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Diffstat (limited to 'test/test_empty.py')
| -rw-r--r-- | test/test_empty.py | 163 |
1 files changed, 163 insertions, 0 deletions
diff --git a/test/test_empty.py b/test/test_empty.py new file mode 100644 index 0000000..4d0a5cf --- /dev/null +++ b/test/test_empty.py @@ -0,0 +1,163 @@ +""" 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_equal, 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_equal(d, 0.0) + prof = spk.isi_profile(st1, st2) + assert_equal(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_equal(d, 0.6*0.4+0.4*0.6) + prof = spk.isi_profile(st1, st2) + assert_equal(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_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.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) + 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_equal(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_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) + + +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_equal(d, 1.0) + prof = spk.spike_sync_profile(st1, st2) + assert_equal(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_equal(d, 0.0) + prof = spk.spike_sync_profile(st1, st2) + assert_equal(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_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.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_equal(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_equal(res, 1.0) + res = spk.spike_sync(st1, st2, interval=[3.0, 4.0]) + assert_equal(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)) - np.diag(np.ones(3))) + + +if __name__ == "__main__": + test_get_non_empty() + test_isi_empty() + test_spike_empty() + test_spike_sync_empty() |