57 lines
1.4 KiB
Python
57 lines
1.4 KiB
Python
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from read_baseline_data import *
|
|
from IPython import embed
|
|
|
|
inch_factor = 2.54
|
|
data_dir = '../data'
|
|
dataset = '2018-11-09-ad-invivo-1'
|
|
time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
|
|
|
|
fig = plt.figure(figsize=(12/inch_factor, 8/inch_factor))
|
|
ax = fig.add_subplot(111)
|
|
ax.plot(time[:1000], eod[:1000])
|
|
ax.set_xlabel('time [ms]', fontsize=12)
|
|
ax.set_ylabel('voltage [mV]', fontsize=12)
|
|
plt.xticks(fontsize = 8)
|
|
plt.yticks(fontsize = 8)
|
|
fig.tight_layout()
|
|
plt.savefig('eod.pdf')
|
|
|
|
#interspikeintervalhistogram, windowsize = 1 ms
|
|
#plt.hist
|
|
#coefficient of variation
|
|
#embed()
|
|
#exit()
|
|
|
|
spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
|
|
interspikeintervals = np.diff(spikes)
|
|
fig = plt.figure()
|
|
plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001))
|
|
plt.show()
|
|
|
|
mu = np.mean(interspikeintervals)
|
|
sigma = np.std(interspikeintervals)
|
|
cv = sigma/mu
|
|
print(cv)
|
|
|
|
# calculate zero crossings of the eod
|
|
# plot mean of eod circles
|
|
# plot std of eod circles
|
|
# plot psth into the same plot
|
|
# calculate vector strength
|
|
|
|
threshold = 0;
|
|
shift_eod = np.roll(eod, 1)
|
|
eod_times = time[(eod >= threshold) & (shift_eod < threshold)]
|
|
sampling_rate = 40000.0
|
|
eod_idx = eod_times*sampling_rate
|
|
|
|
fig = plt.figure()
|
|
for i, idx in enumerate(eod_idx):
|
|
#embed()
|
|
#exit()
|
|
plt.plot(time[int(idx):int(eod_idx[i+1])], eod[int(idx):int(eod_idx[i+1])])
|
|
plt.show()
|
|
|