diff --git a/View/sample_data_tab.py b/View/sample_data_tab.py
index 2b6ffff..64ebc22 100644
--- a/View/sample_data_tab.py
+++ b/View/sample_data_tab.py
@@ -1109,7 +1109,16 @@ class SampleDataTab(QWidget):
     def fill_comboboxes_and_plot_transect(self):
 
         self.combobox_acoustic_data.clear()
-        self.combobox_acoustic_data.addItems(stg.filename_BS_raw_data)
+        for n, m in enumerate(stg.noise_method):
+            print("n = ", n, "m = ", m)
+            if stg.noise_method[n] == 0:
+                print("stg.filename_BS_raw_data ", stg.filename_BS_raw_data)
+                self.combobox_acoustic_data.addItem(stg.filename_BS_raw_data[n])
+            elif stg.noise_method[n]!=0:
+                print("stg.data_preprocessed ", stg.data_preprocessed)
+                print("stg.data_preprocessed[n] ", stg.data_preprocessed[n])
+                self.combobox_acoustic_data.addItem(stg.data_preprocessed[n])
+
         self.plot_sample_position_on_transect()
         self.combobox_acoustic_data.currentIndexChanged.connect(self.update_plot_sample_position_on_transect)
         self.combobox_frequencies.currentIndexChanged.connect(self.update_plot_sample_position_on_transect)
@@ -1131,12 +1140,62 @@ class SampleDataTab(QWidget):
             # self.combobox_frequencies.currentTextChanged.connect(self.update_plot_sample_position_on_transect)
 
             self.verticalLayout_groupbox_plot_transect.removeWidget(self.canvas_plot_sample_position_on_transect)
+
             self.figure_plot_sample_position_on_transect, self.axis_plot_sample_position_on_transect = \
                     plt.subplots(nrows=1, ncols=1, layout="constrained")
             self.canvas_plot_sample_position_on_transect = FigureCanvas(self.figure_plot_sample_position_on_transect)
+
             self.verticalLayout_groupbox_plot_transect.addWidget(self.canvas_plot_sample_position_on_transect)
 
-            if stg.BS_stream_bed[self.combobox_acoustic_data.currentIndex()].shape != (0,):
+            if stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data.currentIndex()].shape != (0,):
+
+                val_min = np.nanmin(stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                                    self.combobox_frequencies.currentIndex(), :, :])
+                val_max = np.nanmax(stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                                    self.combobox_frequencies.currentIndex(), :, :])
+                if val_min == 0:
+                    val_min = 1e-5
+
+                self.axis_plot_sample_position_on_transect.pcolormesh(
+                    stg.time_cross_section[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :],
+                    -stg.depth_cross_section[self.combobox_acoustic_data.currentIndex()][
+                     self.combobox_frequencies.currentIndex(), :],
+                    stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :, :],
+                    cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
+
+                self.axis_plot_sample_position_on_transect.plot(
+                    stg.time_cross_section[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :],
+                    -stg.depth_bottom[self.combobox_acoustic_data.currentIndex()], color='black', linewidth=1,
+                    linestyle="solid")
+
+            elif stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data.currentIndex()].shape != (0,):
+
+                val_min = np.nanmin(stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                                    self.combobox_frequencies.currentIndex(), :, :])
+                val_max = np.nanmax(stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                                    self.combobox_frequencies.currentIndex(), :, :])
+                if val_min == 0:
+                    val_min = 1e-5
+
+                self.axis_plot_sample_position_on_transect.pcolormesh(
+                    stg.time_cross_section[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :],
+                    -stg.depth_cross_section[self.combobox_acoustic_data.currentIndex()][
+                     self.combobox_frequencies.currentIndex(), :],
+                    stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :, :],
+                    cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
+
+                self.axis_plot_sample_position_on_transect.plot(
+                    stg.time_cross_section[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :],
+                    -stg.depth_bottom[self.combobox_acoustic_data.currentIndex()], color='black', linewidth=1,
+                    linestyle="solid")
+
+            elif stg.BS_stream_bed[self.combobox_acoustic_data.currentIndex()].shape != (0,):
 
                 val_min = np.nanmin(stg.BS_stream_bed[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(), :, :])
                 val_max = np.nanmax(stg.BS_stream_bed[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(), :, :])
@@ -1153,6 +1212,46 @@ class SampleDataTab(QWidget):
                         stg.time_cross_section[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(), :],
                         -stg.depth_bottom[self.combobox_acoustic_data.currentIndex()], color='black', linewidth=1, linestyle="solid")
 
+            elif stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data.currentIndex()].shape != (0,):
+
+                val_min = np.nanmin(
+                    stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :, :])
+                val_max = np.nanmax(
+                    stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :, :])
+                if val_min == 0:
+                    val_min = 1e-5
+
+                self.axis_plot_sample_position_on_transect.pcolormesh(
+                    stg.time_cross_section[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :],
+                    -stg.depth_cross_section[self.combobox_acoustic_data.currentIndex()][
+                     self.combobox_frequencies.currentIndex(), :],
+                    stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :, :],
+                    cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
+
+            elif stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data.currentIndex()].shape != (0,):
+
+                val_min = np.nanmin(
+                    stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :, :])
+                val_max = np.nanmax(
+                    stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :, :])
+                if val_min == 0:
+                    val_min = 1e-5
+
+                self.axis_plot_sample_position_on_transect.pcolormesh(
+                    stg.time_cross_section[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :],
+                    -stg.depth_cross_section[self.combobox_acoustic_data.currentIndex()][
+                     self.combobox_frequencies.currentIndex(), :],
+                    stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :, :],
+                    cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
+
             elif stg.BS_cross_section[self.combobox_acoustic_data.currentIndex()].shape != (0,):
 
                 val_min = np.nanmin(
@@ -1168,6 +1267,38 @@ class SampleDataTab(QWidget):
                     stg.BS_cross_section[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(), :, :],
                     cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
 
+            elif stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data.currentIndex()].shape != (0,):
+
+                val_min = np.nanmin(stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                                    self.combobox_frequencies.currentIndex(), :, :])
+                val_max = np.nanmax(stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                                    self.combobox_frequencies.currentIndex(), :, :])
+                if val_min == 0:
+                    val_min = 1e-5
+
+                self.axis_plot_sample_position_on_transect.pcolormesh(
+                    stg.time[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(), :],
+                    -stg.depth[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(), :],
+                    stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :, :],
+                    cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
+
+            elif stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data.currentIndex()].shape != (0,):
+
+                val_min = np.nanmin(stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                                    self.combobox_frequencies.currentIndex(), :, :])
+                val_max = np.nanmax(stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                                    self.combobox_frequencies.currentIndex(), :, :])
+                if val_min == 0:
+                    val_min = 1e-5
+
+                self.axis_plot_sample_position_on_transect.pcolormesh(
+                    stg.time[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(), :],
+                    -stg.depth[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(), :],
+                    stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                    self.combobox_frequencies.currentIndex(), :, :],
+                    cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
+
             elif stg.BS_raw_data[self.combobox_acoustic_data.currentIndex()].shape != (0,):
 
                 val_min = np.nanmin(stg.BS_raw_data[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(), :, :])
@@ -1190,7 +1321,59 @@ class SampleDataTab(QWidget):
         self.axis_plot_sample_position_on_transect.cla()
 
