Question

How do i “carve” or mask a 2D numpy array according to an index formula? I don’t care what the element value is, only its position in the array.

For example, given an mxm array, how do I extract all elements whose address conforms to

for i in range(0,m):
    for j in range(0,m):
        if j-i-k>=0:
            A[i,j] = 1
        elif j-p-k>=0:
            A[i,j] = 1
        elif i-k>=0:
            A[i,j] = 1
        else:
            A[i,j] = 0
        j=j+1
    i=i+1

where

k and p are an arbitrary fences

Assume

k<m
p<m

This ends up looking like a diagonal slice + a horizontal slice + a vertical slice. Can it be done without the for loops above?

Answer 1

xdim,ydim = data.shape
k = 2
a, b = np.meshgrid(range(ydim),range(xdim))
mask = (b - a -k) < 0

new_data = data[mask]

new_data2 = np.array(data) # to force a copy
new_data2[~mask] = 0

new_data is a vector because the masking processes (done this way) flattens the array. Your are selecting a shape with a ragged that cannot be represented as an array. If you just want to set the non-selcted values to 0, use new_data2.