parouby
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acoused
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Signal processing: Remove noise file loading (from fs) at '.acd' opening.

dev
Pierre-Antoine 2025-04-23 17:41:00 +02:00
parent 1fe6b6a1e1
commit 84fe5738a3
2 changed files with 115 additions and 74 deletions
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1
Model/read_table_for_open.py
View File

@ -113,7 +113,6 @@ class ReadTableForOpen:
WHERE (acoustic_data = {k})
'''
data = self.execute(query)[0]
print("data acoustic file", data)
stg.filename_BS_raw_data.append(
str(data[1]) + '.aqa'

188
View/signal_processing_tab.py
View File

@ -548,9 +548,8 @@ class SignalProcessingTab(QWidget):
)
def update_SignalPreprocessingTab(self):
""" The tab is updated in two cases :
- the user remove a file (in the list widget) in the first tab (Acoustic data), so that the combobox
- the user remove a file (in the list widget) in the first tab (Acoustic data), so that the combobox
of data to be processed is updated,
- the user change the limits of one or all the records in the first tab (Acoustic data) """
if len(stg.filename_BS_raw_data) == 0:
@ -627,13 +626,68 @@ class SignalProcessingTab(QWidget):
if stg.noise_method[data_id] == 0:
if stg.filename_BS_noise_data[data_id] != "":
self.load_noise_data_and_compute_SNR()
if len(stg.BS_noise_raw_data) == 0:
self.load_noise_data_and_compute_SNR()
else:
self.compute_noise()
elif stg.noise_method[data_id] == 1:
self.compute_noise_from_profile_tail_value()
self.remove_point_with_snr_filter()
self.compute_averaged_BS_data()
def compute_noise(self):
data_id = max(0, self.combobox_acoustic_data_choice.currentIndex())
if stg.time_cross_section[data_id].shape != (0,):
stg.time_noise[data_id] = (
stg.time_cross_section[data_id]
)
else:
stg.time_noise[data_id] = (
stg.time[data_id]
)
if stg.depth_cross_section[data_id].shape != (0,):
stg.depth_noise[data_id] = (
stg.depth_cross_section[data_id]
)
else:
stg.depth_noise[data_id] = (
stg.depth[data_id]
)
if self._is_correct_shape(stg.BS_stream_bed):
BS_data = stg.BS_stream_bed
SNR_data = stg.SNR_stream_bed
elif self._is_correct_shape(stg.BS_cross_section):
BS_data = stg.BS_cross_section
SNR_data = stg.SNR_cross_section
else:
BS_data = stg.BS_raw_data
SNR_data = stg.SNR_raw_data
noise = np.zeros(BS_data[data_id].shape)
for f, _ in enumerate(BS_data[data_id]):
noise[f, :, :] = np.mean(
stg.BS_noise_raw_data[data_id][f, :, :], axis=(0, 1)
)
stg.BS_noise_averaged_data[data_id] = noise
SNR_data[data_id] = (
np.divide(
(
BS_data[data_id] - stg.BS_noise_averaged_data[data_id]
) ** 2,
stg.BS_noise_averaged_data[data_id] ** 2
)
)
self.combobox_frequency_profile.clear()
self.combobox_frequency_profile.addItems(
[f for f in stg.freq_text[data_id]]
)
def replot(self):
self.plot_averaged_profile_tail()
self.plot_transect_with_SNR_data()
@ -1034,50 +1088,42 @@ class SignalProcessingTab(QWidget):
stg.noise_method[data_id] = 0
noise_data = AcousticDataLoader(stg.path_BS_noise_data[data_id] +
"/" +
stg.filename_BS_noise_data[data_id])
noise_data = AcousticDataLoader(
os.path.join(
stg.path_BS_noise_data[data_id],
stg.filename_BS_noise_data[data_id]
)
)
stg.BS_noise_raw_data[data_id] = noise_data._BS_raw_data
stg.time_noise[data_id] = noise_data._time
stg.depth_noise[data_id] = noise_data._r
if stg.BS_stream_bed[data_id].shape != (0,):
noise = np.zeros(stg.BS_stream_bed[data_id].shape)
for f, _ in enumerate(noise_data._freq):
noise[f, :, :] = np.mean(
stg.BS_noise_raw_data[data_id][f, :, :], axis=(0, 1))
stg.BS_noise_averaged_data[data_id] = noise
stg.SNR_stream_bed[data_id] = (
np.divide((stg.BS_stream_bed[data_id] -
stg.BS_noise_averaged_data[data_id]) ** 2,
stg.BS_noise_averaged_data[data_id] ** 2))
BS_data = stg.BS_stream_bed
SNR_data = stg.SNR_stream_bed
elif stg.BS_cross_section[data_id].shape != (0,):
noise = np.zeros(stg.BS_cross_section[data_id].shape)
