scipy.interpolate.splint

scipy.interpolate.splint(a, b, tck, full_output=0)

Evaluate the definite integral of a B-spline.

Given the knots and coefficients of a B-spline, evaluate the definite integral of the smoothing polynomial between two given points.

Parameters:

a, b : float

The end-points of the integration interval.

tck : tuple

A tuple (t,c,k) containing the vector of knots, the B-spline coefficients, and the degree of the spline (see splev).

full_output : int, optional

Non-zero to return optional output.

Returns:

integral : float

The resulting integral.

wrk : ndarray

An array containing the integrals of the normalized B-splines defined on the set of knots.

See also

splprep, splrep, sproot, spalde, splev, bisplrep, bisplev, UnivariateSpline, BivariateSpline

Notes

splint silently assumes that the spline function is zero outside the data interval (a, b).

References

[R76]

P.W. Gaffney, The calculation of indefinite integrals of b-splines”, J. Inst. Maths Applics, 17, p.37-41, 1976.

[R77]

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