the clear button is partially corrected

Model/create_table_for_save_as.py

@@ -389,10 +389,6 @@ class CreateTableForSaveAs:
                 )
             )
 
-            logger.debug(f"stg.ind_bottom: {stg.ind_bottom[i]}")
-            logger.debug(np.array([stg.ind_bottom[i]]),
-                         np.array(stg.ind_bottom[i]).shape)
-
         # Commit the transaction after executing INSERT.
         cnx.commit()
 
@@ -446,7 +442,7 @@ class CreateTableForSaveAs:
 
         cur.execute(self.create_SedimentsFile)
 
-        if stg.path_fine != "" and path_sand != "":
+        if stg.path_fine != "" and stg.path_sand != "":
             cur.execute(
                 """
                 INSERT into SedimentsFile(

README.md

@@ -1,27 +1,23 @@
 # AcouSed
 
-AcouSed for **Acou**stic Backscattering for Concentration of Suspended **Sed**iments in Rivers is a software developped by INRAE, in collaboation with CNR. 
+AcouSed for **Acou**stic Backscattering for Concentration of Suspended **Sed**iments in Rivers is a software developped by INRAE, in collaboation with CNR.
 
 ![](icons/Logo-INRAE.jpg)
 
-It is divided in six tabs: 
-- Acoustic data : acoustic raw data are downloaded and visualised 
+It is divided in six tabs:
+- Acoustic data : acoustic raw data are downloaded and visualised
 - Signal preprocessing : acoustic raw signal is preprocessed with filters
-- Sample data : fine and sand sediments samples data are downloaded and visualised 
+- Sample data : fine and sand sediments samples data are downloaded and visualised
 - Calibration : calibration parameter are computed
 - Inversion : inversion method is calculated to provide fine and sand sediments fields
 
-## Software documentation
-
-### Installation
+## Installation
 
 Acoused is developped for Linux and Windows on Python version 3.8 or
 greater. By default, Acoused is developped with Pypi package
 dependencies, but is also possible to use Guix package manager to run
 Acoused.
 
