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acoused
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Acoustic data: Add bottom detection setting to SQL and refactoring.

dev
Pierre-Antoine 2025-04-17 17:54:18 +02:00
parent 7bf93bb6d8
commit 24f270a2a4
4 changed files with 286 additions and 343 deletions
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14
Model/create_table_for_save_as.py
View File

@ -106,7 +106,11 @@ class CreateTableForSaveAs:
rmax_index FLOAT, rmax_value FLOAT, rmax_index FLOAT, rmax_value FLOAT,
freq_bottom_detection_index FLOAT, freq_bottom_detection_index FLOAT,
freq_bottom_detection_value STRING, freq_bottom_detection_value STRING,
SNR_filter_value FLOAT, Nb_cells_to_average_BS_signal FLOAT depth_bottom_detection_min FLOAT,
depth_bottom_detection_max FLOAT,
depth_bottom_detection_inverval FLOAT,
SNR_filter_value FLOAT,
Nb_cells_to_average_BS_signal FLOAT
) )
""" """
@ -435,9 +439,12 @@ class CreateTableForSaveAs:
tmin_index, tmin_value, tmax_index, tmax_value, tmin_index, tmin_value, tmax_index, tmax_value,
rmin_index, rmin_value, rmax_index, rmax_value, rmin_index, rmin_value, rmax_index, rmax_value,
freq_bottom_detection_index, freq_bottom_detection_value, freq_bottom_detection_index, freq_bottom_detection_value,
depth_bottom_detection_min,
depth_bottom_detection_max,
depth_bottom_detection_inverval,
SNR_filter_value, Nb_cells_to_average_BS_signal SNR_filter_value, Nb_cells_to_average_BS_signal
) )
VALUES(?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) VALUES(?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
""", """,
( (
stg.acoustic_data[i], stg.temperature, stg.acoustic_data[i], stg.temperature,
@ -448,6 +455,9 @@ class CreateTableForSaveAs:
stg.rmax[i][0], stg.rmax[i][1], stg.rmax[i][0], stg.rmax[i][1],
stg.freq_bottom_detection[i][0], stg.freq_bottom_detection[i][0],
stg.freq_bottom_detection[i][1], stg.freq_bottom_detection[i][1],
stg.depth_bottom_detection_min,
stg.depth_bottom_detection_max,
stg.depth_bottom_detection_interval,
stg.SNR_filter_value[i], stg.SNR_filter_value[i],
stg.Nb_cells_to_average_BS_signal[i] stg.Nb_cells_to_average_BS_signal[i]
) )

27
Model/read_table_for_open.py
View File

@ -497,23 +497,30 @@ class ReadTableForOpen:
tmin_index, tmin_value, tmax_index, tmax_value, tmin_index, tmin_value, tmax_index, tmax_value,
rmin_index, rmin_value, rmax_index, rmax_value, rmin_index, rmin_value, rmax_index, rmax_value,
freq_bottom_detection_index, freq_bottom_detection_value, freq_bottom_detection_index, freq_bottom_detection_value,
depth_bottom_detection_min,
depth_bottom_detection_max,
depth_bottom_detection_inverval,
SNR_filter_value, Nb_cells_to_average_BS_signal SNR_filter_value, Nb_cells_to_average_BS_signal
FROM Settings FROM Settings
WHERE (acoustic_data = {s}) WHERE (acoustic_data = {s})
''' '''
data = self.execute(query3) data = self.execute(query3)
x = data[0] it = iter(data[0])
stg.temperature = [x[1]][0] acoustic_data = next(it)
stg.distance_from_ABS_to_free_surface.append(x[2]) stg.temperature = [next(it)][0]
stg.tmin.append((x[3], x[4])) stg.distance_from_ABS_to_free_surface.append(next(it))
stg.tmax.append((x[5], x[6])) stg.tmin.append((next(it), next(it)))
stg.rmin.append((x[7], x[8])) stg.tmax.append((next(it), next(it)))
stg.rmax.append((x[9], x[10])) stg.rmin.append((next(it), next(it)))
stg.freq_bottom_detection.append((x[11], x[12])) stg.rmax.append((next(it), next(it)))
stg.SNR_filter_value.append(x[13]) stg.freq_bottom_detection.append((next(it), next(it)))
stg.Nb_cells_to_average_BS_signal.append(x[14]) stg.depth_bottom_detection_min = next(it)
stg.depth_bottom_detection_max = next(it)
stg.depth_bottom_detection_interval = next(it)
stg.SNR_filter_value.append(next(it))
stg.Nb_cells_to_average_BS_signal.append(next(it))
logger.debug(f"stg.temperature: {stg.temperature}") logger.debug(f"stg.temperature: {stg.temperature}")
logger.debug(f"stg.tmin: {stg.tmin}") logger.debug(f"stg.tmin: {stg.tmin}")

580
View/acoustic_data_tab.py
View File

@ -750,7 +750,10 @@ class AcousticDataTab(QWidget):
self.fill_table() self.fill_table()
self.plot_backscattered_acoustic_signal_recording() self.plot_backscattered_acoustic_signal_recording()
self.plot_profile() self.plot_profile()
self.update_frequency_combobox() self.update_frequency_combobox()
self.update_bottom_detection_settings()
self.water_attenuation() self.water_attenuation()
self.compute_tmin_tmax() self.compute_tmin_tmax()
self.compute_rmin_rmax() self.compute_rmin_rmax()
@ -2445,7 +2448,6 @@ class AcousticDataTab(QWidget):
self.fig_BS.canvas.draw_idle() self.fig_BS.canvas.draw_idle()
def update_plot_backscattered_acoustic_signal_recording(self): def update_plot_backscattered_acoustic_signal_recording(self):
# --- Condition if table is filled but transect is not plotted # --- Condition if table is filled but transect is not plotted
# --- => Error message if spin box values of tmin or tmax is change # --- => Error message if spin box values of tmin or tmax is change
if self.canvas_BS == None: if self.canvas_BS == None:
@ -2455,116 +2457,85 @@ class AcousticDataTab(QWidget):
msgBox.setText("Plot transect before change x-axis value") msgBox.setText("Plot transect before change x-axis value")
msgBox.setStandardButtons(QMessageBox.Ok) msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec() msgBox.exec()
return
else: data_id = self.fileListWidget.currentRow()
if self.fileListWidget.currentRow() != -1: if data_id == -1:
if len(self.axis_BS.tolist()) != stg.freq[self.fileListWidget.currentRow()].shape[0]: return
self.fig_BS, self.axis_BS = plt.subplots(nrows=stg.freq[self.fileListWidget.currentRow()].shape[0],
ncols=1,
sharex=False, sharey=False, layout="constrained")
for f, _ in enumerate(stg.freq[self.fileListWidget.currentRow()]): if len(self.axis_BS.tolist()) != stg.freq[data_id].shape[0]:
self.axis_BS[f].cla() self.fig_BS, self.axis_BS = plt.subplots(
nrows=stg.freq[data_id].shape[0],
ncols=1,
sharex=False, sharey=False,
layout="constrained"
)
if stg.BS_cross_section[self.fileListWidget.currentRow()].shape != (0,): for f, _ in enumerate(stg.freq[data_id]):
self.axis_BS[f].cla()
val_min = np.nanmin(stg.BS_cross_section[self.fileListWidget.currentRow()][f, :, :]) if stg.BS_cross_section[data_id].shape != (0,):
val_max = np.nanmax(stg.BS_cross_section[self.fileListWidget.currentRow()][f, :, :]) BS_data = stg.BS_cross_section
if val_min == 0: time_data = stg.time_cross_section
val_min = 1e-5 depth_data = stg.depth_cross_section
else:
BS_data = stg.BS_raw_data
time_data = stg.time
depth_data = stg.depth
if self.combobox_ABS_system_choice.currentIndex() == 1: val_min = np.nanmin(BS_data[data_id][f, :, :])
pcm = self.axis_BS[f].pcolormesh( val_max = np.nanmax(BS_data[data_id][f, :, :])
stg.time_cross_section[self.fileListWidget.currentRow()][f, :],
-stg.depth_cross_section[self.fileListWidget.currentRow()][f, :],
stg.BS_cross_section[self.fileListWidget.currentRow()][f, :, :],
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
elif self.combobox_ABS_system_choice.currentIndex() == 2:
pcm = self.axis_BS[f].pcolormesh(
stg.time_cross_section[self.fileListWidget.currentRow()][f, :],
-stg.depth_cross_section[self.fileListWidget.currentRow()][f, :],
np.log(stg.BS_cross_section[self.fileListWidget.currentRow()][f, :,
:]),
cmap='Blues')
