Aquascat data are averaged with new shape of array (freq shape, r shape, t shape)
brahim
parent
bdf900a9bb
commit
1bda848671
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@ -637,10 +637,10 @@ class SignalProcessingTab(QWidget):
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stg.BS_data_section = deepcopy(stg.BS_data)
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elif stg.r_bottom.size != 0:
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stg.BS_data_section = deepcopy(stg.BS_data)
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for f in range(stg.freq.shape[0]):
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for k in range(stg.r_bottom.shape[0]):
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for f, _ in enumerate(stg.freq):
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for k, _ in enumerate(stg.r_bottom):
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# print(k, np.where(stg.r >= stg.r_bottom[k])[0])
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stg.BS_data_section[np.where(stg.r >= stg.r_bottom[k])[0], f, k] = np.nan
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stg.BS_data_section[f, np.where(stg.r[self.combobox_frequency.currentIndex(), :] >= stg.r_bottom[k])[0], k] = np.nan
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# --- Choose frequency (Combo box) to plot transect with profile position ---
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self.combobox_frequency.addItems(stg.freq_text)
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@ -653,7 +653,7 @@ class SignalProcessingTab(QWidget):
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self.combobox_frequency_compute_alphaS.addItems(stg.freq_text)
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# --- Fix maximum value of slider + Edit Label Profile number ---
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self.slider.setMaximum(stg.t.shape[0])
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self.slider.setMaximum(stg.t.shape[1])
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self.label_profile_number.clear()
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self.label_profile_number.setText("Profile " + str(self.slider.value()) + " / " + str(self.slider.maximum()))
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@ -680,13 +680,17 @@ class SignalProcessingTab(QWidget):
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msgBox.setStandardButtons(QMessageBox.Ok)
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msgBox.exec()
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else:
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filter_convolve = np.ones(self.spinbox_average_horizontal.value())
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stg.BS_data_section_averaged = np.zeros((stg.r.shape[0], stg.freq.shape[0], stg.t.shape[0]))
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for f in range(stg.freq.shape[0]):
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for i in range(stg.r.shape[0]):
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stg.BS_data_section_averaged[i, f, :] \
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= convolve1d(stg.BS_data_section[i, f, :], weights=filter_convolve) / filter_convolve.shape[0]
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filter_convolve = np.ones( self.spinbox_average_horizontal.value())
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print(filter_convolve)
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# stg.BS_data_section_averaged = np.zeros((stg.r.shape[0], stg.freq.shape[0], stg.t.shape[0]))
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# stg.BS_data_section_averaged = np.zeros((stg.freq.shape[0], stg.r.shape[1], stg.t.shape[1]))
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stg.BS_data_section_averaged = deepcopy(stg.BS_data_section)
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for f, _ in enumerate(stg.freq):
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for i in range(stg.r.shape[1]):
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stg.BS_data_section_averaged[f, i, :] \
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= convolve1d(stg.BS_data_section[f, i, :], weights=filter_convolve) / filter_convolve.shape[0]
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# stg.BS_data_section_averaged[i, f, :] \
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# = convolve1d(stg.BS_data_section[i, f, :], weights=filter_convolve) / filter_convolve.shape[0]
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self.label_cells_horizontal.clear()
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self.label_cells_horizontal.setText(
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@ -699,6 +703,19 @@ class SignalProcessingTab(QWidget):
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self.plot_averaged_profile()
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self.update_plot_profile_position_on_transect()
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# fig, ax = plt.subplots(nrows=1, ncols=1)
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# val_min = np.nanmin(stg.BS_data_section_averaged[self.combobox_frequency.currentIndex(), :, :])
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# val_max = np.nanmax(stg.BS_data_section_averaged[self.combobox_frequency.currentIndex(), :, :])
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# if val_min == 0:
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# val_min = 1e-5
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#
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# ax.pcolormesh(stg.t[self.combobox_frequency.currentIndex(), :],
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# -stg.r[self.combobox_frequency.currentIndex(), :],
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# stg.BS_data_section_averaged[self.combobox_frequency.currentIndex(), :, :],
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# cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
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#
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# plt.show()
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# ---------------------------------------- Connect Groupbox filter with SNR ----------------------------------------
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def remove_point_with_snr_filter(self):
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@ -716,26 +733,26 @@ class SignalProcessingTab(QWidget):
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if stg.BS_data_section_averaged.size == 0:
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stg.BS_data_section_SNR_filter = deepcopy(stg.BS_data_section)
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stg.SNR_data_average = np.divide(
