path: root/debian/patches/0001-Use-assert_allclose-instead-of-assert_equal-in-tests.patch

From: Gard Spreemann <gspr@nonempty.org>
Date: Sat, 7 Mar 2020 18:51:42 +0100
Subject: Use assert_allclose instead of assert_equal in tests.

---
 test/test_distance.py                      | 70 +++++++++++++++---------------
 test/test_empty.py                         | 36 +++++++--------
 test/test_function.py                      | 52 +++++++++++-----------
 test/test_generic_interfaces.py            | 18 ++++----
 test/test_regression/test_regression_15.py | 20 ++++-----
 test/test_spikes.py                        | 10 ++---
 test/test_sync_filter.py                   | 32 +++++++-------
 7 files changed, 119 insertions(+), 119 deletions(-)

diff --git a/test/test_distance.py b/test/test_distance.py
index fe09f34..7ec3a72 100644
--- a/test/test_distance.py
+++ b/test/test_distance.py
@@ -11,7 +11,7 @@ Distributed under the BSD License
 from __future__ import print_function
 import numpy as np
 from copy import copy
-from numpy.testing import assert_equal, assert_almost_equal, \
+from numpy.testing import assert_allclose, assert_almost_equal, \
     assert_array_almost_equal
 
 import pyspike as spk
@@ -41,10 +41,10 @@ def test_isi():
     # print("ISI: ", f.y)
     print("ISI value:", expected_isi_val)
 
-    assert_equal(f.x, expected_times)
+    assert_allclose(f.x, expected_times)
     assert_array_almost_equal(f.y, expected_isi, decimal=15)
-    assert_equal(f.avrg(), expected_isi_val)
-    assert_equal(spk.isi_distance(t1, t2), expected_isi_val)
+    assert_allclose(f.avrg(), expected_isi_val)
+    assert_allclose(spk.isi_distance(t1, t2), expected_isi_val)
 
     # check with some equal spike times
     t1 = SpikeTrain([0.2, 0.4, 0.6], [0.0, 1.0])
@@ -60,10 +60,10 @@ def test_isi():
 
     f = spk.isi_profile(t1, t2)
 
-    assert_equal(f.x, expected_times)
+    assert_allclose(f.x, expected_times)
     assert_array_almost_equal(f.y, expected_isi, decimal=15)
-    assert_equal(f.avrg(), expected_isi_val)
-    assert_equal(spk.isi_distance(t1, t2), expected_isi_val)
+    assert_allclose(f.avrg(), expected_isi_val)
+    assert_allclose(spk.isi_distance(t1, t2), expected_isi_val)
 
 
 def test_spike():
@@ -75,7 +75,7 @@ def test_spike():
 
     f = spk.spike_profile(t1, t2)
 
-    assert_equal(f.x, expected_times)
+    assert_allclose(f.x, expected_times)
 
     # from SPIKY:
     y_all = np.array([0.000000000000000000, 0.555555555555555580,
@@ -89,7 +89,7 @@ def test_spike():
     assert_array_almost_equal(f.y2, y_all[1::2])
 
     assert_almost_equal(f.avrg(), 0.186309523809523814, decimal=15)
-    assert_equal(spk.spike_distance(t1, t2), f.avrg())
+    assert_allclose(spk.spike_distance(t1, t2), f.avrg())
 
     t1 = SpikeTrain([0.2, 0.4, 0.6, 0.7], 1.0)
     t2 = SpikeTrain([0.3, 0.45, 0.8, 0.9, 0.95], 1.0)
@@ -118,7 +118,7 @@ def test_spike():
 
     f = spk.spike_profile(t1, t2)
 
-    assert_equal(f.x, expected_times)
+    assert_allclose(f.x, expected_times)
     assert_array_almost_equal(f.y1, expected_y1, decimal=15)
     assert_array_almost_equal(f.y2, expected_y2, decimal=15)
     assert_almost_equal(f.avrg(), expected_spike_val, decimal=15)
@@ -157,7 +157,7 @@ def test_spike():
 
     f = spk.spike_profile(t1, t2)
 
-    assert_equal(f.x, expected_times)
+    assert_allclose(f.x, expected_times)
     assert_array_almost_equal(f.y1, expected_y1, decimal=14)
     assert_array_almost_equal(f.y2, expected_y2, decimal=14)
     assert_almost_equal(f.avrg(), expected_spike_val, decimal=16)
@@ -236,8 +236,8 @@ def test_spike_sync():
     f.add(f2)
     i12 = f.integral()
 
