Python mne.VolSourceEstimate类代码示例

def test_transform_data():
    """Test applying linear (time) transform to data"""
    # make up some data
    n_sensors, n_vertices, n_times = 10, 20, 4
    kernel = rng.randn(n_vertices, n_sensors)
    sens_data = rng.randn(n_sensors, n_times)

    vertices = np.arange(n_vertices)
    data = np.dot(kernel, sens_data)

    for idx, tmin_idx, tmax_idx in\
            zip([None, np.arange(n_vertices // 2, n_vertices)],
                [None, 1], [None, 3]):

        if idx is None:
            idx_use = slice(None, None)
        else:
            idx_use = idx

        data_f, _ = _my_trans(data[idx_use, tmin_idx:tmax_idx])

        for stc_data in (data, (kernel, sens_data)):
            stc = VolSourceEstimate(stc_data, vertices=vertices,
                                    tmin=0., tstep=1.)
            stc_data_t = stc.transform_data(_my_trans, idx=idx,
                                            tmin_idx=tmin_idx,
                                            tmax_idx=tmax_idx)
            assert_allclose(data_f, stc_data_t)

def test_plot_volume_source_estimates():
    """Test interactive plotting of volume source estimates."""
    forward = read_forward_solution(fwd_fname)
    sample_src = forward['src']

    vertices = [s['vertno'] for s in sample_src]
    n_verts = sum(len(v) for v in vertices)
    n_time = 2
    data = np.random.RandomState(0).rand(n_verts, n_time)
    vol_stc = VolSourceEstimate(data, vertices, 1, 1)

    vol_vec_stc = VolVectorSourceEstimate(
        np.tile(vol_stc.data[:, np.newaxis], (1, 3, 1)), vol_stc.vertices,
        0, 1)
    for mode, stc in zip(['glass_brain', 'stat_map'], (vol_stc, vol_vec_stc)):
        with pytest.warns(None):  # sometimes get scalars/index warning
            fig = stc.plot(sample_src, subject='sample',
                           subjects_dir=subjects_dir,
                           mode=mode)
        # [ax_time, ax_y, ax_x, ax_z]
        for ax_idx in [0, 2, 3, 4]:
            _fake_click(fig, fig.axes[ax_idx], (0.3, 0.5))
        fig.canvas.key_press_event('left')
        fig.canvas.key_press_event('shift+right')

    with pytest.raises(ValueError, match='must be one of'):
        vol_stc.plot(sample_src, 'sample', subjects_dir, mode='abcd')
    vertices.append([])
    surface_stc = SourceEstimate(data, vertices, 1, 1)
    with pytest.raises(ValueError, match='Only Vol'):
        plot_volume_source_estimates(surface_stc, sample_src, 'sample',
                                     subjects_dir)
    with pytest.raises(ValueError, match='Negative colormap limits'):
        vol_stc.plot(sample_src, 'sample', subjects_dir,
                     clim=dict(lims=[-1, 2, 3], kind='value'))

def test_vol_mask():
    """Test extraction of volume mask."""
    src = read_source_spaces(fname_vsrc)
    mask = _get_vol_mask(src)
    # Let's use an alternative way that should be equivalent
    vertices = src[0]['vertno']
    n_vertices = len(vertices)
    data = (1 + np.arange(n_vertices))[:, np.newaxis]
    stc_tmp = VolSourceEstimate(data, vertices, tmin=0., tstep=1.)
    img = stc_tmp.as_volume(src, mri_resolution=False)
    img_data = img.get_data()[:, :, :, 0].T
    mask_nib = (img_data != 0)
    assert_array_equal(img_data[mask_nib], data[:, 0])
    assert_array_equal(np.where(mask_nib.ravel())[0], src[0]['vertno'])
    assert_array_equal(mask, mask_nib)
    assert_array_equal(img_data.shape, mask.shape)

