def METHOD_NAME():
    """This tests the number of initial directions."
    """
    hsph_updated = HemiSphere.from_sphere(unit_icosahedron).subdivide(2)
    vertices = hsph_updated.vertices
    bvecs = vertices
    bvals = np.ones(len(vertices)) * 1000
    bvecs = np.insert(bvecs, 0, np.array([0, 0, 0]), axis=0)
    bvals = np.insert(bvals, 0, 0)
    gtab = gradient_table(bvals, bvecs)
    # test that we get one direction when we have a single tensor
    sphere = HemiSphere.from_sphere(get_sphere('symmetric724'))
    voxel = single_tensor(gtab).reshape([1, 1, 1, -1])
    dti_model = dti.TensorModel(gtab)
    with warnings.catch_warnings():
        warnings.filterwarnings(
            "ignore", message=shm.descoteaux07_legacy_msg,
            category=PendingDeprecationWarning)
        boot_dg = BootDirectionGetter.from_data(voxel, dti_model, 30,
                                                sphere=sphere, sh_order=6)
    initial_direction = boot_dg.initial_direction(np.zeros(3))
    npt.assert_equal(len(initial_direction), 1)
    npt.assert_allclose(initial_direction[0], [1, 0, 0], atol=0.1)
    # test that we get multiple directions when we have a multi-tensor
    mevals = np.array([[1.5, 0.4, 0.4], [1.5, 0.4, 0.4]]) * 1e-3
    fracs = [60, 40]
    voxel, primary_evecs = multi_tensor(gtab, mevals, fractions=fracs,
                                        snr=None)
    voxel = voxel.reshape([1, 1, 1, -1])
    response = (np.array([0.0015, 0.0004, 0.0004]), 1)
    with warnings.catch_warnings():
        warnings.filterwarnings(
            "ignore", message=shm.descoteaux07_legacy_msg,
            category=PendingDeprecationWarning)
        csd_model = ConstrainedSphericalDeconvModel(gtab, response=response,
                                                    sh_order=4)
        boot_dg = BootDirectionGetter.from_data(voxel, csd_model, 30,
                                                sphere=sphere,)
        initial_direction = boot_dg.initial_direction(np.zeros(3))
    npt.assert_equal(len(initial_direction), 2)
    npt.assert_allclose(initial_direction, primary_evecs, atol=0.1)