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(stg.BS_data_section_SNR_filter - stg.Noise_data[:, :, :stg.t.shape[0]])**2,
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stg.Noise_data[:, :, :stg.t.shape[0]]**2)
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(stg.BS_data_section_SNR_filter - stg.Noise_data[:, :, :stg.t.shape[1]])**2,
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stg.Noise_data[:, :, :stg.t.shape[1]]**2)
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for f in range(stg.freq.shape[0]):
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stg.BS_data_section_SNR_filter[np.where(stg.SNR_data_average[:, f, :] < self.spinbox_SNR_criterion.value())[0],
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f,
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np.where(stg.SNR_data_average[:, f, :] < self.spinbox_SNR_criterion.value())[1]] \
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for f, _ in enumerate(stg.freq):
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stg.BS_data_section_SNR_filter[f,
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np.where(stg.SNR_data_average[f, :, :] < self.spinbox_SNR_criterion.value())[0],
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np.where(stg.SNR_data_average[f, :, :] < self.spinbox_SNR_criterion.value())[1]] \
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= np.nan
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elif stg.BS_data_section_averaged.size != 0:
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stg.BS_data_section_SNR_filter = deepcopy(stg.BS_data_section_averaged)
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stg.SNR_data_average = np.divide(
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(stg.BS_data_section_SNR_filter - stg.Noise_data[:, :, :stg.t.shape[0]]) ** 2,
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stg.Noise_data[:, :, :stg.t.shape[0]] ** 2)
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(stg.BS_data_section_SNR_filter - stg.Noise_data[:, :, :stg.t.shape[1]]) ** 2,
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stg.Noise_data[:, :, :stg.t.shape[1]] ** 2)
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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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stg.BS_data_section_SNR_filter[
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np.where(stg.SNR_data_average[:, f, :] < self.spinbox_SNR_criterion.value())[0],
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np.where(stg.SNR_data_average[f, :, :] < self.spinbox_SNR_criterion.value())[0],
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f,
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np.where(stg.SNR_data_average[:, f, :] < self.spinbox_SNR_criterion.value())[1]] \
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np.where(stg.SNR_data_average[f, :, :] < self.spinbox_SNR_criterion.value())[1]] \
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= np.nan
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self.update_plot_profile_position_on_transect()
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@ -895,21 +912,22 @@ class SignalProcessingTab(QWidget):
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self.verticalLayout_groupbox_display_profile_position.addWidget(self.canvas_plot_profile_position_on_transect)
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# --- Plot transect with profile position ---
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val_min = np.nanmin(stg.BS_data_section[:, stg.freq_bottom_detection, :])
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val_max = np.nanmax(stg.BS_data_section[:, stg.freq_bottom_detection, :])
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val_min = np.nanmin(stg.BS_data_section[stg.freq_bottom_detection, :, :])
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val_max = np.nanmax(stg.BS_data_section[stg.freq_bottom_detection, :, :])
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if val_min == 0:
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val_min = 1e-5
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self.axis_plot_profile_position_on_transect.pcolormesh(
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stg.t, -stg.r, stg.BS_data_section[:, stg.freq_bottom_detection, :],
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stg.t[stg.freq_bottom_detection, :], -stg.r[stg.freq_bottom_detection, :],
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stg.BS_data_section[stg.freq_bottom_detection, :, :],
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cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
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if stg.r_bottom.size != 0:
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self.axis_plot_profile_position_on_transect.plot(
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stg.t, -stg.r_bottom, color='black', linewidth=1, linestyle="solid")
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stg.t[stg.freq_bottom_detection, :], -stg.r_bottom, color='black', linewidth=1, linestyle="solid")
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self.axis_plot_profile_position_on_transect.plot(
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stg.t[self.slider.value() - 1] * np.ones(stg.r.shape[0]), -stg.r,
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stg.t[stg.freq_bottom_detection, self.slider.value() - 1] * np.ones(stg.r.shape[1]), -stg.r[stg.freq_bottom_detection, :],
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color='red', linestyle="solid", linewidth=2)
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self.axis_plot_profile_position_on_transect.set_xticks([])
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@ -939,21 +957,24 @@ class SignalProcessingTab(QWidget):
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self.axis_plot_profile_position_on_transect.cla()
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val_min = np.nanmin(stg.BS_data_section[:, self.combobox_frequency.currentIndex(), :])
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val_max = np.nanmax(stg.BS_data_section[:, self.combobox_frequency.currentIndex(), :])
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val_min = np.nanmin(stg.BS_data_section[self.combobox_frequency.currentIndex(), :, :])
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val_max = np.nanmax(stg.BS_data_section[self.combobox_frequency.currentIndex(), :, :])
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if val_min == 0:
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val_min = 1e-5
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self.axis_plot_profile_position_on_transect.pcolormesh(
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stg.t, -stg.r, stg.BS_data_section[:, self.combobox_frequency.currentIndex(), :],
