diff options
Diffstat (limited to 'test')
-rw-r--r-- | test/test_distance.py | 70 | ||||
-rw-r--r-- | test/test_empty.py | 16 | ||||
-rw-r--r-- | test/test_regression/test_regression_15.py | 80 | ||||
-rw-r--r-- | test/test_spikes.py | 9 |
4 files changed, 154 insertions, 21 deletions
diff --git a/test/test_distance.py b/test/test_distance.py index e45ac16..8cf81e2 100644 --- a/test/test_distance.py +++ b/test/test_distance.py @@ -17,6 +17,8 @@ from numpy.testing import assert_equal, assert_almost_equal, \ import pyspike as spk from pyspike import SpikeTrain +import os +TEST_PATH = os.path.dirname(os.path.realpath(__file__)) def test_isi(): # generate two spike trains: @@ -36,6 +38,7 @@ def test_isi(): f = spk.isi_profile(t1, t2) # print("ISI: ", f.y) + print("ISI value:", expected_isi_val) assert_equal(f.x, expected_times) assert_array_almost_equal(f.y, expected_isi, decimal=15) @@ -73,8 +76,19 @@ def test_spike(): 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) + # from SPIKY: + y_all = np.array([0.000000000000000000, 0.555555555555555580, + 0.222222222222222210, 0.305555555555555580, + 0.255102040816326536, 0.000000000000000000, + 0.000000000000000000, 0.255102040816326536, + 0.255102040816326536, 0.285714285714285698, + 0.285714285714285698, 0.285714285714285698]) + + #assert_array_almost_equal(f.y1, y_all[::2]) + assert_array_almost_equal(f.y2, y_all[1::2]) + + assert_almost_equal(f.avrg(), 0.186309523809523814, decimal=15) + assert_equal(spk.spike_distance(t1, t2), f.avrg()) 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) @@ -99,6 +113,8 @@ def test_spike(): (expected_y1+expected_y2)/2) expected_spike_val /= (expected_times[-1]-expected_times[0]) + print("SPIKE value:", expected_spike_val) + f = spk.spike_profile(t1, t2) assert_equal(f.x, expected_times) @@ -117,9 +133,14 @@ def test_spike(): # 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, + # 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]) + # eero's edge correction: + s2_r = np.array([0.1, 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, + s2_l = np.array([0.1, 0.1*0.3/0.3, 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]) @@ -275,8 +296,8 @@ def test_multi_spike_sync(): expected, decimal=15) # multivariate regression test - spike_trains = spk.load_spike_trains_from_txt("test/SPIKE_Sync_Test.txt", - edges=[0, 4000]) + spike_trains = spk.load_spike_trains_from_txt( + os.path.join(TEST_PATH, "SPIKE_Sync_Test.txt"), edges=[0, 4000]) # extract all spike times spike_times = np.array([]) for st in spike_trains: @@ -309,10 +330,10 @@ def check_dist_matrix(dist_func, dist_matrix_func): f_matrix = dist_matrix_func(spike_trains) # check zero diagonal - for i in xrange(4): + for i in range(4): assert_equal(0.0, f_matrix[i, i]) - for i in xrange(4): - for j in xrange(i+1, 4): + for i in range(4): + for j in range(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]) @@ -345,15 +366,15 @@ def test_regression_spiky(): 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) + assert_equal(spike_dist, 0.211058782487353908) 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)) + spike_trains = spk.load_spike_trains_from_txt( + os.path.join(TEST_PATH, "PySpike_testdata.txt"), (0.0, 4000.0)) isi_dist = spk.isi_distance_multi(spike_trains) # get the full precision from SPIKY assert_almost_equal(isi_dist, 0.17051816816999129656, decimal=15) @@ -363,16 +384,35 @@ def test_regression_spiky(): spike_dist = spk.spike_distance_multi(spike_trains) # get the full precision from SPIKY - assert_almost_equal(spike_dist, 2.4432433330596512e-01, decimal=15) + assert_almost_equal(spike_dist, 0.25188056475463755, decimal=15) spike_sync = spk.spike_sync_multi(spike_trains) # get the full precision from SPIKY assert_equal(spike_sync, 0.7183531505298066) + # Eero's edge correction example + st1 = SpikeTrain([0.5, 1.5, 2.5], 6.0) + st2 = SpikeTrain([3.5, 4.5, 5.5], 6.0) + + f = spk.spike_profile(st1, st2) + + expected_times = np.array([0.0, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.0]) + y_all = np.array([0.271604938271605, 0.271604938271605, 0.271604938271605, + 0.617283950617284, 0.617283950617284, 0.444444444444444, + 0.285714285714286, 0.285714285714286, 0.444444444444444, + 