How to take n block of a 3d array in python?

Question

I had 328 grayscale images with the size 128*128 and I convert all of them into a 3D array with the shape (128,128,328). Now I want to convert it into 5 separated 3d arrays with the shape of (128,128,64) without changing in sequence.

As you can see 328 is not divisible by 64 and using dsplit function is not working.

Is there any way to slice the 3d array on depth axes dynamically?

So how do you want to do it if 328 is not divisible by 64? Please explain what exactly should the result be. How to handle the images which will remain after the splitting of this 3D array into 5 smaller arrays? — user11718531
– user11718531, Commented Apr 1, 2022 at 18:39
I would discard the residual units. I want 5 separated arrays with 64 images in them individually, I don't need the rest of my images — Pegtomous
– Pegtomous, Commented Apr 1, 2022 at 18:47

score 1 · Accepted Answer · 2022-04-01 19:30:03Z

1

import numpy as np

arr = np.zeros(shape=(128,128,328))

# make aray divisible by 5 (trim it to the depth of 320)
arr = arr[:, :, :320]

# Split array
arrays = np.dsplit(arr, 5)

for array in arrays:
    print(array.shape)

Output:

(128, 128, 64)
(128, 128, 64)
(128, 128, 64)
(128, 128, 64)
(128, 128, 64)

EDIT: Here is the same thing written in a dynamic way.

import numpy as np

num_subarrays = 5
subarray_depth = 64

# Initialize array
arr = np.zeros(shape=(128,128,328))

# make aray divisible by subarray_depth
arr = arr[:, :, :(arr.shape[2] // subarray_depth) * subarray_depth]

# Split array
arrays = np.dsplit(arr, num_subarrays)

for array in arrays:
    print(array.shape)

edited Apr 1, 2022 at 19:30

answered Apr 1, 2022 at 19:08

user11718531

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2 Comments

Pegtomous Over a year ago

Thanks. This is working now, but I wanted a dynamic code, not hard coding.

user11718531 Over a year ago

@Pegtomous I have updated the answer. Hope that's what you wanted

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