plot noise program is cleaned from useless commented lines and useless print

Model/AquascatDataLoader.py

@@ -721,14 +721,14 @@ class RawAquascatData:
                 elif pktType == 41:
                     chan = st.unpack("H", f.read(2))[0] + 1
                     # Increase the Index
-       
+
 
                     AbsIndex[chan-1] = AbsIndex[chan-1] + 1
                     Tmp = [st.unpack(
                             "h", f.read(2))[0] for ui in np.arange(
                                     1, 2 * AbsNumBins[chan-1]+1)]
                     sss = [x / 32768 for x in Tmp]
-                    
+
                     AbsData[:, int(
                             AbsIndex[chan-1]), int(chan-1)] = np.abs(np.array(sss[0::2])+complex(0,1)*np.array(sss[1::2]))
 
@@ -1097,19 +1097,3 @@ class MeanAquascatProfile:
 
         file.close()
 
-# ------------------------- Test  --------------------------------------#
-start_time = time.time()
-
-if __name__ == "__main__":
-#     path1 = r'C:\Users\vergne\Documents\Donnees_aquascat\2017_juillet - experience cuve sediments fins\2017_07_19 - mercredi eau claire\20170719114700.aqa'
-    path1 = r'//home/brahim.moudjed/Documents/3 Software_Project/river_inversion_project/Data/Aquascat data test/20171213135800.aqa'
-    data1 = RawAquascatData(path1)
-    
-#     path2 = r'C:\Users\vergne\Documents\Donnees_aquascat\2017_juillet - experience cuve sediments fins\2017_07_19 - mercredi eau claire\20170719114700.aqa.txt'
-    path2 = r'//home/brahim.moudjed/Documents/3 Software_Project/river_inversion_project/Data/Aquascat data test/20171213135800.txt'
-    data2 = RawAquascatData(path2)
-
-    print(data1.PingRate)
-    print(data2.PingRate)
-
-print("Computational time: %.2f min" %((time.time() - start_time)/60) )

Model/acoustic_data_loader_UBSediFlow.py

@@ -1,49 +1,35 @@
+# ============================================================================== #
+    # acoustic_data_loder_UBSediFlow.py - AcouSed                                   #
+    # Copyright (C) 2024  INRAE                                              #
+    #                                                                        #
+    # 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/>. #
+
+    #                                                      by Brahim MOUDJED #
+# ============================================================================== #
+
+# -*- coding: utf-8 -*-
+
 
 import numpy as np
-import pandas as pd
-import datetime
-import matplotlib.pyplot as plt
-from matplotlib.colors import LogNorm, BoundaryNorm
-from copy import deepcopy
-
-from scipy.signal import savgol_filter
 
 from Model.udt_extract.raw_extract import raw_extract
-# raw_20210519_102332.udt raw_20210520_135452.udt raw_20210525_092759.udt raw_20210525_080454.udt
-
-# path_BS_raw_data0 = ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/APAVER_2021/Raw_data_udt/")
-# filename0 = "raw_20210519_135400.udt"
-
-# path_BS_raw_data0 = ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/"
-#                      "APAVER_2021/transect_ubsediflow/01-raw_20210519_115128/Raw_data_udt/")
-# filename0 = "raw_20210519_115128.udt"
-
-# path_BS_raw_data0 = ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/"
-#                      "APAVER_2021/transect_ubsediflow/02-bb0077eda128f3f7887052eb3e8b0884/Raw_data_udt/")
-# filename0 = "raw_20210519_161400.udt"
-
-# path_BS_raw_data0 = ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/"
-#                      "APAVER_2021/transect_ubsediflow/04-fb53d0e92c9c88e2a6cf45e0320fbc76/Raw_data_udt/")
-# filename0 = "raw_20210520_133200.udt"
-
-    # ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/APAVER_2021/"
-    #                 "Rhone_20210519/Rhone_20210519/record/")
-
-# path_BS_raw_data0 = ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/Raw_data_udt/")
-# filename0 = "raw_20210519_130643.udt"
-
-# path_BS_raw_data0 = ("/home/bmoudjed/Documents/2 Data/APAVER_2021/Raw_data_udt/")
-# filename0 = "raw_20210520_085958.udt"
-
-# filename = "raw_20210519_115128.udt"
-    # "raw_20210526_153310.udt"
 
 
 class AcousticDataLoaderUBSediFlow:
 
     def __init__(self, path_BS_raw_data: str):
 
-        # path_BS_raw_data = path_BS_raw_data0 + filename0
         self.path_BS_raw_data = path_BS_raw_data
 
         # --- Extract Backscatter acoustic raw data with  class ---
@@ -53,23 +39,10 @@ class AcousticDataLoaderUBSediFlow:
         device_name, time_begin, time_end, param_us_dicts, data_us_dicts, data_dicts, settings_dict \
             = raw_extract(self.path_BS_raw_data)
 
-        print(f"device_name : {device_name}")
-        print(f"date begin : {time_begin.date()}")
-        print(f"time begin : {time_begin.time()}")
-        print(f"settings_dict : {settings_dict}")
-
         # # --- Date and Hour of measurements read on udt data file ---
-        # filename = self.path_BS_raw_data[-23:]
-        # date_and_time = datetime.datetime(year=int(filename[4:8]),
-        #                                   month=int(filename[8:10]),
-        #                                   day=int(filename[10:12]),
-        #                                   hour=int(filename[13:15]),
-        #                                   minute=int(filename[15:17]),
-        #                                   second=int(filename[17:19]))
+
         self._date = time_begin.date()
-        print(f"date : {self._date}")
         self._hour = time_begin.time()
-        print(f"time : {self._hour}")
 
         self._freq = np.array([[]])
 
@@ -88,19 +61,11 @@ class AcousticDataLoaderUBSediFlow:
         self._time = np.array([[]])
         self._time_snr = np.array([[]])
         self._BS_raw_data = np.array([[[]]])
-        # self._SNR_data = np.array([[[]]])
         time_len = []
-        time_snr_len = []
 
         for config in param_us_dicts.keys():
-            # print("-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x")
-            # print(f"config : {config} \n")
+
             for channel in param_us_dicts[config].keys():
-                print("-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x")
-                # print(f"channel : {channel} \n")
-                # print("param_us_dicts[config][channel] ", param_us_dicts[config][channel])
-                # print("param_us_dicts ", param_us_dicts)
-                # print(data_us_dicts[config][channel]['echo_avg_profile'])
 
                 # --- Frequencies ---
                 self._freq = np.append(self._freq, param_us_dicts[config][channel]['f0'])
@@ -115,31 +80,19 @@ class AcousticDataLoaderUBSediFlow:
                 self._gain_tx = np.append(self._gain_tx, param_us_dicts[config][channel]['a1'])
 
                 # --- Depth for each frequencies ---
-                print("r_dcell : ", param_us_dicts[config][channel]['r_dcell'])
-                print("n_cell : ", param_us_dicts[config][channel]['n_cell'])
                 depth = [param_us_dicts[config][channel]['r_dcell'] * i
                          for i in list(range(param_us_dicts[config][channel]['n_cell']))]
-                print(f"depth : {depth}")
-                print(f"lenght of depth : {len(depth)}")
+
                 if self._r.shape[1] == 0:
                     self._r = np.array([depth])
                 else:
                     if len(depth) == self._r.shape[1]:
-                        print("Je suis là")
-                        print(f"depth lenght : {len(depth)}")
-                        print(f"r shape : {self._r.shape}")
                         self._r = np.append(self._r, np.array([depth]), axis=0)
-                        print("C'est encore moi")
                     elif len(depth) < self._r.shape[1]:
-                        print(f"depth lenght : {len(depth)}")
                         self._r = self._r[:, :len(depth)]
                         self._r = np.append(self._r, np.array([depth]), axis=0)
-                        print(f"r shape : {self._r.shape}")
                     elif len(depth) > self._r.shape[1]:
-                        print(f"depth lenght : {len(depth)}")
                         self._r = np.append(self._r, np.array([depth[:self._r.shape[1]]]), axis=0)
-                        print(f"r shape : {self._r.shape}")
-                print(f"self._r : {self._r.shape}")
 
                 # --- BS Time for each frequencies ---
                 time = [[(t - data_us_dicts[config][channel]['echo_avg_profile']['time'][0]).total_seconds()
@@ -147,69 +100,28 @@ class AcousticDataLoaderUBSediFlow:
                 time_len = np.append(time_len, len(time[0]))
 
                 if len(time_len) == 1:
-                    print(f"1 time length : {len(time[0])}")
                     self._time = np.array(time)
-                    print(f"self._time.shape {self._time.shape}")
                 elif self._time.shape[1] == len(time[0]):
-                    print(f"2 time length : {len(time[0])}")
                     self._time = np.append(self._time, time, axis=0)
-                    print(f"self._time.shape {self._time.shape}")
                 elif self._time.shape[1] > len(time[0]):
-                    print(f"3 time length : {len(time[0])}")
-                    # print(f"self._time.shape {self._time.shape}")
-                    # print([int(np.min(time_len)) + int(i) - 1 for i in range(1, int(np.max(time_len))-int(np.min(time_len))+1)])
                     self._time = np.delete(self._time,
                                            [int(np.min(time_len)) + int(i) - 1 for i in range(1, int(np.max(time_len))-int(np.min(time_len))+1)],
                                            axis=1)
                     self._time = np.append(self._time, time, axis=0)
-                    print(f"self._time.shape {self._time.shape}")
                 elif self._time.shape[1] < len(time[0]):
-                    print(f"4 time length : {len(time[0])}")
                     time = time[:int(np.max(time_len)) - (int(np.max(time_len)) - int(np.min(time_len)))]
                     self._time = np.append(self._time, time, axis=0)
-                    print(f"self._time.shape {self._time.shape}")
 
                 self._nb_profiles = np.append(self._nb_profiles, self._time.shape[1])
                 self._nb_profiles_per_sec = np.append(self._nb_profiles_per_sec,
                                                       param_us_dicts[config][channel]['n_avg'])
 
-                # --- SNR Time for each frequencies ---
-                # time_snr = [[(t - data_us_dicts[config][channel]['snr_doppler_avg_profile']['time'][0]).total_seconds()
-                #          for t in data_us_dicts[config][channel]['snr_doppler_avg_profile']['time']]]
-                # time_snr_len = np.append(time_snr_len, len(time_snr[0]))
-                #
-                # if len(time_snr_len) == 1:
-                #     # print(f"1 time length : {len(time[0])}")
-                #     self._time_snr = np.array(time_snr)
-                #     # print(f"self._time.shape {self._time.shape}")
-                # elif self._time_snr.shape[1] == len(time_snr[0]):
-                #     # print(f"2 time length : {len(time[0])}")
-                #     self._time_snr = np.append(self._time_snr, time_snr, axis=0)
-                #     # print(f"self._time.shape {self._time.shape}")
-                # elif self._time_snr.shape[1] > len(time_snr[0]):
-                #     # print(f"3 time length : {len(time[0])}")
-                #     # print(f"self._time.shape {self._time.shape}")
-                #     # print([int(np.min(time_len)) + int(i) - 1 for i in range(1, int(np.max(time_len))-int(np.min(time_len))+1)])
-                #     self._time_snr = np.delete(self._time_snr,
-                #                             [int(np.min(time_snr_len)) + int(i) - 1 for i in
-                #                             range(1, int(np.max(time_snr_len)) - int(np.min(time_snr_len)) + 1)],
-                #                             axis=1)
-                #     self._time_snr = np.append(self._time_snr, time_snr, axis=0)
-                #     # print(f"self._time.shape {self._time.shape}")
-                # elif self._time_snr.shape[1] < len(time_snr[0]):
-                #     # print(f"4 time length : {len(time[0])}")
-                #     time_snr = time_snr[:int(np.max(time_snr_len)) - (int(np.max(time_snr_len)) - int(np.min(time_snr_len)))]
-                #     self._time_snr = np.append(self._time_snr, time_snr, axis=0)
-                #     # print(f"self._time.shape {self._time.shape}")
-
                 # --- US Backscatter raw signal ---
                 BS_data = np.array([[]])
 
                 if config == 1:
 
                     BS_data = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
-                    print(f"cas 1 : BS_raw_data shape = {self._BS_raw_data.shape}")
-                    print(f"cas 1 : BS_data shape = {BS_data.shape}")
 
                     for i in range(self._time.shape[1]):
                         BS_data = np.append(BS_data,
@@ -218,27 +130,21 @@ class AcousticDataLoaderUBSediFlow:
                                             axis=0)
 
                     self._BS_raw_data = np.array([BS_data[:self._time.shape[1], :].transpose()])
-                    #                     print(f"a) BS_data shape = {BS_data.shape}")
-                    #                     print(f"a) BS_raw_data shape = {BS_raw_data.shape}")
 
                 else:
 
                     BS_data = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
-                    print(f"{config}) BS_data shape = {BS_data.shape}")
+
                     for j in range(self._time.shape[1]):
                         BS_data = np.append(BS_data,
                                             np.array(
                                                 [data_us_dicts[config][channel]['echo_avg_profile']['data'][j]]),
                                             axis=0)
                     BS_data = np.array([BS_data.transpose()])
-                    print(f"xxxx BS_data shape = {BS_data.shape}")
-                        #                     print(f"b) BS_raw_data shape = {BS_raw_data.shape}")
 
                     # 1- time shape > BS data shape
                     #    <=> data recorded with the frequency are longer than data recorded with the other lower frequencies
                     if (BS_data.shape[2] > self._BS_raw_data.shape[2]):
-                        print(f"cas 2 : BS_raw_data shape = {self._BS_raw_data.shape}")
-                        print(f"cas 2 : BS_data shape = {BS_data.shape}")
 
                         if (BS_data.shape[1] > self._BS_raw_data.shape[1]):
                                 #                         print(f"BS_data shape[0] = {BS_data.shape[0]}")
@@ -260,8 +166,6 @@ class AcousticDataLoaderUBSediFlow:
                     # 2- time shape < BS data shape
                     #    <=> data recorded with the frequency are shorter than data recorded with the other lower frequencies
                     elif BS_data.shape[2] < self._BS_raw_data.shape[2]:
-                        print(f"cas 3 : BS_raw_data shape = {self._BS_raw_data.shape}")
-                        print(f"cas 3 : BS_data shape = {BS_data.shape}")
 