-## Development documentation
-
 ### **TODO** Windows
 
 ### Linux
@@ -49,16 +45,15 @@ script `guix.sh` to run the program.
   guix shell sqlitebrowser -- ./guix.sh
 ```
 
-## License
+## Support files & References
 
-AcouSed 
-Copyright (C) 2024 - INRAE 
+- [ ] [Acoustic inversion method diagram](https://forgemia.inra.fr/theophile.terraz/acoused/-/blob/main/Acoustic_Inversion_theory.pdf?ref_type=heads)
+- [ ] [Tutorial AQUAscat software : AQUAtalk](https://forgemia.inra.fr/theophile.terraz/acoused/-/blob/main/Tutorial_AQUAscat_software.pdf?ref_type=heads)
 
-This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 
 
-This program 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 General Public License for more details. 
-
-You should have received a copy of the GNU General Public License along with this program.  If not, see <https://www.gnu.org/licenses/>.
+- [ ] [Adrien Vergne thesis (2018)](https://theses.fr/2018GREAU046)
+- [ ] [Vergne A., Le Coz J., Berni C., & Pierrefeu G. (2020), Water Resources Research, 56(2)](https://doi.org/10.1029/2019WR024877)
+- [ ] [Vergne A., Berni C., Le Coz J., & Tencé F., (2021), Water Resources Research, 57(9)](https://doi.org/10.1029/2021WR029589)
 
 ## Authors & Contacts
 
@@ -68,15 +63,16 @@ You should have received a copy of the GNU General Public License along with thi
 If you have any questions or suggestions, please contact us to celine.berni@inrae.fr and/or jerome.lecoz@inrae.fr.
 
 ## Acknowledgment (Funding)
-This study was conducted within the [Rhône Sediment Observatory](https://observatoire-sediments-rhone.fr/) (OSR), a multi-partner research program funded through the Plan Rhône by the European Regional Development Fund (ERDF), Agence de l’Eau RMC, CNR, EDF and three regional councils (Auvergne-Rhône-Alpes, PACA and Occitanie). It was also support by CNR. 
 
-## Support files & References 
+This study was conducted within the [Rhône Sediment Observatory](https://observatoire-sediments-rhone.fr/) (OSR), a multi-partner research program funded through the Plan Rhône by the European Regional Development Fund (ERDF), Agence de l’Eau RMC, CNR, EDF and three regional councils (Auvergne-Rhône-Alpes, PACA and Occitanie). It was also support by CNR.
 
-- [ ] [Acoustic inversion method diagram](https://forgemia.inra.fr/theophile.terraz/acoused/-/blob/main/Acoustic_Inversion_theory.pdf?ref_type=heads) 
-- [ ] [Tutorial AQUAscat software : AQUAtalk](https://forgemia.inra.fr/theophile.terraz/acoused/-/blob/main/Tutorial_AQUAscat_software.pdf?ref_type=heads)
+## License
 
+AcouSed
+Copyright (C) 2024-2025 - INRAE
 
-- [ ] [Adrien Vergne thesis (2018)](https://theses.fr/2018GREAU046)
-- [ ] [Vergne A., Le Coz J., Berni C., & Pierrefeu G. (2020), Water Resources Research, 56(2)](https://doi.org/10.1029/2019WR024877)
-- [ ] [Vergne A., Berni C., Le Coz J., & Tencé F., (2021), Water Resources Research, 57(9)](https://doi.org/10.1029/2021WR029589)
+This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
 
+This program 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 General Public License for more details.
+
+You should have received a copy of the GNU General Public License along with this program.  If not, see <https://www.gnu.org/licenses/>.

View/acoustic_inversion_tab.py

@@ -20,16 +20,11 @@
 
 # -*- coding: utf-8 -*-
 
-
-import pandas as pd
-from PyQt5.QtWidgets import (QWidget, QVBoxLayout, QHBoxLayout, QGroupBox, QComboBox,
-                             QLabel, QPushButton, QSpacerItem,
-                             QSlider, QLineEdit, QMessageBox, QFileDialog)
-
-from PyQt5.QtCore import QCoreApplication, Qt
-from PyQt5.QtGui import QIcon, QPixmap
-
+import os
 import numpy as np
+import pandas as pd
+
+from copy import deepcopy
 
 import matplotlib.pyplot as plt
 
@@ -37,9 +32,14 @@ from matplotlib.colors import LogNorm
 from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
 from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolBar
 
-from os import chdir
+from PyQt5.QtWidgets import (
+    QWidget, QVBoxLayout, QHBoxLayout, QGroupBox, QComboBox,
+    QLabel, QPushButton, QSpacerItem, QSlider, QLineEdit,
+    QMessageBox, QFileDialog
+)
 
-from copy import deepcopy
+from PyQt5.QtCore import QCoreApplication, Qt
+from PyQt5.QtGui import QIcon, QPixmap
 
 from View.checkable_combobox import CheckableComboBox
 
@@ -1671,96 +1671,81 @@ class AcousticInversionTab(QWidget):
                 self.figure_measured_vs_inverted_sand.canvas.draw_idle()
 
     def save_result_in_excel_file(self):
-
         if self.combobox_acoustic_data_choice.count() > 0:
-
             name = QFileDialog.getSaveFileName(
-                caption="Save As - Inversion results", directory="", filter="Excel Files (*.xlsx)",
-                options=QFileDialog.DontUseNativeDialog)
+                caption="Save As - Inversion results",
+                directory="",
+                filter="Excel Files (*.xlsx)",
+                options=QFileDialog.DontUseNativeDialog
+            )
 
             if name[0]:
-                dirname = "/".join(name[0].split("/")[:-1]) + "/"
-                filename = name[0].split("/")[-1]
-                chdir(dirname)
+                dirname = os.path.dirname(name[0])
+                filename = os.path.basename(name[0])
+                os.chdir(dirname)
+
+            results = []
 
             for k in range(self.combobox_acoustic_data_choice.count()):
-
                 if stg.time_cross_section[k].shape != (0,):
-
-                    if stg.depth_cross_section[k].shape != (0,):
-
-                        t = np.repeat(stg.time_cross_section[k][stg.frequency_for_inversion[1]],
-                                      stg.depth_cross_section[k].shape[1])
-
-                        r = np.zeros((stg.depth_cross_section[k].shape[1] *stg.time_cross_section[k].shape[1],1))
-
-                        for i in range(stg.time_cross_section[k].shape[1]):
-                            for j in range(stg.depth_cross_section[k].shape[1]):
-                                r[i * stg.depth_cross_section[k].shape[1] + j] = (
-                                    stg.depth_cross_section[k][int(stg.frequency_for_inversion[1]), j])
-
-                        if stg.SSC_fine[k].shape == (0,):
-                            stg.SSC_fine[k] = np.zeros(r.shape[0])
-                        if stg.SSC_sand[k].shape == (0,):
-                            stg.SSC_sand[k] = np.zeros(r.shape[0])
-
-                    else:
-
-                        t = np.repeat(stg.time_cross_section[k][stg.frequency_for_inversion[1]], stg.depth[k].shape[1])
-
-                        r = np.zeros((stg.depth[k].shape[1] * stg.time_cross_section[k].shape[1], 1))
-                        for i in range(stg.time_cross_section[k].shape[1]):
-                            for j in range(stg.depth[k].shape[1]):
-                                r[i * stg.depth[k].shape[1] + j] = (
-                                    stg.depth[k][int(stg.frequency_for_inversion[1]), j])
-
-                        if stg.SSC_fine[k].shape == (0,):
-                            stg.SSC_fine[k] = np.zeros(r.shape[0])
-                        if stg.SSC_sand[k].shape == (0,):
-                            stg.SSC_sand[k] = np.zeros(r.shape[0])
-
+                    time_data = stg.time_cross_section
                 else:
+                    time_data = stg.time
 
-                    if stg.depth_cross_section[k].shape != (0,):
-
-                        t = np.repeat(stg.time[k][stg.frequency_for_inversion[1]], stg.depth_cross_section[k].shape[1])
-
-                        r = np.zeros((stg.depth_cross_section[k].shape[1] * stg.time[k].shape[1], 1))
-                        for i in range(stg.time[k].shape[1]):
-                            for j in range(stg.depth_cross_section[k].shape[1]):
-                                r[i * stg.depth_cross_section[k].shape[1] + j] = (
-                                    stg.depth_cross_section[k][int(stg.frequency_for_inversion[1]), j])
-
-                        if stg.SSC_fine[k].shape == (0,):
-                            stg.SSC_fine[k] = np.zeros(r.shape[0])
-                        if stg.SSC_sand[k].shape == (0,):
-                            stg.SSC_sand[k] = np.zeros(r.shape[0])
-
-                    else:
-
-                        t = np.repeat(stg.time[k][stg.frequency_for_inversion[1]], stg.depth[k].shape[1])
-
-                        r = np.zeros(stg.depth[k].shape[1] * stg.time[k].shape[1])
-
-                        for i in range(stg.time[k].shape[1]):
-                            for j in range(stg.depth[k].shape[1]):
-                                r[i * stg.depth[k].shape[1] + j] = (
-                                    stg.depth[k][int(stg.frequency_for_inversion[1]), j])
-
-                        if stg.SSC_fine[k].shape == (0,):
-                            stg.SSC_fine[k] = np.zeros(r.shape[0])
-                        if stg.SSC_sand[k].shape == (0,):
-                            stg.SSC_sand[k] = np.zeros(r.shape[0])
-
-                exec("result_" + str(k)  + "= pd.DataFrame({'Time (sec)': t," +
-                                                           "'Depth (m)': r," +
-                                                           "'SSC_fine (g/L)': stg.SSC_fine[" + str(k) + "].reshape(t.shape[0])," +
-                                                           "'SSC_sand (g/L)': stg.SSC_sand[" + str(k) + "].reshape(t.shape[0])})")
+                if stg.depth_cross_section[k].shape != (0,):
+                    depth_data = stg.depth_cross_section
+                else:
+                    depth_data = stg.depth
 
 
+                t = np.repeat(
+                    time_data[k][stg.frequency_for_inversion[1]],
+                    depth_data[k].shape[1]
+                )
 
-            with pd.ExcelWriter(dirname + filename + '.xlsx') as writer:
+                r = np.zeros(
+                    depth_data[k].shape[1] * time_data[k].shape[1]
