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)
# strip channel names
raw.info['ch_names'] = [chn.strip('.') for chn in raw.info['ch_names']]
# Apply band-pass filter
raw.filter(7., 35., method='iir')
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
exclude='bads')
# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
epochs = Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
baseline=None, preload=True, add_eeg_ref=False,verbose=False)
labels = epochs.events[:, -1] - 2
# get epochs
epochs_data = epochs.get_data()
# compute covariance matrices
covmats = Covariances().fit_transform(epochs_data)
p_test = PermutationTest(5000)
p,F = p_test.test(covmats,labels)
p_test.plot()
print p_test.summary()
plt.show()
eog=False,
exclude='bads')
# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
proj=True,
picks=picks,
baseline=None,
preload=True,
add_eeg_ref=False,
verbose=False)
labels = epochs.events[:, -1] - 2
# get epochs
epochs_data = epochs.get_data()
# compute covariance matrices
covmats = Covariances().fit_transform(epochs_data)
p_test = PermutationTest(5000)
p, F = p_test.test(covmats, labels)
p_test.plot()
print(p_test.summary())
plt.show()
