Python mne.read_forward_solution函数代码示例

def test_convert_forward():
    """Test converting forward solution between different representations
    """
    fwd = read_forward_solution(fname_meeg)
    print(fwd)  # __repr__
    assert_true(isinstance(fwd, Forward))
    # look at surface orientation
    fwd_surf = convert_forward_solution(fwd, surf_ori=True)
    fwd_surf_io = read_forward_solution(fname_meeg, surf_ori=True)
    compare_forwards(fwd_surf, fwd_surf_io)
    # go back
    fwd_new = convert_forward_solution(fwd_surf, surf_ori=False)
    print(fwd_new)
    assert_true(isinstance(fwd, Forward))
    compare_forwards(fwd, fwd_new)
    # now go to fixed
    fwd_fixed = convert_forward_solution(fwd_surf, surf_ori=False,
                                         force_fixed=True)
    print(fwd_fixed)
    assert_true(isinstance(fwd_fixed, Forward))
    fwd_fixed_io = read_forward_solution(fname_meeg, surf_ori=False,
                                         force_fixed=True)
    compare_forwards(fwd_fixed, fwd_fixed_io)
    # now go back to cartesian (original condition)
    fwd_new = convert_forward_solution(fwd_fixed)
    print(fwd_new)
    assert_true(isinstance(fwd_new, Forward))
    compare_forwards(fwd, fwd_new)

def test_make_inverse_operator_fixed():
    """Test MNE inverse computation w/ fixed orientation (& no depth weighting)
    """
    fwd_op = read_forward_solution(fname_fwd, surf_ori=True)
    fwd_1 = read_forward_solution(fname_fwd, surf_ori=False, force_fixed=False)
    fwd_2 = read_forward_solution(fname_fwd, surf_ori=False, force_fixed=True)

    # can't make fixed inv without surf ori fwd
    assert_raises(ValueError, make_inverse_operator, evoked.info, fwd_1,
                  noise_cov, depth=0.8, loose=None, fixed=True)
    # can't make fixed inv with depth weighting without free ori fwd
    assert_raises(ValueError, make_inverse_operator, evoked.info, fwd_2,
                  noise_cov, depth=0.8, loose=None, fixed=True)
    inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov, depth=0.8,
                                   loose=None, fixed=True)
    _compare_io(inv_op)
    inverse_operator_fixed = read_inverse_operator(fname_inv_fixed)
    _compare_inverses_approx(inverse_operator_fixed, inv_op, evoked, 2)
    # Inverse has 306 channels - 4 proj = 302
    assert_true(compute_rank_inverse(inverse_operator_fixed) == 302)

    # Now without depth weighting, these should be equivalent
    inv_op = make_inverse_operator(evoked.info, fwd_2, noise_cov, depth=None,
                                   loose=None, fixed=True)
    inv_2 = make_inverse_operator(evoked.info, fwd_op, noise_cov, depth=None,
                                  loose=None, fixed=True)
    _compare_inverses_approx(inv_op, inv_2, evoked, 2)
    _compare_io(inv_op)
    # now compare to C solution
    inverse_operator_nodepth = read_inverse_operator(fname_inv_nodepth)
    _compare_inverses_approx(inverse_operator_nodepth, inv_op, evoked, 2)
    # Inverse has 306 channels - 4 proj = 302
    assert_true(compute_rank_inverse(inverse_operator_fixed) == 302)

def test_make_inverse_operator_fixed():
    """Test MNE inverse computation (fixed orientation)
    """
    fwd_op = read_forward_solution(fname_fwd, surf_ori=True)
    fwd_1 = read_forward_solution(fname_fwd, surf_ori=False, force_fixed=False)
    fwd_2 = read_forward_solution(fname_fwd, surf_ori=False, force_fixed=True)
    evoked = _get_evoked()
    noise_cov = read_cov(fname_cov)

