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acoused
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Exceptions are added to manage error on algorithm bottom detection

dev-brahim
brahim 2023-09-11 13:50:59 +02:00
parent 45aa5ae2f5
commit 5b63855bd8
1 changed files with 128 additions and 94 deletions
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222
View/acoustic_data_tab.py
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@ -431,7 +431,7 @@ class AcousticDataTab(QWidget):
self.gridlayout_compute_bathymetry.addWidget(self.combobox_freq_choice, 0, 0, 2, 1) self.gridlayout_compute_bathymetry.addWidget(self.combobox_freq_choice, 0, 0, 2, 1)
self.label_from_bathy = QLabel() self.label_from_bathy = QLabel()
self.label_from_bathy.setText("From ") self.label_from_bathy.setText("From - ")
self.gridlayout_compute_bathymetry.addWidget(self.label_from_bathy, 0, 1, 1, 1) self.gridlayout_compute_bathymetry.addWidget(self.label_from_bathy, 0, 1, 1, 1)
self.spinbox_depth_min = QSpinBox() self.spinbox_depth_min = QSpinBox()
@ -443,7 +443,7 @@ class AcousticDataTab(QWidget):
self.gridlayout_compute_bathymetry.addWidget(self.label_depth_min_unit, 0, 3, 1, 1) self.gridlayout_compute_bathymetry.addWidget(self.label_depth_min_unit, 0, 3, 1, 1)
self.label_to_bathy = QLabel() self.label_to_bathy = QLabel()
self.label_to_bathy.setText("to ") self.label_to_bathy.setText("to - ")
self.gridlayout_compute_bathymetry.addWidget(self.label_to_bathy, 0, 4, 1, 1) self.gridlayout_compute_bathymetry.addWidget(self.label_to_bathy, 0, 4, 1, 1)
self.spinbox_depth_max = QSpinBox() self.spinbox_depth_max = QSpinBox()
@ -822,8 +822,14 @@ class AcousticDataTab(QWidget):
def plot_transect_with_BS_raw_data(self): def plot_transect_with_BS_raw_data(self):
# --- Condition if table is not filled --- # --- Condition if table is not filled ---
if not self.lineEdit_acoustic_file.text():
if self.tableModel.rowCount(1) == 10: msgBox = QMessageBox()
msgBox.setWindowTitle("Plot transect Error")
msgBox.setIcon(QMessageBox.Warning)
msgBox.setText("Load data before plot transect 2D field")
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec()
elif self.tableModel.rowCount(1) == 10:
msgBox = QMessageBox() msgBox = QMessageBox()
msgBox.setWindowTitle("Plot transect Error") msgBox.setWindowTitle("Plot transect Error")
msgBox.setIcon(QMessageBox.Warning) msgBox.setIcon(QMessageBox.Warning)
@ -925,7 +931,14 @@ class AcousticDataTab(QWidget):
self.fig_BS.canvas.draw_idle() self.fig_BS.canvas.draw_idle()
def plot_transect_with_SNR_data(self): def plot_transect_with_SNR_data(self):
if self.tableModel.rowCount(1) == 10: if not self.lineEdit_noise_file.text():
msgBox = QMessageBox()
msgBox.setWindowTitle("Plot transect Error")
msgBox.setIcon(QMessageBox.Warning)
msgBox.setText("Load data before plot SNR 2D field")
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec()
elif self.tableModel.rowCount(1) == 10:
msgBox = QMessageBox() msgBox = QMessageBox()
msgBox.setWindowTitle("Plot transect Error") msgBox.setWindowTitle("Plot transect Error")
msgBox.setIcon(QMessageBox.Warning) msgBox.setIcon(QMessageBox.Warning)
@ -1076,8 +1089,8 @@ class AcousticDataTab(QWidget):
# msgBox.setText("Plot transect before compute bathymety algorithm") # msgBox.setText("Plot transect before compute bathymety algorithm")
# msgBox.setStandardButtons(QMessageBox.Ok) # msgBox.setStandardButtons(QMessageBox.Ok)
# msgBox.exec() # msgBox.exec()
# elif (self.canvas_BS) and (self.canvas_SNR == None): elif self.canvas_BS != None:
else: # else:
# --- Record frequency choose for bottom detection --- # --- Record frequency choose for bottom detection ---
stg.freq_bottom_detection = self.combobox_freq_choice.currentIndex() stg.freq_bottom_detection = self.combobox_freq_choice.currentIndex()
@ -1095,114 +1108,135 @@ class AcousticDataTab(QWidget):
ind_min = np.where(stg.r >= rmin)[0][0] ind_min = np.where(stg.r >= rmin)[0][0]
ind_max = np.where(stg.r <= rmax)[0][-1] ind_max = np.where(stg.r <= rmax)[0][-1]
# Getting the peak # Getting the peak
val_bottom[d] = np.nanmax(stg.BS_raw_data[ind_min:ind_max, self.combobox_freq_choice.currentIndex(), d]) try:
# Getting the range cell of the peak val_bottom[d] = np.nanmax(stg.BS_raw_data[ind_min:ind_max, self.combobox_freq_choice.currentIndex(), d])
ind_bottom = np.where(stg.BS_raw_data[ind_min:ind_max, self.combobox_freq_choice.currentIndex(), d] except ValueError as e:
== val_bottom[d])[0][0] msgBox = QMessageBox()
np.append(stg.ind_bottom, ind_bottom) msgBox.setWindowTitle("Detect bottom Error")
msgBox.setIcon(QMessageBox.Warning)
msgBox.setText(f"{e} : maximum value of section bottom is not found. \n "
f"Please change parameter of algorithm")
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox_return = msgBox.exec()
if msgBox_return == msgBox.Ok:
break #msgBox.close()
else:
# Getting the range cell of the peak
ind_bottom = np.where(stg.BS_raw_data[ind_min:ind_max, self.combobox_freq_choice.currentIndex(), d]
== val_bottom[d])[0][0]
np.append(stg.ind_bottom, ind_bottom)
r_bottom[d] = stg.r[ind_bottom + ind_min] r_bottom[d] = stg.r[ind_bottom + ind_min]
r_bottom_ind.append(ind_bottom + ind_min) r_bottom_ind.append(ind_bottom + ind_min)
# Updating the range where we will look for the peak (in the next cell) # Updating the range where we will look for the peak (in the next cell)
rmin = r_bottom[d] - locale.atof(self.doublespinbox_next_cell.text()) rmin = r_bottom[d] - locale.atof(self.doublespinbox_next_cell.text())
rmax = r_bottom[d] + locale.atof(self.doublespinbox_next_cell.text()) rmax = r_bottom[d] + locale.atof(self.doublespinbox_next_cell.text())
BS_section_bottom = np.zeros((stg.r.shape[0], stg.time.shape[0])) BS_section_bottom = np.zeros((stg.r.shape[0], stg.time.shape[0]))
for i in range(BS_section_bottom.shape[0]): for i in range(BS_section_bottom.shape[0]):
# print(r_bottom_temp_ind[i]) try:
# print(i) BS_section_bottom[r_bottom_ind[i]][i] = 1
BS_section_bottom[r_bottom_ind[i]][i] = 1 except IndexError as e:
# print(BS_section_bottom[r_bottom_temp_ind[i]][i]) msgBox = QMessageBox()
msgBox.setWindowTitle("Detect bottom Error")
msgBox.setIcon(QMessageBox.Warning)
msgBox.setText(f"{e} : maximum value of section bottom is not found. \n "
f"Please change parameter of algorithm")
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox_return = msgBox.exec()
if msgBox_return == msgBox.Ok:
break # msgBox.close()
# --- Record r_bottom for other tabs --- if BS_section_bottom.sum() > 2:
stg.r_bottom = r_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]: # --- Record r_bottom for other tabs ---
np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]] stg.r_bottom = r_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
stg.val_bottom = val_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]] np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]]
stg.val_bottom = val_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]]
# --- Plot transect BS with bathymetry --- # --- Plot transect BS with bathymetry ---
for f in range(stg.freq.shape[0]): for f in range(stg.freq.shape[0]):
self.axis_BS[f].cla() self.axis_BS[f].cla()
val_min = np.min(stg.BS_raw_data[:, f, :]) val_min = np.min(stg.BS_raw_data[:, f, :])
val_max = np.max(stg.BS_raw_data[:, f, :]) val_max = np.max(stg.BS_raw_data[:, f, :])
if val_min == 0: if val_min == 0:
val_min = 1e-5 val_min = 1e-5
pcm = self.axis_BS[f].pcolormesh( pcm = self.axis_BS[f].pcolormesh(
stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]: stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]], np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
-stg.r, -stg.r,
(stg.BS_raw_data[:, f, (stg.BS_raw_data[:, f,
np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]: np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]]), np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]]),
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max)) cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
self.axis_BS[f].plot( self.axis_BS[f].plot(
stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]: stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]], np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
- r_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]: - r_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]], np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
