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""" test_empty.py
Tests the distance measure for empty spike trains
Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net>
Distributed under the BSD License
"""
from __future__ import print_function
import numpy as np
from numpy.testing import assert_allclose, assert_almost_equal, \
assert_array_equal, assert_array_almost_equal
import pyspike as spk
from pyspike import SpikeTrain
def test_get_non_empty():
st = SpikeTrain([], edges=(0.0, 1.0))
spikes = st.get_spikes_non_empty()
assert_array_equal(spikes, [0.0, 1.0])
st = SpikeTrain([0.5, ], edges=(0.0, 1.0))
spikes = st.get_spikes_non_empty()
# assert_array_equal(spikes, [0.0, 0.5, 1.0])
# spike trains with one spike don't get edge spikes anymore
assert_array_equal(spikes, [0.5, ])
def test_isi_empty():
st1 = SpikeTrain([], edges=(0.0, 1.0))
st2 = SpikeTrain([], edges=(0.0, 1.0))
d = spk.isi_distance(st1, st2)
assert_allclose(d, 0.0)
prof = spk.isi_profile(st1, st2)
assert_allclose(d, prof.avrg())
assert_array_equal(prof.x, [0.0, 1.0])
assert_array_equal(prof.y, [0.0, ])
st1 = SpikeTrain([], edges=(0.0, 1.0))
st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
d = spk.isi_distance(st1, st2)
assert_allclose(d, 0.6*0.4+0.4*0.6)
prof = spk.isi_profile(st1, st2)
assert_allclose(d, prof.avrg())
assert_array_equal(prof.x, [0.0, 0.4, 1.0])
assert_array_equal(prof.y, [0.6, 0.4])
st1 = SpikeTrain([0.6, ], edges=(0.0, 1.0))
st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
d = spk.isi_distance(st1, st2)
assert_almost_equal(d, 0.2/0.6*0.4 + 0.0 + 0.2/0.6*0.4, decimal=15)
prof = spk.isi_profile(st1, st2)
assert_allclose(d, prof.avrg())
assert_array_almost_equal(prof.x, [0.0, 0.4, 0.6, 1.0], decimal=15)
assert_array_almost_equal(prof.y, [0.2/0.6, 0.0, 0.2/0.6], decimal=15)
def test_spike_empty():
st1 = SpikeTrain([], edges=(0.0, 1.0))
st2 = SpikeTrain([], edges=(0.0, 1.0))
d = spk.spike_distance(st1, st2)
assert_allclose(d, 0.0)
prof = spk.spike_profile(st1, st2)
assert_allclose(d, prof.avrg())
assert_array_equal(prof.x, [0.0, 1.0])
assert_array_equal(prof.y1, [0.0, ])
assert_array_equal(prof.y2, [0.0, ])
st1 = SpikeTrain([], edges=(0.0, 1.0))
st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
d = spk.spike_distance(st1, st2)
d_expect = 2*0.4*0.4*1.0/(0.4+1.0)**2 + 2*0.6*0.4*1.0/(0.6+1.0)**2
assert_almost_equal(d, d_expect, decimal=15)
prof = spk.spike_profile(st1, st2)
assert_allclose(d, prof.avrg())
assert_array_equal(prof.x, [0.0, 0.4, 1.0])
assert_array_almost_equal(prof.y1, [2*0.4*1.0/(0.4+1.0)**2,
2*0.4*1.0/(0.6+1.0)**2],
decimal=15)
assert_array_almost_equal(prof.y2, [2*0.4*1.0/(0.4+1.0)**2,
2*0.4*1.0/(0.6+1.0)**2],
decimal=15)
st1 = SpikeTrain([0.6, ], edges=(0.0, 1.0))
st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
d = spk.spike_distance(st1, st2)
s1 = np.array([0.2, 0.2, 0.2, 0.2])
s2 = np.array([0.2, 0.2, 0.2, 0.2])
isi1 = np.array([0.6, 0.6, 0.4])
isi2 = np.array([0.4, 0.6, 0.6])
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([0.0, 0.4, 0.6, 1.0])
expected_spike_val = sum((expected_times[1:] - expected_times[:-1]) *
(expected_y1+expected_y2)/2)
expected_spike_val /= (expected_times[-1]-expected_times[0])
assert_almost_equal(d, expected_spike_val, decimal=15)
prof = spk.spike_profile(st1, st2)
assert_allclose(d, prof.avrg())
assert_array_almost_equal(prof.x, [0.0, 0.4, 0.6, 1.0], decimal=15)
assert_array_almost_equal(prof.y1, expected_y1, decimal=15)
assert_array_almost_equal(prof.y2, expected_y2, decimal=15)
def test_spike_sync_empty():
st1 = SpikeTrain([], edges=(0.0, 1.0))
st2 = SpikeTrain([], edges=(0.0, 1.0))
d = spk.spike_sync(st1, st2)
assert_allclose(d, 1.0)
prof = spk.spike_sync_profile(st1, st2)
assert_allclose(d, prof.avrg())
assert_array_equal(prof.x, [0.0, 1.0])
assert_array_equal(prof.y, [1.0, 1.0])
st1 = SpikeTrain([], edges=(0.0, 1.0))
st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
d = spk.spike_sync(st1, st2)
assert_allclose(d, 0.0)
prof = spk.spike_sync_profile(st1, st2)
assert_allclose(d, prof.avrg())
assert_array_equal(prof.x, [0.0, 0.4, 1.0])
assert_array_equal(prof.y, [0.0, 0.0, 0.0])
st1 = SpikeTrain([0.6, ], edges=(0.0, 1.0))
st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
d = spk.spike_sync(st1, st2)
assert_almost_equal(d, 1.0, decimal=15)
prof = spk.spike_sync_profile(st1, st2)
assert_allclose(d, prof.avrg())
assert_array_almost_equal(prof.x, [0.0, 0.4, 0.6, 1.0], decimal=15)
assert_array_almost_equal(prof.y, [1.0, 1.0, 1.0, 1.0], decimal=15)
st1 = SpikeTrain([0.2, ], edges=(0.0, 1.0))
st2 = SpikeTrain([0.8, ], edges=(0.0, 1.0))
d = spk.spike_sync(st1, st2)
assert_almost_equal(d, 0.0, decimal=15)
prof = spk.spike_sync_profile(st1, st2)
assert_allclose(d, prof.avrg())
assert_array_almost_equal(prof.x, [0.0, 0.2, 0.8, 1.0], decimal=15)
assert_array_almost_equal(prof.y, [0.0, 0.0, 0.0, 0.0], decimal=15)
# test with empty intervals
st1 = SpikeTrain([2.0, 5.0], [0, 10.0])
st2 = SpikeTrain([2.1, 7.0], [0, 10.0])
st3 = SpikeTrain([5.1, 6.0], [0, 10.0])
res = spk.spike_sync_profile(st1, st2).avrg(interval=[3.0, 4.0])
assert_allclose(res, 1.0)
res = spk.spike_sync(st1, st2, interval=[3.0, 4.0])
assert_allclose(res, 1.0)
sync_matrix = spk.spike_sync_matrix([st1, st2, st3], interval=[3.0, 4.0])
assert_array_equal(sync_matrix, np.ones((3, 3)))
if __name__ == "__main__":
test_get_non_empty()
test_isi_empty()
test_spike_empty()
test_spike_sync_empty()
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