-    assert_equal(i1[0]+i2[0], i12[0])
-    assert_equal(i1[1]+i2[1], i12[1])
+    assert_allclose(i1[0]+i2[0], i12[0])
+    assert_allclose(i1[1]+i2[1], i12[1])
 
 
 def check_multi_profile(profile_func, profile_func_multi, dist_func_multi):
@@ -258,7 +258,7 @@ def check_multi_profile(profile_func, profile_func_multi, dist_func_multi):
     f_multi = profile_func_multi(spike_trains, [0, 1])
     assert f_multi.almost_equal(f12, decimal=14)
     d = dist_func_multi(spike_trains, [0, 1])
-    assert_equal(f_multi.avrg(), d)
+    assert_allclose(f_multi.avrg(), d)
 
     f_multi1 = profile_func_multi(spike_trains, [1, 2, 3])
     f_multi2 = profile_func_multi(spike_trains[1:])
@@ -329,11 +329,11 @@ def test_multi_spike_sync():
 
     f = spk.spike_sync_profile_multi(spike_trains)
 
-    assert_equal(spike_times, f.x[1:-1])
-    assert_equal(len(f.x), len(f.y))
+    assert_allclose(spike_times, f.x[1:-1])
+    assert_allclose(len(f.x), len(f.y))
 
-    assert_equal(np.sum(f.y[1:-1]), 39932)
-    assert_equal(np.sum(f.mp[1:-1]), 85554)
+    assert_allclose(np.sum(f.y[1:-1]), 39932)
+    assert_allclose(np.sum(f.mp[1:-1]), 85554)
 
     # example with 2 empty spike trains
     sts = []
@@ -365,16 +365,16 @@ def check_dist_matrix(dist_func, dist_matrix_func):
     f_matrix = dist_matrix_func(spike_trains)
     # check zero diagonal
     for i in range(4):
-        assert_equal(0.0, f_matrix[i, i])
+        assert_allclose(0.0, f_matrix[i, i])
     for i in range(4):
         for j in range(i+1, 4):
-            assert_equal(f_matrix[i, j], f_matrix[j, i])
-    assert_equal(f12, f_matrix[1, 0])
-    assert_equal(f13, f_matrix[2, 0])
-    assert_equal(f14, f_matrix[3, 0])
-    assert_equal(f23, f_matrix[2, 1])
-    assert_equal(f24, f_matrix[3, 1])
-    assert_equal(f34, f_matrix[3, 2])
+            assert_allclose(f_matrix[i, j], f_matrix[j, i])
+    assert_allclose(f12, f_matrix[1, 0])
+    assert_allclose(f13, f_matrix[2, 0])
+    assert_allclose(f14, f_matrix[3, 0])
+    assert_allclose(f23, f_matrix[2, 1])
+    assert_allclose(f24, f_matrix[3, 1])
+    assert_allclose(f34, f_matrix[3, 2])
 
 
 def test_isi_matrix():
@@ -397,13 +397,13 @@ def test_regression_spiky():
     isi_dist = spk.isi_distance(st1, st2)
     assert_almost_equal(isi_dist, 9.0909090909090939e-02, decimal=15)
     isi_profile = spk.isi_profile(st1, st2)
-    assert_equal(isi_profile.y, 0.1/1.1 * np.ones_like(isi_profile.y))
+    assert_allclose(isi_profile.y, 0.1/1.1 * np.ones_like(isi_profile.y))
 
     spike_dist = spk.spike_distance(st1, st2)
-    assert_equal(spike_dist, 0.211058782487353908)
+    assert_allclose(spike_dist, 0.211058782487353908)
 
     spike_sync = spk.spike_sync(st1, st2)
-    assert_equal(spike_sync, 8.6956521739130432e-01)
+    assert_allclose(spike_sync, 8.6956521739130432e-01)
 
     # multivariate check
 
@@ -414,7 +414,7 @@ def test_regression_spiky():
     assert_almost_equal(isi_dist, 0.17051816816999129656, decimal=15)
 
     spike_profile = spk.spike_profile_multi(spike_trains)
-    assert_equal(len(spike_profile.y1)+len(spike_profile.y2), 1252)
+    assert_allclose(len(spike_profile.y1)+len(spike_profile.y2), 1252)
 
     spike_dist = spk.spike_distance_multi(spike_trains)
     # get the full precision from SPIKY
@@ -422,7 +422,7 @@ def test_regression_spiky():
 
     spike_sync = spk.spike_sync_multi(spike_trains)
     # get the full precision from SPIKY
-    assert_equal(spike_sync, 0.7183531505298066)
+    assert_allclose(spike_sync, 0.7183531505298066)
 
