diff options
Diffstat (limited to 'test/test_distance.py')
| -rw-r--r-- | test/test_distance.py | 192 |
1 files changed, 132 insertions, 60 deletions
diff --git a/test/test_distance.py b/test/test_distance.py index ba19f5e..19da35f 100644 --- a/test/test_distance.py +++ b/test/test_distance.py @@ -15,12 +15,13 @@ from numpy.testing import assert_equal, assert_almost_equal, \ assert_array_almost_equal import pyspike as spk +from pyspike import SpikeTrain 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]) + t1 = SpikeTrain([0.2, 0.4, 0.6, 0.7], 1.0) + t2 = SpikeTrain([0.3, 0.45, 0.8, 0.9, 0.95], 1.0) # 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] @@ -32,8 +33,6 @@ def test_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) @@ -44,19 +43,17 @@ def test_isi(): 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]) + t1 = SpikeTrain([0.2, 0.4, 0.6], [0.0, 1.0]) + t2 = SpikeTrain([0.1, 0.4, 0.5, 0.6], [0.0, 1.0]) 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_isi = [0.1/0.3, 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) @@ -67,14 +64,27 @@ def test_isi(): 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]) + t1 = SpikeTrain([0.0, 2.0, 5.0, 8.0], 10.0) + t2 = SpikeTrain([0.0, 1.0, 5.0, 9.0], 10.0) + + expected_times = np.array([0.0, 1.0, 2.0, 5.0, 8.0, 9.0, 10.0]) + + f = spk.spike_profile(t1, t2) + + assert_equal(f.x, expected_times) + + assert_almost_equal(f.avrg(), 1.6624149659863946e-01, decimal=15) + assert_almost_equal(f.y2[-1], 0.1394558, decimal=6) + + t1 = SpikeTrain([0.2, 0.4, 0.6, 0.7], 1.0) + t2 = SpikeTrain([0.3, 0.45, 0.8, 0.9, 0.95], 1.0) # 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, + 0.15, 0.1, (0.1*0.1+0.1*0.2)/0.3, (0.1*0.2+0.1*0.1)/0.3, + (0.1*0.05+0.1*0.25)/0.3, 0.1]) + s2 = np.array([0.1, (0.1*0.2+0.1*0.1)/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]) @@ -89,27 +99,32 @@ def test_spike(): (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) + assert_almost_equal(f.avrg(), expected_spike_val, decimal=15) + assert_almost_equal(spk.spike_distance(t1, t2), expected_spike_val, + decimal=15) # 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]) + t1 = SpikeTrain([0.2, 0.4, 0.6], [0.0, 1.0]) + t2 = SpikeTrain([0.1, 0.4, 0.5, 0.6], [0.0, 1.0]) 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]) + # due to the edge correction in the beginning, s1 and s2 are different + # for left and right values + s1_r = np.array([0.1, (0.1*0.1+0.1*0.1)/0.2, 0.1, 0.0, 0.0, 0.0, 0.0]) + s1_l = np.array([0.1, (0.1*0.1+0.1*0.1)/0.2, 0.1, 0.0, 0.0, 0.0, 0.0]) + s2_r = np.array([0.1*0.1/0.3, 0.1*0.3/0.3, 0.1*0.2/0.3, + 0.0, 0.1, 0.0, 0.0]) + s2_l = np.array([0.1*0.1/0.3, 0.1*0.1/0.3, 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_y1 = (s1_r[:-1]*isi2+s2_r[:-1]*isi1) / (0.5*(isi1+isi2)**2) + expected_y2 = (s1_l[1:]*isi2+s2_l[1:]*isi1) / (0.5*(isi1+isi2)**2) expected_times = np.array(expected_times) expected_y1 = np.array(expected_y1) @@ -118,8 +133,6 @@ def test_spike(): (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) @@ -131,10 +144,17 @@ def test_spike(): 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) + spikes1 = SpikeTrain([1.0, 2.0, 3.0], 4.0) + spikes2 = SpikeTrain([2.1], 4.0) + + expected_x = np.array([0.0, 1.0, 2.0, 2.1, 3.0, 4.0]) + expected_y = np.array([0.0, 0.0, 1.0, 1.0, 0.0, 0.0]) + + f = spk.spike_sync_profile(spikes1, spikes2) + + assert_array_almost_equal(f.x, expected_x, decimal=16) + assert_array_almost_equal(f.y, expected_y, decimal=16) + assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16) @@ -142,28 +162,46 @@ def test_spike_sync(): 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) + spikes2 = SpikeTrain([3.1], 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) + spikes2 = SpikeTrain([1.1], 4.0) + + expected_x = np.array([0.0, 1.0, 1.1, 2.0, 3.0, 4.0]) + expected_y = np.array([1.0, 1.0, 1.0, 0.0, 0.0, 0.0]) + + f = spk.spike_sync_profile(spikes1, spikes2) + + assert_array_almost_equal(f.x, expected_x, decimal=16) + assert_array_almost_equal(f.y, expected_y, decimal=16) + 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) + spikes2 = SpikeTrain([0.9], 4.0) assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=16) + spikes2 = SpikeTrain([3.0], 4.0) + assert_almost_equal(spk.spike_sync(spikes1, spikes2), + 0.5, decimal=16) + + spikes2 = SpikeTrain([1.0], 4.0) + assert_almost_equal(spk.spike_sync(spikes1, spikes2), + 0.5, decimal=16) + + spikes2 = SpikeTrain([1.5, 3.0], 4.0) + assert_almost_equal(spk.spike_sync(spikes1, spikes2), + 0.4, 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) + t1 = SpikeTrain([0.2, 0.4, 0.6, 0.7], 1.0) + t2 = SpikeTrain([0.3, 0.45, 0.8, 0.9, 0.95], 1.0) + t3 = SpikeTrain([0.2, 0.4, 0.6], 1.0) + t4 = SpikeTrain([0.1, 0.4, 0.5, 0.6], 1.0) spike_trains = [t1, t2, t3, t4] f12 = profile_func(t1, t2) @@ -206,15 +244,12 @@ def test_multi_spike(): 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) + spikes1 = SpikeTrain([100, 300, 400, 405, 410, 500, 700, 800, + 805, 810, 815, 900], 1000) + spikes2 = SpikeTrain([100, 200, 205, 210, 295, 350, 400, 510, + 600, 605, 700, 910], 1000) + spikes3 = SpikeTrain([100, 180, 198, 295, 412, 420, 510, 640, + 695, 795, 820, 920], 1000) assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=15) assert_almost_equal(spk.spike_sync(spikes1, spikes3), @@ -233,18 +268,28 @@ def test_multi_spike_sync(): # multivariate regression test spike_trains = spk.load_spike_trains_from_txt("test/SPIKE_Sync_Test.txt", - time_interval=(0, 4000)) + edges=[0, 4000]) + # extract all spike times + spike_times = np.array([]) + for st in spike_trains: + spike_times = np.append(spike_times, st.spikes) + spike_times = np.unique(np.sort(spike_times)) + f = spk.spike_sync_profile_multi(spike_trains) + + assert_equal(spike_times, f.x[1:-1]) + assert_equal(len(f.x), len(f.y)) + 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) + t1 = SpikeTrain([0.2, 0.4, 0.6, 0.7], 1.0) + t2 = SpikeTrain([0.3, 0.45, 0.8, 0.9, 0.95], 1.0) + t3 = SpikeTrain([0.2, 0.4, 0.6], 1.0) + t4 = SpikeTrain([0.1, 0.4, 0.5, 0.6], 1.0) spike_trains = [t1, t2, t3, t4] f12 = dist_func(t1, t2) @@ -282,19 +327,39 @@ def test_spike_sync_matrix(): def test_regression_spiky(): + # standard example + st1 = SpikeTrain(np.arange(100, 1201, 100), 1300) + st2 = SpikeTrain(np.arange(100, 1201, 110), 1300) + + isi_dist = spk.isi_distance(st1, st2) + assert_almost_equal(isi_dist, 9.0909090909090939e-02, decimal=15) + isi_profile = spk.isi_profile(st1, st2) + assert_equal(isi_profile.y, 0.1/1.1 * np.ones_like(isi_profile.y)) + + spike_dist = spk.spike_distance(st1, st2) + assert_equal(spike_dist, 2.1105878248735391e-01) + + spike_sync = spk.spike_sync(st1, st2) + assert_equal(spike_sync, 8.6956521739130432e-01) + + # multivariate check + 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) + isi_dist = spk.isi_distance_multi(spike_trains) # get the full precision from SPIKY - # assert_equal(isi_dist, 0.1832) + assert_almost_equal(isi_dist, 0.17051816816999129656, decimal=15) spike_profile = spk.spike_profile_multi(spike_trains) - spike_dist = spike_profile.avrg() - print(spike_dist) + assert_equal(len(spike_profile.y1)+len(spike_profile.y2), 1252) + + spike_dist = spk.spike_distance_multi(spike_trains) + # get the full precision from SPIKY + assert_almost_equal(spike_dist, 2.4432433330596512e-01, decimal=15) + + spike_sync = spk.spike_sync_multi(spike_trains) # get the full precision from SPIKY - # assert_equal(spike_dist, 0.2445) + assert_equal(spike_sync, 0.7183531505298066) def test_multi_variate_subsets(): @@ -319,5 +384,12 @@ def test_multi_variate_subsets(): if __name__ == "__main__": test_isi() test_spike() + test_spike_sync() test_multi_isi() test_multi_spike() + test_multi_spike_sync() + test_isi_matrix() + test_spike_matrix() + test_spike_sync_matrix() + test_regression_spiky() + test_multi_variate_subsets() |