How to use the pykrige.ok.OrdinaryKriging function in PyKrige

def test_ok_geographic():
    # Generate random data:
    np.random.seed(89239413)
    lon = 360.0 * np.random.rand(50, 1)
    lat = 180.0 * np.random.rand(50, 1) - 90.0
    z = np.random.rand(50, 1)
    # Generate grid:
    grid_lon = 360.0 * np.random.rand(120, 1)
    grid_lat = 180.0 * np.random.rand(120, 1) - 90.0
    # Create ordinary kriging object:
    OK = OrdinaryKriging(
        variogram_model="linear",
        verbose=False,
        enable_plotting=False,
        coordinates_type="geographic",
    # Execute on grid:
    z, ss = OK.execute("grid", grid_lon, grid_lat)

# of the workflow works as intended (tested: 2e-9 is currently the
    # match for this setup):
    d_eucl = cdist(
        np.concatenate([x[:, np.newaxis], y[:, np.newaxis]], axis=1),
        np.concatenate([grid_x[:, np.newaxis], grid_y[:, np.newaxis]], axis=1),
    d_geo = core.great_circle_distance(
        lon[:, np.newaxis],
        lat[:, np.newaxis],
        grid_lon[np.newaxis, :],
        grid_lat[np.newaxis, :],
    assert_allclose(d_eucl, d_geo, rtol=2e-9)
    # Create ordinary kriging objects:
    OK_geo = OrdinaryKriging(
        variogram_model="linear",
        verbose=False,
        enable_plotting=False,
        coordinates_type="geographic",
    OK_xy = OrdinaryKriging(
        x, y, z, variogram_model="linear", verbose=False, enable_plotting=False
    OK_wrong = OrdinaryKriging(
        lon, lat, z, variogram_model="linear", verbose=False, enable_plotting=False
    # Execute on grid:

def test_ok_backends_produce_same_result(validation_ref):
    data, _, (uk_test_answer, gridx_ref, gridy_ref) = validation_ref
    gridx = np.linspace(1067000.0, 1072000.0, 100)
    gridy = np.linspace(241500.0, 244000.0, 100)
    ok = OrdinaryKriging(
        data[:, 0],
        data[:, 1],
        data[:, 2],
        variogram_model="linear",
        verbose=False,
        enable_plotting=False,
    z_ok_v, ss_ok_v = ok.execute("grid", gridx, gridy, backend="vectorized")
    z_ok_l, ss_ok_l = ok.execute("grid", gridx, gridy, backend="loop")
    assert_allclose(z_ok_v, z_ok_l)
    assert_allclose(ss_ok_v, ss_ok_l)

data[:, 0],
            data[:, 1],
            data[:, 2],
            variogram_model="custom",
            variogram_function=func,
    ok = OrdinaryKriging(
        data[:, 0],
        data[:, 1],
        data[:, 2],
        variogram_model="custom",
        variogram_parameters=[1.0, 1.0, 1.0],
        variogram_function=func,
    assert ok.variogram_function([1.0, 1.0, 1.0], 1.0) == approx(1.3010, rel=1e-4)
    ok = OrdinaryKriging(data[:, 0], data[:, 1], data[:, 2], variogram_model="linear")
    ok.update_variogram_model(
        "custom", variogram_parameters=[1.0, 1.0, 1.0], variogram_function=func
    assert ok.variogram_function([1.0, 1.0, 1.0], 1.0) == approx(1.3010, rel=1e-4)

OrdinaryKriging(
            data[:, 0],
            data[:, 1],
            data[:, 2],
            variogram_model="custom",
            variogram_function=0,
    with pytest.raises(ValueError):
        OrdinaryKriging(
            data[:, 0],
            data[:, 1],
            data[:, 2],
            variogram_model="custom",
            variogram_function=func,
    ok = OrdinaryKriging(
        data[:, 0],
        data[:, 1],
        data[:, 2],
        variogram_model="custom",
        variogram_parameters=[1.0, 1.0, 1.0],
        variogram_function=func,
    assert ok.variogram_function([1.0, 1.0, 1.0], 1.0) == approx(1.3010, rel=1e-4)
    ok = OrdinaryKriging(data[:, 0], data[:, 1], data[:, 2], variogram_model="linear")
    ok.update_variogram_model(
        "custom", variogram_parameters=[1.0, 1.0, 1.0], variogram_function=func
    assert ok.variogram_function([1.0, 1.0, 1.0], 1.0) == approx(1.3010, rel=1e-4)

def test_kriging_tools(sample_data_2d):
    data, (gridx, gridy, gridx_2), mask_ref = sample_data_2d
    ok = OrdinaryKriging(data[:, 0], data[:, 1], data[:, 2])
    z_write, ss_write = ok.execute("grid", gridx, gridy)
    kt.write_asc_grid(
        gridx,
        gridy,
        z_write,
        filename=os.path.join(BASE_DIR, "test_data/temp.asc"),
        style=1,
    z_read, x_read, y_read, cellsize, no_data = kt.read_asc_grid(
        os.path.join(BASE_DIR, "test_data/temp.asc")
    assert_allclose(z_write, z_read, 0.01, 0.01)
    assert_allclose(gridx, x_read)
    assert_allclose(gridy, y_read)

def test_ok_get_variogram_points(validation_ref):
    # Test to compare the variogram of OK results to those obtained using
    # KT3D_H2O.
    # (M. Karanovic, M. Tonkin, and D. Wilson, 2009, Groundwater,
    # vol. 47, no. 4, 580-586.)
    # Variogram parameters
    _variogram_parameters = [500.0, 3000.0, 0.0]
    data, _, (ok_test_answer, gridx, gridy) = validation_ref
    ok = OrdinaryKriging(
        data[:, 0],
        data[:, 1],
        data[:, 2],
        variogram_model="exponential",
        variogram_parameters=_variogram_parameters,
    # Get the variogram points from the UniversalKriging instance
    lags, calculated_variogram = ok.get_variogram_points()
    # Generate the expected variogram points according to the
    # exponential variogram model
    expected_variogram = variogram_models.exponential_variogram_model(
        _variogram_parameters, lags
    )

# Create ordinary kriging object:
OK = OrdinaryKriging(
    variogram_model="linear",
    verbose=False,
    enable_plotting=False,
    coordinates_type="geographic",
# Execute on grid:
z1, ss1 = OK.execute("grid", grid_lon, grid_lat)
# Create ordinary kriging object ignoring curvature:
OK = OrdinaryKriging(
    lon, lat, z, variogram_model="linear", verbose=False, enable_plotting=False
# Execute on grid:
z2, ss2 = OK.execute("grid", grid_lon, grid_lat)
# Print data at equator (last longitude index will show periodicity):
print("Original data:")
print("Longitude:", lon.astype(int))
print("Latitude: ", lat.astype(int))
print("z:        ", np.array_str(z, precision=2))
print("\nKrige at 60° latitude:\n======================")
print("Longitude:", grid_lon)
print("Value:    ", np.array_str(z1[5, :], precision=2))
print("Sigma²:   ", np.array_str(ss1[5, :], precision=2))
print("\nIgnoring curvature:\n=====================")

number of dimensions (default=2)
    Returns
    -------
    res : dict
      a dictionary with the timing results
    X_train = np.random.rand(n_train, n_dim)
    y_train = np.random.rand(n_train)
    X_test = np.random.rand(n_test, n_dim)
    res = {}
    for variogram_model in VARIOGRAM_MODELS:
        tic = time()
        OK = OrdinaryKriging(X_train[:, 0], X_train[:, 1], y_train,
                             variogram_model='linear',
                             verbose=False, enable_plotting=False)
        res['t_train_{}'.format(variogram_model)] = time() - tic
    # All the following tests are performed with the linear variogram model
    for backend in BACKENDS:
        for n_closest_points in N_MOVING_WINDOW:
            if backend == 'vectorized' and n_closest_points is not None:
                continue  # this is not supported
            tic = time()
            OK.execute('points', X_test[:, 0], X_test[:, 1],
                       backend=backend,
                       n_closest_points=n_closest_points)
            res['t_test_{}_{}'.format(backend, n_closest_points)] = time() - tic