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Module « numpy.matlib »

Fonction choose - module numpy.matlib

Signature de la fonction choose

def choose(a, choices, out=None, mode='raise') 

Description

help(numpy.matlib.choose)

Construct an array from an index array and a list of arrays to choose from.

First of all, if confused or uncertain, definitely look at the Examples -
in its full generality, this function is less simple than it might
seem from the following code description::

    np.choose(a,c) == np.array([c[a[I]][I] for I in np.ndindex(a.shape)])

But this omits some subtleties.  Here is a fully general summary:

Given an "index" array (`a`) of integers and a sequence of ``n`` arrays
(`choices`), `a` and each choice array are first broadcast, as necessary,
to arrays of a common shape; calling these *Ba* and *Bchoices[i], i =
0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape``
for each ``i``.  Then, a new array with shape ``Ba.shape`` is created as
follows:

* if ``mode='raise'`` (the default), then, first of all, each element of
  ``a`` (and thus ``Ba``) must be in the range ``[0, n-1]``; now, suppose
  that ``i`` (in that range) is the value at the ``(j0, j1, ..., jm)``
  position in ``Ba`` - then the value at the same position in the new array
  is the value in ``Bchoices[i]`` at that same position;

* if ``mode='wrap'``, values in `a` (and thus `Ba`) may be any (signed)
  integer; modular arithmetic is used to map integers outside the range
  `[0, n-1]` back into that range; and then the new array is constructed
  as above;

* if ``mode='clip'``, values in `a` (and thus ``Ba``) may be any (signed)
  integer; negative integers are mapped to 0; values greater than ``n-1``
  are mapped to ``n-1``; and then the new array is constructed as above.

Parameters
----------
a : int array
    This array must contain integers in ``[0, n-1]``, where ``n`` is the
    number of choices, unless ``mode=wrap`` or ``mode=clip``, in which
    cases any integers are permissible.
choices : sequence of arrays
    Choice arrays. `a` and all of the choices must be broadcastable to the
    same shape.  If `choices` is itself an array (not recommended), then
    its outermost dimension (i.e., the one corresponding to
    ``choices.shape[0]``) is taken as defining the "sequence".
out : array, optional
    If provided, the result will be inserted into this array. It should
    be of the appropriate shape and dtype. Note that `out` is always
    buffered if ``mode='raise'``; use other modes for better performance.
mode : {'raise' (default), 'wrap', 'clip'}, optional
    Specifies how indices outside ``[0, n-1]`` will be treated:

    * 'raise' : an exception is raised
    * 'wrap' : value becomes value mod ``n``
    * 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1

Returns
-------
merged_array : array
    The merged result.

Raises
------
ValueError: shape mismatch
    If `a` and each choice array are not all broadcastable to the same
    shape.

See Also
--------
ndarray.choose : equivalent method
numpy.take_along_axis : Preferable if `choices` is an array

Notes
-----
To reduce the chance of misinterpretation, even though the following
"abuse" is nominally supported, `choices` should neither be, nor be
thought of as, a single array, i.e., the outermost sequence-like container
should be either a list or a tuple.

Examples
--------

>>> import numpy as np
>>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],
...   [20, 21, 22, 23], [30, 31, 32, 33]]
>>> np.choose([2, 3, 1, 0], choices
... # the first element of the result will be the first element of the
... # third (2+1) "array" in choices, namely, 20; the second element
... # will be the second element of the fourth (3+1) choice array, i.e.,
... # 31, etc.
... )
array([20, 31, 12,  3])
>>> np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)
array([20, 31, 12,  3])
>>> # because there are 4 choice arrays
>>> np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)
array([20,  1, 12,  3])
>>> # i.e., 0

A couple examples illustrating how choose broadcasts:

>>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
>>> choices = [-10, 10]
>>> np.choose(a, choices)
array([[ 10, -10,  10],
       [-10,  10, -10],
       [ 10, -10,  10]])

>>> # With thanks to Anne Archibald
>>> a = np.array([0, 1]).reshape((2,1,1))
>>> c1 = np.array([1, 2, 3]).reshape((1,3,1))
>>> c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5))
>>> np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
array([[[ 1,  1,  1,  1,  1],
        [ 2,  2,  2,  2,  2],
        [ 3,  3,  3,  3,  3]],
       [[-1, -2, -3, -4, -5],
        [-1, -2, -3, -4, -5],
        [-1, -2, -3, -4, -5]]])

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