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
|
""" test_directionality.py
Tests the directionality functions
Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net>
Distributed under the BSD License
"""
import numpy as np
from numpy.testing import assert_equal, assert_almost_equal, \
assert_array_equal
import pyspike as spk
from pyspike import SpikeTrain, DiscreteFunc
def test_spike_directionality():
st1 = SpikeTrain([100, 200, 300], [0, 1000])
st2 = SpikeTrain([105, 205, 300], [0, 1000])
assert_almost_equal(spk.spike_directionality(st1, st2), 2.0/3.0)
assert_almost_equal(spk.spike_directionality(st1, st2, normalize=False),
2.0)
# exchange order of spike trains should give exact negative profile
assert_almost_equal(spk.spike_directionality(st2, st1), -2.0/3.0)
assert_almost_equal(spk.spike_directionality(st2, st1, normalize=False),
-2.0)
st3 = SpikeTrain([105, 195, 500], [0, 1000])
assert_almost_equal(spk.spike_directionality(st1, st3), 0.0)
assert_almost_equal(spk.spike_directionality(st1, st3, normalize=False),
0.0)
assert_almost_equal(spk.spike_directionality(st3, st1), 0.0)
D = spk.spike_directionality_matrix([st1, st2, st3], normalize=False)
D_expected = np.array([[0, 2.0, 0.0], [-2.0, 0.0, -1.0], [0.0, 1.0, 0.0]])
assert_array_equal(D, D_expected)
dir_profs = spk.spike_directionality_values([st1, st2, st3])
assert_array_equal(dir_profs[0], [1.0, 0.0, 0.0])
assert_array_equal(dir_profs[1], [-0.5, -1.0, 0.0])
def test_spike_train_order():
st1 = SpikeTrain([100, 200, 300], [0, 1000])
st2 = SpikeTrain([105, 205, 300], [0, 1000])
st3 = SpikeTrain([105, 195, 500], [0, 1000])
expected_x12 = np.array([0, 100, 105, 200, 205, 300, 1000])
expected_y12 = np.array([1, 1, 1, 1, 1, 0, 0])
expected_mp12 = np.array([1, 1, 1, 1, 1, 2, 2])
f = spk.spike_train_order_profile(st1, st2)
assert f.almost_equal(DiscreteFunc(expected_x12, expected_y12,
expected_mp12))
assert_almost_equal(f.avrg(), 2.0/3.0)
assert_almost_equal(f.avrg(normalize=False), 4.0)
assert_almost_equal(spk.spike_train_order(st1, st2), 2.0/3.0)
assert_almost_equal(spk.spike_train_order(st1, st2, normalize=False), 4.0)
expected_x23 = np.array([0, 105, 195, 205, 300, 500, 1000])
expected_y23 = np.array([0, 0, -1, -1, 0, 0, 0])
expected_mp23 = np.array([2, 2, 1, 1, 1, 1, 1])
f = spk.spike_train_order_profile(st2, st3)
assert_array_equal(f.x, expected_x23)
assert_array_equal(f.y, expected_y23)
assert_array_equal(f.mp, expected_mp23)
assert f.almost_equal(DiscreteFunc(expected_x23, expected_y23,
expected_mp23))
assert_almost_equal(f.avrg(), -1.0/3.0)
assert_almost_equal(f.avrg(normalize=False), -2.0)
assert_almost_equal(spk.spike_train_order(st2, st3), -1.0/3.0)
assert_almost_equal(spk.spike_train_order(st2, st3, normalize=False), -2.0)
f = spk.spike_train_order_profile_multi([st1, st2, st3])
expected_x = np.array([0, 100, 105, 195, 200, 205, 300, 500, 1000])
expected_y = np.array([2, 2, 2, -2, 0, 0, 0, 0, 0])
expected_mp = np.array([2, 2, 4, 2, 2, 2, 4, 2, 2])
assert_array_equal(f.x, expected_x)
assert_array_equal(f.y, expected_y)
assert_array_equal(f.mp, expected_mp)
# Averaging the profile should be the same as computing the synfire indicator directly.
assert_almost_equal(f.avrg(), spk.spike_train_order([st1, st2, st3]))
# We can also compute the synfire indicator from the Directionality Matrix:
D_matrix = spk.spike_directionality_matrix([st1, st2, st3], normalize=False)
num_spikes = np.sum(len(st) for st in [st1, st2, st3])
syn_fire = np.sum(np.triu(D_matrix)) / num_spikes
assert_almost_equal(f.avrg(), syn_fire)
|
