Signal processing: Fix #42.
parent
74137405fc
commit
5f7c81f866
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@ -515,7 +515,7 @@ class SignalProcessingTab(QWidget):
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self.combobox_acoustic_data_choice.blockSignals(True)
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self.full_update_fill_text()
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self.update_SignalPreprocessingTab(recompute=True)
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self.update_SignalPreprocessingTab()
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self.combobox_acoustic_data_choice.blockSignals(False)
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self.blockSignals(False)
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@ -535,7 +535,7 @@ class SignalProcessingTab(QWidget):
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str(stg.Nb_cells_to_average_BS_signal[data_id])
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)
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def update_SignalPreprocessingTab(self, recompute=True):
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def update_SignalPreprocessingTab(self):
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""" The tab is updated in two cases :
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- the user remove a file (in the list widget) in the first tab (Acoustic data), so that the combobox
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@ -573,9 +573,7 @@ class SignalProcessingTab(QWidget):
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stg.freq_text[data_id]
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)
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if recompute:
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self.recompute()
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self.recompute()
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self.replot()
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logger.debug("Update the Signal preprocessing tab... Done")
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@ -583,6 +581,33 @@ class SignalProcessingTab(QWidget):
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self.combobox_freq_noise_from_profile_tail.blockSignals(False)
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self.combobox_acoustic_data_choice.blockSignals(False)
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def _is_correct_shape(self, data):
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data_id = self.combobox_acoustic_data_choice.currentIndex()
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if stg.time_cross_section[data_id].shape != (0,):
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x_time = stg.time_cross_section[data_id]
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else:
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x_time = stg.time[data_id]
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if stg.depth_cross_section[data_id].shape != (0,):
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y_depth = stg.depth_cross_section[data_id]
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else:
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y_depth = stg.depth[data_id]
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time_shape, = x_time[data_id].shape
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depth_shape, = y_depth[data_id].shape
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logger.debug(f"_is_correct_shape: time shape: {time_shape}")
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logger.debug(f"_is_correct_shape: depth shape: {depth_shape}")
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logger.debug(f"_is_correct_shape: data shape: {data[data_id].shape}")
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if data[data_id].shape == (0,):
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return False
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_, y, z = data[data_id].shape
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return (y == depth_shape and z == time_shape)
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def recompute(self):
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data_id = self.combobox_acoustic_data_choice.currentIndex()
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@ -835,28 +860,29 @@ class SignalProcessingTab(QWidget):
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if len(stg.filename_BS_raw_data) == 0:
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pass
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else:
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data_id = self.combobox_acoustic_data_choice.currentIndex()
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
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stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
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stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
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stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
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stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
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stg.time_noise[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
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stg.SNR_filter_value[self.combobox_acoustic_data_choice.currentIndex()] = 0
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stg.BS_noise_raw_data[data_id] = np.array([])
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stg.BS_noise_averaged_data[data_id] = np.array([])
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stg.SNR_raw_data[data_id] = np.array([])
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stg.SNR_cross_section[data_id] = np.array([])
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stg.SNR_stream_bed[data_id] = np.array([])
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stg.time_noise[data_id] = np.array([])
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stg.SNR_filter_value[data_id] = 0
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stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
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stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
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stg.BS_raw_data_pre_process_SNR[data_id] = np.array([])
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stg.BS_raw_data_pre_process_average[data_id] = np.array([])
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stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
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stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
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stg.BS_cross_section_pre_process_SNR[data_id] = np.array([])
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stg.BS_cross_section_pre_process_average[data_id] = np.array([])
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stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
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stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
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print("stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()]", stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()])
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if stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] == 0:
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stg.BS_stream_bed_pre_process_SNR[data_id] = np.array([])
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stg.BS_stream_bed_pre_process_average[data_id] = np.array([])
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print("stg.noise_method[data_id]", stg.noise_method[data_id])
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if stg.noise_method[data_id] == 0:
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self.lineEdit_noise_file.clear()
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elif stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] == 1:
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elif stg.noise_method[data_id] == 1:
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self.lineEdit_val1.clear()
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self.lineEdit_val1.setText("0.00")
