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histogram

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Ramona 2018-11-29 09:48:12 +01:00
parent 9f80891616
commit d77d377849
1 changed files with 6 additions and 12 deletions
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18
code/plot_eodform_spikehist.py
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@ -7,14 +7,13 @@ 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
eod_norm = eod - np.mean(eod)
# calculate eod times and indices by zero crossings
@ -22,20 +21,15 @@ threshold = 0
shift_eod = np.roll(eod_norm, 1)
eod_times = time[(eod_norm >= threshold) & (shift_eod < threshold)]
eod_duration = eod_times[2]- eod_times[1] #time in s
eod_duration = eod_times[2]- eod_times[1]
=======
>>>>>>> 5cd62554fa5af12a6a50661f0a60cd2b0457e702
# 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)