Module « scipy.interpolate »

Fonction krogh_interpolate - module scipy.interpolate

Signature de la fonction krogh_interpolate

def krogh_interpolate(xi, yi, x, der=0, axis=0) 

Description

krogh_interpolate.__doc__

    Convenience function for polynomial interpolation.

    See `KroghInterpolator` for more details.

    Parameters
    ----------
    xi : array_like
        Known x-coordinates.
    yi : array_like
        Known y-coordinates, of shape ``(xi.size, R)``. Interpreted as
        vectors of length R, or scalars if R=1.
    x : array_like
        Point or points at which to evaluate the derivatives.
    der : int or list, optional
        How many derivatives to extract; None for all potentially
        nonzero derivatives (that is a number equal to the number
        of points), or a list of derivatives to extract. This number
        includes the function value as 0th derivative.
    axis : int, optional
        Axis in the yi array corresponding to the x-coordinate values.

    Returns
    -------
    d : ndarray
        If the interpolator's values are R-D then the
        returned array will be the number of derivatives by N by R.
        If `x` is a scalar, the middle dimension will be dropped; if
        the `yi` are scalars then the last dimension will be dropped.

    See Also
    --------
    KroghInterpolator : Krogh interpolator

    Notes
    -----
    Construction of the interpolating polynomial is a relatively expensive
    process. If you want to evaluate it repeatedly consider using the class
    KroghInterpolator (which is what this function uses).

    Examples
    --------
    We can interpolate 2D observed data using krogh interpolation:

    >>> import matplotlib.pyplot as plt
    >>> from scipy.interpolate import krogh_interpolate
    >>> x_observed = np.linspace(0.0, 10.0, 11)
    >>> y_observed = np.sin(x_observed)
    >>> x = np.linspace(min(x_observed), max(x_observed), num=100)
    >>> y = krogh_interpolate(x_observed, y_observed, x)
    >>> plt.plot(x_observed, y_observed, "o", label="observation")
    >>> plt.plot(x, y, label="krogh interpolation")
    >>> plt.legend()
    >>> plt.show()