Acoustic data tab is all updated with global variables from settings.py file
brahim
parent
58138d0e4e
commit
89fc888797
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@ -339,7 +339,7 @@ class AcousticDataTab(QWidget):
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self.spinbox_tmin.setRange(0, 9999)
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self.gridLayout_groupbox_xaxis_time.addWidget(self.spinbox_tmin, 0, 2, 1, 1)
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self.spinbox_tmin.valueChanged.connect(self.update_xaxis_transect_with_BS_raw_data)
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# self.spinbox_tmin.valueChanged.connect(self.plot_transect_with_BS_raw_data)
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self.spinbox_tmin.valueChanged.connect(self.update_xaxis_transect_with_SNR_data)
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self.label_tmin_unit = QLabel()
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self.label_tmin_unit.setText("sec")
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@ -357,7 +357,7 @@ class AcousticDataTab(QWidget):
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self.spinbox_tmax.setRange(0, 9999)
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self.gridLayout_groupbox_xaxis_time.addWidget(self.spinbox_tmax, 0, 6, 1, 1)
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self.spinbox_tmax.valueChanged.connect(self.update_xaxis_transect_with_BS_raw_data)
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# self.spinbox_tmax.valueChanged.connect(self.update_xaxis_transect_with_SNR_data)
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self.spinbox_tmax.valueChanged.connect(self.update_xaxis_transect_with_SNR_data)
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self.label_tmax_unit = QLabel()
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self.label_tmax_unit.setText("sec")
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@ -836,7 +836,7 @@ class AcousticDataTab(QWidget):
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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.supxlabel('Distance from left bank (m)', fontsize=10)
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self.fig_BS.supxlabel('Time (sec)', fontsize=10)
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self.fig_BS.supylabel('Depth (m)', fontsize=10)
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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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@ -878,7 +878,7 @@ class AcousticDataTab(QWidget):
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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.supxlabel('Distance from left bank (m)', fontsize=10)
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self.fig_BS.supxlabel('Time (sec)', fontsize=10)
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self.fig_BS.supylabel('Depth (m)', fontsize=10)
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self.fig_BS.canvas.draw_idle()
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@ -897,10 +897,9 @@ class AcousticDataTab(QWidget):
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msgBox.setText("Plot backscatter acoustic raw data 2D field 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) > 11:
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noise_data = self.compute_SNR()
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elif (self.lineEdit_noise_file.text()) and (self.tableModel.rowCount(1) > 11):
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self.fig_SNR, self.axis_SNR = plt.subplots(nrows=noise_data._freq.shape[0] , ncols=1,
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self.fig_SNR, self.axis_SNR = plt.subplots(nrows=stg.freq.shape[0] , ncols=1,
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sharex=True, sharey=False, layout="constrained")
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self.canvas_SNR = FigureCanvas(self.fig_SNR)
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self.verticalLayout_groupbox_transect_2Dplot_snr_data.addWidget(self.canvas_SNR)
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@ -915,14 +914,14 @@ class AcousticDataTab(QWidget):
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# self.spinbox_tmax.setValue(np.round(np.max(noise_data._time_snr), 2))
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x, y = np.meshgrid(
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noise_data._time_snr[np.where(np.round(noise_data._time_snr, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(noise_data._time_snr, 2) == self.spinbox_tmax.value())[0][0]],
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noise_data._r)
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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(noise_data._freq.shape[0]):
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for f in range(stg.freq.shape[0]):
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val_min = np.min(noise_data._snr[:, f, :])
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val_max = np.max(noise_data._snr[:, f, :])
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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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@ -932,40 +931,47 @@ class AcousticDataTab(QWidget):
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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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noise_data._snr[:, f,
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np.where(np.round(noise_data._time_snr, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(noise_data._time_snr, 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].