for f, _ in enumerate(noise_data._freq):
noise[f, :, :] = np.mean(
stg.BS_noise_raw_data[data_id][f, :, :], axis=(0, 1))
stg.BS_noise_averaged_data[data_id] = noise
stg.SNR_cross_section[data_id] = (
np.divide((stg.BS_cross_section[data_id] -
stg.BS_noise_averaged_data[data_id]) ** 2,
stg.BS_noise_averaged_data[data_id] ** 2))
# stg.SNR_reshape = np.reshape(stg.SNR_cross_section, (stg.r.shape[1] * stg.t.shape[1], stg.freq.shape[0]), order="F")
BS_data = stg.BS_cross_section
SNR_data = stg.SNR_cross_section
else:
BS_data = stg.BS_raw_data
SNR_data = stg.SNR_raw_data
noise = np.zeros(stg.BS_raw_data[data_id].shape)
for f, _ in enumerate(noise_data._freq):
noise[f, :, :] = np.mean(
stg.BS_noise_raw_data[data_id][f, :, :], axis=(0, 1))
stg.BS_noise_averaged_data[data_id] = noise
stg.SNR_raw_data[data_id] = (
np.divide((stg.BS_raw_data[data_id] -
stg.BS_noise_averaged_data[data_id]) ** 2,
stg.BS_noise_averaged_data[data_id] ** 2))
noise = np.zeros(BS_data[data_id].shape)
for f, _ in enumerate(noise_data._freq):
noise[f, :, :] = np.mean(
stg.BS_noise_raw_data[data_id][f, :, :], axis=(0, 1)
)
stg.BS_noise_averaged_data[data_id] = noise
SNR_data[data_id] = (
np.divide(
(
BS_data[data_id] - stg.BS_noise_averaged_data[data_id]
) ** 2,
stg.BS_noise_averaged_data[data_id] ** 2
)
)
def open_plot_noise_window(self):
pnw = PlotNoiseWindow()
@ -1091,6 +1137,11 @@ class SignalProcessingTab(QWidget):
float(self.lineEdit_profile_tail_value.text().replace(",", "."))
)
self.compute_noise_from_profile_tail_value_compute()
def compute_noise_from_profile_tail_value_compute(self):
data_id = max(0, self.combobox_acoustic_data_choice.currentIndex())
if stg.time_cross_section[data_id].shape != (0,):
stg.time_noise[data_id] = (
stg.time_cross_section[data_id]
@ -1110,47 +1161,38 @@ class SignalProcessingTab(QWidget):
# --- Compute noise from value and compute SNR ---
if self._is_correct_shape(stg.BS_stream_bed):
stg.BS_noise_raw_data[data_id] = np.array([])
stg.BS_noise_raw_data[data_id] = (
np.full(stg.BS_stream_bed[data_id].shape,
float(self.lineEdit_profile_tail_value.text().replace(",", "."))))
stg.BS_noise_averaged_data[data_id] = (
stg.BS_noise_raw_data[data_id][:, :,
:stg.BS_stream_bed[data_id].shape[2]])
stg.SNR_stream_bed[data_id] = (
np.divide((stg.BS_stream_bed[data_id]
- stg.BS_noise_raw_data[data_id]) ** 2,
stg.BS_noise_raw_data[data_id] ** 2))
BS_data = stg.BS_stream_bed
SNR_data = stg.SNR_stream_bed
elif self._is_correct_shape(stg.BS_cross_section):
stg.BS_noise_raw_data[data_id] = (
np.full(stg.BS_cross_section[data_id].shape,
float(self.lineEdit_profile_tail_value.text().replace(",", "."))))
stg.BS_noise_averaged_data[data_id] = (
stg.BS_noise_raw_data[data_id][:, :,
:stg.BS_cross_section[data_id].shape[2]])
stg.SNR_cross_section[data_id] = (
np.divide((stg.BS_cross_section[data_id]
- stg.BS_noise_raw_data[data_id]) ** 2,
stg.BS_noise_raw_data[data_id] ** 2)) #
BS_data = stg.BS_cross_section
SNR_data = stg.SNR_cross_section
else:
stg.BS_noise_raw_data[data_id] = (
np.full(stg.BS_raw_data[data_id].shape,
float(self.lineEdit_profile_tail_value.text().replace(",", "."))))
BS_data = stg.BS_raw_data
SNR_data = stg.SNR_raw_data
stg.BS_noise_averaged_data[data_id] = (
stg.BS_noise_raw_data[data_id])
stg.SNR_raw_data[data_id] = (
np.divide((stg.BS_raw_data[data_id]
- stg.BS_noise_raw_data[data_id]) ** 2,
stg.BS_noise_raw_data[data_id] ** 2))
stg.BS_noise_raw_data[data_id] = (
np.full(
BS_data[data_id].shape,
float(self.lineEdit_profile_tail_value.text().replace(",", "."))
)
)
stg.BS_noise_averaged_data[data_id] = (
stg.BS_noise_raw_data[data_id]
)
SNR_data[data_id] = (
np.divide(
(
BS_data[data_id] - stg.BS_noise_raw_data[data_id]
) ** 2,
stg.BS_noise_raw_data[data_id] ** 2
)
)
self.combobox_frequency_profile.clear()
self.combobox_frequency_profile.addItems(
[f for f in stg.freq_text[data_id]])
[f for f in stg.freq_text[data_id]]
)
# --- Trigger graphic widgets ---