def test_volume_stc():
    """Test volume STCs
    """
    N = 100
    data = np.arange(N)[:, np.newaxis]
    datas = [data, data, np.arange(2)[:, np.newaxis]]
    vertno = np.arange(N)
    vertnos = [vertno, vertno[:, np.newaxis], np.arange(2)[:, np.newaxis]]
    vertno_reads = [vertno, vertno, np.arange(2)]
    for data, vertno, vertno_read in zip(datas, vertnos, vertno_reads):
        stc = VolSourceEstimate(data, vertno, 0, 1)
        fname_temp = op.join(tempdir, 'temp-vl.stc')
        stc_new = stc
        for _ in xrange(2):
            stc_new.save(fname_temp)
            stc_new = read_source_estimate(fname_temp)
            assert_true(isinstance(stc_new, VolSourceEstimate))
            assert_array_equal(vertno_read, stc_new.vertno)
            assert_array_almost_equal(stc.data, stc_new.data)
    # now let's actually read a MNE-C processed file
    stc = read_source_estimate(fname_vol, 'sample')
    assert_true(isinstance(stc, VolSourceEstimate))

    assert_true('sample' in repr(stc))
    stc_new = stc
    assert_raises(ValueError, stc.save, fname_vol, ftype='whatever')
    for _ in xrange(2):
        fname_temp = op.join(tempdir, 'temp-vol.w')
        stc_new.save(fname_temp, ftype='w')
        stc_new = read_source_estimate(fname_temp)
        assert_true(isinstance(stc_new, VolSourceEstimate))
        assert_array_equal(stc.vertno, stc_new.vertno)
        assert_array_almost_equal(stc.data, stc_new.data)

    # save the stc as a nifti file and export
    try:
        import nibabel as nib
        src = read_source_spaces(fname_vsrc)
        vol_fname = op.join(tempdir, 'stc.nii.gz')
        stc.save_as_volume(vol_fname, src,
                           dest='surf', mri_resolution=False)
        img = nib.load(vol_fname)
        assert_true(img.shape == src[0]['shape'] + (len(stc.times),))

        t1_img = nib.load(fname_t1)
        stc.save_as_volume(op.join(tempdir, 'stc.nii.gz'), src,
                           dest='mri', mri_resolution=True)
        img = nib.load(vol_fname)
        assert_true(img.shape == t1_img.shape + (len(stc.times),))
        assert_array_almost_equal(img.get_affine(), t1_img.get_affine(),
                                  decimal=5)

        # export without saving
        img = stc.as_volume(src, dest='mri', mri_resolution=True)
        assert_true(img.shape == t1_img.shape + (len(stc.times),))
        assert_array_almost_equal(img.get_affine(), t1_img.get_affine(),
                                  decimal=5)

    except ImportError:
        print 'Save as nifti test skipped, needs NiBabel'

def test_save_vol_stc_as_nifti():
    """Save the stc as a nifti file and export."""
    import nibabel as nib
    tempdir = _TempDir()
    src = read_source_spaces(fname_vsrc)
    vol_fname = op.join(tempdir, 'stc.nii.gz')

    # now let's actually read a MNE-C processed file
    stc = read_source_estimate(fname_vol, 'sample')
    assert (isinstance(stc, VolSourceEstimate))

    stc.save_as_volume(vol_fname, src,
                       dest='surf', mri_resolution=False)
    with pytest.warns(None):  # nib<->numpy
        img = nib.load(vol_fname)
    assert (img.shape == src[0]['shape'] + (len(stc.times),))

    with pytest.warns(None):  # nib<->numpy
        t1_img = nib.load(fname_t1)
    stc.save_as_volume(op.join(tempdir, 'stc.nii.gz'), src,
                       dest='mri', mri_resolution=True)
    with pytest.warns(None):  # nib<->numpy
        img = nib.load(vol_fname)
    assert (img.shape == t1_img.shape + (len(stc.times),))
    assert_allclose(img.affine, t1_img.affine, atol=1e-5)

    # export without saving
    img = stc.as_volume(src, dest='mri', mri_resolution=True)
    assert (img.shape == t1_img.shape + (len(stc.times),))
    assert_allclose(img.affine, t1_img.affine, atol=1e-5)

    src = SourceSpaces([src[0], src[0]])
    stc = VolSourceEstimate(np.r_[stc.data, stc.data],
                            [stc.vertices, stc.vertices],
                            tmin=stc.tmin, tstep=stc.tstep)
    img = stc.as_volume(src, dest='mri', mri_resolution=False)
    assert (img.shape == src[0]['shape'] + (len(stc.times),))