Modified the plotting routines so that they show the data interpolated on common spatial and temporal grids. Export to Excel files is now working.
Bjarne Vincent
parent
fc438ee3a6
commit
1b46baa6d0
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@ -1199,6 +1199,7 @@ class AcousticInversionTab(QWidget):
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self.figure_SSC_fine.canvas.draw_idle()
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def plot_SSC_sand(self):
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data_id = self.combobox_acoustic_data_choice.currentIndex()
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if self.combobox_acoustic_data_choice.count() <= 0:
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@ -1315,9 +1316,11 @@ class AcousticInversionTab(QWidget):
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def plot_SSC_sand_vertical_profile(self):
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data_id = self.combobox_acoustic_data_choice.currentIndex()
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if self.combobox_acoustic_data_choice.count() > 0:
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if stg.SSC_sand[self.combobox_acoustic_data_choice.currentIndex()].shape == (0,):
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if stg.SSC_sand[data_id].shape == (0,):
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self.verticalLayout_groupbox_plot_vertical_profile_sand.removeWidget(self.toolbar_profile_sand)
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self.verticalLayout_groupbox_plot_vertical_profile_sand.removeWidget(self.canvas_profile_sand)
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@ -1328,9 +1331,9 @@ class AcousticInversionTab(QWidget):
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self.verticalLayout_groupbox_plot_vertical_profile_sand.addWidget(self.toolbar_profile_sand)
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self.verticalLayout_groupbox_plot_vertical_profile_sand.addWidget(self.canvas_profile_sand)
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if stg.SSC_sand[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
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if stg.SSC_sand[data_id].shape != (0,):
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self.slider_sand.setMaximum(stg.SSC_fine[self.combobox_acoustic_data_choice.currentIndex()].shape[1])
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self.slider_sand.setMaximum(stg.SSC_fine[data_id].shape[1])
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self.verticalLayout_groupbox_plot_vertical_profile_sand.removeWidget(self.toolbar_profile_sand)
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self.verticalLayout_groupbox_plot_vertical_profile_sand.removeWidget(self.canvas_profile_sand)
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@ -1351,51 +1354,39 @@ class AcousticInversionTab(QWidget):
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self.verticalLayout_groupbox_plot_vertical_profile_sand.addWidget(self.toolbar_profile_sand)
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self.verticalLayout_groupbox_plot_vertical_profile_sand.addWidget(self.canvas_profile_sand)
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if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
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if stg.depth_cross_section[data_id].shape != (0,):
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self.plot_sand , = self.axis_vertical_profile_SSC_sand.plot(
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stg.SSC_sand[self.combobox_acoustic_data_choice.currentIndex()][:, self.slider_sand.value() -1],
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-stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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stg.frequency_for_inversion[1]],
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stg.SSC_sand[data_id][:, self.slider_sand.value() -1],
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-stg.r2D_interp[data_id][:,0],
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linestyle="solid", linewidth=1, color="k")
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self.pcm_SSC_sand_vertical_line.set_data(
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stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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stg.frequency_for_inversion[1],
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self.slider_sand.value() - 1] *
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np.ones(stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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stg.frequency_for_inversion[1]].shape),
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-stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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stg.frequency_for_inversion[1]])
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stg.t2D_interp[data_id][0, self.slider_sand.value() - 1]
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* np.ones( ( stg.r2D_interp[data_id].shape[0], 1 ) ),
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-stg.r2D_interp[data_id][:, 0] )
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self.figure_SSC_sand.canvas.draw_idle()
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self.axis_vertical_profile_SSC_sand.set_ylim(
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[-np.nanmax(stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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stg.frequency_for_inversion[1]]),
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-np.nanmin(stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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stg.frequency_for_inversion[1]])])
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[ -np.nanmax( stg.r2D_interp[data_id][:,0] ), -np.nanmin( stg.r2D_interp[data_id][:,0] ) ] )
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else:
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self.plot_sand , = self.axis_vertical_profile_SSC_sand.plot(
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stg.SSC_sand[self.combobox_acoustic_data_choice.currentIndex()][:, self.slider_sand.value() -1],
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-stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
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stg.frequency_for_inversion[1]],
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stg.SSC_sand[data_id][:, self.slider_sand.value() -1],
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-stg.depth[data_id][stg.frequency_for_inversion[1].shape],
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linestyle="solid", linewidth=1, color="k")
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self.pcm_SSC_sand_vertical_line.set_data(
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stg.time[self.combobox_acoustic_data_choice.currentIndex()][
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stg.frequency_for_inversion[1], self.slider_sand.value() - 1] *
