Acoustic data tab is simplified for time, depth and BS cross section computation. The button clear all works. The signal processing is updated.
brahim
parent
8320a9a0e1
commit
44b87378b8
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@ -1727,9 +1727,14 @@ class AcousticDataTab(QWidget):
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print("axis BS : ", self.axis_BS)
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self.axis_BS.tolist().clear()
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print("clear axis BS : ", self.axis_BS)
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self.canvas_BS = FigureCanvas()
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self.scroll_BS.setWidget(self.canvas_BS)
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self.canvas_plot_profile.figure.clear()
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self.fig_profile.clear()
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self.axis_profile.clear()
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self.canvas_plot_profile = FigureCanvas()
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self.verticalLayout_groupbox_plot_profile.addWidget(self.canvas_plot_profile)
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self.slider.setValue(0)
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self.slider.setMaximum(10)
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@ -1893,6 +1898,17 @@ class AcousticDataTab(QWidget):
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stg.freq_bottom_detection.append([])
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stg.depth_bottom_detection_1st_int_area.append([])
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stg.BS_noise_raw_data.append(np.array([]))
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stg.BS_noise_averaged_data.append(np.array([]))
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stg.SNR_raw_data.append(np.array([]))
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stg.SNR_cross_section.append(np.array([]))
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stg.SNR_stream_bed.append(np.array([]))
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stg.time_noise.append(np.array([]))
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stg.noise_method.append(0)
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stg.SNR_filter_value.append(0)
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stg.filename_BS_noise_data.append("")
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stg.path_BS_noise_data.append("")
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stg.BS_raw_data_pre_process_SNR.append(np.array([]))
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stg.BS_raw_data_pre_process_average.append(np.array([]))
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stg.BS_raw_data_pre_process_SNR_average.append(np.array([]))
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@ -2984,7 +3000,7 @@ class AcousticDataTab(QWidget):
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else:
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# print("stg.BS_cross_section[self.fileListWidget.currentRow()].shape ", stg.BS_cross_section[self.fileListWidget.currentRow()].shape)
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if self.fileListWidget.currentRow() != -1:
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if len(self.axis_BS.tolist()) != stg.freq[self.fileListWidget.currentRow()].shape[0]:
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self.fig_BS, self.axis_BS = plt.subplots(nrows=stg.freq[self.fileListWidget.currentRow()].shape[0],
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ncols=1,
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@ -3400,6 +3416,8 @@ class AcousticDataTab(QWidget):
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def update_plot_profile(self):
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# print("len(stg.BS_cross_section) ", len(stg.BS_cross_section))
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# if (len(stg.BS_cross_section) == 0) and (len(stg.BS_raw_data) != 0):
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if self.fileListWidget.currentRow() != -1:
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if stg.BS_cross_section[self.fileListWidget.currentRow()].shape == (0,):
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self.axis_profile.cla()
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@ -100,16 +100,13 @@ class SignalProcessingTab(QWidget):
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self.radiobutton_file = QRadioButton("File")
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self.radiobutton_file.setChecked(True)
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self.radiobutton_file.toggled.connect(self.onClicked_radiobutton_noise_data)
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self.gridLayout_radiobutton_noise_data.addWidget(self.radiobutton_file, 0, 0, 1, 1)
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self.radiobutton_profile_tail = QRadioButton("Profile tail")
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# self.radiobutton_file.setChecked(False)
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self.radiobutton_profile_tail.toggled.connect(self.onClicked_radiobutton_noise_data)
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self.gridLayout_radiobutton_noise_data.addWidget(self.radiobutton_profile_tail, 0, 1, 1, 1)
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self.radiobutton_value = QRadioButton("Value")
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self.radiobutton_value.toggled.connect(self.onClicked_radiobutton_noise_data)
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self.gridLayout_radiobutton_noise_data.addWidget(self.radiobutton_value, 0, 2, 1, 1)
