scipy.interpolate.BarycentricInterpolator¶
- class scipy.interpolate.BarycentricInterpolator(xi, yi=None, axis=0)[source]¶
The interpolating polynomial for a set of points
Constructs a polynomial that passes through a given set of points. Allows evaluation of the polynomial, efficient changing of the y values to be interpolated, and updating by adding more x values. For reasons of numerical stability, this function does not compute the coefficients of the polynomial.
The values yi need to be provided before the function is evaluated, but none of the preprocessing depends on them, so rapid updates are possible.
Parameters: xi : array_like
1-d array of x coordinates of the points the polynomial should pass through
yi : array_like, optional
The y coordinates of the points the polynomial should pass through. If None, the y values will be supplied later via the set_y method.
axis : int, optional
Axis in the yi array corresponding to the x-coordinate values.
Notes
This class uses a “barycentric interpolation” method that treats the problem as a special case of rational function interpolation. This algorithm is quite stable, numerically, but even in a world of exact computation, unless the x coordinates are chosen very carefully - Chebyshev zeros (e.g. cos(i*pi/n)) are a good choice - polynomial interpolation itself is a very ill-conditioned process due to the Runge phenomenon.
Based on Berrut and Trefethen 2004, “Barycentric Lagrange Interpolation”.
Methods
__call__(x) Evaluate the interpolating polynomial at the points x :Parameters: x : array_like Points to evaluate the interpolant at. add_xi(xi[, yi]) Add more x values to the set to be interpolated The barycentric interpolation algorithm allows easy updating by adding more points for the polynomial to pass through. set_yi(yi[, axis]) Update the y values to be interpolated The barycentric interpolation algorithm requires the calculation of weights, but these depend only on the xi.