Exceptions are added to manage error on algorithm bottom detection
brahim
parent
45aa5ae2f5
commit
5b63855bd8
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@ -431,7 +431,7 @@ class AcousticDataTab(QWidget):
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self.gridlayout_compute_bathymetry.addWidget(self.combobox_freq_choice, 0, 0, 2, 1)
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self.label_from_bathy = QLabel()
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self.label_from_bathy.setText("From ")
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self.label_from_bathy.setText("From - ")
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self.gridlayout_compute_bathymetry.addWidget(self.label_from_bathy, 0, 1, 1, 1)
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self.spinbox_depth_min = QSpinBox()
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@ -443,7 +443,7 @@ class AcousticDataTab(QWidget):
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self.gridlayout_compute_bathymetry.addWidget(self.label_depth_min_unit, 0, 3, 1, 1)
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self.label_to_bathy = QLabel()
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self.label_to_bathy.setText("to ")
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self.label_to_bathy.setText("to - ")
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self.gridlayout_compute_bathymetry.addWidget(self.label_to_bathy, 0, 4, 1, 1)
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self.spinbox_depth_max = QSpinBox()
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@ -822,8 +822,14 @@ class AcousticDataTab(QWidget):
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def plot_transect_with_BS_raw_data(self):
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# --- Condition if table is not filled ---
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if self.tableModel.rowCount(1) == 10:
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if not self.lineEdit_acoustic_file.text():
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msgBox = QMessageBox()
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msgBox.setWindowTitle("Plot transect Error")
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msgBox.setIcon(QMessageBox.Warning)
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msgBox.setText("Load data before plot transect 2D field")
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msgBox.setStandardButtons(QMessageBox.Ok)
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msgBox.exec()
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elif self.tableModel.rowCount(1) == 10:
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msgBox = QMessageBox()
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msgBox.setWindowTitle("Plot transect Error")
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msgBox.setIcon(QMessageBox.Warning)
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@ -925,7 +931,14 @@ class AcousticDataTab(QWidget):
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self.fig_BS.canvas.draw_idle()
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def plot_transect_with_SNR_data(self):
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if self.tableModel.rowCount(1) == 10:
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if not self.lineEdit_noise_file.text():
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msgBox = QMessageBox()
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msgBox.setWindowTitle("Plot transect Error")
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msgBox.setIcon(QMessageBox.Warning)
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msgBox.setText("Load data before plot SNR 2D field")
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msgBox.setStandardButtons(QMessageBox.Ok)
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msgBox.exec()
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elif self.tableModel.rowCount(1) == 10:
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msgBox = QMessageBox()
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msgBox.setWindowTitle("Plot transect Error")
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msgBox.setIcon(QMessageBox.Warning)
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@ -1076,8 +1089,8 @@ class AcousticDataTab(QWidget):
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# msgBox.setText("Plot transect before compute bathymety algorithm")
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# msgBox.setStandardButtons(QMessageBox.Ok)
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# msgBox.exec()
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# elif (self.canvas_BS) and (self.canvas_SNR == None):
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else:
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elif self.canvas_BS != None:
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# else:
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# --- Record frequency choose for bottom detection ---
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stg.freq_bottom_detection = self.combobox_freq_choice.currentIndex()
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@ -1095,114 +1108,135 @@ class AcousticDataTab(QWidget):
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ind_min = np.where(stg.r >= rmin)[0][0]
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ind_max = np.where(stg.r <= rmax)[0][-1]
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# Getting the peak
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val_bottom[d] = np.nanmax(stg.BS_raw_data[ind_min:ind_max, self.combobox_freq_choice.currentIndex(), d])
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# Getting the range cell of the peak
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ind_bottom = np.where(stg.BS_raw_data[ind_min:ind_max, self.combobox_freq_choice.currentIndex(), d]
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== val_bottom[d])[0][0]
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np.append(stg.ind_bottom, ind_bottom)
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try:
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val_bottom[d] = np.nanmax(stg.BS_raw_data[ind_min:ind_max, self.combobox_freq_choice.currentIndex(), d])
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except ValueError as e:
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msgBox = QMessageBox()
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msgBox.setWindowTitle("Detect bottom Error")
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msgBox.setIcon(QMessageBox.Warning)
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msgBox.setText(f"{e} : maximum value of section bottom is not found. \n "
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f"Please change parameter of algorithm")
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msgBox.setStandardButtons(QMessageBox.Ok)
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msgBox_return = msgBox.exec()
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if msgBox_return == msgBox.Ok:
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break #msgBox.close()
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else:
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# Getting the range cell of the peak
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ind_bottom = np.where(stg.BS_raw_data[ind_min:ind_max, self.combobox_freq_choice.currentIndex(), d]
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== val_bottom[d])[0][0]
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np.append(stg.ind_bottom, ind_bottom)
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r_bottom[d] = stg.r[ind_bottom + ind_min]
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r_bottom_ind.append(ind_bottom + ind_min)
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# Updating the range where we will look for the peak (in the next cell)
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rmin = r_bottom[d] - locale.atof(self.doublespinbox_next_cell.text())
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rmax = r_bottom[d] + locale.atof(self.doublespinbox_next_cell.text())
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r_bottom[d] = stg.r[ind_bottom + ind_min]
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r_bottom_ind.append(ind_bottom + ind_min)
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# Updating the range where we will look for the peak (in the next cell)
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rmin = r_bottom[d] - locale.atof(self.doublespinbox_next_cell.text())
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rmax = r_bottom[d] + locale.atof(self.doublespinbox_next_cell.text())
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BS_section_bottom = np.zeros((stg.r.shape[0], stg.time.shape[0]))
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for i in range(BS_section_bottom.shape[0]):
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# print(r_bottom_temp_ind[i])
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# print(i)
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BS_section_bottom[r_bottom_ind[i]][i] = 1
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# print(BS_section_bottom[r_bottom_temp_ind[i]][i])
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try:
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BS_section_bottom[r_bottom_ind[i]][i] = 1
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except IndexError as e:
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msgBox = QMessageBox()
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msgBox.setWindowTitle("Detect bottom Error")
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msgBox.setIcon(QMessageBox.Warning)
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msgBox.setText(f"{e} : maximum value of section bottom is not found. \n "
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f"Please change parameter of algorithm")
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msgBox.setStandardButtons(QMessageBox.Ok)
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msgBox_return = msgBox.exec()
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if msgBox_return == msgBox.Ok:
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break # msgBox.close()
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# --- Record r_bottom for other tabs ---
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stg.r_bottom = r_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]]
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stg.val_bottom = val_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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if BS_section_bottom.sum() > 2:
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# --- Record r_bottom for other tabs ---
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stg.r_bottom = r_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]]
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stg.val_bottom = val_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]]
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# --- Plot transect BS with bathymetry ---
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for f in range(stg.freq.shape[0]):
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self.axis_BS[f].cla()
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# --- Plot transect BS with bathymetry ---
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for f in range(stg.freq.shape[0]):
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self.axis_BS[f].cla()
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val_min = np.min(stg.BS_raw_data[:, f, :])
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val_max = np.max(stg.BS_raw_data[:, f, :])
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if val_min == 0:
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val_min = 1e-5
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val_min = np.min(stg.BS_raw_data[:, f, :])
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val_max = np.max(stg.BS_raw_data[:, f, :])
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if val_min == 0:
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val_min = 1e-5
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pcm = self.axis_BS[f].pcolormesh(
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stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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-stg.r,
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(stg.BS_raw_data[:, f,
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np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]]),
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cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
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pcm = self.axis_BS[f].pcolormesh(
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stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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-stg.r,
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(stg.BS_raw_data[:, f,
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np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]]),
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cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
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self.axis_BS[f].plot(
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stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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- r_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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color='black', linewidth=1, linestyle="solid")
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self.axis_BS[f].plot(
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stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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- r_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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color='black', linewidth=1, linestyle="solid")
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self.axis_BS[f].text(1, .70, stg.freq_text[f],
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fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
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horizontalalignment='right', verticalalignment='bottom',
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transform=self.axis_BS[f].transAxes)
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self.axis_BS[f].text(1, .70, stg.freq_text[f],
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fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
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horizontalalignment='right', verticalalignment='bottom',
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transform=self.axis_BS[f].transAxes)
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self.fig_BS.canvas.draw_idle()
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self.fig_BS.canvas.draw_idle()
