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# 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

  [R61] | P.W. Gaffney, The calculation of indefinite integrals of b-splines”, J. Inst. Maths Applics, 17, p.37-41, 1976.

  [R62] | P. Dierckx, “Curve and surface fitting with splines”, Monographs on Numerical Analysis, Oxford University Press, 1993.