parouby
/
acoused
mirror of https://forgemia.inra.fr/theophile.terraz/acoused
1
0
Fork 0
Code Issues Packages Projects Releases Activity

Signal processing: Fix recompute pipeline for new data.

dev
Pierre-Antoine 2025-04-30 11:27:20 +02:00
parent 31e145fd74
commit 1c770f58d3
1 changed files with 13 additions and 15 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
View/signal_processing_tab.py
View File

@ -625,7 +625,9 @@ class SignalProcessingTab(QWidget):
self.compute_average_profile_tail() self.compute_average_profile_tail()
if stg.noise_method[data_id] == 0: if stg.noise_method[data_id] == 0:
if stg.filename_BS_noise_data[data_id] != "": if stg.filename_BS_noise_data[data_id] == "":
return
if len(stg.BS_noise_raw_data) == 0: if len(stg.BS_noise_raw_data) == 0:
self.load_noise_data_and_compute_SNR() self.load_noise_data_and_compute_SNR()
else: else:
@ -920,8 +922,6 @@ class SignalProcessingTab(QWidget):
self.compute_average_profile_tail() self.compute_average_profile_tail()
logger.debug(f"stg.SNR_filter_value: {stg.SNR_filter_value}")
self.lineEdit_profile_tail_value.setText( self.lineEdit_profile_tail_value.setText(
str(stg.noise_value[data_id]) str(stg.noise_value[data_id])
) )
@ -1649,7 +1649,6 @@ class SignalProcessingTab(QWidget):
BS_data_ppa = stg.BS_raw_data_pre_process_average BS_data_ppa = stg.BS_raw_data_pre_process_average
for i in range(len(BS)): for i in range(len(BS)):
bs = BS[i] bs = BS[i]
logger.debug(f"BS data shape {bs[data_id].shape}")
if bs[data_id].shape == (0,): if bs[data_id].shape == (0,):
continue continue
@ -1658,7 +1657,6 @@ class SignalProcessingTab(QWidget):
BS_data_ppa = BS_ppa[i] BS_data_ppa = BS_ppa[i]
break break
logger.debug(f"BS_data: {BS_data[data_id].shape}")
# BS_data_ppa[data_id] = deepcopy(BS_data[data_id]) # BS_data_ppa[data_id] = deepcopy(BS_data[data_id])
# #
@ -1687,10 +1685,10 @@ class SignalProcessingTab(QWidget):
BS_data_ppa[data_id] = np.array([temp_list[0]]) BS_data_ppa[data_id] = np.array([temp_list[0]])
for j in range(stg.freq[data_id].shape[0]-1): for j in range(stg.freq[data_id].shape[0]-1):
BS_data_ppa[data_id] = np.append(BS_data_ppa[data_id], np.array([temp_list[j+1]]), axis=0) BS_data_ppa[data_id] = np.append(
BS_data_ppa[data_id],
logger.debug( np.array([temp_list[j+1]]),
f"BS_data_ppa: {BS_data_ppa[data_id].shape}" axis=0
) )
def plot_pre_processed_profile(self): def plot_pre_processed_profile(self):