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Module « numpy »

Fonction take - module numpy

Signature de la fonction take

def take(a, indices, axis=None, out=None, mode='raise') 

Description

help(numpy.take)

Take elements from an array along an axis.

When axis is not None, this function does the same thing as "fancy"
indexing (indexing arrays using arrays); however, it can be easier to use
if you need elements along a given axis. A call such as
``np.take(arr, indices, axis=3)`` is equivalent to
``arr[:,:,:,indices,...]``.

Explained without fancy indexing, this is equivalent to the following use
of `ndindex`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of
indices::

    Ni, Nk = a.shape[:axis], a.shape[axis+1:]
    Nj = indices.shape
    for ii in ndindex(Ni):
        for jj in ndindex(Nj):
            for kk in ndindex(Nk):
                out[ii + jj + kk] = a[ii + (indices[jj],) + kk]

Parameters
----------
a : array_like (Ni..., M, Nk...)
    The source array.
indices : array_like (Nj...)
    The indices of the values to extract.
    Also allow scalars for indices.
axis : int, optional
    The axis over which to select values. By default, the flattened
    input array is used.
out : ndarray, optional (Ni..., Nj..., Nk...)
    If provided, the result will be placed in 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', 'wrap', 'clip'}, optional
    Specifies how out-of-bounds indices will behave.

    * 'raise' -- raise an error (default)
    * 'wrap' -- wrap around
    * 'clip' -- clip to the range

    'clip' mode means that all indices that are too large are replaced
    by the index that addresses the last element along that axis. Note
    that this disables indexing with negative numbers.

Returns
-------
out : ndarray (Ni..., Nj..., Nk...)
    The returned array has the same type as `a`.

See Also
--------
compress : Take elements using a boolean mask
ndarray.take : equivalent method
take_along_axis : Take elements by matching the array and the index arrays

Notes
-----
By eliminating the inner loop in the description above, and using `s_` to
build simple slice objects, `take` can be expressed  in terms of applying
fancy indexing to each 1-d slice::

    Ni, Nk = a.shape[:axis], a.shape[axis+1:]
    for ii in ndindex(Ni):
        for kk in ndindex(Nj):
            out[ii + s_[...,] + kk] = a[ii + s_[:,] + kk][indices]

For this reason, it is equivalent to (but faster than) the following use
of `apply_along_axis`::

    out = np.apply_along_axis(lambda a_1d: a_1d[indices], axis, a)

Examples
--------
>>> import numpy as np
>>> a = [4, 3, 5, 7, 6, 8]
>>> indices = [0, 1, 4]
>>> np.take(a, indices)
array([4, 3, 6])

In this example if `a` is an ndarray, "fancy" indexing can be used.

>>> a = np.array(a)
>>> a[indices]
array([4, 3, 6])

If `indices` is not one dimensional, the output also has these dimensions.

>>> np.take(a, [[0, 1], [2, 3]])
array([[4, 3],
       [5, 7]])

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