diff --git a/create_ethogram.py b/create_ethogram.py
index 832cb87..12e0fdd 100644
--- a/create_ethogram.py
+++ b/create_ethogram.py
@@ -1,66 +1,100 @@
 from matplotlib import pyplot as plt
 from matplotlib import patches as mpatches
 import numpy as np
 
 #enter the names of the csv files used to create the ethogram
 csv_files = ['test_animals/test1.csv', 'test_animals/test2.csv', 'test_animals/test3.csv', 'test_animals/test4.csv', 'test_animals/test5.csv', 'test_animals/test6.csv']
 labels = ['fly 1\n13A', 'fly 1\n7.5A', 'fly 2\n7.5A', 'fly 2\n13A', 'fly 3\n13A', 'fly 3\n7.5A']
+csv_files = ['test_animals/test2.csv']
+labels = ['']
 inter_frame_interval = 1./30.
 
 N = len(csv_files)
 
 colors = np.array([[1, 0, 0],
                    [0, 1, 0],
                    [0, 0, 1],
                    [1, 1, 0],
                    [1, 0, 1],
                    [0, 1, 1]])
 
 fig = plt.figure()
 
 ax1 = fig.add_subplot(111)
 
+signals = np.zeros((N,3,9000))
+event_index = np.zeros((N,4)) #index for signals that separate the stimulation events
+
 #plot individual animals
 for i in range(N):
     a = np.genfromtxt(csv_files[i]).astype(np.int_)
     if a[1,2]==-1:
         a[2:,:2] = a[2:,:2] - np.ones(a[2:,:2].shape)*a[1,1]
     else:
         print('Cannot determine off-set. The two initial blinks of the LED should be in the first two rows.')
     index0=np.where(a[:,2]==0)
     index1=np.where(a[:,2]==1)
     index2=np.where(a[:,2]==2)
-    data0 = np.empty(sum((a[index0,1]-a[index0,0])[0]))
-    data1 = np.empty(sum((a[index1,1]-a[index1,0])[0]))
-    data2 = np.empty(sum((a[index2,1]-a[index2,0])[0]))
+    data0 = np.empty(sum((a[index0,1]-a[index0,0])[0]),dtype=np.int_)
+    data1 = np.empty(sum((a[index1,1]-a[index1,0])[0]),dtype=np.int_)
+    data2 = np.empty(sum((a[index2,1]-a[index2,0])[0]),dtype=np.int_)
     l0 = 0
     l1 = 0
     l2 = 0
     for j in range(len(a)):
         if a[j,2]==0:
             #print('difference',a[j,1]-a[j,0])
             #print(a[j,0])
             #print(a[j,1])
             data0[l0:l0+a[j,1]-a[j,0]]=np.arange(a[j,0],a[j,1])
             l0 = l0+a[j,1]-a[j,0]
         elif a[j,2]==1:
             data1[l1:l1+a[j,1]-a[j,0]]=np.arange(a[j,0],a[j,1])
             l1 = l1+a[j,1]-a[j,0]
         elif a[j,2]==2:
             data2[l2:l2+a[j,1]-a[j,0]]=np.arange(a[j,0],a[j,1])
             l2 = l2+a[j,1]-a[j,0]
     ax1.eventplot(data0*inter_frame_interval, colors=[colors[0]], lineoffsets=[i+1], linelengths=[1])
     ax1.eventplot(data1*inter_frame_interval, colors=[colors[1]], lineoffsets=[i+1], linelengths=[0.8])
     ax1.eventplot(data2*inter_frame_interval, colors=[colors[2]], lineoffsets=[i+1], linelengths=[0.8])
 
+    signals[i,0,data0]=1
+    signals[i,1,data1]=1
+    signals[i,2,data2]=1
+
 plt.rcParams.update({'font.size': 20})
 ax1.set_title('Grooming behaviour')
 ax1.set_xlabel('time in seconds')
 plt.yticks(range(1,N+1),labels)
 ax1.set_xlim(-5,300)
 ax1.set_ylim(0.5,N+1)
 patch0 = mpatches.Patch(color=colors[0], label='Magnet on')
 patch1 = mpatches.Patch(color=colors[1], label='foreleg grooming')
 patch2 = mpatches.Patch(color=colors[2], label='head related grooming')
 plt.legend(handles=[patch0, patch1, patch2],prop={'size': 12}) #only works in python 3
+plt.savefig('ethogram.pdf')
+#plt.show()
+
+fig2 = plt.figure()
+ax21 = fig2.add_subplot(141)
+ax22 = fig2.add_subplot(142)
+ax23 = fig2.add_subplot(143)
+ax24 = fig2.add_subplot(144)
+#ax25 = fig2.add_subplot(155)
+
+for i in range(N):
+    #ax2.plot(np.arange(9000)*inter_frame_interval, np.correlate(signals[i,0,:], signals[i,1,:],'same'), label="foreleg grooming")
+    ax21.plot(np.arange(2098)*inter_frame_interval, np.correlate(signals[i,0,0:2098], signals[i,2,0:2098],'same'), label="head grooming")
+    ax22.plot(np.arange(2098)*inter_frame_interval, np.correlate(signals[i,0,2099:4197], signals[i,2,2099:4197],'same'), label="head grooming")
+    ax23.plot(np.arange(2098)*inter_frame_interval, np.correlate(signals[i,0,4198:6296], signals[i,2,4198:6296],'same'), label="head grooming")
+    ax24.plot(np.arange(2098)*inter_frame_interval, np.correlate(signals[i,0,6297:8395], signals[i,2,6297:8395],'same'), label="head grooming")
+#plt.legend()
+
+#for ji in range(N):
+#    ax25.plot(np.arange(2098)*inter_frame_interval, signals[ji,0,0:2098])
+#    ax25.plot(np.arange(2098)*inter_frame_interval, signals[ji,0,2099:4197])
+#    ax25.plot(np.arange(2098)*inter_frame_interval, signals[ji,0,4198:6296])
+#    ax25.plot(np.arange(2098)*inter_frame_interval, signals[ji,0,6297:8395])
+
+plt.savefig('correlation.pdf')
 plt.show()