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### --- Groupbox download noise file ---
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@ -131,7 +128,6 @@ class SignalProcessingTab(QWidget):
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# self.gridLayout_groupbox_noise_file.addWidget(self.label_hour_groupbox_noise_file, 1, 2, 1, 1)
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# Download Push Button event : connect button clicked signal to open file slot
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self.pushbutton_noise_file.clicked.connect(self.open_dialog_box)
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self.verticalLayout_groupbox_study_data.addWidget(self.groupbox_download_noise_file)
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@ -158,7 +154,6 @@ class SignalProcessingTab(QWidget):
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self.pushbutton_compute_noise_from_value = QPushButton()
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self.pushbutton_compute_noise_from_value.setText("Apply noise")
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self.gridLayout_compute_noise_from_value.addWidget(self.pushbutton_compute_noise_from_value, 0, 3, 1, 1)
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self.pushbutton_compute_noise_from_value.clicked.connect(self.compute_noise_from_value)
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self.verticalLayout_groupbox_study_data.addWidget(self.groupbox_compute_noise_from_value)
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@ -180,8 +175,6 @@ class SignalProcessingTab(QWidget):
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self.pushbutton_Apply_SNR_filter = QPushButton()
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self.gridLayout_SNR_criterion.addWidget(self.pushbutton_Apply_SNR_filter, 0, 2, 1, 1)
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self.pushbutton_Apply_SNR_filter.clicked.connect(self.remove_point_with_snr_filter)
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self.verticalLayout_groupbox_study_data.addWidget(self.groupbox_SNR_criterion)
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# +++++++++++++++++++++++++++++
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@ -408,6 +401,19 @@ class SignalProcessingTab(QWidget):
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# --- Connect signal of widget ---
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self.pushbutton_update.clicked.connect(self.event_combobobx_fileListWidget)
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self.pushbutton_update.clicked.connect(self.remove_point_with_snr_filter)
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self.radiobutton_file.toggled.connect(self.onClicked_radiobutton_noise_data)
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self.radiobutton_profile_tail.toggled.connect(self.onClicked_radiobutton_noise_data)
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self.radiobutton_value.toggled.connect(self.onClicked_radiobutton_noise_data)
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self.pushbutton_noise_file.clicked.connect(self.open_dialog_box)
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self.pushbutton_compute_noise_from_value.clicked.connect(self.compute_noise_from_value)
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self.pushbutton_Apply_SNR_filter.clicked.connect(self.remove_point_with_snr_filter)
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self.pushbutton_average.clicked.connect(self.compute_averaged_BS_data)
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self.pushbutton_average.clicked.connect(self.plot_profile_and_position_on_transect_with_slider)
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@ -1037,28 +1043,30 @@ class SignalProcessingTab(QWidget):
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for i in range(len(stg.filename_BS_raw_data)):
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self.combobox_fileListWidget.addItem(stg.filename_BS_raw_data[i])
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print("len stg.BS_noise_raw_data : ", len(stg.BS_noise_raw_data))
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if len(stg.BS_noise_raw_data) == 0:
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stg.BS_noise_raw_data = [np.array([]) for _ in range(self.combobox_fileListWidget.count() - 1)]
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print("stg.BS_noise_raw_data : ", stg.BS_noise_raw_data)
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print("len stg.BS_noise_raw_data : ", len(stg.BS_noise_raw_data))
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stg.BS_noise_averaged_data = [np.array([]) for _ in range(self.combobox_fileListWidget.count() - 1)]
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stg.SNR_raw_data = [np.array([]) for _ in range(self.combobox_fileListWidget.count() - 1)]
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stg.SNR_cross_section = [np.array([]) for _ in range(self.combobox_fileListWidget.count() - 1)]
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stg.SNR_stream_bed = [np.array([]) for _ in range(self.combobox_fileListWidget.count() - 1)]
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stg.time_noise = [np.array([]) for _ in range(self.combobox_fileListWidget.count() - 1)]
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stg.noise_method = [0]*(self.combobox_fileListWidget.count() - 1)
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elif len(stg.BS_noise_raw_data) < (self.combobox_fileListWidget.count() - 1):
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stg.BS_noise_raw_data.append(np.array([]))
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stg.BS_noise_averaged_data.append(np.array([]))
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stg.SNR_raw_data.append(np.array([]))
