masking a numpy array using an existing mask of the same shape

Question

Say I have a numpy array mask called m1 = [[False, True, False], [True, False, True]] And I want to find a mask m2 such that its (i,j) entry is True iff j >= 0 and m1[i, j+1] == True. Any elegant and efficient ideas as to how to pull that off? Thanks

Answer 1

Here's a way slicing and using binary operators:

m1 = np.array([[False, True, False], [True, False, True]])

m2 = np.full(m1.shape, False)
m2[:, :-1] = m1[:, 1:] | m2[:, :-1]

---

print(m2)

array([[ True, False, False],
       [False,  True, False]])