c5ad56010a3302472f6d25ba1a34d1f826d2d2cb,nisl/tests/test_masking.py,,test_apply_mask,#,90

Before Change

    mask = np.ones((40, 40, 40))
    for affine in (np.eye(4), np.diag((1, 1, -1, 1)),
                   np.diag((.5, 1, .5, 1))):
        series = masking.apply_mask(Nifti1Image(data, affine),
                                    Nifti1Image(mask, affine), smooth=9)
        series = np.reshape(series[0, :], (40, 40, 40))
        vmax = series.max()
        // We are expecting a full-width at half maximum of
        // 9mm/voxel_size:

After Change

    data = np.zeros((40, 40, 40, 2))
    data[20, 20, 20] = 1
    mask = np.ones((40, 40, 40))
    for create_files in (False, True):
        for affine in (np.eye(4), np.diag((1, 1, -1, 1)),
                       np.diag((.5, 1, .5, 1))):
            data_img = Nifti1Image(data, affine)
            mask_img = Nifti1Image(mask, affine)
            with write_tmp_imgs(data_img, mask_img, create_files=create_files)\
                     as filenames:
                series = masking.apply_mask(filenames[0], filenames[1],
                                            smooth=9)

            series = np.reshape(series[0, :], (40, 40, 40))
            vmax = series.max()
            // We are expecting a full-width at half maximum of
            // 9mm/voxel_size:
            above_half_max = series > .5 * vmax
            for axis in (0, 1, 2):
                proj = np.any(np.any(np.rollaxis(above_half_max,
                              axis=axis), axis=-1), axis=-1)
                np.testing.assert_equal(proj.sum(),
                                        9 / np.abs(affine[axis, axis]))

    // Check that NaNs in the data do not propagate
    data[10, 10, 10] = np.NaN
    data_img = Nifti1Image(data, affine)
    mask_img = Nifti1Image(mask, affine)
    series = masking.apply_mask(data_img, mask_img, smooth=9)

Instances