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diff --git a/test/test_empty.py b/test/test_empty.py
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+""" test_empty.py
+
+Tests the distance measure for empty spike trains
+
+Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net>
+
+Distributed under the BSD License
+
+"""
+
+from __future__ import print_function
+import numpy as np
+from numpy.testing import assert_equal, assert_almost_equal, \
+    assert_array_equal, assert_array_almost_equal
+
+import pyspike as spk
+from pyspike import SpikeTrain
+
+
+def test_get_non_empty():
+    st = SpikeTrain([], edges=(0.0, 1.0))
+    spikes = st.get_spikes_non_empty()
+    assert_array_equal(spikes, [0.0, 1.0])
+
+    st = SpikeTrain([0.5, ], edges=(0.0, 1.0))
+    spikes = st.get_spikes_non_empty()
+    assert_array_equal(spikes, [0.0, 0.5, 1.0])
+
+
+def test_isi_empty():
+    st1 = SpikeTrain([], edges=(0.0, 1.0))
+    st2 = SpikeTrain([], edges=(0.0, 1.0))
+    d = spk.isi_distance(st1, st2)
+    assert_equal(d, 0.0)
+    prof = spk.isi_profile(st1, st2)
+    assert_equal(d, prof.avrg())
+    assert_array_equal(prof.x, [0.0, 1.0])
+    assert_array_equal(prof.y, [0.0, ])
+
+    st1 = SpikeTrain([], edges=(0.0, 1.0))
+    st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
+    d = spk.isi_distance(st1, st2)
+    assert_equal(d, 0.6*0.4+0.4*0.6)
+    prof = spk.isi_profile(st1, st2)
+    assert_equal(d, prof.avrg())
+    assert_array_equal(prof.x, [0.0, 0.4, 1.0])
+    assert_array_equal(prof.y, [0.6, 0.4])
+
+    st1 = SpikeTrain([0.6, ], edges=(0.0, 1.0))
+    st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
+    d = spk.isi_distance(st1, st2)
+    assert_almost_equal(d, 0.2/0.6*0.4 + 0.0 + 0.2/0.6*0.4, decimal=15)
+    prof = spk.isi_profile(st1, st2)
+    assert_equal(d, prof.avrg())
+    assert_array_almost_equal(prof.x, [0.0, 0.4, 0.6, 1.0], decimal=15)
+    assert_array_almost_equal(prof.y, [0.2/0.6, 0.0, 0.2/0.6], decimal=15)
+
+
+def test_spike_empty():
+    st1 = SpikeTrain([], edges=(0.0, 1.0))
+    st2 = SpikeTrain([], edges=(0.0, 1.0))
+    d = spk.spike_distance(st1, st2)
+    assert_equal(d, 0.0)
+    prof = spk.spike_profile(st1, st2)
+    assert_equal(d, prof.avrg())
+    assert_array_equal(prof.x, [0.0, 1.0])
+    assert_array_equal(prof.y1, [0.0, ])
+    assert_array_equal(prof.y2, [0.0, ])
+
+    st1 = SpikeTrain([], edges=(0.0, 1.0))
+    st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
+    d = spk.spike_distance(st1, st2)
+    assert_almost_equal(d, 0.4*0.4*1.0/(0.4+1.0)**2 + 0.6*0.4*1.0/(0.6+1.0)**2,
+                        decimal=15)
+    prof = spk.spike_profile(st1, st2)
+    assert_equal(d, prof.avrg())
+    assert_array_equal(prof.x, [0.0, 0.4, 1.0])
+    assert_array_almost_equal(prof.y1, [0.0, 2*0.4*1.0/(0.6+1.0)**2],
+                              decimal=15)
+    assert_array_almost_equal(prof.y2, [2*0.4*1.0/(0.4+1.0)**2, 0.0],
+                              decimal=15)
+
+    st1 = SpikeTrain([0.6, ], edges=(0.0, 1.0))
+    st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
+    d = spk.spike_distance(st1, st2)
+    s1 = np.array([0.0, 0.4*0.2/0.6, 0.2, 0.0])
+    s2 = np.array([0.0, 0.2, 0.2*0.4/0.6, 0.0])
+    isi1 = np.array([0.6, 0.6, 0.4])
+    isi2 = np.array([0.4, 0.6, 0.6])
+    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)
+    expected_times = np.array([0.0, 0.4, 0.6, 1.0])
+    expected_spike_val = sum((expected_times[1:] - expected_times[:-1]) *
+                             (expected_y1+expected_y2)/2)
+    expected_spike_val /= (expected_times[-1]-expected_times[0])
+
+    assert_almost_equal(d, expected_spike_val, decimal=15)
+    prof = spk.spike_profile(st1, st2)
+    assert_equal(d, prof.avrg())
+    assert_array_almost_equal(prof.x, [0.0, 0.4, 0.6, 1.0], decimal=15)
+    assert_array_almost_equal(prof.y1, expected_y1, decimal=15)
+    assert_array_almost_equal(prof.y2, expected_y2, decimal=15)
+
+
+def test_spike_sync_empty():
+    st1 = SpikeTrain([], edges=(0.0, 1.0))
+    st2 = SpikeTrain([], edges=(0.0, 1.0))
+    d = spk.spike_sync(st1, st2)
+    assert_equal(d, 1.0)
+    prof = spk.spike_sync_profile(st1, st2)
+    assert_equal(d, prof.avrg())
+    assert_array_equal(prof.x, [0.0, 1.0])
+    assert_array_equal(prof.y, [1.0, 1.0])
+
+    st1 = SpikeTrain([], edges=(0.0, 1.0))
+    st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
+    d = spk.spike_sync(st1, st2)
+    assert_equal(d, 0.0)
+    prof = spk.spike_sync_profile(st1, st2)
+    assert_equal(d, prof.avrg())
+    assert_array_equal(prof.x, [0.0, 0.4, 1.0])
+    assert_array_equal(prof.y, [0.0, 0.0, 0.0])
+
+    st1 = SpikeTrain([0.6, ], edges=(0.0, 1.0))
+    st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
+    d = spk.spike_sync(st1, st2)
+    assert_almost_equal(d, 1.0, decimal=15)
+    prof = spk.spike_sync_profile(st1, st2)
+    assert_equal(d, prof.avrg())
+    assert_array_almost_equal(prof.x, [0.0, 0.4, 0.6, 1.0], decimal=15)
+    assert_array_almost_equal(prof.y, [1.0, 1.0, 1.0, 1.0], decimal=15)
+
+    st1 = SpikeTrain([0.2, ], edges=(0.0, 1.0))
+    st2 = SpikeTrain([0.8, ], edges=(0.0, 1.0))
+    d = spk.spike_sync(st1, st2)
+    assert_almost_equal(d, 0.0, decimal=15)
+    prof = spk.spike_sync_profile(st1, st2)
+    assert_equal(d, prof.avrg())
+    assert_array_almost_equal(prof.x, [0.0, 0.2, 0.8, 1.0], decimal=15)
+    assert_array_almost_equal(prof.y, [0.0, 0.0, 0.0, 0.0], decimal=15)
+
+
+if __name__ == "__main__":
+    test_get_non_empty()
+    test_isi_empty()
+    test_spike_empty()
+    test_spike_sync_empty()