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stg.SNR_cross_section.append(np.array([]))
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stg.SNR_stream_bed.append(np.array([]))
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stg.time_noise.append(np.array([]))
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stg.noise_method.append(0)
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# print("len stg.BS_noise_raw_data : ", len(stg.BS_noise_raw_data))
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# if len(stg.BS_noise_raw_data) == 0:
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# stg.BS_noise_raw_data = [np.array([]) for _ in range(self.combobox_fileListWidget.count() - 1)]
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# print("stg.BS_noise_raw_data : ", stg.BS_noise_raw_data)
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# print("len stg.BS_noise_raw_data : ", len(stg.BS_noise_raw_data))
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# stg.BS_noise_averaged_data = [np.array([]) for _ in range(self.combobox_fileListWidget.count() - 1)]
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# stg.SNR_raw_data = [np.array([]) for _ in range(self.combobox_fileListWidget.count() - 1)]
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# stg.SNR_cross_section = [np.array([]) for _ in range(self.combobox_fileListWidget.count() - 1)]
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# stg.SNR_stream_bed = [np.array([]) for _ in range(self.combobox_fileListWidget.count() - 1)]
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# stg.time_noise = [np.array([]) for _ in range(self.combobox_fileListWidget.count() - 1)]
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# stg.noise_method = [0]*(self.combobox_fileListWidget.count() - 1)
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# elif len(stg.BS_noise_raw_data) < (self.combobox_fileListWidget.count() - 1):
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# stg.BS_noise_raw_data.append(np.array([]))
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# stg.BS_noise_averaged_data.append(np.array([]))
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# stg.SNR_raw_data.append(np.array([]))
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# stg.SNR_cross_section.append(np.array([]))
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# stg.SNR_stream_bed.append(np.array([]))
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# stg.time_noise.append(np.array([]))
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# stg.noise_method.append(0)
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if (stg.noise_method == 1) or (stg.noise_method == 0):
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self.radiobutton_file.setChecked(True)
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if stg.filename_BS_noise_data[self.combobox_fileListWidget.currentIndex() - 1] != "":
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self.lineEdit_noise_file.setText(stg.filename_BS_noise_data[self.combobox_fileListWidget.currentIndex() - 1])
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self.radiobutton_profile_tail.setChecked(False)
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self.radiobutton_value.setChecked(False)
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elif stg.noise_method == 2:
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@ -1101,8 +1109,8 @@ class SignalProcessingTab(QWidget):
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name = path.basename(filename[0])
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try:
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stg.path_BS_noise_data = dir_name
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stg.filename_BS_noise_data = name
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stg.path_BS_noise_data[self.combobox_fileListWidget.currentIndex() - 1] = dir_name
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stg.filename_BS_noise_data[self.combobox_fileListWidget.currentIndex() - 1] = name
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print("stg.path_BS_noise_data : ", stg.path_BS_noise_data)
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print("stg.filename_BS_noise_data : ", stg.filename_BS_noise_data)
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self.load_noise_data_and_compute_SNR()
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@ -1117,8 +1125,8 @@ class SignalProcessingTab(QWidget):
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msgBox.exec()
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else:
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self.lineEdit_noise_file.setText(stg.filename_BS_noise_data)
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self.lineEdit_noise_file.setToolTip(stg.path_BS_noise_data)
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self.lineEdit_noise_file.setText(stg.filename_BS_noise_data[self.combobox_fileListWidget.currentIndex() - 1])
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self.lineEdit_noise_file.setToolTip(stg.path_BS_noise_data[self.combobox_fileListWidget.currentIndex() - 1])
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# self.plot_noise()
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self.plot_transect_with_SNR_data()
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# self.combobox_freq_noise.addItems([f for f in stg.freq_text])
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@ -1129,12 +1137,7 @@ class SignalProcessingTab(QWidget):
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self.combobox_frequency_profile.addItems([f for f in stg.freq_text[self.combobox_fileListWidget.currentIndex() - 1]])
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self.combobox_frequency_profile.currentIndexChanged.connect(self.plot_profile_and_position_on_transect_with_slider)
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if len(stg.BS_stream_bed) and stg.BS_stream_bed[self.combobox_fileListWidget.currentIndex() - 1].shape:
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self.slider.setMaximum(stg.time_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape[1])
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elif (stg.BS_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape[2]
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< stg.BS_raw_data[self.combobox_fileListWidget.currentIndex() - 1].shape[2]):
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if stg.time_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
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self.slider.setMaximum(stg.time_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape[1])
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@ -1142,6 +1145,19 @@ class SignalProcessingTab(QWidget):
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self.slider.setMaximum(stg.time[self.combobox_fileListWidget.currentIndex() - 1].shape[1])
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# if len(stg.BS_stream_bed) and stg.BS_stream_bed[self.combobox_fileListWidget.currentIndex() - 1].shape:
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#
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# self.slider.setMaximum(stg.time_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape[1])
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#
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# elif (stg.BS_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape[2]
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# < stg.BS_raw_data[self.combobox_fileListWidget.currentIndex() - 1].shape[2]):
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#
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# self.slider.setMaximum(stg.time_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape[1])
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#
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# else:
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#
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# self.slider.setMaximum(stg.time[self.combobox_fileListWidget.currentIndex() - 1].shape[1])
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self.compute_averaged_BS_data()
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self.plot_profile_and_position_on_transect_with_slider()
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@ -1158,25 +1174,24 @@ class SignalProcessingTab(QWidget):
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# stg.hour_noise = noise_data._hour
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stg.time_noise[self.combobox_fileListWidget.currentIndex() - 1] = noise_data._time
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# stg.time_snr_reshape = stg.time_reshape
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print("len(stg.BS_cross_section) : ", len(stg.BS_cross_section))
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print("stg.BS_cross_section[0] : ", stg.BS_cross_section[0].shape)
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print("len(stg.BS_stream_bed) : ", len(stg.BS_stream_bed))
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# print("len(stg.BS_cross_section) : ", len(stg.BS_cross_section))
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# print("stg.BS_cross_section[0] : ", stg.BS_cross_section[0].shape)
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# print("len(stg.BS_stream_bed) : ", len(stg.BS_stream_bed))
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if (stg.BS_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape ==
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stg.BS_raw_data[self.combobox_fileListWidget.currentIndex() - 1].shape):
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print("Je suis dans raw")
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if stg.BS_stream_bed[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
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print("Je suis dans stream bed")
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noise = np.zeros(stg.BS_raw_data[self.combobox_fileListWidget.currentIndex() - 1].shape)
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noise = np.zeros(stg.BS_stream_bed[self.combobox_fileListWidget.currentIndex() - 1].shape)
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for f, _ in enumerate(noise_data._freq):
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noise[f, :, :] = np.mean(
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stg.BS_noise_raw_data[self.combobox_fileListWidget.currentIndex() - 1][f, :, :], axis=(0, 1))
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stg.BS_noise_averaged_data[self.combobox_fileListWidget.currentIndex() - 1] = noise
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stg.SNR_raw_data[self.combobox_fileListWidget.currentIndex() - 1] = (
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np.divide((stg.BS_raw_data[self.combobox_fileListWidget.currentIndex() - 1] -
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stg.SNR_stream_bed[self.combobox_fileListWidget.currentIndex() - 1] = (
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np.divide((stg.BS_stream_bed[self.combobox_fileListWidget.currentIndex() - 1] -
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stg.BS_noise_averaged_data[self.combobox_fileListWidget.currentIndex() - 1]) ** 2,
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stg.BS_noise_averaged_data[self.combobox_fileListWidget.currentIndex() - 1] ** 2))
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elif len(stg.BS_stream_bed) < len(stg.BS_cross_section):
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elif stg.BS_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
