def test_setup_source_space():
    """Test setting up ico, oct, and all source spaces
    """
    fname_all = op.join(data_path, "subjects", "sample", "bem", "sample-all-src.fif")
    fname_ico = op.join(data_path, "subjects", "fsaverage", "bem", "fsaverage-ico-5-src.fif")
    # first lets test some input params
    assert_raises(ValueError, setup_source_space, "sample", spacing="oct")
    assert_raises(ValueError, setup_source_space, "sample", spacing="octo")
    assert_raises(ValueError, setup_source_space, "sample", spacing="oct6e")
    assert_raises(ValueError, setup_source_space, "sample", spacing="7emm")
    assert_raises(ValueError, setup_source_space, "sample", spacing="alls")
    assert_raises(IOError, setup_source_space, "sample", spacing="oct6", subjects_dir=subjects_dir)

    # ico 5 (fsaverage) - write to temp file
    src = read_source_spaces(fname_ico)
    temp_name = op.join(tempdir, "temp-src.fif")
    with warnings.catch_warnings(record=True):  # sklearn equiv neighbors
        src_new = setup_source_space("fsaverage", temp_name, spacing="ico5", subjects_dir=subjects_dir)
    _compare_source_spaces(src, src_new, mode="approx")

    # oct-6 (sample) - auto filename + IO
    src = read_source_spaces(fname)
    temp_name = op.join(tempdir, "temp-src.fif")
    with warnings.catch_warnings(record=True):  # sklearn equiv neighbors
        src_new = setup_source_space("sample", temp_name, spacing="oct6", subjects_dir=subjects_dir, overwrite=True)
    _compare_source_spaces(src, src_new, mode="approx")
    src_new = read_source_spaces(temp_name)
    _compare_source_spaces(src, src_new, mode="approx")

    # all source points - no file writing
    src = read_source_spaces(fname_all)
    src_new = setup_source_space("sample", None, spacing="all", subjects_dir=subjects_dir)
    _compare_source_spaces(src, src_new, mode="approx")

def create_source_space(sbj_dir, sbj_id):
    import os.path as op
    import mne
    
    bem_dir     = op.join(sbj_dir, sbj_id, 'bem') 
    
    src_fname = op.join(bem_dir, '%s-ico-5-src.fif' %sbj_id)
    if not op.isfile(src_fname):
        mne.setup_source_space(sbj_id, fname=True, spacing='ico5', subjects_dir=sbj_dir, 
                               overwrite=True, n_jobs=2)

def test_setup_source_space():
    """Test setting up ico, oct, and all source spaces
    """
    tempdir = _TempDir()
    fname_ico = op.join(data_path, 'subjects', 'fsaverage', 'bem',
                        'fsaverage-ico-5-src.fif')
    # first lets test some input params
    assert_raises(ValueError, setup_source_space, 'sample', spacing='oct',
                  add_dist=False)
    assert_raises(ValueError, setup_source_space, 'sample', spacing='octo',
                  add_dist=False)
    assert_raises(ValueError, setup_source_space, 'sample', spacing='oct6e',
                  add_dist=False)
    assert_raises(ValueError, setup_source_space, 'sample', spacing='7emm',
                  add_dist=False)
    assert_raises(ValueError, setup_source_space, 'sample', spacing='alls',
                  add_dist=False)
    assert_raises(IOError, setup_source_space, 'sample', spacing='oct6',
                  subjects_dir=subjects_dir, add_dist=False)

    # ico 5 (fsaverage) - write to temp file
    src = read_source_spaces(fname_ico)
    temp_name = op.join(tempdir, 'temp-src.fif')
    with warnings.catch_warnings(record=True):  # sklearn equiv neighbors
        warnings.simplefilter('always')
        src_new = setup_source_space('fsaverage', temp_name, spacing='ico5',
                                     subjects_dir=subjects_dir, add_dist=False,
                                     overwrite=True)
    _compare_source_spaces(src, src_new, mode='approx')
    assert_equal(repr(src), repr(src_new))
    assert_equal(repr(src).count('surface ('), 2)
    assert_array_equal(src[0]['vertno'], np.arange(10242))
    assert_array_equal(src[1]['vertno'], np.arange(10242))

    # oct-6 (sample) - auto filename + IO
    src = read_source_spaces(fname)
    temp_name = op.join(tempdir, 'temp-src.fif')
    with warnings.catch_warnings(record=True):  # sklearn equiv neighbors
        warnings.simplefilter('always')
        src_new = setup_source_space('sample', temp_name, spacing='oct6',
                                     subjects_dir=subjects_dir,
                                     overwrite=True, add_dist=False)
    _compare_source_spaces(src, src_new, mode='approx', nearest=False)
    src_new = read_source_spaces(temp_name)
    _compare_source_spaces(src, src_new, mode='approx', nearest=False)

