diff --git a/code/eod_freq_normal.py b/code/eod_freq_normal.py
index e1a16a9..7b0e192 100644
--- a/code/eod_freq_normal.py
+++ b/code/eod_freq_normal.py
@@ -4,21 +4,27 @@ import matplotlib.pyplot as plt
 import numpy as np
 
 data_dir = '../data'
-dataset = '2018-11-09-ad-invivo-1'
+#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"]
 
+for dataset in data:
 # read eod and time of baseline
-time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
+    time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
 
-eod_norm = eod - np.mean(eod)
+    eod_norm = eod - np.mean(eod)
 
 # calculate eod times and indices by zero crossings
-threshold = 0
-shift_eod = np.roll(eod_norm, 1)
-eod_times = time[(eod_norm >= threshold) & (shift_eod < threshold)]
+    threshold = 0
+    shift_eod = np.roll(eod_norm, 1)
+    eod_times = time[(eod_norm >= threshold) & (shift_eod < threshold)]
 
 
 ## normal
-eod_freq_normal = 1/np.diff(eod_times)
+    eod_freq_normal = 1/np.diff(eod_times)
+    overallfreq = np.mean(eod_freq_normal)
+    print(overallfreq)
+
+exit()
 kernel = np.ones(7)/7
 
 smooth_eod_freq_normal = np.convolve(eod_freq_normal, kernel, mode = 'valid')
diff --git a/code/repetition_firingrate.py b/code/repetition_firingrate.py
index 6d54fc8..f1f05a4 100644
--- a/code/repetition_firingrate.py
+++ b/code/repetition_firingrate.py
@@ -8,7 +8,7 @@ from IPython import embed
 # define sampling rate and data path
 sampling_rate = 40 #kHz
 data_dir = "../data"
-dataset = "2018-11-09-ad-invivo-1"
+dataset = "2018-11-13-aj-invivo-1"
 inch_factor = 2.54
 # parameters for binning, smoothing and plotting
 cut_window = 60