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Plot of SNR field is corrected when the user set a profile tail level #4

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brahim committed 2025-03-10 14:30:45 +01:00
1 parent 190c0f0b80
commit 320971160d
1 file changed
+12 -18
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View/signal_processing_tab.py
+12 -18
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@@ -1084,23 +1084,20 @@ class SignalProcessingTab(QWidget):
val_max = np.nanmax(stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
if val_min == val_max:
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
else:
if val_min == 0:
val_min = 1e-5
if val_max > 1000:
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
else:
levels = np.array([00.1, 1, 2, 10, 100, val_max])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000]
levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
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,
stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, :, :],
levels, cmap='gist_rainbow',
@@ -1140,24 +1137,21 @@ class SignalProcessingTab(QWidget):
val_max = np.nanmax(stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
if val_min == val_max:
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
else:
if val_min == 0:
val_min = 1e-5
if val_max > 1000:
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
else:
levels = np.array([00.1, 1, 2, 10, 100, val_max])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000]
levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
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,
stg.SNR_cross_section[
self.combobox_acoustic_data_choice.currentIndex()][f, :, :],
@@ -1172,18 +1166,18 @@ class SignalProcessingTab(QWidget):
val_max = np.nanmax(stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
if val_min == val_max:
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
else:
if val_min == 0:
val_min = 1e-5
if val_max > 1000:
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
else:
levels = np.array([00.1, 1, 2, 10, 100, val_max])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000]
levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max*1000 + 1])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
cf = (self.axis_SNR[f].contourf(x, -y,