Python io.read_raw_ctf函数代码示例

def test_rawctf_clean_names():
    """Test RawCTF _clean_names method."""
    # read test data
    with pytest.warns(RuntimeWarning, match='ref channel RMSP did not'):
        raw = read_raw_ctf(op.join(ctf_dir, ctf_fname_catch))
        raw_cleaned = read_raw_ctf(op.join(ctf_dir, ctf_fname_catch),
                                   clean_names=True)
    test_channel_names = _clean_names(raw.ch_names)
    test_info_comps = copy.deepcopy(raw.info['comps'])

    # channel names should not be cleaned by default
    assert raw.ch_names != test_channel_names

    chs_ch_names = [ch['ch_name'] for ch in raw.info['chs']]

    assert chs_ch_names != test_channel_names

    for test_comp, comp in zip(test_info_comps, raw.info['comps']):
        for key in ('row_names', 'col_names'):
            assert not array_equal(_clean_names(test_comp['data'][key]),
                                   comp['data'][key])

    # channel names should be cleaned if clean_names=True
    assert raw_cleaned.ch_names == test_channel_names

    for ch, test_ch_name in zip(raw_cleaned.info['chs'], test_channel_names):
        assert ch['ch_name'] == test_ch_name

    for test_comp, comp in zip(test_info_comps, raw_cleaned.info['comps']):
        for key in ('row_names', 'col_names'):
            assert _clean_names(test_comp['data'][key]) == comp['data'][key]

def convert_ds_to_raw_fif(ds_file):
    import os
    import os.path as op
    
    from nipype.utils.filemanip import split_filename as split_f
    from mne.io import read_raw_ctf
    
    
    subj_path,basename,ext = split_f(ds_file)
    print subj_path,basename,ext
    raw = read_raw_ctf(ds_file)
    #raw_fif_file = os.path.abspath(basename + "_raw.fif")
    
    #raw.save(raw_fif_file)
    #return raw_fif_file

    
    raw_fif_file = os.path.join(subj_path,basename + "_raw.fif")
    if not op.isfile(raw_fif_file):
        raw = read_raw_ctf(ds_file)
        raw.save(raw_fif_file)
    else:
        print '*** RAW FIF file %s exists!!!' % raw_fif_file
        
    return raw_fif_file

def test_plot_ref_meg():
    """Test plotting ref_meg."""
    import matplotlib.pyplot as plt
    raw_ctf = read_raw_ctf(ctf_fname_continuous).crop(0, 1).load_data()
    raw_ctf.plot()
    plt.close('all')
    assert_raises(ValueError, raw_ctf.plot, group_by='selection')

def test_find_ch_connectivity():
    """Test computing the connectivity matrix."""
    data_path = testing.data_path()

    raw = read_raw_fif(raw_fname, preload=True)
    sizes = {'mag': 828, 'grad': 1700, 'eeg': 386}
    nchans = {'mag': 102, 'grad': 204, 'eeg': 60}
    for ch_type in ['mag', 'grad', 'eeg']:
        conn, ch_names = find_ch_connectivity(raw.info, ch_type)
        # Silly test for checking the number of neighbors.
        assert_equal(conn.getnnz(), sizes[ch_type])
        assert_equal(len(ch_names), nchans[ch_type])
    pytest.raises(ValueError, find_ch_connectivity, raw.info, None)

    # Test computing the conn matrix with gradiometers.
    conn, ch_names = _compute_ch_connectivity(raw.info, 'grad')
    assert_equal(conn.getnnz(), 2680)

    # Test ch_type=None.
    raw.pick_types(meg='mag')
    find_ch_connectivity(raw.info, None)

    bti_fname = op.join(data_path, 'BTi', 'erm_HFH', 'c,rfDC')
    bti_config_name = op.join(data_path, 'BTi', 'erm_HFH', 'config')
    raw = read_raw_bti(bti_fname, bti_config_name, None)
    _, ch_names = find_ch_connectivity(raw.info, 'mag')
    assert 'A1' in ch_names

    ctf_fname = op.join(data_path, 'CTF', 'testdata_ctf_short.ds')
    raw = read_raw_ctf(ctf_fname)
    _, ch_names = find_ch_connectivity(raw.info, 'mag')
    assert 'MLC11' in ch_names

    pytest.raises(ValueError, find_ch_connectivity, raw.info, 'eog')