                         if (BS_data.shape[1] > self._BS_raw_data.shape[1]):
                             self._BS_raw_data = np.append(self._BS_raw_data[:, :, BS_data.shape[2]],
@@ -277,16 +181,11 @@ class AcousticDataLoaderUBSediFlow:
                             self._BS_raw_data = np.append(self._BS_raw_data[:, :, BS_data.shape[0]],
                                                     BS_data,
                                                     axis=0)
-                        #                     print(f"d) BS_data shape = {BS_data.shape}")
-                        #                     print(f"d) BS_raw_data shape = {BS_raw_data.shape}")
 
                     # 3- time shape = BS data shape
                     #    <=> data recorded with the frequency have the same duration than data recorded with the other lower frequency
                     else:
 
-                        print(f"cas 4 : BS_raw_data shape = {self._BS_raw_data.shape}")
-                        print(f"cas 4 : BS_data shape = {BS_data.shape}")
-
                         if (BS_data.shape[1] > self._BS_raw_data.shape[1]):
 
                             self._BS_raw_data = np.append(self._BS_raw_data,
@@ -301,115 +200,6 @@ class AcousticDataLoaderUBSediFlow:
 
                             self._BS_raw_data = np.append(self._BS_raw_data,
                                                         BS_data, axis=0)
-                    #                     print(f"e) BS_data shape = {BS_data.shape}")
-
-                print("Final BS_raw_data shape = ", self._BS_raw_data.shape)
-
-            print("********************************************")
-
-            #                 # --- US Backscatter raw signal + SNR data ---
-#                 BS_data = np.array([[]])
-#
-#                 if config == 1:
-#                     BS_data = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
-#                     # print("BS_data shape ", BS_data.shape)
-#                     # print("******************************")
-#                     # date_list = [np.abs(datetime.datetime(2021, 5, 19, 14, 10, 00).timestamp()
-#                     #       - date.timestamp()) for date in data_us_dicts[config][channel]['echo_avg_profile']['time']]
-#                     # print(date_list)
-#                     # print(np.where(date_list == np.min(date_list)))
-#                     # print((data_us_dicts[config][channel]['echo_avg_profile']['time'][np.where(date_list == np.min(date_list))[0][0]] -
-#                     #       data_us_dicts[config][channel]['echo_avg_profile']['time'][0]).total_seconds())
-#                     # # == datetime.datetime(2021, 5, 19, 14, 10, 2, 644000))
-#                     # print("******************************")
-#
-#                     for i in range(self._time.shape[1]):
-#                         BS_data = np.append(BS_data,
-#                                             np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][i]]),
-#                                             axis=0)
-#                     print("0. BS_data shape ", BS_data.shape)
-#
-#                     self._BS_raw_data = np.array([BS_data[:self._time.shape[1], :].transpose()])
-#
-#                     print("0. BS_raw_data shape ", self._BS_raw_data.shape)
-#
-#                     # fig, ax = plt.subplots(nrows=1, ncols=1, layout="constrained")
-#                     # pcm = ax.pcolormesh(list(range(self._BS_raw_data.shape[2])), list(range(self._BS_raw_data.shape[1])),
-#                     #                     np.log(self._BS_raw_data[0, :, :]),
-#                     #                     cmap='Blues')
-#                     # fig.colorbar(pcm, ax=ax, shrink=1, location='right')
-#                     # plt.show()
-#
-#                 else:
-#
-#                     BS_data = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
-#                     # print("BS_data shape ", BS_data.shape)
-#                     for i in range(self._time.shape[1]):
-#                         BS_data = np.append(BS_data,
-#                                             np.array(
-#                                                 [data_us_dicts[config][channel]['echo_avg_profile']['data'][i]]),
-#                                             axis=0)
-#                     print("1. BS_data shape ", BS_data.shape)
-#
-# #-----------------------------------------------------------------------------------------------------------------------
-#                     # Ici il faut écrire les conditions sur les tailles selon r et selon time
-#                     # donc sur BS_data.shape[0] (time) et BS_data.shape[1] (depth)
-# #-----------------------------------------------------------------------------------------------------------------------
-#
-#                     # 1- time shape > BS data shape
-#                     #    <=> data recorded with the frequency are longer than data recorded with the other lower frequencies
-#                     if (BS_data.shape[0] > self._BS_raw_data.shape[2]):
-#                         self._BS_raw_data = np.append(self._BS_raw_data,
-#                                                       np.array([BS_data[:self._BS_raw_data.shape[2], :].transpose()]),
-#                                                       axis=0)
-#
-#                     # 2- time shape < BS data shape
-#                     #    <=> data recorded with the frequency are shorter than data recorded with the other lower frequencies
-#                     elif BS_data.shape[0] < self._BS_raw_data.shape[2]:
-#                         self._BS_raw_data = np.append(self._BS_raw_data[config-1, :, BS_data.shape[0]],
-#                                                       np.array([BS_data.transpose()]),
-#                                                       axis=0)
-#
-#                     # 3- time shape = BS data shape
-#                     #    <=> data recorded with the frequency have the same duration than data recorded with the other lower frequency
-#                     else:
-#                         self._BS_raw_data = np.append(self._BS_raw_data, np.array([BS_data.transpose()]), axis=0)
-#
-#
-#                     print("1. BS_raw_data shape ", self._BS_raw_data.shape)
-#
-#                     # if f == 0:
-#                     #     print(np.array(data_us_dicts[config][channel]['echo_avg_profile']['data'][0]).shape)
-#                     #     self._BS_raw_data[f, :, :] = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
-#                     #     # self._BS_raw_data = np.array([np.reshape(data_us_dicts[config][channel]['echo_avg_profile']['data'],
-#                     #     #                                (self._time.shape[1], self._r.shape[1])).transpose()])
-#                     #     print("self._BS_raw_data.shape ", self._BS_raw_data.shape)
-#                     #     self._SNR_data = np.array(
-#                     #         [np.reshape(np.abs(data_us_dicts[config][channel]['snr_doppler_avg_profile']['data']),
-#                     #                     (self._time.shape[1], self._r.shape[1])).transpose()])
-#                     # else:
-#                     #     # self._BS_raw_data = np.append(self._BS_raw_data,
-#                     #     #                               np.array(data_us_dicts[config][channel]['echo_avg_profile']['data']),
-#                     #     #                                   (self._r.shape[1], self._time.shape[1]))]),
-#                     #     #                               axis=0)
-#                     #     # self._BS_raw_data = np.append(self._BS_raw_data,
-#                     #     #                               np.array([np.reshape(np.array(
-#                     #     #                                   data_us_dicts[config][channel]['echo_avg_profile']['data']),
-#                     #     #                                         (self._time.shape[1], self._r.shape[1])).transpose()]),
-#                     #     #                               axis=0)
-#                     #
-#                     #     self._SNR_data = np.append(self._SNR_data,
-#                     #                                   np.array([np.reshape(np.array(
-#                     #                                       np.abs(data_us_dicts[config][channel]['snr_doppler_avg_profile']['data'])),
-#                     #                                       (self._time.shape[1], self._r.shape[1])).transpose()]),
-#                     #                                   axis=0)
-#                     #     # print(self._BS_raw_data.shape)
-#
-#                     # --- US Backscatter raw signal ---
-#
-#
-#                 # print(len(self._BS_raw_data))
-#                 # print(self._BS_raw_data)
 
         if self._time.shape[1] > self._BS_raw_data.shape[2]:
             self._time = self._time[:, :self._BS_raw_data.shape[2]]
@@ -420,275 +210,39 @@ class AcousticDataLoaderUBSediFlow:
             self._BS_raw_data = self._BS_raw_data
 
         self._time = self._time[:, :self._BS_raw_data.shape[2]]
-        print("self._time.shape ", self._time.shape)
-        # print(f"time : {self._time}")
-        # print("****************************")
-        # for i in range(len(self._time[0, :])-1):
-        #     print(self._time[0, i+1] - self._time[0, i])
-        # print("****************************")
-
-        print("self._r.shape ", self._r.shape)
 
         self._freq_text = np.array([str(f) + " MHz" for f in [np.round(f*1e-6, 2) for f in self._freq]])
-        print("self._freq_text ", self._freq_text)
-        print("self._freq_text ", self._freq)
-
-        # self._BS_raw_data = np.array(np.reshape(self._BS_raw_data, (len(self._freq), self._r.shape[1], self._time.shape[1])))
-        print("self._BS_raw_data.shape ", self._BS_raw_data.shape)
-
-        # print("self._SNR_data.shape ", self._SNR_data.shape)
-        # print(self._SNR_data)
-
-        # print("device_name ", device_name, "\n")
-
-        # print("time_begin ", time_begin, "\n")
-
-        # print("time_end ", time_end, "\n")
-
-        # print(f"param_dicts keys {param_us_dicts.keys()} \n")
-        # print(param_us_dicts, "\n")
-
-        # for i in range(len(list(param_us_dicts.keys()))):
-        #     print(f"param_us_dicts {i} : {list(param_us_dicts.items())[i]} \n")
-
-        # # print("settings_dict ", settings_dict, "\n")
-        # print(f"keys in data_us_dicts {data_us_dicts[1][1].keys()} \n")
-        # # les clés du dictionnaire data_us_dicts :
-        # # dict_keys(['echo_avg_profile', 'saturation_avg_profile', 'velocity_avg_profile', 'snr_doppler_avg_profile',
-        # #            'velocity_std_profile', 'a1_param', 'a0_param', 'noise_g_high', 'noise_g_low'])
-
-        # print(f"data_us_dicts keys in echo avg profile {data_us_dicts[1][1]['echo_avg_profile'].keys()} \n")
-        # print(f"number of profiles {len(data_us_dicts[1][1]['echo_avg_profile']['data'])} \n")
-        # print(f"number of cells {data_us_dicts[1][1]['echo_avg_profile']['data'][0].shape} \n")
-
-        # self._data_BS = RawAquascatData(self.path_BS_raw_data)
-
-        # self._nb_profiles = self._data_BS.NumProfiles
-        # self._nb_profiles_per_sec = self._data_BS.ProfileRate
-        # self._nb_cells = self._data_BS.NumCells
-        # self._cell_size = self._data_BS.cellSize
-        # self._pulse_length = self._data_BS.TxPulseLength
-        # self._nb_pings_per_sec = self._data_BS.PingRate
-        # self._nb_pings_averaged_per_profile = self._data_BS.Average
-        # self._kt = self._data_BS.Kt
-        # self._gain_rx = self._data_BS.RxGain
-        # self._gain_tx = self._data_BS.TxGain
-
-        # self._snr = np.array([])
-        # self._snr_reshape = np.array([])
-        # self._time_snr = np.array([])
-
-        # print(type(self._gain_tx))
-
-        # print(["BS - " + f for f in self._freq_text])
-        # print(self._time.shape[0]*self._r.shape[0]*4)
-
-        # print(self._time[np.where(np.floor(self._time) == 175)])
-        # print(np.where((self._time) == 155)[0][0])
-
-        # --- Plot Backscatter US data ---
-
-        # fig, ax = plt.subplots(nrows=1, ncols=1, layout="constrained")
-        # pcm = ax.pcolormesh(self._time[0, :], -self._r[0, :], np.log(self._BS_raw_data[0, :, :]),
-        #                            cmap='plasma')#, shading='gouraud')
-        # # pcm = ax.pcolormesh(list(range(self._BS_raw_data.shape[2])), list(range(self._BS_raw_data.shape[1])),
-        # #                     np.log(self._BS_raw_data[0, :, :]),
-        # #                     cmap='Blues')  # , shading='gouraud')
-        #     # norm=LogNorm(vmin=np.min(self._BS_raw_data[f, :, :]), vmax=np.max(self._BS_raw_data[f, :, :])), shading='gouraud')
-        #     # ax.pcolormesh(range(self._BS_raw_data.shape[2]), range(self._BS_raw_data.shape[0]), self._BS_raw_data[:, 1, :], cmap='viridis',
-        #     #               norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[:, 0, :])))  # , shading='gouraud')
-        # fig.colorbar(pcm, ax=ax, shrink=1, location='right')
-        # plt.show()
-        
-        # fig, ax = plt.subplots(nrows=len(self._freq), ncols=1, layout="constrained")
-        # for f, freq in enumerate(self._freq):
-        #     print(f"{f} : {freq} \n")
-        #     # pcm = ax[f].imshow(np.log(self._BS_raw_data[f, :, :self._time.shape[1]]),
-        #     #              cmap='Blues')
-        #     # pcm = ax[f].pcolormesh(list(range(self._BS_raw_data.shape[2])), list(range(self._BS_raw_data.shape[1])),
-        #     #                        np.log(self._BS_raw_data[f, :, :]),
-        #     #                        cmap='Blues', shading='gouraud')
-        #     pcm = ax[f].pcolormesh(self._time[f, 50:247], -self._r[f, :], np.log(self._BS_raw_data[f, :, 50:247]),
-        #                         cmap='Blues')#, shading='gouraud')
-        #                         # norm=LogNorm(vmin=np.min(self._BS_raw_data[f, :, :]), vmax=np.max(self._BS_raw_data[f, :, :])), shading='gouraud')
-        #     # ax.pcolormesh(range(self._BS_raw_data.shape[2]), range(self._BS_raw_data.shape[0]), self._BS_raw_data[:, 1, :], cmap='viridis',
-        #     #               norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[:, 0, :])))  # , shading='gouraud')
-        # fig.colorbar(pcm, ax=ax[:], shrink=1, location='right')
-        # # plt.show()
-        #
-        # # --- Smooth value with savgol_filter ---
-        # BS_smooth = deepcopy(self._BS_raw_data[0, :, :])
-        # for k in range(self._time[0, :].shape[0]):
-        #     BS_smooth[:, k] = savgol_filter(BS_smooth[:, k], 10, 2)
-        #
-        # fig1, ax1 = plt.subplots(nrows=1, ncols=1, layout="constrained")
-        # pcm1 = ax1.pcolormesh(self._time[0, :], -self._r[0, :], np.log(BS_smooth[:, :]), cmap='Blues')
-        # fig1.colorbar(pcm1, ax=ax1, shrink=1, location='right')
-
-        # print("find value in depth", np.where(np.abs(self._r - 3.3) == np.min(np.abs(self._r - 3.3))))
-        #
-        # fig, ax = plt.subplots(nrows=1, ncols=1, layout="constrained")
-        # ax.plot(self._r[0, :], self._BS_raw_data[0, :, 766])
-        # plt.show()
-
-        # # --- Plot vertical profile for bottom detection ---
-        # n = 60
-        # t0 = 200
-        # t1 = np.where(np.abs(self._time[0, :] - t0) == np.nanmin(np.abs(self._time[0, :] - t0)))[0][0]
-        # # print(np.abs(self._time[0, :] - 200))
-        # # print(f"x0 = {x0}")
-        # r1 = 98
-        # r2 = 150
-        # fig2, ax2 = plt.subplots(nrows=1, ncols=n, layout="constrained")
-        # for i in range(n):
-        #     ax2[i].plot(self._BS_raw_data[0, r1:r2, t1+i], -self._r[0, r1:r2], 'b')
-        #     ax2[i].plot(BS_smooth[r1:r2, t1+i], -self._r[0, r1:r2], 'r')
-        #     ax2[i].set_xticks([])
-        #     if i != 0:
-        #         ax2[i].set_yticks([])
-        # plt.show()
-
-        # --- Plot SNR data ---
-        # fig_snr, ax_snr = plt.subplots(nrows=len(self._freq), ncols=1)
-        #
-        # x, y = np.meshgrid(self._time[0, :], self._r[0, :])
-        #
-        # for f, freq in enumerate(self._freq):
-        #
-        #     val_min = np.nanmin(abs(self._SNR_data[f, :, :]))
-        #     print(f"val_min = {val_min}")
-        #     val_max = np.nanmax(self._SNR_data[f, :, :])
-        #     print(f"val_max = {val_max}")
-        #     if int(val_min) == 0:
-        #         val_min = 1e-5
-        #     if int(val_max) < 1000:
-        #         levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
-        #         bounds = [00.1, 1, 2, 10, 100, 1000, 1e6, 1e6 * 1.2]
-        #     else:
-        #         levels = np.array([00.1, 1, 2, 10, 100, val_max])
-        #         bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
-        #     norm = BoundaryNorm(boundaries=bounds, ncolors=300)
-        #
-        #     print(f"levels = {levels}")
-        #     print(f"norm = {norm.boundaries}")
-        #
-        #     cf = ax_snr[f].contourf(x, y, self._SNR_data[f, :, :])#, levels, cmap='gist_rainbow', norm=norm)
-        #
-        #     ax_snr[f].text(1, .70, self._freq_text[f],
-        #                           fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
-        #                           horizontalalignment='right', verticalalignment='bottom',
-        #                           transform=ax_snr[f].transAxes)
-        #
-        # fig_snr.supxlabel('Time (sec)', fontsize=10)
-        # fig_snr.supylabel('Depth (m)', fontsize=10)
-        # cbar = fig_snr.colorbar(cf, ax=ax_snr[:], 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$^6$'])
-        # plt.show()
-
-        # fig, ax = plt.subplots(nrows=1, ncols=1)
-        # ax.plot(list(range(self._time.shape[1])), self._time[0, :])
-        # # ax.set_ylim(2, 20)
-        # plt.show()
-
-        # print(self.reshape_BS_raw_cross_section())
-
-        # self.reshape_BS_raw_cross_section()
-        # self.reshape_r()
-        # self.reshape_t()
-        # self.compute_r_2D()
-
-        # Lecture du fichier excel
-
-        # path = ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/APAVER_2021/"
-        #         "transect_ubsediflow/01-raw_20210519_115128/Raw_data_csv/config_1/"
-        #         "echo_avg_profile_1_1_20210519_115128.csv")
-        #
-        # df = pd.read_csv(path, sep="\t")
-        #
-        # arr = []
-        # for column in df.columns:
-        #     arr.append(df[column].to_numpy())
-        #     # arr = np.append(arr, np.array([df[column].to_numpy()]), axis=0)
-        # arr = arr[1:]
-        # print(len(arr))
-        #
-        # matrix = np.array([arr[0]])
-        # print(matrix.shape)
-        # for i in range(len(arr)-1):
-        #     matrix = np.append(matrix, np.array([arr[i]]), axis=0)
-        # print(matrix.shape)
-
-        # fig, ax = plt.subplots(nrows=1, ncols=1, layout="constrained")
-        # pcm = ax.pcolormesh(list(range(matrix.shape[1])), list(range(matrix.shape[0])), np.log(matrix),
-        #                            cmap='Blues')#, shading='gouraud')
-        #     # norm=LogNorm(vmin=np.min(self._BS_raw_data[f, :, :]), vmax=np.max(self._BS_raw_data[f, :, :])), shading='gouraud')
-        #     # ax.pcolormesh(range(self._BS_raw_data.shape[2]), range(self._BS_raw_data.shape[0]), self._BS_raw_data[:, 1, :], cmap='viridis',
-        #     #               norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[:, 0, :])))  # , shading='gouraud')
-        # fig.colorbar(pcm, ax=ax, shrink=1, location='right')
-        # plt.show()
 
