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apply_over_axes(func, a, axes)

:None:None:`func` is called as :None:None:`res = func(a, axis)`, where :None:None:`axis` is the first element of :None:None:`axes`. The result :None:None:`res` of the function call must have either the same dimensions as a or one less dimension. If :None:None:`res` has one less dimension than a, a dimension is inserted before :None:None:`axis`. The call to :None:None:`func` is then repeated for each axis in :None:None:`axes`, with :None:None:`res` as the first argument.

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.

Parameters

func : function: This function must take two arguments, :None:None:`func(a, axis)`.
a : array_like: Input array.
axes : array_like: Axes over which :None:None:`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 :None:None:`func` changes the shape of its output with respect to its input.

Apply a function repeatedly over multiple axes.

Examples

>>> 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]]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.apply_along_axis numpy.ma.extras.apply_along_axis dask.array.routines.apply_over_axes

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