code formatting following PEP8

1 files changed, 13 insertions, 14 deletions

diff --git a/test/test_spikes.py b/test/test_spikes.py
index e008207..349e0bf 100644
--- a/test/test_spikes.py
+++ b/test/test_spikes.py
@@ -23,13 +23,13 @@ def test_auxiliary_spikes():
 
 
 def test_load_from_txt():
-    spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", 
-                                              time_interval=(0,4000))
+    spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt",
+                                                  time_interval=(0, 4000))
     assert len(spike_trains) == 40
 
     # check the first spike train
-    spike_times = [0, 64.886, 305.81, 696, 937.77, 1059.7, 1322.2, 1576.1, 
-                   1808.1, 2121.5, 2381.1, 2728.6, 2966.9, 3223.7, 3473.7, 
+    spike_times = [0, 64.886, 305.81, 696, 937.77, 1059.7, 1322.2, 1576.1,
+                   1808.1, 2121.5, 2381.1, 2728.6, 2966.9, 3223.7, 3473.7,
                    3644.3, 3936.3, 4000]
     assert_equal(spike_times, spike_trains[0])
 
@@ -39,15 +39,15 @@ def test_load_from_txt():
         assert spike_train[-1] == 4000
 
     # load without adding auxiliary spikes
-    spike_trains2 = spk.load_spike_trains_from_txt("PySpike_testdata.txt", 
-                                                  time_interval=None)
+    spike_trains2 = spk.load_spike_trains_from_txt("PySpike_testdata.txt",
+                                                   time_interval=None)
     assert len(spike_trains2) == 40
     # check auxiliary spikes
     for i in xrange(len(spike_trains)):
-        assert len(spike_trains[i]) == len(spike_trains2[i])+2 # two spikes less
+        assert len(spike_trains[i]) == len(spike_trains2[i])+2  # 2 spikes less
     
 
-def check_merged_spikes( merged_spikes, spike_trains ):
+def check_merged_spikes(merged_spikes, spike_trains):
     # create a flat array with all spike events
     all_spikes = np.array([])
     for spike_train in spike_trains:
@@ -55,7 +55,7 @@ def check_merged_spikes( merged_spikes, spike_trains ):
     indices = np.zeros_like(all_spikes, dtype='bool')
     # check if we find all the spike events in the original spike trains
     for x in merged_spikes:
-        i = np.where(all_spikes == x)[0][0] # the first axis and the first entry
+        i = np.where(all_spikes == x)[0][0]  # first axis and first entry
         # change to something impossible so we dont find this event again
         all_spikes[i] = -1.0
         indices[i] = True
@@ -64,23 +64,22 @@ 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("PySpike_testdata.txt", 
-                                                  time_interval=(0,4000))
+    spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt",
+                                                  time_interval=(0, 4000))
     
     spikes = spk.merge_spike_trains([spike_trains[0], spike_trains[1]])
     # test if result is sorted
     assert((spikes == np.sort(spikes)).all())
     # check merging
-    check_merged_spikes( spikes, [spike_trains[0], spike_trains[1]] )
+    check_merged_spikes(spikes, [spike_trains[0], spike_trains[1]])
 
     spikes = spk.merge_spike_trains(spike_trains)
     # test if result is sorted
     assert((spikes == np.sort(spikes)).all())
     # check merging
-    check_merged_spikes( spikes, spike_trains )
+    check_merged_spikes(spikes, spike_trains)
 
 if __name__ == "main":
     test_auxiliary_spikes()
     test_load_from_txt()
     test_merge_spike_trains()
-