Lets say I have a numpy array of 4×4 dimension and want to change it to 2×2 by taking its halve. So, theoretically do something like this:
is this possible without using any loop and for it to work on not only a 4×4 but lets say a 500×500?
#input:
x_4= np.array([[1, 2, 4, 5], [3, 4, 6, 8], [5, 3, 1, -1], [2, 3, 5, 0]])
# thinking it would work with something like this:
new = x_4[:2, :2]/4 + x_4[:2, -2:]/4 + x_4[-2:, :2]/4 + x_4[-2:, -2:]/4
new
# output: array([[11, 9],[16, 15]])
#Expected output: array([[2.5, 5.75], [3.25, 1.25]])
>Solution :
Numpy Version:
you can do a reshape and perform mean over two axis to get the desired result
import numpy as np
blocksize = 500
Mat = np.random.rand(blocksize,blocksize)
## reshape into (blocksize/2 x blocksize/2 ) 2x2 matrices
blocks = Mat.reshape(blocksize//2, 2, blocksize//2, 2)
block_mean = np.mean(blocks, axis=(1,-1))