Vous êtes un professionnel et vous avez besoin d'une formation ? Coder avec une
Intelligence Artificielle Voir le programme détaillé

Module « numpy.matlib »

Fonction clip - module numpy.matlib

Signature de la fonction clip

def clip(a, a_min=<no value>, a_max=<no value>, out=None, *, min=<no value>, max=<no value>, **kwargs) 

Description

help(numpy.matlib.clip)

Clip (limit) the values in an array.

Given an interval, values outside the interval are clipped to
the interval edges.  For example, if an interval of ``[0, 1]``
is specified, values smaller than 0 become 0, and values larger
than 1 become 1.

Equivalent to but faster than ``np.minimum(a_max, np.maximum(a, a_min))``.

No check is performed to ensure ``a_min < a_max``.

Parameters
----------
a : array_like
    Array containing elements to clip.
a_min, a_max : array_like or None
    Minimum and maximum value. If ``None``, clipping is not performed on
    the corresponding edge. If both ``a_min`` and ``a_max`` are ``None``,
    the elements of the returned array stay the same. Both are broadcasted
    against ``a``.
out : ndarray, optional
    The results will be placed in this array. It may be the input
    array for in-place clipping.  `out` must be of the right shape
    to hold the output.  Its type is preserved.
min, max : array_like or None
    Array API compatible alternatives for ``a_min`` and ``a_max``
    arguments. Either ``a_min`` and ``a_max`` or ``min`` and ``max``
    can be passed at the same time. Default: ``None``.

    .. versionadded:: 2.1.0
**kwargs
    For other keyword-only arguments, see the
    :ref:`ufunc docs <ufuncs.kwargs>`.

Returns
-------
clipped_array : ndarray
    An array with the elements of `a`, but where values
    < `a_min` are replaced with `a_min`, and those > `a_max`
    with `a_max`.

See Also
--------
:ref:`ufuncs-output-type`

Notes
-----
When `a_min` is greater than `a_max`, `clip` returns an
array in which all values are equal to `a_max`,
as shown in the second example.

Examples
--------
>>> import numpy as np
>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.clip(a, 1, 8)
array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])
>>> np.clip(a, 8, 1)
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
>>> np.clip(a, 3, 6, out=a)
array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])
>>> a
array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])
>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
array([3, 4, 2, 3, 4, 5, 6, 7, 8, 8])

Vous êtes un professionnel et vous avez besoin d'une formation ? Programmation Python
Les compléments Voir le programme détaillé