Signal processing tab is modified. According to the input data choice, the user can loaded a file for noise data and plot SNR field, BS corrected field with SNR criterion and averaged pre-processed signal. A button is added (close to the combobox of data to processed) to update the input data.

Model/acoustic_data_loader.py

@@ -117,7 +117,7 @@ class AcousticDataLoader:
         print(r2D.shape)
         return r2D
 
-    def reshape_time(self):
+    def reshape_t(self):
         # t = np.reshape(np.repeat(self._time, self._r.shape[0]), (self._time.shape[0]*self._r.shape[0], 1))
         t = np.zeros((self._r.shape[1] * self._time.shape[1], self._freq.shape[0]))
         for i, _ in enumerate(self._freq):

View/acoustic_data_tab.py

@@ -2988,6 +2988,7 @@ class AcousticDataTab(QWidget):
             self.fig_BS.supxlabel('Time (sec)', fontsize=10)
             self.fig_BS.supylabel('Depth (m)', fontsize=10)
             self.fig_BS.canvas.draw_idle()
+            # self.fig_BS.canvas.flush_events()
             # plt.close(self.fig_BS)
 
     def plot_profile(self):
@@ -3083,7 +3084,9 @@ class AcousticDataTab(QWidget):
 
             self.fig_profile.supxlabel("Acoustic Backscatter Signal (V)")
             self.fig_profile.supylabel("Depth (m)")
+            # self.fig_profile.canvas.draw()
             self.fig_profile.canvas.draw_idle()
+            # self.fig_profile.canvas.flush_events()
             # plt.close(self.fig_profile)
 
         elif len(stg.BS_cross_section) != 0:
@@ -3144,7 +3147,9 @@ class AcousticDataTab(QWidget):
 
             self.fig_profile.supxlabel("Acoustic Backscatter Signal (V)")
             self.fig_profile.supylabel("Depth (m)")
+            # self.fig_profile.canvas.draw()
             self.fig_profile.canvas.draw_idle()
+            # self.fig_profile.canvas.flush_events()
             # plt.close(self.fig_profile)
 
     def slide_profile_number_to_begin(self):

settings.py

@@ -19,8 +19,10 @@ time = []
 
 path_BS_noise_data = ""
 filename_BS_noise_data = ""
-BS_noise_raw_data = np.array([])            # BS noise raw data : BS signal listen
-BS_noise_averaged_data = np.array([])       # BS noise raw data averaged (array has the same shape than BS_raw_data shape)
+BS_noise_raw_data = []            # BS noise raw data : BS signal listen
+BS_noise_averaged_data = []       # BS noise raw data averaged (array has the same shape than BS_raw_data shape)
+
+noise_method = []
 
 date = []
 date_noise = []
@@ -38,8 +40,8 @@ kt_corrected = []
 gain_rx = []
 gain_tx = []
 
-SNR_data = np.array([])              # SNR is computed with BS_noise_averaged_data
-time_snr = np.array([])
+SNR_raw_data = []              # SNR is computed with BS_noise_averaged_data
+time_noise = []
 
 # --- reshape raw data for table of values in Acoustic Data tab ---
 time_reshape = []
@@ -56,14 +58,14 @@ tmax = []                 # maximum boundary of time (spin box tmin)
 tmax_snr = np.array([])
 rmin = []
 rmax = []
-BS_cross_section = []     # BS data limited with tmin and tmax values of spin box
-                                    # BS_data = stg.BS_raw_data[f, :, int(stg.tmin[f]):int(stg.tmax[f])]
-BS_stream_bed = []      # BS_data_section = BS data in the section. Values NaN outside the bottom of the section are deleted
-BS_noise_cross_section = np.array([])         # BS_noise_cros_section = BS_noise_data[:, :, tmin:tmax]  (former Noise_data)
-SNR_cross_section = np.array([])             # SNR_data = snr[:, :, tmin:tmax]
-SNR_stream_bed = np.array([])
+BS_cross_section = []               # BS data limited with tmin and tmax values of spin box
+
+BS_stream_bed = []                  # BS_data_section = BS data in the section. Values NaN outside the bottom of the section are deleted
+# BS_noise_cross_section = []         # BS_noise_cross_section = BS_noise_data[:, :, tmin:tmax]  (former Noise_data)
+SNR_cross_section = []              # SNR_data = snr[:, :, tmin:tmax]
+SNR_stream_bed = []
 time_cross_section = []
-t_snr = np.array([])
+time_snr = []
 depth_cross_section = []
 depth_bottom = []
 val_bottom = []
@@ -80,13 +82,17 @@ depth_bottom_detection_1st_int_area = []
 # BS_stream_bed_averaged = np.array([])               # BS data averaged - bottom is detected
 # BS_stream_bed_averaged_SNR = np.array([])               # BS data averaged and filtered with SNR - bottom is detected
 
-BS_cross_section_pre_process_SNR = np.array([[[]]])      # BS data filtered with SNR values (remove point if SNR < value) - bottom is not detected
-BS_cross_section_pre_process_average = np.array([[[]]])        # BS data averaged - bottom is not detected
-BS_cross_section_pre_process_SNR_average = np.array([[[]]])        # BS data averaged and filtered with SNR - bottom is not detected
+BS_raw_data_pre_process_SNR = []
+BS_raw_data_pre_process_average = []
+BS_raw_data_pre_process_SNR_average = []
 
-BS_stream_bed_pre_process_SNR = np.array([])             # BS data filtered with SNR values (remove point if SNR < value) - bottom is detected
-BS_stream_bed_pre_process_average = np.array([])               # BS data averaged - bottom is detected
-BS_stream_bed_pre_process_SNR_average = np.array([])               # BS data averaged and filtered with SNR - bottom is detected
+BS_cross_section_pre_process_SNR = []      # BS data filtered with SNR values (remove point if SNR < value) - bottom is not detected
+BS_cross_section_pre_process_average = []        # BS data averaged - bottom is not detected
+BS_cross_section_pre_process_SNR_average = []        # BS data averaged and filtered with SNR - bottom is not detected
+
+BS_stream_bed_pre_process_SNR = []             # BS data filtered with SNR values (remove point if SNR < value) - bottom is detected
+BS_stream_bed_pre_process_average = []               # BS data averaged - bottom is detected
+BS_stream_bed_pre_process_SNR_average = []               # BS data averaged and filtered with SNR - bottom is detected
 
 time_average = np.array([])
 SNR_data_average = np.array([])             # SNR data computed with BS signal averaged (not with BS raw signal)