# scipy.interpolate.sproot

scipy.interpolate.sproot(tck, mest=10)

  Find the roots of a cubic B-spline.

  Given the knots (>=8) and coefficients of a cubic B-spline return the roots of the spline.

  Parameters:
    tck tuple or a BSpline object

      If a tuple, then it should be a sequence of length 3, containing the vector of knots, the B-spline coefficients, and the degree of the spline. The number of knots must be >= 8, and the degree must be 3. The knots must be a montonically increasing sequence.

    mest int, optional

      An estimate of the number of zeros (Default is 10).

  Returns:
    zeros ndarray

      An array giving the roots of the spline.

  See also

  `splprep`, `splrep`, `splint`, `spalde`, `splev`
  `bisplrep`, `bisplev`
  `BSpline`

  Notes

  Manipulating the tck-tuples directly is not recommended. In new code, prefer using the `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

  For some data, this method may miss a root. This happens when one of the spline knots (which FITPACK places automatically) happens to coincide with the true root. A workaround is to convert to `PPoly`, which uses a different root-finding algorithm.

  For example,

      >>> x = [1.96, 1.97, 1.98, 1.99, 2.00, 2.01, 2.02, 2.03, 2.04, 2.05]
      >>> y = [-6.365470e-03, -4.790580e-03, -3.204320e-03, -1.607270e-03,
      ...      4.440892e-16,  1.616930e-03,  3.243000e-03,  4.877670e-03,
      ...      6.520430e-03,  8.170770e-03]
      >>> from scipy.interpolate import splrep, sproot, PPoly
      >>> tck = splrep(x, y, s=0)
      >>> sproot(tck)
      array([], dtype=float64)

  Converting to a PPoly object does find the roots at x=2:

      >>> ppoly = PPoly.from_spline(tck)
      >>> ppoly.roots(extrapolate=False)
      array([2.])

  Further examples are given in the tutorial.