     # Eero's edge correction example
     st1 = SpikeTrain([0.5, 1.5, 2.5], 6.0)
@@ -439,7 +439,7 @@ def test_regression_spiky():
     expected_y1 = y_all[::2]
     expected_y2 = y_all[1::2]
 
-    assert_equal(f.x, expected_times)
+    assert_allclose(f.x, expected_times)
     assert_array_almost_equal(f.y1, expected_y1, decimal=14)
     assert_array_almost_equal(f.y2, expected_y2, decimal=14)
 
@@ -452,15 +452,15 @@ def test_multi_variate_subsets():
 
     v1 = spk.isi_distance_multi(spike_trains_sub_set)
     v2 = spk.isi_distance_multi(spike_trains, sub_set)
-    assert_equal(v1, v2)
+    assert_allclose(v1, v2)
 
     v1 = spk.spike_distance_multi(spike_trains_sub_set)
     v2 = spk.spike_distance_multi(spike_trains, sub_set)
-    assert_equal(v1, v2)
+    assert_allclose(v1, v2)
 
     v1 = spk.spike_sync_multi(spike_trains_sub_set)
     v2 = spk.spike_sync_multi(spike_trains, sub_set)
-    assert_equal(v1, v2)
+    assert_allclose(v1, v2)
 
 
 if __name__ == "__main__":
diff --git a/test/test_empty.py b/test/test_empty.py
index 4d0a5cf..93fd2c1 100644
--- a/test/test_empty.py
+++ b/test/test_empty.py
@@ -10,7 +10,7 @@ Distributed under the BSD License
 
 from __future__ import print_function
 import numpy as np
-from numpy.testing import assert_equal, assert_almost_equal, \
+from numpy.testing import assert_allclose, assert_almost_equal, \
     assert_array_equal, assert_array_almost_equal
 
 import pyspike as spk
@@ -33,18 +33,18 @@ 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)
+    assert_allclose(d, 0.0)
     prof = spk.isi_profile(st1, st2)
-    assert_equal(d, prof.avrg())
+    assert_allclose(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)
+    assert_allclose(d, 0.6*0.4+0.4*0.6)
     prof = spk.isi_profile(st1, st2)
-    assert_equal(d, prof.avrg())
+    assert_allclose(d, prof.avrg())
     assert_array_equal(prof.x, [0.0, 0.4, 1.0])
     assert_array_equal(prof.y, [0.6, 0.4])
 
@@ -53,7 +53,7 @@ def test_isi_empty():
     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_allclose(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)
 
@@ -62,9 +62,9 @@ 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)
+    assert_allclose(d, 0.0)
     prof = spk.spike_profile(st1, st2)
-    assert_equal(d, prof.avrg())
+    assert_allclose(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, ])
@@ -75,7 +75,7 @@ def test_spike_empty():
     d_expect = 2*0.4*0.4*1.0/(0.4+1.0)**2 + 2*0.6*0.4*1.0/(0.6+1.0)**2
     assert_almost_equal(d, d_expect, decimal=15)
     prof = spk.spike_profile(st1, st2)
-    assert_equal(d, prof.avrg())
+    assert_allclose(d, prof.avrg())
     assert_array_equal(prof.x, [0.0, 0.4, 1.0])
     assert_array_almost_equal(prof.y1, [2*0.4*1.0/(0.4+1.0)**2,
                                         2*0.4*1.0/(0.6+1.0)**2],
@@ -100,7 +100,7 @@ def test_spike_empty():
 
     assert_almost_equal(d, expected_spike_val, decimal=15)
     prof = spk.spike_profile(st1, st2)
-    assert_equal(d, prof.avrg())
+    assert_allclose(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)
@@ -110,18 +110,18 @@ 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)
+    assert_allclose(d, 1.0)
     prof = spk.spike_sync_profile(st1, st2)
-    assert_equal(d, prof.avrg())
+    assert_allclose(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)
+    assert_allclose(d, 0.0)
     prof = spk.spike_sync_profile(st1, st2)
-    assert_equal(d, prof.avrg())
+    assert_allclose(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])
 