    # can't make depth-weighted fixed inv without surf ori fwd
    assert_raises(ValueError, make_inverse_operator, evoked.info, fwd_1,
                  noise_cov, depth=0.8, loose=None, fixed=True)
    # can't make fixed inv with depth weighting without free ori fwd
    assert_raises(ValueError, make_inverse_operator, evoked.info, fwd_2,
                  noise_cov, depth=0.8, loose=None, fixed=True)

    # compare to C solution w/fixed
    inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov, depth=0.8,
                                   loose=None, fixed=True)
    _compare_io(inv_op)
    inverse_operator_fixed = read_inverse_operator(fname_inv_fixed)
    _compare_inverses_approx(inverse_operator_fixed, inv_op, evoked, 2)
    # Inverse has 306 channels - 4 proj = 302
    assert_true(compute_rank_inverse(inverse_operator_fixed) == 302)

    # now compare to C solution
    # note that the forward solution must not be surface-oriented
    # to get equivalency (surf_ori=True changes the normals)
    inv_op = make_inverse_operator(evoked.info, fwd_2, noise_cov, depth=None,
                                   loose=None, fixed=True)
    inverse_operator_nodepth = read_inverse_operator(fname_inv_nodepth)
    _compare_inverses_approx(inverse_operator_nodepth, inv_op, evoked, 2)
    # Inverse has 306 channels - 4 proj = 302
    assert_true(compute_rank_inverse(inverse_operator_fixed) == 302)

def test_io_forward():
    """Test IO for forward solutions
    """
    fwd = mne.read_forward_solution(fname)
    fwd = mne.read_forward_solution(fname, force_fixed=True)
    fwd = mne.read_forward_solution(fname, surf_ori=True)
    leadfield = fwd['sol']['data']

def test_psf_ctf():
    """Test computation of PSFs and CTFs for linear estimators
    """

    inverse_operator = read_inverse_operator(fname_inv)
    forward = read_forward_solution(fname_fwd, force_fixed=False,
                                    surf_ori=True)
    forward = pick_types_forward(forward, meg=True, eeg=False)
    labels = [mne.read_label(ss) for ss in fname_label]

    method = 'MNE'
    n_svd_comp = 2

    # Test PSFs (then CTFs)
    for mode in ('sum', 'svd'):
        stc_psf, psf_ev = point_spread_function(inverse_operator,
                                                forward,
                                                method=method,
                                                labels=labels,
                                                lambda2=lambda2,
                                                pick_ori='normal',
                                                mode=mode,
                                                n_svd_comp=n_svd_comp)

        n_vert, n_samples = stc_psf.shape
        should_n_vert = (inverse_operator['src'][1]['vertno'].shape[0] +
                         inverse_operator['src'][0]['vertno'].shape[0])
        if mode == 'svd':
            should_n_samples = len(labels) * n_svd_comp + 1
        else:
            should_n_samples = len(labels) + 1

        assert_true(n_vert == should_n_vert)
        assert_true(n_samples == should_n_samples)

        n_chan, n_samples = psf_ev.data.shape
        assert_true(n_chan == forward['nchan'])

    forward = read_forward_solution(fname_fwd, force_fixed=True, surf_ori=True)
    forward = pick_types_forward(forward, meg=True, eeg=False)

    # Test CTFs
    for mode in ('sum', 'svd'):
        stc_ctf = cross_talk_function(inverse_operator, forward,
                                      labels, method=method,
                                      lambda2=lambda2,
                                      signed=False, mode=mode,
                                      n_svd_comp=n_svd_comp)

        n_vert, n_samples = stc_ctf.shape
        should_n_vert = (inverse_operator['src'][1]['vertno'].shape[0] +
                         inverse_operator['src'][0]['vertno'].shape[0])
        if mode == 'svd':
            should_n_samples = len(labels) * n_svd_comp + 1
        else:
            should_n_samples = len(labels) + 1

        assert_true(n_vert == should_n_vert)
        assert_true(n_samples == should_n_samples)

