def test_compute_proj_epochs(): """Test SSP computation on epochs""" event_id, tmin, tmax = 1, -0.2, 0.3 raw = Raw(raw_fname, preload=True) events = read_events(event_fname) bad_ch = 'MEG 2443' picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False, exclude=[]) epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=None, proj=False) evoked = epochs.average() projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=1) write_proj(op.join(tempdir, 'proj.fif.gz'), projs) for p_fname in [proj_fname, proj_gz_fname, op.join(tempdir, 'proj.fif.gz')]: projs2 = read_proj(p_fname) assert_true(len(projs) == len(projs2)) for p1, p2 in zip(projs, projs2): assert_true(p1['desc'] == p2['desc']) assert_true(p1['data']['col_names'] == p2['data']['col_names']) assert_true(p1['active'] == p2['active']) # compare with sign invariance p1_data = p1['data']['data'] * np.sign(p1['data']['data'][0, 0]) p2_data = p2['data']['data'] * np.sign(p2['data']['data'][0, 0]) if bad_ch in p1['data']['col_names']: bad = p1['data']['col_names'].index('MEG 2443') mask = np.ones(p1_data.size, dtype=np.bool) mask[bad] = False p1_data = p1_data[:, mask] p2_data = p2_data[:, mask] corr = np.corrcoef(p1_data, p2_data)[0, 1] assert_array_almost_equal(corr, 1.0, 5) # test that you can compute the projection matrix projs = activate_proj(projs) proj, nproj, U = make_projector(projs, epochs.ch_names, bads=[]) assert_true(nproj == 2) assert_true(U.shape[1] == 2) # test that you can save them epochs.info['projs'] += projs evoked = epochs.average() evoked.save(op.join(tempdir, 'foo.fif')) projs = read_proj(proj_fname) projs_evoked = compute_proj_evoked(evoked, n_grad=1, n_mag=1, n_eeg=0) assert_true(len(projs_evoked) == 2) # XXX : test something # test parallelization projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=2) projs = activate_proj(projs) proj_par, _, _ = make_projector(projs, epochs.ch_names, bads=[]) assert_allclose(proj, proj_par, rtol=1e-8, atol=1e-16)
def test_compute_proj_raw(): """Test SSP computation on raw""" # Test that the raw projectors work raw_time = 2.5 # Do shorter amount for speed raw = Raw(raw_fname, preload=True).crop(0, raw_time, False) for ii in (0.25, 0.5, 1, 2): with warnings.catch_warnings(True) as w: projs = compute_proj_raw(raw, duration=ii - 0.1, stop=raw_time, n_grad=1, n_mag=1, n_eeg=0) assert_true(len(w) == 1) # test that you can compute the projection matrix projs = activate_proj(projs) proj, nproj, U = make_projector(projs, raw.ch_names, bads=[]) assert_true(nproj == 2) assert_true(U.shape[1] == 2) # test that you can save them raw.info['projs'] += projs raw.save(op.join(tempdir, 'foo_%d_raw.fif' % ii), overwrite=True) # Test that purely continuous (no duration) raw projection works with warnings.catch_warnings(True) as w: projs = compute_proj_raw(raw, duration=None, stop=raw_time, n_grad=1, n_mag=1, n_eeg=0) assert_true(len(w) == 1) # test that you can compute the projection matrix projs = activate_proj(projs) proj, nproj, U = make_projector(projs, raw.ch_names, bads=[]) assert_true(nproj == 2) assert_true(U.shape[1] == 2) # test that you can save them raw.info['projs'] += projs raw.save(op.join(tempdir, 'foo_rawproj_continuous_raw.fif')) # test resampled-data projector, upsampling instead of downsampling # here to save an extra filtering (raw would have to be LP'ed to be equiv) raw_resamp = cp.deepcopy(raw) raw_resamp.resample(raw.info['sfreq'] * 2, n_jobs=2) with warnings.catch_warnings(True) as w: projs = compute_proj_raw(raw_resamp, duration=None, stop=raw_time, n_grad=1, n_mag=1, n_eeg=0) projs = activate_proj(projs) proj_new, _, _ = make_projector(projs, raw.ch_names, bads=[]) assert_array_almost_equal(proj_new, proj, 4) # test with bads raw.load_bad_channels(bads_fname) # adds 2 bad mag channels with warnings.catch_warnings(True) as w: projs = compute_proj_raw(raw, n_grad=0, n_mag=0, n_eeg=1) # test that bad channels can be excluded proj, nproj, U = make_projector(projs, raw.ch_names, bads=raw.ch_names) assert_array_almost_equal(proj, np.eye(len(raw.ch_names)))