    # all source points - no file writing
    src_new = setup_source_space('sample', None, spacing='all',
                                 subjects_dir=subjects_dir, add_dist=False)
    assert_true(src_new[0]['nuse'] == len(src_new[0]['rr']))
    assert_true(src_new[1]['nuse'] == len(src_new[1]['rr']))

    # dense source space to hit surf['inuse'] lines of _create_surf_spacing
    assert_raises(RuntimeError, setup_source_space, 'sample', None,
                  spacing='ico6', subjects_dir=subjects_dir, add_dist=False)

def test_setup_source_space():
    """Test setting up ico, oct, and all source spaces
    """
    fname_all = op.join(data_path, 'subjects', 'sample', 'bem',
                        'sample-all-src.fif')
    fname_ico = op.join(data_path, 'subjects', 'fsaverage', 'bem',
                        'fsaverage-ico-5-src.fif')
    # first lets test some input params
    assert_raises(ValueError, setup_source_space, 'sample', spacing='oct',
                  add_dist=False)
    assert_raises(ValueError, setup_source_space, 'sample', spacing='octo',
                  add_dist=False)
    assert_raises(ValueError, setup_source_space, 'sample', spacing='oct6e',
                  add_dist=False)
    assert_raises(ValueError, setup_source_space, 'sample', spacing='7emm',
                  add_dist=False)
    assert_raises(ValueError, setup_source_space, 'sample', spacing='alls',
                  add_dist=False)
    assert_raises(IOError, setup_source_space, 'sample', spacing='oct6',
                  subjects_dir=subjects_dir, add_dist=False)

    # ico 5 (fsaverage) - write to temp file
    src = read_source_spaces(fname_ico)
    temp_name = op.join(tempdir, 'temp-src.fif')
    with warnings.catch_warnings(record=True):  # sklearn equiv neighbors
        warnings.simplefilter('always')
        src_new = setup_source_space('fsaverage', temp_name, spacing='ico5',
                                     subjects_dir=subjects_dir, add_dist=False,
                                     overwrite=True)
    _compare_source_spaces(src, src_new, mode='approx')

    # oct-6 (sample) - auto filename + IO
    src = read_source_spaces(fname)
    temp_name = op.join(tempdir, 'temp-src.fif')
    with warnings.catch_warnings(record=True):  # sklearn equiv neighbors
        warnings.simplefilter('always')
        src_new = setup_source_space('sample', temp_name, spacing='oct6',
                                     subjects_dir=subjects_dir,
                                     overwrite=True, add_dist=False)
    _compare_source_spaces(src, src_new, mode='approx')
    src_new = read_source_spaces(temp_name)
    _compare_source_spaces(src, src_new, mode='approx')

    # all source points - no file writing
    src = read_source_spaces(fname_all)
    src_new = setup_source_space('sample', None, spacing='all',
                                 subjects_dir=subjects_dir, add_dist=False)
    _compare_source_spaces(src, src_new, mode='approx')

def create_src_space(sbj_dir, sbj_id, spacing, is_blind):
    import os.path as op
    import mne

    bem_dir = op.join(sbj_dir, sbj_id, 'bem')

    # check if source space exists, if not it creates using mne-python fun
    # we have to create the cortical surface source space even when aseg is
    # True
    if is_blind:
        # if is_blind we have to precomputed the source space sincw we had
        # to remove some labels
        src_fname = op.join(bem_dir, '%s-blind-%s-src.fif' % (sbj_id, spacing))
        if not op.isfile(src_fname):
            raise '\n *** you have to compute the source space blind!!! ***\n'
        else:
            print '\n*** source space file %s exists!!!\n' % src_fname
            src = mne.read_source_spaces(src_fname)
    else:
        src_fname = op.join(bem_dir, '%s-%s-src.fif' % (sbj_id, spacing))
        if not op.isfile(src_fname):
            src = mne.setup_source_space(sbj_id, subjects_dir=sbj_dir,
                                         fname=True,
                                         spacing=spacing.replace('-', ''),
                                         add_dist=False, overwrite=True,
                                         n_jobs=2)
            print '\n*** source space file %s written ***\n' % src_fname
        else:
            print '\n*** source space file %s exists!!!\n' % src_fname
            src = mne.read_source_spaces(src_fname)

    return src

def _mne_source_space(subject, src_tag, subjects_dir):
    """Load mne source space