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@ -973,136 +999,140 @@ class SignalProcessingTab(QWidget):
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def load_noise_data_and_compute_SNR(self):
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data_id = self.combobox_acoustic_data_choice.currentIndex()
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stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] = 0
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stg.noise_method[data_id] = 0
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noise_data = AcousticDataLoader(stg.path_BS_noise_data[self.combobox_acoustic_data_choice.currentIndex()] +
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noise_data = AcousticDataLoader(stg.path_BS_noise_data[data_id] +
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"/" +
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stg.filename_BS_noise_data[self.combobox_acoustic_data_choice.currentIndex()])
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = noise_data._BS_raw_data
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stg.filename_BS_noise_data[data_id])
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stg.BS_noise_raw_data[data_id] = noise_data._BS_raw_data
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stg.time_noise[self.combobox_acoustic_data_choice.currentIndex()] = noise_data._time
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stg.depth_noise[self.combobox_acoustic_data_choice.currentIndex()] = noise_data._r
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stg.time_noise[data_id] = noise_data._time
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stg.depth_noise[data_id] = noise_data._r
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if stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
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if stg.BS_stream_bed[data_id].shape != (0,):
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noise = np.zeros(stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape)
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noise = np.zeros(stg.BS_stream_bed[data_id].shape)
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for f, _ in enumerate(noise_data._freq):
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noise[f, :, :] = np.mean(
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :], axis=(0, 1))
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stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = noise
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stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()] = (
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np.divide((stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()] -
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stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
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stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
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stg.BS_noise_raw_data[data_id][f, :, :], axis=(0, 1))
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stg.BS_noise_averaged_data[data_id] = noise
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stg.SNR_stream_bed[data_id] = (
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np.divide((stg.BS_stream_bed[data_id] -
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stg.BS_noise_averaged_data[data_id]) ** 2,
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stg.BS_noise_averaged_data[data_id] ** 2))
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elif stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
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elif stg.BS_cross_section[data_id].shape != (0,):
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noise = np.zeros(stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape)
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noise = np.zeros(stg.BS_cross_section[data_id].shape)
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for f, _ in enumerate(noise_data._freq):
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noise[f, :, :] = np.mean(
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :], axis=(0, 1))
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stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = noise
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stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()] = (
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np.divide((stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()] -
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stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
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stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
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stg.BS_noise_raw_data[data_id][f, :, :], axis=(0, 1))
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stg.BS_noise_averaged_data[data_id] = noise
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stg.SNR_cross_section[data_id] = (
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np.divide((stg.BS_cross_section[data_id] -
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stg.BS_noise_averaged_data[data_id]) ** 2,
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stg.BS_noise_averaged_data[data_id] ** 2))
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# stg.SNR_reshape = np.reshape(stg.SNR_cross_section, (stg.r.shape[1] * stg.t.shape[1], stg.freq.shape[0]), order="F")
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else:
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noise = np.zeros(stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()].shape)
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noise = np.zeros(stg.BS_raw_data[data_id].shape)
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for f, _ in enumerate(noise_data._freq):
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noise[f, :, :] = np.mean(
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :], axis=(0, 1))
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stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = noise
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stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
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np.divide((stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()] -
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stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
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stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
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stg.BS_noise_raw_data[data_id][f, :, :], axis=(0, 1))
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stg.BS_noise_averaged_data[data_id] = noise
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stg.SNR_raw_data[data_id] = (
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np.divide((stg.BS_raw_data[data_id] -
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stg.BS_noise_averaged_data[data_id]) ** 2,
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stg.BS_noise_averaged_data[data_id] ** 2))
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def open_plot_noise_window(self):
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pnw = PlotNoiseWindow()
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pnw.exec()
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def compute_noise_from_profile_tail_value(self):
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data_id = self.combobox_acoustic_data_choice.currentIndex()
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stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] = 1
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stg.noise_method[data_id] = 1
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stg.noise_value[data_id] = (
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float(self.lineEdit_profile_tail_value.text().replace(",", "."))
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)
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stg.noise_value[self.combobox_acoustic_data_choice.currentIndex()] = (
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float(self.lineEdit_profile_tail_value.text().replace(",", ".")))
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if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
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stg.time_noise[self.combobox_acoustic_data_choice.currentIndex()] = (
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stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()])
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if stg.time_cross_section[data_id].shape != (0,):
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stg.time_noise[data_id] = (
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stg.time_cross_section[data_id]
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)
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else:
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stg.time_noise[self.combobox_acoustic_data_choice.currentIndex()] = (
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stg.time[self.combobox_acoustic_data_choice.currentIndex()])
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if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
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stg.depth_noise[self.combobox_acoustic_data_choice.currentIndex()] = (
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stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()])
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stg.time_noise[data_id] = (
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stg.time[data_id]
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)
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if stg.depth_cross_section[data_id].shape != (0,):
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stg.depth_noise[data_id] = (
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stg.depth_cross_section[data_id]
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)
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else:
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stg.depth_noise[self.combobox_acoustic_data_choice.currentIndex()] = (
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stg.depth[self.combobox_acoustic_data_choice.currentIndex()])
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stg.depth_noise[data_id] = (
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stg.depth[data_id]
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)
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# --- Compute noise from value and compute SNR ---
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if stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
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np.full(stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape,
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if self._is_correct_shape(stg.BS_stream_bed):
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stg.BS_noise_raw_data[data_id] = np.array([])
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stg.BS_noise_raw_data[data_id] = (
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np.full(stg.BS_stream_bed[data_id].shape,
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float(self.lineEdit_profile_tail_value.text().replace(",", "."))))
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stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = (
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][:, :,
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:stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape[2]])
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stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()] = (
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np.divide((stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()]
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- stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
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stg.BS_noise_averaged_data[data_id] = (
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stg.BS_noise_raw_data[data_id][:, :,
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:stg.BS_stream_bed[data_id].shape[2]])
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stg.SNR_stream_bed[data_id] = (
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np.divide((stg.BS_stream_bed[data_id]
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- stg.BS_noise_raw_data[data_id]) ** 2,
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stg.BS_noise_raw_data[data_id] ** 2))
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elif stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
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np.full(stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape,
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elif self._is_correct_shape(stg.BS_cross_section):
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stg.BS_noise_raw_data[data_id] = (
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np.full(stg.BS_cross_section[data_id].shape,
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float(self.lineEdit_profile_tail_value.text().replace(",", "."))))
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stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = (
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][:, :,
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:stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape[2]])