# --- Plot red solid line on transect to visualize position of plotted profile --- if val_min == 0:
slider_value = \ val_min = 1e-5
[self.slider.value() - 1 if self.slider.value() - 1 <=
stg.time_cross_section[self.fileListWidget.currentRow()].shape[
1] - 1
else np.max(stg.time_cross_section[self.fileListWidget.currentRow()].shape[1] - 1)][0]
self.axis_BS[self.combobox_frequency_profile.currentIndex()].plot( if self.combobox_ABS_system_choice.currentIndex() == 1:
stg.time_cross_section[self.fileListWidget.currentRow()][ pcm = self.axis_BS[f].pcolormesh(
0, # self.combobox_frequency_profile.currentIndex(), time_data[data_id][f, :],
slider_value] * np.ones( -depth_data[data_id][f, :],
stg.depth_cross_section[self.fileListWidget.currentRow()].shape[1]), BS_data[data_id][f, :, :],
-stg.depth_cross_section[self.fileListWidget.currentRow()][ cmap='viridis',
self.combobox_frequency_profile.currentIndex(), :], norm=LogNorm(vmin=val_min, vmax=val_max)
color='red', linestyle="solid", linewidth=2) )
elif self.combobox_ABS_system_choice.currentIndex() == 2:
pcm = self.axis_BS[f].pcolormesh(
time_data[data_id][f, :],
-depth_data[data_id][f, :],
np.log(BS_data[data_id][f, :, :]),
cmap='Blues'
)
# --- Plot river bottom line --- # --- Plot red solid line on transect to visualize position of plotted profile ---
if stg.depth_bottom[self.fileListWidget.currentRow()].shape != (0,): slider_value = [
self.slider.value() - 1
if self.slider.value() - 1 <= time_data[data_id].shape[1] - 1
else np.max(time_data[data_id].shape[1] - 1)
][0]
self.axis_BS[f].plot(stg.time_cross_section[self.fileListWidget.currentRow()][ freq_id = self.combobox_frequency_profile.currentIndex()
self.combobox_frequency_bathymetry.currentIndex(), :],
-stg.depth_bottom[self.fileListWidget.currentRow()],
color='black', linewidth=1, linestyle="solid")
else: self.axis_BS[freq_id].plot(
time_data[data_id][0, slider_value] * np.ones(
depth_data[data_id].shape[1]
),
-depth_data[data_id][freq_id, :],
color='red', linestyle="solid", linewidth=2
)
val_min = np.nanmin(stg.BS_raw_data[self.fileListWidget.currentRow()][f, :, :]) # --- Plot river bottom line ---
val_max = np.nanmax(stg.BS_raw_data[self.fileListWidget.currentRow()][f, :, :]) if stg.depth_bottom[data_id].shape != (0,):
if val_min == 0: self.axis_BS[f].plot(
val_min = 1e-5 time_data[data_id][
self.combobox_frequency_bathymetry.currentIndex(), :
],
-stg.depth_bottom[data_id],
color='black', linewidth=1, linestyle="solid"
)
if self.combobox_ABS_system_choice.currentIndex() == 1: self.fig_BS.supxlabel('Time (sec)', fontsize=10)
pcm = self.axis_BS[f].pcolormesh(stg.time[self.fileListWidget.currentRow()][f, :], self.fig_BS.supylabel('Depth (m)', fontsize=10)
-stg.depth[self.fileListWidget.currentRow()][f, self.fig_BS.canvas.draw_idle()
:],
stg.BS_raw_data[self.fileListWidget.currentRow()][f, :,
:],
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
elif self.combobox_ABS_system_choice.currentIndex() == 2:
pcm = self.axis_BS[f].pcolormesh(stg.time[self.fileListWidget.currentRow()][f, :],
-stg.depth[self.fileListWidget.currentRow()][f,
:],
np.log(
stg.BS_raw_data[self.fileListWidget.currentRow()][f,
:, :]),
cmap='Blues')
# --- Plot red solid line on transect to visualize position of plotted profile ---
slider_value = \
[self.slider.value() - 1 if self.slider.value() - 1 <=
stg.time[self.fileListWidget.currentRow()].shape[
1] - 1
else np.max(stg.time[self.fileListWidget.currentRow()].shape[1] - 1)][0]
self.axis_BS[self.combobox_frequency_profile.currentIndex()].plot(
stg.time[self.fileListWidget.currentRow()][0, slider_value] *
np.ones(stg.depth[self.fileListWidget.currentRow()].shape[1]),
