-                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
+                                [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
+                            )
+                            bounds = [
+                                00.1, 1, 2, 10, 100, 1000,
+                                val_max * 1000 + 1
+                            ]
+
                     norm = BoundaryNorm(boundaries=bounds, ncolors=300)
 
-                        cf = (self.axis_SNR[f].contourf(x, -y,
+                    cf = self.axis_SNR[f].contourf(
-                                                        stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, :, :],
+                        x, -y,
+                        SNR_data[data_id][f, :, :],
                         levels, cmap='gist_rainbow',
-                                                        norm=norm))
+                        norm=norm
+                    )
 
-                    elif stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+                    self.axis_SNR[f].text(
-
+                        1, .70, stg.freq_text[data_id][f],
-                        if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+                        fontsize=14, fontweight='bold', fontname="DejaVu Sans",
-
+                        c="black", alpha=0.5,
-                            if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+                        horizontalalignment='right',
-
+                        verticalalignment='bottom',
-                                x, y = np.meshgrid(
+                        transform=self.axis_SNR[f].transAxes
-                                    stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :],
+                    )
-                                    stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :])
-
-                            elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
-
-                                x, y = np.meshgrid(
-                                    stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :],
-                                    stg.depth[self.combobox_acoustic_data_choice.currentIndex()][f, :])
-
-                        else:
-
-                            if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
-
-                                x, y = np.meshgrid(
-                                    stg.time[self.combobox_acoustic_data_choice.currentIndex()][f, :],
-                                    stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :])
-
-                            elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
-
-                                x, y = np.meshgrid(
-                                    stg.time[self.combobox_acoustic_data_choice.currentIndex()][f, :],
-                                    stg.depth[self.combobox_acoustic_data_choice.currentIndex()][f, :])
-
-                        val_min = np.nanmin(stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
-                        val_max = np.nanmax(stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
-                        if val_min == val_max:
-                            levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
-                            bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
-                            norm = BoundaryNorm(boundaries=bounds, ncolors=300)
-                        else:
-                            if val_min == 0:
-                                val_min = 1e-5
-                            if val_max > 1000:
-                                levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
-                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
-                                norm = BoundaryNorm(boundaries=bounds, ncolors=300)
-
-                            else:
-                                levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1])
-                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
-                                norm = BoundaryNorm(boundaries=bounds, ncolors=300)
-
-                        cf = (self.axis_SNR[f].contourf(x, -y,
-                                                        stg.SNR_cross_section[
-                                                            self.combobox_acoustic_data_choice.currentIndex()][f, :, :],
-                                                        levels, cmap='gist_rainbow', norm=norm))
-
-                    else:
-
-                        x, y = np.meshgrid(stg.time[self.combobox_acoustic_data_choice.currentIndex()][0, :],
-                                           stg.depth[self.combobox_acoustic_data_choice.currentIndex()][0, :])
-
-                        val_min = np.nanmin(stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
-                        val_max = np.nanmax(stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
-                        if val_min == val_max:
-                            levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
-                            bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
-                            norm = BoundaryNorm(boundaries=bounds, ncolors=300)
-                        else:
-                            if val_min == 0:
-                                val_min = 1e-5
-                            if val_max > 1000:
-                                levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
-                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
-                                norm = BoundaryNorm(boundaries=bounds, ncolors=300)
-                            else:
-                                levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max*1000 + 1])
-                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
-                                norm = BoundaryNorm(boundaries=bounds, ncolors=300)
-
-                        cf = (self.axis_SNR[f].contourf(x, -y,
-                                                        stg.SNR_raw_data[
-                                                            self.combobox_acoustic_data_choice.currentIndex()][f, :, :],
-                                                        levels, cmap='gist_rainbow', norm=norm))
-
-
-                    self.axis_SNR[f].text(1, .70, stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()][f],
-                                            fontsize=14, fontweight='bold', fontname="DejaVu Sans", c="black", alpha=0.5,
-                                            horizontalalignment='right', verticalalignment='bottom',
-                                            transform=self.axis_SNR[f].transAxes)
 
