Python mne.pick_types_forward函数代码示例

def test_make_forward_solution_sphere():
    """Test making a forward solution with a sphere model."""
    temp_dir = _TempDir()
    fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
    src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir,
                             add_dist=False)
    write_source_spaces(fname_src_small, src)  # to enable working with MNE-C
    out_name = op.join(temp_dir, 'tmp-fwd.fif')
    run_subprocess(['mne_forward_solution', '--meg', '--eeg',
                    '--meas', fname_raw, '--src', fname_src_small,
                    '--mri', fname_trans, '--fwd', out_name])
    fwd = read_forward_solution(out_name)
    sphere = make_sphere_model(verbose=True)
    fwd_py = make_forward_solution(fname_raw, fname_trans, src, sphere,
                                   meg=True, eeg=True, verbose=True)
    _compare_forwards(fwd, fwd_py, 366, 108,
                      meg_rtol=5e-1, meg_atol=1e-6,
                      eeg_rtol=5e-1, eeg_atol=5e-1)
    # Since the above is pretty lax, let's check a different way
    for meg, eeg in zip([True, False], [False, True]):
        fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
        fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
        assert_allclose(np.corrcoef(fwd_['sol']['data'].ravel(),
                                    fwd_py_['sol']['data'].ravel())[0, 1],
                        1.0, rtol=1e-3)

def test_make_forward_solution_sphere():
    """Test making a forward solution with a sphere model"""
    temp_dir = _TempDir()
    fname_src_small = op.join(temp_dir, "sample-oct-2-src.fif")
    src = setup_source_space("sample", fname_src_small, "oct2", subjects_dir=subjects_dir, add_dist=False)
    out_name = op.join(temp_dir, "tmp-fwd.fif")
    run_subprocess(
        [
            "mne_forward_solution",
            "--meg",
            "--eeg",
            "--meas",
            fname_raw,
            "--src",
            fname_src_small,
            "--mri",
            fname_trans,
            "--fwd",
            out_name,
        ]
    )
    fwd = read_forward_solution(out_name)
    sphere = make_sphere_model(verbose=True)
    fwd_py = make_forward_solution(fname_raw, fname_trans, src, sphere, meg=True, eeg=True, verbose=True)
    _compare_forwards(fwd, fwd_py, 366, 108, meg_rtol=5e-1, meg_atol=1e-6, eeg_rtol=5e-1, eeg_atol=5e-1)
    # Since the above is pretty lax, let's check a different way
    for meg, eeg in zip([True, False], [False, True]):
        fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
        fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
        assert_allclose(np.corrcoef(fwd_["sol"]["data"].ravel(), fwd_py_["sol"]["data"].ravel())[0, 1], 1.0, rtol=1e-3)

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 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_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),
                          proj=False)
    evoked.resample(50, npad=100)
    evoked.crop(tmin=0.1, tmax=0.16)  # crop to nice window near samp border

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

    alpha = 0.5
    stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
                    xyz_same_gamma=True, update_mode=1)
    _check_stc(stc, evoked, 68477)

    stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
                    xyz_same_gamma=False, update_mode=1)
    _check_stc(stc, evoked, 82010)

    # force fixed orientation
    stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
                    xyz_same_gamma=False, update_mode=2,
                    loose=None, return_residual=False)
    _check_stc(stc, evoked, 85739, 20)

def _get_fwd_labels():
    fwd = read_forward_solution(fname_fwd)
    fwd = convert_forward_solution(fwd, force_fixed=True, use_cps=True)
    fwd = pick_types_forward(fwd, meg=True, eeg=False)
    labels = [read_label(op.join(data_path, 'MEG', 'sample', 'labels',
                         '%s.label' % label)) for label in label_names]
    return fwd, labels

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 test_gamma_map():
    """Test Gamma MAP inverse"""
    forward = read_forward_solution(fname_fwd)
    forward = convert_forward_solution(forward, surf_ori=True)

    forward = pick_types_forward(forward, meg=False, eeg=True)
    evoked = read_evokeds(fname_evoked, condition=0, baseline=(None, 0),
                          proj=False)
    evoked.resample(50, npad=100)
    evoked.crop(tmin=0.1, tmax=0.16)  # crop to window around peak

