diff options
Diffstat (limited to 'test')
-rw-r--r-- | test/test_distance.py | 14 | ||||
-rw-r--r-- | test/test_empty.py | 147 | ||||
-rw-r--r-- | test/test_function.py | 37 |
3 files changed, 194 insertions, 4 deletions
diff --git a/test/test_distance.py b/test/test_distance.py index 19da35f..e45ac16 100644 --- a/test/test_distance.py +++ b/test/test_distance.py @@ -196,7 +196,7 @@ def test_spike_sync(): 0.4, decimal=16) -def check_multi_profile(profile_func, profile_func_multi): +def check_multi_profile(profile_func, profile_func_multi, dist_func_multi): # generate spike trains: 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) @@ -213,10 +213,14 @@ def check_multi_profile(profile_func, profile_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) f_multi1 = profile_func_multi(spike_trains, [1, 2, 3]) f_multi2 = profile_func_multi(spike_trains[1:]) assert f_multi1.almost_equal(f_multi2, decimal=14) + d = dist_func_multi(spike_trains, [1, 2, 3]) + assert_almost_equal(f_multi1.avrg(), d, decimal=14) f = copy(f12) f.add(f13) @@ -224,6 +228,8 @@ def check_multi_profile(profile_func, profile_func_multi): f.mul_scalar(1.0/3) f_multi = profile_func_multi(spike_trains, [0, 1, 2]) assert f_multi.almost_equal(f, decimal=14) + d = dist_func_multi(spike_trains, [0, 1, 2]) + assert_almost_equal(f_multi.avrg(), d, decimal=14) f.mul_scalar(3) # revert above normalization f.add(f14) @@ -235,11 +241,13 @@ def check_multi_profile(profile_func, profile_func_multi): def test_multi_isi(): - check_multi_profile(spk.isi_profile, spk.isi_profile_multi) + check_multi_profile(spk.isi_profile, spk.isi_profile_multi, + spk.isi_distance_multi) def test_multi_spike(): - check_multi_profile(spk.spike_profile, spk.spike_profile_multi) + check_multi_profile(spk.spike_profile, spk.spike_profile_multi, + spk.spike_distance_multi) def test_multi_spike_sync(): diff --git a/test/test_empty.py b/test/test_empty.py new file mode 100644 index 0000000..48be25d --- /dev/null +++ b/test/test_empty.py @@ -0,0 +1,147 @@ +""" 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() diff --git a/test/test_function.py b/test/test_function.py index d81b03a..92d378d 100644 --- a/test/test_function.py +++ b/test/test_function.py @@ -10,7 +10,8 @@ Distributed under the BSD License from __future__ import print_function import numpy as np from copy import copy -from numpy.testing import assert_almost_equal, assert_array_almost_equal +from numpy.testing import assert_equal, assert_almost_equal, \ + assert_array_equal, assert_array_almost_equal import pyspike as spk @@ -20,6 +21,20 @@ def test_pwc(): x = [0.0, 1.0, 2.0, 2.5, 4.0] y = [1.0, -0.5, 1.5, 0.75] 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_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]) + xp, yp = f.get_plottable_data() xp_expected = [0.0, 1.0, 1.0, 2.0, 2.0, 2.5, 2.5, 4.0] @@ -38,11 +53,17 @@ def test_pwc(): assert_almost_equal(a, (-0.5*1.0+0.5*1.5+1.0*0.75)/2.5, decimal=16) a = f.avrg([1.0, 4.0]) assert_almost_equal(a, (-0.5*1.0+0.5*1.5+1.5*0.75)/3.0, decimal=16) + a = f.avrg([0.0, 2.2]) + assert_almost_equal(a, (1.0*1.0-0.5*1.0+0.2*1.5)/2.2, decimal=15) # averaging over multiple intervals a = f.avrg([(0.5, 1.5), (1.5, 3.5)]) assert_almost_equal(a, (0.5-0.5+0.5*1.5+1.0*0.75)/3.0, decimal=16) + # averaging over multiple intervals + a = f.avrg([(0.5, 1.5), (2.2, 3.5)]) + assert_almost_equal(a, (0.5*1.0-0.5*0.5+0.3*1.5+1.0*0.75)/2.3, decimal=15) + def test_pwc_add(): # some random data @@ -105,6 +126,20 @@ def test_pwl(): y1 = [1.0, -0.5, 1.5, 0.75] y2 = [1.5, -0.4, 1.5, 0.25] 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_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]) + xp, yp = f.get_plottable_data() xp_expected = [0.0, 1.0, 1.0, 2.0, 2.0, 2.5, 2.5, 4.0] |