+                )
+
+                for i in range(time_data[k].shape[1]):
+                    for j in range(depth_data[k].shape[1]):
+                        r_id = i * depth_data[k].shape[1] + j
+
+                        r[r_id] = (
+                            depth_data[k][
+                                int(stg.frequency_for_inversion[1]), j
+                            ]
+                        )
+
+                if stg.SSC_fine[k].shape == (0,):
+                    stg.SSC_fine[k] = np.zeros(r.shape[0])
+                if stg.SSC_sand[k].shape == (0,):
+                    stg.SSC_sand[k] = np.zeros(r.shape[0])
+
+                results.append(
+                    pd.DataFrame(
+                        {
+                            'Time (sec)': list(t),
+                            'Depth (m)': list(r),
+                            'SSC_fine (g/L)': list(
+                                stg.SSC_fine[k].reshape(t.shape[0])
+                            ),
+                            'SSC_sand (g/L)': list(
+                                stg.SSC_sand[k].reshape(t.shape[0])
+                            ),
+                        }
+                    )
+                )
+
+            if os.path.splitext(filename)[1] != ".xlsx":
+                filename += ".xlsx"
+
+            with pd.ExcelWriter(
+                    os.path.join(dirname, filename)
+            ) as writer:
                 for k in range(self.combobox_acoustic_data_choice.count()):
-                    eval("result_" + str(k) + ".to_excel(writer, index=False, " +
-                        "engine='xlsxwriter', na_rep='NA', " +
-                        "sheet_name=stg.data_preprocessed[" + str(k) + "])")
+                    results[k].to_excel(
+                        writer, index=False,
+                        engine='xlsxwriter', na_rep='NA',
+                        sheet_name=stg.data_preprocessed[k],
+                    )

View/mainwindow.py

@@ -147,12 +147,14 @@ class Ui_MainWindow(object):
         icon6.addPixmap(QtGui.QPixmap("icons/en.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
         self.actionEnglish.setIcon(icon6)
         self.actionEnglish.setObjectName("actionEnglish")
+        self.actionEnglish.setEnabled(False)
 
         self.actionFrench = QtWidgets.QAction(self.mainwindow)
         icon7 = QtGui.QIcon()
         icon7.addPixmap(QtGui.QPixmap("icons/fr.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
         self.actionFrench.setIcon(icon7)
         self.actionFrench.setObjectName("actionFrench")
+        self.actionFrench.setEnabled(False)
 
         self.action_ABSCalibrationConstant = QtWidgets.QAction(self.mainwindow)
         self.action_ABSCalibrationConstant.setText("ABS constant calibration kt")
@@ -268,7 +270,10 @@ class Ui_MainWindow(object):
         )
 
     def save(self):
-        UpdateTableForSave()
+        if stg.dirname_save_as:
+            UpdateTableForSave()
+        else:
+            self.save_as()
 
     def open(self):
         reader = ReadTableForOpen()

View/sediment_calibration_tab.py

@@ -1716,14 +1716,18 @@ class SedimentCalibrationTab(QWidget):
         self.animaiton_groupbox_compute.start()
 
     def import_calibration_file(self):
-
         filename = QFileDialog.getOpenFileName(
             self, "Open calibration",
-            [stg.path_calibration_file if stg.path_calibration_file else
-                                                stg.path_BS_raw_data[
-                                                    -1] if self.combobox_acoustic_data_choice.count() > 0 else ""][0],
+            [
+                stg.path_calibration_file
+                if stg.path_calibration_file
+                else stg.path_BS_raw_data[-1]
+                if self.combobox_acoustic_data_choice.count() > 0
+                else ""
+            ][0],
             "Calibration file (*.xls, *.ods, *csv)",
-        options=QFileDialog.DontUseNativeDialog)
+            options=QFileDialog.DontUseNativeDialog
+        )
 
         dir_name = os.path.dirname(filename[0])
         name = os.path.basename(filename[0])
@@ -1736,114 +1740,188 @@ class SedimentCalibrationTab(QWidget):
 
         self.lineEdit_import_calibration.setToolTip(dir_name)
 
+        self.compute_depth_2D()
         self.read_calibration_file_and_fill_parameter()
 
     def update_label_freq1_for_calibration(self):
         self.label_freq1.clear()
-        self.label_freq1.setText(str(self.combobox_freq1.currentText()))
+        self.label_freq1.setText(
+            str(self.combobox_freq1.currentText())
+        )
 
     def update_label_freq2_for_calibration(self):
         self.label_freq2.clear()
-        self.label_freq2.setText(self.combobox_freq2.currentText())
+        self.label_freq2.setText(
+            self.combobox_freq2.currentText()
+        )
 
     def update_label_kt_value_for_calibration(self):
+        print("self.combobox_freq1.currentIndex() ",
+              self.combobox_freq1.currentIndex(),
+              self.combobox_freq1.currentText())
+
+        freq_1 = self.combobox_freq1.currentIndex()
+        freq_2 = self.combobox_freq2.currentIndex()
+
         self.label_kt_freq1.clear()
-        print("self.combobox_freq1.currentIndex() ", self.combobox_freq1.currentIndex(), self.combobox_freq1.currentText())
-        if stg.kt_corrected[self.combobox_freq1.currentIndex()] != stg.kt_read[self.combobox_freq1.currentIndex()]:
-            self.label_kt_freq1.setText(str('%.4f' % stg.kt_corrected[self.combobox_freq1.currentIndex()]))
+        if stg.kt_corrected[freq_1] != stg.kt_read[freq_1]:
+            self.label_kt_freq1.setText(
+                str('%.4f' % stg.kt_corrected[freq_1])
+            )
         else:
-            self.label_kt_freq1.setText(str('%.4f' % stg.kt_read[self.combobox_freq1.currentIndex()]))
+            self.label_kt_freq1.setText(
+                str('%.4f' % stg.kt_read[freq_1])
+            )
 
         self.label_kt_freq2.clear()
-        if stg.kt_corrected[self.combobox_freq2.currentIndex()] != stg.kt_read[self.combobox_freq2.currentIndex()]:
-            self.label_kt_freq2.setText(str('%.4f' % stg.kt_corrected[self.combobox_freq2.currentIndex()]))
+        if stg.kt_corrected[freq_2] != stg.kt_read[freq_2]:
+            self.label_kt_freq2.setText(
+                str('%.4f' % stg.kt_corrected[freq_2])
+            )
         else:
-            self.label_kt_freq2.setText(str('%.4f' % stg.kt_read[self.combobox_freq2.currentIndex()]))
+            self.label_kt_freq2.setText(
+                str('%.4f' % stg.kt_read[freq_2])
+            )
 
     def read_calibration_file_and_fill_parameter(self):
         if self.combobox_acoustic_data_choice.count() == 0:
-
             msgBox = QMessageBox()
             msgBox.setWindowTitle("Calibration import error")
             msgBox.setIconPixmap(
-                QPixmap(self._path_icon("no_approved.png")).scaledToHeight(32, Qt.SmoothTransformation))
+                QPixmap(
+                    self._path_icon("no_approved.png")
+                ).scaledToHeight(32, Qt.SmoothTransformation)
+            )
             msgBox.setText("Update data before importing calibration")
             msgBox.setStandardButtons(QMessageBox.Ok)
             msgBox.exec()
 
-        else:
+        elif stg.filename_calibration_file == "":
 
+            pass
+
+        else:
             # --- Read calibration file ---
-            data = pd.read_csv(stg.path_calibration_file + "/" + stg.filename_calibration_file, header=0, index_col=0)
+            data = pd.read_csv(os.path.join(stg.path_calibration_file, stg.filename_calibration_file), header=0, index_col=0)
 
             # --- Fill spinboxes of calibration parameter ---
             self.label_temperature.clear()
-            self.label_temperature.setText("T = " + str(stg.temperature) + " °C")
+            self.label_temperature.setText(
+                "T = " + str(stg.temperature) + " °C"
+            )
 
             self.label_freq1.clear()
             self.label_freq1.setText(data.columns[0])
 
-            index_freq1 = np.where(np.asarray(stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()]) ==
-                                   data.columns[0])[0][0]
+            data_id = self.combobox_acoustic_data_choice.currentIndex()
+
+            index_freq1 = np.where(
+                np.asarray(
+                    stg.freq_text[data_id]
+                ) == data.columns[0]
+            )[0][0]
+
             stg.frequencies_for_calibration.clear()
-            stg.frequencies_for_calibration.append((stg.freq[self.combobox_acoustic_data_choice.currentIndex()][
-                                                        index_freq1],
-                                                    index_freq1))
+            stg.frequencies_for_calibration.append(
+                (
+                    stg.freq[data_id][index_freq1],
+                    index_freq1
+                )
+            )
 
             self.label_freq2.clear()
             self.label_freq2.setText(data.columns[1])
 
-            index_freq2 = np.where(np.asarray(stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()]) ==
-                                   data.columns[1])[0][0]
-            stg.frequencies_for_calibration.append((stg.freq[self.combobox_acoustic_data_choice.currentIndex()][
-                                                        index_freq2],
-                                                    index_freq2))
+            index_freq2 = np.where(
+                np.asarray(
+                    stg.freq_text[data_id]
+                ) == data.columns[1]
+            )[0][0]
+
+            stg.frequencies_for_calibration.append(
+                (
+                    stg.freq[data_id][index_freq2],
+                    index_freq2
+                )
+            )
 
             stg.frequency_for_inversion = tuple()
-            stg.frequency_for_inversion = (stg.freq[self.combobox_acoustic_data_choice.currentIndex()][index_freq2],
-                                           index_freq2)
+            stg.frequency_for_inversion = (
+                stg.freq[data_id][index_freq2],
+                index_freq2
+            )
 
             self.lineEdit_ks_freq1.clear()
-            self.lineEdit_ks_freq1.setText(str("%.5f" % float(data.iloc[0][0])))
+            self.lineEdit_ks_freq1.setText(
+                str("%.5f" % float(data.iloc[0][0]))
+            )
 
             self.lineEdit_ks_freq2.clear()
-            self.lineEdit_ks_freq2.setText(str("%.5f" % float(data.iloc[0][1])))
+            self.lineEdit_ks_freq2.setText(
+                str("%.5f" % float(data.iloc[0][1]))
+            )
 
             stg.ks.clear()
-            stg.ks = [float(self.lineEdit_ks_freq1.text()), float(self.lineEdit_ks_freq2.text())]
+            stg.ks = [
+                float(self.lineEdit_ks_freq1.text()),
+                float(self.lineEdit_ks_freq2.text())
+            ]
 
             self.lineEdit_sv_freq1.clear()
-            self.lineEdit_sv_freq1.setText(str("%.5f" % float(data.iloc[1][0])))
+            self.lineEdit_sv_freq1.setText(
+                str("%.5f" % float(data.iloc[1][0]))
+            )
 
             self.lineEdit_sv_freq2.clear()
-            self.lineEdit_sv_freq2.setText(str("%.5f" % float(data.iloc[1][1])))
+            self.lineEdit_sv_freq2.setText(
+                str("%.5f" % float(data.iloc[1][1]))
+            )
 
             stg.sv.clear()
-            stg.sv = [float(self.lineEdit_sv_freq1.text()), float(self.lineEdit_sv_freq2.text())]
+            stg.sv = [
+                float(self.lineEdit_sv_freq1.text()),
+                float(self.lineEdit_sv_freq2.text())
+            ]
 
             self.lineEdit_X.clear()
-            self.lineEdit_X.setText(str("%.2f" % float(data.iloc[2][0])))
+            self.lineEdit_X.setText(
+                str("%.2f" % float(data.iloc[2][0]))
+            )
 
             stg.X_exponent.clear()
             stg.X_exponent.append(float(self.lineEdit_X.text()))
 
             self.lineEdit_alphas_freq1.clear()
-            self.lineEdit_alphas_freq1.setText(str("%.5f" % float(data.iloc[3][0])))
+            self.lineEdit_alphas_freq1.setText(
+                str("%.5f" % float(data.iloc[3][0]))
+            )
 
             self.lineEdit_alphas_freq2.clear()
-            self.lineEdit_alphas_freq2.setText(str("%.5f" % float(data.iloc[3][1])))
+            self.lineEdit_alphas_freq2.setText(
+                str("%.5f" % float(data.iloc[3][1]))
+            )
 
             stg.alpha_s.clear()
-            stg.alpha_s = [float(self.lineEdit_alphas_freq1.text()), float(self.lineEdit_alphas_freq2.text())]
+            stg.alpha_s = [
+                float(self.lineEdit_alphas_freq1.text()),
+                float(self.lineEdit_alphas_freq2.text())
+            ]
 
             self.lineEdit_zeta_freq1.clear()
-            self.lineEdit_zeta_freq1.setText(str("%.5f" % float(data.iloc[4][0])))
+            self.lineEdit_zeta_freq1.setText(
+                str("%.5f" % float(data.iloc[4][0]))
+            )
 
             self.lineEdit_zeta_freq2.clear()
-            self.lineEdit_zeta_freq2.setText(str("%.5f" % float(data.iloc[4][1])))
+            self.lineEdit_zeta_freq2.setText(
+                str("%.5f" % float(data.iloc[4][1]))
+            )
 
             stg.zeta.clear()
-            stg.zeta = [float(self.lineEdit_zeta_freq1.text()), float(self.lineEdit_zeta_freq2.text())]
+            stg.zeta = [
+                float(self.lineEdit_zeta_freq1.text()),
+                float(self.lineEdit_zeta_freq2.text())
+            ]
 
             self.compute_kt2D_kt3D()
             self.compute_J_cross_section()
@@ -1931,8 +2009,8 @@ class SedimentCalibrationTab(QWidget):
         self.compute_zeta()
 
     def compute_ks(self):
+        data_id = self.combobox_acoustic_data_choice.currentIndex()
 
-        # --- Compute ks ---
         psd_number_of_particles = (
             self.inv_hc.compute_particle_size_distribution_in_number_of_particles(
                 num_sample=stg.sand_sample_target[0][1],
@@ -1943,17 +2021,13 @@ class SedimentCalibrationTab(QWidget):
 
         ks_freq1 = self.inv_hc.ks(
             proba_num=psd_number_of_particles,
-            freq=stg.freq[self.combobox_acoustic_data_choice.currentIndex()][
-                self.combobox_freq1.currentIndex()
-            ],
+            freq=stg.freq[data_id][self.combobox_freq1.currentIndex()],
             C=stg.water_velocity
         )
 
         ks_freq2 = self.inv_hc.ks(
             proba_num=psd_number_of_particles,
-            freq=stg.freq[self.combobox_acoustic_data_choice.currentIndex()][
-                self.combobox_freq2.currentIndex()
-            ],
+            freq=stg.freq[data_id][self.combobox_freq2.currentIndex()],
             C=stg.water_velocity
         )
 
@@ -1961,12 +2035,12 @@ class SedimentCalibrationTab(QWidget):
 
         logger.debug(
             "ks for frequency of "
-            + f"{stg.freq[self.combobox_acoustic_data_choice.currentIndex()][self.combobox_freq1.currentIndex()]} : "
+            + f"{stg.freq[data_id][self.combobox_freq1.currentIndex()]} : "
             + f"{ks_freq1} m/kg^0.5 \n"
         )
         logger.debug(
             "ks for frequency of "
-            + f"{stg.freq[self.combobox_acoustic_data_choice.currentIndex()][self.combobox_freq2.currentIndex()]} : " +
+            + f"{stg.freq[data_id][self.combobox_freq2.currentIndex()]} : " +
             f"{ks_freq2} m/kg^0.5"
         )
 
@@ -1977,13 +2051,21 @@ class SedimentCalibrationTab(QWidget):
         self.lineEdit_ks_freq2.setText(str("%.5f" % ks_freq2))
 
     def compute_sv(self):
+        data_id = self.combobox_acoustic_data_choice.currentIndex()
+
         sv_freq1 = self.inv_hc.sv(ks=stg.ks[0], M_sand=stg.Ctot_sand[stg.sand_sample_target[0][1]])
         sv_freq2 = self.inv_hc.sv(ks=stg.ks[1], M_sand=stg.Ctot_sand[stg.sand_sample_target[0][1]])
 
         stg.sv = [sv_freq1, sv_freq2]
 
-        print(f"sv for frequency of {stg.freq[self.combobox_acoustic_data_choice.currentIndex()][self.combobox_freq1.currentIndex()]} : {sv_freq1:.8f} /m \n")
-        print(f"sv for frequency of {stg.freq[self.combobox_acoustic_data_choice.currentIndex()][self.combobox_freq2.currentIndex()]} : {sv_freq2:.8f} /m")
+        print(
+            f"sv for frequency of {stg.freq[data_id][self.combobox_freq1.currentIndex()]}"
+            + f" : {sv_freq1:.8f} /m \n"
+        )
+        print(
+            f"sv for frequency of {stg.freq[data_id][self.combobox_freq2.currentIndex()]}"
+            + f" : {sv_freq2:.8f} /m"
+        )
 
         self.lineEdit_sv_freq1.clear()
         self.lineEdit_sv_freq1.setText(str("%.5f" % sv_freq1))
@@ -1992,9 +2074,13 @@ class SedimentCalibrationTab(QWidget):
         self.lineEdit_sv_freq2.setText(str("%.5f" % sv_freq2))
 
     def compute_X(self):
-        X_exponent = self.inv_hc.X_exponent(freq1=stg.freq[self.combobox_acoustic_data_choice.currentIndex()][self.combobox_freq1.currentIndex()],
-                                            freq2=stg.freq[self.combobox_acoustic_data_choice.currentIndex()][self.combobox_freq2.currentIndex()],
-                                            sv_freq1=stg.sv[0], sv_freq2=stg.sv[1])
+        data_id = self.combobox_acoustic_data_choice.currentIndex()
+
+        X_exponent = self.inv_hc.X_exponent(
+            freq1=stg.freq[data_id][self.combobox_freq1.currentIndex()],
+            freq2=stg.freq[data_id][self.combobox_freq2.currentIndex()],
+            sv_freq1=stg.sv[0], sv_freq2=stg.sv[1]
+        )
 
         stg.X_exponent.clear()
         stg.X_exponent.append(X_exponent)
@@ -2037,190 +2123,90 @@ class SedimentCalibrationTab(QWidget):
             )
 
     def compute_J_cross_section(self):
+        lst_bs_data = [
+            stg.BS_stream_bed_pre_process_average,
+            stg.BS_stream_bed_pre_process_SNR,
+            stg.BS_stream_bed,
+            stg.BS_cross_section_pre_process_average,
+            stg.BS_cross_section_pre_process_SNR,
+            stg.BS_cross_section,
+            stg.BS_raw_data_pre_process_average,
+            stg.BS_raw_data_pre_process_SNR,
+            stg.BS_raw_data
+        ]
+
         for i in range(self.combobox_acoustic_data_choice.count()):
 
             J_cross_section_freq1 = np.array([])
             J_cross_section_freq2 = np.array([])
 
-            # --- Compute J ---
-            if stg.BS_stream_bed_pre_process_average[i].shape != (0,):
+            for data in lst_bs_data:
+                if data[i].shape != (0,):
+                    bs_data = data
+                    break
 
-                J_cross_section_freq1 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_stream_bed_pre_process_average[i][
-                       stg.frequencies_for_calibration[0][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[0][1],
-                        :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[0][1], :, :])
+            print(f"{stg.depth_2D[i].shape}")
+            print(f"{stg.depth_2D[i]}")
 
-                J_cross_section_freq2 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_stream_bed_pre_process_average[i][
-                       stg.frequencies_for_calibration[1][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[1][1],
-                        :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[1][1], :, :])
+            J_cross_section_freq1 = self.inv_hc.j_cross_section(
+                BS = bs_data[i][
+                    stg.frequencies_for_calibration[0][1], :, :
+                ],
+                r2D = stg.depth_2D[i][
+                        stg.frequencies_for_calibration[0][1], :, :
+                ],
+                kt = stg.kt3D[i][
+                    stg.frequencies_for_calibration[0][1], :, :