@@ -130,7 +130,7 @@ def test_spike_sync_empty():
     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_allclose(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)
 
@@ -139,7 +139,7 @@ def test_spike_sync_empty():
     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_allclose(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)
 
@@ -148,9 +148,9 @@ def test_spike_sync_empty():
     st2 = SpikeTrain([2.1, 7.0], [0, 10.0])
     st3 = SpikeTrain([5.1, 6.0], [0, 10.0])
     res = spk.spike_sync_profile(st1, st2).avrg(interval=[3.0, 4.0])
-    assert_equal(res, 1.0)
+    assert_allclose(res, 1.0)
     res = spk.spike_sync(st1, st2, interval=[3.0, 4.0])
-    assert_equal(res, 1.0)
+    assert_allclose(res, 1.0)
 
     sync_matrix = spk.spike_sync_matrix([st1, st2, st3], interval=[3.0, 4.0])
     assert_array_equal(sync_matrix, np.ones((3, 3)) - np.diag(np.ones(3)))
diff --git a/test/test_function.py b/test/test_function.py
index 6c04839..ba10ae7 100644
--- a/test/test_function.py
+++ b/test/test_function.py
@@ -11,7 +11,7 @@ from __future__ import print_function
 import numpy as np
 from copy import copy
 from nose.tools import raises
-from numpy.testing import assert_equal, assert_almost_equal, \
+from numpy.testing import assert_allclose, assert_almost_equal, \
     assert_array_equal, assert_array_almost_equal
 
 import pyspike as spk
@@ -24,14 +24,14 @@ def test_pwc():
     f = spk.PieceWiseConstFunc(x, y)
 
     # function values
-    assert_equal(f(0.0), 1.0)
-    assert_equal(f(0.5), 1.0)
-    assert_equal(f(1.0), 0.25)
-    assert_equal(f(2.0), 0.5)
-    assert_equal(f(2.25), 1.5)
-    assert_equal(f(2.5), 2.25/2)
-    assert_equal(f(3.5), 0.75)
-    assert_equal(f(4.0), 0.75)
+    assert_allclose(f(0.0), 1.0)
+    assert_allclose(f(0.5), 1.0)
+    assert_allclose(f(1.0), 0.25)
+    assert_allclose(f(2.0), 0.5)
+    assert_allclose(f(2.25), 1.5)
+    assert_allclose(f(2.5), 2.25/2)
+    assert_allclose(f(3.5), 0.75)
+    assert_allclose(f(4.0), 0.75)
 
     assert_array_equal(f([0.0, 0.5, 1.0, 2.0, 2.25, 2.5, 3.5, 4.0]),
                        [1.0, 1.0, 0.25, 0.5, 1.5, 2.25/2, 0.75, 0.75])
@@ -131,21 +131,21 @@ def test_pwc_integral():
 
     # test full interval
     full = 1.0*1.0 + 1.0*-0.5 + 0.5*1.5 + 1.5*0.75;
-    assert_equal(f1.integral(), full)
-    assert_equal(f1.integral((np.min(x),np.max(x))), full)
+    assert_allclose(f1.integral(), full)
+    assert_allclose(f1.integral((np.min(x),np.max(x))), full)
     # test part interval, spanning an edge
-    assert_equal(f1.integral((0.5,1.5)), 0.5*1.0 + 0.5*-0.5)
+    assert_allclose(f1.integral((0.5,1.5)), 0.5*1.0 + 0.5*-0.5)
     # test part interval, just over two edges
     assert_almost_equal(f1.integral((1.0-1e-16,2+1e-16)), 1.0*-0.5, decimal=14)
     # test part interval, between two edges
-    assert_equal(f1.integral((1.0,2.0)), 1.0*-0.5)
-    assert_equal(f1.integral((1.2,1.7)), (1.7-1.2)*-0.5)
+    assert_allclose(f1.integral((1.0,2.0)), 1.0*-0.5)
+    assert_allclose(f1.integral((1.2,1.7)), (1.7-1.2)*-0.5)
     # test part interval, start to before and after edge
-    assert_equal(f1.integral((0.0,0.7)), 0.7*1.0)
-    assert_equal(f1.integral((0.0,1.1)), 1.0*1.0+0.1*-0.5)
+    assert_allclose(f1.integral((0.0,0.7)), 0.7*1.0)
+    assert_allclose(f1.integral((0.0,1.1)), 1.0*1.0+0.1*-0.5)
     # test part interval, before and after edge till end
-    assert_equal(f1.integral((2.6,4.0)), (4.0-2.6)*0.75)
-    assert_equal(f1.integral((2.4,4.0)), (2.5-2.4)*1.5+(4-2.5)*0.75)
+    assert_allclose(f1.integral((2.6,4.0)), (4.0-2.6)*0.75)
+    assert_allclose(f1.integral((2.4,4.0)), (2.5-2.4)*1.5+(4-2.5)*0.75)
 
