River bottom detection is implemented. Plot of bottom line on transect is avalaible with the choice of the frequency and x-axis boundaries
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parent
444ca27567
commit
6817ffe4d8
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@ -18,6 +18,8 @@ from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as Navigatio
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from os import path
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from copy import deepcopy
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import locale
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locale.setlocale(locale.LC_ALL, '')
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# import Translation.biblio_string as bs
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import Translation.constant_string as cs
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@ -392,7 +394,7 @@ class AcousticDataTab(QWidget):
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self.label_xmax_m.setText("m")
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self.gridLayout_groupbox_xaxis_space.addWidget(self.label_xmax_m, 0, 7, 1, 1)
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# --- Group Box bathymetry computation algorithm to plot bottom of transect---
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# --- Group Box bathymetry computation algorithm to detect and plot bottom of transect---
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self.groupbox_compute_bathymetry = QGroupBox()
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# # self.groupbox_crosssectionbottom.setTitle("Plot bottom of cross section")
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@ -433,9 +435,7 @@ class AcousticDataTab(QWidget):
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self.pushbutton_compute_bathymetry_algorithm.setText("Compute \n&& \nPlot")
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self.gridlayout_compute_bathymetry.addWidget(self.pushbutton_compute_bathymetry_algorithm, 0, 7, 2, 1)
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# self.pushButton_plot = QPushButton()
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# self.pushButton_plot.setText("Plot")
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# self.verticalLayout_displayoption.addWidget(self.pushButton_plot)
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self.pushbutton_compute_bathymetry_algorithm.clicked.connect(self.detect_bottom)
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# =====================================================
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# BOTTOM HORIZONTAL BOX LAYOUT
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@ -1047,57 +1047,102 @@ class AcousticDataTab(QWidget):
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# cbar.set_ticklabels(['0', '1', '2', '10', '100', r'10$^3$', r'10$^6$'])
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self.fig_SNR.canvas.draw_idle()
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def detect_bottom(self):
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if self.lineEdit_acoustic_file.text() == "":
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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("Load data before compute bathymety algorithm")
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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("Detect bottom Error")
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msgBox.setIcon(QMessageBox.Warning)
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msgBox.setText("Fill table 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 == None:
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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("Plot transect before compute bathymety algorithm")
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msgBox.setStandardButtons(QMessageBox.Ok)
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msgBox.exec()
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else:
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acoustic_data = self.load_BS_acoustic_raw_data()
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# Selecting the range in which we look for the bottom reflection
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rmin = np.int(self.spinbox_depth_min.text()) # 4
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rmax = np.int(self.spinbox_depth_max.text()) # 8
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# self.ax[f].plot(self.model.r_bottom_cross_section, color='red', linewidth=2)
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#
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# def plot_snr_data(self):
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#
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# # self.canvas_snrdata = FigureCanvas(self.figure)
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#
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# frequency = self._model.Freq
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# for f in range(len(frequency)):
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# # self.axis = self.figure.add_subplot(4, 1, f+1)
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# val_min = np.nanmin(self._model.snr[:, f, :])
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# val_max = np.nanmax(self._model.snr[:, f, :])
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# # val_min = np.nanmin(self.model.V_noise[:, f, :])
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# # val_max = np.nanmax(self.model.V_noise[:, f, :])
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# # if val_min == 0:
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# # val_min = 0.5
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# if val_min == 0:
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# val_min = 0.5
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# # self.axis[f].imshow(np.asarray(np.array(self.model.V_noise[:, f, :], dtype=float)), aspect='auto',
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# # extent=[0, 2300, self.model.depth[-1][0], self.model.depth[0][0]],
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# # cmap='hsv', norm=LogNorm(vmin=val_min, vmax=val_max))
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#
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# # self.axis[f].pcolormesh(self.model.dist_BS_section, np.flipud(self.model.BS_raw_cross_section.r),
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# # self.model.snr[:, f, :],
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# # cmap='hsv', norm=LogNorm(vmin=val_min, vmax=val_max), shading='gouraud')
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#
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# x, y = np.meshgrid(self._model.dist_BS_section, self._model.BS_raw_cross_section.r)
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# if val_max > 1000:
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# levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max])
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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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# pcm_snr = self.axis[f].contourf(x, y, np.flipud(self._model.snr[:, f, :]), levels,
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# cmap='gist_rainbow', linewidths=None, norm=norm,
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# extent=[self._model.dist_BS_section[0], self._model.dist_BS_section[-1],
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# self._model.BS_raw_cross_section.r[-1], self._model.BS_raw_cross_section.r[0]])
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#
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# self.axis[f].plot(self._model.dist_BS_section,
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# np.max(self._model.r_bottom_cross_section) - self._model.r_bottom_cross_section + np.min(
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# self._model.r_bottom_cross_section),
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# color='k', linewidth=2)
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# self.axis[f].text(1, .70, self._model.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', transform=self.axis[f].transAxes)
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#
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# self.figure.supxlabel('Distance from left bank (m)', fontsize=10)
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# self.figure.supylabel('Depth (m)', fontsize=10)
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# # self.figure.tight_layout()
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# cbar_snr = self.figure.colorbar(pcm_snr, ax=self.axis[:], shrink=1, location='right')
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# cbar_snr.set_label(label='SNR', rotation=270)
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# self.figure.canvas.draw_idle()
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#
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#
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# empty result arrays
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r_bottom = np.zeros(acoustic_data._nb_profiles)
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val_bottom = np.zeros(acoustic_data._nb_profiles)
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r_bottom_ind = []
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# ----------- Detecting the bottom -------------
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for d in range(acoustic_data._nb_profiles):
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# Index of the range where we look for the peak
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ind_min = np.where(acoustic_data._r >= rmin)[0][0]
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ind_max = np.where(acoustic_data._r <= rmax)[0][-1]
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# Getting the peak
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val_bottom[d] = np.nanmax(acoustic_data._BS_raw_data[ind_min:ind_max,
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self.combobox_freq_choice.currentIndex() - 1, d])
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# Getting the range cell of the peak
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ind_bottom = np.where(acoustic_data._BS_raw_data[ind_min:ind_max,
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self.combobox_freq_choice.currentIndex() - 1, d] == val_bottom[d])[0][0]
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r_bottom[d] = acoustic_data._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()) # 0.75
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rmax = r_bottom[d] + locale.atof(self.doublespinbox_next_cell.text()) # 0.75
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BS_section_bottom = np.zeros((acoustic_data._BS_raw_data.shape[0], acoustic_data._BS_raw_data.shape[2]))
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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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# --- Plot transect with bathymetry ---
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for f in range(acoustic_data._freq.shape[0]):
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self.axis_BS[f].cla()
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val_min = np.min(acoustic_data._BS_raw_data[:, f, :])
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val_max = np.max(acoustic_data._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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acoustic_data._time[np.where(np.round(acoustic_data._time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(acoustic_data._time, 2) == self.spinbox_tmax.value())[0][0]],
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acoustic_data._r ,
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np.flipud(acoustic_data._BS_raw_data[:, f,
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np.where(np.round(acoustic_data._time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(acoustic_data._time, 2) == self.spinbox_tmax.value())[0][0]]),
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cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))#, shading='gouraud')
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self.axis_BS[f].plot(
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acoustic_data._time[np.where(np.round(acoustic_data._time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(acoustic_data._time, 2) == self.spinbox_tmax.value())[0][0]],
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np.max(r_bottom[np.where(np.round(acoustic_data._time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(acoustic_data._time, 2) == self.spinbox_tmax.value())[0][0]])
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- r_bottom[np.where(np.round(acoustic_data._time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(acoustic_data._time, 2) == self.spinbox_tmax.value())[0][0]]
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+ np.min(r_bottom[np.where(np.round(acoustic_data._time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(acoustic_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="--")
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# self.axis_BS[f].text(1, .70, acoustic_data._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', transform=self.axis_BS[f].transAxes)
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# self.fig_BS.supxlabel('Distance from left bank (m)', fontsize=10)
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# self.fig_BS.supylabel('Depth (m)', fontsize=10)
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# # plt.subplots_adjust(bottom=0.125, top=0.98, right=1.03, left=0.08, hspace=0.1)
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# # self.fig.tight_layout()
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# cbar = self.fig_BS.colorbar(pcm, ax=self.axis_BS[:], shrink=1, location='right')
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# cbar.set_label(label='Backscatter acoustic signal (V)', rotation=270, labelpad=10)
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self.fig_BS.canvas.draw_idle()
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return r_bottom, val_bottom, r_bottom_ind, BS_section_bottom
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