num.py 文件源码

def polyval(fit, points, der=0, avg=False):
    """Evaluate polynomial generated by ``polyfit()`` on `points`.

    Parameters
    ----------
    fit, points : see polyfit()
    der : int, optional
        Derivative order. Only for 1D, uses np.polyder().
    avg : bool, optional
        Internal hack, only used by ``avgpolyval()``.

    Notes
    -----
    For 1D we provide "analytic" derivatives using np.polyder(). For ND, we
    didn't implement an equivalent machinery. For 2D, you might get away with
    fitting a bispline (see Interpol2D) and use it's derivs. For ND, try rbf.py's RBF
    interpolator which has at least 1st derivatives for arbitrary dimensions.

    See Also
    --------
    :class:`PolyFit`, :class:`PolyFit1D`, :func:`polyfit`
    """
    pscale, pmin = fit['pscale'], fit['pmin']
    vscale, vmin = fit['vscale'], fit['vmin']
    if der > 0:
        assert points.shape[1] == 1, "deriv only for 1d poly (ndim=1)"
        # ::-1 b/c numpy stores poly coeffs in reversed order
        dcoeffs = np.polyder(fit['coeffs'][::-1], m=der)
        return np.polyval(dcoeffs, (points[:,0] - pmin[0,0]) / pscale[0,0]) / \
            pscale[0,0]**der * vscale
    else:
        vand = vander((points - pmin) / pscale, fit['deg'])
        if avg:
            return np.dot(vand, fit['coeffs']) * vscale
        else:
            return np.dot(vand, fit['coeffs']) * vscale + vmin