Signal processing: Fix #8 and clean debug display code.
parent
be7627d93f
commit
998e1e7f8f
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@ -475,6 +475,7 @@ class SignalProcessingTab(QWidget):
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self.lineEdit_horizontal_average.returnPressed.connect(self.update_label_cells_sec)
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self.pushbutton_average.clicked.connect(self.compute_averaged_BS_data)
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self.pushbutton_average.clicked.connect(self.replot)
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self.pushbutton_slider_left_to_begin.clicked.connect(self.slide_profile_number_to_begin)
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@ -561,8 +562,7 @@ class SignalProcessingTab(QWidget):
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self.combobox_freq_noise_from_profile_tail.blockSignals(False)
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self.combobox_acoustic_data_choice.blockSignals(False)
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@trace
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def recompute(self, *args):
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def recompute(self):
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data_id = self.combobox_acoustic_data_choice.currentIndex()
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self.compute_average_profile_tail()
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@ -573,10 +573,10 @@ class SignalProcessingTab(QWidget):
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elif stg.noise_method[data_id] == 1:
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self.compute_noise_from_profile_tail_value()
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self.remove_point_with_snr_filter()
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self.compute_averaged_BS_data()
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@trace
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def replot(self, *args):
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def replot(self):
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self.plot_averaged_profile_tail()
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self.plot_transect_with_SNR_data()
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self.plot_pre_processed_BS_signal()
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@ -646,8 +646,7 @@ class SignalProcessingTab(QWidget):
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self.animation_groupbox_option_profile_tail.start()
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# ------------------------------------------------------
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@trace
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def compute_average_profile_tail(self, *args):
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def compute_average_profile_tail(self):
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data_id = self.combobox_acoustic_data_choice.currentIndex()
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freq_noise_id = self.combobox_freq_noise_from_profile_tail.currentIndex()
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@ -712,8 +711,7 @@ class SignalProcessingTab(QWidget):
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)
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)
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@trace
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def plot_averaged_profile_tail(self, *args):
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def plot_averaged_profile_tail(self):
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# --- Plot averaged signal ---
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@ -998,8 +996,7 @@ class SignalProcessingTab(QWidget):
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pnw = PlotNoiseWindow()
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pnw.exec()
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@trace
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def compute_noise_from_profile_tail_value(self, *args):
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def compute_noise_from_profile_tail_value(self):
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stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] = 1
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@ -1080,8 +1077,7 @@ class SignalProcessingTab(QWidget):
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# self.activate_list_of_pre_processed_data()
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@trace
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def plot_noise(self, *args):
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def plot_noise(self):
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self.horizontalLayout_groupbox_plot_noise_data.removeWidget(self.canvas_noise)
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self.fig_noise, self.axis_noise = plt.subplots(nrows=1, ncols=1, layout="constrained")
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@ -1112,8 +1108,7 @@ class SignalProcessingTab(QWidget):
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self.axis_noise.tick_params(axis='both', which='minor', labelsize=10)
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@trace
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def plot_transect_with_SNR_data(self, *args):
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def plot_transect_with_SNR_data(self):
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# --- Condition if table is not filled ---
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# if not self.lineEdit_noise_file.text():
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if len(stg.BS_noise_raw_data) == 0:
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@ -1305,6 +1300,8 @@ class SignalProcessingTab(QWidget):
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self.verticalLayout_groupbox_plot_SNR.addWidget(self.scroll_SNR)
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def remove_point_with_snr_filter(self):
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if self.lineEdit_SNR_criterion.text() == '':
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return
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if len(stg.filename_BS_raw_data) == 0:
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msgBox = QMessageBox()
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@ -1369,8 +1366,7 @@ class SignalProcessingTab(QWidget):
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self.compute_averaged_BS_data()
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@trace
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def plot_pre_processed_BS_signal(self, *args):
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def plot_pre_processed_BS_signal(self):
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data_id = self.combobox_acoustic_data_choice.currentIndex()
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self.lineEdit_horizontal_average.setText(
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@ -1400,7 +1396,6 @@ class SignalProcessingTab(QWidget):
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.addWidget(self.scroll_BS)
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if stg.time_cross_section[data_id].shape != (0,):
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logger.info("time_cross_section")
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if stg.depth_cross_section[data_id].shape != (0,):
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x_time = stg.time_cross_section[data_id]
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y_depth = stg.depth_cross_section[data_id]
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@ -1408,7 +1403,6 @@ class SignalProcessingTab(QWidget):
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x_time = stg.time_cross_section[data_id]
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y_depth = stg.depth[data_id]
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else:
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logger.info("time")
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if stg.depth_cross_section[data_id].shape != (0,):
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x_time = stg.time[data_id]
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y_depth = stg.depth_cross_section[data_id]
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@ -1416,31 +1410,31 @@ class SignalProcessingTab(QWidget):
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x_time = stg.time[data_id]
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y_depth = stg.depth[data_id]
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for f, _ in enumerate(stg.freq[data_id]):
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bed = False
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logger.debug(f"x_time: {x_time[data_id].shape}")
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logger.debug(f"y_depth: {y_depth[data_id].shape}")
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for f, _ in enumerate(stg.freq[data_id]):
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if stg.BS_stream_bed_pre_process_average[data_id].shape != (0,):
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BS_data = stg.BS_stream_bed_pre_process_average
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bed = True
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elif stg.BS_cross_section_pre_process_average[data_id].shape != (0,):
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BS_data = stg.BS_cross_section_pre_process_average
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elif stg.BS_raw_data_pre_process_average[data_id].shape != (0,):
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BS_data = stg.BS_raw_data_pre_process_average
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elif stg.BS_stream_bed_pre_process_SNR[data_id].shape != (0,):
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BS_data = stg.BS_stream_bed_pre_process_SNR
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bed = True
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elif stg.BS_cross_section_pre_process_SNR[data_id].shape != (0,):
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BS_data = stg.BS_cross_section_pre_process_SNR
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elif stg.BS_raw_data_pre_process_SNR[data_id].shape != (0,):
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BS_data = stg.BS_raw_data_pre_process_SNR
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elif stg.BS_stream_bed[data_id].shape != (0,):
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BS_data = stg.BS_stream_bed
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bed = True
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elif stg.BS_cross_section[data_id].shape != (0,):
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BS_data = stg.BS_cross_section
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elif stg.BS_raw_data[data_id].shape != (0,):
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BS_data = stg.BS_raw_data
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logger.debug(f"BS_data: {BS_data[data_id].shape}")
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val_min = np.nanmin(
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BS_data[data_id][f, :, :]
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)
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@ -1457,7 +1451,7 @@ class SignalProcessingTab(QWidget):
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cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max)
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)
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if bed:
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if stg.depth_bottom[data_id].shape != (0,):
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self.axis_BS[f].plot(
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x_time[f, :], -stg.depth_bottom[data_id],
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color='black', linewidth=1, linestyle="solid"
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@ -1516,8 +1510,7 @@ class SignalProcessingTab(QWidget):
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(1 / stg.nb_profiles_per_sec[self.combobox_acoustic_data_choice.currentIndex()][0])) +
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" sec")
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@trace
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def compute_averaged_BS_data(self, *args):
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def compute_averaged_BS_data(self):
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if len(stg.filename_BS_raw_data) == 0:
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msgBox = QMessageBox()
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msgBox.setWindowTitle("Compute noise from profile tail error")
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@ -1525,7 +1518,6 @@ class SignalProcessingTab(QWidget):
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msgBox.setText("Download acoustic data in previous tab before applying SNR filter")
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msgBox.setStandardButtons(QMessageBox.Ok)
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msgBox.exec()
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elif len(stg.BS_noise_raw_data) == 0:
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msgBox = QMessageBox()
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msgBox.setWindowTitle("SNR filter Error")
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@ -1533,108 +1525,96 @@ class SignalProcessingTab(QWidget):
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msgBox.setText("Define noise data (file or profile tail) before using SNR filter")
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msgBox.setStandardButtons(QMessageBox.Ok)
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msgBox.exec()
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else:
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data_id = self.combobox_acoustic_data_choice.currentIndex()
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kernel_avg = np.ones(2 * int(float(self.lineEdit_horizontal_average.text().replace(",", "."))) + 1)
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print(kernel_avg)
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kernel_avg = np.ones(
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2 * int(
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float(
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self.lineEdit_horizontal_average\
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.text()\
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.replace(",", ".")
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)
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) + 1
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)
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logger.debug(f"kernel_avg: {kernel_avg}")
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stg.Nb_cells_to_average_BS_signal[data_id] = (
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float(self.lineEdit_horizontal_average.text().replace(",", ".")))
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float(
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self.lineEdit_horizontal_average\
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.text().replace(",", ".")
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)
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)
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if stg.time_cross_section[data_id].shape != (0,):
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if stg.depth_cross_section[data_id].shape != (0,):
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x_time = stg.time_cross_section[data_id]
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y_depth = stg.depth_cross_section[data_id]
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elif stg.depth[data_id].shape != (0,):
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x_time = stg.time_cross_section[data_id]
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y_depth = stg.depth[data_id]
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else:
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if stg.depth_cross_section[data_id].shape != (0,):
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x_time = stg.time[data_id]
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y_depth = stg.depth_cross_section[data_id]
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elif stg.depth[data_id].shape != (0,):
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x_time = stg.time[data_id]
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y_depth = stg.depth[data_id]
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if stg.BS_stream_bed_pre_process_SNR[data_id].shape != (0,):
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BS = [
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stg.BS_stream_bed_pre_process_SNR,
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stg.BS_cross_section_pre_process_SNR,
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stg.BS_raw_data_pre_process_SNR,
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stg.BS_stream_bed,
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stg.BS_cross_section,
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stg.BS_raw_data,
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]
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stg.BS_stream_bed_pre_process_average[data_id] = (deepcopy(
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stg.BS_stream_bed_pre_process_SNR[data_id]))
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BS_ppa = [
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stg.BS_stream_bed_pre_process_average,
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stg.BS_cross_section_pre_process_average,
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stg.BS_raw_data_pre_process_average,
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stg.BS_stream_bed_pre_process_average,
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stg.BS_cross_section_pre_process_average,
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stg.BS_raw_data_pre_process_average,
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]
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for f, _ in enumerate(stg.freq[data_id]):
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for i in range(y_depth.shape[1]):
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time_shape, = x_time[data_id].shape
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depth_shape, = y_depth[data_id].shape
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logger.debug(f"time_shape: {time_shape}")
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logger.debug(f"depth_shape: {depth_shape}")
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stg.BS_stream_bed_pre_process_average[data_id][f, i, :] = (
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convolve(stg.BS_stream_bed_pre_process_SNR[data_id][f, i, :],
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kernel_avg))
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BS_data = stg.BS_raw_data
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BS_data_ppa = stg.BS_raw_data_pre_process_average
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for i in range(len(BS)):
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bs = BS[i]
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logger.debug(f"BS data shape {bs[data_id].shape}")
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if bs[data_id].shape == (0,):
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continue
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elif stg.BS_cross_section_pre_process_SNR[data_id].shape != (0,):
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x, y, z = bs[data_id].shape
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if y == depth_shape and z == time_shape:
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BS_data = bs
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BS_data_ppa = BS_ppa[i]
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break
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stg.BS_cross_section_pre_process_average[data_id] = (deepcopy(
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stg.BS_cross_section_pre_process_SNR[data_id]))
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logger.debug(f"BS_data: {BS_data[data_id].shape}")
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for f, _ in enumerate(stg.freq[data_id]):
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for i in range(y_depth.shape[1]):
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BS_data_ppa[data_id] = deepcopy(BS_data[data_id])
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stg.BS_cross_section_pre_process_average[data_id][f, i, :] = (
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convolve(stg.BS_cross_section_pre_process_SNR[data_id][f, i, :],
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kernel_avg))
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for f, _ in enumerate(stg.freq[data_id]):
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for i in range(y_depth.shape[1]):
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BS_data_ppa[data_id][f, i, :] = (
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convolve(
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BS_data[data_id][f, i, :],
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kernel_avg
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)
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)
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elif stg.BS_raw_data_pre_process_SNR[data_id].shape != (0,):
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logger.debug(
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f"BS_data_ppa: {BS_data_ppa[data_id].shape}"
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)
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stg.BS_raw_data_pre_process_average[data_id] = (deepcopy(
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stg.BS_raw_data_pre_process_SNR[data_id]))
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for f, _ in enumerate(stg.freq[data_id]):
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for i in range(y_depth.shape[1]):
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stg.BS_raw_data_pre_process_average[data_id][f, i, :] = (
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convolve(stg.BS_raw_data_pre_process_SNR[data_id][f, i, :],
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kernel_avg))
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elif stg.BS_stream_bed[data_id].shape != (0,):
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stg.BS_stream_bed_pre_process_average[data_id] = (deepcopy(
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stg.BS_stream_bed[data_id]))
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for f, _ in enumerate(stg.freq[data_id]):
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for i in range(y_depth.shape[1]):
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stg.BS_stream_bed_pre_process_average[data_id][f, i, :] = (
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convolve(stg.BS_stream_bed[data_id][f, i, :], kernel_avg))
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elif stg.BS_cross_section[data_id].shape != (0,):
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stg.BS_cross_section_pre_process_average[data_id] = (deepcopy(
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stg.BS_cross_section[data_id]))
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for f, _ in enumerate(stg.freq[data_id]):
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for i in range(y_depth.shape[1]):
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stg.BS_cross_section_pre_process_average[data_id][f, i, :] = (
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convolve(stg.BS_cross_section[data_id][f, i, :],
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kernel_avg))
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elif stg.BS_raw_data[data_id].shape != (0,):
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stg.BS_raw_data_pre_process_average[data_id] = (deepcopy(
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stg.BS_raw_data[data_id]))
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for f, _ in enumerate(stg.freq[data_id]):
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for i in range(y_depth.shape[1]):
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stg.BS_raw_data_pre_process_average[data_id][f, i, :] = (
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convolve(stg.BS_raw_data[data_id][f, i, :], kernel_avg))
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@trace
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def plot_pre_processed_profile(self, *args):
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def plot_pre_processed_profile(self):
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data_id = self.combobox_acoustic_data_choice.currentIndex()
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if ((data_id != -1) and
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