Python mne.compute_proj_raw函数代码示例

def test_sss_proj():
    """Test `meg` proj option."""
    raw = read_raw_fif(raw_fname)
    raw.crop(0, 1.0).load_data().pick_types(exclude=())
    raw.pick_channels(raw.ch_names[:51]).del_proj()
    with pytest.raises(ValueError, match='can only be used with Maxfiltered'):
        compute_proj_raw(raw, meg='combined')
    raw_sss = maxwell_filter(raw, int_order=5, ext_order=2)
    sss_rank = 21  # really low due to channel picking
    assert len(raw_sss.info['projs']) == 0
    for meg, n_proj, want_rank in (('separate', 6, sss_rank),
                                   ('combined', 3, sss_rank - 3)):
        proj = compute_proj_raw(raw_sss, n_grad=3, n_mag=3, meg=meg,
                                verbose='error')
        this_raw = raw_sss.copy().add_proj(proj).apply_proj()
        assert len(this_raw.info['projs']) == n_proj
        sss_proj_rank = _compute_rank_int(this_raw)
        cov = compute_raw_covariance(this_raw, verbose='error')
        W, ch_names, rank = compute_whitener(cov, this_raw.info,
                                             return_rank=True)
        assert ch_names == this_raw.ch_names
        assert want_rank == sss_proj_rank == rank  # proper reduction
        if meg == 'combined':
            assert this_raw.info['projs'][0]['data']['col_names'] == ch_names
        else:
            mag_names = ch_names[2::3]
            assert this_raw.info['projs'][3]['data']['col_names'] == mag_names

def test_proj_raw_duration(duration, sfreq):
    """Test equivalence of `duration` options."""
    n_ch, n_dim = 30, 3
    rng = np.random.RandomState(0)
    signals = rng.randn(n_dim, 10000)
    mixing = rng.randn(n_ch, n_dim) + [0, 1, 2]
    data = np.dot(mixing, signals)
    raw = RawArray(data, create_info(n_ch, sfreq, 'eeg'))
    raw.set_eeg_reference(projection=True)
    n_eff = int(round(raw.info['sfreq'] * duration))
    # crop to an even "duration" number of epochs
    stop = ((len(raw.times) // n_eff) * n_eff - 1) / raw.info['sfreq']
    raw.crop(0, stop)
    proj_def = compute_proj_raw(raw, n_eeg=n_dim)
    proj_dur = compute_proj_raw(raw, duration=duration, n_eeg=n_dim)
    proj_none = compute_proj_raw(raw, duration=None, n_eeg=n_dim)
    assert len(proj_dur) == len(proj_none) == len(proj_def) == n_dim
    # proj_def is not in here because it does not necessarily evenly divide
    # the signal length:
    for pu, pn in zip(proj_dur, proj_none):
        assert_allclose(pu['data']['data'], pn['data']['data'])
    # but we can test it here since it should still be a small subspace angle:
    for proj in (proj_dur, proj_none, proj_def):
        computed = np.concatenate([p['data']['data'] for p in proj], 0)
        angle = np.rad2deg(linalg.subspace_angles(computed.T, mixing)[0])
        assert angle < 1e-5

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_rank_estimation():
    """Test raw rank estimation
    """
    iter_tests = itt.product([fif_fname, hp_fif_fname], ["norm", dict(mag=1e11, grad=1e9, eeg=1e5)])  # sss
    for fname, scalings in iter_tests:
        raw = Raw(fname)
        (_, picks_meg), (_, picks_eeg) = _picks_by_type(raw.info, meg_combined=True)
        n_meg = len(picks_meg)
        n_eeg = len(picks_eeg)

        raw = Raw(fname, preload=True)
        if "proc_history" not in raw.info:
            expected_rank = n_meg + n_eeg
        else:
            mf = raw.info["proc_history"][0]["max_info"]
            expected_rank = _get_sss_rank(mf) + n_eeg
        assert_array_equal(raw.estimate_rank(scalings=scalings), expected_rank)

        assert_array_equal(raw.estimate_rank(picks=picks_eeg, scalings=scalings), n_eeg)

        raw = Raw(fname, preload=False)
        if "sss" in fname:
            tstart, tstop = 0.0, 30.0
            raw.add_proj(compute_proj_raw(raw))
            raw.apply_proj()
        else:
            tstart, tstop = 10.0, 20.0

        raw.apply_proj()
        n_proj = len(raw.info["projs"])

        assert_array_equal(
            raw.estimate_rank(tstart=tstart, tstop=tstop, scalings=scalings),
            expected_rank - (1 if "sss" in fname else n_proj),
        )

def apply_empty_room_projections(raw, raw_empty_room):
    '''
    Calculates empty room projections from empty room data and applies it to raw.
    Note: Make sure the empty room data is also filtered. This may affect the projections.

