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Implementation of SNR data from UBSediFlow ABS tool
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@@ -783,6 +783,8 @@ class AcousticDataTab(QWidget):
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stg.BS_raw_data_reshape = acoustic_data.reshape_BS_raw_cross_section()
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stg.r_reshape = acoustic_data.reshape_r()
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stg.time_reshape = acoustic_data.reshape_t()
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stg.snr = acoustic_data._SNR_data
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stg.snr_reshape = acoustic_data.reshape_SNR_data()
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def load_noise_data_and_compute_SNR(self):
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if self.combobox_ABS_system_choice.currentIndex() == 1:
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@@ -804,13 +806,10 @@ class AcousticDataTab(QWidget):
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noise_data = AcousticDataLoaderUBSediFlow(stg.path_BS_noise_data + "/" + stg.filename_BS_noise_data)
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stg.date_noise = noise_data._date
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stg.hour_noise = noise_data._hour
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stg.time_snr = noise_data._time
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noise = np.zeros(stg.BS_raw_data.shape)
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for f in range(noise_data._freq.shape[0]):
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noise[:, f, :] = np.mean(noise_data._BS_raw_data[:, f, :], axis=(0, 1))
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stg.BS_noise_data = noise
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stg.snr = np.divide((stg.BS_raw_data - stg.BS_noise_data) ** 2, stg.BS_noise_data ** 2)
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stg.snr_reshape = np.reshape(stg.snr, (stg.r.shape[0] * stg.time.shape[0], stg.freq.shape[0]), order="F")
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stg.time_snr = noise_data._time_snr
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stg.time_snr_reshape = noise_data.reshape_t_snr()
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stg.snr = noise_data._SNR_data
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stg.snr_reshape = noise_data.reshape_SNR_data()
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def fill_measurements_information_groupbox(self):
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if self.combobox_ABS_system_choice.currentIndex() == 1:
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@@ -872,15 +871,17 @@ class AcousticDataTab(QWidget):
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elif self.combobox_ABS_system_choice.currentIndex() == 2:
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if ((self.lineEdit_acoustic_file.text()) and (self.lineEdit_noise_file.text())):
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stg.DataFrame_acoustic = pd.DataFrame(
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np.concatenate((stg.time_reshape, stg.BS_raw_data_reshape, stg.snr_reshape), axis=1),
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columns=list(map(str, ["Time"] + ["BS - " + f for f in stg.freq_text] +
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np.concatenate((stg.time_reshape, stg.BS_raw_data_reshape, stg.time_snr_reshape, stg.snr_reshape), axis=1),
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columns=list(map(str, ["Time BS - " + f for f in stg.freq_text] +
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["BS - " + f for f in stg.freq_text] +
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["Time SNR - " + f for f in stg.freq_text] +
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["SNR - " + f for f in stg.freq_text])))
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self.tableModel = TableModel(stg.DataFrame_acoustic)
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self.tableView.setModel(self.tableModel)
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elif self.lineEdit_acoustic_file.text():
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stg.DataFrame_acoustic = pd.DataFrame(
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np.concatenate((stg.time_reshape, stg.BS_raw_data_reshape), axis=1),
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columns=list(map(str, ["Time - " + f for f in stg.freq_text] + ["BS - " + f for f in stg.freq_text])))
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columns=list(map(str, ["Time BS - " + f for f in stg.freq_text] + ["BS - " + f for f in stg.freq_text])))
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self.tableModel = TableModel(stg.DataFrame_acoustic)
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self.tableView.setModel(self.tableModel)
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else:
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@@ -1105,42 +1106,92 @@ class AcousticDataTab(QWidget):
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# self.spinbox_tmin.setValue(np.min(noise_data._time_snr))
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# self.spinbox_tmax.setValue(np.round(np.max(noise_data._time_snr), 2))
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x, y = np.meshgrid(
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if self.combobox_ABS_system_choice.currentIndex() == 1:
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x, y = np.meshgrid(
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stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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stg.r)
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for f in range(stg.freq.shape[0]):
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for f in range(stg.freq.shape[0]):
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val_min = np.min(stg.snr[:, f, :])
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val_max = np.max(stg.snr[:, 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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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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val_min = np.min(stg.snr[:, f, :])
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val_max = np.max(stg.snr[:, 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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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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cf = (self.axis_SNR[f].
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contourf(x, -y,
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stg.snr[:, f,
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np.where(np.round(stg.snr, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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levels, cmap='gist_rainbow', norm=norm))
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cf = (self.axis_SNR[f].
