diff --git a/code/plot_functions.py b/code/plot_functions.py
index f8ea237..7bf001f 100644
--- a/code/plot_functions.py
+++ b/code/plot_functions.py
@@ -110,11 +110,14 @@ def plot_highlighted_integrals(frequency, power, points, color_mapping, points_c
             # Define color based on the category of the point
             color = next((c for cat, c in color_mapping.items() if point in points_categories[cat]), 'gray')
             
+            # Find the category of the point
+            point_category = next((cat for cat, pts in points_categories.items() if point in pts), "Unknown")
+            
             # Shade the region around the point where the integral was calculated
             ax.axvspan(point - delta, point + delta, color=color, alpha=0.3, label=f'{point:.2f} Hz')
 
-            # Print out point and color
-            print(f"Integral around {point:.2f} Hz: {integral:.5e}, Color: {color}")
+            # Print out point, category, and color
+            print(f"{point_category}: Integral: {integral:.5e}, Color: {color}")
             
             # Annotate the plot with the point and its color
             ax.text(point, max(power) * 0.9, f'{point:.2f}', color=color, fontsize=10, ha='center')
diff --git a/code/useful_functions.py b/code/useful_functions.py
index a4a2d27..c0861c3 100644
--- a/code/useful_functions.py
+++ b/code/useful_functions.py
@@ -154,6 +154,59 @@ def calculate_integral(freq, power, point, delta = 2.5):
     local_mean = np.mean([l_integral, r_integral])
     return integral, local_mean, p_power
 
+def contrast_sorting(sams, con_1 = 20, con_2 = 10, con_3 = 5, stim_count = 3, stim_dur = 2):
+    '''
+    sorts the sams into three contrasts
+
+    Parameters
+    ----------
+    sams : ReproRuns
+        The sams to be sorted.
+    con_1 : int, optional
+        the first contrast. The default is 20.
+    con_2 : int, optional
+        the second contrast. The default is 10.
+    con_3 : int, optional
+        the third contrast. The default is 5.
+    stim_count : int, optional
+        the amount of stimuli per sam in a good sam. The default is 3.
+    stim_dur : int, optional
+        The stimulus duration. The default is 2.
+
+    Returns
+    -------
+    contrast_sams : dictionary
+        A dictionary containing all sams sorted to the contrasts.
+
+    '''
+    # dictionary for the contrasts
+    contrast_sams = {con_1 : [],
+                     con_2 : [],
+                     con_3 : []}
+    # loop over all sams
+    for sam in sams:
+        # get the contrast
+        avg_dur, contrast, _, _, _, _, _ = sam_data(sam)
+        # check for valid trails
+        if np.isnan(contrast):
+            continue
+        elif sam.stimulus_count < stim_count: #aborted trials
+            continue
+        elif avg_dur < (stim_dur * 0.8):
+            continue
+        else:
+            contrast = int(contrast) # get integer of contrast
+            # sort them accordingly
+            if contrast == con_1:
+                contrast_sams[con_1].append(sam)
+            elif contrast == con_2:
+                contrast_sams[con_2].append(sam)
+            elif contrast == con_3:
+                contrast_sams[con_3].append(sam)
+            else:
+                continue
+    return contrast_sams
+
 def extract_stim_data(stimulus):
     '''
     extracts all necessary metadata for each stimulus
@@ -483,9 +536,9 @@ def valid_integrals(integral, local_mean, point, threshold = 0.1):
     """
     valid = integral > (local_mean * (1 + threshold))
     if valid:
-        print(f"The point {point} is valid, as its integral exceeds the threshold.")
+        print(f"The point {point} is valid.")
     else:
-        print(f"The point {point} is not valid, as its integral does not exceed the threshold.")
+        print(f"The point {point} is not valid.")
     return valid
 
 '''TODO Sarah: AM-freq plot: