Computing noise from a value is implemented in signal pre-processing tab.
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d9927d5868
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343fc27aec
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@ -129,6 +129,19 @@ class SignalProcessingTab(QWidget):
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self.groupbox_compute_noise_from_value = QGroupBox()
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self.groupbox_compute_noise_from_value.setVisible(False)
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self.gridLayout_compute_noise_from_value = QGridLayout(self.groupbox_compute_noise_from_value)
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self.label_noise_value = QLabel()
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self.label_noise_value.setText("Value : ")
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self.gridLayout_compute_noise_from_value.addWidget(self.label_noise_value, 0, 0, 1, 1)
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self.spinbox_compute_noise_from_value = QDoubleSpinBox()
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self.gridLayout_compute_noise_from_value.addWidget(self.spinbox_compute_noise_from_value, 0, 1, 1, 1)
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self.label_Volts = QLabel("Volts")
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self.gridLayout_compute_noise_from_value.addWidget(self.label_Volts, 0, 2, 1, 1)
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self.pushbutton_compute_noise_from_value = QPushButton()
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self.pushbutton_compute_noise_from_value.setText("Apply noise")
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self.gridLayout_compute_noise_from_value.addWidget(self.pushbutton_compute_noise_from_value, 0, 3, 1, 1)
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self.pushbutton_compute_noise_from_value.clicked.connect(self.compute_noise_from_value)
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self.verticalLayout_groupbox_study_data.addWidget(self.groupbox_compute_noise_from_value)
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# +++++++++++++++++++++++++++++
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@ -1038,6 +1051,61 @@ class SignalProcessingTab(QWidget):
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stg.SNR_reshape = np.reshape(stg.SNR_stream_bed, (stg.r.shape[1] * stg.t.shape[1], stg.freq.shape[0]),
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order="F")
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def compute_noise_from_value(self):
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# --- Compute noise from value and compute SNR ---
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if stg.BS_stream_bed.size == 0:
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stg.time_snr = stg.t
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if self.spinbox_compute_noise_from_value.value() == 0:
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stg.BS_noise_raw_data = np.full(stg.BS_cross_section.shape,
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self.spinbox_compute_noise_from_value.value())
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print(f"stg.BS_noise_raw_data.shape : {stg.BS_noise_raw_data.shape}")
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stg.SNR_cross_section = np.full(stg.BS_cross_section.shape, 1e3)
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else:
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stg.BS_noise_raw_data = np.full(stg.BS_cross_section.shape, self.spinbox_compute_noise_from_value.value())
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stg.SNR_cross_section = np.divide((stg.BS_cross_section - stg.BS_noise_raw_data) ** 2,
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stg.BS_noise_raw_data ** 2)
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else:
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stg.time_snr = stg.t
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if self.spinbox_compute_noise_from_value.value() == 0:
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stg.BS_noise_raw_data = np.full(stg.BS_stream_bed.shape,
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self.spinbox_compute_noise_from_value.value())
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stg.SNR_stream_bed = np.full(stg.BS_stream_bed.shape, 1e3)
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else:
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stg.BS_noise_raw_data = np.full(stg.BS_stream_bed.shape,
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self.spinbox_compute_noise_from_value.value())
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stg.SNR_stream_bed = np.divide((stg.BS_stream_bed - stg.BS_noise_raw_data) ** 2,
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stg.BS_noise_raw_data ** 2)
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# --- Trigger graphic widgets ---
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self.combobox_freq_noise.addItems([f for f in stg.freq_text])
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self.plot_noise()
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self.plot_transect_with_SNR_data()
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self.combobox_freq_noise.currentIndexChanged.connect(self.plot_noise)
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self.spinbox_SNR_criterion.setValue(0)
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self.remove_point_with_snr_filter()
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self.combobox_frequency_profile.addItems([f for f in stg.freq_text])
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self.combobox_frequency_profile.currentIndexChanged.connect(self.plot_profile_and_position_on_transect_with_slider)
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self.slider.setMaximum(stg.t.shape[1])
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self.compute_averaged_BS_data()
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self.plot_profile_and_position_on_transect_with_slider()
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def plot_noise(self):
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self.horizontalLayout_groupbox_plot_noise_data.removeWidget(self.canvas_noise)
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@ -1048,13 +1116,27 @@ class SignalProcessingTab(QWidget):
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val_min = np.nanmin(stg.BS_noise_raw_data[self.combobox_freq_noise.currentIndex(), :, :])
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val_max = np.nanmax(stg.BS_noise_raw_data[self.combobox_freq_noise.currentIndex(), :, :])
