diff --git a/code/useful_functions.py b/code/useful_functions.py
index 4ea8522..dde5944 100644
--- a/code/useful_functions.py
+++ b/code/useful_functions.py
@@ -18,10 +18,10 @@ def all_coming_together(freq_array, power_array, points_list, categories, num_ha
         color = colors[i]
         
         # Calculate the integral for the point
-        integral, local_mean = calculate_integral_2(freq_array, power_array, point, delta)
+        integral, local_mean = calculate_integral_2(freq_array, power_array, point)
         
         # Check if the point is valid
-        valid = valid_integrals(integral, local_mean, point, threshold)
+        valid = valid_integrals(integral, local_mean, point)
         if valid:
             # Prepare harmonics if the point is valid
             harmonics, color_map, category_harm = prepare_harmonic(point, category, num_harmonics, color)
@@ -148,10 +148,11 @@ def calculate_integral_2(freq, power, peak_freq, delta=2.5):
     local_mean : float
         The local mean value (adjacent integrals).
     """
-    # Calculate integral around the nearest peak
+    # Calculate integral around the peak frequency
     indices = (freq >= peak_freq - delta) & (freq <= peak_freq + delta)
     integral = np.trapz(power[indices], freq[indices])
     
+    # Calculate local mean from adjacent ranges
     left_indices = (freq >= peak_freq - 5 * delta) & (freq < peak_freq - delta)
     right_indices = (freq > peak_freq + delta) & (freq <= peak_freq + 5 * delta)
     
@@ -162,7 +163,6 @@ def calculate_integral_2(freq, power, peak_freq, delta=2.5):
     
     return integral, local_mean
 
-
 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
@@ -269,7 +269,8 @@ def find_AM(eodf, nyquist, stimulus_frequency):
     AM = t2[np.argmin(np.abs(x_values - stimulus_frequency))]
     return AM
 
-def find_nearest_peak(freq, power, point, threshold=0.5e-6, peak_search_range=10):
+
+def find_nearest_peak(freq, power, point, peak_search_range=30, threshold=None):
     """
     Find the nearest peak within a specified range around a given point.
 
@@ -283,27 +284,34 @@ def find_nearest_peak(freq, power, point, threshold=0.5e-6, peak_search_range=10
         The harmonic frequency for which to find the nearest peak.
     peak_search_range : float, optional
         Range in Hz to search for peaks around the specified point. The default is 30.
+    threshold : float, optional
+        Minimum height of peaks to consider. If None, no threshold is applied.
 
     Returns
     -------
     peak_freq : float
-        The frequency of the nearest peak within the specified range.
+        The frequency of the nearest peak within the specified range, or the input point if no peak is found.
     """
     # Define the range for peak searching
     search_indices = (freq >= point - peak_search_range) & (freq <= point + peak_search_range)
     
     # Find peaks in the specified range
-    peaks, _ = find_peaks(power[search_indices], height=threshold)
-    
+    peaks, properties = find_peaks(power[search_indices], height=threshold)
+ 
     # Adjust peak indices to match the original frequency array
     peaks_freq = freq[search_indices][peaks]
     
+    if peaks_freq.size == 0:
+        # No peaks detected, return the input point
+        return point
+    
     # Find the nearest peak to the specified point
     nearest_peak_index = np.argmin(np.abs(peaks_freq - point))
     peak_freq = peaks_freq[nearest_peak_index]
     
     return peak_freq
 
+
 def firing_rate(binary_spikes, dt = 0.000025, box_width = 0.01):
     '''
     Calculates the firing rate from binary spikes