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Module « numpy.matlib »

Fonction apply_along_axis - module numpy.matlib

Signature de la fonction apply_along_axis

def apply_along_axis(func1d, axis, arr, *args, **kwargs) 

Description

help(numpy.matlib.apply_along_axis)

Apply a function to 1-D slices along the given axis.

Execute `func1d(a, *args, **kwargs)` where `func1d` operates on 1-D arrays
and `a` is a 1-D slice of `arr` along `axis`.

This is equivalent to (but faster than) the following use of `ndindex` and
`s_`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of indices::

    Ni, Nk = a.shape[:axis], a.shape[axis+1:]
    for ii in ndindex(Ni):
        for kk in ndindex(Nk):
            f = func1d(arr[ii + s_[:,] + kk])
            Nj = f.shape
            for jj in ndindex(Nj):
                out[ii + jj + kk] = f[jj]

Equivalently, eliminating the inner loop, this can be expressed as::

    Ni, Nk = a.shape[:axis], a.shape[axis+1:]
    for ii in ndindex(Ni):
        for kk in ndindex(Nk):
            out[ii + s_[...,] + kk] = func1d(arr[ii + s_[:,] + kk])

Parameters
----------
func1d : function (M,) -> (Nj...)
    This function should accept 1-D arrays. It is applied to 1-D
    slices of `arr` along the specified axis.
axis : integer
    Axis along which `arr` is sliced.
arr : ndarray (Ni..., M, Nk...)
    Input array.
args : any
    Additional arguments to `func1d`.
kwargs : any
    Additional named arguments to `func1d`.

Returns
-------
out : ndarray  (Ni..., Nj..., Nk...)
    The output array. The shape of `out` is identical to the shape of
    `arr`, except along the `axis` dimension. This axis is removed, and
    replaced with new dimensions equal to the shape of the return value
    of `func1d`. So if `func1d` returns a scalar `out` will have one
    fewer dimensions than `arr`.

See Also
--------
apply_over_axes : Apply a function repeatedly over multiple axes.

Examples
--------
>>> import numpy as np
>>> def my_func(a):
...     """Average first and last element of a 1-D array"""
...     return (a[0] + a[-1]) * 0.5
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(my_func, 0, b)
array([4., 5., 6.])
>>> np.apply_along_axis(my_func, 1, b)
array([2.,  5.,  8.])

For a function that returns a 1D array, the number of dimensions in
`outarr` is the same as `arr`.

>>> b = np.array([[8,1,7], [4,3,9], [5,2,6]])
>>> np.apply_along_axis(sorted, 1, b)
array([[1, 7, 8],
       [3, 4, 9],
       [2, 5, 6]])

For a function that returns a higher dimensional array, those dimensions
are inserted in place of the `axis` dimension.

>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(np.diag, -1, b)
array([[[1, 0, 0],
        [0, 2, 0],
        [0, 0, 3]],
       [[4, 0, 0],
        [0, 5, 0],
        [0, 0, 6]],
       [[7, 0, 0],
        [0, 8, 0],
        [0, 0, 9]]])

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