def METHOD_NAME(lambdas, params=None, ax=None, **kwargs):
    """
    Plot a decomposed orthogonal polynomial from a single set of lambda coefficients.
    Parameters
    ----------
    lambdas
        1D array of lambdas.
    params : :class:`list`
        List of orthongonal polynomial parameters, if defaults are not used.
    ax : :class:`matplotlib.axes.Axes`
        Optionally specified axes to plot on.
    index : :class:`str`
        Index to use for the plot (one of :code:`"index", "radii", "z"`).
    Returns
    --------
    :class:`matplotlib.axes.Axes`
    """
    degree = lambdas.size
    params = _get_params(params=params, degree=degree)
    # check the degree and parameters are of consistent degree?
    reconstructed_func = get_lambda_poly_function(lambdas, params)
    ax = plot.spider.REE_v_radii(ax=ax)
    radii = np.array(get_ionic_radii(REE(), charge=3, coordination=8))
    xs = np.linspace(np.max(radii), np.min(radii), 100)
    ax.plot(xs, reconstructed_func(xs), label="Regression", color="k", **kwargs)
    for w, p in zip(lambdas, params):  # plot the components
        l_func = get_lambda_poly_function(
            [w], [p]
        )  # pasing singluar vaules and one tuple
        label = (
            r"$r^{}: \lambda_{}".format(len(p), len(p))
            + [r"\cdot f_{}".format(len(p)), ""][int(len(p) == 0)]
            + "$"
        )
        ax.plot(xs, l_func(xs), label=label, ls="--", **kwargs)  # plot the polynomials
    return ax