diff --git a/View/signal_processing_tab.py b/View/signal_processing_tab.py
index b9f9cee..3d29e53 100644
--- a/View/signal_processing_tab.py
+++ b/View/signal_processing_tab.py
@@ -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,