- scipy.interpolate.splev(x, tck, der=0, ext=0)#
Evaluate a B-spline or its derivatives.
Given the knots and coefficients of a B-spline representation, evaluate the value of the smoothing polynomial and its derivatives. This is a wrapper around the FORTRAN routines splev and splder of FITPACK.
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.
- tck3-tuple or a BSpline object
- derint, optional
The order of derivative of the spline to compute (must be less than or equal to k, the degree of the spline).
- extint, optional
Controls the value returned for elements of
xnot 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.
- yndarray 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 an N-D space.
Manipulating the tck-tuples directly is not recommended. In new code, prefer using
C. de Boor, “On calculating with b-splines”, J. Approximation Theory, 6, p.50-62, 1972.
M. G. Cox, “The numerical evaluation of b-splines”, J. Inst. Maths Applics, 10, p.134-149, 1972.
P. Dierckx, “Curve and surface fitting with splines”, Monographs on Numerical Analysis, Oxford University Press, 1993.
Examples are given in the tutorial.