code formatting following PEP8

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()