+                ]
+            )
 
-            elif stg.BS_stream_bed_pre_process_SNR[i].shape != (0,):
-
-                J_cross_section_freq1 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_stream_bed_pre_process_SNR[i][
-                       stg.frequencies_for_calibration[0][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[0][1],
-                        :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[0][1], :, :])
-
-                J_cross_section_freq2 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_stream_bed_pre_process_SNR[i][
-                       stg.frequencies_for_calibration[1][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[1][1],
-                        :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[1][1], :, :])
-
-            elif stg.BS_stream_bed[i].shape != (0,):
-
-                J_cross_section_freq1 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_stream_bed[i][stg.frequencies_for_calibration[0][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[0][1], :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[0][1], :, :])
-
-                J_cross_section_freq2 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_stream_bed[i][
-                       stg.frequencies_for_calibration[1][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[1][1], :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[1][1], :, :])
-
-            elif stg.BS_cross_section_pre_process_average[i].shape != (0,):
-
-                J_cross_section_freq1 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_cross_section_pre_process_average[i][
-                       stg.frequencies_for_calibration[0][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[0][1],
-                        :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[0][1], :, :])
-
-                J_cross_section_freq2 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_cross_section_pre_process_average[i][
-                       stg.frequencies_for_calibration[1][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[1][1],
-                        :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[1][1], :, :])
-
-            elif stg.BS_cross_section_pre_process_SNR[i].shape != (0,):
-
-                J_cross_section_freq1 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_cross_section_pre_process_SNR[i][
-                       stg.frequencies_for_calibration[0][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[0][1],
-                        :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[0][1], :, :])
-
-                J_cross_section_freq2 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_cross_section_pre_process_SNR[i][
-                       stg.frequencies_for_calibration[1][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[1][1],
-                        :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[1][1], :, :])
-
-            elif stg.BS_cross_section[i].shape != (0,):
-
-                J_cross_section_freq1 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_cross_section[i][
-                       stg.frequencies_for_calibration[0][1], :, :],
-                    r2D=stg.depth_2D[i, :, :][stg.frequencies_for_calibration[0][1], :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[0][1], :, :])
-
-                J_cross_section_freq2 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_cross_section[i][
-                       stg.frequencies_for_calibration[1][1], :, :],
-                    r2D=stg.depth_2D[i, :, :][stg.frequencies_for_calibration[1][1], :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[1][1], :, :])
-
-            elif stg.BS_raw_data_pre_process_average[i].shape != (0,):
-
-                J_cross_section_freq1 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_raw_data_pre_process_average[i][
-                       stg.frequencies_for_calibration[0][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[0][1], :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[0][1], :, :])
-
-                J_cross_section_freq2 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_raw_data_pre_process_average[i][
-                       stg.frequencies_for_calibration[1][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[1][1], :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[1][1], :, :])
-
-            elif stg.BS_raw_data_pre_process_SNR[i].shape != (0,):
-
-                J_cross_section_freq1 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_raw_data_pre_process_SNR[i][
-                       stg.frequencies_for_calibration[0][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[0][1], :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[0][1], :, :])
-
-                J_cross_section_freq2 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_raw_data_pre_process_SNR[i][
-                       stg.frequencies_for_calibration[1][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[1][1], :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[1][1], :, :])
-
-            elif stg.BS_raw_data:
-
-                J_cross_section_freq1 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_raw_data[i][
-                       stg.frequencies_for_calibration[0][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[0][1], :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[0][1], :, :])
-
-                J_cross_section_freq2 = self.inv_hc.j_cross_section(
-                    BS=stg.BS_raw_data[i][
-                       stg.frequencies_for_calibration[1][1], :, :],
-                    r2D=stg.depth_2D[i][stg.frequencies_for_calibration[1][1], :, :],
-                    kt=stg.kt3D[i][stg.frequencies_for_calibration[1][1], :, :])
+            J_cross_section_freq2 = self.inv_hc.j_cross_section(
+                BS = bs_data[i][
+                    stg.frequencies_for_calibration[1][1], :, :
+                ],
+                r2D = stg.depth_2D[i][
+                    stg.frequencies_for_calibration[1][1], :, :
+                ],
+                kt = stg.kt3D[i][
+                    stg.frequencies_for_calibration[1][1], :, :
+                ]
+            )
 
             stg.J_cross_section[i][0] = J_cross_section_freq1
             stg.J_cross_section[i][1] = J_cross_section_freq2
 
     def compute_alpha_s(self):
+        data_id = self.combobox_acoustic_data_choice.currentIndex()
+        freq_1 = self.combobox_freq1.currentIndex()
+        freq_2 = self.combobox_freq2.currentIndex()
 
-        # --- Compute alpha_s ---
-        if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+        depth_data = stg.depth
+        if stg.depth_cross_section[data_id].shape != (0,):
+            depth_data = stg.depth_cross_section
 
-            alpha_s_freq1 = self.inv_hc.alpha_s(
-                sv=stg.sv[0],
-                j_cross_section=stg.J_cross_section[self.combobox_acoustic_data_choice.currentIndex()][0][
-                    stg.sand_sample_target_indice[0][0], stg.sand_sample_target_indice[0][1]],
-                depth=stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
-                    self.combobox_freq1.currentIndex(), stg.sand_sample_target_indice[0][0]],
-                alpha_w=stg.water_attenuation[self.combobox_acoustic_data_choice.currentIndex()][
-                    self.combobox_freq1.currentIndex()])
+        alpha_s_freq1 = self.inv_hc.alpha_s(
+            sv=stg.sv[0],
+            j_cross_section=stg.J_cross_section[data_id][0][
+                stg.sand_sample_target_indice[0][0],
+                stg.sand_sample_target_indice[0][1]
+            ],
+            depth=depth_data[data_id][
+                freq_1, stg.sand_sample_target_indice[0][0]
+            ],
+            alpha_w=stg.water_attenuation[data_id][freq_1]
+        )
 
-            alpha_s_freq2 = self.inv_hc.alpha_s(
-                sv=stg.sv[1],
-                j_cross_section=stg.J_cross_section[self.combobox_acoustic_data_choice.currentIndex()][1][
-                    stg.sand_sample_target_indice[1][0], stg.sand_sample_target_indice[1][1]],
-                depth=stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
-                    self.combobox_freq2.currentIndex(), stg.sand_sample_target_indice[1][0]],
-                alpha_w=stg.water_attenuation[self.combobox_acoustic_data_choice.currentIndex()][
-                    self.combobox_freq2.currentIndex()])
-
-        else:
-
-            alpha_s_freq1 = self.inv_hc.alpha_s(
-                sv=stg.sv[0],
-                j_cross_section=stg.J_cross_section[self.combobox_acoustic_data_choice.currentIndex()][0][
-                    stg.sand_sample_target_indice[0][0], stg.sand_sample_target_indice[0][1]],
-                depth=stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
-                    self.combobox_freq1.currentIndex(), stg.sand_sample_target_indice[0][0]],
-                alpha_w=stg.water_attenuation[self.combobox_acoustic_data_choice.currentIndex()][
-                    self.combobox_freq1.currentIndex()])
-
-            alpha_s_freq2 = self.inv_hc.alpha_s(
-                sv=stg.sv[1],
-                j_cross_section=stg.J_cross_section[self.combobox_acoustic_data_choice.currentIndex()][1][
-                    stg.sand_sample_target_indice[1][0], stg.sand_sample_target_indice[1][1]],
-                depth=stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
-                    self.combobox_freq2.currentIndex(), stg.sand_sample_target_indice[1][0]],
-                alpha_w=stg.water_attenuation[self.combobox_acoustic_data_choice.currentIndex()][
-                    self.combobox_freq2.currentIndex()])
+        alpha_s_freq2 = self.inv_hc.alpha_s(
+            sv=stg.sv[1],
+            j_cross_section=stg.J_cross_section[data_id][1][
+                stg.sand_sample_target_indice[1][0],
+                stg.sand_sample_target_indice[1][1]
+            ],
