scipy.interpolate.krogh_interpolate(xi, yi, x, der=0, axis=0)[source]

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

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-dimensional 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.


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).