1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
|
""" test_distance.py
Tests the isi- and spike-distance computation
Copyright 2014, Mario Mulansky <mario.mulansky@gmx.net>
"""
from __future__ import print_function
import numpy as np
from numpy.testing import assert_equal, assert_array_almost_equal
import pyspike as spk
def test_auxiliary_spikes():
t = np.array([0.2, 0.4, 0.6, 0.7])
t_aux = spk.add_auxiliary_spikes(t, T_end=1.0, T_start=0.1)
assert_equal(t_aux, [0.1, 0.2, 0.4, 0.6, 0.7, 1.0])
t_aux = spk.add_auxiliary_spikes(t_aux, 1.0)
assert_equal(t_aux, [0.0, 0.1, 0.2, 0.4, 0.6, 0.7, 1.0])
def test_isi():
# generate two spike trains:
t1 = np.array([0.2, 0.4, 0.6, 0.7])
t2 = np.array([0.3, 0.45, 0.8, 0.9, 0.95])
# pen&paper calculation of the isi distance
expected_times = [0.0,0.2,0.3,0.4,0.45,0.6,0.7,0.8,0.9,0.95,1.0]
expected_isi = [-0.1/0.3, -0.1/0.3, 0.05/0.2, 0.05/0.2, -0.15/0.35,
-0.25/0.35, -0.05/0.35, 0.2/0.3, 0.25/0.3, 0.25/0.3]
t1 = spk.add_auxiliary_spikes(t1, 1.0)
t2 = spk.add_auxiliary_spikes(t2, 1.0)
f = spk.isi_distance(t1, t2)
print("ISI: ", f.y)
assert_equal(f.x, expected_times)
assert_array_almost_equal(f.y, expected_isi, decimal=14)
# check with some equal spike times
t1 = np.array([0.2,0.4,0.6])
t2 = np.array([0.1,0.4,0.5,0.6])
expected_times = [0.0,0.1,0.2,0.4,0.5,0.6,1.0]
expected_isi = [0.1/0.2, -0.1/0.3, -0.1/0.3, 0.1/0.2, 0.1/0.2, -0.0/0.5]
t1 = spk.add_auxiliary_spikes(t1, 1.0)
t2 = spk.add_auxiliary_spikes(t2, 1.0)
f = spk.isi_distance(t1, t2)
assert_equal(f.x, expected_times)
assert_array_almost_equal(f.y, expected_isi, decimal=14)
def test_spike():
# generate two spike trains:
t1 = np.array([0.2, 0.4, 0.6, 0.7])
t2 = np.array([0.3, 0.45, 0.8, 0.9, 0.95])
# pen&paper calculation of the spike distance
expected_times = [0.0,0.2,0.3,0.4,0.45,0.6,0.7,0.8,0.9,0.95,1.0]
s1 = np.array([0.1, 0.1, (0.1*0.1+0.05*0.1)/0.2, 0.05, (0.05*0.15 * 2)/0.2,
0.15, 0.1, 0.1*0.2/0.3, 0.1**2/0.3, 0.1*0.05/0.3, 0.1])
s2 = np.array([0.1, 0.1*0.2/0.3, 0.1, (0.1*0.05 * 2)/.15, 0.05,
(0.05*0.2+0.1*0.15)/0.35, (0.05*0.1+0.1*0.25)/0.35,
0.1, 0.1, 0.05, 0.05])
isi1 = np.array([0.2, 0.2, 0.2, 0.2, 0.2, 0.1, 0.3, 0.3, 0.3, 0.3])
isi2 = np.array([0.3, 0.3, 0.15, 0.15, 0.35, 0.35, 0.35, 0.1, 0.05, 0.05])
expected_y1 = (s1[:-1]*isi2+s2[:-1]*isi1) / (0.5*(isi1+isi2)**2)
expected_y2 = (s1[1:]*isi2+s2[1:]*isi1) / (0.5*(isi1+isi2)**2)
t1 = spk.add_auxiliary_spikes(t1, 1.0)
t2 = spk.add_auxiliary_spikes(t2, 1.0)
f = spk.spike_distance(t1, t2)
assert_equal(f.x, expected_times)
assert_array_almost_equal(f.y1, expected_y1, decimal=14)
assert_array_almost_equal(f.y2, expected_y2, decimal=14)
# check with some equal spike times
t1 = np.array([0.2,0.4,0.6])
t2 = np.array([0.1,0.4,0.5,0.6])
expected_times = [0.0,0.1,0.2,0.4,0.5,0.6,1.0]
s1 = np.array([0.1, 0.1*0.1/0.2, 0.1, 0.0, 0.0, 0.0, 0.0])
s2 = np.array([0.1*0.1/0.3, 0.1, 0.1*0.2/0.3, 0.0, 0.1, 0.0, 0.0])
isi1 = np.array([0.2, 0.2, 0.2, 0.2, 0.2, 0.4])
isi2 = np.array([0.3, 0.3, 0.3, 0.1, 0.1, 0.4])
expected_y1 = (s1[:-1]*isi2+s2[:-1]*isi1) / (0.5*(isi1+isi2)**2)
expected_y2 = (s1[1:]*isi2+s2[1:]*isi1) / (0.5*(isi1+isi2)**2)
t1 = spk.add_auxiliary_spikes(t1, 1.0)
t2 = spk.add_auxiliary_spikes(t2, 1.0)
f = spk.spike_distance(t1, t2)
assert_equal(f.x, expected_times)
assert_array_almost_equal(f.y1, expected_y1, decimal=14)
assert_array_almost_equal(f.y2, expected_y2, decimal=14)
if __name__ == "__main__":
test_auxiliary_spikes()
test_isi()
test_spike()
|
