Vous êtes un professionnel et vous avez besoin d'une formation ? Machine Learning
avec Scikit-Learn Voir le programme détaillé

Module « scipy.interpolate »

Fonction splev - module scipy.interpolate

Signature de la fonction splev

def splev(x, tck, der=0, ext=0) 

Description

help(scipy.interpolate.splev)

Evaluate a B-spline or its derivatives.

.. legacy:: function

    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.

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 :ref:`in the tutorial <tutorial-interpolate_splXXX>`.

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

Vous êtes un professionnel et vous avez besoin d'une formation ? Deep Learning avec Python
et Keras et Tensorflow Voir le programme détaillé