    Parameters
    ----------
    subject : str
        Subejct
    src_tag : str
        Spacing (e.g., 'ico-4').
    """
    src_file = os.path.join(subjects_dir, subject, 'bem',
                            '%s-%s-src.fif' % (subject, src_tag))
    src, spacing = src_tag.split('-')
    if os.path.exists(src_file):
        return mne.read_source_spaces(src_file, False)
    elif src == 'ico':
        ss = mne.setup_source_space(subject, spacing=src + spacing,
                                    subjects_dir=subjects_dir, add_dist=True)
    elif src == 'vol':
        mri_file = os.path.join(subjects_dir, subject, 'mri', 'orig.mgz')
        bem_file = os.path.join(subjects_dir, subject, 'bem',
                                'sample-5120-5120-5120-bem-sol.fif')
        ss = mne.setup_volume_source_space(subject, pos=float(spacing),
                                           mri=mri_file, bem=bem_file,
                                           mindist=0., exclude=0.,
                                           subjects_dir=subjects_dir)
    else:
        raise ValueError("src_tag=%s" % repr(src_tag))
    mne.write_source_spaces(src_file, ss)
    return ss

def run_forward(subject_id):
    subject = "sub%03d" % subject_id
    print("processing subject: %s" % subject)
    data_path = op.join(meg_dir, subject)

    fname_ave = op.join(data_path, '%s-ave.fif' % subject)
    fname_fwd = op.join(data_path, '%s-meg-%s-fwd.fif' % (subject, spacing))
    fname_trans = op.join(study_path, 'ds117', subject, 'MEG', '%s-trans.fif' % subject)

    src = mne.setup_source_space(subject, spacing=spacing,
                                 subjects_dir=subjects_dir, overwrite=True,
                                 n_jobs=1, add_dist=False)

    src_fname = op.join(subjects_dir, subject, '%s-src.fif' % spacing)
    mne.write_source_spaces(src_fname, src)

    bem_model = mne.make_bem_model(subject, ico=4, subjects_dir=subjects_dir,
                                   conductivity=(0.3,))
    bem = mne.make_bem_solution(bem_model)
    info = mne.read_evokeds(fname_ave, condition=0).info
    fwd = mne.make_forward_solution(info, trans=fname_trans, src=src, bem=bem,
                                    fname=None, meg=True, eeg=False,
                                    mindist=mindist, n_jobs=1, overwrite=True)
    fwd = mne.convert_forward_solution(fwd, surf_ori=True)
    mne.write_forward_solution(fname_fwd, fwd, overwrite=True)

def test_setup_source_space_spacing(tmpdir, spacing):
    """Test setting up surface source spaces using a given spacing."""
    tempdir = str(tmpdir)
    copytree(op.join(subjects_dir, 'sample'), op.join(tempdir, 'sample'))
    args = [] if spacing == 7 else ['--spacing', str(spacing)]
    with modified_env(SUBJECTS_DIR=tempdir, SUBJECT='sample'):
        run_subprocess(['mne_setup_source_space'] + args)
    src = read_source_spaces(op.join(tempdir, 'sample', 'bem',
                                     'sample-%d-src.fif' % spacing))
    src_new = setup_source_space('sample', spacing=spacing, add_dist=False,
                                 subjects_dir=subjects_dir)
    _compare_source_spaces(src, src_new, mode='approx', nearest=True)
    # Degenerate conditions
    with pytest.raises(TypeError, match='spacing must be.*got.*float.*'):
        setup_source_space('sample', 7., subjects_dir=subjects_dir)
    with pytest.raises(ValueError, match='spacing must be >= 2, got 1'):
        setup_source_space('sample', 1, subjects_dir=subjects_dir)

def test_scale_mri():
    """Test creating fsaverage and scaling it"""
    # create fsaverage
    tempdir = _TempDir()
    create_default_subject(subjects_dir=tempdir)
    is_mri = _is_mri_subject("fsaverage", tempdir)
    assert_true(is_mri, "Creating fsaverage failed")

    fid_path = os.path.join(tempdir, "fsaverage", "bem", "fsaverage-fiducials.fif")
    os.remove(fid_path)
    create_default_subject(update=True, subjects_dir=tempdir)
    assert_true(os.path.exists(fid_path), "Updating fsaverage")

    # remove redundant label files
    label_temp = os.path.join(tempdir, "fsaverage", "label", "*.label")
    label_paths = glob(label_temp)
    for label_path in label_paths[1:]:
        os.remove(label_path)

