diff --git a/code/useful_functions.py b/code/useful_functions.py
index d07000a..4038291 100644
--- a/code/useful_functions.py
+++ b/code/useful_functions.py
@@ -54,6 +54,40 @@ def binary_spikes(spike_times, duration, dt):
     binary[spike_indices] = 1 # put the indices into binary
     return binary
 
+def calculate_integral(freq, power, point, delta):   
+    """
+    Calculate the integral around a single specified point.
+
+    Parameters
+    ----------
+    frequency : np.array
+        An array of frequencies corresponding to the power values.
+    power : np.array
+        An array of power spectral density values.
+    point : float
+        The harmonic frequency at which to calculate the integral.
+    delta : float
+        Radius of the range for integration around the point.
+
+    Returns
+    -------
+    integral : float
+        The calculated integral around the point.
+    local_mean : float
+        The local mean value (adjacent integrals).
+    """
+    indices = (freq >= point - delta) & (freq <= point + delta)
+    integral = np.trapz(power[indices], freq[indices])
+            
+    left_indices = (freq >= point - 5 * delta) & (freq < point - delta)
+    right_indices = (freq > point + delta) & (freq <= point + 5 * delta)
+           
+    l_integral = np.trapz(power[left_indices], freq[left_indices])
+    r_integral = np.trapz(power[right_indices], freq[right_indices])
+           
+    local_mean = np.mean([l_integral, r_integral])
+    return integral, local_mean
+
 def extract_stim_data(stimulus):
     '''
     extracts all necessary metadata for each stimulus
@@ -252,39 +286,6 @@ def spike_times(stim):
     dt = ti.sampling_interval  
     return spikes, stim_dur, dt # se changed spike_times to spikes so its not the same as name of function
 
-def calculate_integral(freq, power, point, delta):   
-    """
-    Calculate the integral around a single specified point.
-
-    Parameters
-    ----------
-    frequency : np.array
-        An array of frequencies corresponding to the power values.
-    power : np.array
-        An array of power spectral density values.
-    point : float
-        The harmonic frequency at which to calculate the integral.
-    delta : float
-        Half-width of the range for integration around the point.
-
-    Returns
-    -------
-    integral : float
-        The calculated integral around the point.
-    local_mean : float
-        The local mean value (adjacent integrals).
-    """
-    indices = (freq >= point - delta) & (freq <= point + delta)
-    integral = np.trapz(power[indices], freq[indices])
-            
-    left_indices = (freq >= point - 5 * delta) & (freq < point - delta)
-    right_indices = (freq > point + delta) & (freq <= point + 5 * delta)
-           
-    l_integral = np.trapz(power[left_indices], freq[left_indices])
-    r_integral = np.trapz(power[right_indices], freq[right_indices])
-           
-    local_mean = np.mean([l_integral, r_integral])
-    return integral, local_mean
 
 def valid_integrals(integral, local_mean, threshold, point):
     """