-stg.depth[self.fileListWidget.currentRow()][
self.combobox_frequency_profile.currentIndex(), :],
color='red', linestyle="solid", linewidth=2)
# --- Plot river bottom line ---
if stg.depth_bottom[self.fileListWidget.currentRow()].shape != (0,):
self.axis_BS[f].plot(stg.time[self.fileListWidget.currentRow()][
self.combobox_frequency_bathymetry.currentIndex(), :],
-stg.depth_bottom[self.fileListWidget.currentRow()],
color='black', linewidth=1, linestyle="solid")
self.axis_BS[f].text(1, .70, stg.freq_text[self.fileListWidget.currentRow()][f],
fontsize=14, fontweight='bold', fontname="DejaVu Sans", c="black", alpha=0.5,
horizontalalignment='right', verticalalignment='bottom',
transform=self.axis_BS[f].transAxes)
self.fig_BS.supxlabel('Time (sec)', fontsize=10)
self.fig_BS.supylabel('Depth (m)', fontsize=10)
self.fig_BS.canvas.draw_idle()
def plot_profile(self): def plot_profile(self):
if self.fileListWidget.currentRow() != -1: if self.fileListWidget.currentRow() != -1:
@ -2782,7 +2753,33 @@ class AcousticDataTab(QWidget):
str(stg.time[self.fileListWidget.currentRow()][self.combobox_frequency_profile.currentIndex(), self.slider.value()-1])) str(stg.time[self.fileListWidget.currentRow()][self.combobox_frequency_profile.currentIndex(), self.slider.value()-1]))
def update_bottom_detection_settings(self):
self.lineEdit_depth_min_bathy.setText(
str(stg.depth_bottom_detection_min)
)
self.lineEdit_depth_max_bathy.setText(
str(stg.depth_bottom_detection_max)
)
self.lineEdit_next_cell_bathy.setText(
str(stg.depth_bottom_detection_interval)
)
def save_bottom_detection_settings(self):
stg.depth_bottom_detection_min = float(
self.lineEdit_depth_min_bathy.text()
)
stg.depth_bottom_detection_max = float(
self.lineEdit_depth_max_bathy.text()
)
stg.depth_bottom_detection_interval = float(
self.lineEdit_next_cell_bathy.text()
)
def detect_bottom(self): def detect_bottom(self):
self.save_bottom_detection_settings()
if self.fileListWidget.count() == 0: if self.fileListWidget.count() == 0:
msgBox = QMessageBox() msgBox = QMessageBox()
msgBox.setWindowTitle("Detect bottom Error") msgBox.setWindowTitle("Detect bottom Error")
@ -2790,7 +2787,7 @@ class AcousticDataTab(QWidget):
msgBox.setText("Load data before compute bathymety algorithm") msgBox.setText("Load data before compute bathymety algorithm")
msgBox.setStandardButtons(QMessageBox.Ok) msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec() msgBox.exec()
return
elif self.canvas_BS == None: elif self.canvas_BS == None:
msgBox = QMessageBox() msgBox = QMessageBox()
msgBox.setWindowTitle("Detect bottom Error") msgBox.setWindowTitle("Detect bottom Error")
@ -2798,258 +2795,187 @@ 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()
return
elif self.lineEdit_next_cell_bathy.text() == "0.00": elif self.lineEdit_next_cell_bathy.text() == "0.00":
return
pass self.detect_bottom_compute()
else: def detect_bottom_compute(self):
data_id = self.fileListWidget.currentRow()
freq_id = self.combobox_frequency_bathymetry.currentIndex()
freq_text = self.combobox_frequency_bathymetry.currentText()
if stg.BS_cross_section[self.fileListWidget.currentRow()].shape != (0,): if stg.BS_cross_section[data_id].shape != (0,):
BS_data = stg.BS_cross_section
time_data = stg.time_cross_section
depth_data = stg.depth_cross_section
elif stg.BS_raw_data[data_id].shape != (0,):
BS_data = stg.BS_raw_data
time_data = stg.time
depth_data = stg.depth
stg.freq_bottom_detection[self.fileListWidget.currentRow()] = \ stg.freq_bottom_detection[data_id] = (freq_id, freq_text)
(self.combobox_frequency_bathymetry.currentIndex(), self.combobox_frequency_bathymetry.currentText())
