Python SourceEstimate.subject方法代码示例

# 需要导入模块: from mne import SourceEstimate [as 别名]
# 或者: from mne.SourceEstimate import subject [as 别名]
def test_morphed_source_space_return():
    """Test returning a morphed source space to the original subject"""
    # let's create some random data on fsaverage
    data = rng.randn(20484, 1)
    tmin, tstep = 0, 1.
    src_fs = read_source_spaces(fname_fs)
    stc_fs = SourceEstimate(data, [s['vertno'] for s in src_fs],
                            tmin, tstep, 'fsaverage')

    # Create our morph source space
    src_morph = morph_source_spaces(src_fs, 'sample',
                                    subjects_dir=subjects_dir)

    # Morph the data over using standard methods
    stc_morph = stc_fs.morph('sample', [s['vertno'] for s in src_morph],
                             smooth=1, subjects_dir=subjects_dir)

    # We can now pretend like this was real data we got e.g. from an inverse.
    # To be complete, let's remove some vertices
    keeps = [np.sort(rng.permutation(np.arange(len(v)))[:len(v) - 10])
             for v in stc_morph.vertices]
    stc_morph = SourceEstimate(
        np.concatenate([stc_morph.lh_data[keeps[0]],
                        stc_morph.rh_data[keeps[1]]]),
        [v[k] for v, k in zip(stc_morph.vertices, keeps)], tmin, tstep,
        'sample')

    # Return it to the original subject
    stc_morph_return = stc_morph.to_original_src(
        src_fs, subjects_dir=subjects_dir)

    # Compare to the original data
    stc_morph_morph = stc_morph.morph('fsaverage', stc_morph_return.vertices,
                                      smooth=1,
                                      subjects_dir=subjects_dir)
    assert_equal(stc_morph_return.subject, stc_morph_morph.subject)
    for ii in range(2):
        assert_array_equal(stc_morph_return.vertices[ii],
                           stc_morph_morph.vertices[ii])
    # These will not match perfectly because morphing pushes data around
    corr = np.corrcoef(stc_morph_return.data[:, 0],
                       stc_morph_morph.data[:, 0])[0, 1]
    assert_true(corr > 0.99, corr)

    # Degenerate cases
    stc_morph.subject = None  # no .subject provided
    assert_raises(ValueError, stc_morph.to_original_src,
                  src_fs, subject_orig='fsaverage', subjects_dir=subjects_dir)
    stc_morph.subject = 'sample'
    del src_fs[0]['subject_his_id']  # no name in src_fsaverage
    assert_raises(ValueError, stc_morph.to_original_src,
                  src_fs, subjects_dir=subjects_dir)
    src_fs[0]['subject_his_id'] = 'fsaverage'  # name mismatch
    assert_raises(ValueError, stc_morph.to_original_src,
                  src_fs, subject_orig='foo', subjects_dir=subjects_dir)
    src_fs[0]['subject_his_id'] = 'sample'
    src = read_source_spaces(fname)  # wrong source space
    assert_raises(RuntimeError, stc_morph.to_original_src,
                  src, subjects_dir=subjects_dir)

# 需要导入模块: from mne import SourceEstimate [as 别名]
# 或者: from mne.SourceEstimate import subject [as 别名]
def test_morphed_source_space_return():
    """Test returning a morphed source space to the original subject."""
    # let's create some random data on fsaverage
    data = rng.randn(20484, 1)
    tmin, tstep = 0, 1.
    src_fs = read_source_spaces(fname_fs)
    stc_fs = SourceEstimate(data, [s['vertno'] for s in src_fs],
                            tmin, tstep, 'fsaverage')
    n_verts_fs = sum(len(s['vertno']) for s in src_fs)

    # Create our morph source space
    src_morph = morph_source_spaces(src_fs, 'sample',
                                    subjects_dir=subjects_dir)
    n_verts_sample = sum(len(s['vertno']) for s in src_morph)
    assert n_verts_fs == n_verts_sample

    # Morph the data over using standard methods
    stc_morph = compute_source_morph(
        src_fs, 'fsaverage', 'sample',
        spacing=[s['vertno'] for s in src_morph], smooth=1,
        subjects_dir=subjects_dir, warn=False).apply(stc_fs)
    assert stc_morph.data.shape[0] == n_verts_sample

    # We can now pretend like this was real data we got e.g. from an inverse.
    # To be complete, let's remove some vertices
    keeps = [np.sort(rng.permutation(np.arange(len(v)))[:len(v) - 10])
             for v in stc_morph.vertices]
    stc_morph = SourceEstimate(
        np.concatenate([stc_morph.lh_data[keeps[0]],
                        stc_morph.rh_data[keeps[1]]]),
        [v[k] for v, k in zip(stc_morph.vertices, keeps)], tmin, tstep,
        'sample')

    # Return it to the original subject
    stc_morph_return = stc_morph.to_original_src(
        src_fs, subjects_dir=subjects_dir)

    # This should fail (has too many verts in SourceMorph)
    with pytest.warns(RuntimeWarning, match='vertices not included'):
        morph = compute_source_morph(
            src_morph, subject_from='sample',
            spacing=stc_morph_return.vertices, smooth=1,
            subjects_dir=subjects_dir)
    with pytest.raises(ValueError, match='vertices do not match'):
        morph.apply(stc_morph)

    # Compare to the original data
    with pytest.warns(RuntimeWarning, match='vertices not included'):
        stc_morph_morph = compute_source_morph(
            src=stc_morph, subject_from='sample',
            spacing=stc_morph_return.vertices, smooth=1,
            subjects_dir=subjects_dir).apply(stc_morph)

    assert_equal(stc_morph_return.subject, stc_morph_morph.subject)
    for ii in range(2):
        assert_array_equal(stc_morph_return.vertices[ii],
                           stc_morph_morph.vertices[ii])
    # These will not match perfectly because morphing pushes data around
    corr = np.corrcoef(stc_morph_return.data[:, 0],
                       stc_morph_morph.data[:, 0])[0, 1]
    assert corr > 0.99, corr

    # Explicitly test having two vertices map to the same target vertex. We
    # simulate this by having two vertices be at the same position.
    src_fs2 = src_fs.copy()
    vert1, vert2 = src_fs2[0]['vertno'][:2]
    src_fs2[0]['rr'][vert1] = src_fs2[0]['rr'][vert2]
    stc_morph_return = stc_morph.to_original_src(
        src_fs2, subjects_dir=subjects_dir)

    # test to_original_src method result equality
    for ii in range(2):
        assert_array_equal(stc_morph_return.vertices[ii],
                           stc_morph_morph.vertices[ii])

    # These will not match perfectly because morphing pushes data around
    corr = np.corrcoef(stc_morph_return.data[:, 0],
                       stc_morph_morph.data[:, 0])[0, 1]
    assert corr > 0.99, corr

    # Degenerate cases
    stc_morph.subject = None  # no .subject provided
    pytest.raises(ValueError, stc_morph.to_original_src,
                  src_fs, subject_orig='fsaverage', subjects_dir=subjects_dir)
    stc_morph.subject = 'sample'
    del src_fs[0]['subject_his_id']  # no name in src_fsaverage
    pytest.raises(ValueError, stc_morph.to_original_src,
                  src_fs, subjects_dir=subjects_dir)
    src_fs[0]['subject_his_id'] = 'fsaverage'  # name mismatch
    pytest.raises(ValueError, stc_morph.to_original_src,
                  src_fs, subject_orig='foo', subjects_dir=subjects_dir)
    src_fs[0]['subject_his_id'] = 'sample'
    src = read_source_spaces(fname)  # wrong source space
    pytest.raises(RuntimeError, stc_morph.to_original_src,
                  src, subjects_dir=subjects_dir)