def test_spike_directionality():
st1 = SpikeTrain([100, 200, 300], [0, 1000])
st2 = SpikeTrain([105, 205, 300], [0, 1000])
assert_almost_equal(spk.spike_directionality(st1, st2), 2.0/3.0)
assert_almost_equal(spk.spike_directionality(st1, st2, normalize=False),
2.0)
# exchange order of spike trains should give exact negative profile
assert_almost_equal(spk.spike_directionality(st2, st1), -2.0/3.0)
assert_almost_equal(spk.spike_directionality(st2, st1, normalize=False),
-2.0)
st3 = SpikeTrain([105, 195, 500], [0, 1000])
assert_almost_equal(spk.spike_directionality(st1, st3), 0.0)
assert_almost_equal(spk.spike_directionality(st1, st3, normalize=False),
0.0)
assert_almost_equal(spk.spike_directionality(st3, st1), 0.0)
D = spk.spike_directionality_matrix([st1, st2, st3], normalize=False)
D_expected = np.array([[0, 2.0, 0.0], [-2.0, 0.0, -1.0], [0.0, 1.0, 0.0]])
assert_array_equal(D, D_expected)
dir_profs = spk.spike_directionality_values([st1, st2, st3])
assert_array_equal(dir_profs[0], [1.0, 0.0, 0.0])
assert_array_equal(dir_profs[1], [-0.5, -1.0, 0.0])
spike_profile = spk.spike_profile(spike_trains[0], spike_trains[1])
x, y = spike_profile.get_plottable_data()
plt.plot(x, y, '--k')
print("SPIKE distance: %.8f" % spike_profile.avrg())
plt.show()
import numpy as np
from matplotlib import pyplot as plt
import pyspike as spk
st1 = spk.generate_poisson_spikes(1.0, [0, 20])
st2 = spk.generate_poisson_spikes(1.0, [0, 20])
d = spk.spike_directionality(st1, st2)
#print "Spike Directionality of two Poissonian spike trains:", d
E = spk.spike_train_order_profile(st1, st2)
plt.figure()
x, y = E.get_plottable_data()
plt.plot(x, y, '-ob')
plt.ylim(-1.1, 1.1)
plt.xlabel("t")
plt.ylabel("E")
plt.title("Spike Train Order Profile")
plt.show()