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:

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-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.

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-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.