scipy.interpolate.splev¶
- scipy.interpolate.splev(x, tck, der=0, ext=0)[source]¶
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.
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 : 3-tuple 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 N-dimensional space.
Notes
Manipulating the tck-tuples directly is not recommended. In new code, prefer using BSpline objects.
References
[R78] C. de Boor, “On calculating with b-splines”, J. Approximation Theory, 6, p.50-62, 1972. [R79] M. G. Cox, “The numerical evaluation of b-splines”, J. Inst. Maths Applics, 10, p.134-149, 1972. [R80] P. Dierckx, “Curve and surface fitting with splines”, Monographs on Numerical Analysis, Oxford University Press, 1993.