diff --git a/code/eod_freq_normal.py b/code/eod_freq_normal.py
index 7b0e192..5228031 100644
--- a/code/eod_freq_normal.py
+++ b/code/eod_freq_normal.py
@@ -5,7 +5,13 @@ import numpy as np
 
 data_dir = '../data'
 #dataset = '2018-11-09-ad-invivo-1'
-data = ["2018-11-09-ad-invivo-1",  "2018-11-13-aa-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", "2018-11-20-ai-invivo-1"]
+data = ["2018-11-09-ad-invivo-1",
+        "2018-11-13-aa-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1",
+        "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1",
+        "2018-11-14-ac-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ak-invivo-1",
+        "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1",
+        "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1", "2018-11-20-af-invivo-1",
+        "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", "2018-11-20-ai-invivo-1"]
 
 for dataset in data:
 # read eod and time of baseline
diff --git a/code/plot_eodform_spikehist.py b/code/plot_eodform_spikehist.py
index 44892bc..24d3590 100644
--- a/code/plot_eodform_spikehist.py
+++ b/code/plot_eodform_spikehist.py
@@ -8,7 +8,8 @@ from IPython import embed
 # plot and data values
 inch_factor = 2.54
 data_dir = '../data'
-dataset = '2018-11-09-ad-invivo-1'
+#dataset = '2018-11-09-ad-invivo-1'
+dataset = '2018-11-14-al-invivo-1'
 
 # read eod and time of baseline
 time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
@@ -29,8 +30,9 @@ plt.yticks(fontsize = 18)
 ax.spines["top"].set_visible(False)
 ax.spines["right"].set_visible(False)
 fig.tight_layout()
+plt.show()
 #plt.show()
-plt.savefig('isis.pdf')
+#plt.savefig('isis.pdf')
 exit()
 
 # calculate coefficient of variation
diff --git a/code/response_beat.py b/code/response_beat.py
index e57b1c7..a8837e3 100644
--- a/code/response_beat.py
+++ b/code/response_beat.py
@@ -1,6 +1,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from read_chirp_data import *
+from read_baseline_data import *
 from utility import *
 from IPython import embed
 
@@ -11,27 +12,37 @@ cut_window = 40
 cut_range = np.arange(-cut_window * sampling_rate, 0, 1)
 window = 1
 
-'''
-# norm: -150, 150, 300
-data = ["2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1","2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1",
-        "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
+# norm: -150, 150, 300  aa, #ac, aj??
+data = ["2018-11-13-al-invivo-1"]#, "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1",
+        #"2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
 
+'''
 # norm: -50
 data = ["2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1","2018-11-20-ad-invivo-1", 
         "2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1",
         "2018-11-20-ai-invivo-1"]
 
-'''
 data = ["2018-11-14-aa-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1",
         "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-invivo-1", "2018-11-14-ak-invivo-1",
         "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1"]
+'''
+#data = ["2018-11-09-ad-invivo-1", "2018-11-14-af-invivo-1"]
 
 rates = {}
 
 for dataset in data:
+    print(dataset)
+    # read baseline spikes
+    base_spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
+    base_spikes = base_spikes[1000:2000]
+    spikerate = len(base_spikes)/base_spikes[-1]
+    print(spikerate)
+
+    # read spikes during chirp stimulation
     spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
     df_map = map_keys(spikes)
-    print(dataset)
+
+    # iterate over df
     for df in df_map.keys():
         '''
         if df == 50:
@@ -39,8 +50,8 @@ for dataset in data:
         else:
             continue
         '''
-        
-        print(df)
+
+        #print(df)
         rep_rates = []
         beat_duration = int(abs(1 / df) * 1000)
         beat_window = 0
@@ -56,9 +67,12 @@ for dataset in data:
                 binary_spikes = np.isin(cut_range, spikes_idx) * 1
                 smoothed_data = smooth(binary_spikes, window, 1 / sampling_rate)
                 train = smoothed_data[window:beat_window+window]
-                rep_rates.append(np.std(train))
+                norm_train = train*1000#/spikerate
+                rep_rates.append(np.std(norm_train))#/spikerate)
                 break
-        df_rate = np.mean(rep_rates)
+        df_rate = np.median(rep_rates)/spikerate
+        #embed()
+        #exit()
         if df in rates.keys():
             rates[df].append(df_rate)
         else: