How to use the pykrige.ok3d.OrdinaryKriging3D function in PyKrige

def test_ok3d_update_variogram_model(sample_data_3d):

    data, (gridx_ref, gridy_ref, gridz_ref), mask_ref = sample_data_3d

    with pytest.raises(ValueError):
        OrdinaryKriging3D(
            data[:, 0], data[:, 1], data[:, 2], data[:, 3], variogram_model="blurg"
        )

    k3d = OrdinaryKriging3D(data[:, 0], data[:, 1], data[:, 2], data[:, 3])
    variogram_model = k3d.variogram_model
    variogram_parameters = k3d.variogram_model_parameters
    anisotropy_scaling_y = k3d.anisotropy_scaling_y
    anisotropy_scaling_z = k3d.anisotropy_scaling_z
    anisotropy_angle_x = k3d.anisotropy_angle_x
    anisotropy_angle_y = k3d.anisotropy_angle_y
    anisotropy_angle_z = k3d.anisotropy_angle_z

    with pytest.raises(ValueError):
        k3d.update_variogram_model("blurg")
    k3d.update_variogram_model(
        "power",
        anisotropy_scaling_y=3.0,
        anisotropy_scaling_z=3.0,
        anisotropy_angle_x=45.0,
        anisotropy_angle_y=45.0,

assert_allclose(k[0, :, :], 0.0, atol=0.01)
    assert_allclose(k[1, :, :], 1.0, rtol=1.0e-2)
    assert_allclose(k[2, :, :], 2.0, rtol=1.0e-2)
    assert_allclose(k[3, :, :], 3.0, rtol=1.0e-2)
    k, ss = k3d.execute(
        "grid",
        np.arange(2.0, 3.0, 0.1),
        np.arange(2.0, 3.0, 0.1),
        np.arange(0.0, 4.0, 1.0),
        backend="loop",
    )
    assert_allclose(k[0, :, :], 0.0, atol=0.01)
    assert_allclose(k[1, :, :], 1.0, rtol=1.0e-2)
    assert_allclose(k[2, :, :], 2.0, rtol=1.0e-2)
    assert_allclose(k[3, :, :], 3.0, rtol=1.0e-2)
    k3d = OrdinaryKriging3D(
        data[:, 0], data[:, 1], data[:, 2], data[:, 3], variogram_model="linear"
    )
    k, ss = k3d.execute(
        "points",
        [2.5, 2.5, 2.5],
        [2.5, 2.5, 2.5],
        [1.0, 2.0, 3.0],
        backend="vectorized",
    )
    assert_allclose(k[0], 1.0, atol=0.01)
    assert_allclose(k[1], 2.0, rtol=1.0e-2)
    assert_allclose(k[2], 3.0, rtol=1.0e-2)
    k, ss = k3d.execute(
        "points", [2.5, 2.5, 2.5], [2.5, 2.5, 2.5], [1.0, 2.0, 3.0], backend="loop"
    )
    assert_allclose(k[0], 1.0, atol=0.01)

)
    uk_v, uss_v = uk3d.execute(
        "grid", gridx_ref, gridy_ref, np.array([0.0]), backend="vectorized"
    )
    assert_allclose(uk_v, ok_v)
    uk_l, uss_l = uk3d.execute(
        "grid", gridx_ref, gridy_ref, np.array([0.0]), backend="loop"
    )
    assert_allclose(uk_l, ok_l)
    assert_allclose(uk_l, uk_v)
    assert_allclose(uss_l, uss_v)

    data = np.genfromtxt(
        os.path.join(BASE_DIR, "test_data", "test3d_data.txt"), skip_header=1
    )
    ok3d = OrdinaryKriging3D(
        data[:, 0],
        data[:, 1],
        data[:, 2],
        data[:, 3],
        variogram_model="linear",
        variogram_parameters=[1.0, 0.1],
    )
    ok_v, oss_v = ok3d.execute(
        "grid", np.arange(10.0), np.arange(10.0), np.arange(10.0), backend="vectorized"
    )
    ok_l, oss_l = ok3d.execute(
        "grid", np.arange(10.0), np.arange(10.0), np.arange(10.0), backend="loop"
    )

    uk3d = UniversalKriging3D(
        data[:, 0],

)
    k, ss = k3d.execute(
        "grid", gridx_ref, gridy_ref, np.array([0.0]), backend="vectorized"
    )
    assert_allclose(np.squeeze(k), ok_test_answer)
    k, ss = k3d.execute("grid", gridx_ref, gridy_ref, np.array([0.0]), backend="loop")
    assert_allclose(np.squeeze(k), ok_test_answer)

    # Test to compare K3D results to those obtained using KT3D.
    data = np.genfromtxt(
        os.path.join(BASE_DIR, "test_data", "test3d_data.txt"), skip_header=1
    )
    ans = np.genfromtxt(os.path.join(BASE_DIR, "test_data", "test3d_answer.txt"))
    ans_z = ans[:, 0].reshape((10, 10, 10))
    ans_ss = ans[:, 1].reshape((10, 10, 10))
    k3d = OrdinaryKriging3D(
        data[:, 0],
        data[:, 1],
        data[:, 2],
        data[:, 3],
        variogram_model="linear",
        variogram_parameters=[1.0, 0.1],
    )
    k, ss = k3d.execute(
        "grid", np.arange(10.0), np.arange(10.0), np.arange(10.0), backend="vectorized"
    )
    assert_allclose(k, ans_z, rtol=1e-3, atol=1e-8)
    assert_allclose(ss, ans_ss, rtol=1e-3, atol=1e-8)
    k3d = OrdinaryKriging3D(
        data[:, 0],
        data[:, 1],
        data[:, 2],

def test_ok3d_backends_produce_same_result(sample_data_3d):

    data, (gridx_ref, gridy_ref, gridz_ref), mask_ref = sample_data_3d

    k3d = OrdinaryKriging3D(
        data[:, 0], data[:, 1], data[:, 2], data[:, 3], variogram_model="linear"
    )
    k_k3d_v, ss_k3d_v = k3d.execute(
        "grid", gridx_ref, gridy_ref, gridz_ref, backend="vectorized"
    )
    k_k3d_l, ss_k3d_l = k3d.execute(
        "grid", gridx_ref, gridy_ref, gridz_ref, backend="loop"
    )
    assert_allclose(k_k3d_v, k_k3d_l, rtol=1e-05, atol=1e-8)
    assert_allclose(ss_k3d_v, ss_k3d_l, rtol=1e-05, atol=1e-8)

def test_ok3d(validation_ref):

    data, (ok_test_answer, gridx_ref, gridy_ref), _ = validation_ref

    # Test to compare K3D results to those obtained using KT3D_H2O.
    # (M. Karanovic, M. Tonkin, and D. Wilson, 2009, Groundwater, vol. 47,
    # no. 4, 580-586.)
    k3d = OrdinaryKriging3D(
        data[:, 0],
        data[:, 1],
        np.zeros(data[:, 1].shape),
        data[:, 2],
        variogram_model="exponential",
        variogram_parameters=[500.0, 3000.0, 0.0],
    )
    k, ss = k3d.execute(
        "grid", gridx_ref, gridy_ref, np.array([0.0]), backend="vectorized"
    )
    assert_allclose(np.squeeze(k), ok_test_answer)
    k, ss = k3d.execute("grid", gridx_ref, gridy_ref, np.array([0.0]), backend="loop")
    assert_allclose(np.squeeze(k), ok_test_answer)

    # Test to compare K3D results to those obtained using KT3D.
    data = np.genfromtxt(

def test_force_exact_3d(sample_data_3d):

    data, (gridx_ref, gridy_ref, gridz_ref), mask_ref = sample_data_3d

    k3d = OrdinaryKriging3D(
        data[:, 0], data[:, 1], data[:, 2], data[:, 3], variogram_model="linear"
    )
    k, ss = k3d.execute(
        "grid", [0.1, 0.2, 0.3], [0.1, 0.2, 0.3], [0.1, 0.2, 0.3], backend="vectorized"
    )
    assert k[2, 0, 0] == approx(0.9)
    assert ss[2, 0, 0] == approx(0.0)
    assert k[0, 2, 0] == approx(0.9)
    assert ss[0, 2, 0] == approx(0.0)
    assert k[1, 2, 2] == approx(0.7)
    assert ss[1, 2, 2] == approx(0.0)
    assert ss[2, 2, 2] != approx(0.0)
    assert ss[0, 0, 0] != approx(0.0)

    k, ss = k3d.execute(
        "grid", [0.1, 0.2, 0.3], [0.1, 0.2, 0.3], [0.1, 0.2, 0.3], backend="loop"

def test_ok3d_update_variogram_model(sample_data_3d):

    data, (gridx_ref, gridy_ref, gridz_ref), mask_ref = sample_data_3d

    with pytest.raises(ValueError):
        OrdinaryKriging3D(
            data[:, 0], data[:, 1], data[:, 2], data[:, 3], variogram_model="blurg"
        )

    k3d = OrdinaryKriging3D(data[:, 0], data[:, 1], data[:, 2], data[:, 3])
    variogram_model = k3d.variogram_model
    variogram_parameters = k3d.variogram_model_parameters
    anisotropy_scaling_y = k3d.anisotropy_scaling_y
    anisotropy_scaling_z = k3d.anisotropy_scaling_z
    anisotropy_angle_x = k3d.anisotropy_angle_x
    anisotropy_angle_y = k3d.anisotropy_angle_y
    anisotropy_angle_z = k3d.anisotropy_angle_z

    with pytest.raises(ValueError):
        k3d.update_variogram_model("blurg")
    k3d.update_variogram_model(
        "power",

def execute(self, points, *args, **kwargs):
        # TODO array of Points, (x, y) pairs of shape (N, 2)
        """
        Parameters
        ----------
        points: dict

        Returns:
        -------
        Prediction array
        Variance array
        """
        if isinstance(self.model, OrdinaryKriging) or isinstance(
            self.model, OrdinaryKriging3D
        ):
            prediction, variance = self.model.execute(
                "points",
                n_closest_points=self.n_closest_points,
                backend="loop",
                **points
            )
        else:
            print("n_closest_points will be ignored for UniversalKriging")
            prediction, variance = self.model.execute(
                "points", backend="loop", **points
            )

        return prediction, variance