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:
- xarray_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.- tck3-tuple or a BSpline object
If a tuple, then it should be a sequence of length 3 returned by
splrep
orsplprep
containing the knots, coefficients, and degree of the spline. (Also see Notes.)- 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
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:
- 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.
Notes
Manipulating the tck-tuples directly is not recommended. In new code, prefer using
BSpline
objects.References
[1]C. de Boor, “On calculating with b-splines”, J. Approximation Theory, 6, p.50-62, 1972.
[2]M. G. Cox, “The numerical evaluation of b-splines”, J. Inst. Maths Applics, 10, p.134-149, 1972.
[3]P. Dierckx, “Curve and surface fitting with splines”, Monographs on Numerical Analysis, Oxford University Press, 1993.
Examples
Examples are given in the tutorial.