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stg.t[self.combobox_frequency.currentIndex(), :], -stg.r[self.combobox_frequency.currentIndex(), :],
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stg.BS_data_section[self.combobox_frequency.currentIndex(), :, :],
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cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
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if stg.r_bottom.size != 0:
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self.axis_plot_profile_position_on_transect.plot(stg.t, -stg.r_bottom,
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self.axis_plot_profile_position_on_transect.plot(stg.t[self.combobox_frequency.currentIndex(), :],
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-stg.r_bottom,
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color='black', linewidth=1, linestyle="solid")
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self.axis_plot_profile_position_on_transect.plot(
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stg.t[self.slider.value()-1] * np.ones(stg.r.shape[0]), -stg.r,
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stg.t[self.combobox_frequency.currentIndex(), self.slider.value()-1] * np.ones(stg.r.shape[1]),
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-stg.r[self.combobox_frequency.currentIndex(), :],
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color='red', linestyle="solid", linewidth=2)
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self.axis_plot_profile_position_on_transect.set_xticks([])
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@ -965,21 +986,24 @@ class SignalProcessingTab(QWidget):
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self.axis_plot_profile_position_on_transect.cla()
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val_min = np.nanmin(stg.BS_data_section_averaged[:, self.combobox_frequency.currentIndex(), :])
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val_max = np.nanmax(stg.BS_data_section_averaged[:, self.combobox_frequency.currentIndex(), :])
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val_min = np.nanmin(stg.BS_data_section_averaged[self.combobox_frequency.currentIndex(), :, :])
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val_max = np.nanmax(stg.BS_data_section_averaged[self.combobox_frequency.currentIndex(), :, :])
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if val_min == 0:
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val_min = 1e-5
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self.axis_plot_profile_position_on_transect.pcolormesh(
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stg.t, -stg.r, stg.BS_data_section_averaged[:, self.combobox_frequency.currentIndex(), :],
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stg.t[self.combobox_frequency.currentIndex(), :], -stg.r[self.combobox_frequency.currentIndex(), :],
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stg.BS_data_section_averaged[self.combobox_frequency.currentIndex(), :, :],
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cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
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if stg.r_bottom.size != 0:
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self.axis_plot_profile_position_on_transect.plot(stg.t, -stg.r_bottom,
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self.axis_plot_profile_position_on_transect.plot(stg.t[self.combobox_frequency.currentIndex(), :],
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-stg.r_bottom,
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color='black', linewidth=1, linestyle="solid")
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self.axis_plot_profile_position_on_transect.plot(
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stg.t[self.slider.value() - 1] * np.ones(stg.r.shape[0]), -stg.r,
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stg.t[self.combobox_frequency.currentIndex(), self.slider.value() - 1] * np.ones(stg.r.shape[1]),
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-stg.r[self.combobox_frequency.currentIndex(), :],
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color='red', linestyle="solid", linewidth=2)
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self.axis_plot_profile_position_on_transect.set_xticks([])
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@ -991,21 +1015,24 @@ class SignalProcessingTab(QWidget):
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self.axis_plot_profile_position_on_transect.cla()
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val_min = np.nanmin(stg.BS_data_section_SNR_filter[:, self.combobox_frequency.currentIndex(), :])
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val_max = np.nanmax(stg.BS_data_section_SNR_filter[:, self.combobox_frequency.currentIndex(), :])
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val_min = np.nanmin(stg.BS_data_section_SNR_filter[self.combobox_frequency.currentIndex(), :, :])
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val_max = np.nanmax(stg.BS_data_section_SNR_filter[self.combobox_frequency.currentIndex(), :, :])
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if val_min == 0:
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val_min = 1e-5
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self.axis_plot_profile_position_on_transect.pcolormesh(
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stg.t, -stg.r, stg.BS_data_section_SNR_filter[:, self.combobox_frequency.currentIndex(), :],
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stg.t[self.combobox_frequency.currentIndex(), :], -stg.r[self.combobox_frequency.currentIndex(), :],
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stg.BS_data_section_SNR_filter[self.combobox_frequency.currentIndex(), :, :],
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cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
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if stg.r_bottom.size != 0:
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self.axis_plot_profile_position_on_transect.plot(stg.t, -stg.r_bottom,
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self.axis_plot_profile_position_on_transect.plot(stg.t[self.combobox_frequency.currentIndex(), :],
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-stg.r_bottom,
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color='black', linewidth=1, linestyle="solid")
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self.axis_plot_profile_position_on_transect.plot(
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stg.t[self.slider.value() - 1] * np.ones(stg.r.shape[0]), -stg.r,
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stg.t[self.combobox_frequency.currentIndex(), self.slider.value() - 1] * np.ones(stg.r.shape[1]),