+            depth=depth_data[data_id][
+                freq_2, stg.sand_sample_target_indice[1][0]
+            ],
+            alpha_w=stg.water_attenuation[data_id][freq_2]
+        )
 
         stg.alpha_s = [alpha_s_freq1, alpha_s_freq2]
 

View/signal_processing_tab.py

@@ -447,8 +447,8 @@ class SignalProcessingTab(QWidget):
         # --------------------------------------------------------------------------------------------------------------
 
         self.pushbutton_update.clicked.connect(self.update_SignalPreprocessingTab)
-        self.pushbutton_update.clicked.connect(self.compute_average_profile_tail)
-        self.pushbutton_update.clicked.connect(self.plot_averaged_profile_tail)
+        # self.pushbutton_update.clicked.connect(self.compute_average_profile_tail)
+        # self.pushbutton_update.clicked.connect(self.plot_averaged_profile_tail)
 
         self.combobox_acoustic_data_choice.currentIndexChanged.connect(self.combobox_acoustic_data_choice_change_index)
 
@@ -501,26 +501,39 @@ class SignalProcessingTab(QWidget):
                 - the user remove a file (in the list widget) in the first tab (Acoustic data), so that the combobox
                     of data to be processed is updated,
                 - the user change the limits of one or all the records in the first tab (Acoustic data) """
+        if len(stg.filename_BS_raw_data) == 0:
 
-        self.combobox_acoustic_data_choice.clear()
-        self.combobox_acoustic_data_choice.addItems(stg.filename_BS_raw_data)
+            msgBox = QMessageBox()
+            msgBox.setWindowTitle("Compute noise from profile tail error")
+            msgBox.setIcon(QMessageBox.Warning)
+            msgBox.setText("Download acoustic data in previous tab before updating data")
+            msgBox.setStandardButtons(QMessageBox.Ok)
+            msgBox.exec()
 
-        if stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] == 0:
+        else:
 
-            self.groupbox_download_noise_file.setChecked(True)
-            self.groupbox_compute_noise_from_profile_tail.setChecked(False)
-            self.groupbox_download_noise_file_toggle()
+            self.combobox_acoustic_data_choice.clear()
+            self.combobox_acoustic_data_choice.addItems(stg.filename_BS_raw_data)
 
-        elif stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] == 1:
+            if stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] == 0:
 
-            self.groupbox_download_noise_file.setChecked(False)
-            self.groupbox_compute_noise_from_profile_tail.setChecked(True)
-            self.groupbox_option_profile_tail_toggle()
+                self.groupbox_download_noise_file.setChecked(True)
+                self.groupbox_compute_noise_from_profile_tail.setChecked(False)
+                self.groupbox_download_noise_file_toggle()
 
-        self.combobox_freq_noise_from_profile_tail.clear()
-        self.combobox_freq_noise_from_profile_tail.addItems(stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()])
+            elif stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] == 1:
 
-        self.combobox_acoustic_data_choice.currentIndexChanged.connect(self.combobox_acoustic_data_choice_change_index)
+                self.groupbox_download_noise_file.setChecked(False)
+                self.groupbox_compute_noise_from_profile_tail.setChecked(True)
+                self.groupbox_option_profile_tail_toggle()
+
+            self.combobox_freq_noise_from_profile_tail.clear()
+            self.combobox_freq_noise_from_profile_tail.addItems(stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()])
+
+            self.combobox_acoustic_data_choice.currentIndexChanged.connect(self.combobox_acoustic_data_choice_change_index)
+
+            self.compute_average_profile_tail()
+            self.plot_averaged_profile_tail()
 
     def activate_list_of_pre_processed_data(self):
         for i in range(self.combobox_acoustic_data_choice.count()):
@@ -654,45 +667,65 @@ class SignalProcessingTab(QWidget):
 
         # --- Plot averaged signal ---
 
-        if stg.BS_mean[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+        if len(stg.filename_BS_raw_data) == 0:
 
-            self.verticalLayout_groupbox_plot_profile_tail.removeWidget(self.canvas_profile_tail)
+            msgBox = QMessageBox()
+            msgBox.setWindowTitle("Compute noise from profile tail error")
+            msgBox.setIcon(QMessageBox.Warning)
+            msgBox.setText("Download acoustic data in previous tab before computing noise from profile tail")
+            msgBox.setStandardButtons(QMessageBox.Ok)
+            msgBox.exec()
 
-            self.fig_profile_tail, self.axis_profile_tail = plt.subplots(nrows=1, ncols=1, layout='constrained')
-            self.canvas_profile_tail = FigureCanvas(self.fig_profile_tail)
+        elif self.combobox_acoustic_data_choice.count() == 0:
 
-            self.verticalLayout_groupbox_plot_profile_tail.addWidget(self.canvas_profile_tail)
+            msgBox = QMessageBox()
+            msgBox.setWindowTitle("Compute noise from profile tail error")
+            msgBox.setIcon(QMessageBox.Warning)
+            msgBox.setText("Refresh acoustic data before computing noise from profile tail")
+            msgBox.setStandardButtons(QMessageBox.Ok)
+            msgBox.exec()
 
-            self.axis_profile_tail.plot(
-                    -stg.depth[self.combobox_acoustic_data_choice.currentIndex()][self.combobox_freq_noise_from_profile_tail.currentIndex()],
-                    stg.BS_mean[self.combobox_acoustic_data_choice.currentIndex()][self.combobox_freq_noise_from_profile_tail.currentIndex()],
-                    color="blue", linewidth=1)
-            self.axis_profile_tail.plot(
-                    -stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
-                        self.combobox_freq_noise_from_profile_tail.currentIndex()],
-                    float(self.lineEdit_profile_tail_value.text().replace(",", ".")) *
-                        np.ones(stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
-                            self.combobox_freq_noise_from_profile_tail.currentIndex()].shape[0]),
-                    linestyle='dashed', linewidth=2, color='red')
+        else:
 
-            self.axis_profile_tail.set_yscale('log')
-            self.axis_profile_tail.tick_params(axis='both', labelsize=8)
-            self.axis_profile_tail.text(.98, .03, "Depth (m)",
-                                        fontsize=8, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.9,
-                                        horizontalalignment='right', verticalalignment='bottom', rotation='horizontal',
-                                        transform=self.axis_profile_tail.transAxes)
-            self.axis_profile_tail.text(.1, .45, "BS signal (v)",
-                                        fontsize=8, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.9,
-                                        horizontalalignment='right', verticalalignment='bottom', rotation='vertical',
-                                        transform=self.axis_profile_tail.transAxes)
-            self.axis_profile_tail.text(.98, .85,
-                                        stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()][
-                                            self.combobox_freq_noise_from_profile_tail.currentIndex()],
-                                        fontsize=10, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
-                                        horizontalalignment='right', verticalalignment='bottom',
-                                        transform=self.axis_profile_tail.transAxes)
+            if stg.BS_mean[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
 
-            self.fig_profile_tail.canvas.draw_idle()
+                self.verticalLayout_groupbox_plot_profile_tail.removeWidget(self.canvas_profile_tail)
+
+                self.fig_profile_tail, self.axis_profile_tail = plt.subplots(nrows=1, ncols=1, layout='constrained')
+                self.canvas_profile_tail = FigureCanvas(self.fig_profile_tail)
+
+                self.verticalLayout_groupbox_plot_profile_tail.addWidget(self.canvas_profile_tail)
+
+                self.axis_profile_tail.plot(
+                        -stg.depth[self.combobox_acoustic_data_choice.currentIndex()][self.combobox_freq_noise_from_profile_tail.currentIndex()],
+                        stg.BS_mean[self.combobox_acoustic_data_choice.currentIndex()][self.combobox_freq_noise_from_profile_tail.currentIndex()],
+                        color="blue", linewidth=1)
+                self.axis_profile_tail.plot(
+                        -stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
+                            self.combobox_freq_noise_from_profile_tail.currentIndex()],
+                        float(self.lineEdit_profile_tail_value.text().replace(",", ".")) *
+                            np.ones(stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
+                                self.combobox_freq_noise_from_profile_tail.currentIndex()].shape[0]),
+                        linestyle='dashed', linewidth=2, color='red')
+
+                self.axis_profile_tail.set_yscale('log')
+                self.axis_profile_tail.tick_params(axis='both', labelsize=8)
+                self.axis_profile_tail.text(.98, .03, "Depth (m)",
+                                            fontsize=8, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.9,
+                                            horizontalalignment='right', verticalalignment='bottom', rotation='horizontal',
+                                            transform=self.axis_profile_tail.transAxes)
+                self.axis_profile_tail.text(.1, .45, "BS signal (v)",
+                                            fontsize=8, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.9,
