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Acoustic inversion method for high concentration is implemented. It is tested with Aquascat data acquired during Adrien PhD (Confluence Rhône-Isère, 2018.01.07)
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@@ -200,12 +200,12 @@ class NoteTab(QWidget):
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f" Fine sediments data file: {stg.fine_sediment_path}/{stg.fine_sediment_filename} \n"
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f" Sand sediments data file: {stg.sand_sediment_path}/{stg.sand_sediment_filename} \n"
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"\n\n"
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"------------------------------------------------------------------------- \n\n\n"
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"------------------------------------------------------------------------- \n\n\n")
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"Acoustic Inversion parameters: \n"
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f" frequencies to compute VBI: {stg.freq_text[int(stg.frequencies_to_compute_VBI[0, 0])]}, "
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f"{stg.freq_text[int(stg.frequencies_to_compute_VBI[1, 0])]} \n"
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f" frequency to compute SSC: {stg.freq_text[int(stg.frequency_to_compute_SSC[0])]}")
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# "Acoustic Inversion parameters: \n"
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# f" frequencies to compute VBI: {stg.freq_text[int(stg.frequencies_to_compute_VBI[0, 0])]}, "
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# f"{stg.freq_text[int(stg.frequencies_to_compute_VBI[1, 0])]} \n"
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# f" frequency to compute SSC: {stg.freq_text[int(stg.frequency_to_compute_SSC[0])]}")
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# if __name__ == "__main__":
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+23
-7
@@ -773,7 +773,7 @@ class SampleDataTab(QWidget):
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def plot_sample_position_on_transect(self):
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if stg.BS_data.size == 0:
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if stg.BS_cross_section.size == 0:
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msgBox = QMessageBox()
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msgBox.setWindowTitle("Plot transect Error")
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msgBox.setIcon(QMessageBox.Warning)
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@@ -968,7 +968,8 @@ class SampleDataTab(QWidget):
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else:
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indices = []
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for i in position_list:
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indices.append(np.where(stg.t == stg.sample_time[i])[0][0])
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# indices.append(np.where(stg.t == stg.sample_time[i])[0][0])
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indices.append(np.where(np.abs(stg.t - stg.sample_time[i]) == np.nanmin(np.abs(stg.t - stg.sample_time[i])))[0][0])
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self.axis_total_concentration.scatter(stg.Ctot_fine[position_list],
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-stg.sample_depth[position_list] / stg.r_bottom[indices],
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# np.max(stg.sample_depth[position_list]),
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@@ -1002,7 +1003,10 @@ class SampleDataTab(QWidget):
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else:
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indices = []
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for i in position_list:
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indices.append(np.where(stg.t == stg.sample_time[i])[0][0])
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# indices.append(np.where(stg.t == stg.sample_time[i])[0][0])
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indices.append(np.where(
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np.abs(stg.t - stg.sample_time[i]) == np.nanmin(np.abs(stg.t - stg.sample_time[i])))[0][
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0])
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self.axis_total_concentration.scatter(stg.Ctot_fine_per_cent[position_list],
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-stg.sample_depth[position_list] / stg.r_bottom[indices],
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# np.max(stg.sample_depth[position_list]),
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@@ -1050,7 +1054,10 @@ class SampleDataTab(QWidget):
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else:
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indices = []
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for i in position_list:
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indices.append(np.where(stg.t == stg.sample_time[i])[0][0])
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# indices.append(np.where(stg.t == stg.sample_time[i])[0][0])
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indices.append(np.where(
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np.abs(stg.t - stg.sample_time[i]) == np.nanmin(np.abs(stg.t - stg.sample_time[i])))[0][
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0])
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self.axis_total_concentration.scatter(stg.Ctot_sand[position_list],
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-stg.sample_depth[position_list] / stg.r_bottom[indices],
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# np.max(stg.sample_depth[position_list]),
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@@ -1084,7 +1091,10 @@ class SampleDataTab(QWidget):
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else:
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indices = []
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for i in position_list:
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indices.append(np.where(stg.t == stg.sample_time[i])[0][0])
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# indices.append(np.where(stg.t == stg.sample_time[i])[0][0])
