fixed spike-sync bug

fixed ugly bugs in code for computing multi-variate spike sync profile and
multi-variate spike sync value.

2 files changed, 52 insertions, 8 deletions

diff --git a/test/numeric/test_regression_random_spikes.py b/test/numeric/test_regression_random_spikes.py
index 6156bb4..73d39a6 100644
--- a/test/numeric/test_regression_random_spikes.py
+++ b/test/numeric/test_regression_random_spikes.py
@@ -35,7 +35,9 @@ def test_regression_random():
 
         spike = spk.spike_distance_multi(spike_trains)
         spike_prof = spk.spike_profile_multi(spike_trains).avrg()
-        # spike_sync = spk.spike_sync_multi(spike_trains)
+
+        spike_sync = spk.spike_sync_multi(spike_trains)
+        spike_sync_prof = spk.spike_sync_profile_multi(spike_trains).avrg()
 
         assert_almost_equal(isi, results_cSPIKY[i][0], decimal=14,
                             err_msg="Index: %d, ISI" % i)
@@ -47,6 +49,9 @@ def test_regression_random():
         assert_almost_equal(spike_prof, results_cSPIKY[i][1], decimal=14,
                             err_msg="Index: %d, SPIKE" % i)
 
+        assert_almost_equal(spike_sync, spike_sync_prof, decimal=14,
+                            err_msg="Index: %d, SPIKE-Sync" % i)
+
 
 def check_regression_dataset(spike_file="benchmark.mat",
                              spikes_name="spikes",
@@ -109,19 +114,25 @@ def check_single_spike_train_set(index):
     spike_train_data = spike_train_sets[index]
 
     spike_trains = []
+    N = 0
     for spikes in spike_train_data[0]:
-        print("Spikes:", spikes.flatten())
-        spike_trains.append(spk.SpikeTrain(spikes.flatten(), 100.0))
+        N += len(spikes.flatten())
+        print("Spikes:", len(spikes.flatten()))
+        spikes_array = spikes.flatten()
+        if len(spikes_array > 0) and (spikes_array[-1] > 100.0):
+            spikes_array[-1] = 100.0
+        spike_trains.append(spk.SpikeTrain(spikes_array, 100.0))
+        print(spike_trains[-1].spikes)
 
-    print(spk.spike_distance_multi(spike_trains))
+    print(N)
 
-    print(results_cSPIKY[index][1])
+    print(spk.spike_sync_multi(spike_trains))
 
-    print(spike_trains[1].spikes)
+    print(spk.spike_sync_profile_multi(spike_trains).integral())
 
 
 if __name__ == "__main__":
 
-    test_regression_random()
+    # test_regression_random()
     # check_regression_dataset()
-    # check_single_spike_train_set(7633)
+    check_single_spike_train_set(4)
diff --git a/test/test_distance.py b/test/test_distance.py
index 083d8a3..fe09f34 100644
--- a/test/test_distance.py
+++ b/test/test_distance.py
@@ -217,6 +217,28 @@ def test_spike_sync():
     assert_almost_equal(spk.spike_sync(spikes1, spikes2),
                         0.4, decimal=16)
 
+    spikes1 = SpikeTrain([1.0, 2.0, 4.0], 4.0)
+    spikes2 = SpikeTrain([3.8], 4.0)
+    spikes3 = SpikeTrain([3.9, ], 4.0)
+
+    expected_x = np.array([0.0, 1.0, 2.0, 3.8, 4.0, 4.0])
+    expected_y = np.array([0.0, 0.0, 0.0, 1.0, 1.0, 1.0])
+
+    f = spk.spike_sync_profile(spikes1, spikes2)
+
+    assert_array_almost_equal(f.x, expected_x, decimal=16)
+    assert_array_almost_equal(f.y, expected_y, decimal=16)
+
+    f2 = spk.spike_sync_profile(spikes2, spikes3)
+
+    i1 = f.integral()
+    i2 = f2.integral()
+    f.add(f2)
+    i12 = f.integral()
+
+    assert_equal(i1[0]+i2[0], i12[0])
+    assert_equal(i1[1]+i2[1], i12[1])
+
 
 def check_multi_profile(profile_func, profile_func_multi, dist_func_multi):
     # generate spike trains:
@@ -313,6 +335,17 @@ def test_multi_spike_sync():
     assert_equal(np.sum(f.y[1:-1]), 39932)
     assert_equal(np.sum(f.mp[1:-1]), 85554)
 
+    # example with 2 empty spike trains
+    sts = []
+    sts.append(SpikeTrain([1, 9], [0, 10]))
+    sts.append(SpikeTrain([1, 3], [0, 10]))
+    sts.append(SpikeTrain([], [0, 10]))
+    sts.append(SpikeTrain([], [0, 10]))
+
+    assert_almost_equal(spk.spike_sync_multi(sts), 1.0/6.0, decimal=15)
+    assert_almost_equal(spk.spike_sync_profile_multi(sts).avrg(), 1.0/6.0,
+                        decimal=15)
+
 
 def check_dist_matrix(dist_func, dist_matrix_func):
     # generate spike trains: