Python interpolate.piecewise_polynomial_interpolate方法代碼示例

# 需要導入模塊: from scipy import interpolate [as 別名]
# 或者: from scipy.interpolate import piecewise_polynomial_interpolate [as 別名]
def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
    """
    Convenience function for interpolate.BPoly.from_derivatives

    Construct a piecewise polynomial in the Bernstein basis, compatible
    with the specified values and derivatives at breakpoints.

    Parameters
    ----------
    xi : array_like
        sorted 1D array of x-coordinates
    yi : array_like or list of array-likes
        yi[i][j] is the j-th derivative known at xi[i]
    orders : None or int or array_like of ints. Default: None.
        Specifies the degree of local polynomials. If not None, some
        derivatives are ignored.
    der : int or list
        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 numberincludes the function
        value as 0th derivative.
     extrapolate : bool, optional
        Whether to extrapolate to ouf-of-bounds points based on first and last
        intervals, or to return NaNs. Default: True.

    See Also
    --------
    scipy.interpolate.BPoly.from_derivatives

    Returns
    -------
    y : scalar or array_like
        The result, of length R or length M or M by R,

    """
    import scipy
    from scipy import interpolate

    if LooseVersion(scipy.__version__) < LooseVersion('0.18.0'):
        try:
            method = interpolate.piecewise_polynomial_interpolate
            return method(xi, yi.reshape(-1, 1), x,
                          orders=order, der=der)
        except AttributeError:
            pass

    # return the method for compat with scipy version & backwards compat
    method = interpolate.BPoly.from_derivatives
    m = method(xi, yi.reshape(-1, 1),
               orders=order, extrapolate=extrapolate)

    return m(x) 

# 需要導入模塊: from scipy import interpolate [as 別名]
# 或者: from scipy.interpolate import piecewise_polynomial_interpolate [as 別名]
def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None,
                               bounds_error=False, order=None, **kwargs):
    """
    passed off to scipy.interpolate.interp1d. method is scipy's kind.
    Returns an array interpolated at new_x.  Add any new methods to
    the list in _clean_interp_method
    """
    try:
        from scipy import interpolate
        from pandas import DatetimeIndex
    except ImportError:
        raise ImportError('{0} interpolation requires Scipy'.format(method))

    new_x = np.asarray(new_x)

    # ignores some kwargs that could be passed along.
    alt_methods = {
        'barycentric': interpolate.barycentric_interpolate,
        'krogh': interpolate.krogh_interpolate,
        'piecewise_polynomial': interpolate.piecewise_polynomial_interpolate,
    }

    if getattr(x, 'is_all_dates', False):
        # GH 5975, scipy.interp1d can't hande datetime64s
        x, new_x = x.values.astype('i8'), new_x.astype('i8')

    try:
        alt_methods['pchip'] = interpolate.pchip_interpolate
    except AttributeError:
        if method == 'pchip':
            raise ImportError("Your version of scipy does not support "
                              "PCHIP interpolation.")

    interp1d_methods = ['nearest', 'zero', 'slinear', 'quadratic', 'cubic',
                        'polynomial']
    if method in interp1d_methods:
        if method == 'polynomial':
            method = order
        terp = interpolate.interp1d(x, y, kind=method, fill_value=fill_value,
                                    bounds_error=bounds_error)
        new_y = terp(new_x)
    elif method == 'spline':
        terp = interpolate.UnivariateSpline(x, y, k=order)
        new_y = terp(new_x)
    else:
        method = alt_methods[method]
        new_y = method(x, y, new_x)
    return new_y 

# 需要導入模塊: from scipy import interpolate [as 別名]
# 或者: from scipy.interpolate import piecewise_polynomial_interpolate [as 別名]
def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
    """
    Convenience function for interpolate.BPoly.from_derivatives

    Construct a piecewise polynomial in the Bernstein basis, compatible
    with the specified values and derivatives at breakpoints.

    Parameters
    ----------
    xi : array_like
        sorted 1D array of x-coordinates
    yi : array_like or list of array-likes
        yi[i][j] is the j-th derivative known at xi[i]
    orders : None or int or array_like of ints. Default: None.
        Specifies the degree of local polynomials. If not None, some
        derivatives are ignored.
    der : int or list
        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 numberincludes the function
        value as 0th derivative.
     extrapolate : bool, optional
        Whether to extrapolate to ouf-of-bounds points based on first and last
        intervals, or to return NaNs. Default: True.

    See Also
    --------
    scipy.interpolate.BPoly.from_derivatives

    Returns
    -------
    y : scalar or array_like
        The result, of length R or length M or M by R,

    """
    import scipy
    from scipy import interpolate

    if LooseVersion(scipy.__version__) < '0.18.0':
        try:
            method = interpolate.piecewise_polynomial_interpolate
            return method(xi, yi.reshape(-1, 1), x,
                          orders=order, der=der)
        except AttributeError:
            pass

    # return the method for compat with scipy version & backwards compat
    method = interpolate.BPoly.from_derivatives
    m = method(xi, yi.reshape(-1, 1),
               orders=order, extrapolate=extrapolate)

    return m(x)