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

Execute :None:None:`func1d(a, *args, **kwargs)` where :None:None:`func1d` operates on 1-D arrays and a is a 1-D slice of :None:None:`arr` along :None:None:`axis`.

This is equivalent to (but faster than) the following use of ndindex and :None:None:`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 :None:None:`arr` along the specified axis.

axis : integer

Axis along which :None:None:`arr` is sliced.

arr : ndarray (Ni..., M, Nk...)

Input array.

args : any

Additional arguments to :None:None:`func1d`.

kwargs : any

Additional named arguments to :None:None:`func1d`.

versionadded

Returns

out : ndarray (Ni..., Nj..., Nk...)

The output array. The shape of :None:None:`out` is identical to the shape of :None:None:`arr`, except along the :None:None:`axis` dimension. This axis is removed, and replaced with new dimensions equal to the shape of the return value of :None:None:`func1d`. So if :None:None:`func1d` returns a scalar :None:None:`out` will have one fewer dimensions than :None:None:`arr`.

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

See Also

apply_over_axes

Apply a function repeatedly over multiple axes.

Examples

>>> 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 :None:None:`outarr` is the same as :None:None:`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 :None:None:`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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GitHub : /numpy/ma/extras.py#371
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