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stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()] = (
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np.divide((stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()]
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- stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2)) #
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stg.BS_noise_averaged_data[data_id] = (
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stg.BS_noise_raw_data[data_id][:, :,
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:stg.BS_cross_section[data_id].shape[2]])
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stg.SNR_cross_section[data_id] = (
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np.divide((stg.BS_cross_section[data_id]
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- stg.BS_noise_raw_data[data_id]) ** 2,
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stg.BS_noise_raw_data[data_id] ** 2)) #
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else:
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
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np.full(stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()].shape,
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stg.BS_noise_raw_data[data_id] = (
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np.full(stg.BS_raw_data[data_id].shape,
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float(self.lineEdit_profile_tail_value.text().replace(",", "."))))
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stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = (
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()])
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stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
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np.divide((stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()]
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- stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
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stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
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stg.BS_noise_averaged_data[data_id] = (
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stg.BS_noise_raw_data[data_id])
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stg.SNR_raw_data[data_id] = (
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np.divide((stg.BS_raw_data[data_id]
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- stg.BS_noise_raw_data[data_id]) ** 2,
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stg.BS_noise_raw_data[data_id] ** 2))
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self.combobox_frequency_profile.clear()
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self.combobox_frequency_profile.addItems(
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[f for f in stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()]])
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[f for f in stg.freq_text[data_id]])
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# --- Trigger graphic widgets ---
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||||
if stg.SNR_filter_value[self.combobox_acoustic_data_choice.currentIndex()] == 0:
|
||||
if stg.SNR_filter_value[data_id] == 0:
|
||||
self.lineEdit_SNR_criterion.setText("0.00")
|
||||
else:
|
||||
self.lineEdit_SNR_criterion.setText(str(stg.SNR_filter_value[self.combobox_acoustic_data_choice.currentIndex()]))
|
||||
self.lineEdit_SNR_criterion.setText(str(stg.SNR_filter_value[data_id]))
|
||||
|
||||
if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
|
||||
self.slider.setMaximum(stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape[1])
|
||||
if stg.time_cross_section[data_id].shape != (0,):
|
||||
self.slider.setMaximum(stg.time_cross_section[data_id].shape[1])
|
||||
else:
|
||||
self.slider.setMaximum(stg.time[self.combobox_acoustic_data_choice.currentIndex()].shape[1])
|
||||
self.slider.setMaximum(stg.time[data_id].shape[1])
|
||||
|
||||
# self.activate_list_of_pre_processed_data()
|
||||
|
||||
|
|
@ -1150,14 +1180,17 @@ class SignalProcessingTab(QWidget):
|
|||
|
||||
# elif self.canvas_SNR == None:
|
||||
else:
|
||||
data_id = self.combobox_acoustic_data_choice.currentIndex()
|
||||
|
||||
if ((self.combobox_acoustic_data_choice.currentIndex() != -1)
|
||||
and (stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,))):
|
||||
|
||||
if ((data_id != -1)
|
||||
and (stg.BS_noise_raw_data[data_id].shape != (0,))):
|
||||
self.verticalLayout_groupbox_plot_SNR.removeWidget(self.toolbar_SNR)
|
||||
self.verticalLayout_groupbox_plot_SNR.removeWidget(self.scroll_SNR)
|
||||
|
||||
self.fig_SNR, self.axis_SNR = plt.subplots(nrows=stg.freq[self.combobox_acoustic_data_choice.currentIndex()].shape[0], ncols=1, sharex=True, sharey=False, layout='constrained')
|
||||
self.fig_SNR, self.axis_SNR = plt.subplots(
|
||||
nrows=stg.freq[data_id].shape[0], ncols=1,
|
||||
sharex=True, sharey=False, layout='constrained'
|
||||
)
|
||||
self.canvas_SNR = FigureCanvas(self.fig_SNR)
|
||||
self.toolbar_SNR = NavigationToolBar(self.canvas_SNR, self)
|
||||
|
||||
|
|
@ -1166,158 +1199,82 @@ class SignalProcessingTab(QWidget):
