Question

I have the following code which first selects elements of a NumPy array with a logical index mask:

import numpy as np

grid = np.random.rand(4,4) 
mask = grid > 0.5

I wish to use a second boolean mask against this one to pick out objects with :

masklength = len(grid[mask])
prob = 0.5
# generates an random array of bools
second_mask = np.random.rand(masklength) < prob 

# this fails to act on original object
grid[mask][second_mask] = 100

This is not quite the same problem as listed in this SO question:
Numpy array, how to select indices satisfying multiple conditions? – as I am using random number generation, I don’t want to have to generate a full mask, only for the elements selected by the first mask.

Answer 1

I believe the following does what you’re asking:

grid[[a[second_mask] for a in np.where(mask)]] = 100

It works as follows:

• np.where(mask) converts the boolean mask into the indices where mask is True;
• [a[second_mask] for a in ...] subsets the indices to only select those where second_mask is True.

The reason your original version doesn’t work is that grid[mask] involves fancy indexing. This creates a copy of the data, which in turn results in ...[second_mask] = 100 modifying that copy rather than the original array.