NumPy 在二维数组中切片正方形

Question

I want to create a heightfield map that consists of squares of random height. Given an array of NxN, I want that every square of size MxM, where M<N, will be at the same random height, with the height sampled from a uniform distribution. For example, if we have N = 6 and M = 2, we would have:

0.2, 0.2, 0.6, 0.6, 0.1, 0.1,
0.2, 0.2, 0.6, 0.6, 0.1, 0.1,
0.5, 0.5, 0.3, 0.3, 0.8, 0.8,
0.5, 0.5, 0.3, 0.3, 0.8, 0.8,
0.6, 0.6, 0.4, 0.4, 0.9, 0.9,
0.6, 0.6, 0.4, 0.4, 0.9, 0.9

For now, I've come up with an inefficient way of doing it with 2 nested for loops. I'm sure there must be an efficient and elegant way to do that with NumPy slicing.

Answer 1

This solution using the repeat() method should work for N/M integer.

import numpy as np

N = 6
M = 2

values = np.random.random( [N//M, N//M] )
y = values.repeat( M, axis=0 ).repeat( M, axis=1 )
print(y)