rmin = float("".join(findall("[.0-9]", self.lineEdit_depth_max_bathy.text()))) rmin = float(
rmax = float("".join(findall("[.0-9]", self.lineEdit_depth_min_bathy.text()))) "".join(findall(
"[.0-9]",
self.lineEdit_depth_max_bathy.text()
))
)
rmax = float(
"".join(findall(
"[.0-9]", self.lineEdit_depth_min_bathy.text()
))
)
r_bottom = np.zeros(stg.time_cross_section[self.fileListWidget.currentRow()].shape[1]) r_bottom = np.zeros(time_data[data_id].shape[1])
val_bottom = np.zeros(stg.time_cross_section[self.fileListWidget.currentRow()].shape[1]) val_bottom = np.zeros(time_data[data_id].shape[1])
r_bottom_ind = [] r_bottom_ind = []
BS_smooth = deepcopy(stg.BS_cross_section[self.fileListWidget.currentRow()][self.combobox_frequency_bathymetry.currentIndex(), :, :]) BS_smooth = deepcopy(BS_data[data_id][freq_id, :, :])
for k in range(stg.time_cross_section[self.fileListWidget.currentRow()].shape[1]): for k in range(time_data[data_id].shape[1]):
BS_smooth[:, k] = savgol_filter(BS_smooth[:, k], 10, 2) BS_smooth[:, k] = savgol_filter(BS_smooth[:, k], 10, 2)
# ----------- Detecting the bottom ------------- # ----------- Detecting the bottom -------------
for d in range(stg.time_cross_section[self.fileListWidget.currentRow()].shape[1]): for d in range(time_data[data_id].shape[1]):
ind_min = np.where(
depth_data[data_id][freq_id, :]
>= rmin
)[0][0]
ind_max = np.where(
depth_data[data_id][freq_id, :]
<= rmax
)[0][-1]
ind_min = np.where(stg.depth_cross_section[self.fileListWidget.currentRow()][int(self.combobox_frequency_bathymetry.currentIndex()), :] >= rmin)[0][0] # Getting the peak
ind_max = np.where(stg.depth_cross_section[self.fileListWidget.currentRow()][int(self.combobox_frequency_bathymetry.currentIndex()), :] <= rmax)[0][-1] try:
val_bottom[d] = np.nanmax(BS_smooth[ind_min:ind_max, d])
except ValueError as e:
msgBox = QMessageBox()
msgBox.setWindowTitle("Detect bottom Error")
msgBox.setIcon(QMessageBox.Warning)
msgBox.setText(
f"1/ {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
else:
ind_bottom = np.where(
(BS_smooth[ind_min:ind_max, d])
== val_bottom[d]
)[0][0]
np.append(stg.ind_bottom, ind_bottom)
r_bottom[d] = depth_data[data_id][
freq_id, ind_bottom + ind_min
]
r_bottom_ind.append(ind_bottom + ind_min)
# Getting the peak # Updating the range where we will look for the peak (in the next cell)
try: rmin = r_bottom[d] - float(
val_bottom[d] = np.nanmax(BS_smooth[ind_min:ind_max, d]) "".join(findall(
"[.0-9]", self.lineEdit_next_cell_bathy.text()
))
)
rmax = r_bottom[d] + float(
"".join(findall(
"[.0-9]", self.lineEdit_next_cell_bathy.text()
))
)
except ValueError as e: BS_section_bottom = np.zeros((
msgBox = QMessageBox() depth_data[data_id].shape[1],
msgBox.setWindowTitle("Detect bottom Error") time_data[data_id].shape[1]
msgBox.setIcon(QMessageBox.Warning) ))
msgBox.setText(f"1/ {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
else:
ind_bottom = np.where((BS_smooth[ind_min:ind_max, d]) == val_bottom[d])[0][0] for i in range(BS_section_bottom.shape[0]):
np.append(stg.ind_bottom, ind_bottom) try:
BS_section_bottom[r_bottom_ind[i]][i] = 1
except IndexError as e:
msgBox = QMessageBox()
msgBox.setWindowTitle("Detect bottom Error")
msgBox.setIcon(QMessageBox.Warning)
msgBox.setText(
f"2/ {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
r_bottom[d] = stg.depth_cross_section[self.fileListWidget.currentRow()][self.combobox_frequency_bathymetry.currentIndex(), ind_bottom + ind_min] if BS_section_bottom.sum() > 2:
r_bottom_ind.append(ind_bottom + ind_min) stg.depth_bottom[data_id] = r_bottom
# Updating the range where we will look for the peak (in the next cell) stg.val_bottom[data_id] = val_bottom
rmin = r_bottom[d] - float("".join(findall("[.0-9]", self.lineEdit_next_cell_bathy.text()))) stg.ind_bottom[data_id] = r_bottom_ind
rmax = r_bottom[d] + float("".join(findall("[.0-9]", self.lineEdit_next_cell_bathy.text())))
BS_section_bottom = np.zeros((stg.depth_cross_section[self.fileListWidget.currentRow()].shape[1], BS_stream_bed_copy = deepcopy(BS_data[data_id])
stg.time_cross_section[self.fileListWidget.currentRow()].shape[1])) for f, _ in enumerate(stg.freq[data_id]):
for k, _ in enumerate(stg.depth_bottom[data_id]):
BS_stream_bed_copy[
f, np.where(
depth_data[data_id][freq_id, :]
>= stg.depth_bottom[data_id][k]
)[0], k
] = np.nan
for i in range(BS_section_bottom.shape[0]): stg.BS_stream_bed[data_id] = BS_stream_bed_copy
try:
BS_section_bottom[r_bottom_ind[i]][i] = 1
except IndexError as e:
msgBox = QMessageBox()
msgBox.setWindowTitle("Detect bottom Error")
msgBox.setIcon(QMessageBox.Warning)
msgBox.setText(f"2/ {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
if BS_section_bottom.sum() > 2: self.detect_bottom_compute_plot_BS_with_bathymetry(
BS_data, time_data, depth_data
)
stg.depth_bottom[self.fileListWidget.currentRow()] = r_bottom # --- Update plot profile ---
self.update_plot_profile()
self.fig_BS.canvas.draw_idle()
stg.val_bottom[self.fileListWidget.currentRow()] = val_bottom def detect_bottom_compute_plot_BS_with_bathymetry(
self, BS_data, time_data, depth_data
):
data_id = self.fileListWidget.currentRow()
freq_id = self.combobox_frequency_bathymetry.currentIndex()
stg.ind_bottom[self.fileListWidget.currentRow()] = r_bottom_ind for f, _ in enumerate(stg.freq[data_id]):
self.axis_BS[f].cla()
val_min = np.min(stg.BS_raw_data[data_id][f, :, :])
val_max = np.max(stg.BS_raw_data[data_id][f, :, :])
if val_min == 0:
val_min = 1e-5
BS_stream_bed_copy = deepcopy(stg.BS_cross_section[self.fileListWidget.currentRow()]) if self.combobox_ABS_system_choice.currentIndex() == 1:
for f, _ in enumerate(stg.freq[self.fileListWidget.currentRow()]): pcm = self.axis_BS[f].pcolormesh(
for k, _ in enumerate(stg.depth_bottom[self.fileListWidget.currentRow()]): time_data[data_id][f, :],
BS_stream_bed_copy[ -depth_data[data_id][f, :],
f, np.where(stg.depth_cross_section[self.fileListWidget.currentRow()][self.combobox_frequency_bathymetry.currentIndex(), :] BS_data[data_id][f, :, :],
>= stg.depth_bottom[self.fileListWidget.currentRow()][k])[ cmap='viridis',
0], k] = np.nan norm=LogNorm(vmin=val_min, vmax=val_max)
)
elif self.combobox_ABS_system_choice.currentIndex() == 2:
pcm = self.axis_BS[f].pcolormesh(
time_data[data_id][f, :],
-depth_data[data_id][f, :],
np.log(BS_data[data_id][f, :, :]),
cmap='Blues'
)
stg.BS_stream_bed[self.fileListWidget.currentRow()] = BS_stream_bed_copy self.axis_BS[f].plot(
time_data[data_id][freq_id, :],
-stg.depth_bottom[data_id],
color='black', linewidth=1, linestyle="solid"
)
# --- Plot transect BS with bathymetry --- self.axis_BS[f].text(
for f, _ in enumerate(stg.freq[self.fileListWidget.currentRow()]): 1, .70, stg.freq_text[data_id][f],
self.axis_BS[f].cla() fontsize=14, fontweight='bold',
fontname="DejaVu Sans", c="black", alpha=0.5,
val_min = np.min(stg.BS_raw_data[self.fileListWidget.currentRow()][f, :, :]) horizontalalignment='right',
val_max = np.max(stg.BS_raw_data[self.fileListWidget.currentRow()][f, :, :]) verticalalignment='bottom',
if val_min == 0: transform=self.axis_BS[f].transAxes
val_min = 1e-5 )
if self.combobox_ABS_system_choice.currentIndex() == 1:
pcm = self.axis_BS[f].pcolormesh(stg.time_cross_section[self.fileListWidget.currentRow()][f, :],
-stg.depth_cross_section[self.fileListWidget.currentRow()][f, :],
stg.BS_cross_section[self.fileListWidget.currentRow()][f, :, :],