def _get_data(tmin=-0.1, tmax=0.15, all_forward=True, epochs=True,
              epochs_preload=True, data_cov=True):
    """Read in data used in tests
    """
    label = mne.read_label(fname_label)
    events = mne.read_events(fname_event)
    raw = mne.fiff.Raw(fname_raw, preload=True)
    forward = mne.read_forward_solution(fname_fwd)
    if all_forward:
        forward_surf_ori = mne.read_forward_solution(fname_fwd, surf_ori=True)
        forward_fixed = mne.read_forward_solution(fname_fwd, force_fixed=True,
                                                  surf_ori=True)
        forward_vol = mne.read_forward_solution(fname_fwd_vol, surf_ori=True)
    else:
        forward_surf_ori = None
        forward_fixed = None
        forward_vol = None

    event_id, tmin, tmax = 1, tmin, tmax

    # Setup for reading the raw data
    raw.info['bads'] = ['MEG 2443', 'EEG 053']  # 2 bads channels

    if epochs:
        # Set up pick list: MEG - bad channels
        left_temporal_channels = mne.read_selection('Left-temporal')
        picks = mne.fiff.pick_types(raw.info, meg=True, eeg=False,
                                    stim=True, eog=True, ref_meg=False,
                                    exclude='bads',
                                    selection=left_temporal_channels)

        # Read epochs
        epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
                            picks=picks, baseline=(None, 0),
                            preload=epochs_preload,
                            reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))
        if epochs_preload:
            epochs.resample(200, npad=0, n_jobs=2)
        evoked = epochs.average()
        info = evoked.info
    else:
        epochs = None
        evoked = None
        info = raw.info

    noise_cov = mne.read_cov(fname_cov)
    noise_cov = mne.cov.regularize(noise_cov, info,
                                   mag=0.05, grad=0.05, eeg=0.1, proj=True)
    if data_cov:
        data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.15)
    else:
        data_cov = None

    return raw, epochs, evoked, data_cov, noise_cov, label, forward,\
        forward_surf_ori, forward_fixed, forward_vol

def test_restrict_forward_to_label():
    """Test restriction of source space to label
    """
    fwd = read_forward_solution(fname_meeg, force_fixed=True)
    fwd = pick_types_forward(fwd, meg=True)

    label_path = op.join(data_path, 'MEG', 'sample', 'labels')
    labels = ['Aud-lh', 'Vis-rh']
    label_lh = read_label(op.join(label_path, labels[0] + '.label'))
    label_rh = read_label(op.join(label_path, labels[1] + '.label'))

    fwd_out = restrict_forward_to_label(fwd, [label_lh, label_rh])

    src_sel_lh = np.intersect1d(fwd['src'][0]['vertno'], label_lh.vertices)
    src_sel_lh = np.searchsorted(fwd['src'][0]['vertno'], src_sel_lh)
    vertno_lh = fwd['src'][0]['vertno'][src_sel_lh]

    nuse_lh = fwd['src'][0]['nuse']
    src_sel_rh = np.intersect1d(fwd['src'][1]['vertno'], label_rh.vertices)
    src_sel_rh = np.searchsorted(fwd['src'][1]['vertno'], src_sel_rh)
    vertno_rh = fwd['src'][1]['vertno'][src_sel_rh]
    src_sel_rh += nuse_lh

    assert_equal(fwd_out['sol']['ncol'], len(src_sel_lh) + len(src_sel_rh))
    assert_equal(fwd_out['src'][0]['nuse'], len(src_sel_lh))
    assert_equal(fwd_out['src'][1]['nuse'], len(src_sel_rh))
    assert_equal(fwd_out['src'][0]['vertno'], vertno_lh)
    assert_equal(fwd_out['src'][1]['vertno'], vertno_rh)

    fwd = read_forward_solution(fname_meeg, force_fixed=False)
    fwd = pick_types_forward(fwd, meg=True)

    label_path = op.join(data_path, 'MEG', 'sample', 'labels')
    labels = ['Aud-lh', 'Vis-rh']
    label_lh = read_label(op.join(label_path, labels[0] + '.label'))
    label_rh = read_label(op.join(label_path, labels[1] + '.label'))

    fwd_out = restrict_forward_to_label(fwd, [label_lh, label_rh])

