scipy.interpolate.splev

scipy.interpolate.splev(x, tck, der=0)

Evaluate a B-spline and its derivatives.

Given the knots and coefficients of a B-spline representation, evaluate the value of the smoothing polynomial and it’s derivatives. This is a wrapper around the FORTRAN routines splev and splder of FITPACK.

Parameters :

x (u) – a 1-D 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 – A sequence of length 3 returned by splrep or splprep containg the :

knots, coefficients, and degree of the spline.

der – The order of derivative of the spline to compute (must be less than :

or equal to k).

Returns :

y – an array of values representing the spline function or curve. :

If tck was returned from splrep, then this is a list of arrays representing the curve in N-dimensional space.

See also

splprep, splrep, sproot, spalde, splint, roots, integral, bisplrep, bisplev, UnivariateSpline, BivariateSpline

References

[R5]	C. de Boor, “On calculating with b-splines”, J. Approximation Theory, 6, p.50-62, 1972.

[R6]	M.G. Cox, “The numerical evaluation of b-splines”, J. Inst. Maths Applics, 10, p.134-149, 1972.

[R7]	P. Dierckx, “Curve and surface fitting with splines”, Monographs on Numerical Analysis, Oxford University Press, 1993.