         # --- Create canvas of Matplotlib figure ---
-        if stg.BS_stream_bed[self.combobox_acoustic_data.currentIndex()].shape != (0,):
+        if stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data.currentIndex()].shape != (0,):
+
+            val_min = np.nanmin(
+                stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(),
+                :, :])
+            val_max = np.nanmax(
+                stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(),
+                :, :])
+            if val_min == 0:
+                val_min = 1e-5
+
+            self.axis_plot_sample_position_on_transect.pcolormesh(
+                stg.time_cross_section[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(), :],
+                -stg.depth_cross_section[self.combobox_acoustic_data.currentIndex()][
+                 self.combobox_frequencies.currentIndex(), :],
+                stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(),
+                :, :],
+                cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
+
+            self.axis_plot_sample_position_on_transect.plot(
+                stg.time_cross_section[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(), :],
+                -stg.depth_bottom[self.combobox_acoustic_data.currentIndex()],
+                color='black', linewidth=1, linestyle="solid")
+
+        elif stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data.currentIndex()].shape != (0,):
+
+            val_min = np.nanmin(
+                stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(),
+                :, :])
+            val_max = np.nanmax(
+                stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(),
+                :, :])
+            if val_min == 0:
+                val_min = 1e-5
+
+            self.axis_plot_sample_position_on_transect.pcolormesh(
+                stg.time_cross_section[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(), :],
+                -stg.depth_cross_section[self.combobox_acoustic_data.currentIndex()][
+                 self.combobox_frequencies.currentIndex(), :],
+                stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(),
+                :, :],
+                cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
+
+            self.axis_plot_sample_position_on_transect.plot(
+                stg.time_cross_section[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(), :],
+                -stg.depth_bottom[self.combobox_acoustic_data.currentIndex()],
+                color='black', linewidth=1, linestyle="solid")
+
+        elif stg.BS_stream_bed[self.combobox_acoustic_data.currentIndex()].shape != (0,):
 
             val_min = np.nanmin(stg.BS_stream_bed[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(), :, :])
             val_max = np.nanmax(stg.BS_stream_bed[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex(), :, :])
@@ -1208,6 +1391,52 @@ class SampleDataTab(QWidget):
                 -stg.depth_bottom[self.combobox_acoustic_data.currentIndex()],
                 color='black', linewidth=1, linestyle="solid")
 
+        elif stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data.currentIndex()].shape != (0,):
+
+            val_min = np.nanmin(
+                stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(),
+                :, :])
+            val_max = np.nanmax(
+                stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(),
+                :, :])
+            if val_min == 0:
+                val_min = 1e-5
+
+            self.axis_plot_sample_position_on_transect.pcolormesh(
+                stg.time_cross_section[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(), :],
+                -stg.depth_cross_section[self.combobox_acoustic_data.currentIndex()][
+                 self.combobox_frequencies.currentIndex(), :],
+                stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(),
+                :, :],
+                cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
+
+        elif stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data.currentIndex()].shape != (0,):
+
+            val_min = np.nanmin(
+                stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(),
+                :, :])
+            val_max = np.nanmax(
+                stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(),
+                :, :])
+            if val_min == 0:
+                val_min = 1e-5
+
+            self.axis_plot_sample_position_on_transect.pcolormesh(
+                stg.time_cross_section[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(), :],
+                -stg.depth_cross_section[self.combobox_acoustic_data.currentIndex()][
+                 self.combobox_frequencies.currentIndex(), :],
+                stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(),
+                :, :],
+                cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
+
         elif stg.BS_cross_section[self.combobox_acoustic_data.currentIndex()].shape != (0,):
 
             val_min = np.nanmin(
@@ -1228,6 +1457,52 @@ class SampleDataTab(QWidget):
                 :, :],
                 cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
 
+        elif stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data.currentIndex()].shape != (0,):
+
+            val_min = np.nanmin(
+                stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(),
+                :, :])
+            val_max = np.nanmax(
+                stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(),
+                :, :])
+            if val_min == 0:
+                val_min = 1e-5
+
+            self.axis_plot_sample_position_on_transect.pcolormesh(
+                stg.time[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(), :],
+                -stg.depth[self.combobox_acoustic_data.currentIndex()][
+                 self.combobox_frequencies.currentIndex(), :],
+                stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(),
+                :, :],
+                cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
+
+        elif stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data.currentIndex()].shape != (0,):
+
+            val_min = np.nanmin(
+                stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(),
+                :, :])
+            val_max = np.nanmax(
+                stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(),
+                :, :])
+            if val_min == 0:
+                val_min = 1e-5
+
+            self.axis_plot_sample_position_on_transect.pcolormesh(
+                stg.time[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(), :],
+                -stg.depth[self.combobox_acoustic_data.currentIndex()][
+                 self.combobox_frequencies.currentIndex(), :],
+                stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data.currentIndex()][
+                self.combobox_frequencies.currentIndex(),
+                :, :],
+                cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
+
         elif stg.BS_raw_data[self.combobox_acoustic_data.currentIndex()].shape != (0,):
 
             val_min = np.nanmin(