+                                            horizontalalignment='right', verticalalignment='bottom', rotation='vertical',
+                                            transform=self.axis_profile_tail.transAxes)
+                self.axis_profile_tail.text(.98, .85,
+                                            stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()][
+                                                self.combobox_freq_noise_from_profile_tail.currentIndex()],
+                                            fontsize=10, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
+                                            horizontalalignment='right', verticalalignment='bottom',
+                                            transform=self.axis_profile_tail.transAxes)
+
+                self.fig_profile_tail.canvas.draw_idle()
 
     # ------------------------------------------------------
 
@@ -722,82 +755,87 @@ class SignalProcessingTab(QWidget):
 
     def clear_noise_data(self):
 
-        stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
-        stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
-        stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
-        stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
-        stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
-        stg.time_noise[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
-        stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] = 0
-        stg.SNR_filter_value[self.combobox_acoustic_data_choice.currentIndex()] = 0
+        if len(stg.filename_BS_raw_data) == 0:
 
-        stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
-        stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            pass
 
-        stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
-        stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+        else:
 
-        stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
-        stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.time_noise[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.SNR_filter_value[self.combobox_acoustic_data_choice.currentIndex()] = 0
 
-        if stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] == 0:
-            self.lineEdit_noise_file.clear()
+            stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
 
-        elif stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] == 1:
-            self.lineEdit_val1.clear()
-            self.lineEdit_val1.setText("0.00")
+            stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
 
-            self.lineEdit_val2.clear()
-            self.lineEdit_val2.setText("0.00")
+            stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
+            print("stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()]", stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()])
+            if stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] == 0:
+                self.lineEdit_noise_file.clear()
 
-            self.lineEdit_profile_tail_value.clear()
-            self.lineEdit_profile_tail_value.setText("0.0000")
+            elif stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] == 1:
+                self.lineEdit_val1.clear()
+                self.lineEdit_val1.setText("0.00")
 
-            self.verticalLayout_groupbox_plot_profile_tail.removeWidget(self.canvas_profile_tail)
-            self.canvas_profile_tail = FigureCanvas()
-            self.verticalLayout_groupbox_plot_profile_tail.addWidget(self.canvas_profile_tail)
+                self.lineEdit_val2.clear()
+                self.lineEdit_val2.setText("0.00")
 
-        self.lineEdit_SNR_criterion.setText("0.00")
-        self.lineEdit_horizontal_average.setText("0.00")
+                self.lineEdit_profile_tail_value.clear()
+                self.lineEdit_profile_tail_value.setText("0.0000")
 
-        # --- Clear SNR plot ---
-        self.verticalLayout_groupbox_plot_SNR.removeWidget(self.toolbar_SNR)
-        self.verticalLayout_groupbox_plot_SNR.removeWidget(self.scroll_SNR)
+                self.verticalLayout_groupbox_plot_profile_tail.removeWidget(self.canvas_profile_tail)
+                self.canvas_profile_tail = FigureCanvas()
+                self.verticalLayout_groupbox_plot_profile_tail.addWidget(self.canvas_profile_tail)
 
-        self.canvas_SNR = FigureCanvas()
-        self.toolbar_SNR = NavigationToolBar(self.canvas_SNR, self)
-        self.scroll_SNR.setWidget(self.canvas_SNR)
+            self.lineEdit_SNR_criterion.setText("0.00")
+            self.lineEdit_horizontal_average.setText("0.00")
 
-        self.verticalLayout_groupbox_plot_SNR.addWidget(self.toolbar_SNR)
-        self.verticalLayout_groupbox_plot_SNR.addWidget(self.scroll_SNR)
+            # --- Clear SNR plot ---
+            self.verticalLayout_groupbox_plot_SNR.removeWidget(self.toolbar_SNR)
+            self.verticalLayout_groupbox_plot_SNR.removeWidget(self.scroll_SNR)
 
-        # --- Clear BS plot ---
-        self.verticalLayout_groupbox_plot_pre_processed_data_2D_field.removeWidget(self.toolbar_BS)
-        self.verticalLayout_groupbox_plot_pre_processed_data_2D_field.removeWidget(self.scroll_BS)
+            self.canvas_SNR = FigureCanvas()
+            self.toolbar_SNR = NavigationToolBar(self.canvas_SNR, self)
+            self.scroll_SNR.setWidget(self.canvas_SNR)
 
-        self.canvas_BS = FigureCanvas()
-        self.toolbar_BS = NavigationToolBar(self.canvas_BS, self)
-        self.scroll_BS.setWidget(self.canvas_BS)
+            self.verticalLayout_groupbox_plot_SNR.addWidget(self.toolbar_SNR)
+            self.verticalLayout_groupbox_plot_SNR.addWidget(self.scroll_SNR)
 
-        self.verticalLayout_groupbox_plot_pre_processed_data_2D_field.addWidget(self.toolbar_BS)
-        self.verticalLayout_groupbox_plot_pre_processed_data_2D_field.addWidget(self.scroll_BS)
+            # --- Clear BS plot ---
+            self.verticalLayout_groupbox_plot_pre_processed_data_2D_field.removeWidget(self.toolbar_BS)
+            self.verticalLayout_groupbox_plot_pre_processed_data_2D_field.removeWidget(self.scroll_BS)
 
-        self.combobox_frequency_profile.clear()
+            self.canvas_BS = FigureCanvas()
+            self.toolbar_BS = NavigationToolBar(self.canvas_BS, self)
+            self.scroll_BS.setWidget(self.canvas_BS)
 
-        self.verticalLayout_groupbox_plot_profile.removeWidget(self.toolbar_profile)
-        self.verticalLayout_groupbox_plot_profile.removeWidget(self.canvas_profile)
+            self.verticalLayout_groupbox_plot_pre_processed_data_2D_field.addWidget(self.toolbar_BS)
+            self.verticalLayout_groupbox_plot_pre_processed_data_2D_field.addWidget(self.scroll_BS)
 
-        self.canvas_profile = FigureCanvas()
-        self.toolbar_profile = NavigationToolBar(self.canvas_profile, self)
+            self.combobox_frequency_profile.clear()
 
-        self.verticalLayout_groupbox_plot_profile.addWidget(self.toolbar_profile)
-        self.verticalLayout_groupbox_plot_profile.addWidget(self.canvas_profile)
+            self.verticalLayout_groupbox_plot_profile.removeWidget(self.toolbar_profile)
+            self.verticalLayout_groupbox_plot_profile.removeWidget(self.canvas_profile)
 
-        self.slider.setValue(1)
-        self.slider.setMaximum(10)
+            self.canvas_profile = FigureCanvas()
+            self.toolbar_profile = NavigationToolBar(self.canvas_profile, self)
 
-        self.slider.setValue(0)
-        self.slider.setMaximum(10)
+            self.verticalLayout_groupbox_plot_profile.addWidget(self.toolbar_profile)
+            self.verticalLayout_groupbox_plot_profile.addWidget(self.canvas_profile)
+
+            self.slider.setValue(1)
+            self.slider.setMaximum(10)
+
+            self.slider.setValue(0)
+            self.slider.setMaximum(10)
 
     def open_dialog_box(self):
 
@@ -1221,11 +1259,19 @@ class SignalProcessingTab(QWidget):
 
     def remove_point_with_snr_filter(self):
 
-        if len(stg.BS_noise_raw_data) == 0:
+        if len(stg.filename_BS_raw_data) == 0:
+            msgBox = QMessageBox()
+            msgBox.setWindowTitle("Compute noise from profile tail error")
+            msgBox.setIcon(QMessageBox.Warning)
+            msgBox.setText("Download acoustic data in previous tab before applying SNR filter")
+            msgBox.setStandardButtons(QMessageBox.Ok)
+            msgBox.exec()
+
+        elif len(stg.BS_noise_raw_data) == 0:
             msgBox = QMessageBox()
             msgBox.setWindowTitle("SNR filter Error")
             msgBox.setIcon(QMessageBox.Warning)
-            msgBox.setText("Load Noise data from acoustic data tab before using SNR filter")
+            msgBox.setText("Define noise data (file or profile tail) before using SNR filter")
             msgBox.setStandardButtons(QMessageBox.Ok)
             msgBox.exec()
 
@@ -1504,105 +1550,123 @@ class SignalProcessingTab(QWidget):
 
     def compute_averaged_BS_data(self):
 
-        kernel_avg = np.ones(2 * int(float(self.lineEdit_horizontal_average.text().replace(",", "."))) + 1)
-        print(kernel_avg)
+        if len(stg.filename_BS_raw_data) == 0:
+            msgBox = QMessageBox()
+            msgBox.setWindowTitle("Compute noise from profile tail error")
+            msgBox.setIcon(QMessageBox.Warning)
+            msgBox.setText("Download acoustic data in previous tab before applying SNR filter")
+            msgBox.setStandardButtons(QMessageBox.Ok)
+            msgBox.exec()
 
-        stg.Nb_cells_to_average_BS_signal[self.combobox_acoustic_data_choice.currentIndex()] = (
-            float(self.lineEdit_horizontal_average.text().replace(",", ".")))
-
-        if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
-
-            if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
-
-                x_time = stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()]
-                y_depth = stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()]