     def reshape_BS_raw_data(self):
         BS_raw_cross_section = np.reshape(self._BS_raw_data,
                                           (self._r.shape[1]*self._time.shape[1], len(self._freq)),
                                           order="F")
-        # print(BS_raw_cross_section.shape)
         return BS_raw_cross_section
 
-    # def reshape_SNR_data(self):
-    #     SNR_data = np.reshape(self._SNR_data,
-    #                                       (self._r.shape[1]*self._time.shape[1], len(self._freq)),
-    #                                       order="F")
-    #     # print(BS_raw_cross_section.shape)
-    #     return SNR_data
-
     def reshape_r(self):
         r = np.zeros((self._r.shape[1]*self._time.shape[1], len(self._freq)))
         for i, _ in enumerate(self._freq):
             r[:, i] = np.repeat(self._r[i, :], self._time.shape[1])
-        # print(r.shape)
         return r
 
     def compute_r_2D(self):
         r2D = np.zeros((self._freq.shape[0], self._r.shape[1], self._time.shape[1]))
         for f, _ in enumerate(self._freq):
             r2D[f, :, :] = np.repeat(np.transpose(self._r[0, :])[:, np.newaxis], self._time.shape[1], axis=1)
-        print("r2D.shape ", r2D.shape)
         return r2D
 
-    # def compute_r_2D(self):
-    #     r2D = np.repeat(self._r, self._time.size, axis=1)
-    #     return r2D
-
     def reshape_t(self):
         t = np.zeros((self._r.shape[1]*self._time.shape[1], len(self._freq)))
         for i, _ in enumerate(self._freq):
             t[:, i] = np.repeat(self._time[i, :], self._r.shape[1])
-        # print(t.shape)
         return t
 
     def reshape_t_snr(self):
         t = np.zeros((self._r.shape[1]*self._time_snr.shape[1], len(self._freq)))
         for i, _ in enumerate(self._freq):
             t[:, i] = np.repeat(self._time_snr[i, :], self._r.shape[1])
-        # print(t.shape)
         return t
 
-    def detect_bottom(self):
-        rmin = 2.5
-        rmax = 3.5
-
-
-
-    # def concatenate_data(self):
-
-    #     self.reshape_BS_raw_cross_section()
-    #     # print(self.reshape_t().shape)
-    #     # print(se.lf.reshape_BS_raw_cross_section().shape)
-    #     df = pd.DataFrame(np.concatenate((self.reshape_t(), self.reshape_BS_raw_cross_section()), axis=1),
-    #                       columns=["time"] + self._freq_text)
-    #     return df
-
 
 # if __name__ == "__main__":
 #     AcousticDataLoaderUBSediFlow(path_BS_raw_data0 + filename0)

Model/calibration_constant_kt.py

@@ -25,7 +25,6 @@ from PyQt5.QtWidgets import (QWidget, QVBoxLayout, QDialog, QTabWidget, QGridLay
                              QFileDialog, QMessageBox, QLabel)
 from PyQt5.QtCore import Qt
 
-
 class CalibrationConstantKt(QDialog):
 
     def __init__(self, parent=None):
@@ -113,11 +112,3 @@ class CalibrationConstantKt(QDialog):
                 eval("self.gridLayout_tab_" + str(t_index) + ".addWidget(self.label_kt_" + str(x) + "_ABS_" + str(t_index) +
                      ", " + str(x+1) + ", 1, 1, 1, Qt.AlignCenter)")
 
-
-# if __name__ == "__main__":
-#     app = QApplication(sys.argv)
-#     cal = CalibrationConstantKt()
-#     cal.show()
-#     # sys.exit(app.exec_())
-#     app.exec()
-

Model/granulo_loader.py

@@ -1,7 +1,5 @@
-import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-from Model.GrainSizeTools import demodul_granulo, mix_gaussian_model
 
 class GranuloLoader:
 
@@ -16,8 +14,7 @@ class GranuloLoader:
         self._y = self._data.iloc[:, 1].tolist()                        # distance from left bank (m)
         self._z = self._data.iloc[:, 2].tolist()                        # depth (m)
 
-        self._r_grain = 1e-6 * np.array(self._data.columns.values)[5:].astype(float) / 2
-        # self._r_grain = 1e-6 * np.array(self._data.columns.values)[5:].astype(float) / 2         # grain radius (m)
+        self._r_grain = 1e-6 * np.array(self._data.columns.values)[5:].astype(float) / 2        # grain radius (m)
 
         self._Ctot = self._data.iloc[:, 3].tolist()                # Total concentration (g/L)
         self._D50 = self._data.iloc[:, 4].tolist()               # median diameter (um)
@@ -25,147 +22,3 @@ class GranuloLoader:
 
         self._frac_vol_cumul = np.cumsum(self._frac_vol, axis=1)    # Cumulated volume fraction (%)
 
-        # print(type(self._frac_vol_cumul), self._frac_vol_cumul.shape, self._frac_vol_cumul)
-
-        # # --- Load sand sediments data file ---
-        # self.path_sand = path_sand
-        # self._data_sand = pd.read_excel(self.path_sand, engine="odf", header=0)
-        #
-        # self._Ctot_sand = np.array(self._data_sand.iloc[:, 2])                # Total concentration (g/L)
-        # self._D50_sand = np.array(self._data_sand.iloc[:, 3])                 # median diameter (um)
-        # self._frac_vol_sand = np.array(self._data_sand.iloc[:, 4:])           # Volume fraction (%)
-        #
-        # self._frac_vol_sand_cumul = np.cumsum(self._frac_vol_sand, axis=1)    # Cumulated volume fraction (%)
-        #
-        # # --- Compute % of fine and % of sand sediment in total concentration ---
-        #
-        # self._Ctot_fine_per_cent = 100 * self._Ctot_fine / (self._Ctot_fine + self._Ctot_sand)
-        # self._Ctot_sand_per_cent = 100 * self._Ctot_sand / (self._Ctot_fine + self._Ctot_sand)
-
-        # ==============================================================================================================
-        # ==============================================================================================================
-
-#         N_sample = 0
-# #
-#         fig, ax = plt.subplots(1, 2)
-#         ax[0].plot(self._r_grain, self._frac_vol[N_sample, :], color="k", marker='.')
-#         ax[0].set_xscale('log')
-#         ax[0].set_xlabel('Radius ($\mu m$)')
-#         ax[0].set_ylabel('Class size volume fraction')
-#
-#         ax[1].plot([self._r_grain[i+1]-self._r_grain[i] for i in range(self._r_grain.shape[0]-1)], list(range(self._r_grain.shape[0]-1)), color="k", marker="x")
-#         ax[1].set_xlabel('Ecart inter-class')
-#         ax[1].set_ylabel('n° échantillon')
-#
-#         plt.show()
-#
-#         print(f"self._r_grain.shape : {self._r_grain.shape}")
-#         print(f"self._r_grain : {self._r_grain}")
-#         print(f"self._frac_vol_cumul.shape : {self._frac_vol_cumul[N_sample, :].shape}")
-#         print(f"self._frac_vol_cumul[N_sample, :] : {self._frac_vol_cumul[N_sample, :]}")
-#         print(np.where(self._frac_vol_cumul[N_sample, :] > 0))
-# #
-#         min_demodul = 1e-6
-#         max_demodul = 500e-6
-#         sample_demodul = demodul_granulo(self._r_grain[:],
-#                                               self._frac_vol_cumul[N_sample, :],
-#                                               min_demodul, max_demodul)
-#
-#         print(f"sample_demodul : {sample_demodul.demodul_data_list}")
-
-#         N_modes = 3
-#         sample_demodul.print_mode_data(N_modes)
-#         sample_demodul.plot_interpolation()
-#         sample_demodul.plot_modes(N_modes)
-#
-#         print(f"mu_list : {sample_demodul.demodul_data_list[3 - 1].mu_list}")
-#         print(f"sigma_list : {sample_demodul.demodul_data_list[3 - 1].sigma_list}")
-#         print(f"w_list : {sample_demodul.demodul_data_list[3 - 1].w_list}")
-#
-#         resampled_log_array = np.log(np.logspace(-10, -2, 3000))
-#         proba_vol_demodul = mix_gaussian_model(resampled_log_array,
-#                                                sample_demodul.demodul_data_list[2].mu_list,
-#                                                sample_demodul.demodul_data_list[2].sigma_list,
-#                                                sample_demodul.demodul_data_list[2].w_list)
-#
-#
-#
-#         proba_vol_demodul = proba_vol_demodul / np.sum(proba_vol_demodul)
-#         ss = np.sum(proba_vol_demodul / np.exp(resampled_log_array) ** 3)
-#         proba_num = proba_vol_demodul / np.exp(resampled_log_array) ** 3 / ss
-#
-#         print(f"proba_num : {proba_num}")
-#         freq = 5e6
-#
-#         a2f2pdf = 0
-#         a3pdf = 0
-#         for i in range(len(resampled_log_array)):
-#             a = np.exp(resampled_log_array)[i]
-#             a2f2pdf += a**2 * form_factor_function_MoateThorne2012(a, freq)**2 * proba_num[i]
-#             a3pdf += a**3 * proba_num[i]
-#
-#         print(f"a2f2pdf = {a2f2pdf}")
-#         print(f"a3pdf = {a3pdf}")
-#
-#         ks = (a2f2pdf / a3pdf)
-#
-#         print(f"ks = {ks}")
-#
-#
-# def form_factor_function_MoateThorne2012(a_s, freq, C=1500):
-#     """This function computes the form factor based on the equation of
-#     Moate and Thorne (2012)"""
-#     # computing the wave number
-#     k = 2 * np.pi * freq / C
-#     x = k * a_s
-#     f = (x ** 2 * (1 - 0.25 * np.exp(-((x - 1.5) / 0.35) ** 2)) * (
-#             1 + 0.6 * np.exp(-((x - 2.9) / 1.15) ** 2))) / (
-#                 42 + 28 * x ** 2)
-#     return f
-
-
-# if __name__ == "__main__":
-#     GranuloLoader("/home/bmoudjed/Documents/2 Data/Confluence_Rhône_Isere_2018/Granulo_data/fine_sample_file.ods")
-    # GranuloLoader("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/Granulo_data/"
-    #               "sand_sample_file.ods")
-    # GranuloLoader("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/"
-    #               "pt_acoused_cnr_7nov2023/echantil/fine_new_file_acoused_101RDS 3.ods")
-    # GranuloLoader("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/"
-    #               "pt_acoused_cnr_7nov2023/echantil/sand_new_file_acoused_101RDS 3.ods")
-
-
-
-
-
-
-
-
-
-
-# # form_factor = form_factor_function_MoateThorne2012(GranuloLoader.)
-#
-# def ks(a_s, rho_s, freq, pdf):
-#     # --- Calcul de la fonction de form ---
-#     form_factor = form_factor_function_MoateThorne2012(a_s, freq)
-#     print(f"form_factor shape = {len(form_factor)}")
-#     # print(f"form_factor = {form_factor}")
-#
-#     # --- Gaussian mixture ---
-#     # sample_demodul = demodul_granulo(a_s.astype(float), pdf, 0.17e-6, 200e-6)
-#     # sample_demodul.plot_interpolation()
-#
-#     # ss = np.sum(pdf / a_s ** 3)
-#     # proba_num = (pdf / a_s ** 3) / ss
-#
-#     # --- Compute k_s by dividing two integrals ---
-#     a2f2pdf = 0
-#     a3pdf = 0
-#     for i in range(len(pdf)):
-#         a2f2pdf += a_s[i] ** 2 * form_factor[i] * proba_num[i]
-#         a3pdf += a_s[i] ** 3 * proba_num[i]
-#
-#     ks = np.sqrt(a2f2pdf / (rho_s * a3pdf))
-#
-#     # ks = np.array([0.04452077, 0.11415143, 0.35533713, 2.47960051])
-#     # ks = ks0[ind]
-#     return ks