    src_sel_lh = np.intersect1d(fwd['src'][0]['vertno'], label_lh.vertices)
    src_sel_lh = np.searchsorted(fwd['src'][0]['vertno'], src_sel_lh)
    vertno_lh = fwd['src'][0]['vertno'][src_sel_lh]

    nuse_lh = fwd['src'][0]['nuse']
    src_sel_rh = np.intersect1d(fwd['src'][1]['vertno'], label_rh.vertices)
    src_sel_rh = np.searchsorted(fwd['src'][1]['vertno'], src_sel_rh)
    vertno_rh = fwd['src'][1]['vertno'][src_sel_rh]
    src_sel_rh += nuse_lh

    assert_equal(fwd_out['sol']['ncol'],
                 3 * (len(src_sel_lh) + len(src_sel_rh)))
    assert_equal(fwd_out['src'][0]['nuse'], len(src_sel_lh))
    assert_equal(fwd_out['src'][1]['nuse'], len(src_sel_rh))
    assert_equal(fwd_out['src'][0]['vertno'], vertno_lh)
    assert_equal(fwd_out['src'][1]['vertno'], vertno_rh)

def test_restrict_forward_to_stc():
    """Test restriction of source space to source SourceEstimate
    """
    start = 0
    stop = 5
    n_times = stop - start - 1
    sfreq = 10.0
    t_start = 0.123

    fwd = read_forward_solution(fname_meeg)
    fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=True,
                                   use_cps=True)
    fwd = pick_types_forward(fwd, meg=True)

    vertno = [fwd['src'][0]['vertno'][0:15], fwd['src'][1]['vertno'][0:5]]
    stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
    stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)

    fwd_out = restrict_forward_to_stc(fwd, stc)
    assert_true(isinstance(fwd_out, Forward))

    assert_equal(fwd_out['sol']['ncol'], 20)
    assert_equal(fwd_out['src'][0]['nuse'], 15)
    assert_equal(fwd_out['src'][1]['nuse'], 5)
    assert_equal(fwd_out['src'][0]['vertno'], fwd['src'][0]['vertno'][0:15])
    assert_equal(fwd_out['src'][1]['vertno'], fwd['src'][1]['vertno'][0:5])

    fwd = read_forward_solution(fname_meeg)
    fwd = convert_forward_solution(fwd, surf_ori=True, force_fixed=False)
    fwd = pick_types_forward(fwd, meg=True)

    vertno = [fwd['src'][0]['vertno'][0:15], fwd['src'][1]['vertno'][0:5]]
    stc_data = np.ones((len(vertno[0]) + len(vertno[1]), n_times))
    stc = SourceEstimate(stc_data, vertno, tmin=t_start, tstep=1.0 / sfreq)

    fwd_out = restrict_forward_to_stc(fwd, stc)

    assert_equal(fwd_out['sol']['ncol'], 60)
    assert_equal(fwd_out['src'][0]['nuse'], 15)
    assert_equal(fwd_out['src'][1]['nuse'], 5)
    assert_equal(fwd_out['src'][0]['vertno'], fwd['src'][0]['vertno'][0:15])
    assert_equal(fwd_out['src'][1]['vertno'], fwd['src'][1]['vertno'][0:5])

    # Test saving the restricted forward object. This only works if all fields
    # are properly accounted for.
    temp_dir = _TempDir()
    fname_copy = op.join(temp_dir, 'copy-fwd.fif')
    with warnings.catch_warnings(record=True):
        warnings.simplefilter('always')
        write_forward_solution(fname_copy, fwd_out, overwrite=True)
    fwd_out_read = read_forward_solution(fname_copy)
    fwd_out_read = convert_forward_solution(fwd_out_read, surf_ori=True,
                                            force_fixed=False)
    compare_forwards(fwd_out, fwd_out_read)

def test_convert_forward():
    """Test converting forward solution between different representations
    """
    fwd = read_forward_solution(fname_meeg_grad)
    fwd_repr = repr(fwd)
    assert_true('306' in fwd_repr)
    assert_true('60' in fwd_repr)
    assert_true(fwd_repr)
    assert_true(isinstance(fwd, Forward))
    # look at surface orientation
    fwd_surf = convert_forward_solution(fwd, surf_ori=True)