-
-            elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
-
-                x_time = stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()]
-                y_depth = stg.depth[self.combobox_acoustic_data_choice.currentIndex()]
+        elif len(stg.BS_noise_raw_data) == 0:
+            msgBox = QMessageBox()
+            msgBox.setWindowTitle("SNR filter Error")
+            msgBox.setIcon(QMessageBox.Warning)
+            msgBox.setText("Define noise data (file or profile tail) before using SNR filter")
+            msgBox.setStandardButtons(QMessageBox.Ok)
+            msgBox.exec()
 
         else:
 
-            if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+            kernel_avg = np.ones(2 * int(float(self.lineEdit_horizontal_average.text().replace(",", "."))) + 1)
+            print(kernel_avg)
 
-                x_time = stg.time[self.combobox_acoustic_data_choice.currentIndex()]
-                y_depth = stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()]
+            stg.Nb_cells_to_average_BS_signal[self.combobox_acoustic_data_choice.currentIndex()] = (
+                float(self.lineEdit_horizontal_average.text().replace(",", ".")))
 
-            elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+            if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
 
-                x_time = stg.time[self.combobox_acoustic_data_choice.currentIndex()]
-                y_depth = stg.depth[self.combobox_acoustic_data_choice.currentIndex()]
+                if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
 
-        if stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+                    x_time = stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()]
+                    y_depth = stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()]
 
-            stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = (deepcopy(
-                stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()]))
+                elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
 
-            for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
-                for i in range(y_depth.shape[1]):
+                    x_time = stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()]
+                    y_depth = stg.depth[self.combobox_acoustic_data_choice.currentIndex()]
 
-                    stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()][f, i, :] = (
-                        convolve(stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()][f, i, :],
-                                 kernel_avg))
+            else:
 
-        elif stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+                if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
 
-            stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = (deepcopy(
-                stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()]))
+                    x_time = stg.time[self.combobox_acoustic_data_choice.currentIndex()]
+                    y_depth = stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()]
 
-            for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
-                for i in range(y_depth.shape[1]):
+                elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
 
-                    stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()][f, i, :] = (
-                        convolve(stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()][f, i, :],
-                                 kernel_avg))
+                    x_time = stg.time[self.combobox_acoustic_data_choice.currentIndex()]
+                    y_depth = stg.depth[self.combobox_acoustic_data_choice.currentIndex()]
 
-        elif stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+            if stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
 
-            stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = (deepcopy(
-                stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()]))
+                stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = (deepcopy(
+                    stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()]))
 
-            for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
-                for i in range(y_depth.shape[1]):
+                for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
+                    for i in range(y_depth.shape[1]):
 
-                    stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()][f, i, :] = (
-                        convolve(stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()][f, i, :],
-                                 kernel_avg))
+                        stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()][f, i, :] = (
+                            convolve(stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()][f, i, :],
+                                     kernel_avg))
 
-        elif stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+            elif stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
 
-            stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = (deepcopy(
-                stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()]))
+                stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = (deepcopy(
+                    stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()]))
 
-            for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
-                for i in range(y_depth.shape[1]):
-                    stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()][f, i, :] = (
-                        convolve(stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, i, :], kernel_avg))
+                for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
+                    for i in range(y_depth.shape[1]):
 
-        elif stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+                        stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()][f, i, :] = (
+                            convolve(stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()][f, i, :],
+                                     kernel_avg))
 
-            stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = (deepcopy(
-                stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()]))
+            elif stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
 
-            for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
-                for i in range(y_depth.shape[1]):
-                    stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()][f, i, :] = (
-                        convolve(stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, i, :],
-                                 kernel_avg))
+                stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = (deepcopy(
+                    stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()]))
 
-        elif stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+                for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
+                    for i in range(y_depth.shape[1]):
 
-            stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = (deepcopy(
-                stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()]))
+                        stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()][f, i, :] = (
+                            convolve(stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()][f, i, :],
+                                     kernel_avg))
 
-            for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
-                for i in range(y_depth.shape[1]):
-                    stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()][f, i, :] = (
-                        convolve(stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, i, :], kernel_avg))
+            elif stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
 
-        self.plot_pre_processed_BS_signal()
-        self.update_plot_pre_processed_profile()
+                stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = (deepcopy(
+                    stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()]))
+
+                for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
+                    for i in range(y_depth.shape[1]):
+                        stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()][f, i, :] = (
+                            convolve(stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, i, :], kernel_avg))
+
+            elif stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+
+                stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = (deepcopy(
+                    stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()]))
+
+                for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
+                    for i in range(y_depth.shape[1]):
+                        stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()][f, i, :] = (
+                            convolve(stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, i, :],
+                                     kernel_avg))
+
+            elif stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
+
+                stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = (deepcopy(
+                    stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()]))
+
+                for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
+                    for i in range(y_depth.shape[1]):
+                        stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()][f, i, :] = (
+                            convolve(stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, i, :], kernel_avg))
+
+            self.plot_pre_processed_BS_signal()
+            self.update_plot_pre_processed_profile()
 
     def plot_pre_processed_profile(self):