    # create source space
    path = os.path.join(tempdir, "fsaverage", "bem", "fsaverage-ico-0-src.fif")
    mne.setup_source_space("fsaverage", path, "ico0", overwrite=True, subjects_dir=tempdir, add_dist=False)

    # scale fsaverage
    os.environ["_MNE_FEW_SURFACES"] = "true"
    scale_mri("fsaverage", "flachkopf", [1, 0.2, 0.8], True, subjects_dir=tempdir)
    del os.environ["_MNE_FEW_SURFACES"]
    is_mri = _is_mri_subject("flachkopf", tempdir)
    assert_true(is_mri, "Scaling fsaverage failed")
    src_path = os.path.join(tempdir, "flachkopf", "bem", "flachkopf-ico-0-src.fif")
    assert_true(os.path.exists(src_path), "Source space was not scaled")
    scale_labels("flachkopf", subjects_dir=tempdir)

    # scale source space separately
    os.remove(src_path)
    scale_source_space("flachkopf", "ico-0", subjects_dir=tempdir)
    assert_true(os.path.exists(src_path), "Source space was not scaled")

    # add distances to source space
    src = mne.read_source_spaces(path)
    mne.add_source_space_distances(src)
    src.save(path)

    # scale with distances
    os.remove(src_path)
    scale_source_space("flachkopf", "ico-0", subjects_dir=tempdir)

def test_forward_mixed_source_space():
    """Test making the forward solution for a mixed source space
    """
    # get bem file
    fname_bem = op.join(subjects_dir, 'sample', 'bem',
                        'sample-5120-5120-5120-bem-sol.fif')
    # get the aseg file
    fname_aseg = op.join(subjects_dir, 'sample', 'mri', 'aseg.mgz')

    # get the surface source space
    surf = setup_source_space('sample', fname=None, spacing='ico2')

    # setup two volume source spaces
    label_names = get_volume_labels_from_aseg(fname_aseg)
    vol_labels = [label_names[int(np.random.rand() * len(label_names))]
                  for _ in range(2)]
    vol1 = setup_volume_source_space('sample', fname=None, pos=20.,
                                     mri=fname_aseg,
                                     volume_label=vol_labels[0])
    vol2 = setup_volume_source_space('sample', fname=None, pos=20.,
                                     mri=fname_aseg,
                                     volume_label=vol_labels[1])

    # merge surfaces and volume
    src = surf + vol1 + vol2

    # calculate forward solution
    fwd = make_forward_solution(fname_raw, mri=fname_mri, src=src,
                                bem=fname_bem, fname=None)

    # extract source spaces
    src_from_fwd = fwd['src']

    # get the coordinate frame of each source space
    coord_frames = np.array([s['coord_frame'] for s in src_from_fwd])

    # assert that all source spaces are in head coordinates
    assert_true((coord_frames == FIFF.FIFFV_COORD_HEAD).all())

    # run tests for SourceSpaces.export_volume
    fname_img = op.join(temp_dir, 'temp-image.mgz')

    # head coordinates and mri_resolution, but trans file
    assert_raises(ValueError, src_from_fwd.export_volume, fname_img,
                  mri_resolution=True, trans=None)

    # head coordinates and mri_resolution, but wrong trans file
    vox_mri_t = vol1[0]['vox_mri_t']
    assert_raises(RuntimeError, src_from_fwd.export_volume, fname_img,
                  mri_resolution=True, trans=vox_mri_t)