|
|||
self.verticalLayout_groupbox_plot_SNR.addWidget(self.toolbar_SNR)
|
||||
self.verticalLayout_groupbox_plot_SNR.addWidget(self.scroll_SNR)
|
||||
|
||||
for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
|
||||
|
||||
if stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
|
||||
|
||||
if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
|
||||
|
||||
if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
|
||||
|
||||
x, y = np.meshgrid(
|
||||
stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :],
|
||||
stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :])
|
||||
|
||||
elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
|
||||
|
||||
x, y = np.meshgrid(
|
||||
stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :],
|
||||
stg.depth[self.combobox_acoustic_data_choice.currentIndex()][f, :])
|
||||
|
||||
else:
|
||||
|
||||
if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
|
||||
|
||||
x, y = np.meshgrid(
|
||||
stg.time[self.combobox_acoustic_data_choice.currentIndex()][f, :],
|
||||
stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :])
|
||||
|
||||
elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
|
||||
|
||||
x, y = np.meshgrid(
|
||||
stg.time[self.combobox_acoustic_data_choice.currentIndex()][f, :],
|
||||
stg.depth[self.combobox_acoustic_data_choice.currentIndex()][f, :])
|
||||
|
||||
val_min = np.nanmin(stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
|
||||
val_max = np.nanmax(stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
|
||||
if val_min == val_max:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
|
||||
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
|
||||
else:
|
||||
if val_min == 0:
|
||||
val_min = 1e-5
|
||||
if val_max > 1000:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
|
||||
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
|
||||
else:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1])
|
||||
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
|
||||
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
|
||||
|
||||
cf = (self.axis_SNR[f].contourf(x, -y,
|
||||
stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, :, :],
|
||||
levels, cmap='gist_rainbow',
|
||||
norm=norm))
|
||||
|
||||
elif stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
|
||||
|
||||
if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
|
||||
|
||||
if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
|
||||
|
||||
x, y = np.meshgrid(
|
||||
stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :],
|
||||
stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :])
|
||||
|
||||
elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
|
||||
|
||||
x, y = np.meshgrid(
|
||||
stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :],
|
||||
stg.depth[self.combobox_acoustic_data_choice.currentIndex()][f, :])
|
||||
|
||||
else:
|
||||
|
||||
if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
|
||||
|
||||
x, y = np.meshgrid(
|
||||
stg.time[self.combobox_acoustic_data_choice.currentIndex()][f, :],
|
||||
stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :])
|
||||
|
||||
elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
|
||||
|
||||
x, y = np.meshgrid(
|
||||
stg.time[self.combobox_acoustic_data_choice.currentIndex()][f, :],
|
||||
stg.depth[self.combobox_acoustic_data_choice.currentIndex()][f, :])
|
||||
|
||||
val_min = np.nanmin(stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
|
||||
val_max = np.nanmax(stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
|
||||
if val_min == val_max:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
|
||||
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
|
||||
else:
|
||||
if val_min == 0:
|
||||
val_min = 1e-5
|
||||
if val_max > 1000:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
|
||||
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
|
||||
|
||||
else:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1])
|
||||
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
|
||||
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
|
||||
|
||||
cf = (self.axis_SNR[f].contourf(x, -y,
|
||||
stg.SNR_cross_section[
|
||||
self.combobox_acoustic_data_choice.currentIndex()][f, :, :],
|
||||
levels, cmap='gist_rainbow', norm=norm))
|
||||
for f, _ in enumerate(stg.freq[data_id]):
|
||||
if stg.SNR_stream_bed[data_id].shape != (0,):
|
||||
SNR_data = stg.SNR_stream_bed
|
||||
elif stg.SNR_cross_section[data_id].shape != (0,):
|
||||
SNR_data = stg.SNR_cross_section
|
||||
|
||||
if stg.time_cross_section[data_id].shape != (0,):
|
||||
time_data = stg.time_cross_section
|
||||
else:
|
||||
time_data = stg.time
|
||||
|
||||
x, y = np.meshgrid(stg.time[self.combobox_acoustic_data_choice.currentIndex()][0, :],
|
||||
stg.depth[self.combobox_acoustic_data_choice.currentIndex()][0, :])
|
||||
if stg.depth_cross_section[data_id].shape != (0,):
|
||||
depth_data = stg.depth_cross_section
|
||||
elif stg.depth[data_id].shape != (0,):
|
||||
depth_data = stg.depth
|
||||
|
||||
val_min = np.nanmin(stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
|
||||
val_max = np.nanmax(stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
|
||||
if val_min == val_max:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
|
||||
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
|
||||
x, y = np.meshgrid(
|
||||
time_data[data_id][f, :],
|
||||
depth_data[data_id][f, :]
|
||||
)
|
||||
|
||||
val_min = np.nanmin(SNR_data[data_id][f, :, :])
|
||||
val_max = np.nanmax(SNR_data[data_id][f, :, :])
|
||||
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
|
||||
|
||||
if val_min != val_max:
|
||||
if val_min == 0:
|
||||
val_min = 1e-5
|
||||
else:
|
||||
if val_min == 0:
|
||||
val_min = 1e-5
|
||||
if val_max > 1000:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
|
||||
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
|
||||
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
|
||||
else:
|
||||
levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max*1000 + 1])
|
||||
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
|
||||
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
|
||||
levels = np.array(
|
||||
[00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
|
||||
)
|
||||
bounds = [
|
||||
00.1, 1, 2, 10, 100, 1000,
|
||||
val_max * 1000 + 1
|
||||
]
|
||||
|
||||
cf = (self.axis_SNR[f].contourf(x, -y,
|
||||
stg.SNR_raw_data[
|
||||
self.combobox_acoustic_data_choice.currentIndex()][f, :, :],
|
||||
levels, cmap='gist_rainbow', norm=norm))
|
||||
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
|
||||
|
||||
cf = self.axis_SNR[f].contourf(
|
||||
x, -y,
|
||||
SNR_data[data_id][f, :, :],
|
||||
levels, cmap='gist_rainbow',
|
||||
norm=norm
|
||||
)
|
||||
|
||||
self.axis_SNR[f].text(1, .70, stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()][f],
|
||||
fontsize=14, fontweight='bold', fontname="DejaVu Sans", 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[data_id][f],
|
||||
fontsize=14, fontweight='bold', fontname="DejaVu Sans",
|
||||
c="black", alpha=0.5,
|
||||
horizontalalignment='right',
|
||||
verticalalignment='bottom',
|
||||
transform=self.axis_SNR[f].transAxes
|
||||
)
|
||||
|
||||
self.fig_SNR.supxlabel('Time (sec)', fontsize=10)
|
||||
self.fig_SNR.supylabel('Depth (m)', fontsize=10)
|
||||
cbar = self.fig_SNR.colorbar(cf, ax=self.axis_SNR[:], shrink=1, location='right')
|
||||
cbar.set_label(label='Signal to Noise Ratio', rotation=270, labelpad=10)
|
||||
cbar.set_ticklabels(['0', '1', '2', '10', '100', r'10$^3$', r'10$^6$'])
|
||||
cbar = self.fig_SNR.colorbar(
|
||||
cf, ax=self.axis_SNR[:],
|
||||
shrink=1, location='right'
|
||||
)
|
||||
cbar.set_label(
|
||||
label='Signal to Noise Ratio',
|
||||
rotation=270, labelpad=10
|
||||
)
|
||||
cbar.set_ticklabels(
|
||||
[
|
||||
'0', '1', '2', '10', '100',
|
||||
r'10$^3$', r'10$^6$'
|
||||
]
|
||||
)
|
||||
self.fig_SNR.canvas.draw_idle()
|
||||
|
||||
else:
|
||||
|
||||
self.verticalLayout_groupbox_plot_SNR.removeWidget(self.toolbar_SNR)
|
||||
self.verticalLayout_groupbox_plot_SNR.removeWidget(self.scroll_SNR)
|
||||
|
||||
|
|
@ -1439,9 +1396,6 @@ class SignalProcessingTab(QWidget):
|
|||
x_time = stg.time[data_id]
|
||||
y_depth = stg.depth[data_id]
|
||||
|
||||
logger.debug(f"x_time: {x_time[data_id].shape}")
|
||||
logger.debug(f"y_depth: {y_depth[data_id].shape}")
|
||||
|
||||
for f, _ in enumerate(stg.freq[data_id]):
|
||||
if stg.BS_stream_bed_pre_process_average[data_id].shape != (0,):
|
||||
BS_data = stg.BS_stream_bed_pre_process_average
|
||||
|
|
@ -1462,8 +1416,6 @@ class SignalProcessingTab(QWidget):
|
|||
elif stg.BS_raw_data[data_id].shape != (0,):
|
||||
BS_data = stg.BS_raw_data
|
||||
|
||||
logger.debug(f"BS_data: {BS_data[data_id].shape}")
|
||||
|
||||
val_min = np.nanmin(
|
||||
BS_data[data_id][f, :, :]
|
||||
)
|
||||
|
|
@ -1574,20 +1526,10 @@ class SignalProcessingTab(QWidget):
|
|||
)
|
||||
)
|
||||
|
||||
if stg.time_cross_section[data_id].shape != (0,):
|
||||
if stg.depth_cross_section[data_id].shape != (0,):
|
||||
x_time = stg.time_cross_section[data_id]
|
||||
y_depth = stg.depth_cross_section[data_id]
|
||||
elif stg.depth[data_id].shape != (0,):
|
||||
x_time = stg.time_cross_section[data_id]
|
||||
y_depth = stg.depth[data_id]
|
||||
else:
|
||||
if stg.depth_cross_section[data_id].shape != (0,):
|
||||
x_time = stg.time[data_id]
|
||||
y_depth = stg.depth_cross_section[data_id]
|
||||
elif stg.depth[data_id].shape != (0,):
|
||||
x_time = stg.time[data_id]
|
||||
y_depth = stg.depth[data_id]
|
||||
if stg.depth_cross_section[data_id].shape != (0,):
|
||||
y_depth = stg.depth_cross_section[data_id]
|
||||
elif stg.depth[data_id].shape != (0,):
|
||||
y_depth = stg.depth[data_id]
|
||||
|
||||
BS = [
|
||||
stg.BS_stream_bed_pre_process_SNR,
|
||||
|
|
@ -1607,11 +1549,6 @@ class SignalProcessingTab(QWidget):
|
|||
stg.BS_raw_data_pre_process_average,
|
||||
]
|
||||
|
||||
time_shape, = x_time[data_id].shape
|
||||
depth_shape, = y_depth[data_id].shape
|
||||
logger.debug(f"time_shape: {time_shape}")
|
||||
logger.debug(f"depth_shape: {depth_shape}")
|
||||
|
||||
BS_data = stg.BS_raw_data
|
||||
BS_data_ppa = stg.BS_raw_data_pre_process_average
|
||||
for i in range(len(BS)):
|
||||
|
|
@ -1620,8 +1557,7 @@ class SignalProcessingTab(QWidget):
|
|||
if bs[data_id].shape == (0,):
|
||||
continue
|
||||
|
||||
x, y, z = bs[data_id].shape
|
||||
if y == depth_shape and z == time_shape:
|
||||
if self._is_correct_shape(bs):
|
||||
BS_data = bs
|
||||
BS_data_ppa = BS_ppa[i]
|
||||
break
|
||||
|
|
|
|||
Loading…
Reference in New Issue