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if val_min == 0:
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val_min = 1e-5
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pcm = self.axis_noise.pcolormesh(stg.time_snr[0, :],
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-stg.r[self.combobox_freq_noise.currentIndex(), :],
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stg.BS_noise_raw_data[self.combobox_freq_noise.currentIndex(), :, :],
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cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
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print(f"0/ val_min for plot noise = {val_min}")
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print(f"0/ val_max for plot noise = {val_max}")
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if val_min == val_max:
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pcm = self.axis_noise.pcolormesh(stg.time_snr[0, :],
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-stg.r[self.combobox_freq_noise.currentIndex(), :],
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stg.BS_noise_raw_data[self.combobox_freq_noise.currentIndex(), :, :],
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cmap='viridis')
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else:
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if val_min == 0:
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val_min = 1e-5
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pcm = self.axis_noise.pcolormesh(stg.time_snr[0, :],
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-stg.r[self.combobox_freq_noise.currentIndex(), :],
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stg.BS_noise_raw_data[self.combobox_freq_noise.currentIndex(), :, :],
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cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
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self.axis_noise.tick_params(axis='both', which='minor', labelsize=10)
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# cbar = self.fig_noise.colorbar(pcm, ax=self.axis_noise, shrink=1, location='top')
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@ -1067,7 +1149,8 @@ class SignalProcessingTab(QWidget):
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def plot_transect_with_SNR_data(self):
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# --- Condition if table is not filled ---
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if not self.lineEdit_noise_file.text():
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# if not self.lineEdit_noise_file.text():
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if stg.BS_noise_raw_data.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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@ -1075,7 +1158,8 @@ class SignalProcessingTab(QWidget):
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msgBox.setStandardButtons(QMessageBox.Ok)
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msgBox.exec()
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elif self.canvas_SNR == None:
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# elif self.canvas_SNR == None:
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else:
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self.fig_SNR, self.axis_SNR = plt.subplots(nrows=stg.freq.shape[0], ncols=1, sharex=True, sharey=False, layout='constrained')
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self.canvas_SNR = FigureCanvas(self.fig_SNR)
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@ -1097,12 +1181,16 @@ class SignalProcessingTab(QWidget):
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val_min = np.nanmin(stg.SNR_cross_section[f, :, :])
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val_max = np.nanmax(stg.SNR_cross_section[f, :, :])
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if val_min == 0:
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val_min = 1e-5
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if val_max > 1000:
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if val_min == val_max:
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levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
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else:
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levels = np.array([00.1, 1, 2, 10, 100, val_max])
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if val_min == 0:
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val_min = 1e-5
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if val_max > 1000:
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levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
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else:
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levels = np.array([00.1, 1, 2, 10, 100, val_max])
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bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
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norm = BoundaryNorm(boundaries=bounds, ncolors=300)
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@ -1112,12 +1200,17 @@ class SignalProcessingTab(QWidget):
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val_min = np.nanmin(stg.SNR_stream_bed[f, :, :])
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val_max = np.nanmax(stg.SNR_stream_bed[f, :, :])
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if val_min == 0:
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val_min = 1e-5
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if val_max > 1000:
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if val_min == val_max:
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levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
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else:
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levels = np.array([00.1, 1, 2, 10, 100, val_max])
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if val_min == 0:
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val_min = 1e-5
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if val_max > 1000:
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levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
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else:
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levels = np.array([00.1, 1, 2, 10, 100, val_max])
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bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
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norm = BoundaryNorm(boundaries=bounds, ncolors=300)
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