def test_simulate_raw_bem(raw_data):
    """Test simulation of raw data with BEM."""
    raw, src, stc, trans, sphere = raw_data
    src = setup_source_space('sample', 'oct1', subjects_dir=subjects_dir)
    for s in src:
        s['nuse'] = 3
        s['vertno'] = src[1]['vertno'][:3]
        s['inuse'].fill(0)
        s['inuse'][s['vertno']] = 1
    # use different / more complete STC here
    vertices = [s['vertno'] for s in src]
    stc = SourceEstimate(np.eye(sum(len(v) for v in vertices)), vertices,
                         0, 1. / raw.info['sfreq'])
    with pytest.deprecated_call():
        raw_sim_sph = simulate_raw(raw, stc, trans, src, sphere, cov=None,
                                   verbose=True)
    with pytest.deprecated_call():
        raw_sim_bem = simulate_raw(raw, stc, trans, src, bem_fname, cov=None,
                                   n_jobs=2)
    # some components (especially radial) might not match that well,
    # so just make sure that most components have high correlation
    assert_array_equal(raw_sim_sph.ch_names, raw_sim_bem.ch_names)
    picks = pick_types(raw.info, meg=True, eeg=True)
    n_ch = len(picks)
    corr = np.corrcoef(raw_sim_sph[picks][0], raw_sim_bem[picks][0])
    assert_array_equal(corr.shape, (2 * n_ch, 2 * n_ch))
    med_corr = np.median(np.diag(corr[:n_ch, -n_ch:]))
    assert med_corr > 0.65
    # do some round-trip localization
    for s in src:
        transform_surface_to(s, 'head', trans)
    locs = np.concatenate([s['rr'][s['vertno']] for s in src])
    tmax = (len(locs) - 1) / raw.info['sfreq']
    cov = make_ad_hoc_cov(raw.info)
    # The tolerance for the BEM is surprisingly high (28) but I get the same
    # result when using MNE-C and Xfit, even when using a proper 5120 BEM :(
    for use_raw, bem, tol in ((raw_sim_sph, sphere, 2),
                              (raw_sim_bem, bem_fname, 31)):
        events = find_events(use_raw, 'STI 014')
        assert len(locs) == 6
        evoked = Epochs(use_raw, events, 1, 0, tmax, baseline=None).average()
        assert len(evoked.times) == len(locs)
        fits = fit_dipole(evoked, cov, bem, trans, min_dist=1.)[0].pos
        diffs = np.sqrt(np.sum((locs - fits) ** 2, axis=-1)) * 1000
        med_diff = np.median(diffs)
        assert med_diff < tol, '%s: %s' % (bem, med_diff)

def test_make_forward_solution_compensation():
    """Test making forward solution from python with compensation
    """
    fname_ctf_raw = op.join(op.dirname(__file__), '..', '..', 'fiff', 'tests',
                            'data', 'test_ctf_comp_raw.fif')
    fname_bem = op.join(subjects_dir, 'sample', 'bem',
                        'sample-5120-bem-sol.fif')
    fname_src = op.join(temp_dir, 'oct2-src.fif')
    src = setup_source_space('sample', fname_src, 'oct2',
                             subjects_dir=subjects_dir)
    fwd_py = make_forward_solution(fname_ctf_raw, mindist=0.0,
                                   src=src, eeg=False, meg=True,
                                   bem=fname_bem, mri=fname_mri)

    fwd = do_forward_solution('sample', fname_ctf_raw, src=fname_src,
                              mindist=0.0, bem=fname_bem, mri=fname_mri,
                              eeg=False, meg=True, subjects_dir=subjects_dir)
    _compare_forwards(fwd, fwd_py, 274, 108)

def _mne_source_space(subject, src_tag, subjects_dir):
    """Load mne source space"""
    src_file = os.path.join(subjects_dir, subject, 'bem',
                            '%s-%s-src.fif' % (subject, src_tag))
    src = src_tag[:3]
    if os.path.exists(src_file):
        return mne.read_source_spaces(src_file, False)
    elif src == 'ico':
        return mne.setup_source_space(subject, src_file, 'ico4',
                                      subjects_dir=subjects_dir, add_dist=True)
    elif src == 'vol':
        mri_file = os.path.join(subjects_dir, subject, 'mri', 'orig.mgz')
        bem_file = os.path.join(subjects_dir, subject, 'bem',
                                'sample-5120-5120-5120-bem-sol.fif')
        return mne.setup_volume_source_space(subject, src_file, pos=10.,
                                             mri=mri_file, bem=bem_file,
                                             mindist=0., exclude=0.,
                                             subjects_dir=subjects_dir)
    else:
        raise ValueError("src_tag=%s" % repr(src_tag))

def create_src_space(sbj_dir, sbj_id, spacing):
    import os.path as op
    import mne

    bem_dir = op.join(sbj_dir, sbj_id, 'bem')

    # check if source space exists, if not it creates using mne-python fun
    # we have to create the cortical surface source space even when aseg is
    # True
    src_fname = op.join(bem_dir, '%s-%s-src.fif' % (sbj_id, spacing))
    if not op.isfile(src_fname):
        src = mne.setup_source_space(sbj_id, subjects_dir=sbj_dir,
                                     fname=True,
                                     spacing=spacing.replace('-', ''),
                                     add_dist=False, overwrite=True,
                                     n_jobs=2)
        print '*** source space file %s written ***' % src_fname
    else:
        print '*** source space file %s exists!!!' % src_fname
        src = mne.read_source_spaces(src_fname)

    return src

def test_source_estimate():
    "Test SourceSpace dimension"
    ds = datasets.get_mne_sample(src='ico')
    dsa = ds.aggregate('side')