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

    alpha = 0.5
    stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
                    xyz_same_gamma=True, update_mode=1)
    _check_stc(stc, evoked, 68477)

    stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
                    xyz_same_gamma=False, update_mode=1)
    _check_stc(stc, evoked, 82010)

    dips = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
                     xyz_same_gamma=False, update_mode=1,
                     return_as_dipoles=True)
    assert_true(isinstance(dips[0], Dipole))
    stc_dip = make_stc_from_dipoles(dips, forward['src'])
    _check_stcs(stc, stc_dip)

    # force fixed orientation
    stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
                    xyz_same_gamma=False, update_mode=2,
                    loose=0, return_residual=False)
    _check_stc(stc, evoked, 85739, 20)

def test_simulate_evoked():
    """ Test simulation of evoked data """

    raw = Raw(raw_fname)
    fwd = read_forward_solution(fwd_fname, force_fixed=True)
    fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
    cov = read_cov(cov_fname)

    evoked_template = read_evokeds(ave_fname, condition=0, baseline=None)
    evoked_template.pick_types(meg=True, eeg=True, exclude=raw.info['bads'])

    snr = 6  # dB
    tmin = -0.1
    sfreq = 1000.  # Hz
    tstep = 1. / sfreq
    n_samples = 600
    times = np.linspace(tmin, tmin + n_samples * tstep, n_samples)

    # Generate times series for 2 dipoles
    stc = simulate_sparse_stc(fwd['src'], n_dipoles=2, times=times)
    stc._data *= 1e-9

    # Generate noisy evoked data
    iir_filter = [1, -0.9]
    evoked = simulate_evoked(fwd, stc, evoked_template.info, cov, snr,
                             tmin=0.0, tmax=0.2, iir_filter=iir_filter)
    assert_array_almost_equal(evoked.times, stc.times)
    assert_true(len(evoked.data) == len(fwd['sol']['data']))

    # make a vertex that doesn't exist in fwd, should throw error
    stc_bad = stc.copy()
    mv = np.max(fwd['src'][0]['vertno'][fwd['src'][0]['inuse']])
    stc_bad.vertices[0][0] = mv + 1

    assert_raises(RuntimeError, simulate_evoked, fwd, stc_bad,
                  evoked_template.info, cov, snr, tmin=0.0, tmax=0.2)
    evoked_1 = simulate_evoked(fwd, stc, evoked_template.info, cov, np.inf,
                               tmin=0.0, tmax=0.2)
    evoked_2 = simulate_evoked(fwd, stc, evoked_template.info, cov, np.inf,
                               tmin=0.0, tmax=0.2)
    assert_array_equal(evoked_1.data, evoked_2.data)

    # test snr definition in dB
    evoked_noise = simulate_evoked(fwd, stc, evoked_template.info, cov,
                                   snr=snr, tmin=None, tmax=None,
                                   iir_filter=None)
    evoked_clean = simulate_evoked(fwd, stc, evoked_template.info, cov,
                                   snr=np.inf, tmin=None, tmax=None,
                                   iir_filter=None)
    noise = evoked_noise.data - evoked_clean.data

    empirical_snr = 10 * np.log10(np.mean((evoked_clean.data ** 2).ravel()) /
                                  np.mean((noise ** 2).ravel()))

    assert_almost_equal(snr, empirical_snr, decimal=5)

    cov['names'] = cov.ch_names[:-2]  # Error channels are different.
    assert_raises(ValueError, simulate_evoked, fwd, stc, evoked_template.info,
                  cov, snr=3., tmin=None, tmax=None, iir_filter=None)

