def METHOD_NAME(self):
    # Test from Neri et al. (Meas. Sci. Technol. 1 (1990) 1007-1010.)
    i, x, Wx, y, Wy = np.loadtxt(
        f"{path}/../burnman/data/" "input_fitting/Pearson_York.dat", unpack=True
    )
    data = np.array([x, y]).T
    cov = np.array([[1.0 / Wx, 0.0 * Wx], [0.0 * Wy, 1.0 / Wy]]).T
    class m:
        def __init__(self, data, cov, guessed_params, delta_params):
            self.data = data
            self.data_covariances = cov
            self.set_params(guessed_params)
            self.delta_params = delta_params
            self.mle_tolerances = np.array([1.0e-8] * len(data[:, 0]))
        def set_params(self, param_values):
            self.params = param_values
        def get_params(self):
            return self.params
        def function(self, x, flag):
            return np.array(
                [
                    x[0],
                    self.params[0] * x[0] * x[0] * x[0]
                    + self.params[1] * x[0] * x[0]
                    + self.params[2] * x[0]
                    + self.params[3],
                ]
            )
        def normal(self, x, flag):
            n = np.array(
                [
                    3.0 * self.params[0] * x[0] * x[0]
                    + 2.0 * self.params[1] * x[0]
                    + 1.0 * self.params[2],
                    -1.0,
                ]
            )
            return n / np.linalg.norm(n)
    guessed_params = np.array([-1.2e-2, 0.161, -1.15, 6.142])
    delta_params = np.array([1.0e-5, 1.0e-5, 1.0e-5, 1.0e-5])
    fitted_curve = m(data, cov, guessed_params, delta_params)
    nonlinear_least_squares_fit(model=fitted_curve, param_tolerance=1.0e-5)
    self.assertArraysAlmostEqual([fitted_curve.WSS], [10.486904577])