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Module « numpy.matlib »

Fonction apply_over_axes - module numpy.matlib

Signature de la fonction apply_over_axes

def apply_over_axes(func, a, axes) 

Description

help(numpy.matlib.apply_over_axes)

Apply a function repeatedly over multiple axes.

`func` is called as `res = func(a, axis)`, where `axis` is the first
element of `axes`.  The result `res` of the function call must have
either the same dimensions as `a` or one less dimension.  If `res`
has one less dimension than `a`, a dimension is inserted before
`axis`.  The call to `func` is then repeated for each axis in `axes`,
with `res` as the first argument.

Parameters
----------
func : function
    This function must take two arguments, `func(a, axis)`.
a : array_like
    Input array.
axes : array_like
    Axes over which `func` is applied; the elements must be integers.

Returns
-------
apply_over_axis : ndarray
    The output array.  The number of dimensions is the same as `a`,
    but the shape can be different.  This depends on whether `func`
    changes the shape of its output with respect to its input.

See Also
--------
apply_along_axis :
    Apply a function to 1-D slices of an array along the given axis.

Notes
-----
This function is equivalent to tuple axis arguments to reorderable ufuncs
with keepdims=True. Tuple axis arguments to ufuncs have been available since
version 1.7.0.

Examples
--------
>>> import numpy as np
>>> a = np.arange(24).reshape(2,3,4)
>>> a
array([[[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]],
       [[12, 13, 14, 15],
        [16, 17, 18, 19],
        [20, 21, 22, 23]]])

Sum over axes 0 and 2. The result has same number of dimensions
as the original array:

>>> np.apply_over_axes(np.sum, a, [0,2])
array([[[ 60],
        [ 92],
        [124]]])

Tuple axis arguments to ufuncs are equivalent:

>>> np.sum(a, axis=(0,2), keepdims=True)
array([[[ 60],
        [ 92],
        [124]]])

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