Model/udt_extract/raw_extract.py

@@ -43,7 +43,7 @@ def raw_extract(_raw_file):
         while 1:
             flag, size, data = fileraw.read_chunk()
 
-            # Pour raw UDT005 (ie. UB-Lab P, UB-SediFlow, UB-Lab 3C) on peut 
+            # Pour raw UDT005 (ie. UB-Lab P, UB-SediFlow, UB-Lab 3C) on peut
             # rencontrer 4 flags: const, settings json, configs (HW), profils
             if flag == CONST_TAG:
                 try:
@@ -55,7 +55,6 @@ def raw_extract(_raw_file):
                         .replace("True", "true")
                         .replace("False", "false")
                     )
-                print("const: %s" % const_dict)
 
                 ubt_data = ubt_raw_data( const_dict )
 
@@ -70,12 +69,9 @@ def raw_extract(_raw_file):
                         .replace("True", "true")
                         .replace("False", "false")
                     )
-                print("settings: %s" % settings_dict)
 
                 ubt_data.set_config(settings_dict)
 
-                print("ubt_data.set_config(settings_dict) : ", ubt_data.set_config(settings_dict))
-
             if flag == CONFIG_TAG:
                 # what is needed from here and which is not in param_us_dict is only blind_ca0 and blind_ca1
                 # note: this is not useful on APF06, but could be used for double check
@@ -100,7 +96,7 @@ def raw_extract(_raw_file):
 
     #print("%d profiles read" % profile_id)
     # last timestamp of udt file for time_end definition of run:
-    # based on the last profile processed 
+    # based on the last profile processed
     time_end = timestamp
 
     return (
@@ -111,4 +107,4 @@ def raw_extract(_raw_file):
         ubt_data.data_us_dicts,
         ubt_data.data_dicts,
         settings_dict,
-    )
+    )

View/mainwindow.py

@@ -43,8 +43,6 @@ from subprocess import check_call, run
 
 import time
 
-from View.acoustic_data_tab import AcousticDataTab
-
 
 class Ui_MainWindow(object):
     def setupUi(self, MainWindow):
@@ -55,10 +53,13 @@ class Ui_MainWindow(object):
         self.mainwindow.setDocumentMode(False)
         self.mainwindow.setDockNestingEnabled(False)
         self.mainwindow.setUnifiedTitleAndToolBarOnMac(False)
+
         self.centralwidget = QtWidgets.QWidget(self.mainwindow)
         self.centralwidget.setObjectName("centralwidget")
+
         self.verticalLayout = QtWidgets.QVBoxLayout(self.centralwidget)
         self.verticalLayout.setObjectName("verticalLayout")
+
         self.tabWidget = QtWidgets.QTabWidget(self.centralwidget)
         self.tabWidget.setAutoFillBackground(False)
         self.tabWidget.setLocale(QtCore.QLocale(QtCore.QLocale.French, QtCore.QLocale.France))
@@ -66,115 +67,125 @@ class Ui_MainWindow(object):
         self.tabWidget.setTabsClosable(False)
         self.tabWidget.setTabBarAutoHide(False)
         self.tabWidget.setObjectName("tabWidget")
+
         self.tab1 = QtWidgets.QWidget()
         self.tab1.setContextMenuPolicy(QtCore.Qt.ActionsContextMenu)
         self.tab1.setObjectName("tab1")
         self.tabWidget.addTab(self.tab1, "")
+
         self.tab2 = QtWidgets.QWidget()
         self.tab2.setObjectName("tab2")
         self.tabWidget.addTab(self.tab2, "")
+
         self.tab3 = QtWidgets.QWidget()
         self.tab3.setObjectName("tab3")
         self.tabWidget.addTab(self.tab3, "")
+
         self.tab4 = QtWidgets.QWidget()
         self.tab4.setObjectName("tab4")
         self.tabWidget.addTab(self.tab4, "")
+
         self.tab5 = QtWidgets.QWidget()
         self.tab5.setObjectName("tab5")
         self.tabWidget.addTab(self.tab5, "")
+
         self.tab6 = QtWidgets.QWidget()
         self.tab6.setObjectName("tab6")
         self.tabWidget.addTab(self.tab6, "")
-        # self.tab7 = QtWidgets.QWidget()
-        # self.tab7.setObjectName("tab7")
-        # self.tabWidget.addTab(self.tab7, "")
+
         self.verticalLayout.addWidget(self.tabWidget)
         self.mainwindow.setCentralWidget(self.centralwidget)
+
         self.menubar = QtWidgets.QMenuBar(self.mainwindow)
         self.menubar.setGeometry(QtCore.QRect(0, 0, 898, 22))
         self.menubar.setObjectName("menubar")
+
         self.menuFile = QtWidgets.QMenu(self.menubar)
         self.menuFile.setLocale(QtCore.QLocale(QtCore.QLocale.French, QtCore.QLocale.France))
         self.menuFile.setObjectName("menuFile")
+
         self.menuSettings = QtWidgets.QMenu(self.menuFile)
         self.menuSettings.setObjectName("menuSettings")
+
         self.menuLanguage = QtWidgets.QMenu(self.menuSettings)
         self.menuLanguage.setObjectName("menuLanguage")
+
         self.menuExport = QtWidgets.QMenu(self.menuFile)
         self.menuExport.setObjectName("menuExport")
+
         self.menuTools = QtWidgets.QMenu(self.menubar)
         self.menuTools.setLocale(QtCore.QLocale(QtCore.QLocale.French, QtCore.QLocale.France))
         self.menuTools.setObjectName("menuTools")
+
         self.menuHelp = QtWidgets.QMenu(self.menubar)
         self.menuHelp.setObjectName("menuHelp")
+
         self.mainwindow.setMenuBar(self.menubar)
+
         self.statusbar = QtWidgets.QStatusBar(self.mainwindow)
         self.statusbar.setObjectName("statusbar")
         self.mainwindow.setStatusBar(self.statusbar)
+
         self.toolBar = QtWidgets.QToolBar(self.mainwindow)
         self.toolBar.setObjectName("toolBar")
         self.mainwindow.addToolBar(QtCore.Qt.TopToolBarArea, self.toolBar)
-        # self.actionNew = QtWidgets.QAction(self.mainwindow)
-        # icon = QtGui.QIcon()
-        # icon.addPixmap(QtGui.QPixmap("icons/new.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
-        # self.actionNew.setIcon(icon)
-        # self.actionNew.setObjectName("actionNew")
+
         self.actionOpen = QtWidgets.QAction(self.mainwindow)
         icon1 = QtGui.QIcon()
         icon1.addPixmap(QtGui.QPixmap("icons/icon_folder.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
         self.actionOpen.setIcon(icon1)
         self.actionOpen.setObjectName("actionOpen")
+
         self.actionSave = QtWidgets.QAction(self.mainwindow)
         icon2 = QtGui.QIcon()
         icon2.addPixmap(QtGui.QPixmap("icons/save.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
         self.actionSave.setIcon(icon2)
         self.actionSave.setObjectName("actionSave")
-        # self.actionCopy = QtWidgets.QAction(self.mainwindow)
-        # icon3 = QtGui.QIcon()
-        # icon3.addPixmap(QtGui.QPixmap("icons/copy.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
-        # self.actionCopy.setIcon(icon3)
-        # self.actionCopy.setObjectName("actionCopy")
-        # self.actionCut = QtWidgets.QAction(self.mainwindow)
-        # icon4 = QtGui.QIcon()
-        # icon4.addPixmap(QtGui.QPixmap("icons/cut.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
-        # self.actionCut.setIcon(icon4)
-        # self.actionCut.setObjectName("actionCut")
-        # self.actionPaste = QtWidgets.QAction(self.mainwindow)
-        # icon5 = QtGui.QIcon()
-        # icon5.addPixmap(QtGui.QPixmap("icons/paste.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
-        # self.actionPaste.setIcon(icon5)
-        # self.actionPaste.setObjectName("actionPaste")
+
         self.actionEnglish = QtWidgets.QAction(self.mainwindow)
         icon6 = QtGui.QIcon()
         icon6.addPixmap(QtGui.QPixmap("icons/en.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
         self.actionEnglish.setIcon(icon6)
         self.actionEnglish.setObjectName("actionEnglish")
+
         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.action_ABSCalibrationConstant = QtWidgets.QAction(self.mainwindow)
         self.action_ABSCalibrationConstant.setText("ABS constant calibration kt")
+
         self.actionTable_of_Backscatter_values = QtWidgets.QAction(self.mainwindow)
         self.actionTable_of_Backscatter_values.setObjectName("actionTable_of_Backscatter_values")
+
         self.actionSave_As = QtWidgets.QAction(self.mainwindow)
         self.actionSave_As.setObjectName("actionSave_As")
+
         self.actionAbout = QtWidgets.QAction(self.mainwindow)
         self.actionAbout.setObjectName("actionAbout")
+
         self.actionUserManual = QtWidgets.QAction(self.mainwindow)
         self.actionUserManual.setText("User Manual")
+
         self.action_AcousticInversionTheory = QtWidgets.QAction(self.mainwindow)
         self.action_AcousticInversionTheory.setText("Acoustic inversion theory")
+
         self.action_AQUAscatUserManual = QtWidgets.QAction(self.mainwindow)
         self.action_AQUAscatUserManual.setText("Tutorial AQUAscat software")
+
         self.actionDB_Browser_for_SQLite = QtWidgets.QAction(self.mainwindow)
         self.actionDB_Browser_for_SQLite.setObjectName("actionDB_Browser_for_SQLite")
+
         self.menuLanguage.addAction(self.actionEnglish)
         self.menuLanguage.addAction(self.actionFrench)
+
         self.menuSettings.addAction(self.menuLanguage.menuAction())
         self.menuSettings.addAction(self.action_ABSCalibrationConstant)
+
         self.menuExport.addAction(self.actionTable_of_Backscatter_values)
+
         self.menuFile.addAction(self.actionOpen)
         self.menuFile.addAction(self.actionSave)
         self.menuFile.addAction(self.actionSave_As)
@@ -182,21 +193,21 @@ class Ui_MainWindow(object):
         self.menuFile.addAction(self.menuSettings.menuAction())
         self.menuFile.addSeparator()
         self.menuFile.addAction(self.menuExport.menuAction())
+
         self.menuTools.addAction(self.actionDB_Browser_for_SQLite)
+
         self.menuHelp.addAction(self.actionAbout)
         self.menuHelp.addAction(self.actionUserManual)
         self.menuHelp.addAction(self.action_AcousticInversionTheory)
         self.menuHelp.addAction(self.action_AQUAscatUserManual)
+
         self.menubar.addAction(self.menuFile.menuAction())
         self.menubar.addAction(self.menuTools.menuAction())
         self.menubar.addAction(self.menuHelp.menuAction())
-        # self.toolBar.addAction(self.actionNew)
+
         self.toolBar.addAction(self.actionOpen)
         self.toolBar.addAction(self.actionSave)
         self.toolBar.addSeparator()
-        # self.toolBar.addAction(self.actionCopy)
-        # self.toolBar.addAction(self.actionCut)
-        # self.toolBar.addAction(self.actionPaste)
         self.toolBar.addSeparator()
         self.toolBar.addAction(self.actionEnglish)
         self.toolBar.addAction(self.actionFrench)
@@ -257,12 +268,7 @@ class Ui_MainWindow(object):
         UpdateTableForSave()
 
     def open(self):
-        pass
-        # ReadTableForOpen()
-
-        # acoustic_data_tab = AcousticDataTab()
-        #
-        # acoustic_data_tab.fileListWidget.addItems(stg.acoustic_data)
+        ReadTableForOpen()
 
     def load_calibration_constant_values(self):
         cc_kt = CalibrationConstantKt()
@@ -330,13 +336,6 @@ class Ui_MainWindow(object):
                     exec("DataFrame_acoustic_" + str(i) + "= pd.DataFrame(None)")
                     exec("DataFrame_acoustic_" + str(i) + "= pd.DataFrame(data=table_data.transpose(), columns=header_list)")
 
