SNR is computed from Aquascat data and implemented following UBSediFlow array layout for acoustic data tab.
brahim
parent
0e4899da9c
commit
bdf900a9bb
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@ -681,6 +681,9 @@ class AcousticDataTab(QWidget):
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self.WindowNoiseLevelTailAveragedProfile().show()
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def open_dialog_box(self):
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print(self.combobox_ABS_system_choice.currentText())
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print(self.sender().objectName())
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# --- Open dialog box + choice directory and select file ---
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if self.combobox_ABS_system_choice.currentIndex() == 0:
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msgBox = QMessageBox()
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@ -695,7 +698,7 @@ class AcousticDataTab(QWidget):
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"Aquascat file (*.aqa)")
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dir_name = path.dirname(filename[0])
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name = path.basename(filename[0])
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print(dir_name, name)
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elif self.combobox_ABS_system_choice.currentIndex() == 2:
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filename = QFileDialog.getOpenFileName(self, "Open file", "", "UBSediFlow file (*.udt)")
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dir_name = path.dirname(filename[0])
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@ -710,7 +713,9 @@ class AcousticDataTab(QWidget):
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stg.path_BS_raw_data = dir_name
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stg.filename_BS_raw_data = name
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self.load_BS_acoustic_raw_data()
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print("0 Je suis sur la donnée BS")
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except ValueError as e:
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print("1 Je suis sur la donnée BS")
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msgBox = QMessageBox()
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msgBox.setWindowTitle("Download Error")
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msgBox.setIcon(QMessageBox.Warning)
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@ -731,8 +736,12 @@ class AcousticDataTab(QWidget):
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try:
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stg.path_BS_noise_data = dir_name
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stg.filename_BS_noise_data = name
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print("dir_name ", stg.path_BS_noise_data)
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print("filename ", stg.filename_BS_noise_data)
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self.load_noise_data_and_compute_SNR()
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print("0 je suis sur la donnée SNR")
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except ValueError as e:
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print("1 je suis sur la donnée SNR")
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msgBox = QMessageBox()
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msgBox.setWindowTitle("Download Error")
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msgBox.setIcon(QMessageBox.Warning)
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@ -793,13 +802,15 @@ class AcousticDataTab(QWidget):
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# stg.BS_noise_data = noise_data._BS_raw_data
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stg.date_noise = noise_data._date
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stg.hour_noise = noise_data._hour
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stg.time_snr = noise_data._time
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stg.time_snr = stg.time
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stg.time_snr_reshape = stg.time_reshape
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print(stg.time_snr.shape)
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noise = np.zeros(stg.BS_raw_data.shape)
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for f in range(noise_data._freq.shape[0]):
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noise[:, f, :] = np.mean(noise_data._BS_raw_data[:, f, :], axis=(0, 1))
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for f, _ in enumerate(noise_data._freq):
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noise[f, :, :] = np.mean(noise_data._BS_raw_data[f, :, :], axis=(0, 1))
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stg.BS_noise_data = noise
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stg.snr = np.divide((stg.BS_raw_data - stg.BS_noise_data) ** 2, stg.BS_noise_data ** 2)
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stg.snr_reshape = np.reshape(stg.snr, (stg.r.shape[0] * stg.time.shape[0], stg.freq.shape[0]), order="F")
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stg.snr_reshape = np.reshape(stg.snr, (stg.r.shape[1] * stg.time.shape[1], stg.freq.shape[0]), order="F")
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elif self.combobox_ABS_system_choice.currentIndex() == 2:
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@ -1106,92 +1117,98 @@ class AcousticDataTab(QWidget):