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print("Je suis dans cross section")
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noise = np.zeros(stg.BS_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape)
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@ -1191,19 +1206,19 @@ class SignalProcessingTab(QWidget):
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# stg.SNR_reshape = np.reshape(stg.SNR_cross_section, (stg.r.shape[1] * stg.t.shape[1], stg.freq.shape[0]), order="F")
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else:
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print("Je suis dans stream bed")
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print("Je suis dans raw")
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noise = np.zeros(stg.BS_stream_bed[self.combobox_fileListWidget.currentIndex() - 1].shape)
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noise = np.zeros(stg.BS_raw_data[self.combobox_fileListWidget.currentIndex() - 1].shape)
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for f, _ in enumerate(noise_data._freq):
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noise[f, :, :] = np.mean(
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stg.BS_noise_raw_data[self.combobox_fileListWidget.currentIndex() - 1][f, :, :], axis=(0, 1))
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stg.BS_noise_averaged_data[self.combobox_fileListWidget.currentIndex() - 1] = noise
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stg.SNR_stream_bed[self.combobox_fileListWidget.currentIndex() - 1] = (
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np.divide((stg.BS_stream_bed[self.combobox_fileListWidget.currentIndex() - 1] -
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stg.SNR_raw_data[self.combobox_fileListWidget.currentIndex() - 1] = (
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np.divide((stg.BS_raw_data[self.combobox_fileListWidget.currentIndex() - 1] -
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stg.BS_noise_averaged_data[self.combobox_fileListWidget.currentIndex() - 1]) ** 2,
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stg.BS_noise_averaged_data[self.combobox_fileListWidget.currentIndex() - 1] ** 2))
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# stg.SNR_reshape = np.reshape(stg.SNR_stream_bed, (stg.r.shape[1] * stg.t.shape[1], stg.freq.shape[0]),
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# order="F")
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# print("stg.SNR_raw_data[0].shape ", stg.SNR_raw_data)
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@ -1391,60 +1406,7 @@ class SignalProcessingTab(QWidget):
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for f, _ in enumerate(stg.freq[self.combobox_fileListWidget.currentIndex() - 1]):
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if (stg.BS_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape ==
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stg.BS_raw_data[self.combobox_fileListWidget.currentIndex() - 1].shape):
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x, y = np.meshgrid(stg.time[self.combobox_fileListWidget.currentIndex() - 1][0, :],
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stg.depth[self.combobox_fileListWidget.currentIndex() - 1][0, :])
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print("0 plot SNR with SNR_raw_data")
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val_min = np.nanmin(stg.SNR_raw_data[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
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val_max = np.nanmax(stg.SNR_raw_data[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
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if val_min == val_max:
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levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
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else:
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if val_min == 0:
|
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val_min = 1e-5
|
||||
if val_max > 1000:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
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)
|
||||
|
||||
cf = (self.axis_SNR[f].contourf(x, -y,
|
||||
stg.SNR_raw_data[
|
||||
self.combobox_fileListWidget.currentIndex() - 1][f, :, :],
|
||||
levels, cmap='gist_rainbow', norm=norm))
|
||||
|
||||
|
||||
elif len(stg.BS_stream_bed) < len(stg.BS_cross_section):
|
||||
|
||||
x, y = np.meshgrid(stg.time_cross_section[self.combobox_fileListWidget.currentIndex() - 1][0, :],
|
||||
stg.depth_cross_section[self.combobox_fileListWidget.currentIndex() - 1][0, :])
|
||||
|
||||
print("1 plot SNR with SNR_cross_section")
|
||||
val_min = np.nanmin(stg.SNR_cross_section[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
val_max = np.nanmax(stg.SNR_cross_section[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
if val_min == val_max:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
else:
|
||||
if val_min == 0:
|
||||
val_min = 1e-5
|
||||
if val_max > 1000:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
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)
|
||||
|
||||
cf = (self.axis_SNR[f].contourf(x, -y,
|
||||
stg.SNR_cross_section[self.combobox_fileListWidget.currentIndex() - 1][f, :, :],
|
||||
levels, cmap='gist_rainbow', norm=norm))
|
||||
|
||||
else:
|
||||
if stg.SNR_stream_bed[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
|
||||
x, y = np.meshgrid(stg.time_cross_section[self.combobox_fileListWidget.currentIndex() - 1][0, :],
|
||||
stg.depth_cross_section[self.combobox_fileListWidget.currentIndex() - 1][0, :])
|
||||
|
|
@ -1471,6 +1433,59 @@ class SignalProcessingTab(QWidget):
|
|||
levels, cmap='gist_rainbow',
|
||||
norm=norm))
|
||||
|
||||
elif stg.SNR_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
