cupyx.scipy.signal.cspline1d_eval(cj, newx, dx=1.0, x0=0)[source]#

Evaluate a cubic spline at the new set of points.

dx is the old sample-spacing while x0 was the old origin. In other-words the old-sample points (knot-points) for which the cj represent spline coefficients were at equally-spaced points of:

oldx = x0 + j*dx j=0…N-1, with N=len(cj)

Edges are handled using mirror-symmetric boundary conditions.

  • cj (ndarray) – cublic spline coefficients

  • newx (ndarray) – New set of points.

  • dx (float, optional) – Old sample-spacing, the default value is 1.0.

  • x0 (int, optional) – Old origin, the default value is 0.


res – Evaluated a cubic spline points.

Return type:


See also


Compute cubic spline coefficients for rank-1 array.