 @raises(ValueError)
 def test_pwc_integral_bad_bounds_inv():
@@ -178,14 +178,14 @@ def test_pwl():
     f = spk.PieceWiseLinFunc(x, y1, y2)
 
     # function values
-    assert_equal(f(0.0), 1.0)
-    assert_equal(f(0.5), 1.25)
-    assert_equal(f(1.0), 0.5)
-    assert_equal(f(2.0), 1.1/2)
-    assert_equal(f(2.25), 1.5)
-    assert_equal(f(2.5), 2.25/2)
-    assert_equal(f(3.5), 0.75-0.5*1.0/1.5)
-    assert_equal(f(4.0), 0.25)
+    assert_allclose(f(0.0), 1.0)
+    assert_allclose(f(0.5), 1.25)
+    assert_allclose(f(1.0), 0.5)
+    assert_allclose(f(2.0), 1.1/2)
+    assert_allclose(f(2.25), 1.5)
+    assert_allclose(f(2.5), 2.25/2)
+    assert_allclose(f(3.5), 0.75-0.5*1.0/1.5)
+    assert_allclose(f(4.0), 0.25)
 
     assert_array_equal(f([0.0, 0.5, 1.0, 2.0, 2.25, 2.5, 3.5, 4.0]),
                        [1.0, 1.25, 0.5, 0.55, 1.5, 2.25/2, 0.75-0.5/1.5, 0.25])
diff --git a/test/test_generic_interfaces.py b/test/test_generic_interfaces.py
index 7f08067..553f3f4 100644
--- a/test/test_generic_interfaces.py
+++ b/test/test_generic_interfaces.py
@@ -9,7 +9,7 @@ Distributed under the BSD License
 """
 
 from __future__ import print_function
-from numpy.testing import assert_equal
+from numpy.testing import assert_allclose
 
 import pyspike as spk
 from pyspike import SpikeTrain
@@ -43,33 +43,33 @@ def check_func(dist_func):
 
     isi12 = dist_func(t1, t2)
     isi12_ = dist_func([t1, t2])
-    assert_equal(isi12, isi12_)
+    assert_allclose(isi12, isi12_)
 
     isi12_ = dist_func(spike_trains, indices=[0, 1])
-    assert_equal(isi12, isi12_)
+    assert_allclose(isi12, isi12_)
 
     isi123 = dist_func(t1, t2, t3)
     isi123_ = dist_func([t1, t2, t3])
-    assert_equal(isi123, isi123_)
+    assert_allclose(isi123, isi123_)
 
     isi123_ = dist_func(spike_trains, indices=[0, 1, 2])
-    assert_equal(isi123, isi123_)
+    assert_allclose(isi123, isi123_)
 
     # run the same test with an additional interval parameter
 
     isi12 = dist_func(t1, t2, interval=[0.0, 0.5])
     isi12_ = dist_func([t1, t2], interval=[0.0, 0.5])
-    assert_equal(isi12, isi12_)
+    assert_allclose(isi12, isi12_)
 
     isi12_ = dist_func(spike_trains, indices=[0, 1], interval=[0.0, 0.5])
-    assert_equal(isi12, isi12_)
+    assert_allclose(isi12, isi12_)
 
     isi123 = dist_func(t1, t2, t3, interval=[0.0, 0.5])
     isi123_ = dist_func([t1, t2, t3], interval=[0.0, 0.5])
-    assert_equal(isi123, isi123_)
+    assert_allclose(isi123, isi123_)
 
     isi123_ = dist_func(spike_trains, indices=[0, 1, 2], interval=[0.0, 0.5])
-    assert_equal(isi123, isi123_)
+    assert_allclose(isi123, isi123_)
 