    # The following test can be removed in 0.16
    fwd_surf_io = read_forward_solution(fname_meeg_grad, surf_ori=True)
    compare_forwards(fwd_surf, fwd_surf_io)
    del fwd_surf_io
    gc.collect()

    # go back
    fwd_new = convert_forward_solution(fwd_surf, surf_ori=False)
    assert_true(repr(fwd_new))
    assert_true(isinstance(fwd_new, Forward))
    compare_forwards(fwd, fwd_new)
    del fwd_new
    gc.collect()

    # now go to fixed
    fwd_fixed = convert_forward_solution(fwd_surf, surf_ori=True,
                                         force_fixed=True, use_cps=False)
    del fwd_surf
    gc.collect()
    assert_true(repr(fwd_fixed))
    assert_true(isinstance(fwd_fixed, Forward))
    assert_true(is_fixed_orient(fwd_fixed))

    # The following test can be removed in 0.16
    fwd_fixed_io = read_forward_solution(fname_meeg_grad, force_fixed=True)
    assert_true(repr(fwd_fixed_io))
    assert_true(isinstance(fwd_fixed_io, Forward))
    assert_true(is_fixed_orient(fwd_fixed_io))
    compare_forwards(fwd_fixed, fwd_fixed_io)
    del fwd_fixed_io
    gc.collect()

    # now go back to cartesian (original condition)
    fwd_new = convert_forward_solution(fwd_fixed, surf_ori=False,
                                       force_fixed=False)
    assert_true(repr(fwd_new))
    assert_true(isinstance(fwd_new, Forward))
    compare_forwards(fwd, fwd_new)
    del fwd, fwd_new, fwd_fixed
    gc.collect()

def _load_forward():
    """Load forward models."""
    fwd_free = mne.read_forward_solution(fname_fwd)
    fwd_free = mne.pick_types_forward(fwd_free, meg=True, eeg=False)
    fwd_free = mne.convert_forward_solution(fwd_free, surf_ori=False)
    fwd_surf = mne.convert_forward_solution(fwd_free, surf_ori=True,
                                            use_cps=False)
    fwd_fixed = mne.convert_forward_solution(fwd_free, force_fixed=True,
                                             use_cps=False)
    fwd_vol = mne.read_forward_solution(fname_fwd_vol)
    label = mne.read_label(fname_label)

    return fwd_free, fwd_surf, fwd_fixed, fwd_vol, label

def _get_data(tmin=-0.11, tmax=0.15, read_all_forward=True, compute_csds=True):
    """Read in data used in tests."""
    label = mne.read_label(fname_label)
    events = mne.read_events(fname_event)[:10]
    raw = mne.io.read_raw_fif(fname_raw, preload=False)
    raw.add_proj([], remove_existing=True)  # we'll subselect so remove proj
    forward = mne.read_forward_solution(fname_fwd)
    if read_all_forward:
        forward_surf_ori = _read_forward_solution_meg(
            fname_fwd, surf_ori=True)
        forward_fixed = _read_forward_solution_meg(
            fname_fwd, force_fixed=True, use_cps=False)
        forward_vol = mne.read_forward_solution(fname_fwd_vol)
        forward_vol = mne.convert_forward_solution(forward_vol, surf_ori=True)
    else:
        forward_surf_ori = None
        forward_fixed = None
        forward_vol = None

    event_id, tmin, tmax = 1, tmin, tmax

    # Setup for reading the raw data
    raw.info['bads'] = ['MEG 2443', 'EEG 053']  # 2 bads channels

    # Set up pick list: MEG - bad channels
    left_temporal_channels = mne.read_selection('Left-temporal')
    picks = mne.pick_types(raw.info, meg=True, eeg=False,
                           stim=True, eog=True, exclude='bads',
                           selection=left_temporal_channels)

