Use 2D matrix as indexes for a 3D matrix in numpy?

It seems you are using 2D array as index array and 3D array to select values. Thus, you could use NumPy's advanced-indexing -

# a : 2D array of indices, b : 3D array from where values are to be picked up
m,n = a.shape
I,J = np.ogrid[:m,:n]
out = b[a, I, J] # or b[a, np.arange(m)[:,None],np.arange(n)]

If you meant to use a to index into the last axis instead, just move a there : b[I, J, a].

Sample run -

>>> np.random.seed(1234)
>>> a = np.random.randint(0,2,(3,3))
>>> b = np.random.randint(11,99,(2,3,3))
>>> a  # Index array
array([[1, 1, 0],
       [1, 0, 0],
       [0, 1, 1]])
>>> b  # values array
array([[[60, 34, 37],
        [41, 54, 41],
        [37, 69, 80]],

       [[91, 84, 58],
        [61, 87, 48],
        [45, 49, 78]]])
>>> m,n = a.shape
>>> I,J = np.ogrid[:m,:n]
>>> out = b[a, I, J]
>>> out
array([[91, 84, 37],
       [61, 54, 41],
       [37, 49, 78]])

If your matrices get much bigger than 3x3, to the point that memory involved in np.ogrid is an issue, and if your indexes remain binary, you could also do:

np.where(a, b[1], b[0])

But other than that corner case (or if you like code golfing one-liners) the other answer is probably better.