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# self.spinbox_tmin.setValue(np.min(noise_data._time_snr))
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# self.spinbox_tmax.setValue(np.round(np.max(noise_data._time_snr), 2))
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if self.combobox_ABS_system_choice.currentIndex() == 1:
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# if self.combobox_ABS_system_choice.currentIndex() == 1:
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#
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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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#
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# for f in range(stg.freq.shape[0]):
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#
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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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#
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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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#
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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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#
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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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# 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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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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# elif self.combobox_ABS_system_choice.currentIndex() == 2:
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for f in range(stg.freq.shape[0]):
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x = np.array([[[]]])
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y = np.array([[[]]])
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print(f"x : {x.shape}, y : {y.shape}")
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for f, freq in enumerate(stg.freq):
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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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if x.shape[2] == 0:
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x, y = np.meshgrid(stg.time_snr[f, :], stg.r[f, :])
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x = np.array([x])
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y = np.array([y])
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print(f"x : {x.shape}, y : {y.shape}")
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else:
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x0, y0 = np.meshgrid(stg.time_snr[f, :], stg.r[f, :])
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x = np.append(x, np.array([x0]), axis=0)
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y = np.append(y, np.array([y0]), axis=0)
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print(f"x : {x.shape}, y : {y.shape}")
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val_min = np.nanmin(abs(stg.snr[f, :, :]))
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# print(f"val_min = {val_min}")
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val_max = np.nanmax(abs(stg.snr[f, :, :]))
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# print(f"val_max = {val_max}")
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if int(val_min) == 0:
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val_min = 1e-5
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if int(val_max) < 1000:
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levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
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bounds = [00.1, 1, 2, 10, 100, 1000, 1e6, 1e6 * 1.2]
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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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norm = BoundaryNorm(boundaries=bounds, ncolors=300)
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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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# print(f"levels = {levels}")
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# print(f"norm = {norm.boundaries}")
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if self.combobox_ABS_system_choice.currentIndex() == 1:
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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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cf = self.axis_SNR[f].contourf(x[f, :, :], -y[f, :, :],
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stg.snr[f, :, :], levels, cmap='gist_rainbow', norm=norm)
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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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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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elif self.combobox_ABS_system_choice.currentIndex() == 2:
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x = np.array([[[]]])
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y = np.array([[[]]])
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print(f"x : {x.shape}, y : {y.shape}")
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for f, freq in enumerate(stg.freq):
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if x.shape[2] == 0:
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x, y = np.meshgrid(stg.time_snr[f, :], stg.r[f, :])
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x = np.array([x])
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y = np.array([y])