    # Read epochs
    epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
                        picks=picks, baseline=(None, 0), preload=True,
                        reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))
    epochs.resample(200, npad=0, n_jobs=2)
    evoked = epochs.average().crop(0, None)

    # Computing the data and noise cross-spectral density matrices
    if compute_csds:
        data_csd = csd_epochs(epochs, mode='multitaper', tmin=0.045,
                              tmax=None, fmin=8, fmax=12,
                              mt_bandwidth=72.72)
        noise_csd = csd_epochs(epochs, mode='multitaper', tmin=None,
                               tmax=0.0, fmin=8, fmax=12,
                               mt_bandwidth=72.72)
    else:
        data_csd, noise_csd = None, None

    return raw, epochs, evoked, data_csd, noise_csd, label, forward,\
        forward_surf_ori, forward_fixed, forward_vol

def test_sensitivity_maps():
    """Test sensitivity map computation."""
    fwd = mne.read_forward_solution(fwd_fname)
    fwd = mne.convert_forward_solution(fwd, surf_ori=True)
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        projs = read_proj(eog_fname)
        projs.extend(read_proj(ecg_fname))
    decim = 6
    for ch_type in ['eeg', 'grad', 'mag']:
        w = read_source_estimate(sensmap_fname % (ch_type, 'lh')).data
        stc = sensitivity_map(fwd, projs=None, ch_type=ch_type,
                              mode='free', exclude='bads')
        assert_array_almost_equal(stc.data, w, decim)
        assert_true(stc.subject == 'sample')
        # let's just make sure the others run
        if ch_type == 'grad':
            # fixed (2)
            w = read_source_estimate(sensmap_fname % (ch_type, '2-lh')).data
            stc = sensitivity_map(fwd, projs=None, mode='fixed',
                                  ch_type=ch_type, exclude='bads')
            assert_array_almost_equal(stc.data, w, decim)
        if ch_type == 'mag':
            # ratio (3)
            w = read_source_estimate(sensmap_fname % (ch_type, '3-lh')).data
            stc = sensitivity_map(fwd, projs=None, mode='ratio',
                                  ch_type=ch_type, exclude='bads')
            assert_array_almost_equal(stc.data, w, decim)
        if ch_type == 'eeg':
            # radiality (4), angle (5), remaining (6), and  dampening (7)
            modes = ['radiality', 'angle', 'remaining', 'dampening']
            ends = ['4-lh', '5-lh', '6-lh', '7-lh']
            for mode, end in zip(modes, ends):
                w = read_source_estimate(sensmap_fname % (ch_type, end)).data
                stc = sensitivity_map(fwd, projs=projs, mode=mode,
                                      ch_type=ch_type, exclude='bads')
                assert_array_almost_equal(stc.data, w, decim)

    # test corner case for EEG
    stc = sensitivity_map(fwd, projs=[make_eeg_average_ref_proj(fwd['info'])],
                          ch_type='eeg', exclude='bads')
    # test corner case for projs being passed but no valid ones (#3135)
    assert_raises(ValueError, sensitivity_map, fwd, projs=None, mode='angle')
    assert_raises(RuntimeError, sensitivity_map, fwd, projs=[], mode='angle')
    # test volume source space
    fname = op.join(sample_path, 'sample_audvis_trunc-meg-vol-7-fwd.fif')
    fwd = mne.read_forward_solution(fname)
    sensitivity_map(fwd)

def test_gamma_map():
    """Test Gamma MAP inverse"""
    forward = read_forward_solution(fname_fwd, force_fixed=False,
                                    surf_ori=True)
    forward = pick_types_forward(forward, meg=False, eeg=True)
    evoked = read_evokeds(fname_evoked, condition=0, baseline=(None, 0))
    evoked.resample(50)
    evoked.crop(tmin=0, tmax=0.3)

    cov = read_cov(fname_cov)
    cov = regularize(cov, evoked.info)

    alpha = 0.2
    stc = gamma_map(evoked, forward, cov, alpha, tol=1e-5,
                    xyz_same_gamma=True, update_mode=1, verbose=False)
    idx = np.argmax(np.sum(stc.data ** 2, axis=1))
    assert_true(np.concatenate(stc.vertices)[idx] == 96397)

    stc = gamma_map(evoked, forward, cov, alpha, tol=1e-5,
                    xyz_same_gamma=False, update_mode=1, verbose=False)
    idx = np.argmax(np.sum(stc.data ** 2, axis=1))
    assert_true(np.concatenate(stc.vertices)[idx] == 82010)

