diff options
Diffstat (limited to 'test/test_distance.py')
| -rw-r--r-- | test/test_distance.py | 34 |
1 files changed, 22 insertions, 12 deletions
diff --git a/test/test_distance.py b/test/test_distance.py index b54e908..4af0e63 100644 --- a/test/test_distance.py +++ b/test/test_distance.py @@ -64,13 +64,26 @@ 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(), 0.1662415, decimal=6) + 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]) + 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*0.2/0.3, 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]) @@ -86,19 +99,18 @@ 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]) @@ -115,8 +127,6 @@ 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) @@ -315,6 +325,6 @@ def test_multi_variate_subsets(): if __name__ == "__main__": test_isi() - # test_spike() + test_spike() # test_multi_isi() # test_multi_spike() |