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indices.append(np.where(
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np.abs(stg.t - stg.sample_time[i]) == np.nanmin(np.abs(stg.t - stg.sample_time[i])))[0][
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0])
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self.axis_total_concentration.scatter(stg.Ctot_sand_per_cent[position_list],
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-stg.sample_depth[position_list] / stg.r_bottom[indices],
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# np.max(stg.sample_depth[position_list]),
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@@ -1138,7 +1148,10 @@ class SampleDataTab(QWidget):
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else:
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indices = []
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for i in position_list:
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indices.append(np.where(stg.t == stg.sample_time[i])[0][0])
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# indices.append(np.where(stg.t == stg.sample_time[i])[0][0])
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indices.append(np.where(
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np.abs(stg.t - stg.sample_time[i]) == np.nanmin(np.abs(stg.t - stg.sample_time[i])))[0][
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0])
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self.axis_total_concentration.scatter(stg.Ctot_fine[position_list],
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-stg.sample_depth[position_list] / stg.r_bottom[indices], #np.max(stg.sample_depth[position_list]),
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s=100, facecolor=color_list, edgecolor="None", alpha=0.5)
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@@ -1180,7 +1193,10 @@ class SampleDataTab(QWidget):
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else:
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indices = []
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for i in position_list:
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indices.append(np.where(stg.t == stg.sample_time[i])[0][0])
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# indices.append(np.where(stg.t == stg.sample_time[i])[0][0])
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indices.append(np.where(
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np.abs(stg.t - stg.sample_time[i]) == np.nanmin(np.abs(stg.t - stg.sample_time[i])))[0][
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0])
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self.axis_total_concentration.scatter(stg.Ctot_fine_per_cent[position_list],
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-stg.sample_depth[position_list] / stg.r_bottom[indices], #np.max(stg.sample_depth[position_list]),
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s=100, facecolor=color_list, edgecolor="None", alpha=0.5)
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@@ -633,9 +633,9 @@ 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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if stg.r_bottom.size == 0:
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stg.BS_stream_bed = deepcopy(stg.BS_cross_section)
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elif stg.r_bottom.size != 0:
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# if stg.r_bottom.size == 0:
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# stg.BS_stream_bed = deepcopy(stg.BS_cross_section)
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if stg.r_bottom.size != 0:
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stg.BS_stream_bed = deepcopy(stg.BS_cross_section)
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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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@@ -757,50 +757,52 @@ class SignalProcessingTab(QWidget):
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else:
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filter_convolve = np.ones(self.spinbox_average_horizontal.value())
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# print(filter_convolve)
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print(filter_convolve)
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if stg.BS_stream_bed_SNR_filter.size != 0:
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# stg.BS_stream_bed_averaged = np.zeros((stg.r.shape[0], stg.freq.shape[0], stg.t.shape[0]))
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# stg.BS_stream_bed_averaged = np.zeros((stg.freq.shape[0], stg.r.shape[1], stg.t.shape[1]))
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stg.BS_stream_bed_averaged = deepcopy(stg.BS_stream_bed_SNR_filter)
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# print(f"stg.BS_cross_section_averaged : {stg.BS_cross_section_averaged.shape}")
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print(f"1/ stg.BS_stream_bed_averaged : {stg.BS_stream_bed_averaged}")
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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_stream_bed_averaged[f, i, :] \
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= convolve1d(stg.BS_stream_bed_SNR_filter[f, i, :], weights=filter_convolve)# / filter_convolve.shape[0]
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= convolve1d(stg.BS_stream_bed_SNR_filter[f, i, :], weights=filter_convolve) / len(filter_convolve)
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# stg.BS_stream_bed_averaged[i, f, :] \
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# = convolve1d(stg.BS_cross_section[i, f, :], weights=filter_convolve) / filter_convolve.shape[0]
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elif stg.BS_cross_section_SNR_filter.size != 0:
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stg.BS_cross_section_averaged = deepcopy(stg.BS_cross_section_SNR_filter)
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print(f"stg.BS_cross_section_averaged : {stg.BS_cross_section_averaged.shape}")
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print(f"2/ stg.BS_cross_section_averaged : {stg.BS_cross_section_averaged}")
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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_stream_bed_averaged[f, i, :] \
