def test_permutation_test():
"""Test one way permutation test"""
covset = generate_cov(10, 30)
labels = np.array([0, 1]).repeat(5)
# base
p = PermutationTest(10)
p.test(covset, labels)
# fit perm
p = PermutationTest(10, fit_perms=True)
p.test(covset, labels)
# unique perms
p = PermutationTest(1000)
p.test(covset, labels)
p.summary()
p.plot(nbins=2)
def test_permutation_test():
"""Test one way permutation test"""
covset = generate_cov(10, 30)
labels = np.array([0, 1]).repeat(5)
p = PermutationTest(10)
p.test(covset, labels)
p.summary()
covest = Covariances()
Fs = 160
window = 2 * Fs
Nwindow = 20
Ns = epochs_data.shape[2]
step = int((Ns - window) / Nwindow)
time_bins = range(0, Ns - window, step)
pv = []
Fv = []
# For each frequency bin, estimate the stats
for t in time_bins:
covmats = covest.fit_transform(epochs_data[:, ::1, t:(t + window)])
p_test = PermutationTest(5000)
p, F = p_test.test(covmats, labels)
print p_test.summary()
pv.append(p)
Fv.append(F[0])
time = np.array(time_bins) / float(Fs) + tmin
plot(time, Fv, lw=2)
plt.xlabel('Time')
plt.ylabel('F-value')
significant = np.array(pv) < 0.001
plot(time, significant, 'r', lw=2)
plt.legend(['F-value', 'p<0.001'])
plt.grid()
plt.show()