-                # exec("DataFrame_acoustic_" + str(i) + ".to_csv(" +
-                #         "excel_writer=" +
-                #         '/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/BS_raw_data_table.xlsx' + "," +
-                #         "sheet_name=stg.filename_BS_raw_data[i]," +
-                #         "header=DataFrame_acoustic.columns," +
-                #         "engine=" + "xlsxwriter" + ")")
-
                     exec("DataFrame_acoustic_" + str(i) + ".to_csv(" +
                          "path_or_buf=" +
                          "'" + name + "/" + "Table_" +
@@ -357,7 +356,6 @@ class Ui_MainWindow(object):
         self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab4), _translate("MainWindow", "Sediment Calibration"))
         self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab5), _translate("MainWindow", "Acoustic inversion"))
         self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab6), _translate("MainWindow", "Note"))
-        # self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab7), _translate("MainWindow", "User manual"))
         self.menuFile.setTitle(_translate("MainWindow", "File"))
         self.menuSettings.setTitle(_translate("MainWindow", "Settings"))
         self.menuLanguage.setTitle(_translate("MainWindow", "Language"))
@@ -365,12 +363,8 @@ class Ui_MainWindow(object):
         self.menuTools.setTitle(_translate("MainWindow", "Tools"))
         self.menuHelp.setTitle(_translate("MainWindow", "Help"))
         self.toolBar.setWindowTitle(_translate("MainWindow", "toolBar"))
-        # self.actionNew.setText(_translate("MainWindow", "New"))
         self.actionOpen.setText(_translate("MainWindow", "Open ..."))
         self.actionSave.setText(_translate("MainWindow", "Save"))
-        # self.actionCopy.setText(_translate("MainWindow", "Copy"))
-        # self.actionCut.setText(_translate("MainWindow", "Cut"))
-        # self.actionPaste.setText(_translate("MainWindow", "Paste"))
         self.actionEnglish.setText(_translate("MainWindow", "English"))
         self.actionFrench.setText(_translate("MainWindow", "French"))
         self.actionTable_of_Backscatter_values.setText(_translate("MainWindow", "Table of Backscatter values"))

View/plot_noise_window.py

@@ -1,4 +1,3 @@
-import sys
 
 from PyQt5.QtGui import QIcon, QPixmap
 from PyQt5.QtWidgets import (QWidget, QLabel, QHBoxLayout, QVBoxLayout, QApplication, QMainWindow, QGridLayout,
@@ -72,74 +71,6 @@ class PlotNoiseWindow(QDialog):
                              "-stg.depth_noise[" + str(i) + "][" + str(freq_ind) + ", :]," +
                              "stg.BS_noise_raw_data[" + str(i) + "][" + str(freq_ind) + ", :, :]," +
                              "cmap='hsv')")
-                    # , norm = LogNorm(vmin=val_min, vmax=val_max)
-
-                    # if stg.time_cross_section[i].shape != (0,):
-                    #
-                    #     if depth_cross_section[i].shape != (0,):
-                    #         if val_min == val_max:
-                    #             exec("pcm = self.ax" + str(i) + "[" + str(freq_ind) + "]" + ".pcolormesh(" +
-                    #                         "stg.time_cross_section[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                         "-stg.depth_cross_section[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                         "stg.BS_noise_raw_data[" + str(i) + "][" + str(freq_ind) + ", :, :]," +
-                    #                         "cmap='viridis')" )
-                    #         else:
-                    #             val_min = 0
-                    #             val_max = 1e-5
-                    #             exec("pcm = self.ax" + str(i) + "[" + str(freq_ind) + "]" + ".pcolormesh(" +
-                    #                          "stg.time_cross_section[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                          "-stg.depth_cross_section[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                          "stg.BS_noise_raw_data[" + str(i) + "][" + str(freq_ind) + ", :, :]," +
-                    #                          "cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))")
-                    #     else:
-                    #         if val_min == val_max:
-                    #             exec("pcm = self.ax" + str(i) + "[" + str(freq_ind) + "]" + ".pcolormesh(" +
-                    #                         "stg.time_cross_section[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                         "-stg.depth[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                         "stg.BS_noise_raw_data[" + str(i) + "][" + str(freq_ind) + ", :, :]," +
-                    #                         "cmap='viridis')" )
-                    #         else:
-                    #             val_min = 0
-                    #             val_max = 1e-5
-                    #             exec("pcm = self.ax" + str(i) + "[" + str(freq_ind) + "]" + ".pcolormesh(" +
-                    #                          "stg.time_cross_section[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                          "-stg.depth[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                          "stg.BS_noise_averaged_data[" + str(i) + "][" + str(freq_ind) + ", :, :]," +
-                    #                          "cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))")
-                    #
-                    # else:
-                    #
-                    #     if depth_cross_section[i].shape != (0,):
-                    #         if val_min == val_max:
-                    #             exec("pcm = self.ax" + str(i) + "[" + str(freq_ind) + "]" + ".pcolormesh(" +
-                    #                         "stg.time[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                         "-stg.depth_cross_section[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                         "stg.BS_noise_averaged_data[" + str(i) + "][" + str(freq_ind) + ", :, :]," +
-                    #                         "cmap='viridis')" )
-                    #         else:
-                    #             val_min = 0
-                    #             val_max = 1e-5
-                    #             exec("pcm = self.ax" + str(i) + "[" + str(freq_ind) + "]" + ".pcolormesh(" +
-                    #                      "stg.time[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                      "-stg.depth_cross_section[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                      "stg.BS_noise_averaged_data[" + str(i) + "][" + str(freq_ind) + ", :, :]," +
-                    #                      "cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))")
-                    #     else:
-                    #         if val_min == val_max:
-                    #             exec("pcm = self.ax" + str(i) + "[" + str(freq_ind) + "]" + ".pcolormesh(" +
-                    #                     "stg.time[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                     "-stg.depth[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                     "stg.BS_noise_averaged_data[" + str(i) + "][" + str(freq_ind) + ", :, :]," +
-                    #                     "cmap='viridis')" )
-                    #         else:
-                    #             val_min = 0
-                    #             val_max = 1e-5
-                    #             exec("pcm = self.ax" + str(i) + "[" + str(freq_ind) + "]" + ".pcolormesh(" +
-                    #                      "stg.time[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                      "-stg.depth[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                    #                      "stg.BS_noise_averaged_data[" + str(i) + "][" + str(freq_ind) + ", :, :]," +
-                    #                      "cmap='viridis')")
-                    # # , norm = LogNorm(vmin=val_min, vmax=val_max)
 
                     eval("self.ax" + str(i) + "[" + str(freq_ind) + "]" + ".text(1, .70, stg.freq_text[" + str(i) +
                                         "][" + str(freq_ind) + "]," +
@@ -160,8 +91,6 @@ class PlotNoiseWindow(QDialog):
 
                 pass
 
-            # self.axis_noise.tick_params(axis='both', which='minor', labelsize=10)
-
             exec("self.canvas" + str(i) + "= FigureCanvas(self.fig" + str(i) + ")")
             exec("self.toolbar" + str(i) + "= NavigationToolBar(self.canvas" + str(i) + ", self)")
 
@@ -171,10 +100,3 @@ class PlotNoiseWindow(QDialog):
             exec("self.verticalLayout_tab" + str(i) + ".addWidget(self.toolbar" + str(i) + ")")
             exec("self.verticalLayout_tab" + str(i) + ".addWidget(self.scroll" + str(i) + ")")
 
-
-        # self.tab1 = QWidget()
-        # self.tab.addTab(self.tab1, "Tab 1")
-
-
-
-        # ----------------------------------------------------------

View/sediment_calibration_tab.py

@@ -1,13 +1,33 @@
-import sys
+# ============================================================================== #
+    # acoustic_data_tab.py - AcouSed                                   #
+    # Copyright (C) 2024  INRAE                                              #
+    #                                                                        #
+    # 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/>. #
+
+    #                                                      by Brahim MOUDJED #
+# ============================================================================== #
+
+# -*- coding: utf-8 -*-
+
 
 import matplotlib.pyplot as plt
 import pandas as pd
-from PyQt5.QtWidgets import (QWidget, QMainWindow, QApplication, QVBoxLayout, QHBoxLayout, QGroupBox, QComboBox,
-                             QGridLayout, QLabel, QPushButton, QSpinBox, QDoubleSpinBox, QAbstractSpinBox, QSpacerItem,
-                             QSizePolicy, QSlider, QLineEdit, QDial, QFileDialog, QMessageBox, QFrame)
+from PyQt5.QtWidgets import (QWidget, QVBoxLayout, QHBoxLayout, QGroupBox, QComboBox,
+                             QGridLayout, QLabel, QPushButton, QSlider, QLineEdit, QFileDialog, QMessageBox, QFrame)
 
-from PyQt5.QtCore import QCoreApplication, Qt, QPropertyAnimation, QSize
-from PyQt5.QtGui import QStandardItemModel, QIcon, QPixmap, QFont
+from PyQt5.QtCore import Qt, QPropertyAnimation, QSize
+from PyQt5.QtGui import QIcon, QPixmap, QFont
 
 import settings as stg
 
@@ -24,14 +44,6 @@ from os import path
 from View.checkable_combobox import CheckableComboBox
 
 from Model.acoustic_inversion_method_high_concentration import AcousticInversionMethodHighConcentration
-from settings import J_cross_section, alpha_s
-
-
-# from settings import depth_cross_section, BS_raw_data_pre_process_average, BS_raw_data_pre_process_SNR, BS_raw_data, \
-#     path_calibration_file
-
-
-# from virtual_env.bin.odfimg import directory
 
 
 class SedimentCalibrationTab(QWidget):
@@ -323,9 +335,6 @@ class SedimentCalibrationTab(QWidget):
         self.label_ks.setText("ks")
         self.gridLayout_groupbox_sediment_calibration_parameter.addWidget(self.label_ks, 4, 0, 1, 1, Qt.AlignCenter)
 
-        # self.spinbox_ks_freq1 = QDoubleSpinBox()
-        # self.spinbox_ks_freq1.setDecimals(8)
-        # self.spinbox_ks_freq1.setSuffix(" m/kg^0.5")
         self.lineEdit_ks_freq1 = QLineEdit()
         self.lineEdit_ks_freq1.setMaximumWidth(100)
         self.lineEdit_ks_freq1.setText("0.00")
@@ -336,9 +345,6 @@ class SedimentCalibrationTab(QWidget):
         self.gridLayout_groupbox_sediment_calibration_parameter.addWidget(self.label_ks_freq1_unit, 4, 2, 1, 1,
                                                                           Qt.AlignLeft)
 
-        # self.spinbox_ks_freq2 = QDoubleSpinBox()
-        # self.spinbox_ks_freq2.setDecimals(8)
-        # self.spinbox_ks_freq2.setSuffix(" m/kg^0.5")
         self.lineEdit_ks_freq2 = QLineEdit()
         self.lineEdit_ks_freq2.setMaximumWidth(100)
         self.lineEdit_ks_freq2.setText("0.00")
@@ -353,9 +359,6 @@ class SedimentCalibrationTab(QWidget):
         self.label_sv.setText("sv")
         self.gridLayout_groupbox_sediment_calibration_parameter.addWidget(self.label_sv, 5, 0, 1, 1, Qt.AlignCenter)
 
-        # self.spinbox_sv_freq1 = QDoubleSpinBox()
-        # self.spinbox_sv_freq1.setDecimals(8)
-        # self.spinbox_sv_freq1.setSuffix(" /m")
         self.lineEdit_sv_freq1 = QLineEdit()
         self.lineEdit_sv_freq1.setMaximumWidth(100)
         self.lineEdit_sv_freq1.setText("0.00")
@@ -365,9 +368,6 @@ class SedimentCalibrationTab(QWidget):
         self.label_sv_freq1_unit.setText("m<sup>-1</sup>")
         self.gridLayout_groupbox_sediment_calibration_parameter.addWidget(self.label_sv_freq1_unit, 5, 2, 1, 1)
 
-        # self.spinbox_sv_freq2 = QDoubleSpinBox()
-        # self.spinbox_sv_freq2.setDecimals(8)
-        # self.spinbox_sv_freq2.setSuffix(" /m")
         self.lineEdit_sv_freq2 = QLineEdit()
         self.lineEdit_sv_freq2.setMaximumWidth(100)
         self.lineEdit_sv_freq2.setText("0.00")
@@ -381,8 +381,6 @@ class SedimentCalibrationTab(QWidget):
         self.label_X.setText("X")
         self.gridLayout_groupbox_sediment_calibration_parameter.addWidget(self.label_X, 6, 0, 1, 1, Qt.AlignCenter)
 
-        # self.spinbox_X = QDoubleSpinBox()
-        # self.spinbox_X.setDecimals(2)
         self.lineEdit_X = QLineEdit()
         self.lineEdit_X.setMaximumWidth(100)
         self.lineEdit_X.setText("0.00")
@@ -392,9 +390,6 @@ class SedimentCalibrationTab(QWidget):
         self.label_alphas.setText("\u03B1s")
         self.gridLayout_groupbox_sediment_calibration_parameter.addWidget(self.label_alphas, 7, 0, 1, 1, Qt.AlignCenter)
 
-        # self.spinbox_alphas_freq1 = QDoubleSpinBox()
-        # self.spinbox_alphas_freq1.setDecimals(4)
-        # self.spinbox_alphas_freq1.setSuffix(" /m")
         self.lineEdit_alphas_freq1 = QLineEdit()
         self.lineEdit_alphas_freq1.setMaximumWidth(100)
         self.lineEdit_alphas_freq1.setText("0.00")
@@ -404,9 +399,6 @@ class SedimentCalibrationTab(QWidget):
         self.label_alphas_freq1_unit.setText("m<sup>-1</sup>")
         self.gridLayout_groupbox_sediment_calibration_parameter.addWidget(self.label_alphas_freq1_unit, 7, 2, 1, 1, Qt.AlignLeft)
 
-        # self.spinbox_alphas_freq2 = QDoubleSpinBox()
-        # self.spinbox_alphas_freq2.setDecimals(4)
-        # self.spinbox_alphas_freq2.setSuffix(" /m")
         self.lineEdit_alphas_freq2 = QLineEdit()
         self.lineEdit_alphas_freq2.setMaximumWidth(100)
         self.lineEdit_alphas_freq2.setText("0.00")
@@ -420,9 +412,6 @@ class SedimentCalibrationTab(QWidget):
         self.label_zeta.setText("\u03B6")
         self.gridLayout_groupbox_sediment_calibration_parameter.addWidget(self.label_zeta, 8, 0, 1, 1, Qt.AlignCenter)
 
-        # self.spinbox_zeta_freq1 = QDoubleSpinBox()
-        # self.spinbox_zeta_freq1.setDecimals(4)
-        # self.spinbox_zeta_freq1.setSuffix(" /m")
         self.lineEdit_zeta_freq1 = QLineEdit()
         self.lineEdit_zeta_freq1.setMaximumWidth(100)
         self.lineEdit_zeta_freq1.setText("0.00")
@@ -433,9 +422,6 @@ class SedimentCalibrationTab(QWidget):
         self.gridLayout_groupbox_sediment_calibration_parameter.addWidget(self.label_zeta_freq1_unit, 8, 2, 1, 1,
                                                                           Qt.AlignLeft)
 
-        # self.spinbox_zeta_freq2 = QDoubleSpinBox()
-        # self.spinbox_zeta_freq2.setDecimals(4)
-        # self.spinbox_zeta_freq2.setSuffix(" /m")
         self.lineEdit_zeta_freq2 = QLineEdit()
         self.lineEdit_zeta_freq2.setMaximumWidth(100)
         self.lineEdit_zeta_freq2.setText("0.00")
@@ -450,22 +436,6 @@ class SedimentCalibrationTab(QWidget):
         self.pushbutton_save_calibration.setText("Save calibration")
         self.gridLayout_groupbox_sediment_calibration_parameter.addWidget(self.pushbutton_save_calibration,9, 5, 1, 1)
 
-        # self.groupbox_calibration_compute_size_change()
-
-        # self.animaiton_groupbox_compute = QPropertyAnimation(self.groupbox_sediment_calibration_compute, b"size")
-        # self.animaiton_groupbox_compute.setStartValue(QSize(self.groupbox_sediment_calibration_compute.width(), 25))
-        #
-        # self.animaiton_groupbox_compute.start()
-
-        # setStartValue(QSize(self.groupbox_sediment_calibration_compute.width(),
-        #                                                     self.groupbox_sediment_calibration_compute.height()))
-        #
-        #     self.animaiton_groupbox_compute.setEndValue(
-        #         QSize(self.groupbox_sediment_calibration_compute.width(),
-        #               self.groupbox_sediment_calibration_compute.sizeHint().height()))
-        # else:
-        #     self.animaiton_groupbox_compute.setEndValue(QSize(self.groupbox_sediment_calibration_compute.width(), 25))
-
         # ++++++++++++++++++++
         # +++ Groupbox FCB +++
 
@@ -814,7 +784,6 @@ class SedimentCalibrationTab(QWidget):
 
         elif stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
 
-            print("totototototototoott")
             val_min = np.nanmin(
                 stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][self.combobox_freq2.currentIndex(),
                 :, :])
@@ -1066,8 +1035,6 @@ class SedimentCalibrationTab(QWidget):
                 cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
 
         # --- Plot samples ---
-        print("stg.fine_sample_profile ", stg.fine_sample_profile)
-        print("stg.sand_sample_target ", stg.sand_sample_target)
 
         if (stg.fine_sample_profile) or (stg.sand_sample_target):
 
@@ -1150,8 +1117,6 @@ class SedimentCalibrationTab(QWidget):
                              self.combobox_freq2.currentIndex(), :],
                             color='red', linestyle="solid", linewidth=2))
 
-        # self.axis_BS.set_xticks([])
-        # self.axis_BS.set_yticks([])
         self.axis_BS.set_xlabel("Time (sec)")
         self.axis_BS.set_ylabel("Depth (m)")
         self.fig_BS.canvas.draw_idle()
@@ -1159,13 +1124,11 @@ class SedimentCalibrationTab(QWidget):
     def sample_choice_for_calibration(self):
         # --- List selected fine samples ---
         stg.fine_sample_profile = [(f, int(f[1:]) - 1) for f in self.combobox_fine_sample_choice.currentData()]
-        print(f"stg.fine_sample_profile : {stg.fine_sample_profile}")
 
         # --- List selected sand samples ---
         # stg.sand_sample_target = [(s, int(s[1:]) - 1) for s in self.combobox_sand_sample_choice.currentData()]
         stg.sand_sample_target = [(self.combobox_sand_sample_choice.currentText(),
                                    self.combobox_sand_sample_choice.currentIndex())]
-        print(f"stg.sand_sample_target : {stg.sand_sample_target}")
 
         # --- Find index in time (along acoustic recording) of sand sample target ---
         if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
@@ -1232,7 +1195,6 @@ class SedimentCalibrationTab(QWidget):
             )
 
         stg.sand_sample_target_indice = [(d1, t1), (d2, t2)]
-        print("stg.sand_sample_target_indice ", stg.sand_sample_target_indice)
 
     def summary_samples_choices(self):
 
@@ -1260,17 +1222,9 @@ class SedimentCalibrationTab(QWidget):
 
         for p in range(len(fine_data)):
             for q in range(len(fine_data[0])):
-                # exec("fine_label[" + str(p) + "]_" + "stg.fine_sample_profile[" + str(p) + "][0]" + " = QLabel()")
-                # eval("fine_label[" + str(p) + "]_" + "stg.fine_sample_profile[" + str(p) + "][0]" + ".setText(" + "fine_data[" + str(p) + "][" + str(q) + "])")
-                # eval("self.gridLayout_groupbox_interpolate_info.addWidget(" + "fine_label[" + str(p) + "]_" + "stg.fine_sample_profile[" + str(p) + "], " + str(1+p) + ", " + str(q) + ", 1, 1)")
                 print(f"self.gridLayout_groupbox_interpolate_info.addWidget(QLabel(fine_data[{p}][{q}]), {2 + p}, {q}, 1, 1, Qt.AlignCenter)")
                 eval(f"self.gridLayout_groupbox_interpolate_info.addWidget(QLabel(fine_data[{p}][{q}]), {2 + p}, {q}, 1, 1, Qt.AlignCenter)")
 
-        # self.double_horizontal_line = QFrame()
-        # self.double_horizontal_line.setFrameShape(QFrame.HLine)
-        # self.double_horizontal_line.setFrameShadow(QFrame.Sunken)
-        # self.double_horizontal_line.setLineWidth(1)
-        # self.double_horizontal_line.setMidLineWidth(3)
         self.gridLayout_groupbox_interpolate_info.addWidget(self.double_horizontal_line, 2 + len(fine_data), 0, 1, 4, Qt.AlignCenter)
 
         sand_head = ["Sample", "Depth (m)", "time", "Csand (g/L)"]
@@ -1363,8 +1317,6 @@ class SedimentCalibrationTab(QWidget):
                                                                 )
                                                     )
 
-        # print(f"range_lin_interp : {range_lin_interp}")
-        # print(f"M_profile_fine : {M_profile_fine}")
         else:
 
             if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
@@ -1405,118 +1357,12 @@ class SedimentCalibrationTab(QWidget):
                         ]
                     ))
 
-        print(f"1 M_profile_fine : {stg.M_profile_fine}")
         stg.range_lin_interp = stg.range_lin_interp
         stg.M_profile_fine = stg.M_profile_fine
         stg.M_profile_fine = stg.M_profile_fine[:stg.range_lin_interp.shape[0]]
-        print(f"2 M_profile_fine : {stg.M_profile_fine}")
 
         self.plot_profile_of_concentration_fine()
 
-    # def range_cells_function(self):
-    #     """ Computing the real cell size, that depends on the temperature """
-    #
-    #     # defaut Aquascat cell size
-    #     aquascat_cell_size = stg.r[0, 1] - stg.r[0, 0]
-    #     # Pulse duration
-    #     tau = aquascat_cell_size * 2 / 1500  # figure 2.9  1500 vitesse du son entrée pour le paramètrage des mesures aquascat
-    #     # Sound speed
-    #     cel = self.inv_hc.water_velocity(self.spinbox_temperature_water_attenuation.value())
-    #     # Real cell size
-    #     real_cell_size = cel * tau / 2  # voir fig 2.9
-    #
-    #     # Converting to real cell profile
-    #     real_r = stg.r / aquascat_cell_size * real_cell_size  # (/ aquascat_cell_size) pour ramener BS.r entre 0 et 1
-    #     # (* real_cell_size) pour remettre les échelles spatiales sur la taille réelle des cellules
-    #
-    #     # R with right shape (numpy array)
-    #     R_real = real_r   # np.repeat(real_r, len(stg.freq), axis=1)
-    #
-    #     return R_real
-
-
-    # def compute_FCB(self):
-    #
-    #     print(f"self.range_cells_function() : {self.range_cells_function()}")
-    #     print(f"self.range_cells_function() shape : {self.range_cells_function().shape}")
-    #     R_real = np.repeat(self.range_cells_function()[:, :, np.newaxis], stg.t.shape[1], axis=2)
-    #     print(f"R_real shape : {R_real.shape}")
-    #     if (stg.BS_stream_bed_pre_process_average.size == 0) and (stg.BS_stream_bed_pre_process_SNR.size == 0):
-    #         stg.FCB = (np.log(stg.BS_stream_bed) + np.log(R_real) +
-    #                     2 * stg.water_attenuation * R_real)
-    #     elif stg.BS_stream_bed_pre_process_SNR.size == 0:
-    #         stg.FCB = (np.log(stg.BS_stream_bed_pre_process_average) + np.log(R_real) +
-    #                     2 * stg.water_attenuation * R_real)
-    #     else:
-    #         stg.FCB = (np.log(stg.BS_stream_bed_pre_process_SNR) + np.log(R_real) +
-    #                     2 * stg.water_attenuation * R_real)
-    #         self.plot_FCB()
-
-        # def fit_FCB_profile_with_linear_regression_and_compute_alphaS(self):
-        #
-        #     if stg.FCB.size == 0:
-        #         msgBox = QMessageBox()
-        #         msgBox.setWindowTitle("Linear regression error")
-        #         msgBox.setIcon(QMessageBox.Warning)
-        #         msgBox.setText("Please compute FCB before")
-        #         msgBox.setStandardButtons(QMessageBox.Ok)
-        #         msgBox.exec()
-        #     else:
-        #         try:
-        #             y0 = stg.FCB[self.combobox_frequency_compute_alphaS.currentIndex(), :, self.slider.value()]
-        #             y = y0[np.where(np.isnan(y0) == False)]
-        #
-        #             x0 = stg.r[0, :].reshape(-1)
-        #             x = x0[np.where(np.isnan(y0) == False)]
-        #
-        #             value1 = np.where(np.round(np.abs(x - self.spinbox_alphaS_computation_from.value()), 2)
-        #                               == np.min(np.round(np.abs(x - self.spinbox_alphaS_computation_from.value()), 2)))
-        #             value2 = np.where(np.round(np.abs(x - self.spinbox_alphaS_computation_to.value()), 2)
-        #                               == np.min(np.round(np.abs(x - self.spinbox_alphaS_computation_to.value()), 2)))
-        #
-        #             # print(np.round(np.abs(x - self.spinbox_alphaS_computation_from.value()), 2))
-        #             # # print("value1 ", value1[0][0])
-        #             # print(np.round(np.abs(x - self.spinbox_alphaS_computation_to.value()), 2))
-        #             # print("value2 ", value2[0][0])
-        #
-        #             # print("y limited ", y[value1[0][0]:value2[0][0]])
-        #
-        #             lin_reg_compute = stats.linregress(x[value1[0][0]:value2[0][0]], y[value1[0][0]:value2[0][0]])
-        #         except ValueError:
-        #             msgBox = QMessageBox()
-        #             msgBox.setWindowTitle("Linear regression error")
-        #             msgBox.setIcon(QMessageBox.Warning)
-        #             msgBox.setText("Please check boundaries to fit a linear line")
-        #             msgBox.setStandardButtons(QMessageBox.Ok)
-        #             msgBox.exec()
-        #         else:
-        #             stg.lin_reg = (lin_reg_compute.slope, lin_reg_compute.intercept)
-        #             # print(f"y = {stg.lin_reg[0]}x + {stg.lin_reg[1]}")
-        #
-        #             self.label_alphaS.clear()
-        #             self.label_alphaS.setText(f"\u03B1s = {-0.5*stg.lin_reg[0]:.4f} dB/m")
-        #
-        #             # for i, value_freq in enumerate(stg.freq):
-        #             #     for k, value_t in enumerate(stg.t):
-        #             #         # print(f"indice i: {i}, indice k: {k}")
-        #             #         # print(f"values of FCB: {stg.FCB[:, i, k]}")
-        #             #         y = stg.FCB[:, i, k]
-        #             #         # print("y : ", y)
-        #             #         # print(f"values of FCB where FCB is not Nan {y[np.where(np.isnan(y) == False)]}")
-        #             #         # print(f"values of r where FCB is not Nan {x[np.where(np.isnan(y) == False)]}")
-        #             #         lin_reg_compute = stats.linregress(x[np.where(np.isnan(y) == False)], y[np.where(np.isnan(y) == False)])
-        #             #         lin_reg_tuple = (lin_reg_compute.slope, lin_reg_compute.intercept)
-        #             #         stg.lin_reg.append(lin_reg_tuple)
-        #
-        #             # print(f"y = {lin_reg.slope}x + {lin_reg.intercept}")
-        #
-        #             # plt.figure()
-        #             # plt.plot(stg.r, stg.FCB[:, 0, 825], 'k-', stg.r, lin_reg.slope*stg.r + lin_reg.intercept, "b--")
-        #             # plt.show()
-        #
-        #             # print("lin_reg length ", len(stg.lin_reg))
-        #             # print("lin_reg ", stg.lin_reg)
-
     # ------------------------------------------------------------------
     # --------------- Functions for sediment calibration ---------------
     # ------------------------------------------------------------------
@@ -1551,7 +1397,6 @@ class SedimentCalibrationTab(QWidget):
             self.groupbox_sediment_calibration_import.setChecked(False)
 
     def groupbox_calibration_compute_size_change(self):
-        print("self.groupbox_sediment_calibration_compute.isChecked() ", self.groupbox_sediment_calibration_compute.isChecked())
 
         duration = 500
         self.animaiton_groupbox_compute = QPropertyAnimation(self.groupbox_sediment_calibration_compute, b"size")
@@ -1561,12 +1406,10 @@ class SedimentCalibrationTab(QWidget):
                                               self.groupbox_sediment_calibration_compute.height()))
 
         if self.groupbox_sediment_calibration_compute.isChecked():
-            print("Checked")
             self.animaiton_groupbox_compute.setEndValue(
                 QSize(self.groupbox_sediment_calibration_compute.width(),
                       self.groupbox_sediment_calibration_compute.sizeHint().height()))
         else:
-            print("Non Checked")
             self.animaiton_groupbox_compute.setEndValue(QSize(self.groupbox_sediment_calibration_compute.width(), 25))
 
         self.animaiton_groupbox_compute.start()
@@ -1631,9 +1474,6 @@ class SedimentCalibrationTab(QWidget):
 