    # test auto-conversion
    asndvar('epochs', ds=ds)
    asndvar('epochs', ds=dsa)
    asndvar(dsa['epochs'][0])

    # source space clustering
    res = testnd.ttest_ind('src', 'side', ds=ds, samples=0, pmin=0.05,
                           tstart=0.05, mintime=0.02, minsource=10)
    assert_equal(res.clusters.n_cases, 52)

    # test morphing
    dsa = ds.aggregate('side')
    ndvar = dsa['src']
    stc = mne.SourceEstimate(ndvar.x[0], ndvar.source.vertno,
                             ndvar.time.tmin, ndvar.time.tstep,
                             ndvar.source.subject)
    subjects_dir = ndvar.source.subjects_dir
    path = ndvar.source._src_pattern.format(subject='fsaverage',
                                            src=ndvar.source.src,
                                            subjects_dir=subjects_dir)
    if os.path.exists(path):
        src_to = mne.read_source_spaces(path)
    else:
        src_to = mne.setup_source_space('fsaverage', path, 'ico4',
                                        subjects_dir=subjects_dir)
    vertices_to = [src_to[0]['vertno'], src_to[1]['vertno']]
    mm = mne.compute_morph_matrix('sample', 'fsaverage', ndvar.source.vertno,
                                  vertices_to, None, subjects_dir)
    stc_to = mne.morph_data_precomputed('sample', 'fsaverage', stc,
                                        vertices_to, mm)

    ndvar_m = morph_source_space(ndvar, 'fsaverage')
    assert_array_equal(ndvar_m.x[0], stc_to.data)

def test_scale_mri_xfm():
    """Test scale_mri transforms and MRI scaling."""
    # scale fsaverage
    tempdir = _TempDir()
    os.environ['_MNE_FEW_SURFACES'] = 'true'
    fake_home = testing.data_path()
    # add fsaverage
    create_default_subject(subjects_dir=tempdir, fs_home=fake_home,
                           verbose=True)
    # add sample (with few files)
    sample_dir = op.join(tempdir, 'sample')
    os.mkdir(sample_dir)
    os.mkdir(op.join(sample_dir, 'bem'))
    for dirname in ('mri', 'surf'):
        copytree(op.join(fake_home, 'subjects', 'sample', dirname),
                 op.join(sample_dir, dirname))
    subject_to = 'flachkopf'
    spacing = 'oct2'
    for subject_from in ('fsaverage', 'sample'):
        if subject_from == 'fsaverage':
            scale = 1.  # single dim
        else:
            scale = [0.9, 2, .8]  # separate
        src_from_fname = op.join(tempdir, subject_from, 'bem',
                                 '%s-%s-src.fif' % (subject_from, spacing))
        src_from = mne.setup_source_space(
            subject_from, spacing, subjects_dir=tempdir, add_dist=False)
        write_source_spaces(src_from_fname, src_from)
        print(src_from_fname)
        vertices_from = np.concatenate([s['vertno'] for s in src_from])
        assert len(vertices_from) == 36
        hemis = ([0] * len(src_from[0]['vertno']) +
                 [1] * len(src_from[0]['vertno']))
        mni_from = mne.vertex_to_mni(vertices_from, hemis, subject_from,
                                     subjects_dir=tempdir)
        if subject_from == 'fsaverage':  # identity transform
            source_rr = np.concatenate([s['rr'][s['vertno']]
                                        for s in src_from]) * 1e3
            assert_allclose(mni_from, source_rr)
        if subject_from == 'fsaverage':
            overwrite = skip_fiducials = False
        else:
            with pytest.raises(IOError, match='No fiducials file'):
                scale_mri(subject_from, subject_to,  scale,
                          subjects_dir=tempdir)
            skip_fiducials = True
            with pytest.raises(IOError, match='already exists'):
                scale_mri(subject_from, subject_to,  scale,
                          subjects_dir=tempdir, skip_fiducials=skip_fiducials)
            overwrite = True
        scale_mri(subject_from, subject_to, scale, subjects_dir=tempdir,
                  verbose='debug', overwrite=overwrite,
                  skip_fiducials=skip_fiducials)
        if subject_from == 'fsaverage':
            assert _is_mri_subject(subject_to, tempdir), "Scaling failed"
        src_to_fname = op.join(tempdir, subject_to, 'bem',
                               '%s-%s-src.fif' % (subject_to, spacing))
        assert op.exists(src_to_fname), "Source space was not scaled"
        # Check MRI scaling
        fname_mri = op.join(tempdir, subject_to, 'mri', 'T1.mgz')
        assert op.exists(fname_mri), "MRI was not scaled"
        # Check MNI transform
        src = mne.read_source_spaces(src_to_fname)
        vertices = np.concatenate([s['vertno'] for s in src])
        assert_array_equal(vertices, vertices_from)
        mni = mne.vertex_to_mni(vertices, hemis, subject_to,
                                subjects_dir=tempdir)
        assert_allclose(mni, mni_from, atol=1e-3)  # 0.001 mm
    del os.environ['_MNE_FEW_SURFACES']

