dpnp.apply_over_axes

dpnp.apply_over_axes(func, a, 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.

For full documentation refer to numpy.apply_over_axes.

Parameters:

• func (function) -- This function must take two arguments, func(a, axis).

• a ({dpnp.ndarray, usm_ndarray}) -- Input array.

• axes ({int, sequence of ints}) -- Axes over which func is applied.

Returns:

out -- 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.

Return type:

dpnp.ndarray

See also

dpnp.apply_along_axis

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

Examples

>>> import dpnp 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]]])