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

update for the BS signal averaging #43

dev-brahim
brahim 2025-03-24 17:09:03 +01:00
parent 26bd476772
commit 72dff5a26c
1 changed files with 35 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

58
View/signal_processing_tab.py
View File

@ -14,7 +14,7 @@
# # # #
# You should have received a copy of the GNU General Public License # # You should have received a copy of the GNU General Public License #
# along with this program. If not, see <https://www.gnu.org/licenses/>. # # along with this program. If not, see <https://www.gnu.org/licenses/>. #
import math
# by Brahim MOUDJED # # by Brahim MOUDJED #
# ============================================================================== # # ============================================================================== #
@ -47,6 +47,8 @@ from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as Navigatio
from matplotlib.colors import LogNorm, BoundaryNorm from matplotlib.colors import LogNorm, BoundaryNorm
from os import path from os import path
from numba.np.arraymath import np_average
from View.show_popup_combobox import ComboBoxShowPopUpWindow from View.show_popup_combobox import ComboBoxShowPopUpWindow
from View.plot_noise_window import PlotNoiseWindow from View.plot_noise_window import PlotNoiseWindow
@ -1441,8 +1443,7 @@ class SignalProcessingTab(QWidget):
val_max = np.nanmax( val_max = np.nanmax(
BS_data[data_id][f, :, :] BS_data[data_id][f, :, :]
) )
if val_min == 0 or math.isinf(val_min):
if val_min == 0:
val_min = 1e-5 val_min = 1e-5
pcm = self.axis_BS[f].pcolormesh( pcm = self.axis_BS[f].pcolormesh(
@ -1478,10 +1479,10 @@ class SignalProcessingTab(QWidget):
self.fig_BS.supxlabel('Time (sec)', fontsize=10) self.fig_BS.supxlabel('Time (sec)', fontsize=10)
self.fig_BS.supylabel('Depth (m)', fontsize=10) self.fig_BS.supylabel('Depth (m)', fontsize=10)
cbar = self.fig_BS.colorbar(pcm, ax=self.axis_BS[:], # cbar = self.fig_BS.colorbar(pcm, ax=self.axis_BS[:],
shrink=1, location='right') # shrink=1, location='right')
cbar.set_label(label='Acoustic backscatter signal (V)', # cbar.set_label(label='Acoustic backscatter signal (V)',
rotation=270, labelpad=10) # rotation=270, labelpad=10)
self.fig_BS.canvas.draw_idle() self.fig_BS.canvas.draw_idle()
else: else:
self.verticalLayout_groupbox_plot_pre_processed_data_2D_field\ self.verticalLayout_groupbox_plot_pre_processed_data_2D_field\
@ -1527,15 +1528,8 @@ class SignalProcessingTab(QWidget):
msgBox.exec() msgBox.exec()
else: else:
data_id = self.combobox_acoustic_data_choice.currentIndex() data_id = self.combobox_acoustic_data_choice.currentIndex()
kernel_avg = np.ones( n_average = 2 * int(float(self.lineEdit_horizontal_average.text().replace(",", "."))) + 1
2 * int( kernel_avg = np.ones(n_average)
float(
self.lineEdit_horizontal_average\
.text()\
.replace(",", ".")
)
) + 1
)
logger.debug(f"kernel_avg: {kernel_avg}") logger.debug(f"kernel_avg: {kernel_avg}")
stg.Nb_cells_to_average_BS_signal[data_id] = ( stg.Nb_cells_to_average_BS_signal[data_id] = (
@ -1599,16 +1593,34 @@ class SignalProcessingTab(QWidget):
logger.debug(f"BS_data: {BS_data[data_id].shape}") 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])
#
# for f, _ in enumerate(stg.freq[data_id]):
# for i in range(y_depth.shape[1]):
# BS_data_ppa[data_id][f, i, :] = (
# convolve(
# BS_data[data_id][f, i, :],
# kernel_avg
# )
# )
temp_list = []
for f, _ in enumerate(stg.freq[data_id]): for f, _ in enumerate(stg.freq[data_id]):
temp0 = np.array([])
for i in range(y_depth.shape[1]): for i in range(y_depth.shape[1]):
BS_data_ppa[data_id][f, i, :] = ( # temp = convolve(BS_data[data_id][f, i, :], kernel_avg, "same") / n_average
convolve( temp = convolve(array=BS_data[data_id][f, i, :],
BS_data[data_id][f, i, :], kernel=kernel_avg,
kernel_avg nan_treatment='interpolate')
) if temp0.shape == (0,):
) temp0 = np.array([temp])
else:
temp0 = np.append(temp0, np.array([temp]), axis=0)
temp_list.append(temp0)
BS_data_ppa[data_id] = np.array([temp_list[0]])
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)
logger.debug( logger.debug(
f"BS_data_ppa: {BS_data_ppa[data_id].shape}" f"BS_data_ppa: {BS_data_ppa[data_id].shape}"