def test_scale_mri():
    """Test creating fsaverage and scaling it."""
    # create fsaverage using the testing "fsaverage" instead of the FreeSurfer
    # one
    tempdir = _TempDir()
    fake_home = testing.data_path()
    create_default_subject(subjects_dir=tempdir, fs_home=fake_home,
                           verbose=True)
    assert _is_mri_subject('fsaverage', tempdir), "Creating fsaverage failed"

    fid_path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-fiducials.fif')
    os.remove(fid_path)
    create_default_subject(update=True, subjects_dir=tempdir,
                           fs_home=fake_home)
    assert op.exists(fid_path), "Updating fsaverage"

    # copy MRI file from sample data (shouldn't matter that it's incorrect,
    # so here choose a small one)
    path_from = op.join(testing.data_path(), 'subjects', 'sample', 'mri',
                        'T1.mgz')
    path_to = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz')
    copyfile(path_from, path_to)

    # remove redundant label files
    label_temp = op.join(tempdir, 'fsaverage', 'label', '*.label')
    label_paths = glob(label_temp)
    for label_path in label_paths[1:]:
        os.remove(label_path)

    # create source space
    print('Creating surface source space')
    path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-%s-src.fif')
    src = mne.setup_source_space('fsaverage', 'ico0', subjects_dir=tempdir,
                                 add_dist=False)
    mri = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz')
    print('Creating volume source space')
    vsrc = mne.setup_volume_source_space(
        'fsaverage', pos=50, mri=mri, subjects_dir=tempdir,
        add_interpolator=False)
    write_source_spaces(path % 'vol-50', vsrc)

    # scale fsaverage
    for scale in (.9, [1, .2, .8]):
        write_source_spaces(path % 'ico-0', src, overwrite=True)
        os.environ['_MNE_FEW_SURFACES'] = 'true'
        with pytest.warns(None):  # sometimes missing nibabel
            scale_mri('fsaverage', 'flachkopf', scale, True,
                      subjects_dir=tempdir, verbose='debug')
        del os.environ['_MNE_FEW_SURFACES']
        assert _is_mri_subject('flachkopf', tempdir), "Scaling failed"
        spath = op.join(tempdir, 'flachkopf', 'bem', 'flachkopf-%s-src.fif')

        assert op.exists(spath % 'ico-0'), "Source space ico-0 was not scaled"
        assert os.path.isfile(os.path.join(tempdir, 'flachkopf', 'surf',
                                           'lh.sphere.reg'))
        vsrc_s = mne.read_source_spaces(spath % 'vol-50')
        pt = np.array([0.12, 0.41, -0.22])
        assert_array_almost_equal(
            apply_trans(vsrc_s[0]['src_mri_t'], pt * np.array(scale)),
            apply_trans(vsrc[0]['src_mri_t'], pt))
        scale_labels('flachkopf', subjects_dir=tempdir)

        # add distances to source space after hacking the properties to make
        # it run *much* faster
        src_dist = src.copy()
        for s in src_dist:
            s.update(rr=s['rr'][s['vertno']], nn=s['nn'][s['vertno']],
                     tris=s['use_tris'])
            s.update(np=len(s['rr']), ntri=len(s['tris']),
                     vertno=np.arange(len(s['rr'])),
                     inuse=np.ones(len(s['rr']), int))
        mne.add_source_space_distances(src_dist)
        write_source_spaces(path % 'ico-0', src_dist, overwrite=True)

        # scale with distances
        os.remove(spath % 'ico-0')
        scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir)
        ssrc = mne.read_source_spaces(spath % 'ico-0')
        assert ssrc[0]['dist'] is not None

head_mri_t = mne.read_trans(
    op.join(recordings_path, subject, '{}-head_mri-trans.fif'.format(
            subject)))

##############################################################################
# Now we can setup our source model.
# Note that spacing has to be set to 'all' since no common MNE resampling
# scheme has been employed in the HCP pipelines.
# Since this will take very long time to compute and at this point no other
# decimation scheme is available inside MNE, we will compute the source
# space on fsaverage, the freesurfer average brain, and morph it onto
# the subject's native space. With `oct6` we have ~8000 dipole locations.