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-stg.r[self.combobox_frequency.currentIndex(), :],
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color='red', linestyle="solid", linewidth=2)
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self.axis_plot_profile_position_on_transect.set_xticks([])
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@ -1030,9 +1057,9 @@ class SignalProcessingTab(QWidget):
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# self.scroll_profile.setAlignment(Qt.AlignCenter)
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# self.verticalLayout_groupbox_plot_profile.addWidget(self.scroll_profile)
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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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self.axis_profile[f].cla()
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self.axis_profile[f].plot(stg.BS_data_section[:, f, self.slider.value() - 1], -stg.r,
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self.axis_profile[f].plot(stg.BS_data_section[f, :, self.slider.value() - 1], -stg.r[f, :],
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linestyle='solid', color='k', linewidth=1)
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self.axis_profile[f].text(.95, .05, stg.freq_text[f],
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fontsize=10, fontweight='bold', fontname="Ubuntu",
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@ -1091,9 +1118,9 @@ class SignalProcessingTab(QWidget):
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msgBox.exec()
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else:
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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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self.axis_profile[f].cla()
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self.axis_profile[f].plot(stg.BS_data_section[:, f, self.slider.value() - 1], -stg.r,
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self.axis_profile[f].plot(stg.BS_data_section[f, :, self.slider.value() - 1], -stg.r[f, :],
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linestyle='solid', color='k', linewidth=1)
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self.axis_profile[f].text(.95, .05, stg.freq_text[f],
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fontsize=10, fontweight='bold', fontname="Ubuntu",
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@ -1109,11 +1136,11 @@ class SignalProcessingTab(QWidget):
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def plot_averaged_profile(self):
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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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self.axis_averaged_profile[f].cla()
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self.axis_averaged_profile[f].plot(stg.BS_data_section_averaged[:, f, self.slider.value()-1], -stg.r,
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self.axis_averaged_profile[f].plot(stg.BS_data_section_averaged[f, :, self.slider.value()-1], -stg.r[f, :],
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linestyle='solid', color='k', linewidth=1)
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self.axis_averaged_profile[f].set_ylim(-np.max(stg.r), np.min(stg.r))
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self.axis_averaged_profile[f].set_ylim(-np.max(stg.r[f, :]), np.min(stg.r[f, :]))
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self.axis_averaged_profile[f].text(.95, .05, stg.freq_text[f],
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fontsize=10, fontweight='bold', fontname="Ubuntu",
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fontstyle="normal", c="black", alpha=0.2,
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@ -1139,11 +1166,12 @@ class SignalProcessingTab(QWidget):
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else:
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if stg.BS_data_section_SNR_filter.size == 0:
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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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self.axis_averaged_profile[f].cla()
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self.axis_averaged_profile[f].plot(stg.BS_data_section_averaged[:, f, self.slider.value() - 1], -stg.r,
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linestyle='solid', color='k', linewidth=1)
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self.axis_averaged_profile[f].set_ylim(-np.max(stg.r), np.min(stg.r))
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self.axis_averaged_profile[f].plot(stg.BS_data_section_averaged[f, :, self.slider.value() - 1],
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-stg.r[f, :],
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linestyle='solid', color='k', linewidth=1)
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self.axis_averaged_profile[f].set_ylim(-np.max(stg.r[f, :]), np.min(stg.r[f, :]))
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self.axis_averaged_profile[f].text(.95, .05, stg.freq_text[f],
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fontsize=10, fontweight='bold', fontname="Ubuntu",
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fontstyle="normal", c="black", alpha=0.2,
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@ -1158,9 +1186,10 @@ class SignalProcessingTab(QWidget):
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for f in range(stg.freq.shape[0]):
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self.axis_averaged_profile[f].cla()
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self.axis_averaged_profile[f].plot(stg.BS_data_section_SNR_filter[:, f, self.slider.value() - 1], -stg.r,
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self.axis_averaged_profile[f].plot(stg.BS_data_section_SNR_filter[f, :, self.slider.value() - 1],
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-stg.r[f, :],
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linestyle='solid', color='k', linewidth=1)
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self.axis_averaged_profile[f].set_ylim(-np.max(stg.r), np.min(stg.r))
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||||
self.axis_averaged_profile[f].set_ylim(-np.max(stg.r[f, :]), np.min(stg.r[f, :]))
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self.axis_averaged_profile[f].text(.95, .05, stg.freq_text[f],
|
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fontsize=10, fontweight='bold', fontname="Ubuntu",
|
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fontstyle="normal", c="black", alpha=0.2,
|
||||
|
|
|
|||
Loading…
Reference in New Issue