Question

I have the following 3 x 3 x 3 numpy array called a (the comments will make sense after you read the rest of the question):

array([[[8, 1, 0],     # irrelevant 1 (is at position 1 rather than 0)
        [1, 7, 5],     # the 1 on this line is what I am after!
        [1, 4, 9]],    # irrelevant 1 (out of the "cross")

       [[4, 0, 1],     # irrelevant 1 (is at position 2 rather than 0)
        [1, 0, 1],     # I'm only after the first 1 on this line!
        [6, 2, 1]],    # irrelevant 1 (is at position 2 rather than 0)

       [[0, 2, 2],
        [0, 6, 7],
        [3, 4, 9]]])

furthermore I have this list of indexes that refers to the “central cross” of said matrix, called idx

[array([0, 1, 1, 1, 2]), array([1, 0, 1, 2, 1])]

EDIT: I call it “cross” as it marks the central column and row in the following:

>>> a[..., 0]
array([[8, 1, 1],
       [4, 1, 6],
       [0, 0, 3]])

What I would like to obtain is the indexes of all those arrays located at idx whose first value is 1, but I’m struggling in understanding how to use numpy.where() in the right way. Since…

>>> a[..., 0][idx]
array([1, 4, 1, 6, 0])

…I tried…

>>> np.where(a[..., 0][idx] == 1)
(array([0, 2]),)

…but as you can see it returns the index of the sliced array, not of a, while I would like to get:

[array([0, 1]), array([1, 1])]  #as a[0, 1, 0] and a [1, 1, 0] are equal to 1.

Thank you in advance for your help!

PS: In the comments I have been suggested to try to give a broader scenario of applicability. Although it is not what I am using for, I suppose this could be used to process images as many 2D libraries do, with a source layer, a destination layer and a mask (see for example cairo). In this case the mask would be the idx array, and one might imagine working with the R channel of RGB colors (a[..., 0]).

Answer 1

You can translate the indices back using idx:

>>> w = np.where(a[..., 0][idx] == 1)[0]
>>> array(idx).T[w]
array([[0, 1],
       [1, 1]])