def METHOD_NAME():
    data, gtab = dsi_voxels()
    # Create a new data-set with a b0 measurement:
    new_data = np.concatenate([data, data[..., 0, None]], -1)
    new_bvecs = np.concatenate([gtab.bvecs, np.zeros((1, 3))])
    new_bvals = np.concatenate([gtab.bvals, [0]])
    new_gtab = gradient_table(new_bvals, new_bvecs)
    ds = DiffusionSpectrumModel(new_gtab)
    dsfit = ds.fit(new_data)
    pdf = dsfit.pdf()
    dsfit.odf(default_sphere)
    assert_equal(new_data.shape[:-1] + (17, 17, 17), pdf.shape)
    assert_equal(np.alltrue(np.isreal(pdf)), True)
    # And again, with one more b0 measurement (two in total):
    new_data = np.concatenate([data, data[..., 0, None]], -1)
    new_bvecs = np.concatenate([gtab.bvecs, np.zeros((1, 3))])
    new_bvals = np.concatenate([gtab.bvals, [0]])
    new_gtab = gradient_table(new_bvals, new_bvecs)
    ds = DiffusionSpectrumModel(new_gtab)
    dsfit = ds.fit(new_data)
    pdf = dsfit.pdf()
    dsfit.odf(default_sphere)
    assert_equal(new_data.shape[:-1] + (17, 17, 17), pdf.shape)
    assert_equal(np.alltrue(np.isreal(pdf)), True)