def test_plot_trans():
    """Test plotting of -trans.fif files and MEG sensor layouts
    """
    evoked = read_evokeds(evoked_fname)[0]
    with warnings.catch_warnings(record=True):  # 4D weight tables
        bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True,
                           preload=False).info
    infos = dict(
        Neuromag=evoked.info,
        CTF=read_raw_ctf(ctf_fname).info,
        BTi=bti,
        KIT=read_raw_kit(sqd_fname).info,
    )
    for system, info in infos.items():
        ref_meg = False if system == 'KIT' else True
        plot_trans(info, trans_fname, subject='sample', meg_sensors=True,
                   subjects_dir=subjects_dir, ref_meg=ref_meg)
    # KIT ref sensor coil def not defined
    assert_raises(RuntimeError, plot_trans, infos['KIT'], None,
                  meg_sensors=True, ref_meg=True)
    info = infos['Neuromag']
    assert_raises(ValueError, plot_trans, info, trans_fname,
                  subject='sample', subjects_dir=subjects_dir,
                  ch_type='bad-chtype')
    assert_raises(TypeError, plot_trans, 'foo', trans_fname,
                  subject='sample', subjects_dir=subjects_dir)
    # no-head version
    plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
    # EEG only with strange options
    with warnings.catch_warnings(record=True) as w:
        plot_trans(evoked.copy().pick_types(meg=False, eeg=True).info,
                   trans=trans_fname, meg_sensors=True)
    assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])

def test_check_compensation_consistency():
    """Test check picks compensation."""
    raw = read_raw_ctf(ctf_fname, preload=False)
    events = make_fixed_length_events(raw, 99999)
    picks = pick_types(raw.info, meg=True, exclude=[], ref_meg=True)
    pick_ch_names = [raw.info['ch_names'][idx] for idx in picks]
    for (comp, expected_result) in zip([0, 1], [False, False]):
        raw.apply_gradient_compensation(comp)
        ret, missing = _bad_chans_comp(raw.info, pick_ch_names)
        assert ret == expected_result
        assert len(missing) == 0
        Epochs(raw, events, None, -0.2, 0.2, preload=False, picks=picks)

    picks = pick_types(raw.info, meg=True, exclude=[], ref_meg=False)
    pick_ch_names = [raw.info['ch_names'][idx] for idx in picks]

    for (comp, expected_result) in zip([0, 1], [False, True]):
        raw.apply_gradient_compensation(comp)
        ret, missing = _bad_chans_comp(raw.info, pick_ch_names)
        assert ret == expected_result
        assert len(missing) == 17
        with catch_logging() as log:
            Epochs(raw, events, None, -0.2, 0.2, preload=False,
                   picks=picks, verbose=True)
            assert'Removing 5 compensators' in log.getvalue()

def test_ica_ctf():
    """Test run ICA computation on ctf data with/without compensation."""
    method = 'fastica'
    raw = read_raw_ctf(ctf_fname, preload=True)
    events = make_fixed_length_events(raw, 99999)
    for comp in [0, 1]:
        raw.apply_gradient_compensation(comp)
        epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
        evoked = epochs.average()

        # test fit
        for inst in [raw, epochs]:
            ica = ICA(n_components=2, random_state=0, max_iter=2,
                      method=method)
            with pytest.warns(UserWarning, match='did not converge'):
                ica.fit(inst)

        # test apply and get_sources
        for inst in [raw, epochs, evoked]:
            ica.apply(inst)
            ica.get_sources(inst)

    # test mixed compensation case
    raw.apply_gradient_compensation(0)
    ica = ICA(n_components=2, random_state=0, max_iter=2, method=method)
    with pytest.warns(UserWarning, match='did not converge'):
        ica.fit(raw)
    raw.apply_gradient_compensation(1)
    epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
    evoked = epochs.average()
    for inst in [raw, epochs, evoked]:
        with pytest.raises(RuntimeError, match='Compensation grade of ICA'):
            ica.apply(inst)
        with pytest.raises(RuntimeError, match='Compensation grade of ICA'):
            ica.get_sources(inst)

