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_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)
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)
print("Average:", f.avrg())
f = spk.spike_profile(spike_trains[0], spike_trains[1])
x, y = f.get_plottable_data()
plt.plot(x, y, '-b', label="SPIKE-profile")
plt.axis([0, 4000, -0.1, 1.1])
plt.legend(loc="center right")
plt.figure()
plt.subplot(211)
f = spk.spike_sync_profile_multi(spike_trains)
x, y = f.get_plottable_data()
plt.plot(x, y, '-b', alpha=0.7, label="SPIKE-Sync profile")
x1, y1 = f.get_plottable_data(averaging_window_size=50)
plt.plot(x1, y1, '-k', lw=2.5, label="averaged SPIKE-Sync profile")
plt.subplot(212)
f_psth = spk.psth(spike_trains, bin_size=50.0)
x, y = f_psth.get_plottable_data()
plt.plot(x, y, '-k', alpha=1.0, label="PSTH")
print("Average:", f.avrg())