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
elif self.combobox_ABS_system_choice.currentIndex() == 2:
pcm = self.axis_BS[f].pcolormesh(stg.time_cross_section[self.fileListWidget.currentRow()][f, :],
-stg.depth_cross_section[self.fileListWidget.currentRow()][f, :],
np.log(stg.BS_cross_section[self.fileListWidget.currentRow()][f, :, :]),
cmap='Blues')
self.axis_BS[f].plot(stg.time_cross_section[self.fileListWidget.currentRow()][self.combobox_frequency_bathymetry.currentIndex(), :],
-stg.depth_bottom[self.fileListWidget.currentRow()],
color='black', linewidth=1, linestyle="solid")
self.axis_BS[f].text(1, .70, stg.freq_text[self.fileListWidget.currentRow()][f],
fontsize=14, fontweight='bold', fontname="DejaVu Sans", c="black", alpha=0.5,
horizontalalignment='right', verticalalignment='bottom',
transform=self.axis_BS[f].transAxes)
# --- Update plot profile ---
self.update_plot_profile()
self.fig_BS.canvas.draw_idle()
elif stg.BS_raw_data[self.fileListWidget.currentRow()].shape != (0,):
stg.freq_bottom_detection[self.fileListWidget.currentRow()] = (
self.combobox_frequency_bathymetry.currentIndex(), self.combobox_frequency_bathymetry.currentText())
# Selecting the range in which we look for the bottom reflection
rmin = float("".join(findall("[.0-9]", self.lineEdit_depth_max_bathy.text())))
rmax = float("".join(findall("[.0-9]", self.lineEdit_depth_min_bathy.text())))
# empty result arrays
r_bottom = np.zeros(stg.time[self.fileListWidget.currentRow()].shape[1])
val_bottom = np.zeros(stg.time[self.fileListWidget.currentRow()].shape[1])
r_bottom_ind = []
BS_smooth = deepcopy(stg.BS_raw_data[self.fileListWidget.currentRow()][
self.combobox_frequency_bathymetry.currentIndex(), :, :])
for k in range(stg.time[self.fileListWidget.currentRow()].shape[1]):
BS_smooth[:, k] = savgol_filter(BS_smooth[:, k], 10, 2)
# ----------- Detecting the bottom -------------
for d in range(stg.time[self.fileListWidget.currentRow()].shape[1]):
# Index of the range where we look for the peak
ind_min = np.where(stg.depth[self.fileListWidget.currentRow()][
int(self.combobox_frequency_bathymetry.currentIndex()), :] >= rmin)[0][0]
ind_max = np.where(stg.depth[self.fileListWidget.currentRow()][
int(self.combobox_frequency_bathymetry.currentIndex()), :] <= rmax)[0][-1]
# Getting the peak
try:
val_bottom[d] = np.nanmax(BS_smooth[ind_min:ind_max, d])
except ValueError as e:
msgBox = QMessageBox()
msgBox.setWindowTitle("Detect bottom Error")
msgBox.setIcon(QMessageBox.Warning)
msgBox.setText(f"1/ {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((BS_smooth[ind_min:ind_max, d]) == val_bottom[d])[0][0]
np.append(stg.ind_bottom, ind_bottom)
r_bottom[d] = stg.depth[self.fileListWidget.currentRow()][
self.combobox_frequency_bathymetry.currentIndex(), 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)
rmin = r_bottom[d] - float("".join(findall("[.0-9]", self.lineEdit_next_cell_bathy.text())))
rmax = r_bottom[d] + float("".join(findall("[.0-9]", self.lineEdit_next_cell_bathy.text())))
BS_section_bottom = np.zeros((stg.depth[self.fileListWidget.currentRow()].shape[1],
stg.time[self.fileListWidget.currentRow()].shape[1]))
for i in range(BS_section_bottom.shape[0]):
try:
BS_section_bottom[r_bottom_ind[i]][i] = 1
except IndexError as e:
msgBox = QMessageBox()
msgBox.setWindowTitle("Detect bottom Error")
msgBox.setIcon(QMessageBox.Warning)
msgBox.setText(f"2/ {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()
if BS_section_bottom.sum() > 2:
# --- Record r_bottom for other tabs ---
stg.depth_bottom[self.fileListWidget.currentRow()] = r_bottom
stg.val_bottom[self.fileListWidget.currentRow()] = val_bottom