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np.ones(stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
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stg.frequency_for_inversion[1]].shape),
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-stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
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stg.frequency_for_inversion[1]])
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stg.t2D_interp[data_id][0, self.slider_sand.value() - 1]
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* np.ones( stg.depth[data_id][stg.frequency_for_inversion[1]].shape ),
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-stg.depth[data_id][ stg.frequency_for_inversion[1] ] )
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self.figure_SSC_sand.canvas.draw_idle()
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self.axis_vertical_profile_SSC_sand.set_ylim(
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[-np.nanmax(stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
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[-np.nanmax(stg.depth[data_id][
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stg.frequency_for_inversion[1]]),
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-np.nanmin(stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
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-np.nanmin(stg.depth[data_id][
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stg.frequency_for_inversion[1]])])
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# self.axis_SSC_fine.plot([], [], )
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@ -1407,76 +1398,74 @@ class AcousticInversionTab(QWidget):
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def update_plot_SSC_sand_vertical_profile(self):
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data_id = self.combobox_acoustic_data_choice.currentIndex()
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if stg.filename_BS_noise_data != []:
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if stg.SSC_sand[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
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if stg.SSC_sand[data_id].shape != (0,):
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self.axis_vertical_profile_SSC_sand.cla()
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if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
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if stg.depth_cross_section[data_id].shape != (0,):
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self.axis_vertical_profile_SSC_sand.plot(
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stg.SSC_sand[self.combobox_acoustic_data_choice.currentIndex()][:,
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self.slider_sand.value() - 1],
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-stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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stg.frequency_for_inversion[1]],
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stg.SSC_sand[data_id][:, self.slider_sand.value() - 1],
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-stg.r2D_interp[data_id][:, 0],
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linestyle="solid", linewidth=1, color="k")
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self.pcm_SSC_sand_vertical_line.set_data(
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stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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stg.time_cross_section[data_id][
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stg.frequency_for_inversion[1],
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self.slider_sand.value() - 1] *
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np.ones(stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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np.ones(stg.depth_cross_section[data_id][
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stg.frequency_for_inversion[1]].shape),
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-stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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-stg.depth_cross_section[data_id][
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stg.frequency_for_inversion[1]])
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self.figure_SSC_sand.canvas.draw_idle()
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self.axis_vertical_profile_SSC_sand.set_ylim(
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[-np.nanmax(stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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[-np.nanmax(stg.depth_cross_section[data_id][
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stg.frequency_for_inversion[1]]),
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-np.nanmin(stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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-np.nanmin(stg.depth_cross_section[data_id][
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stg.frequency_for_inversion[1]])])
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else:
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self.axis_vertical_profile_SSC_sand.plot(
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stg.SSC_sand[self.combobox_acoustic_data_choice.currentIndex()][:,
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self.slider_sand.value() - 1],
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-stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
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stg.frequency_for_inversion[1]],
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stg.SSC_sand[data_id][:, self.slider_sand.value() - 1],
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-stg.r2D_interp[data_id][:, 0],
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linestyle="solid", linewidth=1, color="k")
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self.pcm_SSC_sand_vertical_line.set_data(
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stg.time[self.combobox_acoustic_data_choice.currentIndex()][
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stg.time[data_id][
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stg.frequency_for_inversion[1], self.slider_sand.value() - 1] *
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np.ones(stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
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np.ones(stg.depth[data_id][
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stg.frequency_for_inversion[1]].shape),
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-stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
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-stg.depth[data_id][
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stg.frequency_for_inversion[1]])
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self.figure_SSC_sand.canvas.draw_idle()
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self.axis_vertical_profile_SSC_sand.set_ylim(
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[-np.nanmax(stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
