From b6521869cad89eae119391557bfa57e818dc9894 Mon Sep 17 00:00:00 2001 From: Mario Mulansky Date: Mon, 11 May 2015 15:54:19 +0200 Subject: treatment of empty spike trains in spike distance --- test/test_empty.py | 48 +++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 47 insertions(+), 1 deletion(-) (limited to 'test') diff --git a/test/test_empty.py b/test/test_empty.py index 22982c7..e08bd1a 100644 --- a/test/test_empty.py +++ b/test/test_empty.py @@ -10,7 +10,6 @@ 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_equal, assert_array_almost_equal @@ -57,6 +56,53 @@ def test_isi_empty(): 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_equal(d, 0.0) + prof = spk.spike_profile(st1, st2) + assert_equal(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) + assert_almost_equal(d, 0.4*0.4*1.0/(0.4+1.0)**2 + 0.6*0.4*1.0/(0.6+1.0)**2, + decimal=15) + prof = spk.spike_profile(st1, st2) + assert_equal(d, prof.avrg()) + assert_array_equal(prof.x, [0.0, 0.4, 1.0]) + assert_array_almost_equal(prof.y1, [0.0, 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, 0.0], + 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([0.0, 0.4*0.2/0.6, 0.2, 0.0]) + s2 = np.array([0.0, 0.2, 0.2*0.4/0.6, 0.0]) + 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_equal(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) + + if __name__ == "__main__": test_get_non_empty() test_isi_empty() + test_spike_empty() -- cgit v1.2.3