def apply_inverse(fnevo, method='dSPM', snr=3.0, event='LLst', 
                  baseline=False, btmin=-0.3, btmax=-0.1, min_subject='fsaverage'):
    '''  
        Parameter
        ---------
        fnevo: string or list
            The evoked file with ECG, EOG and environmental noise free.
        method: inverse method, 'MNE' or 'dSPM'
        event: string
            The event name related with epochs.
        min_subject: string
            The subject name as the common brain.
        snr: signal to noise ratio for inverse solution. 
    '''
    #Get the default subjects_dir
    from mne.minimum_norm import apply_inverse
    fnlist = get_files_from_list(fnevo)
    # loop across all filenames
    for fname in fnlist:
        fn_path = os.path.split(fname)[0]
        name = os.path.basename(fname)
        stc_name = name[:name.rfind('-ave.fif')] 
        subject = name.split('_')[0]
        subject_path = subjects_dir + '/%s' %subject
        min_dir = subjects_dir + '/%s' %min_subject
        fn_trans = fn_path + '/%s-trans.fif' % subject
        fn_cov = fn_path + '/%s_empty,nr-cov.fif' % subject
        fn_src = subject_path + '/bem/%s-ico-5-src.fif' % subject
        fn_bem = subject_path + '/bem/%s-5120-5120-5120-bem-sol.fif' % subject
        snr = snr
        lambda2 = 1.0 / snr ** 2 
        #noise_cov = mne.read_cov(fn_cov)
        [evoked] = mne.read_evokeds(fname)
        noise_cov = mne.read_cov(fn_cov)
        # this path used for ROI definition
        stc_path = min_dir + '/%s_ROIs/%s' %(method,subject)
        #fn_cov = meg_path + '/%s_empty,fibp1-45,nr-cov.fif' % subject
        set_directory(stc_path)
        noise_cov = mne.cov.regularize(noise_cov, evoked.info,
                                        mag=0.05, grad=0.05, proj=True)
        fwd_ev = mne.make_forward_solution(evoked.info, trans=fn_trans,
                                            src=fn_src, bem=fn_bem,
                                            fname=None, meg=True, eeg=False,
                                            mindist=5.0, n_jobs=2,
                                            overwrite=True)
        fwd_ev = mne.convert_forward_solution(fwd_ev, surf_ori=True)
        forward_meg_ev = mne.pick_types_forward(fwd_ev, meg=True, eeg=False)
        inverse_operator_ev = mne.minimum_norm.make_inverse_operator(
            evoked.info, forward_meg_ev, noise_cov,
            loose=0.2, depth=0.8)
        # Compute inverse solution
        stc = apply_inverse(evoked, inverse_operator_ev, lambda2, method,
                            pick_ori=None)
        # Morph STC
        stc_morph = mne.morph_data(subject, min_subject, stc, grade=5, smooth=5)
        stc_morph.save(stc_path + '/%s' % (stc_name), ftype='stc')
        if baseline == True:
            stc_base = stc_morph.crop(btmin, btmax)
            stc_base.save(stc_path + '/%s_%s_baseline' % (subject, event), ftype='stc')

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)

    src_sel_rh = np.intersect1d(fwd["src"][1]["vertno"], label_rh.vertices)
    src_sel_rh = np.searchsorted(fwd["src"][1]["vertno"], src_sel_rh) + len(fwd["src"][0]["vertno"])

    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"], src_sel_lh)
    assert_equal(fwd_out["src"][1]["vertno"], src_sel_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)

    src_sel_rh = np.intersect1d(fwd["src"][1]["vertno"], label_rh.vertices)
    src_sel_rh = np.searchsorted(fwd["src"][1]["vertno"], src_sel_rh) + len(fwd["src"][0]["vertno"])

    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"], src_sel_lh)
    assert_equal(fwd_out["src"][1]["vertno"], src_sel_rh)

def test_simulate_evoked():
    """Test simulation of evoked data."""

    raw = read_raw_fif(raw_fname)
    fwd = read_forward_solution(fwd_fname)
    fwd = convert_forward_solution(fwd, force_fixed=True, use_cps=False)
    fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
    cov = read_cov(cov_fname)

    evoked_template = read_evokeds(ave_fname, condition=0, baseline=None)
    evoked_template.pick_types(meg=True, eeg=True, exclude=raw.info['bads'])

    cov = regularize(cov, evoked_template.info)
    nave = evoked_template.nave

    tmin = -0.1
    sfreq = 1000.  # Hz
    tstep = 1. / sfreq
    n_samples = 600
    times = np.linspace(tmin, tmin + n_samples * tstep, n_samples)