                 self.fig_SNR.supxlabel('Time (sec)', fontsize=10)
                 self.fig_SNR.supylabel('Depth (m)', fontsize=10)
-                cbar = self.fig_SNR.colorbar(cf, ax=self.axis_SNR[:], shrink=1, location='right')
+                cbar = self.fig_SNR.colorbar(
-                cbar.set_label(label='Signal to Noise Ratio', rotation=270, labelpad=10)
+                    cf, ax=self.axis_SNR[:],
-                cbar.set_ticklabels(['0', '1', '2', '10', '100', r'10$^3$', r'10$^6$'])
+                    shrink=1, location='right'
+                )
+                cbar.set_label(
+                    label='Signal to Noise Ratio',
+                    rotation=270, labelpad=10
+                )
+                cbar.set_ticklabels(
+                    [
+                        '0', '1', '2', '10', '100',
+                        r'10$^3$', r'10$^6$'
+                    ]
+                )
                 self.fig_SNR.canvas.draw_idle()
 
             else:
-
                 self.verticalLayout_groupbox_plot_SNR.removeWidget(self.toolbar_SNR)
                 self.verticalLayout_groupbox_plot_SNR.removeWidget(self.scroll_SNR)
 
@@ -1439,9 +1396,6 @@ class SignalProcessingTab(QWidget):
                     x_time = stg.time[data_id]
                     y_depth = stg.depth[data_id]
 
-            logger.debug(f"x_time: {x_time[data_id].shape}")
-            logger.debug(f"y_depth: {y_depth[data_id].shape}")
-
             for f, _ in enumerate(stg.freq[data_id]):
                 if stg.BS_stream_bed_pre_process_average[data_id].shape != (0,):
                     BS_data = stg.BS_stream_bed_pre_process_average
@@ -1462,8 +1416,6 @@ class SignalProcessingTab(QWidget):
                 elif stg.BS_raw_data[data_id].shape != (0,):
                     BS_data = stg.BS_raw_data
 
-                logger.debug(f"BS_data: {BS_data[data_id].shape}")
-
                 val_min = np.nanmin(
                     BS_data[data_id][f, :, :]
                 )
@@ -1574,19 +1526,9 @@ class SignalProcessingTab(QWidget):
                 )
             )
 
-            if stg.time_cross_section[data_id].shape != (0,):
             if stg.depth_cross_section[data_id].shape != (0,):
-                    x_time = stg.time_cross_section[data_id]
                 y_depth = stg.depth_cross_section[data_id]
             elif stg.depth[data_id].shape != (0,):
-                    x_time = stg.time_cross_section[data_id]
-                    y_depth = stg.depth[data_id]
-            else:
-                if stg.depth_cross_section[data_id].shape != (0,):
-                    x_time = stg.time[data_id]
-                    y_depth = stg.depth_cross_section[data_id]
-                elif stg.depth[data_id].shape != (0,):
-                    x_time = stg.time[data_id]
                 y_depth = stg.depth[data_id]
 
             BS = [
@@ -1607,11 +1549,6 @@ class SignalProcessingTab(QWidget):
                 stg.BS_raw_data_pre_process_average,
             ]
 
-            time_shape, = x_time[data_id].shape
-            depth_shape, = y_depth[data_id].shape
-            logger.debug(f"time_shape: {time_shape}")
-            logger.debug(f"depth_shape: {depth_shape}")
-
             BS_data = stg.BS_raw_data
             BS_data_ppa = stg.BS_raw_data_pre_process_average
             for i in range(len(BS)):
@@ -1620,8 +1557,7 @@ class SignalProcessingTab(QWidget):
                 if bs[data_id].shape == (0,):
                     continue
 
-                x, y, z = bs[data_id].shape
+                if self._is_correct_shape(bs):
-                if y == depth_shape and z == time_shape:
                     BS_data = bs
                     BS_data_ppa = BS_ppa[i]
                     break