    Input
    -----
    raw, raw_empty_room: mne Raw object
        Raw file and Empty room raw file.

    Returns
    -------
    raw: mne Raw object
        Raw file with projections applied.
    empty_room_proj: projections
        Empty room projection vectors. 
    '''
    # Add checks to make sure its empty room.
    # Check for events in ECG, EOG, STI.
    print 'Empty room projections calculated for %s.'%(raw_empty_fname)
    empty_room_proj = mne.compute_proj_raw(raw_empty_room)
    raw.add_proj(empty_room_proj).apply_proj()
    return raw, empty_room_proj

def test_rank():
    """Test cov rank estimation."""
    # Test that our rank estimation works properly on a simple case
    evoked = read_evokeds(ave_fname, condition=0, baseline=(None, 0),
                          proj=False)
    cov = read_cov(cov_fname)
    ch_names = [ch for ch in evoked.info['ch_names'] if '053' not in ch and
                ch.startswith('EEG')]
    cov = prepare_noise_cov(cov, evoked.info, ch_names, None)
    assert_equal(cov['eig'][0], 0.)  # avg projector should set this to zero
    assert_true((cov['eig'][1:] > 0).all())  # all else should be > 0

    # Now do some more comprehensive tests
    raw_sample = read_raw_fif(raw_fname)

    raw_sss = read_raw_fif(hp_fif_fname)
    raw_sss.add_proj(compute_proj_raw(raw_sss))

    cov_sample = compute_raw_covariance(raw_sample)
    cov_sample_proj = compute_raw_covariance(
        raw_sample.copy().apply_proj())

    cov_sss = compute_raw_covariance(raw_sss)
    cov_sss_proj = compute_raw_covariance(
        raw_sss.copy().apply_proj())

    picks_all_sample = pick_types(raw_sample.info, meg=True, eeg=True)
    picks_all_sss = pick_types(raw_sss.info, meg=True, eeg=True)

    info_sample = pick_info(raw_sample.info, picks_all_sample)
    picks_stack_sample = [('eeg', pick_types(info_sample, meg=False,
                                             eeg=True))]
    picks_stack_sample += [('meg', pick_types(info_sample, meg=True))]
    picks_stack_sample += [('all',
                            pick_types(info_sample, meg=True, eeg=True))]

    info_sss = pick_info(raw_sss.info, picks_all_sss)
    picks_stack_somato = [('eeg', pick_types(info_sss, meg=False, eeg=True))]
    picks_stack_somato += [('meg', pick_types(info_sss, meg=True))]
    picks_stack_somato += [('all',
                            pick_types(info_sss, meg=True, eeg=True))]

    iter_tests = list(itt.product(
        [(cov_sample, picks_stack_sample, info_sample),
         (cov_sample_proj, picks_stack_sample, info_sample),
         (cov_sss, picks_stack_somato, info_sss),
         (cov_sss_proj, picks_stack_somato, info_sss)],  # sss
        [dict(mag=1e15, grad=1e13, eeg=1e6)]
    ))

    for (cov, picks_list, this_info), scalings in iter_tests:
        for ch_type, picks in picks_list:

            this_very_info = pick_info(this_info, picks)

            # compute subset of projs
            this_projs = [c['active'] and
                          len(set(c['data']['col_names'])
                              .intersection(set(this_very_info['ch_names']))) >
                          0 for c in cov['projs']]
            n_projs = sum(this_projs)

            # count channel types
            ch_types = [channel_type(this_very_info, idx)
                        for idx in range(len(picks))]
            n_eeg, n_mag, n_grad = [ch_types.count(k) for k in
                                    ['eeg', 'mag', 'grad']]
            n_meg = n_mag + n_grad
            if ch_type in ('all', 'eeg'):
                n_projs_eeg = 1
            else:
                n_projs_eeg = 0