    # Generate times series for 2 dipoles
    stc = simulate_sparse_stc(fwd['src'], n_dipoles=2, times=times,
                              random_state=42)

    # Generate noisy evoked data
    iir_filter = [1, -0.9]
    evoked = simulate_evoked(fwd, stc, evoked_template.info, cov,
                             iir_filter=iir_filter, nave=nave)
    assert_array_almost_equal(evoked.times, stc.times)
    assert_true(len(evoked.data) == len(fwd['sol']['data']))
    assert_equal(evoked.nave, nave)

    # make a vertex that doesn't exist in fwd, should throw error
    stc_bad = stc.copy()
    mv = np.max(fwd['src'][0]['vertno'][fwd['src'][0]['inuse']])
    stc_bad.vertices[0][0] = mv + 1

    assert_raises(RuntimeError, simulate_evoked, fwd, stc_bad,
                  evoked_template.info, cov)
    evoked_1 = simulate_evoked(fwd, stc, evoked_template.info, cov,
                               nave=np.inf)
    evoked_2 = simulate_evoked(fwd, stc, evoked_template.info, cov,
                               nave=np.inf)
    assert_array_equal(evoked_1.data, evoked_2.data)

    # Test the equivalence snr to nave
    with warnings.catch_warnings(record=True):  # deprecation
        evoked = simulate_evoked(fwd, stc, evoked_template.info, cov,
                                 snr=6, random_state=42)
    assert_allclose(np.linalg.norm(evoked.data, ord='fro'),
                    0.00078346820226502716)

    cov['names'] = cov.ch_names[:-2]  # Error channels are different.
    assert_raises(ValueError, simulate_evoked, fwd, stc, evoked_template.info,
                  cov, nave=nave, iir_filter=None)

def test_make_forward_solution_sphere():
    """Test making a forward solution with a sphere model."""
    temp_dir = _TempDir()
    fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
    src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir,
                             add_dist=False)
    write_source_spaces(fname_src_small, src)  # to enable working with MNE-C
    out_name = op.join(temp_dir, 'tmp-fwd.fif')
    run_subprocess(['mne_forward_solution', '--meg', '--eeg',
                    '--meas', fname_raw, '--src', fname_src_small,
                    '--mri', fname_trans, '--fwd', out_name])
    fwd = read_forward_solution(out_name)
    sphere = make_sphere_model(verbose=True)
    fwd_py = make_forward_solution(fname_raw, fname_trans, src, sphere,
                                   meg=True, eeg=True, verbose=True)
    _compare_forwards(fwd, fwd_py, 366, 108,
                      meg_rtol=5e-1, meg_atol=1e-6,
                      eeg_rtol=5e-1, eeg_atol=5e-1)
    # Since the above is pretty lax, let's check a different way
    for meg, eeg in zip([True, False], [False, True]):
        fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
        fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
        assert_allclose(np.corrcoef(fwd_['sol']['data'].ravel(),
                                    fwd_py_['sol']['data'].ravel())[0, 1],
                        1.0, rtol=1e-3)
    # Number of layers in the sphere model doesn't matter for MEG
    # (as long as no sources are omitted due to distance)
    assert len(sphere['layers']) == 4
    fwd = make_forward_solution(fname_raw, fname_trans, src, sphere,
                                meg=True, eeg=False)
    sphere_1 = make_sphere_model(head_radius=None)
    assert len(sphere_1['layers']) == 0
    assert_array_equal(sphere['r0'], sphere_1['r0'])
    fwd_1 = make_forward_solution(fname_raw, fname_trans, src, sphere,
                                  meg=True, eeg=False)
    _compare_forwards(fwd, fwd_1, 306, 108, meg_rtol=1e-12, meg_atol=1e-12)
    # Homogeneous model
    sphere = make_sphere_model(head_radius=None)
    with pytest.raises(RuntimeError, match='zero shells.*EEG'):
        make_forward_solution(fname_raw, fname_trans, src, sphere)

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