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""" test_distance.py
Tests the isi- and spike-distance computation
Copyright 2014, Mario Mulansky <mario.mulansky@gmx.net>
Distributed under the BSD License
"""
from __future__ import print_function
import numpy as np
from copy import copy
from numpy.testing import assert_equal, assert_almost_equal, \
assert_array_almost_equal
import pyspike as spk
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]
expected_times = np.array(expected_times)
expected_isi = np.array(expected_isi)
expected_isi_val = sum((expected_times[1:] - expected_times[:-1]) *
expected_isi)/(expected_times[-1]-expected_times[0])
t1 = spk.add_auxiliary_spikes(t1, 1.0)
t2 = spk.add_auxiliary_spikes(t2, 1.0)
f = spk.isi_profile(t1, t2)
# print("ISI: ", f.y)
assert_equal(f.x, expected_times)
assert_array_almost_equal(f.y, expected_isi, decimal=15)
assert_equal(f.avrg(), expected_isi_val)
assert_equal(spk.isi_distance(t1, t2), expected_isi_val)
# 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]
expected_times = np.array(expected_times)
expected_isi = np.array(expected_isi)
expected_isi_val = sum((expected_times[1:] - expected_times[:-1]) *
expected_isi)/(expected_times[-1]-expected_times[0])
t1 = spk.add_auxiliary_spikes(t1, 1.0)
t2 = spk.add_auxiliary_spikes(t2, 1.0)
f = spk.isi_profile(t1, t2)
assert_equal(f.x, expected_times)
assert_array_almost_equal(f.y, expected_isi, decimal=15)
assert_equal(f.avrg(), expected_isi_val)
assert_equal(spk.isi_distance(t1, t2), expected_isi_val)
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)
expected_times = np.array(expected_times)
expected_y1 = np.array(expected_y1)
expected_y2 = np.array(expected_y2)
expected_spike_val = sum((expected_times[1:] - expected_times[:-1]) *
(expected_y1+expected_y2)/2)
expected_spike_val /= (expected_times[-1]-expected_times[0])
t1 = spk.add_auxiliary_spikes(t1, 1.0)
t2 = spk.add_auxiliary_spikes(t2, 1.0)
f = spk.spike_profile(t1, t2)
assert_equal(f.x, expected_times)
assert_array_almost_equal(f.y1, expected_y1, decimal=15)
assert_array_almost_equal(f.y2, expected_y2, decimal=15)
assert_equal(f.avrg(), expected_spike_val)
assert_equal(spk.spike_distance(t1, t2), expected_spike_val)
# 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)
expected_times = np.array(expected_times)
expected_y1 = np.array(expected_y1)
expected_y2 = np.array(expected_y2)
expected_spike_val = sum((expected_times[1:] - expected_times[:-1]) *
(expected_y1+expected_y2)/2)
expected_spike_val /= (expected_times[-1]-expected_times[0])
t1 = spk.add_auxiliary_spikes(t1, 1.0)
t2 = spk.add_auxiliary_spikes(t2, 1.0)
f = spk.spike_profile(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)
assert_almost_equal(f.avrg(), expected_spike_val, decimal=16)
assert_almost_equal(spk.spike_distance(t1, t2), expected_spike_val,
decimal=16)
def test_spike_sync():
spikes1 = np.array([1.0, 2.0, 3.0])
spikes2 = np.array([2.1])
spikes1 = spk.add_auxiliary_spikes(spikes1, 4.0)
spikes2 = spk.add_auxiliary_spikes(spikes2, 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2),
0.5, decimal=16)
# test with some small max_tau, spike_sync should be 0
assert_almost_equal(spk.spike_sync(spikes1, spikes2, max_tau=0.05),
0.0, decimal=16)
spikes2 = np.array([3.1])
spikes2 = spk.add_auxiliary_spikes(spikes2, 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2),
0.5, decimal=16)
spikes2 = np.array([1.1])
spikes2 = spk.add_auxiliary_spikes(spikes2, 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2),
0.5, decimal=16)
spikes2 = np.array([0.9])
spikes2 = spk.add_auxiliary_spikes(spikes2, 4.0)
assert_almost_equal(spk.spike_sync(spikes1, spikes2),
0.5, decimal=16)
def check_multi_profile(profile_func, profile_func_multi):
# generate spike trains:
t1 = spk.add_auxiliary_spikes(np.array([0.2, 0.4, 0.6, 0.7]), 1.0)
t2 = spk.add_auxiliary_spikes(np.array([0.3, 0.45, 0.8, 0.9, 0.95]), 1.0)
t3 = spk.add_auxiliary_spikes(np.array([0.2, 0.4, 0.6]), 1.0)
t4 = spk.add_auxiliary_spikes(np.array([0.1, 0.4, 0.5, 0.6]), 1.0)
spike_trains = [t1, t2, t3, t4]
f12 = profile_func(t1, t2)
f13 = profile_func(t1, t3)
f14 = profile_func(t1, t4)
f23 = profile_func(t2, t3)
f24 = profile_func(t2, t4)
f34 = profile_func(t3, t4)
f_multi = profile_func_multi(spike_trains, [0, 1])
assert f_multi.almost_equal(f12, decimal=14)
f_multi1 = profile_func_multi(spike_trains, [1, 2, 3])