|
||||
x, y = np.meshgrid(stg.time_cross_section[self.combobox_fileListWidget.currentIndex() - 1][0, :],
|
||||
stg.depth_cross_section[self.combobox_fileListWidget.currentIndex() - 1][0, :])
|
||||
|
||||
print("1 plot SNR with SNR_cross_section")
|
||||
val_min = np.nanmin(stg.SNR_cross_section[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
val_max = np.nanmax(stg.SNR_cross_section[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
if val_min == val_max:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
else:
|
||||
if val_min == 0:
|
||||
val_min = 1e-5
|
||||
if val_max > 1000:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
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)
|
||||
|
||||
cf = (self.axis_SNR[f].contourf(x, -y,
|
||||
stg.SNR_cross_section[
|
||||
self.combobox_fileListWidget.currentIndex() - 1][f, :, :],
|
||||
levels, cmap='gist_rainbow', norm=norm))
|
||||
|
||||
else:
|
||||
|
||||
x, y = np.meshgrid(stg.time[self.combobox_fileListWidget.currentIndex() - 1][0, :],
|
||||
stg.depth[self.combobox_fileListWidget.currentIndex() - 1][0, :])
|
||||
|
||||
print("0 plot SNR with SNR_raw_data")
|
||||
val_min = np.nanmin(stg.SNR_raw_data[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
val_max = np.nanmax(stg.SNR_raw_data[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
if val_min == val_max:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
else:
|
||||
if val_min == 0:
|
||||
val_min = 1e-5
|
||||
if val_max > 1000:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
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)
|
||||
|
||||
cf = (self.axis_SNR[f].contourf(x, -y,
|
||||
stg.SNR_raw_data[
|
||||
self.combobox_fileListWidget.currentIndex() - 1][f, :, :],
|
||||
levels, cmap='gist_rainbow', norm=norm))
|
||||
|
||||
|
||||
self.axis_SNR[f].text(1, .70, stg.freq_text[self.combobox_fileListWidget.currentIndex() - 1][f],
|
||||
fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
|
||||
horizontalalignment='right', verticalalignment='bottom',
|
||||
|
|
@ -1502,33 +1517,7 @@ class SignalProcessingTab(QWidget):
|
|||
|
||||
else:
|
||||
|
||||
if (stg.BS_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape ==
|
||||
stg.BS_raw_data[self.combobox_fileListWidget.currentIndex() - 1].shape):
|
||||
|
||||
stg.BS_raw_data_pre_process_SNR = deepcopy(stg.BS_raw_data)
|
||||
|
||||
for f, _ in enumerate(stg.freq[self.combobox_fileListWidget.currentIndex() - 1]):
|
||||
stg.BS_raw_data_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][
|
||||
f,
|
||||
np.where(stg.SNR_raw_data[self.combobox_fileListWidget.currentIndex() - 1][f, :, :] < self.spinbox_SNR_criterion.value())[0],
|
||||
np.where(stg.SNR_raw_data[self.combobox_fileListWidget.currentIndex() - 1][f, :, :] < self.spinbox_SNR_criterion.value())[1]] \
|
||||
= np.nan
|
||||
|
||||
elif len(stg.BS_stream_bed) < len(stg.BS_cross_section):
|
||||
|
||||
stg.BS_cross_section_pre_process_SNR = deepcopy(stg.BS_cross_section)
|
||||
# stg.Noise_data = deepcopy(stg.BS_noise_averaged_data[:, :, :stg.t.shape[1]])
|
||||
# stg.SNR_data_average = np.divide(
|
||||
# (stg.BS_stream_bed_pre_process_SNR - stg.Noise_data) ** 2, stg.Noise_data ** 2)
|
||||
|
||||
for f, _ in enumerate(stg.freq[self.combobox_fileListWidget.currentIndex() - 1]):
|
||||
stg.BS_cross_section_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][
|
||||
f,
|
||||
np.where(stg.SNR_cross_section[self.combobox_fileListWidget.currentIndex() - 1][f, :, :] < self.spinbox_SNR_criterion.value())[0],
|
||||
np.where(stg.SNR_cross_section[self.combobox_fileListWidget.currentIndex() - 1][f, :, :] < self.spinbox_SNR_criterion.value())[1]] \
|
||||
= np.nan
|
||||
|
||||
else:
|
||||
if stg.BS_stream_bed[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
|
||||
stg.BS_stream_bed_pre_process_SNR = deepcopy(stg.BS_stream_bed)
|
||||
# stg.Noise_data = deepcopy(stg.BS_noise_averaged_data[:, :, :stg.t.shape[1]])
|
||||
|
|
@ -1542,6 +1531,35 @@ class SignalProcessingTab(QWidget):
|
|||
np.where(stg.SNR_stream_bed[self.combobox_fileListWidget.currentIndex() - 1][f, :, :] < self.spinbox_SNR_criterion.value())[1]] \
|
||||
= np.nan
|
||||
|
||||
elif stg.BS_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
|
||||
stg.BS_cross_section_pre_process_SNR = deepcopy(stg.BS_cross_section)
|
||||
# stg.Noise_data = deepcopy(stg.BS_noise_averaged_data[:, :, :stg.t.shape[1]])
|
||||
# stg.SNR_data_average = np.divide(
|
||||
# (stg.BS_stream_bed_pre_process_SNR - stg.Noise_data) ** 2, stg.Noise_data ** 2)
|
||||
|
||||
for f, _ in enumerate(stg.freq[self.combobox_fileListWidget.currentIndex() - 1]):
|
||||
stg.BS_cross_section_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][
|
||||
f,
|
||||
np.where(stg.SNR_cross_section[self.combobox_fileListWidget.currentIndex() - 1][f, :,
|
||||
:] < self.spinbox_SNR_criterion.value())[0],
|
||||
np.where(stg.SNR_cross_section[self.combobox_fileListWidget.currentIndex() - 1][f, :,
|
||||
:] < self.spinbox_SNR_criterion.value())[1]] \
|
||||
= np.nan
|
||||
|
||||
else:
|
||||
|
||||
stg.BS_raw_data_pre_process_SNR = deepcopy(stg.BS_raw_data)
|
||||
|
||||
for f, _ in enumerate(stg.freq[self.combobox_fileListWidget.currentIndex() - 1]):
|
||||
stg.BS_raw_data_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][
|
||||
f,
|
||||
np.where(stg.SNR_raw_data[self.combobox_fileListWidget.currentIndex() - 1][f, :,
|
||||
:] < self.spinbox_SNR_criterion.value())[0],
|
||||
np.where(stg.SNR_raw_data[self.combobox_fileListWidget.currentIndex() - 1][f, :,
|
||||
:] < self.spinbox_SNR_criterion.value())[1]] \
|
||||
= np.nan
|
||||
|
||||
|
||||
# elif stg.BS_stream_bed_pre_process_average.size != 0:
|
||||
# stg.BS_stream_bed_pre_process_SNR = deepcopy(stg.BS_stream_bed_pre_process_average)
|
||||
|
|
@ -1576,8 +1594,8 @@ class SignalProcessingTab(QWidget):
|
|||