color='black', linewidth=1, linestyle="solid") color='black', linewidth=1, linestyle="solid")
self.axis_BS[f].text(1, .70, stg.freq_text[f], self.axis_BS[f].text(1, .70, stg.freq_text[f],
fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5, fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
horizontalalignment='right', verticalalignment='bottom', horizontalalignment='right', verticalalignment='bottom',
transform=self.axis_BS[f].transAxes) transform=self.axis_BS[f].transAxes)
self.fig_BS.canvas.draw_idle() self.fig_BS.canvas.draw_idle()
# --- Plot transect SNR with bathymetry --- # --- Plot transect SNR with bathymetry ---
if self.canvas_SNR != None:
x, y = np.meshgrid(
stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
stg.r)
x, y = np.meshgrid( for f in range(stg.freq.shape[0]):
stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]: self.axis_SNR[f].cla()
np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
stg.r)
for f in range(stg.freq.shape[0]): val_min = np.min(stg.snr[:, f, :])
self.axis_SNR[f].cla() val_max = np.max(stg.snr[:, f, :])
if val_min == 0:
val_min = 1e-5
if val_max > 1000:
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
else:
levels = np.array([00.1, 1, 2, 10, 100, val_max])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
val_min = np.min(stg.snr[:, f, :]) cf = self.axis_SNR[f].contourf(x, -y,
val_max = np.max(stg.snr[:, f, :]) stg.snr[:, f,
if val_min == 0: np.where(np.round(stg.time,
val_min = 1e-5 2) == self.spinbox_tmin.value())[0][0]:
if val_max > 1000: np.where(np.round(stg.time,
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6]) 2) == self.spinbox_tmax.value())[0][0]],
else: levels, cmap='gist_rainbow', norm=norm) # , shading='gouraud')
levels = np.array([00.1, 1, 2, 10, 100, val_max])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
cf = self.axis_SNR[f].contourf(x, -y, self.axis_SNR[f].text(1, .70, stg.freq_text[f],
stg.snr[:, f, fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
np.where(np.round(stg.time, horizontalalignment='right', verticalalignment='bottom',
2) == self.spinbox_tmin.value())[0][0]: transform=self.axis_SNR[f].transAxes)
np.where(np.round(stg.time,
2) == self.spinbox_tmax.value())[0][0]],
levels, cmap='gist_rainbow', norm=norm) # , shading='gouraud')
self.axis_SNR[f].text(1, .70, stg.freq_text[f], self.axis_SNR[f].plot(
fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5, stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
horizontalalignment='right', verticalalignment='bottom', np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
transform=self.axis_SNR[f].transAxes) - r_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]:
np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
# + np.min(r_bottom[np.where(np.round(noise_data._time, 2) == self.spinbox_tmin.value())[0][0]:
# np.where(np.round(noise_data._time, 2) == self.spinbox_tmax.value())[0][0]]),
# np.max(self._model.r_bottom_cross_section) - self._model.r_bottom_cross_section + np.min(self._model.r_bottom_cross_section),
color='black', linewidth=1, linestyle="solid")
self.axis_SNR[f].plot( self.axis_SNR[f].text(1, .70, stg.freq_text[f],
stg.time[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]: fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]], horizontalalignment='right', verticalalignment='bottom',
- r_bottom[np.where(np.round(stg.time, 2) == self.spinbox_tmin.value())[0][0]: transform=self.axis_BS[f].transAxes)
np.where(np.round(stg.time, 2) == self.spinbox_tmax.value())[0][0]],
# + np.min(r_bottom[np.where(np.round(noise_data._time, 2) == self.spinbox_tmin.value())[0][0]:
# np.where(np.round(noise_data._time, 2) == self.spinbox_tmax.value())[0][0]]),
# np.max(self._model.r_bottom_cross_section) - self._model.r_bottom_cross_section + np.min(self._model.r_bottom_cross_section),
color='black', linewidth=1, linestyle="solid")
self.axis_SNR[f].text(1, .70, stg.freq_text[f], self.fig_SNR.canvas.draw_idle()
fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
horizontalalignment='right', verticalalignment='bottom',
transform=self.axis_BS[f].transAxes)
self.fig_SNR.canvas.draw_idle()
# else: # else:
# #