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author | Mario Mulansky <mario.mulansky@gmx.net> | 2014-10-13 10:47:18 +0200 |
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committer | Mario Mulansky <mario.mulansky@gmx.net> | 2014-10-13 10:47:18 +0200 |
commit | 4274c328a4927b392036d1c3b759b0787b05f300 (patch) | |
tree | 37a4f331006c63e7155bfb4c083c7e149f567eb8 /test/test_distance.py | |
parent | ef15a482604d8ce9bef094d470d8a905c6da49a0 (diff) |
code formatting following PEP8
Diffstat (limited to 'test/test_distance.py')
-rw-r--r-- | test/test_distance.py | 37 |
1 files changed, 19 insertions, 18 deletions
diff --git a/test/test_distance.py b/test/test_distance.py index dafe693..3371cbd 100644 --- a/test/test_distance.py +++ b/test/test_distance.py @@ -22,8 +22,8 @@ def test_isi(): t2 = np.array([0.3, 0.45, 0.8, 0.9, 0.95]) # 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] - expected_isi = [-0.1/0.3, -0.1/0.3, 0.05/0.2, 0.05/0.2, -0.15/0.35, + expected_times = [0.0, 0.2, 0.3, 0.4, 0.45, 0.6, 0.7, 0.8, 0.9, 0.95, 1.0] + expected_isi = [-0.1/0.3, -0.1/0.3, 0.05/0.2, 0.05/0.2, -0.15/0.35, -0.25/0.35, -0.05/0.35, 0.2/0.3, 0.25/0.3, 0.25/0.3] t1 = spk.add_auxiliary_spikes(t1, 1.0) @@ -36,10 +36,10 @@ def test_isi(): assert_array_almost_equal(f.y, expected_isi, decimal=14) # 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 = np.array([0.2, 0.4, 0.6]) + t2 = np.array([0.1, 0.4, 0.5, 0.6]) - expected_times = [0.0,0.1,0.2,0.4,0.5,0.6,1.0] + 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] t1 = spk.add_auxiliary_spikes(t1, 1.0) @@ -56,11 +56,11 @@ def test_spike(): t2 = np.array([0.3, 0.45, 0.8, 0.9, 0.95]) # 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] + 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, - (0.05*0.2+0.1*0.15)/0.35, (0.05*0.1+0.1*0.25)/0.35, + 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]) isi1 = np.array([0.2, 0.2, 0.2, 0.2, 0.2, 0.1, 0.3, 0.3, 0.3, 0.3]) isi2 = np.array([0.3, 0.3, 0.15, 0.15, 0.35, 0.35, 0.35, 0.1, 0.05, 0.05]) @@ -76,17 +76,17 @@ def test_spike(): assert_array_almost_equal(f.y2, expected_y2, decimal=14) # 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 = np.array([0.2, 0.4, 0.6]) + t2 = np.array([0.1, 0.4, 0.5, 0.6]) - expected_times = [0.0,0.1,0.2,0.4,0.5,0.6,1.0] + 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]) 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) - + t1 = spk.add_auxiliary_spikes(t1, 1.0) t2 = spk.add_auxiliary_spikes(t2, 1.0) f = spk.spike_distance(t1, t2) @@ -100,8 +100,8 @@ def check_multi_distance(dist_func, dist_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) + 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) spike_trains = [t1, t2, t3, t4] f12 = dist_func(t1, t2) @@ -111,17 +111,17 @@ def check_multi_distance(dist_func, dist_func_multi): f24 = dist_func(t2, t4) f34 = dist_func(t3, t4) - f_multi = dist_func_multi(spike_trains, [0,1]) + f_multi = dist_func_multi(spike_trains, [0, 1]) assert f_multi.almost_equal(f12, decimal=14) f = copy(f12) f.add(f13) f.add(f23) f.mul_scalar(1.0/3) - f_multi = dist_func_multi(spike_trains, [0,1,2]) + f_multi = dist_func_multi(spike_trains, [0, 1, 2]) assert f_multi.almost_equal(f, decimal=14) - f.mul_scalar(3) # revert above normalization + f.mul_scalar(3) # revert above normalization f.add(f14) f.add(f24) f.add(f34) @@ -139,6 +139,7 @@ def test_multi_spike(): if __name__ == "__main__": - test_auxiliary_spikes() test_isi() test_spike() + test_multi_isi() + test_multi_spike() |