# Binning multidimensional array in numpy

I have a 4d numpy array (these are stacks of imaging data) and would like to perform mean binning along all but one of the axes.

starting with say

x=np.random.random((3,100,100,100))

I want to apply binning to axes 1,2,3 with bin size 10 and average the values in each bin.

expected result would be an array of shape (3,10,10,10)

I have looked into np.reshape like so:

result=x.reshape(3,-1,10,100,100).mean(axis=1)
result=result.reshape(3,10,-1,10,100).mean(axis=2)

and so on, but this messes up the structure of the image arrays

is there a more straightforward way to do this?

Try this:

x=np.random.random((3,100,100,100))

x_resized=np.zeros((3,10,10,10))

for i in range(len(x_resized[0])):
for j in range(len(x_resized[0][0])):
for k in range(len(x_resized[0][0][0])):

x_resized[0,i,j,k]=np.average(x[0,i*10:i*10+10,j*10:j*10+10,k*10:k*10+10])
x_resized[1,i,j,k]=np.average(x[1,i*10:i*10+10,j*10:j*10+10,k*10:k*10+10])
x_resized[2,i,j,k]=np.average(x[2,i*10:i*10+10,j*10:j*10+10,k*10:k*10+10])

which performs the averaging blockwise.

import numpy as np
import skimage.measure

a = np.arange(36).reshape(6, 6)

b = skimage.measure.block_reduce(a, (2,2), np.mean)

output:

a =
[[ 0  1  2  3  4  5]
[ 6  7  8  9 10 11]
[12 13 14 15 16 17]
[18 19 20 21 22 23]
[24 25 26 27 28 29]
[30 31 32 33 34 35]]
b =
[[ 3.5  5.5  7.5]
[15.5 17.5 19.5]
[27.5 29.5 31.5]]

But instead of my 2d example, you can do that for a block size of (1, 10, 10, 10) of your data.