 
 def test_isi_profile():
diff --git a/test/test_regression/test_regression_15.py b/test/test_regression/test_regression_15.py
index 54adf23..81b5bb0 100644
--- a/test/test_regression/test_regression_15.py
+++ b/test/test_regression/test_regression_15.py
@@ -11,7 +11,7 @@ Distributed under the BSD License
 from __future__ import division
 
 import numpy as np
-from numpy.testing import assert_equal, assert_almost_equal, \
+from numpy.testing import assert_allclose, assert_almost_equal, \
     assert_array_almost_equal
 
 import pyspike as spk
@@ -28,15 +28,15 @@ def test_regression_15_isi():
     N = len(spike_trains)
 
     dist_mat = spk.isi_distance_matrix(spike_trains)
-    assert_equal(dist_mat.shape, (N, N))
+    assert_allclose(dist_mat.shape, (N, N))
 
     ind = np.arange(N//2)
     dist_mat = spk.isi_distance_matrix(spike_trains, ind)
-    assert_equal(dist_mat.shape, (N//2, N//2))
+    assert_allclose(dist_mat.shape, (N//2, N//2))
 
     ind = np.arange(N//2, N)
     dist_mat = spk.isi_distance_matrix(spike_trains, ind)
-    assert_equal(dist_mat.shape, (N//2, N//2))
+    assert_allclose(dist_mat.shape, (N//2, N//2))
 
 
 def test_regression_15_spike():
@@ -46,15 +46,15 @@ def test_regression_15_spike():
     N = len(spike_trains)
 
     dist_mat = spk.spike_distance_matrix(spike_trains)
-    assert_equal(dist_mat.shape, (N, N))
+    assert_allclose(dist_mat.shape, (N, N))
 
     ind = np.arange(N//2)
     dist_mat = spk.spike_distance_matrix(spike_trains, ind)
-    assert_equal(dist_mat.shape, (N//2, N//2))
+    assert_allclose(dist_mat.shape, (N//2, N//2))
 
     ind = np.arange(N//2, N)
     dist_mat = spk.spike_distance_matrix(spike_trains, ind)
-    assert_equal(dist_mat.shape, (N//2, N//2))
+    assert_allclose(dist_mat.shape, (N//2, N//2))
 
 
 def test_regression_15_sync():
@@ -64,15 +64,15 @@ def test_regression_15_sync():
     N = len(spike_trains)
 
     dist_mat = spk.spike_sync_matrix(spike_trains)
-    assert_equal(dist_mat.shape, (N, N))
+    assert_allclose(dist_mat.shape, (N, N))
 
     ind = np.arange(N//2)
     dist_mat = spk.spike_sync_matrix(spike_trains, ind)
-    assert_equal(dist_mat.shape, (N//2, N//2))
+    assert_allclose(dist_mat.shape, (N//2, N//2))
 
     ind = np.arange(N//2, N)
     dist_mat = spk.spike_sync_matrix(spike_trains, ind)
-    assert_equal(dist_mat.shape, (N//2, N//2))
+    assert_allclose(dist_mat.shape, (N//2, N//2))
 
 
 if __name__ == "__main__":
diff --git a/test/test_spikes.py b/test/test_spikes.py
index ee505b5..579f8e1 100644
--- a/test/test_spikes.py
+++ b/test/test_spikes.py
@@ -9,7 +9,7 @@ Distributed under the BSD License
 
 from __future__ import print_function
 import numpy as np
-from numpy.testing import assert_equal
+from numpy.testing import assert_allclose
 
 import pyspike as spk
 
@@ -29,7 +29,7 @@ def test_load_from_txt():
     spike_times = [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]
-    assert_equal(spike_times, spike_trains[0].spikes)
+    assert_allclose(spike_times, spike_trains[0].spikes)
 
     # check auxiliary spikes
     for spike_train in spike_trains:
@@ -47,9 +47,9 @@ def test_load_time_series():
 
     # check spike trains
     for n in range(len(spike_trains)):
-        assert_equal(spike_trains[n].spikes, spike_trains_check[n].spikes)
-        assert_equal(spike_trains[n].t_start, 0)
-        assert_equal(spike_trains[n].t_end, 4000)
+        assert_allclose(spike_trains[n].spikes, spike_trains_check[n].spikes)
+        assert_allclose(spike_trains[n].t_start, 0)
+        assert_allclose(spike_trains[n].t_end, 4000)
 
