diff --git a/LNP_model b/LNP_model
index 37194ba..ac84f4b 100644
--- a/LNP_model
+++ b/LNP_model
@@ -9,19 +9,19 @@ Created on Tue May  9 18:11:51 2017
 import numpy as np
 from scipy.stats.mstats import mquantiles
 
-total_frames=400000
+total_frames=10000
 dt=0.001    # Time step
 t=np.arange(0,total_frames*dt,dt) # Time vector
 filter_time=.6 # The longest feature RGCs respond to is ~600ms 
 filter_length=int(filter_time/dt) # Filter is filter_length frames long
 
-cweight=.5 # The weight of combination for the two filters
+cweight=1 # The weight of combination for the two filters
 
 def make_noise(): # Generate gaussian noise for stimulus
     return np.random.normal(0,9,total_frames) 
-stimulus=make_noise()
+#stimulus=make_noise()
 
-filter_index1=1   # Change filter type here
+filter_index1=2   # Change filter type here
 filter_index2=1 
 
 def linear_filter(t,filter_index):    # Define filter according to choice
diff --git a/stc.py b/stc.py
index aef2ad3..ff05260 100644
--- a/stc.py
+++ b/stc.py
@@ -7,13 +7,21 @@
 
 Spike-triggered covariance
 """
+from datetime import datetime
+execution_timer=datetime.now()
 
-def stc(spikes,stimulus,filter_length):
-    covariance=np.matrix(np.zeros(filter_length,filter_length))
-    sta=sta(spikes,stimulus,filter_length)
+sta_temp=sta(spikes,stimulus,filter_length)
+def stc(spikes,stimulus,filter_length,sta_temp):
+    covariance=np.matrix(np.zeros((filter_length,filter_length)))
+    
     for i in range(filter_length,total_frames):
-        snippet=stimulus[i:i-filter_length:-1]
-        # Snippets are inverted before being added
-        snpta=[snippet-sta]
-        covariance=covariance+snpta.T*snpta*spikes[i]
-    return covariance/fil    
\ No newline at end of file
+        if spikes[i]!=0:
+                 snippet=stimulus[i:i-filter_length:-1]
+                 # Snippets are inverted before being added
+                 snpta=np.matrix(snippet-sta_temp)
+                 covariance=covariance+(snpta.T*snpta)*spikes[i]
+    return covariance/(sum(spikes)+filter_length-1)
+
+recovered_stc=stc(spikes,stimulus,filter_length,sta(spikes,stimulus,filter_length))
+runtime=str(datetime.now()-execution_timer).split('.')[0]
+print('Duration: {}'.format(runtime))