parouby
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acoused
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parouby:dev-bjvincent-fix-UBSediFlow
parouby:dev-bjvincent
parouby:dev
parouby:fix-save-load
parouby:main
parouby:dev-parouby
parouby:dev-brahim
parouby:v2.2
parouby:v2.1
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compare: parouby:571ac20d37fb441431f222589aa267bda8a6d696
Branches Tags
parouby:dev-bjvincent-fix-UBSediFlow
parouby:dev-bjvincent
parouby:dev
parouby:fix-save-load
parouby:main
parouby:dev-parouby
parouby:dev-brahim
parouby:v2.2
parouby:v2.1

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671f66058c ... 571ac20d37

3 changed files with 195 additions and 259 deletions
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11
View/acoustic_data_tab.py
View File

@ -633,7 +633,7 @@ class AcousticDataTab(QWidget):
self.fileListWidget.itemSelectionChanged.connect(self.compute_rmin_rmax)
self.fileListWidget.itemSelectionChanged.connect(self.update_frequency_combobox)
# self.fileListWidget.itemSelectionChanged.connect(self.plot_backscattered_acoustic_signal_recording)
# self.fileListWidget.itemSelectionChanged.connect(self.plot_profile)
self.fileListWidget.itemSelectionChanged.connect(self.plot_profile)
self.fileListWidget.itemSelectionChanged.connect(self.update_plot_backscattered_acoustic_signal_recording)
self.fileListWidget.itemSelectionChanged.connect(self.update_plot_profile)
self.fileListWidget.itemSelectionChanged.connect(self.set_range_for_spinboxes_bathymetry)
@ -1327,12 +1327,8 @@ class AcousticDataTab(QWidget):
self.fill_measurements_information_groupbox()
self.fill_table()
self.plot_backscattered_acoustic_signal_recording()
self.plot_profile()
self.update_frequency_combobox()
self.water_attenuation()
self.compute_tmin_tmax()
self.compute_rmin_rmax()
self.set_range_for_spinboxes_bathymetry()
stg.acoustic_data = list(
range(
@ -1374,7 +1370,7 @@ class AcousticDataTab(QWidget):
"stg.BS_raw_data_reshape", "stg.time_reshape", "stg.depth_reshape"]
for p in list_to_pop1:
if isinstance(eval(p), list):
if isinstance(p, list):
exec(p + ".pop(current_row)")
if stg.BS_cross_section:
@ -1477,7 +1473,7 @@ class AcousticDataTab(QWidget):
]
for k in list_to_clear:
if isinstance(eval(k), list):
if isinstance(k, list):
exec(k + ".clear()")
self.fileListWidget.clear()
@ -1688,7 +1684,6 @@ class AcousticDataTab(QWidget):
def fill_measurements_information_groupbox_datetime(self):
self.label_date_acoustic_file.clear()
self.label_date_acoustic_file.setText(
"Date: " + str(stg.date[self.fileListWidget.currentRow()])
)

29
View/sediment_calibration_tab.py
View File

@ -1795,14 +1795,15 @@ class SedimentCalibrationTab(QWidget):
msgBox.setText("Update data before importing calibration")
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec()
elif stg.filename_calibration_file == "":
pass
else:
# --- Read calibration file ---
data = pd.read_csv(os.path.join(stg.path_calibration_file, stg.filename_calibration_file), header=0, index_col=0)
data = pd.read_csv(
os.path.join(
stg.path_calibration_file,
stg.filename_calibration_file
),
header=0, index_col=0
)
# --- Fill spinboxes of calibration parameter ---
self.label_temperature.clear()
@ -2136,7 +2137,6 @@ class SedimentCalibrationTab(QWidget):
]
for i in range(self.combobox_acoustic_data_choice.count()):
J_cross_section_freq1 = np.array([])
J_cross_section_freq2 = np.array([])
@ -2220,19 +2220,25 @@ class SedimentCalibrationTab(QWidget):
self.lineEdit_alphas_freq2.setText(str("%.5f" % alpha_s_freq2))
if (alpha_s_freq1 < 0) or (alpha_s_freq2 < 0):
msgBox = QMessageBox()
msgBox.setWindowTitle("Alpha computation error")
msgBox.setIconPixmap(QPixmap(self._path_icon("no_approved.png")).scaledToHeight(32, Qt.SmoothTransformation))
msgBox.setIconPixmap(
QPixmap(
self._path_icon("no_approved.png")
).scaledToHeight(32, Qt.SmoothTransformation)
)
msgBox.setText("Sediment sound attenuation is negative !")
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec()
else:
msgBox = QMessageBox()
msgBox.setWindowTitle("Alpha computation validation")
msgBox.setIconPixmap(QPixmap(self._path_icon("approved.png")).scaledToHeight(32, Qt.SmoothTransformation))
msgBox.setIconPixmap(
QPixmap(
self._path_icon("approved.png")
).scaledToHeight(32, Qt.SmoothTransformation)
)
msgBox.setText("Sediment sound attenuation is positive.")
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec()
@ -2733,4 +2739,3 @@ class SedimentCalibrationTab(QWidget):
self.lineEdit_slider_FCB.setText(
str(stg.time[self.combobox_acoustic_data_choice.currentIndex()][
self.combobox_frequency_FCB.currentIndex(), self.slider_FCB.value()-1]))

414
View/signal_processing_tab.py
View File

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