插值方法“三次样条”有什么作用？和“from_derivatives”做？

Question

pandas 插值纪录片已经为所有其他注释留下了有用的注释，说明它们是否使用实际的数字索引或时间索引进行插值。

method str, default ‘linear’

    Interpolation technique to use. One of:

        ‘linear’: Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes.

        ‘time’: Works on daily and higher resolution data to interpolate given length of interval.

        ‘index’, ‘values’: use the actual numerical values of the index.

        ‘pad’: Fill in NaNs using existing values.

        ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘spline’, ‘barycentric’, ‘polynomial’: Passed to scipy.interpolate.interp1d. These methods use the numerical values of the index. Both ‘polynomial’ and ‘spline’ require that you also specify an order (int), e.g. df.interpolate(method='polynomial', order=5).

        ‘krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’, ‘akima’, ‘cubicspline’: Wrappers around the SciPy interpolation methods of similar names. See Notes.

        ‘from_derivatives’: Refers to scipy.interpolate.BPoly.from_derivatives which replaces ‘piecewise_polynomial’ interpolation method in scipy 0.18.

但不幸的是我找不到最后两个的信息：

cubicspline 
and
from_derivatives

Answer 1

scipy.interpolate.CubicSpline

使用两次连续可导的分段三次多项式。结果表示为带有与给定数据匹配的断点的 PPoly 实例。

scipy.interpolate.BPoly.from_derivatives

在伯恩斯坦基上构造分段多项式，与断点处的指定值和导数兼容。