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print(f"x : {x.shape}, y : {y.shape}")
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else:
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x0, y0 = np.meshgrid(stg.time_snr[f, :], stg.r[f, :])
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x = np.append(x, np.array([x0]), axis=0)
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y = np.append(y, np.array([y0]), axis=0)
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print(f"x : {x.shape}, y : {y.shape}")
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val_min = np.nanmin(abs(stg.snr[f, :, :]))
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# print(f"val_min = {val_min}")
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val_max = np.nanmax(abs(stg.snr[f, :, :]))
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# print(f"val_max = {val_max}")
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if int(val_min) == 0:
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val_min = 1e-5
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if int(val_max) < 1000:
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levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
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bounds = [00.1, 1, 2, 10, 100, 1000, 1e6, 1e6 * 1.2]
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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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# print(f"levels = {levels}")
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# print(f"norm = {norm.boundaries}")
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elif self.combobox_ABS_system_choice.currentIndex() == 2:
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cf = self.axis_SNR[f].contourf(x[f, :, :], -y[f, :, :], stg.snr[f, :, :])#, levels, cmap='gist_rainbow', norm=norm)
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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.fig_SNR.supxlabel('Time (sec)', fontsize=10)
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self.fig_SNR.supylabel('Depth (m)', fontsize=10)
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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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self.fig_SNR.canvas.draw_idle()
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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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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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self.fig_SNR.canvas.draw_idle()
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def update_xaxis_transect_with_SNR_data(self):
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@ -1216,7 +1233,7 @@ class AcousticDataTab(QWidget):
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x = np.array([[[]]])
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y = np.array([[[]]])
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print(f"x : {x.shape}, y : {y.shape}")
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for f in range(stg.freq.shape[0]):
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for f, _ in enumerate(stg.freq):
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if x.shape[2] == 0:
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x, y = np.meshgrid(stg.time_snr[f, :], stg.r[f, :])
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@ -1269,62 +1286,71 @@ class AcousticDataTab(QWidget):
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self.axis_SNR[f].cla()
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# if self.combobox_ABS_system_choice.currentIndex() == 1:
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#
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# val_min = np.nanmin(stg.SNR_data[f, :, :])
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# val_max = np.nanmax(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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#
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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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#
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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, 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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#
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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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#
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# elif self.combobox_ABS_system_choice.currentIndex() == 2:
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val_min = np.nanmin(abs(stg.snr[f, :, :]))
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# print(f"val_min = {val_min}")
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val_max = np.nanmax(abs(stg.snr[f, :, :]))
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# print(f"val_max = {val_max}")
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if int(val_min) == 0:
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val_min = 1e-5
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if int(val_max) < 1000:
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levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
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bounds = [00.1, 1, 2, 10, 100, 1000, 1e6, 1e6 * 1.2]
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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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# print(f"levels = {levels}")
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# print(f"norm = {norm.boundaries}")
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if self.combobox_ABS_system_choice.currentIndex() == 1:
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val_min = np.nanmin(stg.SNR_data[f, :, :])
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val_max = np.nanmax(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, 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, 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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cf = self.axis_SNR[f].contourf(x[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])],
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-y[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])],
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stg.snr[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])],
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levels, cmap='gist_rainbow', norm=norm)
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elif self.combobox_ABS_system_choice.currentIndex() == 2:
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val_min = np.nanmin(abs(stg.snr[f, :, :]))
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# print(f"val_min = {val_min}")
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val_max = np.nanmax(abs(stg.snr[f, :, :]))
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# print(f"val_max = {val_max}")
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if int(val_min) == 0:
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val_min = 1e-5
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if int(val_max) < 1000:
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levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
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bounds = [00.1, 1, 2, 10, 100, 1000, 1e6, 1e6 * 1.2]
|
||||
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)
|
||||
|
||||
# print(f"levels = {levels}")
|
||||
# print(f"norm = {norm.boundaries}")
|
||||
|
||||
cf = self.axis_SNR[f].contourf(x[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])],
|
||||
-y[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])],
|
||||
stg.snr[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])]) # , levels, cmap='gist_rainbow', norm=norm)
|
||||
-y[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])],
|
||||
stg.snr[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])]) # , levels, cmap='gist_rainbow', norm=norm)
|
||||
|
||||
self.axis_SNR[f].text(1, .70, stg.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].text(1, .70, stg.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.fig_SNR.supxlabel('Distance from left bank (m)', fontsize=10)
|
||||
self.fig_SNR.supylabel('Depth (m)', fontsize=10)
|
||||
self.fig_SNR.canvas.draw_idle()
|
||||
self.fig_SNR.supxlabel('Distance from left bank (m)', fontsize=10)
|
||||
self.fig_SNR.supylabel('Depth (m)', fontsize=10)
|
||||
self.fig_SNR.canvas.draw_idle()
|
||||
|
||||
def detect_bottom(self):
|
||||
if self.lineEdit_acoustic_file.text() == "":
|
||||
|
|
@ -1371,11 +1397,12 @@ class AcousticDataTab(QWidget):
|
|||
# ----------- Detecting the bottom -------------
|
||||
for d in range(stg.nb_profiles):
|
||||
# Index of the range where we look for the peak
|
||||
ind_min = np.where(stg.r >= rmin)[0][0]
|
||||
ind_max = np.where(stg.r <= rmax)[0][-1]
|
||||
ind_min = np.where(stg.r[int(self.combobox_freq_choice.currentIndex()), :] >= rmin)[0][0]
|
||||
ind_max = np.where(stg.r[int(self.combobox_freq_choice.currentIndex()), :] <= rmax)[0][-1]
|
||||
|
||||
# Getting the peak
|
||||
try:
|
||||
val_bottom[d] = np.nanmax(stg.BS_raw_data[ind_min:ind_max, self.combobox_freq_choice.currentIndex(), d])
|
||||
val_bottom[d] = np.nanmax(stg.BS_raw_data[self.combobox_freq_choice.currentIndex(), ind_min:ind_max, d])
|
||||
except ValueError as e:
|
||||
msgBox = QMessageBox()
|
||||
msgBox.setWindowTitle("Detect bottom Error")
|
||||
|
|
@ -1388,17 +1415,17 @@ class AcousticDataTab(QWidget):
|
|||
break #msgBox.close()
|
||||
else:
|
||||
# Getting the range cell of the peak
|
||||
ind_bottom = np.where(stg.BS_raw_data[ind_min:ind_max, self.combobox_freq_choice.currentIndex(), d]
|
||||
ind_bottom = np.where(stg.BS_raw_data[self.combobox_freq_choice.currentIndex(), ind_min:ind_max, d]
|
||||
== val_bottom[d])[0][0]
|
||||
np.append(stg.ind_bottom, ind_bottom)
|
||||
|
||||
r_bottom[d] = stg.r[ind_bottom + ind_min]
|
||||
r_bottom[d] = stg.r[self.combobox_freq_choice.currentIndex(), ind_bottom + ind_min]
|
||||
r_bottom_ind.append(ind_bottom + ind_min)
|
||||
# Updating the range where we will look for the peak (in the next cell)
|
||||
rmin = r_bottom[d] - locale.atof(self.doublespinbox_next_cell.text())
|
||||
rmax = r_bottom[d] + locale.atof(self.doublespinbox_next_cell.text())
|
||||
|
||||
BS_section_bottom = np.zeros((stg.r.shape[0], stg.time.shape[0]))
|
||||
BS_section_bottom = np.zeros((stg.r.shape[1], stg.time.shape[1]))
|
||||
|
||||
for i in range(BS_section_bottom.shape[0]):
|
||||
try:
|
||||
|
|
@ -1416,35 +1443,45 @@ class AcousticDataTab(QWidget):
|
|||
|
||||
if BS_section_bottom.sum() > 2:
|
||||
# --- Record r_bottom for other tabs ---
|
||||
stg.r_bottom = 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]]
|
||||
stg.val_bottom = val_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]]
|
||||
# stg.r_bottom = 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]]
|
||||
# stg.val_bottom = val_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]]
|
||||
stg.r_bottom = r_bottom[int(stg.tmin[self.combobox_freq_choice.currentIndex()]):
|
||||
int(stg.tmax[self.combobox_freq_choice.currentIndex()])]
|
||||
stg.val_bottom = val_bottom[int(stg.tmin[self.combobox_freq_choice.currentIndex()]):
