# scipy.interpolate.krogh_interpolate¶

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, 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. 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.

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).

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