f_multi2 = profile_func_multi(spike_trains[1:])
assert f_multi1.almost_equal(f_multi2, decimal=14)
f = copy(f12)
f.add(f13)
f.add(f23)
f.mul_scalar(1.0/3)
f_multi = profile_func_multi(spike_trains, [0, 1, 2])
assert f_multi.almost_equal(f, decimal=14)
f.mul_scalar(3) # revert above normalization
f.add(f14)
f.add(f24)
f.add(f34)
f.mul_scalar(1.0/6)
f_multi = profile_func_multi(spike_trains)
assert f_multi.almost_equal(f, decimal=14)
def test_multi_isi():
check_multi_profile(spk.isi_profile, spk.isi_profile_multi)
def test_multi_spike():
check_multi_profile(spk.spike_profile, spk.spike_profile_multi)
def test_multi_spike_sync():
# some basic multivariate check
spikes1 = np.array([100, 300, 400, 405, 410, 500, 700, 800,
805, 810, 815, 900], dtype=float)
spikes2 = np.array([100, 200, 205, 210, 295, 350, 400, 510,
600, 605, 700, 910], dtype=float)
spikes3 = np.array([100, 180, 198, 295, 412, 420, 510, 640,
695, 795, 820, 920], dtype=float)
spikes1 = spk.add_auxiliary_spikes(spikes1, 1000)
spikes2 = spk.add_auxiliary_spikes(spikes2, 1000)
spikes3 = spk.add_auxiliary_spikes(spikes3, 1000)
assert_almost_equal(spk.spike_sync(spikes1, spikes2),
0.5, decimal=15)
assert_almost_equal(spk.spike_sync(spikes1, spikes3),
0.5, decimal=15)
assert_almost_equal(spk.spike_sync(spikes2, spikes3),
0.5, decimal=15)
f = spk.spike_sync_profile_multi([spikes1, spikes2, spikes3])
# hands on definition of the average multivariate spike synchronization
# expected = (f1.integral() + f2.integral() + f3.integral()) / \
# (np.sum(f1.mp[1:-1])+np.sum(f2.mp[1:-1])+np.sum(f3.mp[1:-1]))
expected = 0.5
assert_almost_equal(f.avrg(), expected, decimal=15)
assert_almost_equal(spk.spike_sync_multi([spikes1, spikes2, spikes3]),
expected, decimal=15)
# multivariate regression test
spike_trains = spk.load_spike_trains_from_txt("test/SPIKE_Sync_Test.txt",
time_interval=(0, 4000))
f = spk.spike_sync_profile_multi(spike_trains)
assert_equal(np.sum(f.y[1:-1]), 39932)
assert_equal(np.sum(f.mp[1:-1]), 85554)
def check_dist_matrix(dist_func, dist_matrix_func):
# generate spike trains:
t1 = spk.add_auxiliary_spikes(np.array([0.2, 0.4, 0.6, 0.7]), 1.0)
t2 = spk.add_auxiliary_spikes(np.array([0.3, 0.45, 0.8, 0.9, 0.95]), 1.0)
t3 = spk.add_auxiliary_spikes(np.array([0.2, 0.4, 0.6]), 1.0)
t4 = spk.add_auxiliary_spikes(np.array([0.1, 0.4, 0.5, 0.6]), 1.0)
spike_trains = [t1, t2, t3, t4]
f12 = dist_func(t1, t2)
f13 = dist_func(t1, t3)
f14 = dist_func(t1, t4)
f23 = dist_func(t2, t3)
f24 = dist_func(t2, t4)
f34 = dist_func(t3, t4)
f_matrix = dist_matrix_func(spike_trains)
# check zero diagonal
for i in xrange(4):
assert_equal(0.0, f_matrix[i, i])
for i in xrange(4):
for j in xrange(i+1, 4):
assert_equal(f_matrix[i, j], f_matrix[j, i])
assert_equal(f12, f_matrix[1, 0])
assert_equal(f13, f_matrix[2, 0])
assert_equal(f14, f_matrix[3, 0])
assert_equal(f23, f_matrix[2, 1])
assert_equal(f24, f_matrix[3, 1])
assert_equal(f34, f_matrix[3, 2])
def test_isi_matrix():
check_dist_matrix(spk.isi_distance, spk.isi_distance_matrix)
def test_spike_matrix():
check_dist_matrix(spk.spike_distance, spk.spike_distance_matrix)
def test_spike_sync_matrix():
check_dist_matrix(spk.spike_sync, spk.spike_sync_matrix)
def test_regression_spiky():
spike_trains = spk.load_spike_trains_from_txt("test/PySpike_testdata.txt",
(0.0, 4000.0))
isi_profile = spk.isi_profile_multi(spike_trains)
isi_dist = isi_profile.avrg()
print(isi_dist)
# get the full precision from SPIKY
# assert_equal(isi_dist, 0.1832)
spike_profile = spk.spike_profile_multi(spike_trains)
spike_dist = spike_profile.avrg()
print(spike_dist)
# get the full precision from SPIKY
# assert_equal(spike_dist, 0.2445)
def test_multi_variate_subsets():
spike_trains = spk.load_spike_trains_from_txt("test/PySpike_testdata.txt",
(0.0, 4000.0))
sub_set = [1, 3, 5, 7]
spike_trains_sub_set = [spike_trains[i] for i in sub_set]
v1 = spk.isi_distance_multi(spike_trains_sub_set)
v2 = spk.isi_distance_multi(spike_trains, sub_set)
assert_equal(v1, v2)
v1 = spk.spike_distance_multi(spike_trains_sub_set)
v2 = spk.spike_distance_multi(spike_trains, sub_set)
assert_equal(v1, v2)
v1 = spk.spike_sync_multi(spike_trains_sub_set)
v2 = spk.spike_sync_multi(spike_trains, sub_set)
assert_equal(v1, v2)
if __name__ == "__main__":
test_isi()
test_spike()
test_multi_isi()
test_multi_spike()
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