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[-np.nanmax(stg.depth[data_id][
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stg.frequency_for_inversion[1]]),
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-np.nanmin(stg.depth[self.combobox_acoustic_data_choice.currentIndex()][
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-np.nanmin(stg.depth[data_id][
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stg.frequency_for_inversion[1]])])
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if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
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if stg.time_cross_section[data_id].shape != (0,):
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time_sand_calibration = (
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np.where(np.abs(stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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np.where(np.abs(stg.time_cross_section[data_id][
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stg.frequency_for_inversion[1]] - stg.time_sand[
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stg.sand_sample_target[0][1]]) ==
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np.nanmin(
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np.abs(stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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np.abs(stg.time_cross_section[data_id][
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stg.frequency_for_inversion[1]] - stg.time_sand[
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stg.sand_sample_target[0][1]])))[0][0]
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)
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if (stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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if (stg.time_cross_section[data_id][
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stg.frequency_for_inversion[1], int(self.slider_sand.value())] ==
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stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][
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stg.time_cross_section[data_id][
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stg.frequency_for_inversion[1], int(time_sand_calibration)]):
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self.axis_vertical_profile_SSC_sand.scatter(stg.Ctot_sand[stg.sand_sample_target[0][1]],
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stg.depth_sand[stg.sand_sample_target[0][1]],
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@ -1489,17 +1478,17 @@ class AcousticInversionTab(QWidget):
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else:
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time_sand_calibration = (
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np.where(np.abs(stg.time[self.combobox_acoustic_data_choice.currentIndex()][
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np.where(np.abs(stg.time[data_id][
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stg.frequency_for_inversion[1]] - stg.time_sand[
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stg.sand_sample_target[0][1]]) ==
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np.nanmin(np.abs(stg.time[self.combobox_acoustic_data_choice.currentIndex()][
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np.nanmin(np.abs(stg.time[data_id][
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stg.frequency_for_inversion[1]] - stg.time_sand[
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stg.sand_sample_target[0][1]])))[0][0]
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)
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if (stg.time[self.combobox_acoustic_data_choice.currentIndex()][
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if (stg.time[data_id][
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stg.frequency_for_inversion[1], int(self.slider_sand.value())] ==
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stg.time[self.combobox_acoustic_data_choice.currentIndex()][
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stg.time[data_id][
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stg.frequency_for_inversion[1], int(time_sand_calibration)]):
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self.axis_vertical_profile_SSC_sand.scatter(stg.Ctot_fine[stg.sand_sample_target[0][1]],
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stg.depth_fine[stg.sand_sample_target[0][1]],
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@ -1756,64 +1745,61 @@ class AcousticInversionTab(QWidget):
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if ".ods" in filename:
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engine = "odf"
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else:
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engine = 'xlsxwriter'
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engine = "xlsxwriter"
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with pd.ExcelWriter(
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os.path.join(dirname, filename),
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engine=engine
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) as writer:
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time_data = stg.t2D_interp
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depth_data = stg.r2D_interp
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for k in range(self.combobox_acoustic_data_choice.count()):
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if stg.time_cross_section[k].shape != (0,):
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time_data = stg.time_cross_section
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else:
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time_data = stg.time
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if stg.depth_cross_section[k].shape != (0,):
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depth_data = stg.depth_cross_section
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else:
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depth_data = stg.depth
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dataset_name = self.combobox_acoustic_data_choice.itemText(k)
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t = np.repeat(
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time_data[k][stg.frequency_for_inversion[1]],
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depth_data[k].shape[1]
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)
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time_shape = time_data[k].shape[1]
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depth_shape = depth_data[k].shape[0]
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r = np.zeros(
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depth_data[k].shape[1] * time_data[k].shape[1]
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)
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t = np.repeat( time_data[k][0,:], depth_shape )
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for i in range(time_data[k].shape[1]):