# self.update_plot_profile_position_on_transect()
|
||||
# self.update_plot_averaged_profile()
|
||||
|
||||
# self.compute_averaged_BS_data()
|
||||
# self.plot_profile_and_position_on_transect_with_slider()
|
||||
self.compute_averaged_BS_data()
|
||||
self.plot_profile_and_position_on_transect_with_slider()
|
||||
|
||||
|
||||
def plot_BS_signal_filtered_with_SNR(self):
|
||||
|
|
@ -1598,36 +1616,7 @@ class SignalProcessingTab(QWidget):
|
|||
|
||||
for f, _ in enumerate(stg.freq[self.combobox_fileListWidget.currentIndex() - 1]):
|
||||
|
||||
if (stg.BS_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape ==
|
||||
stg.BS_raw_data[self.combobox_fileListWidget.currentIndex() - 1].shape):
|
||||
|
||||
val_min = np.nanmin(
|
||||
stg.BS_raw_data_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
val_max = np.nanmax(
|
||||
stg.BS_raw_data_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
|
||||
if val_min == 0:
|
||||
val_min = 1e-5
|
||||
|
||||
pcm = self.axis_BS[f].pcolormesh(stg.time[self.combobox_fileListWidget.currentIndex() - 1][f, :],
|
||||
-stg.depth[self.combobox_fileListWidget.currentIndex() - 1][f, :],
|
||||
stg.BS_raw_data_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][f, :, :],
|
||||
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
|
||||
|
||||
elif len(stg.BS_stream_bed) < len(stg.BS_cross_section):
|
||||
|
||||
val_min = np.nanmin(stg.BS_cross_section_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
val_max = np.nanmax(stg.BS_cross_section_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
|
||||
if val_min == 0:
|
||||
val_min = 1e-5
|
||||
|
||||
pcm = self.axis_BS[f].pcolormesh(stg.time_cross_section[self.combobox_fileListWidget.currentIndex() - 1][f, :],
|
||||
-stg.depth_cross_section[self.combobox_fileListWidget.currentIndex() - 1][f, :],
|
||||
stg.BS_cross_section_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][f, :, :],
|
||||
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
|
||||
|
||||
else:
|
||||
if stg.BS_stream_bed_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
|
||||
val_min = np.nanmin(stg.BS_stream_bed_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
val_max = np.nanmax(stg.BS_stream_bed_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
|
|
@ -1644,6 +1633,38 @@ class SignalProcessingTab(QWidget):
|
|||
-stg.depth_bottom[self.combobox_fileListWidget.currentIndex() - 1],
|
||||
color='black', linewidth=1, linestyle="solid")
|
||||
|
||||
elif stg.BS_cross_section_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
|
||||
val_min = np.nanmin(
|
||||
stg.BS_cross_section_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
val_max = np.nanmax(
|
||||
stg.BS_cross_section_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
|
||||
if val_min == 0:
|
||||
val_min = 1e-5
|
||||
|
||||
pcm = self.axis_BS[f].pcolormesh(
|
||||
stg.time_cross_section[self.combobox_fileListWidget.currentIndex() - 1][f, :],
|
||||
-stg.depth_cross_section[self.combobox_fileListWidget.currentIndex() - 1][f, :],
|
||||
stg.BS_cross_section_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][f, :, :],
|
||||
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
|
||||
|
||||
else:
|
||||
|
||||
val_min = np.nanmin(
|
||||
stg.BS_raw_data_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
val_max = np.nanmax(
|
||||
stg.BS_raw_data_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1][f, :, :])
|
||||
|
||||
if val_min == 0:
|
||||
val_min = 1e-5
|
||||
|
||||
pcm = self.axis_BS[f].pcolormesh(stg.time[self.combobox_fileListWidget.currentIndex() - 1][f, :],
|
||||
-stg.depth[self.combobox_fileListWidget.currentIndex() - 1][f, :],
|
||||
stg.BS_raw_data_pre_process_SNR[
|
||||
self.combobox_fileListWidget.currentIndex() - 1][f, :, :],
|
||||
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
|
||||
|
||||
self.axis_BS[f].text(1, .70, stg.freq_text[self.combobox_fileListWidget.currentIndex() - 1][f],
|
||||
fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
|
||||
horizontalalignment='right', verticalalignment='bottom',
|
||||
|
|
@ -1670,11 +1691,11 @@ class SignalProcessingTab(QWidget):
|
|||
kernel = np.ones(2*self.spinbox_average_horizontal.value()+1)
|
||||
print(kernel)
|
||||
|
||||
if len(stg.BS_stream_bed):
|
||||
if stg.BS_stream_bed[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
|
||||
# --- Données pré-traitées avec SNR : BS stream bed pre process SNR -> BS stream bed pre process SNR average ---
|
||||
|
||||
if len(stg.BS_stream_bed_pre_process_SNR):
|
||||
if stg.BS_stream_bed_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
# ((stg.BS_stream_bed_pre_process_SNR_average[self.combobox_fileListWidget.currentIndex() - 1].shape) or
|
||||
# (stg.BS_stream_bed_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1].shape))):
|
||||
|
||||
|
|
@ -1700,12 +1721,11 @@ class SignalProcessingTab(QWidget):
|
|||
= convolve1d(stg.BS_stream_bed[self.combobox_fileListWidget.currentIndex() - 1][f, i, :], weights=kernel) / len(kernel)
|
||||
print("2 - Je suis dans stg.BS_stream_bed_pre_process_average")
|
||||
|
||||
elif (stg.BS_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape[2]
|
||||
< stg.BS_raw_data[self.combobox_fileListWidget.currentIndex() - 1].shape[2]):
|
||||
elif stg.BS_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
|
||||
# --- Données pré-traitées avec SNR : BS cross section pre process SNR -> BS cross section pre process SNR average ---
|
||||
|
||||
if len(stg.BS_cross_section_pre_process_SNR):
|
||||
if stg.BS_cross_section_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
# if ((stg.BS_cross_section_pre_process_SNR_average[self.combobox_fileListWidget.currentIndex() - 1].shape) or
|
||||
# (stg.BS_cross_section_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1].shape)):
|
||||
|
||||
|
|
@ -1731,13 +1751,11 @@ class SignalProcessingTab(QWidget):
|
|||
