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()