Numpy aggregate across multiple axes

Question

Let's say I have a 3d numpy array.shape of (27,27,27). I want to compress this to (9,9,9) by averaging every 3 elements across every axis simultaneously (e.g. make 3x3x3 pixels into 1x1x1).  The objective is to effectively compress by a single integer across all three axes simultaneously (with the assumption that any array will have a multiple of that integer for the shape of each axes).

My initial attempt was to use np.apply_over_axes, though I'm worried it is not getting the cubic mean of all 3 axes but instead averaging each sequentially.

def mean_over(arr, axis):

    np.average(arr.reshape(-1, 3), axis=axis)

the_array_small = np.apply_over_axes(mean_over, the_array, \[0,1,2\])

However this returns an error:

Traceback (most recent call last):

  File "\<stdin\>", line 1, in \<module\>

  File "\<\__array_function_\_ internals\>", line 180, in apply_over_axes

  File "/opt/homebrew/Caskroom/mambaforge/base/envs/seaborn/lib/python3.10/site-packages/numpy/lib/shape_base.py", line 496, in apply_over_axes

    if res.ndim == val.ndim:

AttributeError: 'NoneType' object has no attribute 'ndim'

I'm not convinced my apply_over_axes solution gets the aggregation I'm aiming for though. Ideally the mean of each (3,3,3) component is returned.

Answer 1

Just a first answer (but again, depending on your answer to my comment, it could be better to rely on some convolution)

arr=np.randon.rand(27,27,27)
the_array_small = arr.reshape(9,3,9,3,9,3).mean(axis=(1,3,5))