def test_multi_spike_sync():
# some basic multivariate check
spikes1 = SpikeTrain(
[100, 300, 400, 405, 410, 500, 700, 800, 805, 810, 815, 900], 1000)
spikes2 = SpikeTrain(
[100, 200, 205, 210, 295, 350, 400, 510, 600, 605, 700, 910], 1000)
spikes3 = SpikeTrain(
[100, 180, 198, 295, 412, 420, 510, 640, 695, 795, 820, 920], 1000)
assert_almost_equal(spk.spike_sync(spikes1, spikes2), 0.5, decimal=15)
assert_almost_equal(spk.spike_sync(spikes1, spikes3), 0.5, decimal=15)
assert_almost_equal(spk.spike_sync(spikes2, spikes3), 0.5, decimal=15)
f = spk.spike_sync_profile_multi([spikes1, spikes2, spikes3])
# hands on definition of the average multivariate spike synchronization
# expected = (f1.integral() + f2.integral() + f3.integral()) / \
# (np.sum(f1.mp[1:-1])+np.sum(f2.mp[1:-1])+np.sum(f3.mp[1:-1]))
expected = 0.5
assert_almost_equal(f.avrg(), expected, decimal=15)
assert_almost_equal(spk.spike_sync_multi([spikes1, spikes2, spikes3]),
expected,
decimal=15)
# multivariate regression test
spike_trains = spk.load_spike_trains_from_txt(os.path.join(
TEST_PATH, "SPIKE_Sync_Test.txt"),
edges=[0, 4000])
# extract all spike times
spike_times = np.array([])
for st in spike_trains:
spike_times = np.append(spike_times, st.spikes)
spike_times = np.unique(np.sort(spike_times))
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_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_single_spike_train_set(index):
""" Debuging function """
np.set_printoptions(precision=15)
spike_file = "regression_random_spikes.mat"
spikes_name = "spikes"
result_name = "Distances"
result_file = "regression_random_results_cSPIKY.mat"
spike_train_sets = loadmat(spike_file)[spikes_name][0]
results_cSPIKY = loadmat(result_file)[result_name]
spike_train_data = spike_train_sets[index]
spike_trains = []
N = 0
for spikes in spike_train_data[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(N)
print(spk.spike_sync_multi(spike_trains))
print(spk.spike_sync_profile_multi(spike_trains).integral())
def test_regression_spiky():
# standard example
st1 = SpikeTrain(np.arange(100, 1201, 100), 1300)
st2 = SpikeTrain(np.arange(100, 1201, 110), 1300)
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))
spike_dist = spk.spike_distance(st1, st2)
assert_equal(spike_dist, 2.1105878248735391e-01)
spike_sync = spk.spike_sync(st1, st2)
assert_equal(spike_sync, 8.6956521739130432e-01)
# multivariate check
spike_trains = spk.load_spike_trains_from_txt("test/PySpike_testdata.txt",
(0.0, 4000.0))
isi_dist = spk.isi_distance_multi(spike_trains)
# get the full precision from 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)
spike_dist = spk.spike_distance_multi(spike_trains)
# get the full precision from SPIKY
assert_almost_equal(spike_dist, 2.4432433330596512e-01, decimal=15)
spike_sync = spk.spike_sync_multi(spike_trains)
# get the full precision from SPIKY
assert_equal(spike_sync, 0.7183531505298066)
def test_multi_variate_subsets():
spike_trains = spk.load_spike_trains_from_txt(
os.path.join(TEST_PATH, "PySpike_testdata.txt"), (0.0, 4000.0))
sub_set = [1, 3, 5, 7]
spike_trains_sub_set = [spike_trains[i] for i in sub_set]
v1 = spk.isi_distance_multi(spike_trains_sub_set)
v2 = spk.isi_distance_multi(spike_trains, sub_set)
assert_equal(v1, v2)
v1 = spk.spike_distance_multi(spike_trains_sub_set)
v2 = spk.spike_distance_multi(spike_trains, sub_set)
assert_equal(v1, v2)
v1 = spk.spike_sync_multi(spike_trains_sub_set)
v2 = spk.spike_sync_multi(spike_trains, sub_set)
assert_equal(v1, v2)
def test_multi_variate_subsets():
spike_trains = spk.load_spike_trains_from_txt("test/PySpike_testdata.txt",
(0.0, 4000.0))
sub_set = [1, 3, 5, 7]
spike_trains_sub_set = [spike_trains[i] for i in sub_set]
v1 = spk.isi_distance_multi(spike_trains_sub_set)
v2 = spk.isi_distance_multi(spike_trains, sub_set)
assert_equal(v1, v2)
v1 = spk.spike_distance_multi(spike_trains_sub_set)
v2 = spk.spike_distance_multi(spike_trains, sub_set)
assert_equal(v1, v2)
v1 = spk.spike_sync_multi(spike_trains_sub_set)