0.617283950617284, 0.617283950617284, 0.271604938271605, + 0.271604938271605, 0.271604938271605]) + expected_y1 = y_all[::2] + expected_y2 = y_all[1::2] + + 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) + def test_multi_variate_subsets(): - spike_trains = spk.load_spike_trains_from_txt("test/PySpike_testdata.txt", - (0.0, 4000.0)) + spike_trains = spk.load_spike_trains_from_txt( + os.path.join(TEST_PATH, "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] diff --git a/test/test_empty.py b/test/test_empty.py index 48be25d..5a0042f 100644 --- a/test/test_empty.py +++ b/test/test_empty.py @@ -70,8 +70,8 @@ def test_spike_empty(): 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) + d_expect = 0.4*0.4*1.0/(0.4+1.0)**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]) @@ -139,6 +139,18 @@ def test_spike_sync_empty(): 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() diff --git a/test/test_regression/test_regression_15.py b/test/test_regression/test_regression_15.py new file mode 100644 index 0000000..dcacae2 --- /dev/null +++ b/test/test_regression/test_regression_15.py @@ -0,0 +1,80 @@ +""" test_regression_15.py + +Regression test for Issue #15 + +Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net> + +Distributed under the BSD License + +""" + +from __future__ import division + +import numpy as np +from numpy.testing import assert_equal, assert_almost_equal, \ + assert_array_almost_equal + +import pyspike as spk + +import os +TEST_PATH = os.path.dirname(os.path.realpath(__file__)) +TEST_DATA = os.path.join(TEST_PATH, "..", "SPIKE_Sync_Test.txt") + +def test_regression_15_isi(): + # load spike trains + spike_trains = spk.load_spike_trains_from_txt(TEST_DATA, edges=[0, 4000]) + + N = len(spike_trains) + + dist_mat = spk.isi_distance_matrix(spike_trains) + assert_equal(dist_mat.shape, (N, N)) + + ind = np.arange(N//2) + dist_mat = spk.isi_distance_matrix(spike_trains, ind) + assert_equal(dist_mat.shape, (N//2, N//2)) + + ind = np.arange(N//2, N) + dist_mat = spk.isi_distance_matrix(spike_trains, ind) + assert_equal(dist_mat.shape, (N//2, N//2)) + + +def test_regression_15_spike(): + # load spike trains + spike_trains = spk.load_spike_trains_from_txt(TEST_DATA, edges=[0, 4000]) + + N = len(spike_trains) + + dist_mat = spk.spike_distance_matrix(spike_trains) + assert_equal(dist_mat.shape, (N, N)) + + ind = np.arange(N//2) + dist_mat = spk.spike_distance_matrix(spike_trains, ind) + assert_equal(dist_mat.shape, (N//2, N//2)) + + ind = np.arange(N//2, N) + dist_mat = spk.spike_distance_matrix(spike_trains, ind) + assert_equal(dist_mat.shape, (N//2, N//2)) + + +def test_regression_15_sync(): + # load spike trains + spike_trains = spk.load_spike_trains_from_txt(TEST_DATA, edges=[0, 4000]) + + N = len(spike_trains) + + dist_mat = spk.spike_sync_matrix(spike_trains) + assert_equal(dist_mat.shape, (N, N)) + + ind = np.arange(N//2) + dist_mat = spk.spike_sync_matrix(spike_trains, ind) + assert_equal(dist_mat.shape, (N//2, N//2)) + + ind = np.arange(N//2, N) + dist_mat = spk.spike_sync_matrix(spike_trains, ind) + assert_equal(dist_mat.shape, (N//2, N//2)) + + +if __name__ == "__main__": + test_regression_15_isi() + test_regression_15_spike() + test_regression_15_sync() diff --git a/test/test_spikes.py b/test/test_spikes.py index d4eb131..609a819 100644 --- a/test/test_spikes.py +++ b/test/test_spikes.py @@ -13,10 +13,12 @@ from numpy.testing import assert_equal import pyspike as spk +import os +TEST_PATH = os.path.dirname(os.path.realpath(__file__)) +TEST_DATA = os.path.join(TEST_PATH, "PySpike_testdata.txt") def test_load_from_txt(): - spike_trains = spk.load_spike_trains_from_txt("test/PySpike_testdata.txt", - edges=(0, 4000)) + spike_trains = spk.load_spike_trains_from_txt(TEST_DATA, edges=(0, 4000)) assert len(spike_trains) == 40 # check the first spike train @@ -48,8 +50,7 @@ def check_merged_spikes(merged_spikes, spike_trains): def test_merge_spike_trains(): # first load the data - spike_trains = spk.load_spike_trains_from_txt("test/PySpike_testdata.txt", - edges=(0, 4000)) + spike_trains = spk.load_spike_trains_from_txt(TEST_DATA, edges=(0, 4000)) merged_spikes = spk.merge_spike_trains([spike_trains[0], spike_trains[1]]) # test if result is sorted |