def test_compute_proj(): """Test SSP computation""" event_id, tmin, tmax = 1, -0.2, 0.3 raw = Raw(raw_fname) events = read_events(event_fname) exclude = [] bad_ch = 'MEG 2443' picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False, exclude=exclude) epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=None, proj=False) evoked = epochs.average() projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0) projs2 = read_proj(proj_fname) assert_true(len(projs) == len(projs2)) for p1, p2 in zip(projs, projs2): assert_true(p1['desc'] == p2['desc']) assert_true(p1['data']['col_names'] == p2['data']['col_names']) assert_true(p1['active'] == p2['active']) # compare with sign invariance p1_data = p1['data']['data'] * np.sign(p1['data']['data'][0, 0]) p2_data = p2['data']['data'] * np.sign(p2['data']['data'][0, 0]) if bad_ch in p1['data']['col_names']: bad = p1['data']['col_names'].index('MEG 2443') mask = np.ones(p1_data.size, dtype=np.bool) mask[bad] = False p1_data = p1_data[:, mask] p2_data = p2_data[:, mask] corr = np.corrcoef(p1_data, p2_data)[0, 1] assert_array_almost_equal(corr, 1.0, 7) # test that you can compute the projection matrix projs = activate_proj(projs) proj, nproj, U = make_projector(projs, epochs.ch_names, bads=[]) assert_true(nproj == 2) assert_true(U.shape[1] == 2) # test that you can save them epochs.info['projs'] += projs evoked = epochs.average() evoked.save('foo.fif') projs = read_proj(proj_fname) projs_evoked = compute_proj_evoked(evoked, n_grad=1, n_mag=1, n_eeg=0)
def test_compute_proj(): """Test SSP computation""" event_id, tmin, tmax = 1, -0.2, 0.3 raw = Raw(raw_fname, preload=True) events = read_events(event_fname) exclude = [] bad_ch = 'MEG 2443' picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False, exclude=exclude) epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=None, proj=False) evoked = epochs.average() projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=1) write_proj('proj.fif.gz', projs) for p_fname in [proj_fname, proj_gz_fname, 'proj.fif.gz']: projs2 = read_proj(p_fname) assert_true(len(projs) == len(projs2)) for p1, p2 in zip(projs, projs2): assert_true(p1['desc'] == p2['desc']) assert_true(p1['data']['col_names'] == p2['data']['col_names']) assert_true(p1['active'] == p2['active']) # compare with sign invariance p1_data = p1['data']['data'] * np.sign(p1['data']['data'][0, 0]) p2_data = p2['data']['data'] * np.sign(p2['data']['data'][0, 0]) if bad_ch in p1['data']['col_names']: bad = p1['data']['col_names'].index('MEG 2443') mask = np.ones(p1_data.size, dtype=np.bool) mask[bad] = False p1_data = p1_data[:, mask] p2_data = p2_data[:, mask] corr = np.corrcoef(p1_data, p2_data)[0, 1] assert_array_almost_equal(corr, 1.0, 5) # test that you can compute the projection matrix projs = activate_proj(projs) proj, nproj, U = make_projector(projs, epochs.ch_names, bads=[]) assert_true(nproj == 2) assert_true(U.shape[1] == 2) # test that you can save them epochs.info['projs'] += projs evoked = epochs.average() evoked.save('foo.fif') projs = read_proj(proj_fname) projs_evoked = compute_proj_evoked(evoked, n_grad=1, n_mag=1, n_eeg=0) # XXX : test something # test parallelization projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=2) projs = activate_proj(projs) proj_par, _, _ = make_projector(projs, epochs.ch_names, bads=[]) assert_array_equal(proj, proj_par) # Test that the raw projectors work for ii in (1, 2, 4, 8, 12, 24): raw = Raw(raw_fname) projs = compute_proj_raw(raw, duration=ii-0.1, n_grad=1, n_mag=1, n_eeg=0) # test that you can compute the projection matrix projs = activate_proj(projs) proj, nproj, U = make_projector(projs, epochs.ch_names, bads=[]) assert_true(nproj == 2) assert_true(U.shape[1] == 2) # test that you can save them raw.info['projs'] += projs raw.save('foo_%d_raw.fif' % ii) # Test that purely continuous (no duration) raw projection works raw = Raw(raw_fname) projs = compute_proj_raw(raw, duration=None, n_grad=1, n_mag=1, n_eeg=0) # test that you can compute the projection matrix projs = activate_proj(projs) proj, nproj, U = make_projector(projs, epochs.ch_names, bads=[]) assert_true(nproj == 2) assert_true(U.shape[1] == 2) # test that you can save them raw.info['projs'] += projs raw.save('foo_rawproj_continuous_raw.fif')