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scipy.interpolate.

# splev

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

  Evaluate a B-spline or its derivatives.

  Legacy

  This function is considered legacy and will no longer receive updates. While we currently have no plans to remove it, we recommend that new code uses more modern alternatives instead. Specifically, we recommend constructing a `BSpline` object and using its `__call__` method.

  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:
    x array_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.

    tck BSpline instance or tuple

      If a tuple, then it should be a sequence of length 3 returned by `splrep` or `splprep` containing the knots, coefficients, and degree of the spline. (Also see Notes.)

    der int, optional

      The order of derivative of the spline to compute (must be less than or equal to k, the degree of the spline).

    ext int, 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:
    y ndarray 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.

  See also

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

  Notes

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

  Array API Standard Support

  `splev` is not in-scope for support of Python Array API Standard compatible backends other than NumPy.

  See Support for the array API standard for more information.

  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.

  A comparison between `splev`, `splder` and `spalde` to compute the derivatives of a B-spline can be found in the `spalde` examples section.