diff --git a/hp4155/ADU for double gate devices/lib/help.py b/hp4155/ADU for double gate devices/lib/help.py
index 11dd42bfd4382c4554970f7b524b7b92685a43a7..6e32487381dfe7c126fd8fc59c17d9b655ce4e2f 100644
--- a/hp4155/ADU for double gate devices/lib/help.py	
+++ b/hp4155/ADU for double gate devices/lib/help.py	
@@ -164,44 +164,47 @@ def calculate_line(VTG,VBG):
 
 # replot results
 def replot_results(replot_dict,df,points,title):
-    if len(df.columns.tolist())!=0 and replot_dict['check'].value==True: # Measurement is done
-        fig,ax = plt.subplots(figsize=(10,6))
-
-        #Retrieve the columns
-        x_col = replot_dict['x_variable'].value
-        y_col = replot_dict['y_variable'].value
-
-        #Scale and Absolute Values
-
-        if replot_dict['x_scale'].value=='linear':
-            x = np.array_split(df[x_col],points)
-        else:
-            x = np.array_split(df[x_col].abs(),points)
-            ax.set_xscale('log')
-
-        if replot_dict['y_scale'].value=='linear':
-            y = np.array_split(df[y_col],points)
-        else:
-            y = np.array_split(df[y_col].abs(),points)
-            ax.set_yscale('log')
-
-        # check auto limits
-        if replot_dict['x_auto'].value== False and replot_dict['x_max'].value > replot_dict['x_min'].value:
-            ax.set_xlim([replot_dict['x_min'].value,replot_dict['x_max'].value])
-
-        if replot_dict['y_auto'].value== False and replot_dict['y_max'].value > replot_dict['y_min'].value:
-            ax.set_ylim([replot_dict['y_min'].value,replot_dict['y_max'].value])
-
-        # Now set the label
-        ax.set_xlabel(x_col)
-        ax.set_ylabel(y_col)
-
-        #And Plot
-        for i in range(points):
-            ax.plot(x[i],y[i])
-
-        fig.suptitle(title, fontweight ="bold")
-        display(fig)
+    try:
+        if len(df.columns.tolist())!=0 and replot_dict['check'].value==True: # Measurement is done
+            fig,ax = plt.subplots(figsize=(10,6))
+    
+            #Retrieve the columns
+            x_col = replot_dict['x_variable'].value
+            y_col = replot_dict['y_variable'].value
+    
+            #Scale and Absolute Values
+    
+            if replot_dict['x_scale'].value=='linear':
+                x = np.array_split(df[x_col],points)
+            else:
+                x = np.array_split(df[x_col].abs(),points)
+                ax.set_xscale('log')
+    
+            if replot_dict['y_scale'].value=='linear':
+                y = np.array_split(df[y_col],points)
+            else:
+                y = np.array_split(df[y_col].abs(),points)
+                ax.set_yscale('log')
+    
+            # check auto limits
+            if replot_dict['x_auto'].value== False and replot_dict['x_max'].value > replot_dict['x_min'].value:
+                ax.set_xlim([replot_dict['x_min'].value,replot_dict['x_max'].value])
+    
+            if replot_dict['y_auto'].value== False and replot_dict['y_max'].value > replot_dict['y_min'].value:
+                ax.set_ylim([replot_dict['y_min'].value,replot_dict['y_max'].value])
+    
+            # Now set the label
+            ax.set_xlabel(x_col)
+            ax.set_ylabel(y_col)
+    
+            #And Plot
+            for i in range(points):
+                ax.plot(x[i],y[i])
+    
+            fig.suptitle(title, fontweight ="bold")
+            display(fig)
+    except:
+        information_box("reploting failed please try again")