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# --- Plot transect SNR with bathymetry ---
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# --- Plot transect SNR with bathymetry ---
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if self.canvas_SNR != None:
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x, y = np.meshgrid(
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stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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stg.r)
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x, y = np.meshgrid(
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stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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stg.r)
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for f in range(stg.freq.shape[0]):
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self.axis_SNR[f].cla()
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for f in range(stg.freq.shape[0]):
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self.axis_SNR[f].cla()
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val_min = np.min(stg.snr[:, f, :])
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val_max = np.max(stg.snr[:, f, :])
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if val_min == 0:
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val_min = 1e-5
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if val_max > 1000:
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levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
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else:
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levels = np.array([00.1, 1, 2, 10, 100, val_max])
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bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
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norm = BoundaryNorm(boundaries=bounds, ncolors=300)
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val_min = np.min(stg.snr[:, f, :])
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val_max = np.max(stg.snr[:, f, :])
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if val_min == 0:
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val_min = 1e-5
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if val_max > 1000:
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levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
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else:
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levels = np.array([00.1, 1, 2, 10, 100, val_max])
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bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
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norm = BoundaryNorm(boundaries=bounds, ncolors=300)
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cf = self.axis_SNR[f].contourf(x, -y,
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stg.snr[:, f,
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np.where(np.round(stg.time,
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2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time,
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2) == self.spinbox_tmax.value())[0][0]],
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levels, cmap='gist_rainbow', norm=norm) # , shading='gouraud')
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cf = self.axis_SNR[f].contourf(x, -y,
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stg.snr[:, f,
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np.where(np.round(stg.time,
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2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time,
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2) == self.spinbox_tmax.value())[0][0]],
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levels, cmap='gist_rainbow', norm=norm) # , shading='gouraud')
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self.axis_SNR[f].text(1, .70, stg.freq_text[f],
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fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
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horizontalalignment='right', verticalalignment='bottom',
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transform=self.axis_SNR[f].transAxes)
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self.axis_SNR[f].text(1, .70, stg.freq_text[f],
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fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
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horizontalalignment='right', verticalalignment='bottom',
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transform=self.axis_SNR[f].transAxes)
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self.axis_SNR[f].plot(
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stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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- r_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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# + np.min(r_bottom[np.where(np.round(noise_data._time, 2) == self.spinbox_tmin.value())[0][0]:
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# np.where(np.round(noise_data._time, 2) == self.spinbox_tmax.value())[0][0]]),
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# np.max(self._model.r_bottom_cross_section) - self._model.r_bottom_cross_section + np.min(self._model.r_bottom_cross_section),
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color='black', linewidth=1, linestyle="solid")
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self.axis_SNR[f].plot(
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stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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- r_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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# + np.min(r_bottom[np.where(np.round(noise_data._time, 2) == self.spinbox_tmin.value())[0][0]:
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# np.where(np.round(noise_data._time, 2) == self.spinbox_tmax.value())[0][0]]),
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# np.max(self._model.r_bottom_cross_section) - self._model.r_bottom_cross_section + np.min(self._model.r_bottom_cross_section),
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color='black', linewidth=1, linestyle="solid")
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self.axis_SNR[f].text(1, .70, stg.freq_text[f],
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fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
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horizontalalignment='right', verticalalignment='bottom',
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transform=self.axis_BS[f].transAxes)
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self.axis_SNR[f].text(1, .70, stg.freq_text[f],
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fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
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horizontalalignment='right', verticalalignment='bottom',
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transform=self.axis_BS[f].transAxes)
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self.fig_SNR.canvas.draw_idle()
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self.fig_SNR.canvas.draw_idle()
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# else:
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#
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