            # check sss
            if len(this_very_info['proc_history']) > 0:
                mf = this_very_info['proc_history'][0]['max_info']
                n_free = _get_sss_rank(mf)
                if 'mag' not in ch_types and 'grad' not in ch_types:
                    n_free = 0
                # - n_projs XXX clarify
                expected_rank = n_free + n_eeg
                if n_projs > 0 and ch_type in ('all', 'eeg'):
                    expected_rank -= n_projs_eeg
            else:
                expected_rank = n_meg + n_eeg - n_projs

            C = cov['data'][np.ix_(picks, picks)]
            est_rank = _estimate_rank_meeg_cov(C, this_very_info,
                                               scalings=scalings)

            assert_equal(expected_rank, est_rank)

def test_rank():
    """Test cov rank estimation"""
    raw_sample = Raw(raw_fname)

    raw_sss = Raw(hp_fif_fname)
    raw_sss.add_proj(compute_proj_raw(raw_sss))

    cov_sample = compute_raw_data_covariance(raw_sample)
    cov_sample_proj = compute_raw_data_covariance(
        raw_sample.copy().apply_proj())

    cov_sss = compute_raw_data_covariance(raw_sss)
    cov_sss_proj = compute_raw_data_covariance(
        raw_sss.copy().apply_proj())

    picks_all_sample = pick_types(raw_sample.info, meg=True, eeg=True)
    picks_all_sss = pick_types(raw_sss.info, meg=True, eeg=True)

    info_sample = pick_info(raw_sample.info, picks_all_sample)
    picks_stack_sample = [('eeg', pick_types(info_sample, meg=False,
                                             eeg=True))]
    picks_stack_sample += [('meg', pick_types(info_sample, meg=True))]
    picks_stack_sample += [('all',
                            pick_types(info_sample, meg=True, eeg=True))]

    info_sss = pick_info(raw_sss.info, picks_all_sss)
    picks_stack_somato = [('eeg', pick_types(info_sss, meg=False, eeg=True))]
    picks_stack_somato += [('meg', pick_types(info_sss, meg=True))]
    picks_stack_somato += [('all',
                            pick_types(info_sss, meg=True, eeg=True))]

    iter_tests = list(itt.product(
        [(cov_sample, picks_stack_sample, info_sample),
         (cov_sample_proj, picks_stack_sample, info_sample),
         (cov_sss, picks_stack_somato, info_sss),
         (cov_sss_proj, picks_stack_somato, info_sss)],  # sss
        [dict(mag=1e15, grad=1e13, eeg=1e6)]
    ))

    for (cov, picks_list, this_info), scalings in iter_tests:
        for ch_type, picks in picks_list:

            this_very_info = pick_info(this_info, picks)

            # compute subset of projs
            this_projs = [c['active'] and
                          len(set(c['data']['col_names'])
                              .intersection(set(this_very_info['ch_names']))) >
                          0 for c in cov['projs']]
            n_projs = sum(this_projs)

            # count channel types
            ch_types = [channel_type(this_very_info, idx)
                        for idx in range(len(picks))]
            n_eeg, n_mag, n_grad = [ch_types.count(k) for k in
                                    ['eeg', 'mag', 'grad']]
            n_meg = n_mag + n_grad
            if ch_type in ('all', 'eeg'):
                n_projs_eeg = 1
            else:
                n_projs_eeg = 0

            # check sss
            if 'proc_history' in this_very_info:
                mf = this_very_info['proc_history'][0]['max_info']
                n_free = _get_sss_rank(mf)
                if 'mag' not in ch_types and 'grad' not in ch_types:
                    n_free = 0
                # - n_projs XXX clarify
                expected_rank = n_free + n_eeg
                if n_projs > 0 and ch_type in ('all', 'eeg'):
                    expected_rank -= n_projs_eeg
            else:
                expected_rank = n_meg + n_eeg - n_projs

            C = cov['data'][np.ix_(picks, picks)]
            est_rank = _estimate_rank_meeg_cov(C, this_very_info,
                                               scalings=scalings)

            assert_equal(expected_rank, est_rank)

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')

def test_low_rank():
    """Test low-rank covariance matrix estimation."""
    raw = read_raw_fif(raw_fname).set_eeg_reference(projection=True).crop(0, 3)
    raw = maxwell_filter(raw, regularize=None)  # heavily reduce the rank
    sss_proj_rank = 139  # 80 MEG + 60 EEG - 1 proj
    n_ch = 366
    proj_rank = 365  # one EEG proj
    events = make_fixed_length_events(raw)
    methods = ('empirical', 'diagonal_fixed', 'oas')
    epochs = Epochs(raw, events, tmin=-0.2, tmax=0, preload=True)
    bounds = {
        'None': dict(empirical=(-6000, -5000),
                     diagonal_fixed=(-1500, -500),
                     oas=(-700, -600)),
        'full': dict(empirical=(-9000, -8000),
                     diagonal_fixed=(-2000, -1600),
                     oas=(-1600, -1000)),
    }
    for rank in ('full', None):
        covs = compute_covariance(
            epochs, method=methods, return_estimators=True,
            verbose='error', rank=rank)
        for cov in covs:
            method = cov['method']
            these_bounds = bounds[str(rank)][method]
            this_rank = _cov_rank(cov, epochs.info)
            if rank is None or method == 'empirical':
                assert this_rank == sss_proj_rank
            else:
                assert this_rank == proj_rank
            assert these_bounds[0] < cov['loglik'] < these_bounds[1], \
                (rank, method)
            if method == 'empirical':
                emp_cov = cov  # save for later, rank param does not matter

    # Test equivalence with mne.cov.regularize subspace
    with pytest.raises(ValueError, match='are dependent.*must equal'):
        regularize(emp_cov, epochs.info, rank=None, mag=0.1, grad=0.2)
    assert _cov_rank(emp_cov, epochs.info) == sss_proj_rank
    reg_cov = regularize(emp_cov, epochs.info, proj=True, rank='full')
    assert _cov_rank(reg_cov, epochs.info) == proj_rank
    del reg_cov
    with catch_logging() as log:
        reg_r_cov = regularize(emp_cov, epochs.info, proj=True, rank=None,
                               verbose=True)
    log = log.getvalue()
    assert 'jointly' in log
    assert _cov_rank(reg_r_cov, epochs.info) == sss_proj_rank
    reg_r_only_cov = regularize(emp_cov, epochs.info, proj=False, rank=None)
    assert _cov_rank(reg_r_only_cov, epochs.info) == sss_proj_rank
    assert_allclose(reg_r_only_cov['data'], reg_r_cov['data'])
    del reg_r_only_cov, reg_r_cov

    # test that rank=306 is same as rank='full'
    epochs_meg = epochs.copy().pick_types()
    assert len(epochs_meg.ch_names) == 306
    epochs_meg.info.update(bads=[], projs=[])
    cov_full = compute_covariance(epochs_meg, method='oas',
                                  rank='full', verbose='error')
    assert _cov_rank(cov_full, epochs_meg.info) == 306
    cov_dict = compute_covariance(epochs_meg, method='oas',
                                  rank=306, verbose='error')
    assert _cov_rank(cov_dict, epochs_meg.info) == 306
    assert_allclose(cov_full['data'], cov_dict['data'])

    # Work with just EEG data to simplify projection / rank reduction
    raw.pick_types(meg=False, eeg=True)
    n_proj = 2
    raw.add_proj(compute_proj_raw(raw, n_eeg=n_proj))
    n_ch = len(raw.ch_names)
    rank = n_ch - n_proj - 1  # plus avg proj
    assert len(raw.info['projs']) == 3
    epochs = Epochs(raw, events, tmin=-0.2, tmax=0, preload=True)
    assert len(raw.ch_names) == n_ch
    emp_cov = compute_covariance(epochs, rank='full', verbose='error')
    assert _cov_rank(emp_cov, epochs.info) == rank
    reg_cov = regularize(emp_cov, epochs.info, proj=True, rank='full')
    assert _cov_rank(reg_cov, epochs.info) == rank
    reg_r_cov = regularize(emp_cov, epochs.info, proj=False, rank=None)
    assert _cov_rank(reg_r_cov, epochs.info) == rank
    dia_cov = compute_covariance(epochs, rank=None, method='diagonal_fixed',
                                 verbose='error')
    assert _cov_rank(dia_cov, epochs.info) == rank
    assert_allclose(dia_cov['data'], reg_cov['data'])
    # test our deprecation: can simply remove later
    epochs.pick_channels(epochs.ch_names[:103])
    # degenerate
    with pytest.raises(ValueError, match='can.*only be used with rank="full"'):
        compute_covariance(epochs, rank=None, method='pca')
    with pytest.raises(ValueError, match='can.*only be used with rank="full"'):
        compute_covariance(epochs, rank=None, method='factor_analysis')

