blob: 1186062e5663e4d964d4a3be246633baa78e4251 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
|
# compute the isi distance of some test data
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
import pyspike as spk
# first load the data
spike_trains = []
spike_file = open("SPIKY_testdata.txt", 'r')
for line in spike_file:
spike_trains.append(spk.spike_train_from_string(line))
spikes = spk.merge_spike_trains([spike_trains[0], spike_trains[1]])
print(spikes)
plt.plot(spike_trains[0], np.ones_like(spike_trains[0]), 'o')
plt.plot(spike_trains[1], np.ones_like(spike_trains[1]), 'x')
plt.plot(spikes, 2*np.ones_like(spikes), 'o')
plt.show()
|