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contourf(x, -y,
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stg.snr[:, f,
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np.where(np.round(stg.snr, 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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levels, cmap='gist_rainbow', norm=norm))
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self.axis_SNR[f].text(1, .70, noise_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_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.fig_SNR.supxlabel('Distance from left bank (m)', fontsize=10)
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self.fig_SNR.supxlabel('Time (sec)', fontsize=10)
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self.fig_SNR.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_SNR.colorbar(cf, ax=self.axis_SNR[:], shrink=1, location='right')
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cbar.set_label(label='Signal to Noise Ratio', rotation=270, labelpad=10)
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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 update_xaxis_transect_with_SNR_data(self):
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if self.lineEdit_noise_file.text():
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noise_data = self.compute_SNR()
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if ((self.canvas_BS != None) and (self.canvas_SNR != None)):
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# if self.canvas_SNR == None:
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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("Plot transect before change x-axis value")
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# msgBox.setStandardButtons(QMessageBox.Ok)
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# msgBox.exec()
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if ((self.canvas_BS != None) and (self.canvas_SNR != None)):
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x, y = np.meshgrid(
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noise_data._time_snr[np.where(np.round(noise_data._time_snr, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(noise_data._time_snr, 2) == self.spinbox_tmax.value())[0][0]],
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noise_data._r)
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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(noise_data._freq.shape[0]):
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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(noise_data._snr[:, f, :])
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val_max = np.max(noise_data._snr[:, f, :])
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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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@ -976,25 +982,18 @@ class AcousticDataTab(QWidget):
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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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noise_data._snr[:, f,
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np.where(np.round(noise_data._time_snr,
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2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(noise_data._time_snr,
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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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stg.snr[:, 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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levels, cmap='gist_rainbow', norm=norm)
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self.axis_SNR[f].text(1, .70, noise_data._freq_text[f],
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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.fig_SNR.supxlabel('Distance from left bank (m)', fontsize=10)
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self.fig_SNR.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_SNR.colorbar(cf, ax=self.axis_SNR[:], shrink=1, location='right')
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# cbar.set_label(label='Signal to Noise Ratio', rotation=270, labelpad=10)
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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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@ -1028,35 +1027,31 @@ class AcousticDataTab(QWidget):
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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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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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rmin = np.int(self.spinbox_depth_min.text())
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rmax = np.int(self.spinbox_depth_max.text())
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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 = np.zeros(stg.nb_profiles)
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val_bottom = np.zeros(stg.nb_profiles)
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r_bottom_ind = []
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# ----------- Detecting the bottom -------------
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progessBar = QProgressBar()
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for d in range(acoustic_data._nb_profiles):
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progessBar.setValue(d)
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for d in range(stg.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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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(acoustic_data._BS_raw_data[ind_min:ind_max,
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self.combobox_freq_choice.currentIndex(), d])
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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(acoustic_data._BS_raw_data[ind_min:ind_max,
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self.combobox_freq_choice.currentIndex(), d] == val_bottom[d])[0][0]
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r_bottom[d] = acoustic_data._r[ind_bottom + ind_min]
