huhu

code/plot_eodform_spikehist.py

@@ -7,14 +7,18 @@ from IPython import embed
 
 # plot and data values
 inch_factor = 2.54
+sampling_rate = 40000
 data_dir = '../data'
-#dataset = '2018-11-09-ad-invivo-1'
-dataset = '2018-11-13-aa-invivo-1'
+dataset = '2018-11-09-ad-invivo-1'
+#dataset = '2018-11-13-aa-invivo-1'
 
 # read eod and time of baseline
 time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
+<<<<<<< HEAD
 
 
+=======
+>>>>>>> d77d377849b65effc29e550f25a9107463c2120b
 eod_norm = eod - np.mean(eod)
 
 # calculate eod times and indices by zero crossings
@@ -22,18 +26,22 @@ threshold = 0
 shift_eod = np.roll(eod_norm, 1)
 eod_times = time[(eod_norm >= threshold) & (shift_eod < threshold)]*40000
 
+eod_duration = eod_times[2]- eod_times[1] #time in s
 
+<<<<<<< HEAD
 
 eod_duration = eod_times[2]- eod_times[1]
 
 
+=======
+>>>>>>> d77d377849b65effc29e550f25a9107463c2120b
 # read spikes during baseline activity
-spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
+spikes = read_baseline_spikes(os.path.join(data_dir, dataset)) #spikes in s
 # calculate interpike intervals and plot them
 interspikeintervals = np.diff(spikes)/eod_duration
 
 fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
-plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='royalblue')
+plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.1), color='royalblue')
 plt.xlabel("eod cycles", fontsize = 22)
 plt.xticks(fontsize = 18)
 plt.ylabel("number of \n interspikeintervals", fontsize = 22)

code/stimulus_chirp.py

@@ -35,7 +35,7 @@ for k, t in enumerate(time):
     p += f * stepsize
     signal[k] = a * np.sin(6.28318530717959 * p)
 
-fig = plt.figure(figsize = (20/inch_factor, 15/inch_factor))
+fig = plt.figure(figsize = (20/inch_factor, 12/inch_factor))
 ax1 = fig.add_subplot(211)
 plt.yticks(fontsize=18)
 ax2 = fig.add_subplot(212, sharex=ax1)