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contourf(x, -y,
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stg.snr[:, f,
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np.where(np.round(stg.snr, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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levels, cmap='gist_rainbow', norm=norm))
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self.axis_SNR[f].text(1, .70, stg.freq_text[f],
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fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
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horizontalalignment='right', verticalalignment='bottom',
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transform=self.axis_SNR[f].transAxes)
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self.axis_SNR[f].text(1, .70, stg.freq_text[f],
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fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
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horizontalalignment='right', verticalalignment='bottom',
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transform=self.axis_SNR[f].transAxes)
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self.fig_SNR.supxlabel('Time (sec)', fontsize=10)
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self.fig_SNR.supylabel('Depth (m)', fontsize=10)
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cbar = self.fig_SNR.colorbar(cf, ax=self.axis_SNR[:], shrink=1, location='right')
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cbar.set_label(label='Signal to Noise Ratio', rotation=270, labelpad=10)
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cbar.set_ticklabels(['0', '1', '2', '10', '100', r'10$^3$', r'10$^6$'])
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self.fig_SNR.canvas.draw_idle()
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self.fig_SNR.supxlabel('Time (sec)', fontsize=10)
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self.fig_SNR.supylabel('Depth (m)', fontsize=10)
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cbar = self.fig_SNR.colorbar(cf, ax=self.axis_SNR[:], shrink=1, location='right')
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cbar.set_label(label='Signal to Noise Ratio', rotation=270, labelpad=10)
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cbar.set_ticklabels(['0', '1', '2', '10', '100', r'10$^3$', r'10$^6$'])
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self.fig_SNR.canvas.draw_idle()
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elif self.combobox_ABS_system_choice.currentIndex() == 2:
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x = np.array([[[]]])
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y = np.array([[[]]])
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print(f"x : {x.shape}, y : {y.shape}")
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for f, freq in enumerate(stg.freq):
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if x.shape[2] == 0:
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x, y = np.meshgrid(stg.time_snr[f, :], stg.r[f, :])
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x = np.array([x])
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y = np.array([y])
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print(f"x : {x.shape}, y : {y.shape}")
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else:
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x0, y0 = np.meshgrid(stg.time_snr[f, :], stg.r[f, :])
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x = np.append(x, np.array([x0]), axis=0)
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y = np.append(y, np.array([y0]), axis=0)
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print(f"x : {x.shape}, y : {y.shape}")
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val_min = np.nanmin(abs(stg.snr[f, :, :]))
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# print(f"val_min = {val_min}")
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val_max = np.nanmax(abs(stg.snr[f, :, :]))
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# print(f"val_max = {val_max}")
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if int(val_min) == 0:
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val_min = 1e-5
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if int(val_max) < 1000:
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levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
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bounds = [00.1, 1, 2, 10, 100, 1000, 1e6, 1e6 * 1.2]
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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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# print(f"levels = {levels}")
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# print(f"norm = {norm.boundaries}")
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cf = self.axis_SNR[f].contourf(x[f, :, :], y[f, :, :], stg.snr[f, :, :])#, levels, cmap='gist_rainbow', norm=norm)
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self.axis_SNR[f].text(1, .70, stg.freq_text[f],
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fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
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horizontalalignment='right', verticalalignment='bottom',
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transform=self.axis_SNR[f].transAxes)
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self.fig_SNR.supxlabel('Time (sec)', fontsize=10)
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self.fig_SNR.supylabel('Depth (m)', fontsize=10)
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cbar = self.fig_SNR.colorbar(cf, ax=self.axis_SNR[:], shrink=1, location='right')
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cbar.set_label(label='Signal to Noise Ratio', rotation=270, labelpad=10)
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self.fig_SNR.canvas.draw_idle()
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def update_xaxis_transect_with_SNR_data(self):
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@@ -1155,42 +1206,121 @@ class AcousticDataTab(QWidget):
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if ((self.canvas_BS != None) and (self.canvas_SNR != None)):
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# --- Backscatter noise signal is recorded for next tab ---
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stg.Noise_data = stg.BS_noise_data[:, :, np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]]
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stg.SNR_data = stg.snr[:, :, np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]]
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stg.tmin_snr = np.array([])
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stg.tmax_snr = np.array([])
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x, y = np.meshgrid(
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stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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stg.r)
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stg.SNR_data = np.array([[[]]])
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stg.t_snr = np.array([[]])
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x = np.array([[[]]])
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y = np.array([[[]]])
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print(f"x : {x.shape}, y : {y.shape}")
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for f in range(stg.freq.shape[0]):
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if x.shape[2] == 0:
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x, y = np.meshgrid(stg.time_snr[f, :], stg.r[f, :])
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x = np.array([x])
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y = np.array([y])
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print(f"x : {x.shape}, y : {y.shape}")
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else:
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x0, y0 = np.meshgrid(stg.time_snr[f, :], stg.r[f, :])
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x = np.append(x, np.array([x0]), axis=0)
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y = np.append(y, np.array([y0]), axis=0)
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print(f"x : {x.shape}, y : {y.shape}")
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# print(np.abs(np.round(stg.time_snr[f, :], 2) - self.spinbox_tmin.value()))
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# print(np.where(np.abs(np.round(stg.time_snr[f, :], 2) - self.spinbox_tmin.value()) ==
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# np.nanmin(np.abs(np.round(stg.time_snr[f, :], 2) - self.spinbox_tmin.value())))[0][0])
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stg.tmin_snr = (
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np.append(stg.tmin_snr,
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np.where(np.abs(np.round(stg.time_snr[f, :], 2) - self.spinbox_tmin.value()) ==
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np.nanmin(np.abs(np.round(stg.time_snr[f, :], 2) - self.spinbox_tmin.value())))[0][
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0])
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)
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stg.tmax_snr = (
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np.append(stg.tmax_snr,
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np.where(np.abs(np.round(stg.time_snr[f, :], 2) - self.spinbox_tmax.value()) ==
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np.nanmin(np.abs(np.round(stg.time_snr[f, :], 2) - self.spinbox_tmax.value())))[0][
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0])
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)
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print("stg.tmin[f] ", stg.tmin_snr[f])
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print("stg.tmax[f] ", stg.tmax_snr[f])
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if stg.SNR_data.shape[2] == 0:
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stg.SNR_data = np.array([stg.snr[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])]])
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else:
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stg.SNR_data = np.append(stg.SNR_data,
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np.array([stg.snr[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])]]),
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axis=0)
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# stg.BS_data = np.stack(np.array([stg.BS_raw_data[f, :, int(stg.tmin[f]):int(stg.tmax[f])]]), axis=0)
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# stg.BS_data = np.append(stg.BS_data, np.array([stg.BS_raw_data[f, :, int(stg.tmin[f]):int(stg.tmax[f])]]), axis=2)
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if stg.t_snr.shape[1] == 0:
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stg.t_snr = np.array([stg.time_snr[f, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])]])
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else:
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stg.t_snr = np.append(stg.t_snr, np.array([stg.time_snr[f, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])]]), axis=0)
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# stg.t = np.append(stg.t, np.array([stg.time[f, int(stg.tmin[f]):int(stg.tmax[f])]]), axis=0)
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print("stg.t shape ", stg.t_snr.shape)
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self.axis_SNR[f].cla()
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val_min = np.min(stg.snr[:, f, :])
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val_max = np.max(stg.snr[:, 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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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 self.combobox_ABS_system_choice.currentIndex() == 1:
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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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val_min = np.nanmin(stg.SNR_data[f, :, :])
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val_max = np.nanmax(stg.snr[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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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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cf = self.axis_SNR[f].contourf(x, -y,
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stg.snr[:, f,
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np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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levels, cmap='gist_rainbow', norm=norm)
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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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self.axis_SNR[f].text(1, .70, stg.freq_text[f],
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fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
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horizontalalignment='right', verticalalignment='bottom',
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transform=self.axis_SNR[f].transAxes)
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cf = self.axis_SNR[f].contourf(x, -y,
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stg.snr[:, f,
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np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
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np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
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levels, cmap='gist_rainbow', norm=norm)
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self.axis_SNR[f].text(1, .70, stg.freq_text[f],
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fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
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horizontalalignment='right', verticalalignment='bottom',
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transform=self.axis_SNR[f].transAxes)
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elif self.combobox_ABS_system_choice.currentIndex() == 2:
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val_min = np.nanmin(abs(stg.snr[f, :, :]))
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# print(f"val_min = {val_min}")
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val_max = np.nanmax(abs(stg.snr[f, :, :]))
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# print(f"val_max = {val_max}")
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if int(val_min) == 0:
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val_min = 1e-5
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if int(val_max) < 1000:
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levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
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bounds = [00.1, 1, 2, 10, 100, 1000, 1e6, 1e6 * 1.2]
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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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# print(f"levels = {levels}")
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# print(f"norm = {norm.boundaries}")
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cf = self.axis_SNR[f].contourf(x[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])],
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y[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])],
|
||||
stg.snr[f, :, int(stg.tmin_snr[f]):int(stg.tmax_snr[f])]) # , levels, cmap='gist_rainbow', norm=norm)
|
||||
|
||||
self.axis_SNR[f].text(1, .70, stg.freq_text[f],
|
||||
fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
|
||||
horizontalalignment='right', verticalalignment='bottom',
|
||||
transform=self.axis_SNR[f].transAxes)
|
||||
|
||||
self.fig_SNR.supxlabel('Distance from left bank (m)', fontsize=10)
|
||||
self.fig_SNR.supylabel('Depth (m)', fontsize=10)
|
||||
|
||||
Reference in new issue
Block a user