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for j in range(depth_data[k].shape[1]):
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r_id = i * depth_data[k].shape[1] + j
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r = np.zeros( time_shape * depth_shape )
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r[r_id] = depth_data[k][
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int(stg.frequency_for_inversion[1]), j
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]
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for i in range(time_shape):
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for j in range(depth_shape):
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r_id = i * depth_shape + j
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r[r_id] = depth_data[k][j, 0]
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# If no SSC has been computed, fill
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if stg.SSC_fine[k].shape == (0,):
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stg.SSC_fine[k] = np.zeros(r.shape[0])
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if stg.SSC_sand[k].shape == (0,):
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stg.SSC_sand[k] = np.zeros(r.shape[0])
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# Flatten the fine and sand SSC arrays in a column-major order (i.e., "the first index
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# runs the fastest"):
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ssc_fine = np.reshape( stg.SSC_fine[k], r.shape[0], 'F' ).tolist()
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ssc_sand = np.reshape( stg.SSC_sand[k], r.shape[0], 'F' ).tolist()
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result = pd.DataFrame(
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{
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'Time (sec)': list(t),
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'Depth (m)': list(r),
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'SSC_fine (g/L)': list(
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stg.SSC_fine[k].reshape(t.shape[0])
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),
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'SSC_sand (g/L)': list(
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stg.SSC_sand[k].reshape(t.shape[0])
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),
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'Time (sec)': t.tolist(),
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'Depth (m)': r.tolist(),
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'SSC_fine (g/L)': ssc_fine,
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'SSC_sand (g/L)': ssc_sand,
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}
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)
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gc.collect() # Force garbade collection
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gc.collect() # Force garbage collection
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result.to_excel(
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writer, index=False, na_rep='NA',
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sheet_name=stg.data_preprocessed[k],
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)
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result.to_excel( writer, index=False, na_rep='NA', engine=engine,
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sheet_name=dataset_name )
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def save_result_in_csv_file(self, dirname, filename):
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@ -1839,6 +1825,9 @@ class AcousticInversionTab(QWidget):
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ssc_fine = [] #List of flattened fine SSC arrays (g/L)
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ssc_sand = [] #List of flattened sand SSC arrays (g/L)
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time_data = stg.t2D_interp
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depth_data = stg.r2D_interp
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# Loop over all datasets loaded into AcouSed:
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@ -1874,50 +1863,29 @@ class AcousticInversionTab(QWidget):
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etc.
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'''
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# Retrieve the time stamps and vertical positions:
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if stg.time_cross_section[k].shape != (0,):
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time_data = stg.time_cross_section
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else:
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time_data = stg.time
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if stg.depth_cross_section[k].shape != (0,):
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depth_data = stg.depth_cross_section
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else:
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depth_data = stg.depth
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'''
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# Form the 1D arrays of time stamps and vertical positions
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# for the k-th dataset:
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time_shape = time_data[k].shape[1]
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depth_shape = depth_data[k].shape[1]
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time_shape = time_data[k].shape[1] #Number of time stamps
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depth_shape = depth_data[k].shape[0] #Number of vertical coordinates
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d_id += np.repeat(k, depth_shape * time_shape).tolist() #Flattened array of the current dataset's index
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tmp_t = np.repeat(
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time_data[k][stg.frequency_for_inversion[1]],
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depth_shape
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) #Flattened array of time stamps
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tmp_t = np.repeat(time_data[k][0,:], depth_shape) #Flattened array of time stamps
|
||||
|
||||
tmp_r = np.zeros(
|
||||
depth_shape * time_shape
|
||||
) #Initialise the flattened array of vertical positions
|
||||
tmp_r = np.zeros(depth_shape * time_shape) #Initialise the flattened array of vertical positions
|
||||
|
||||
|
||||
# Fill the flattened array of vertical positions:
|
||||
|
||||
for i in range(time_shape):
|
||||
for j in range(depth_shape):
|
||||
|
||||
r_id = i * depth_shape + j
|
||||
|
||||
tmp_r[r_id] = (
|
||||
depth_data[k][
|
||||
int(stg.frequency_for_inversion[1]), j
|
||||
]
|
||||
)
|
||||
tmp_r[r_id] = depth_data[k][j, 0]
|
||||
|
||||
|
||||
# Add the flattened arrays of time stamps and vertical positions to their respective
|
||||
|
|
|
|||
Loading…
Reference in New Issue