scipy.interpolate.splev¶

scipy.interpolate.
splev
(x, tck, der=0, ext=0)[source]¶ Evaluate a Bspline or its derivatives.
Given the knots and coefficients of a Bspline representation, evaluate the value of the smoothing polynomial and its derivatives. This is a wrapper around the FORTRAN routines splev and splder of FITPACK.
Parameters: x : array_like
An array of points at which to return the value of the smoothed spline or its derivatives. If tck was returned from
splprep
, then the parameter values, u should be given.tck : 3tuple or a BSpline object
der : int, optional
The order of derivative of the spline to compute (must be less than or equal to k).
ext : int, optional
Controls the value returned for elements of
x
not in the interval defined by the knot sequence. if ext=0, return the extrapolated value.
 if ext=1, return 0
 if ext=2, raise a ValueError
 if ext=3, return the boundary value.
The default value is 0.
Returns: y : ndarray or list of ndarrays
An array of values representing the spline function evaluated at the points in x. If tck was returned from
splprep
, then this is a list of arrays representing the curve in Ndimensional space.Notes
Manipulating the tcktuples directly is not recommended. In new code, prefer using
BSpline
objects.References
[R100] C. de Boor, “On calculating with bsplines”, J. Approximation Theory, 6, p.5062, 1972. [R101] M. G. Cox, “The numerical evaluation of bsplines”, J. Inst. Maths Applics, 10, p.134149, 1972. [R102] P. Dierckx, “Curve and surface fitting with splines”, Monographs on Numerical Analysis, Oxford University Press, 1993.