blob: 0c34608c8c7615299659ddc9ec23a0e35b8438f8 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
|
# 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, ending time = 4000
spike_trains = spk.load_spike_trains_from_txt("SPIKY_testdata.txt", 4000)
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()
|