             # --- Read calibration file ---
             data = pd.read_csv(stg.path_calibration_file + "/" + stg.filename_calibration_file, header=0, index_col=0)
-            print(data.head())
-            print(data.iloc[0][0])
-            print(type(data.iloc[0][0]))
 
             # --- Fill spinboxes of calibration parameter ---
             self.label_temperature.clear()
@@ -1641,10 +1481,7 @@ class SedimentCalibrationTab(QWidget):
 
             self.label_freq1.clear()
             self.label_freq1.setText(data.columns[0])
-            print(stg.freq_text)
-            print(stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()])
-            print(data.columns[0])
-            print("index freq1 ", np.where(np.asarray(stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()]) == data.columns[0]))
+
             index_freq1 = np.where(np.asarray(stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()]) ==
                                    data.columns[0])[0][0]
             stg.frequencies_for_calibration.clear()
@@ -1654,11 +1491,9 @@ class SedimentCalibrationTab(QWidget):
 
             self.label_freq2.clear()
             self.label_freq2.setText(data.columns[1])
-            print("index freq2 ",
-                  np.where(np.asarray(stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()]) == 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.clear()
             stg.frequencies_for_calibration.append((stg.freq[self.combobox_acoustic_data_choice.currentIndex()][
                                                         index_freq2],
                                                     index_freq2))
@@ -1667,77 +1502,52 @@ class SedimentCalibrationTab(QWidget):
             stg.frequency_for_inversion = (stg.freq[self.combobox_acoustic_data_choice.currentIndex()][index_freq2],
                                            index_freq2)
 
-            # self.spinbox_ks_freq1.clear()
-            # self.spinbox_ks_freq1.setValue()
             self.lineEdit_ks_freq1.clear()
             self.lineEdit_ks_freq1.setText(str("%.5f" % float(data.iloc[0][0])))
 
-            # self.spinbox_ks_freq2.clear()
-            # self.spinbox_ks_freq2.setValue(float(data.iloc[0][1]))
             self.lineEdit_ks_freq2.clear()
             self.lineEdit_ks_freq2.setText(str("%.5f" % float(data.iloc[0][1])))
 
             stg.ks.clear()
-            # stg.ks = [self.spinbox_ks_freq1.value(), self.spinbox_ks_freq2.value()]
             stg.ks = [float(self.lineEdit_ks_freq1.text()), float(self.lineEdit_ks_freq2.text())]
 
-            # self.spinbox_sv_freq1.clear()
-            # self.spinbox_sv_freq1.setValue(float(data.iloc[1][0]))
             self.lineEdit_sv_freq1.clear()
             self.lineEdit_sv_freq1.setText(str("%.5f" % float(data.iloc[1][0])))
 
-            # self.spinbox_sv_freq2.clear()
-            # self.spinbox_sv_freq2.setValue(float(data.iloc[1][1]))
             self.lineEdit_sv_freq2.clear()
             self.lineEdit_sv_freq2.setText(str("%.5f" % float(data.iloc[1][1])))
 
             stg.sv.clear()
-            # stg.sv = [self.spinbox_sv_freq1.value(), self.spinbox_sv_freq2.value()]
             stg.sv = [float(self.lineEdit_sv_freq1.text()), float(self.lineEdit_sv_freq2.text())]
 
-            # self.spinbox_X.clear()
-            # self.spinbox_X.setValue(float(data.iloc[2][0]))
             self.lineEdit_X.clear()
             self.lineEdit_X.setText(str("%.2f" % float(data.iloc[2][0])))
 
             stg.X_exponent.clear()
-            # stg.X_exponent.append(self.spinbox_X.value())
             stg.X_exponent.append(float(self.lineEdit_X.text()))
 
-            # self.spinbox_alphas_freq1.clear()
-            # self.spinbox_alphas_freq1.setValue(float(data.iloc[3][0]))
             self.lineEdit_alphas_freq1.clear()
             self.lineEdit_alphas_freq1.setText(str("%.5f" % float(data.iloc[3][0])))
 
-            # self.spinbox_alphas_freq2.clear()
-            # self.spinbox_alphas_freq2.setValue(float(data.iloc[3][1]))
             self.lineEdit_alphas_freq2.clear()
             self.lineEdit_alphas_freq2.setText(str("%.5f" % float(data.iloc[3][1])))
 
             stg.alpha_s.clear()
-            # stg.alpha_s = [self.spinbox_alphas_freq1.value(), self.spinbox_alphas_freq2.value()]
             stg.alpha_s = [float(self.lineEdit_alphas_freq1.text()), float(self.lineEdit_alphas_freq2.text())]
 
-            # self.spinbox_zeta_freq1.clear()
-            # self.spinbox_zeta_freq1.setValue(float(data.iloc[4][0]))
             self.lineEdit_zeta_freq1.clear()
             self.lineEdit_zeta_freq1.setText(str("%.5f" % float(data.iloc[4][0])))
 
-            # self.spinbox_zeta_freq2.clear()
-            # self.spinbox_zeta_freq2.setValue(float(data.iloc[4][1]))
             self.lineEdit_zeta_freq2.clear()
             self.lineEdit_zeta_freq2.setText(str("%.5f" % float(data.iloc[4][1])))
 
             stg.zeta.clear()
-            # stg.zeta = [self.spinbox_zeta_freq1.value(), self.spinbox_zeta_freq2.value()]
             stg.zeta = [float(self.lineEdit_zeta_freq1.text()), float(self.lineEdit_zeta_freq2.text())]
 
-            # self.compute_depth_2D()
             self.compute_kt2D_kt3D()
             self.compute_J_cross_section()
 
     def compute_depth_2D(self):
-        print("self.combobox_acoustic_data_choice.count() ", self.combobox_acoustic_data_choice.count())
         if self.combobox_acoustic_data_choice.count() > 0:
 
             for k in range(self.combobox_acoustic_data_choice.count()):
@@ -1805,9 +1615,6 @@ class SedimentCalibrationTab(QWidget):
                                     stg.time[k].shape[1],
                                     axis=1))
 
-            print("stg.depth_2D[self.combobox_acoustic_data_choice.currentIndex()].shape ",
-                  stg.depth_2D[self.combobox_acoustic_data_choice.currentIndex()].shape)
-
     def function_pushbutton_compute_calibration(self):
 
         self.label_temperature.clear()
@@ -1846,13 +1653,9 @@ class SedimentCalibrationTab(QWidget):
         print(f"ks for frequency of {stg.freq[self.combobox_acoustic_data_choice.currentIndex()][self.combobox_freq1.currentIndex()]} : {ks_freq1} m/kg^0.5 \n")
         print(f"ks for frequency of {stg.freq[self.combobox_acoustic_data_choice.currentIndex()][self.combobox_freq2.currentIndex()]} : {ks_freq2} m/kg^0.5")
 
-        # self.spinbox_ks_freq1.clear()
-        # self.spinbox_ks_freq1.setValue(ks_freq1)
         self.lineEdit_ks_freq1.clear()
         self.lineEdit_ks_freq1.setText(str("%.5f" % ks_freq1))
 
-        # self.spinbox_ks_freq2.clear()
-        # self.spinbox_ks_freq2.setValue(ks_freq2)
         self.lineEdit_ks_freq2.clear()
         self.lineEdit_ks_freq2.setText(str("%.5f" % ks_freq2))
 
@@ -1867,13 +1670,9 @@ class SedimentCalibrationTab(QWidget):
         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")
 
-        # self.spinbox_sv_freq1.clear()
-        # self.spinbox_sv_freq1.setValue(sv_freq1)
         self.lineEdit_sv_freq1.clear()
         self.lineEdit_sv_freq1.setText(str("%.5f" % sv_freq1))
 
-        # self.spinbox_sv_freq2.clear()
-        # self.spinbox_sv_freq2.setValue(sv_freq2)
         self.lineEdit_sv_freq2.clear()
         self.lineEdit_sv_freq2.setText(str("%.5f" % sv_freq2))
 
@@ -1889,8 +1688,6 @@ class SedimentCalibrationTab(QWidget):
 
         print(f"Exponent X = {X_exponent:.2f}\n")
 
-        # self.spinbox_X.clear()
-        # self.spinbox_X.setValue(X_exponent)
         self.lineEdit_X.setText(str("%.2f" % X_exponent))
 
     def compute_kt2D_kt3D(self):
@@ -1957,25 +1754,16 @@ class SedimentCalibrationTab(QWidget):
                         stg.kt2D[i] = np.repeat(np.array([stg.kt_read]).transpose(), stg.time[i].shape[1], axis=1)
                         stg.kt3D[i] = np.repeat(stg.kt2D[i][:, np.newaxis, :], stg.depth[i].shape[1], axis=1)
 
-        print('kt2D ', stg.kt2D)
-        print('kt3D ', stg.kt3D)
-
     def compute_J_cross_section(self):
 
         for i in range(self.combobox_acoustic_data_choice.count()):
 
-            print('kt2D shape ', stg.kt2D[i].shape)
-            print('kt3D shape ', stg.kt3D[i].shape)
-
             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,):
-                print("000000000000000000000000000000000")
-                print(stg.BS_stream_bed_pre_process_average[i][stg.frequencies_for_calibration[0][1], :, :].shape,
-                      stg.depth_2D[i][stg.frequencies_for_calibration[0][1], :, :].shape,
-                      stg.kt3D[i][stg.frequencies_for_calibration[0][1], :, :].shape)
+
                 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], :, :],
@@ -2110,9 +1898,6 @@ class SedimentCalibrationTab(QWidget):
             stg.J_cross_section[i][0] = J_cross_section_freq1
             stg.J_cross_section[i][1] = J_cross_section_freq2
 
-            print(f"J_cross_section {str(i)} freq1 shape ", J_cross_section_freq1.shape, J_cross_section[i][0].shape)
-            print(f"J_cross_section {str(i)} freq2 shape ", J_cross_section_freq2.shape, J_cross_section[i][1].shape)
-
     def compute_alpha_s(self):
 
         # --- Compute alpha_s ---
@@ -2161,13 +1946,9 @@ class SedimentCalibrationTab(QWidget):
         print(f"\u03B1s for frequency of freq1 : {alpha_s_freq1:.2f} /m \n")
         print(f"\u03B1s for frequency of freq2 : {alpha_s_freq2:.2f} /m")
 
-        # self.spinbox_alphas_freq1.clear()
-        # self.spinbox_alphas_freq1.setValue(alpha_s_freq1)
         self.lineEdit_alphas_freq1.clear()
         self.lineEdit_alphas_freq1.setText(str("%.5f" % alpha_s_freq1))
 
-        # self.spinbox_alphas_freq2.clear()
-        # self.spinbox_alphas_freq2.setValue(alpha_s_freq2)
         self.lineEdit_alphas_freq2.clear()
         self.lineEdit_alphas_freq2.setText(str("%.5f" % alpha_s_freq2))
 
@@ -2229,13 +2010,9 @@ class SedimentCalibrationTab(QWidget):
             print(f"\u03B6 for frequency of freq1 : {zeta_freq1:.3f} /m \n")
             print(f"\u03B6 for frequency of freq2 : {zeta_freq2:.3f} /m")
 
-            # self.spinbox_zeta_freq1.clear()
-            # self.spinbox_zeta_freq1.setValue(zeta_freq1)
             self.lineEdit_zeta_freq1.clear()
             self.lineEdit_zeta_freq1.setText(str("%.5f" % zeta_freq1))
 
-            # self.spinbox_zeta_freq2.clear()
-            # self.spinbox_zeta_freq2.setValue(zeta_freq2)
             self.lineEdit_zeta_freq2.clear()
             self.lineEdit_zeta_freq2.setText(str("%.5f" % zeta_freq2))
 
@@ -2248,8 +2025,6 @@ class SedimentCalibrationTab(QWidget):
                 directory=[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],
                 options=QFileDialog.DontUseNativeDialog)
 
-            print("dir_save_cal ", dir_save_cal)
-
             if dir_save_cal:
 
                 stg.path_calibration_file = path.dirname(dir_save_cal)
@@ -2289,12 +2064,10 @@ class SedimentCalibrationTab(QWidget):
             aquascat_cell_size = []
             tau = []
             real_cell_size = []
-            # stg.depth_real[self.combobox_acoustic_data_choice.currentIndex()] = (
-            #     np.zeros(stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape))
+
             stg.depth_real = np.zeros(stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape)
 
             for f in range(stg.freq[self.combobox_acoustic_data_choice.currentIndex()].shape[0]):
-                print("f = ", f)
                 # defaut Aquascat cell size
                 aquascat_cell_size.append(
                     stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, 1] -
@@ -2303,27 +2076,19 @@ class SedimentCalibrationTab(QWidget):
                 # Pulse duration
                 tau.append(aquascat_cell_size[f] * 2 / 1500)  # figure 2.9  1500 vitesse du son entrée pour le paramètrage des mesures aquascat
 
-                print(stg.water_velocity)
-                print(tau)
                 # Real cell size
                 real_cell_size.append(stg.water_velocity * tau[f] / 2)  # voir fig 2.9
 
                 # Converting to real cell profile
-                # stg.depth_real[self.combobox_acoustic_data_choice.currentIndex()][f, :] = \
-                #     (stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :] /
-                #                   aquascat_cell_size[f] * real_cell_size[f])  # (/ aquascat_cell_size) pour ramener BS.r entre 0 et 1
                 stg.depth_real[f, :] = (stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :]
                                         / aquascat_cell_size[f] * real_cell_size[f])
-                print("stg.depth_real ", stg.depth_real)
-                # (* real_cell_size) pour remettre les échelles spatiales sur la taille réelle des cellules
 
         else:
 
             aquascat_cell_size = []
             tau = []
             real_cell_size = []
-            # stg.depth_real[self.combobox_acoustic_data_choice.currentIndex()] = (
-            #     np.zeros(stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape))
+
             stg.depth_real = (np.zeros(stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape))
 
             for f in range(stg.freq[self.combobox_acoustic_data_choice.currentIndex()].shape[0]):
@@ -2340,57 +2105,23 @@ class SedimentCalibrationTab(QWidget):
                 real_cell_size.append(stg.water_velocity * tau[f] / 2)  # voir fig 2.9
 
                 # Converting to real cell profile
-                # stg.depth_real[self.combobox_acoustic_data_choice.currentIndex()][f, :] = \
-                #     (stg.depth[self.combobox_acoustic_data_choice.currentIndex()][f, :] /
-                #         aquascat_cell_size[f] * real_cell_size[f])  # (/ aquascat_cell_size) pour ramener BS.r entre 0 et 1
-                #     # (* real_cell_size) pour remettre les échelles spatiales sur la taille réelle des cellules
+
                 stg.depth_real[f, :] = (stg.depth[self.combobox_acoustic_data_choice.currentIndex()][f, :] /
                                             aquascat_cell_size[f] * real_cell_size[f])
 
-            print("R_real 2D ", stg.depth_real.shape)
-
         if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
 
-            # stg.depth_real[self.combobox_acoustic_data_choice.currentIndex()] = (
-            #     np.repeat(stg.depth_real[self.combobox_acoustic_data_choice.currentIndex()][:, :, np.newaxis],
-            #               stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape[1], axis=2))
             stg.depth_real = \
                 (np.repeat(stg.depth_real[:, :, np.newaxis],
                            stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape[1], axis=2))
 
-            print("R_real 3D ", stg.depth_real.shape)
-
         else:
 
-            # stg.depth_real[self.combobox_acoustic_data_choice.currentIndex()] = (
-            #     np.repeat(stg.depth_real[self.combobox_acoustic_data_choice.currentIndex()][:, :, np.newaxis],
-            #               stg.time[self.combobox_acoustic_data_choice.currentIndex()].shape[1], axis=2))
             stg.depth_real = (
                 np.repeat(stg.depth_real[:, :, np.newaxis],
                           stg.time[self.combobox_acoustic_data_choice.currentIndex()].shape[1], axis=2))
 
-            print("R_real 3D ", stg.depth_real.shape)
-
     def compute_FCB(self):
-        # if stg.BS_stream_bed.size == 0:
-        #     msgBox = QMessageBox()
-        #     msgBox.setWindowTitle("FCB Error")
-        #     msgBox.setIcon(QMessageBox.Warning)
-        #     msgBox.setText("Load Backscatter data from acoustic data tab and compute water attenuation")
-        #     msgBox.setStandardButtons(QMessageBox.Ok)
-        #     msgBox.exec()
-        # else:
-
-        # R_real = np.repeat(self.range_cells_function()[:, :, np.newaxis], stg.t.shape[1], axis=2)
-        # if (stg.BS_stream_bed_pre_process_average.size == 0) and (stg.BS_stream_bed_pre_process_SNR.size == 0):
-        #     stg.FCB = (np.log(stg.BS_stream_bed) + np.log(R_real) +
-        #                    2 * stg.water_attenuation * R_real)
-        # elif stg.BS_stream_bed_pre_process_SNR.size == 0:
-        #     stg.FCB = (np.log(stg.BS_stream_bed_pre_process_average) + np.log(R_real) +
-        #                    2 * stg.water_attenuation * R_real)
-        # else:
-        #     stg.FCB = (np.log(stg.BS_stream_bed_pre_process_SNR) + np.log(R_real) +
-        #                    2 * stg.water_attenuation * R_real)
 
         self.combobox_frequency_FCB.clear()
         self.combobox_frequency_FCB.addItems(stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()])
@@ -2417,13 +2148,6 @@ class SedimentCalibrationTab(QWidget):
 
         elif stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
 
-            print("zzzzzzzzzzzzzzzzzzzzz")
-            print(np.log(stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()]).shape)
-            print(np.log(stg.depth_real).shape)
-            print(stg.water_attenuation)
-            print(stg.depth_real.shape)
-            print("zzzzzzzzzzzzzzzzzzzzz")
-
             stg.FCB = \
                 (np.log(stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()]) +
                  np.log(stg.depth_real) +
@@ -2451,13 +2175,6 @@ class SedimentCalibrationTab(QWidget):
 
         elif stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
 
-            print("ttttttttttttttttttttttttt")
-            print(stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape)
-            print(stg.depth_real.shape)
-            print(stg.water_attenuation[self.combobox_acoustic_data_choice.currentIndex()])
-            print(stg.depth_real.shape)
-            print("ttttttttttttttttttttttttt")
-
             stg.FCB = \
                 (np.log(stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()]) +
                  np.log(stg.depth_real) +
@@ -2492,10 +2209,6 @@ class SedimentCalibrationTab(QWidget):
                      self.combobox_frequency_FCB.currentIndex()] *
                  stg.depth_real)
 
-        print("FCB shape", stg.FCB.shape)
-        print("FCB ", stg.FCB)
-
-
         self.plot_FCB()
 
     def plot_FCB(self):
@@ -2547,10 +2260,6 @@ class SedimentCalibrationTab(QWidget):
                                horizontalalignment='right', verticalalignment='bottom',
                                transform=self.axis_FCB.transAxes)
 
-                # if len(stg.lin_reg) != 0:
-                #     self.axis_FCB_profile[self.combobox_frequency_compute_alphaS.currentIndex()]. \
-                #      plot(stg.r[f, :], stg.lin_reg[0]*stg.r[f, :] + stg.lin_reg[1], linestyle="dashed", linewidth=1, color="b")
-
             self.fig_FCB.supxlabel("Depth (m)")
             self.fig_FCB.supylabel("FCB")
             self.fig_FCB.canvas.draw_idle()
@@ -2660,15 +2369,11 @@ class SedimentCalibrationTab(QWidget):
                                   np.min(np.round(np.abs(x - float(self.lineEdit_FCB_from.text().replace(',','.'))), 2)))[0][0]
                 value2 = np.where(np.round(np.abs(x - float(self.lineEdit_FCB_to.text().replace(',', '.'))), 2) ==
                                   np.min(np.round(np.abs(x - float(self.lineEdit_FCB_to.text().replace(',', '.'))), 2)))[0][0]
-                print("value1 ", value1)
-                print("value2 ", value2)
 
                 lin_reg_compute = linregress(x[value1:value2], y[value1:value2])
-                print("lin_reg_compute ", lin_reg_compute)
 
                 stg.lin_reg.clear()
                 stg.lin_reg = [lin_reg_compute.slope, lin_reg_compute.intercept]
-                print("stg.lin_reg ", stg.lin_reg)
 
                 # --- Plot result of linear regression ---
                 self.axis_FCB.plot(
@@ -2692,11 +2397,8 @@ class SedimentCalibrationTab(QWidget):
                                   np.min(np.round(np.abs(x - float(self.lineEdit_FCB_from.text().replace(',','.'))), 2)))[0][0]
                 value2 = np.where(np.round(np.abs(x - float(self.lineEdit_FCB_to.text().replace(',', '.'))), 2) ==
                                   np.min(np.round(np.abs(x - float(self.lineEdit_FCB_to.text().replace(',', '.'))), 2)))[0][0]
-                print("value1 ", value1)
-                print("value2 ", value2)
 
                 lin_reg_compute = linregress(x[value1:value2], y[value1:value2])
-                print("lin_reg_compute ", lin_reg_compute)
 
                 stg.lin_reg.clear()
                 stg.lin_reg = [lin_reg_compute.slope, lin_reg_compute.intercept]
@@ -2717,69 +2419,6 @@ class SedimentCalibrationTab(QWidget):
             self.label_alphaS_FCB.clear()
             self.label_alphaS_FCB.setText(f"&alpha;<sub>s</sub> = {-0.5*stg.lin_reg[0]:.4f} dB/m")
 
-        # if stg.FCB.size == 0:
-        #     msgBox = QMessageBox()
-        #     msgBox.setWindowTitle("Linear regression error")
-        #     msgBox.setIcon(QMessageBox.Warning)
-        #     msgBox.setText("Please compute FCB before")
-        #     msgBox.setStandardButtons(QMessageBox.Ok)
-        #     msgBox.exec()
-        # else:
-        #     try:
-        #         y0 = stg.FCB[self.combobox_frequency_compute_alphaS.currentIndex(), :, self.slider.value()]
-        #         y = y0[np.where(np.isnan(y0) == False)]
-        #
-        #         x0 = stg.r[0, :].reshape(-1)
-        #         x = x0[np.where(np.isnan(y0) == False)]
-        #
-        #         value1 = np.where(np.round(np.abs(x - self.spinbox_alphaS_computation_from.value()), 2)
-        #                           == np.min(np.round(np.abs(x - self.spinbox_alphaS_computation_from.value()), 2)))
-        #         value2 = np.where(np.round(np.abs(x - self.spinbox_alphaS_computation_to.value()), 2)
-        #                           == np.min(np.round(np.abs(x - self.spinbox_alphaS_computation_to.value()), 2)))
-        #
-        #         # print(np.round(np.abs(x - self.spinbox_alphaS_computation_from.value()), 2))
-        #         # # print("value1 ", value1[0][0])
-        #         # print(np.round(np.abs(x - self.spinbox_alphaS_computation_to.value()), 2))
-        #         # print("value2 ", value2[0][0])
-        #
-        #         # print("y limited ", y[value1[0][0]:value2[0][0]])
-        #
-        #         lin_reg_compute = stats.linregress(x[value1[0][0]:value2[0][0]], y[value1[0][0]:value2[0][0]])
-        #     except ValueError:
-        #         msgBox = QMessageBox()
-        #         msgBox.setWindowTitle("Linear regression error")
-        #         msgBox.setIcon(QMessageBox.Warning)
-        #         msgBox.setText("Please check boundaries to fit a linear line")
-        #         msgBox.setStandardButtons(QMessageBox.Ok)
-        #         msgBox.exec()
-        #     else:
-        #         stg.lin_reg = (lin_reg_compute.slope, lin_reg_compute.intercept)
-        #         # print(f"y = {stg.lin_reg[0]}x + {stg.lin_reg[1]}")
-        #
-        #         self.label_alphaS.clear()
-        #         self.label_alphaS.setText(f"\u03B1s = {-0.5*stg.lin_reg[0]:.4f} dB/m")
-        #
-        #         # for i, value_freq in enumerate(stg.freq):
-        #         #     for k, value_t in enumerate(stg.t):
-        #         #         # print(f"indice i: {i}, indice k: {k}")
-        #         #         # print(f"values of FCB: {stg.FCB[:, i, k]}")
-        #         #         y = stg.FCB[:, i, k]
-        #         #         # print("y : ", y)
-        #         #         # print(f"values of FCB where FCB is not Nan {y[np.where(np.isnan(y) == False)]}")
-        #         #         # print(f"values of r where FCB is not Nan {x[np.where(np.isnan(y) == False)]}")
-        #         #         lin_reg_compute = stats.linregress(x[np.where(np.isnan(y) == False)], y[np.where(np.isnan(y) == False)])
-        #         #         lin_reg_tuple = (lin_reg_compute.slope, lin_reg_compute.intercept)
-        #         #         stg.lin_reg.append(lin_reg_tuple)
-        #
-        #         # print(f"y = {lin_reg.slope}x + {lin_reg.intercept}")
-        #
-        #         # plt.figure()
-        #         # plt.plot(stg.r, stg.FCB[:, 0, 825], 'k-', stg.r, lin_reg.slope*stg.r + lin_reg.intercept, "b--")
-        #         # plt.show()
-        #
-        #         # print("lin_reg length ", len(stg.lin_reg))
-        #         # print("lin_reg ", stg.lin_reg)
-
     def slider_profile_number_to_begin_FCB(self):
         self.slider_FCB.setValue(int(self.slider_FCB.minimum()))
         self.update_lineEdit_by_moving_slider_FCB()

virtualenv/requirements.txt

@@ -0,0 +1,41 @@
+astropy==6.1.7
+astropy-iers-data==0.2025.3.3.0.34.45
+contourpy==1.0.7
+cycler==0.11.0
+defusedxml==0.7.1
+et-xmlfile==1.1.0
+fonttools==4.38.0
+importlib-resources==5.12.0
+joblib==1.2.0
+kiwisolver==1.4.4
+llvmlite==0.39.1
+matplotlib==3.6.3
+numba==0.56.4
+numpy==1.23.5
+odfpy==1.4.1
+openpyxl==3.0.10
+packaging==23.0
+pandas==1.5.3
+Pillow==9.4.0
+profilehooks==1.12.0
+pyerfa==2.0.1.5
+pyparsing==3.0.9
+pyqt-checkbox-table-widget==0.0.14
+pyqt-file-list-widget==0.0.1
+pyqt-files-already-exists-dialog==0.0.1
+pyqt-tooltip-list-widget==0.0.1
+PyQt5==5.15.9
+PyQt5-Qt5==5.15.2
+PyQt5-sip==12.11.0
+python-dateutil==2.8.2
+pytz==2022.7.1
+PyYAML==6.0.2
+scikit-learn==1.2.1
+scipy==1.10.0
+simplePyQt5==0.0.1
+six==1.16.0
+threadpoolctl==3.1.0
+utm==0.7.0
+xlrd==2.0.1
+xmltodict==0.13.0
+zipp==3.15.0