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= convolve1d(stg.BS_cross_section_SNR_filter[f, i, :],
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weights=filter_convolve) # / filter_convolve.shape[0]
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weights=filter_convolve) / len(filter_convolve)
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elif stg.BS_stream_bed.size != 0:
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stg.BS_stream_bed_averaged = deepcopy(stg.BS_stream_bed)
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print(f"stg.BS_cross_section_averaged : {stg.BS_cross_section_averaged.shape}")
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print(f"3/ stg.BS_stream_bed_averaged : {stg.BS_stream_bed_averaged.shape}")
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print(stg.BS_stream_bed_averaged[0, 10, :50])
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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_stream_bed_averaged[f, i, :] \
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= convolve1d(stg.BS_stream_bed[f, i, :],
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weights=filter_convolve) # / filter_convolve.shape[0]
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weights=filter_convolve) / len(filter_convolve)
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print(stg.BS_stream_bed_averaged[0, 10, :50])
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elif stg.BS_cross_section.size != 0:
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stg.BS_cross_section_averaged = deepcopy(stg.BS_cross_section)
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print(f"stg.BS_cross_section_averaged : {stg.BS_cross_section_averaged.shape}")
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print(f"4/ stg.BS_cross_section_averaged : {stg.BS_cross_section_averaged.shape}")
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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_stream_bed_averaged[f, i, :] \
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stg.BS_cross_section_averaged[f, i, :] \
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= convolve1d(stg.BS_cross_section[f, i, :],
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weights=filter_convolve) # / filter_convolve.shape[0]
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weights=filter_convolve) / len(filter_convolve)
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if stg.ABS_name == "Aquascat 1000R":
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self.label_cells_horizontal.clear()
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@@ -983,32 +985,42 @@ 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_stream_bed[stg.freq_bottom_detection, :, :])
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val_max = np.nanmax(stg.BS_stream_bed[stg.freq_bottom_detection, :, :])
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if val_min == 0:
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val_min = 1e-5
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if stg.ABS_name == "Aquascat 1000R":
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if stg.BS_stream_bed.size != 0:
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val_min = np.nanmin(stg.BS_stream_bed[stg.freq_bottom_detection, :, :])
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val_max = np.nanmax(stg.BS_stream_bed[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.freq_bottom_detection, :], -stg.r[stg.freq_bottom_detection, :],
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stg.BS_stream_bed[stg.freq_bottom_detection, :, :],
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cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
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else:
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val_min = np.nanmin(stg.BS_cross_section[stg.freq_bottom_detection, :, :])
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val_max = np.nanmax(stg.BS_cross_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.freq_bottom_detection, :], -stg.r[stg.freq_bottom_detection, :],
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stg.BS_cross_section[stg.freq_bottom_detection, :, :],
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cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
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elif stg.ABS_name == "UB-SediFlow":
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if stg.BS_stream_bed.size != 0:
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self.axis_plot_profile_position_on_transect.pcolormesh(
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stg.t[stg.freq_bottom_detection, :], -stg.r[stg.freq_bottom_detection, :],
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np.log(stg.BS_cross_section[stg.freq_bottom_detection, :, :]),
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np.log(stg.BS_stream_bed[stg.freq_bottom_detection, :, :]),
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cmap='Blues')
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else:
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self.axis_plot_profile_position_on_transect.pcolormesh(
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stg.t[stg.freq_bottom_detection, :], -stg.r[stg.freq_bottom_detection, :],
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np.log(stg.BS_stream_bed[stg.freq_bottom_detection, :, :]),
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np.log(stg.BS_cross_section[stg.freq_bottom_detection, :, :]),
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cmap='Blues')
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if stg.r_bottom.size != 0:
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@@ -1116,8 +1128,8 @@ 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_stream_bed[self.combobox_frequency.currentIndex(), :, :])
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val_max = np.nanmax(stg.BS_stream_bed[self.combobox_frequency.currentIndex(), :, :])
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val_min = np.nanmin(stg.BS_cross_section[self.combobox_frequency.currentIndex(), :, :])
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val_max = np.nanmax(stg.BS_cross_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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