src_fsaverage = mne.setup_source_space(
    subject='fsaverage', subjects_dir=subjects_dir, add_dist=False,
    spacing='oct6', overwrite=True)

# now we morph it onto the subject.

src_subject = mne.morph_source_spaces(
    src_fsaverage, subject, subjects_dir=subjects_dir)

##############################################################################
# For the same reason `ico` has to be set to `None` when computing the bem.
# The headshape is not computed with MNE and has a none standard configuration.

bems = mne.make_bem_model(subject, conductivity=(0.3,),
                          subjects_dir=subjects_dir,
                          ico=None)  # ico = None for morphed SP.
bem_sol = mne.make_bem_solution(bems)

def test_scale_mri():
    """Test creating fsaverage and scaling it"""
    # create fsaverage
    tempdir = _TempDir()
    create_default_subject(subjects_dir=tempdir)
    assert_true(_is_mri_subject('fsaverage', tempdir),
                "Creating fsaverage failed")

    fid_path = os.path.join(tempdir, 'fsaverage', 'bem',
                            'fsaverage-fiducials.fif')
    os.remove(fid_path)
    create_default_subject(update=True, subjects_dir=tempdir)
    assert_true(os.path.exists(fid_path), "Updating fsaverage")

    # copy MRI file from sample data
    path = os.path.join('%s', 'fsaverage', 'mri', 'orig.mgz')
    sample_sdir = os.path.join(mne.datasets.sample.data_path(), 'subjects')
    copyfile(path % sample_sdir, path % tempdir)

    # remove redundant label files
    label_temp = os.path.join(tempdir, 'fsaverage', 'label', '*.label')
    label_paths = glob(label_temp)
    for label_path in label_paths[1:]:
        os.remove(label_path)

    # create source space
    path = os.path.join(tempdir, 'fsaverage', 'bem', 'fsaverage-%s-src.fif')
    src = mne.setup_source_space('fsaverage', 'ico0', subjects_dir=tempdir,
                                 add_dist=False)
    write_source_spaces(path % 'ico-0', src)
    mri = os.path.join(tempdir, 'fsaverage', 'mri', 'orig.mgz')
    vsrc = mne.setup_volume_source_space('fsaverage', pos=50, mri=mri,
                                         subjects_dir=tempdir,
                                         add_interpolator=False)
    write_source_spaces(path % 'vol-50', vsrc)

    # scale fsaverage
    os.environ['_MNE_FEW_SURFACES'] = 'true'
    scale = np.array([1, .2, .8])
    scale_mri('fsaverage', 'flachkopf', scale, True, subjects_dir=tempdir)
    del os.environ['_MNE_FEW_SURFACES']
    assert_true(_is_mri_subject('flachkopf', tempdir),
                "Scaling fsaverage failed")
    spath = os.path.join(tempdir, 'flachkopf', 'bem', 'flachkopf-%s-src.fif')

    assert_true(os.path.exists(spath % 'ico-0'),
                "Source space ico-0 was not scaled")
    vsrc_s = mne.read_source_spaces(spath % 'vol-50')
    pt = np.array([0.12, 0.41, -0.22])
    assert_array_almost_equal(apply_trans(vsrc_s[0]['src_mri_t'], pt * scale),
                              apply_trans(vsrc[0]['src_mri_t'], pt))
    scale_labels('flachkopf', subjects_dir=tempdir)

    # add distances to source space
    mne.add_source_space_distances(src)
    src.save(path % 'ico-0', overwrite=True)

    # scale with distances
    os.remove(spath % 'ico-0')
    scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir)
    ssrc = mne.read_source_spaces(spath % 'ico-0')
    assert_is_not(ssrc[0]['dist'], None)

###############################################################################
# Set up our source space.

# List substructures we are interested in. We select only the
# sub structures we want to include in the source space
labels_vol = ['Left-Amygdala',
              'Left-Thalamus-Proper',
              'Left-Cerebellum-Cortex',
              'Brain-Stem',
              'Right-Amygdala',
              'Right-Thalamus-Proper',
              'Right-Cerebellum-Cortex']

# Get a surface-based source space, here with few source points for speed
# in this demonstration, in general you should use oct6 spacing!
src = mne.setup_source_space(subject, spacing='oct5',
                             add_dist=False, subjects_dir=subjects_dir)

# Now we create a mixed src space by adding the volume regions specified in the
# list l