def test_saving_picked():
    """Test saving picked CTF instances."""
    temp_dir = _TempDir()
    out_fname = op.join(temp_dir, 'test_py_raw.fif')
    raw = read_raw_ctf(op.join(ctf_dir, ctf_fname_1_trial))
    raw.crop(0, 1).load_data()
    assert raw.compensation_grade == get_current_comp(raw.info) == 0
    assert len(raw.info['comps']) == 5
    pick_kwargs = dict(meg=True, ref_meg=False, verbose=True)
    for comp_grade in [0, 1]:
        raw.apply_gradient_compensation(comp_grade)
        with catch_logging() as log:
            raw_pick = raw.copy().pick_types(**pick_kwargs)
        assert len(raw.info['comps']) == 5
        assert len(raw_pick.info['comps']) == 0
        log = log.getvalue()
        assert 'Removing 5 compensators' in log
        raw_pick.save(out_fname, overwrite=True)  # should work
        raw2 = read_raw_fif(out_fname)
        assert (raw_pick.ch_names == raw2.ch_names)
        assert_array_equal(raw_pick.times, raw2.times)
        assert_allclose(raw2[0:20][0], raw_pick[0:20][0], rtol=1e-6,
                        atol=1e-20)  # atol is very small but > 0

        raw2 = read_raw_fif(out_fname, preload=True)
        assert (raw_pick.ch_names == raw2.ch_names)
        assert_array_equal(raw_pick.times, raw2.times)
        assert_allclose(raw2[0:20][0], raw_pick[0:20][0], rtol=1e-6,
                        atol=1e-20)  # atol is very small but > 0

def test_check_compensation_consistency():
    """Test check picks compensation."""
    raw = read_raw_ctf(ctf_fname, preload=False)
    events = make_fixed_length_events(raw, 99999)
    picks = pick_types(raw.info, meg=True, exclude=[], ref_meg=True)
    pick_ch_names = [raw.info['ch_names'][idx] for idx in picks]
    for (comp, expected_result) in zip([0, 1], [False, False]):
        raw.apply_gradient_compensation(comp)
        ret, missing = _bad_chans_comp(raw.info, pick_ch_names)
        assert ret == expected_result
        assert len(missing) == 0
        Epochs(raw, events, None, -0.2, 0.2, preload=False, picks=picks)

    picks = pick_types(raw.info, meg=True, exclude=[], ref_meg=False)
    pick_ch_names = [raw.info['ch_names'][idx] for idx in picks]

    for (comp, expected_result) in zip([0, 1], [False, True]):
        raw.apply_gradient_compensation(comp)
        ret, missing = _bad_chans_comp(raw.info, pick_ch_names)
        assert ret == expected_result
        assert len(missing) == 17
        if comp != 0:
            with pytest.raises(RuntimeError,
                               match='Compensation grade 1 has been applied'):
                Epochs(raw, events, None, -0.2, 0.2, preload=False,
                       picks=picks)
        else:
            Epochs(raw, events, None, -0.2, 0.2, preload=False, picks=picks)

def test_cov_ctf():
    """Test basic cov computation on ctf data with/without compensation."""
    raw = read_raw_ctf(ctf_fname).crop(0., 2.).load_data()
    events = make_fixed_length_events(raw, 99999)
    assert len(events) == 2
    ch_names = [raw.info['ch_names'][pick]
                for pick in pick_types(raw.info, meg=True, eeg=False,
                                       ref_meg=False)]

    for comp in [0, 1]:
        raw.apply_gradient_compensation(comp)
        epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
        with pytest.warns(RuntimeWarning, match='Too few samples'):
            noise_cov = compute_covariance(epochs, tmax=0.,
                                           method=['empirical'])
        prepare_noise_cov(noise_cov, raw.info, ch_names)

    raw.apply_gradient_compensation(0)
    epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
    with pytest.warns(RuntimeWarning, match='Too few samples'):
        noise_cov = compute_covariance(epochs, tmax=0., method=['empirical'])
    raw.apply_gradient_compensation(1)

    # TODO This next call in principle should fail.
    prepare_noise_cov(noise_cov, raw.info, ch_names)

    # make sure comps matrices was not removed from raw
    assert raw.info['comps'], 'Comps matrices removed'

def test_ica_eeg():
    """Test ICA on EEG."""
    method = 'fastica'
    raw_fif = read_raw_fif(fif_fname, preload=True)
    with pytest.warns(RuntimeWarning, match='events'):
        raw_eeglab = read_raw_eeglab(input_fname=eeglab_fname,
                                     montage=eeglab_montage, preload=True)
    for raw in [raw_fif, raw_eeglab]:
        events = make_fixed_length_events(raw, 99999, start=0, stop=0.3,
                                          duration=0.1)
        picks_meg = pick_types(raw.info, meg=True, eeg=False)[:2]
        picks_eeg = pick_types(raw.info, meg=False, eeg=True)[:2]
        picks_all = []
        picks_all.extend(picks_meg)
        picks_all.extend(picks_eeg)
        epochs = Epochs(raw, events, None, -0.1, 0.1, preload=True)
        evoked = epochs.average()

        for picks in [picks_meg, picks_eeg, picks_all]:
            if len(picks) == 0:
                continue
            # test fit
            for inst in [raw, epochs]:
                ica = ICA(n_components=2, random_state=0, max_iter=2,
                          method=method)
                with pytest.warns(None):
                    ica.fit(inst, picks=picks)

            # test apply and get_sources
            for inst in [raw, epochs, evoked]:
                ica.apply(inst)
                ica.get_sources(inst)

    with pytest.warns(RuntimeWarning, match='MISC channel'):
        raw = read_raw_ctf(ctf_fname2,  preload=True)
    events = make_fixed_length_events(raw, 99999, start=0, stop=0.2,
                                      duration=0.1)
    picks_meg = pick_types(raw.info, meg=True, eeg=False)[:2]
    picks_eeg = pick_types(raw.info, meg=False, eeg=True)[:2]
    picks_all = picks_meg + picks_eeg
    for comp in [0, 1]:
        raw.apply_gradient_compensation(comp)
        epochs = Epochs(raw, events, None, -0.1, 0.1, preload=True)
        evoked = epochs.average()

        for picks in [picks_meg, picks_eeg, picks_all]:
            if len(picks) == 0:
                continue
            # test fit
            for inst in [raw, epochs]:
                ica = ICA(n_components=2, random_state=0, max_iter=2,
                          method=method)
                with pytest.warns(None):
                    ica.fit(inst)

            # test apply and get_sources
            for inst in [raw, epochs, evoked]:
                ica.apply(inst)
                ica.get_sources(inst)

def test_interpolation_ctf_comp():
    """Test interpolation with compensated CTF data."""
    ctf_dir = op.join(testing.data_path(download=False), 'CTF')
    raw_fname = op.join(ctf_dir, 'somMDYO-18av.ds')
    raw = io.read_raw_ctf(raw_fname, preload=True)
    raw.info['bads'] = [raw.ch_names[5], raw.ch_names[-5]]
    raw.interpolate_bads(mode='fast')
    assert raw.info['bads'] == []

def test_read_spm_ctf():
    """Test CTF reader with omitted samples."""
    data_path = spm_face.data_path()
    raw_fname = op.join(data_path, 'MEG', 'spm',
                        'SPM_CTF_MEG_example_faces1_3D.ds')
    raw = read_raw_ctf(raw_fname)
    extras = raw._raw_extras[0]
    assert_equal(extras['n_samp'], raw.n_times)
    assert_false(extras['n_samp'] == extras['n_samp_tot'])

def test_calculate_head_pos_ctf():
    """Test extracting of cHPI positions from ctf data."""
    raw = read_raw_ctf(ctf_chpi_fname)
    quats = _calculate_head_pos_ctf(raw)
    mc_quats = read_head_pos(ctf_chpi_pos_fname)
    _assert_quats(quats, mc_quats, dist_tol=0.004, angle_tol=2.5)

    raw = read_raw_fif(ctf_fname)
    pytest.raises(RuntimeError, _calculate_head_pos_ctf, raw)

def test_dipole_fitting_ctf():
    """Test dipole fitting with CTF data."""
    raw_ctf = read_raw_ctf(fname_ctf).set_eeg_reference()
    events = make_fixed_length_events(raw_ctf, 1)
    evoked = Epochs(raw_ctf, events, 1, 0, 0, baseline=None).average()
    cov = make_ad_hoc_cov(evoked.info)
    sphere = make_sphere_model((0., 0., 0.))
    # XXX Eventually we should do some better checks about accuracy, but
    # for now our CTF phantom fitting tutorials will have to do
    # (otherwise we need to add that to the testing dataset, which is
    # a bit too big)
    fit_dipole(evoked, cov, sphere)