= convolve1d(stg.BS_cross_section[self.combobox_fileListWidget.currentIndex() - 1][f, i, :], weights=kernel) / len(kernel)
|
||||
print("4 - Je suis dans stg.BS_cross_section_pre_process_average")
|
||||
|
||||
|
||||
|
||||
else:
|
||||
|
||||
# --- Données pré-traitées avec SNR : BS raw data pre process SNR -> BS raw data pre process SNR average ---
|
||||
|
||||
if len(stg.BS_cross_section_pre_process_SNR):
|
||||
if stg.BS_cross_section_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
# if ((stg.BS_raw_data_pre_process_SNR_average[self.combobox_fileListWidget.currentIndex() - 1].shape) or
|
||||
# (stg.BS_raw_data_pre_process_SNR[self.combobox_fileListWidget.currentIndex() - 1].shape)):
|
||||
|
||||
|
|
@ -1840,8 +1858,7 @@ class SignalProcessingTab(QWidget):
|
|||
self.axis_profile[0].cla()
|
||||
self.axis_profile[1].cla()
|
||||
|
||||
if ((len(stg.BS_stream_bed_pre_process_SNR_average)) and
|
||||
(len(stg.BS_stream_bed_pre_process_SNR_average) == (self.combobox_fileListWidget.count() -1))):
|
||||
if stg.BS_stream_bed_pre_process_SNR_average[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
|
||||
# --- Plot profile ---
|
||||
self.axis_profile[0].plot(
|
||||
|
|
@ -1871,8 +1888,7 @@ class SignalProcessingTab(QWidget):
|
|||
[self.combobox_frequency_profile.currentIndex(), :, :],
|
||||
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
|
||||
|
||||
elif (len(stg.BS_stream_bed_pre_process_average)
|
||||
and (len(stg.BS_stream_bed_pre_process_average) == (self.combobox_fileListWidget.count() -1))):
|
||||
elif stg.BS_stream_bed_pre_process_average[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
|
||||
# --- Plot profile ---
|
||||
self.axis_profile[0].plot(
|
||||
|
|
@ -1907,8 +1923,7 @@ class SignalProcessingTab(QWidget):
|
|||
[self.combobox_frequency_profile.currentIndex(), :, :],
|
||||
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
|
||||
|
||||
elif (len(stg.BS_cross_section_pre_process_SNR_average) and
|
||||
(len(stg.BS_cross_section_pre_process_SNR_average) == (self.combobox_fileListWidget.count() -1))):
|
||||
elif stg.BS_cross_section_pre_process_SNR_average[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
|
||||
# --- Plot profile ---
|
||||
self.axis_profile[0].plot(
|
||||
|
|
@ -1943,8 +1958,7 @@ class SignalProcessingTab(QWidget):
|
|||
[self.combobox_frequency_profile.currentIndex(), :, :],
|
||||
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
|
||||
|
||||
elif (len(stg.BS_cross_section_pre_process_average)
|
||||
and (len(stg.BS_cross_section_pre_process_average) == (self.combobox_fileListWidget.count() - 1))):
|
||||
elif stg.BS_cross_section_pre_process_average[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
|
||||
# --- Plot profile ---
|
||||
self.axis_profile[0].plot(
|
||||
|
|
@ -1979,8 +1993,7 @@ class SignalProcessingTab(QWidget):
|
|||
[self.combobox_frequency_profile.currentIndex(), :, :],
|
||||
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
|
||||
|
||||
elif (len(stg.BS_raw_data_pre_process_SNR_average)
|
||||
and (len(stg.BS_raw_data_pre_process_SNR_average) == (self.combobox_fileListWidget.count() - 1))):
|
||||
elif stg.BS_raw_data_pre_process_SNR_average[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
|
||||
# --- Plot profile ---
|
||||
self.axis_profile[0].plot(
|
||||
|
|
@ -2021,7 +2034,7 @@ class SignalProcessingTab(QWidget):
|
|||
self.axis_profile[0].plot(
|
||||
stg.BS_raw_data_pre_process_average[self.combobox_fileListWidget.currentIndex() - 1]
|
||||
[self.combobox_frequency_profile.currentIndex(), :, self.slider.value() - 1],
|
||||
-stg.depth_cross_section[self.combobox_fileListWidget.currentIndex() - 1][
|
||||
-stg.depth[self.combobox_fileListWidget.currentIndex() - 1][
|
||||
self.combobox_frequency_profile.currentIndex(), :],
|
||||
linestyle='solid', color='k', linewidth=1)
|
||||
self.axis_profile[0].text(.95, .05, stg.freq_text[self.combobox_fileListWidget.currentIndex() - 1][
|
||||
|
|
@ -2041,9 +2054,9 @@ class SignalProcessingTab(QWidget):
|
|||
[self.combobox_frequency_profile.currentIndex(), :, :])
|
||||
if val_min == 0:
|
||||
val_min = 1e-5
|
||||
self.axis_profile[1].pcolormesh(stg.time_cross_section[self.combobox_fileListWidget.currentIndex() - 1][
|
||||
self.axis_profile[1].pcolormesh(stg.time[self.combobox_fileListWidget.currentIndex() - 1][
|
||||
self.combobox_frequency_profile.currentIndex(), :],
|
||||
-stg.depth_cross_section[self.combobox_fileListWidget.currentIndex() - 1][
|
||||
-stg.depth[self.combobox_fileListWidget.currentIndex() - 1][
|
||||
self.combobox_frequency_profile.currentIndex(), :],
|
||||
stg.BS_raw_data_pre_process_average[
|
||||
self.combobox_fileListWidget.currentIndex() - 1]
|
||||
|
|
@ -2060,8 +2073,8 @@ class SignalProcessingTab(QWidget):
|
|||
-stg.depth[self.combobox_fileListWidget.currentIndex() - 1][self.combobox_frequency_profile.currentIndex(), :],
|
||||
color='red', linestyle="solid", linewidth=2)
|
||||
|
||||
if len(stg.depth_bottom):
|
||||
if len(stg.time_cross_section):
|
||||
if len(stg.depth_bottom[self.combobox_fileListWidget.currentIndex() - 1]) != 0:
|
||||
if stg.time_cross_section[self.combobox_fileListWidget.currentIndex() - 1].shape != (0,):
|
||||
self.axis_profile[1].plot(stg.time_cross_section[self.combobox_fileListWidget.currentIndex() - 1][self.combobox_frequency_profile.currentIndex(), :],
|
||||
-stg.depth_bottom[self.combobox_fileListWidget.currentIndex() - 1],
|
||||
color='black', linewidth=1, linestyle="solid")
|
||||
|
|
|
|||
|
|
@ -17,12 +17,13 @@ freq = []
|
|||
freq_text = []
|
||||
time = []
|
||||
|
||||
path_BS_noise_data = ""
|
||||
filename_BS_noise_data = ""
|
||||
path_BS_noise_data = []
|
||||
filename_BS_noise_data = []
|
||||
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 = []
|
||||
SNR_filter_value = []
|
||||
|
||||
date = []
|
||||
date_noise = []
|
||||
|
|
|
|||
Loading…
Reference in New Issue