 
 def check_merged_spikes(merged_spikes, spike_trains):
diff --git a/test/test_sync_filter.py b/test/test_sync_filter.py
index e259903..0b915db 100644
--- a/test/test_sync_filter.py
+++ b/test/test_sync_filter.py
@@ -10,7 +10,7 @@ Distributed under the BSD License
 
 from __future__ import print_function
 import numpy as np
-from numpy.testing import assert_equal, assert_almost_equal, \
+from numpy.testing import assert_allclose, assert_almost_equal, \
     assert_array_almost_equal
 
 import pyspike as spk
@@ -36,21 +36,21 @@ def test_single_prof():
     coincidences = np.array(coincidence_impl(st1, st2, 0, 5.0, 0.0))
     print(coincidences)
     for i, t in enumerate(st1):
-        assert_equal(coincidences[i], sync_prof.y[sync_prof.x == t],
-                     "At index %d" % i)
+        assert_allclose(coincidences[i], sync_prof.y[sync_prof.x == t],
+                     err_msg="At index %d" % i)
 
     coincidences = np.array(coincidence_impl(st2, st1, 0, 5.0, 0.0))
     for i, t in enumerate(st2):
-        assert_equal(coincidences[i], sync_prof.y[sync_prof.x == t],
-                     "At index %d" % i)
+        assert_allclose(coincidences[i], sync_prof.y[sync_prof.x == t],
+                     err_msg="At index %d" % i)
 
     sync_prof = spk.spike_sync_profile(SpikeTrain(st1, 5.0),
                                        SpikeTrain(st3, 5.0))
 
     coincidences = np.array(coincidence_impl(st1, st3, 0, 5.0, 0.0))
     for i, t in enumerate(st1):
-        assert_equal(coincidences[i], sync_prof.y[sync_prof.x == t],
-                     "At index %d" % i)
+        assert_allclose(coincidences[i], sync_prof.y[sync_prof.x == t],
+                     err_msg="At index %d" % i)
 
     st1 = np.array([1.0, 2.0, 3.0, 4.0])
     st2 = np.array([1.0, 2.0, 4.0])
@@ -61,8 +61,8 @@ def test_single_prof():
     coincidences = np.array(coincidence_impl(st1, st2, 0, 5.0, 0.0))
     for i, t in enumerate(st1):
         expected = sync_prof.y[sync_prof.x == t]/sync_prof.mp[sync_prof.x == t]
-        assert_equal(coincidences[i], expected,
-                     "At index %d" % i)
+        assert_allclose(coincidences[i], expected,
+                     err_msg="At index %d" % i)
 
 
 def test_filter():
@@ -72,22 +72,22 @@ def test_filter():
 
     # filtered_spike_trains = spk.filter_by_spike_sync([st1, st2], 0.5)
 
-    # assert_equal(filtered_spike_trains[0].spikes, [1.0, 2.0, 4.0])
-    # assert_equal(filtered_spike_trains[1].spikes, [1.1, 2.1, 3.8])
+    # assert_allclose(filtered_spike_trains[0].spikes, [1.0, 2.0, 4.0])
+    # assert_allclose(filtered_spike_trains[1].spikes, [1.1, 2.1, 3.8])
 
     # filtered_spike_trains = spk.filter_by_spike_sync([st2, st1], 0.5)
 
-    # assert_equal(filtered_spike_trains[0].spikes, [1.1, 2.1, 3.8])
-    # assert_equal(filtered_spike_trains[1].spikes, [1.0, 2.0, 4.0])
+    # assert_allclose(filtered_spike_trains[0].spikes, [1.1, 2.1, 3.8])
+    # assert_allclose(filtered_spike_trains[1].spikes, [1.0, 2.0, 4.0])
 
     filtered_spike_trains = spk.filter_by_spike_sync([st1, st2, st3], 0.75)
 
     for st in filtered_spike_trains:
         print(st.spikes)
 
-    assert_equal(filtered_spike_trains[0].spikes, [1.0, 4.0])
-    assert_equal(filtered_spike_trains[1].spikes, [1.1, 3.8])
-    assert_equal(filtered_spike_trains[2].spikes, [0.9, 4.1])
+    assert_allclose(filtered_spike_trains[0].spikes, [1.0, 4.0])
+    assert_allclose(filtered_spike_trains[1].spikes, [1.1, 3.8])
+    assert_allclose(filtered_spike_trains[2].spikes, [0.9, 4.1])
 
 
 if __name__ == "main":