    # force fixed orientation
    stc, res = gamma_map(evoked, forward, cov, alpha, tol=1e-5,
                         xyz_same_gamma=False, update_mode=2,
                         loose=None, return_residual=True, verbose=False)
    idx = np.argmax(np.sum(stc.data ** 2, axis=1))
    # assert_true(np.concatenate(stc.vertices)[idx] == 83398)  # XXX FIX
    assert_array_almost_equal(evoked.times, res.times)

def _get_data():
    """Read in data used in tests."""
    # read forward model
    forward = mne.read_forward_solution(fname_fwd)
    # read data
    raw = mne.io.read_raw_fif(fname_raw, preload=True)
    events = mne.read_events(fname_event)
    event_id, tmin, tmax = 1, -0.1, 0.15

    # decimate for speed
    left_temporal_channels = mne.read_selection('Left-temporal')
    picks = mne.pick_types(raw.info, selection=left_temporal_channels)
    picks = picks[::2]
    raw.pick_channels([raw.ch_names[ii] for ii in picks])
    del picks

    raw.info.normalize_proj()  # avoid projection warnings

    epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
                        baseline=(None, 0.), preload=True, reject=reject)

    noise_cov = mne.compute_covariance(epochs, tmin=None, tmax=0.)

    data_cov = mne.compute_covariance(epochs, tmin=0.01, tmax=0.15)

    return epochs, data_cov, noise_cov, forward

def test_pick_forward_seeg():
    """Test picking forward with SEEG
    """
    fwd = read_forward_solution(test_forward.fname_meeg)
    counts = channel_indices_by_type(fwd["info"])
    for key in counts.keys():
        counts[key] = len(counts[key])
    counts["meg"] = counts["mag"] + counts["grad"]
    fwd_ = pick_types_forward(fwd, meg=True, eeg=False, seeg=False)
    _check_fwd_n_chan_consistent(fwd_, counts["meg"])
    fwd_ = pick_types_forward(fwd, meg=False, eeg=True, seeg=False)
    _check_fwd_n_chan_consistent(fwd_, counts["eeg"])
    # should raise exception related to emptiness
    assert_raises(ValueError, pick_types_forward, fwd, meg=False, eeg=False, seeg=True)
    # change last chan from EEG to sEEG
    seeg_name = "OTp1"
    rename_channels(fwd["info"], {"EEG 060": seeg_name})
    for ch in fwd["info"]["chs"]:
        if ch["ch_name"] == seeg_name:
            ch["kind"] = FIFF.FIFFV_SEEG_CH
            ch["coil_type"] = FIFF.FIFFV_COIL_EEG
    fwd["sol"]["row_names"][-1] = fwd["info"]["chs"][-1]["ch_name"]
    counts["eeg"] -= 1
    counts["seeg"] += 1
    # repick & check
    fwd_seeg = pick_types_forward(fwd, meg=False, eeg=False, seeg=True)
    assert_equal(fwd_seeg["sol"]["row_names"], [seeg_name])
    assert_equal(fwd_seeg["info"]["ch_names"], [seeg_name])
    # should work fine
    fwd_ = pick_types_forward(fwd, meg=True, eeg=False, seeg=False)
    _check_fwd_n_chan_consistent(fwd_, counts["meg"])
    fwd_ = pick_types_forward(fwd, meg=False, eeg=True, seeg=False)
    _check_fwd_n_chan_consistent(fwd_, counts["eeg"])
    fwd_ = pick_types_forward(fwd, meg=False, eeg=False, seeg=True)
    _check_fwd_n_chan_consistent(fwd_, counts["seeg"])