v2 = spk.spike_sync_multi(spike_trains, sub_set)
assert_equal(v1, v2)
def test_regression_spiky():
# standard example
st1 = SpikeTrain(np.arange(100, 1201, 100), 1300)
st2 = SpikeTrain(np.arange(100, 1201, 110), 1300)
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))
spike_dist = spk.spike_distance(st1, st2)
assert_equal(spike_dist, 0.211058782487353908)
spike_sync = spk.spike_sync(st1, st2)
assert_equal(spike_sync, 8.6956521739130432e-01)
# multivariate check
spike_trains = spk.load_spike_trains_from_txt(
os.path.join(TEST_PATH, "PySpike_testdata.txt"), (0.0, 4000.0))
isi_dist = spk.isi_distance_multi(spike_trains)
# get the full precision from 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)
spike_dist = spk.spike_distance_multi(spike_trains)
# get the full precision from SPIKY
assert_almost_equal(spike_dist, 0.25188056475463755, decimal=15)
spike_sync = spk.spike_sync_multi(spike_trains)
# get the full precision from SPIKY
assert_equal(spike_sync, 0.7183531505298066)
# Eero's edge correction example
st1 = SpikeTrain([0.5, 1.5, 2.5], 6.0)
st2 = SpikeTrain([3.5, 4.5, 5.5], 6.0)
f = spk.spike_profile(st1, st2)
expected_times = np.array([0.0, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.0])
y_all = np.array([
0.271604938271605, 0.271604938271605, 0.271604938271605,
0.617283950617284, 0.617283950617284, 0.444444444444444,
0.285714285714286, 0.285714285714286, 0.444444444444444,
0.617283950617284, 0.617283950617284, 0.271604938271605,
0.271604938271605, 0.271604938271605
])
expected_y1 = y_all[::2]
expected_y2 = y_all[1::2]
assert_equal(f.x, expected_times)
assert_array_almost_equal(f.y1, expected_y1, decimal=14)
assert_array_almost_equal(f.y2, expected_y2, decimal=14)
def test_regression_random():
spike_file = "test/numeric/regression_random_spikes.mat"
spikes_name = "spikes"
result_name = "Distances"
result_file = "test/numeric/regression_random_results_cSPIKY.mat"
spike_train_sets = loadmat(spike_file)[spikes_name][0]
results_cSPIKY = loadmat(result_file)[result_name]
for i, spike_train_data in enumerate(spike_train_sets):
spike_trains = []
for spikes in spike_train_data[0]:
spike_trains.append(spk.SpikeTrain(spikes.flatten(), 100.0))
isi = spk.isi_distance_multi(spike_trains)
isi_prof = spk.isi_profile_multi(spike_trains).avrg()
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_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)
assert_almost_equal(isi_prof,
results_cSPIKY[i][0],
decimal=14,
err_msg="Index: %d, ISI" % i)
assert_almost_equal(spike,
results_cSPIKY[i][1],
decimal=14,
err_msg="Index: %d, SPIKE" % i)
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 test_multi_spike_sync():
# some basic multivariate check
spikes1 = SpikeTrain([100, 300, 400, 405, 410, 500, 700, 800,
805, 810, 815, 900], 1000)
spikes2 = SpikeTrain([100, 200, 205, 210, 295, 350, 400, 510,
600, 605, 700, 910], 1000)
spikes3 = SpikeTrain([100, 180, 198, 295, 412, 420, 510, 640,
695, 795, 820, 920], 1000)
assert_almost_equal(spk.spike_sync(spikes1, spikes2),
0.5, decimal=15)
assert_almost_equal(spk.spike_sync(spikes1, spikes3),
0.5, decimal=15)
assert_almost_equal(spk.spike_sync(spikes2, spikes3),
0.5, decimal=15)
f = spk.spike_sync_profile_multi([spikes1, spikes2, spikes3])
# hands on definition of the average multivariate spike synchronization
# expected = (f1.integral() + f2.integral() + f3.integral()) / \
# (np.sum(f1.mp[1:-1])+np.sum(f2.mp[1:-1])+np.sum(f3.mp[1:-1]))
expected = 0.5
assert_almost_equal(f.avrg(), expected, decimal=15)
assert_almost_equal(spk.spike_sync_multi([spikes1, spikes2, spikes3]),
expected, decimal=15)
# multivariate regression test
spike_trains = spk.load_spike_trains_from_txt("test/SPIKE_Sync_Test.txt",
edges=[0, 4000])
# extract all spike times
spike_times = np.array([])
for st in spike_trains:
spike_times = np.append(spike_times, st.spikes)
spike_times = np.unique(np.sort(spike_times))
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_equal(np.sum(f.y[1:-1]), 39932)
assert_equal(np.sum(f.mp[1:-1]), 85554)