|
||||
int(stg.tmax[self.combobox_freq_choice.currentIndex()])]
|
||||
|
||||
# --- Plot transect BS with bathymetry ---
|
||||
for f in range(stg.freq.shape[0]):
|
||||
for f, _ in enumerate(stg.freq):
|
||||
self.axis_BS[f].cla()
|
||||
|
||||
val_min = np.min(stg.BS_raw_data[:, f, :])
|
||||
val_max = np.max(stg.BS_raw_data[:, f, :])
|
||||
val_min = np.min(stg.BS_raw_data[f, :, :])
|
||||
val_max = np.max(stg.BS_raw_data[f, :, :])
|
||||
if val_min == 0:
|
||||
val_min = 1e-5
|
||||
|
||||
pcm = self.axis_BS[f].pcolormesh(
|
||||
stg.time[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]],
|
||||
-stg.r,
|
||||
(stg.BS_raw_data[:, f,
|
||||
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]]),
|
||||
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
|
||||
# pcm = self.axis_BS[f].pcolormesh(
|
||||
# stg.time[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]],
|
||||
# -stg.r,
|
||||
# (stg.BS_raw_data[:, f,
|
||||
# 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]]),
|
||||
# cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
|
||||
|
||||
self.axis_BS[f].plot(
|
||||
stg.time[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]],
|
||||
- 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]],
|
||||
color='black', linewidth=1, linestyle="solid")
|
||||
pcm = self.axis_BS[f].pcolormesh(stg.t[f, :], -stg.r[f, :], stg.BS_data[f, :, :],
|
||||
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
|
||||
|
||||
# self.axis_BS[f].plot(
|
||||
# stg.time[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]],
|
||||
# - 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]],
|
||||
# color='black', linewidth=1, linestyle="solid")
|
||||
|
||||
self.axis_BS[f].plot(stg.t[self.combobox_freq_choice.currentIndex(), :], -stg.r_bottom,
|
||||
color='black', linewidth=1, linestyle="solid")
|
||||
|
||||
self.axis_BS[f].text(1, .70, stg.freq_text[f],
|
||||
fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
|
||||
|
|
@ -1455,47 +1492,80 @@ class AcousticDataTab(QWidget):
|
|||
|
||||
# --- Plot transect SNR with bathymetry ---
|
||||
if self.canvas_SNR != None:
|
||||
x, y = np.meshgrid(
|
||||
stg.time[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]],
|
||||
stg.r)
|
||||
# x, y = np.meshgrid(
|
||||
# stg.time[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]],
|
||||
# stg.r)
|
||||
|
||||
x = np.array([[[]]])
|
||||
y = np.array([[[]]])
|
||||
print(f"x : {x.shape}, y : {y.shape}")
|
||||
|
||||
for f, _ in enumerate(stg.freq):
|
||||
|
||||
if x.shape[2] == 0:
|
||||
x, y = np.meshgrid(stg.time_snr[f, :], stg.r[f, :])
|
||||
x = np.array([x])
|
||||
y = np.array([y])
|
||||
print(f"x : {x.shape}, y : {y.shape}")
|
||||
else:
|
||||
x0, y0 = np.meshgrid(stg.time_snr[f, :], stg.r[f, :])
|
||||
x = np.append(x, np.array([x0]), axis=0)
|
||||
y = np.append(y, np.array([y0]), axis=0)
|
||||
print(f"x : {x.shape}, y : {y.shape}")
|
||||
|
||||
for f in range(stg.freq.shape[0]):
|
||||
self.axis_SNR[f].cla()
|
||||
|
||||
val_min = np.min(stg.snr[:, f, :])
|
||||
val_max = np.max(stg.snr[:, f, :])
|
||||
val_min = abs(np.nanmin(stg.snr[f, :, :]))
|
||||
val_max = abs(np.nanmax(stg.snr[f, :, :]))
|
||||
if val_min == 0:
|
||||
val_min = 1e-5
|
||||
if val_max > 1000:
|
||||
if val_max < 1000:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
else:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, val_max])
|
||||
bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
|
||||
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
|
||||
|
||||
cf = self.axis_SNR[f].contourf(x, -y,
|
||||
stg.snr[:, f,
|
||||
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]],
|
||||
levels, cmap='gist_rainbow', norm=norm) # , shading='gouraud')
|
||||
# cf = self.axis_SNR[f].contourf(x, -y,
|
||||
# stg.snr[:, f,
|
||||
# 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]],
|
||||
# levels, cmap='gist_rainbow', norm=norm) # , shading='gouraud')
|
||||
|
||||
if self.combobox_ABS_system_choice.currentIndex() == 1:
|
||||
|
||||
cf = self.axis_SNR[f].contourf(x[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])],
|
||||
-y[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])],
|
||||
stg.snr[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])],
|
||||
levels, cmap='gist_rainbow', norm=norm)
|
||||
|
||||
elif self.combobox_ABS_system_choice.currentIndex() == 2:
|
||||
|
||||
cf = self.axis_SNR[f].contourf(x[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])],
|
||||
-y[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])],
|
||||
stg.snr[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[
|
||||
f])]) # , levels, cmap='gist_rainbow', norm=norm)
|
||||
|
||||
self.axis_SNR[f].text(1, .70, stg.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(
|
||||
stg.time[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]],
|
||||
- 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]],
|
||||
self.axis_SNR[f].plot(stg.t[self.combobox_freq_choice.currentIndex(), :], -stg.r_bottom,
|
||||
color='black', linewidth=1, linestyle="solid")
|
||||
|
||||
# self.axis_SNR[f].plot(
|
||||
# stg.time[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]],
|
||||
# - 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]],
|
||||
# + 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")
|
||||
# color='black', linewidth=1, linestyle="solid")
|
||||
|
||||
self.axis_SNR[f].text(1, .70, stg.freq_text[f],
|
||||
fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
|
||||
|
|
@ -1504,6 +1574,7 @@ class AcousticDataTab(QWidget):
|
|||
|
||||
self.fig_SNR.canvas.draw_idle()
|
||||
|
||||
|
||||
# else:
|
||||
#
|
||||
# acoustic_data = self.load_BS_acoustic_raw_data()
|
||||
|
|
|
|||
Loading…
Reference in New Issue