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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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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()) # 0.75
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rmax = r_bottom[d] + locale.atof(self.doublespinbox_next_cell.text()) # 0.75
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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((acoustic_data._BS_raw_data.shape[0], acoustic_data._BS_raw_data.shape[2]))
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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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@ -1065,40 +1060,88 @@ class AcousticDataTab(QWidget):
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# print(BS_section_bottom[r_bottom_temp_ind[i]][i])
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# --- Plot transect BS with bathymetry ---
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for f in range(acoustic_data._freq.shape[0]):
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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(acoustic_data._BS_raw_data[:, f, :])
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val_max = np.max(acoustic_data._BS_raw_data[:, f, :])
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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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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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(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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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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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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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, acoustic_data._freq_text[f],
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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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# --- Plot transect SNR with bathymetry ---
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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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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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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]],
|
||||
- r_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
|
||||
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]]),
|
||||
# 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,
|
||||
horizontalalignment='right', verticalalignment='bottom',
|
||||
transform=self.axis_BS[f].transAxes)
|
||||
|
||||
self.fig_SNR.canvas.draw_idle()
|
||||
|
||||
# else:
|
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#
|
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# acoustic_data = self.load_BS_acoustic_raw_data()
|
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|
|
@ -1171,57 +1214,7 @@ class AcousticDataTab(QWidget):
|
|||
# transform=self.axis_BS[f].transAxes)
|
||||
#
|
||||
#
|
||||
# # --- Plot transect SNR with bathymetry ---
|
||||
#
|
||||
# noise_data = self.compute_SNR()
|
||||
#
|
||||
# x, y = np.meshgrid(
|
||||
# noise_data._time_snr[np.where(np.round(noise_data._time_snr, 2) == self.spinbox_tmin.value())[0][0]:
|
||||
# np.where(np.round(noise_data._time_snr, 2) == self.spinbox_tmax.value())[0][0]],
|
||||
# noise_data._r)
|
||||
#
|
||||
# for f in range(noise_data._freq.shape[0]):
|
||||
# self.axis_SNR[f].cla()
|
||||
#
|
||||
# val_min = np.min(noise_data._snr[:, f, :])
|
||||
# val_max = np.max(noise_data._snr[:, f, :])
|
||||
# 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,
|
||||
# noise_data._snr[:, f,
|
||||
# np.where(np.round(noise_data._time_snr,
|
||||
# 2) == self.spinbox_tmin.value())[0][0]:
|
||||
# np.where(np.round(noise_data._time_snr,
|
||||
# 2) == self.spinbox_tmax.value())[0][0]],
|
||||
# levels, cmap='gist_rainbow', norm=norm) # , shading='gouraud')
|
||||
#
|
||||
# self.axis_SNR[f].text(1, .70, noise_data._freq_text[f],
|
||||
# fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
|
||||
# horizontalalignment='right', verticalalignment='bottom',
|
||||
# transform=self.axis_SNR[f].transAxes)
|
||||
#
|
||||
# self.axis_SNR[f].plot(
|
||||
# noise_data._time_snr[np.where(np.round(noise_data._time_snr, 2) == self.spinbox_tmin.value())[0][0]:
|
||||
# np.where(np.round(noise_data._time_snr, 2) == self.spinbox_tmax.value())[0][0]],
|
||||
# # np.max(r_bottom[np.where(np.round(noise_data._time, 2) == self.spinbox_tmin.value())[0][0]:
|
||||
# # np.where(np.round(noise_data._time, 2) == self.spinbox_tmax.value())[0][0]])
|
||||
# - r_bottom[np.where(np.round(noise_data._time_snr, 2) == self.spinbox_tmin.value())[0][0]:
|
||||
# np.where(np.round(noise_data._time_snr, 2) == self.spinbox_tmax.value())[0][0]],
|
||||
# # + np.min(r_bottom[np.where(np.round(noise_data._time, 2) == self.spinbox_tmin.value())[0][0]:
|
||||
# # np.where(np.round(noise_data._time, 2) == self.spinbox_tmax.value())[0][0]]),
|
||||
# # np.max(self._model.r_bottom_cross_section) - self._model.r_bottom_cross_section + np.min(self._model.r_bottom_cross_section),
|
||||
# color='black', linewidth=1, linestyle="solid")
|
||||
#
|
||||
# self.axis_SNR[f].text(1, .70, acoustic_data._freq_text[f],
|
||||
# fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
|
||||
# horizontalalignment='right', verticalalignment='bottom', transform=self.axis_BS[f].transAxes)
|
||||
|
||||
|
||||
# self.fig_BS.supxlabel('Distance from left bank (m)', fontsize=10)
|
||||
# self.fig_BS.supylabel('Depth (m)', fontsize=10)
|
||||
|
|
@ -1229,6 +1222,6 @@ class AcousticDataTab(QWidget):
|
|||
# # self.fig.tight_layout()
|
||||
# cbar = self.fig_BS.colorbar(pcm, ax=self.axis_BS[:], shrink=1, location='right')
|
||||
# cbar.set_label(label='Backscatter acoustic signal (V)', rotation=270, labelpad=10)
|
||||
self.fig_BS.canvas.draw_idle()
|
||||
|
||||
# self.fig_SNR.canvas.draw_idle()
|
||||
return r_bottom, val_bottom, r_bottom_ind, BS_section_bottom
|
||||
# return r_bottom, val_bottom, r_bottom_ind, BS_section_bottom
|
||||
|
|
|
|||
Loading…
Reference in New Issue