stg.ind_bottom[self.fileListWidget.currentRow()] = r_bottom_ind
BS_stream_bed_copy = deepcopy(stg.BS_raw_data[self.fileListWidget.currentRow()])
for f, _ in enumerate(stg.freq[self.fileListWidget.currentRow()]):
for k, _ in enumerate(stg.depth_bottom[self.fileListWidget.currentRow()]):
BS_stream_bed_copy[
f, np.where(stg.depth[self.fileListWidget.currentRow()][
self.combobox_frequency_bathymetry.currentIndex(), :]
>= stg.depth_bottom[self.fileListWidget.currentRow()][k])[
0], k] = np.nan
stg.BS_stream_bed[self.fileListWidget.currentRow()] = BS_stream_bed_copy
# --- Plot transect BS with bathymetry ---
for f, _ in enumerate(stg.freq[self.fileListWidget.currentRow()]):
self.axis_BS[f].cla()
val_min = np.min(stg.BS_raw_data[self.fileListWidget.currentRow()][f, :, :])
val_max = np.max(stg.BS_raw_data[self.fileListWidget.currentRow()][f, :, :])
if val_min == 0:
val_min = 1e-5
if self.combobox_ABS_system_choice.currentIndex() == 1:
pcm = self.axis_BS[f].pcolormesh(
stg.time[self.fileListWidget.currentRow()][f, :],
-stg.depth[self.fileListWidget.currentRow()][f, :],
stg.BS_raw_data[self.fileListWidget.currentRow()][f, :, :],
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
elif self.combobox_ABS_system_choice.currentIndex() == 2:
pcm = self.axis_BS[f].pcolormesh(
stg.time[self.fileListWidget.currentRow()][f, :],
-stg.depth[self.fileListWidget.currentRow()][f, :],
np.log(stg.BS_raw_data[self.fileListWidget.currentRow()][f, :, :]),
cmap='Blues')
self.axis_BS[f].plot(stg.time[self.fileListWidget.currentRow()][
self.combobox_frequency_bathymetry.currentIndex(), :],
-stg.depth_bottom[self.fileListWidget.currentRow()],
color='black', linewidth=1, linestyle="solid")
self.axis_BS[f].text(1, .70, stg.freq_text[self.fileListWidget.currentRow()][f],
fontsize=14, fontweight='bold', fontname="DejaVu Sans", c="black",
alpha=0.5,
horizontalalignment='right', verticalalignment='bottom',
transform=self.axis_BS[f].transAxes)
# --- Update plot profile ---
self.update_plot_profile()
self.fig_BS.canvas.draw_idle()

8
settings.py
View File

@ -105,11 +105,11 @@ BS_stream_bed = [] # BS data (raw or cross_section) with detected b
depth_bottom = [] # Depth value of th bottom : 1D array # List of arrays depth_bottom = [] # Depth value of th bottom : 1D array # List of arrays
val_bottom = [] # Level of the BS signal on the bottom : 1D array # List of arrays val_bottom = [] # Level of the BS signal on the bottom : 1D array # List of arrays
ind_bottom = [] # Index of bottom in depth array : list of int # List of lists ind_bottom = [] # Index of bottom in depth array : list of int # List of lists
freq_bottom_detection = [] # Frequency use to detect the bottom : (index, string) # List of tuple
# depth_bottom_detection_min = [] # Min value to detect bottom on the first vertical # List of float freq_bottom_detection = [] # Frequency use to detect the bottom : (index, string) # List of tuple
# depth_bottom_detection_max = [] # Max value to detect bottom on the first vertical # List of float depth_bottom_detection_min = [] # Min value to detect bottom on the first vertical # List of float
# depth_bottom_detection_1st_int_area = [] # interval for searching area # List of float depth_bottom_detection_max = [] # Max value to detect bottom on the first vertical # List of float
depth_bottom_detection_interval = [] # interval for searching area # List of float
# ---------------------------------------------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------------------------------------------
# ========================================================= # =========================================================