def test_low_rank_cov(raw_epochs_events):
    """Test additional properties of low rank computations."""
    raw, epochs, events = raw_epochs_events
    sss_proj_rank = 139  # 80 MEG + 60 EEG - 1 proj
    n_ch = 366
    proj_rank = 365  # one EEG proj
    with pytest.warns(RuntimeWarning, match='Too few samples'):
        emp_cov = compute_covariance(epochs)
    # Test equivalence with mne.cov.regularize subspace
    with pytest.raises(ValueError, match='are dependent.*must equal'):
        regularize(emp_cov, epochs.info, rank=None, mag=0.1, grad=0.2)
    assert _cov_rank(emp_cov, epochs.info) == sss_proj_rank
    reg_cov = regularize(emp_cov, epochs.info, proj=True, rank='full')
    assert _cov_rank(reg_cov, epochs.info) == proj_rank
    with pytest.warns(RuntimeWarning, match='exceeds the theoretical'):
        _compute_rank_int(reg_cov, info=epochs.info)
    del reg_cov
    with catch_logging() as log:
        reg_r_cov = regularize(emp_cov, epochs.info, proj=True, rank=None,
                               verbose=True)
    log = log.getvalue()
    assert 'jointly' in log
    assert _cov_rank(reg_r_cov, epochs.info) == sss_proj_rank
    reg_r_only_cov = regularize(emp_cov, epochs.info, proj=False, rank=None)
    assert _cov_rank(reg_r_only_cov, epochs.info) == sss_proj_rank
    assert_allclose(reg_r_only_cov['data'], reg_r_cov['data'])
    del reg_r_only_cov, reg_r_cov

    # test that rank=306 is same as rank='full'
    epochs_meg = epochs.copy().pick_types()
    assert len(epochs_meg.ch_names) == 306
    epochs_meg.info.update(bads=[], projs=[])
    cov_full = compute_covariance(epochs_meg, method='oas',
                                  rank='full', verbose='error')
    assert _cov_rank(cov_full, epochs_meg.info) == 306
    with pytest.deprecated_call(match='int is deprecated'):
        cov_dict = compute_covariance(epochs_meg, method='oas', rank=306)
    assert _cov_rank(cov_dict, epochs_meg.info) == 306
    assert_allclose(cov_full['data'], cov_dict['data'])
    cov_dict = compute_covariance(epochs_meg, method='oas',
                                  rank=dict(meg=306), verbose='error')
    assert _cov_rank(cov_dict, epochs_meg.info) == 306
    assert_allclose(cov_full['data'], cov_dict['data'])

    # Work with just EEG data to simplify projection / rank reduction
    raw = raw.copy().pick_types(meg=False, eeg=True)
    n_proj = 2
    raw.add_proj(compute_proj_raw(raw, n_eeg=n_proj))
    n_ch = len(raw.ch_names)
    rank = n_ch - n_proj - 1  # plus avg proj
    assert len(raw.info['projs']) == 3
    epochs = Epochs(raw, events, tmin=-0.2, tmax=0, preload=True)
    assert len(raw.ch_names) == n_ch
    emp_cov = compute_covariance(epochs, rank='full', verbose='error')
    assert _cov_rank(emp_cov, epochs.info) == rank
    reg_cov = regularize(emp_cov, epochs.info, proj=True, rank='full')
    assert _cov_rank(reg_cov, epochs.info) == rank
    reg_r_cov = regularize(emp_cov, epochs.info, proj=False, rank=None)
    assert _cov_rank(reg_r_cov, epochs.info) == rank
    dia_cov = compute_covariance(epochs, rank=None, method='diagonal_fixed',
                                 verbose='error')
    assert _cov_rank(dia_cov, epochs.info) == rank
    assert_allclose(dia_cov['data'], reg_cov['data'])
    # test our deprecation: can simply remove later
    epochs.pick_channels(epochs.ch_names[:103])
    # degenerate
    with pytest.raises(ValueError, match='can.*only be used with rank="full"'):
        compute_covariance(epochs, rank=None, method='pca')
    with pytest.raises(ValueError, match='can.*only be used with rank="full"'):
        compute_covariance(epochs, rank=None, method='factor_analysis')