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How to determine the number of sub-arrays, each of which is a square, which comprise a larger array?

A 1-dimensional array A has the shape (N, K). It is composed of squares with shape (2, 2) which are shifted by one space to the left.
For example, the array A[0], A[1], A[2],…, A[N-1] is the equivalent of A[0 + N], A[1 + N], A[2 + N],…, A[N-1 + N].
The array A could be the length of an image or a video stream.
I want to determine the number of squares which comprise the array A.
My idea is to use N-long arrays and count the number of 1 or 0, whether the N-long array is made of 1 or 0.
A[0] A[1] A[2] … A[N-1]
0 1 0 … 0
0 0 1 … 1

How can I count the number of 1s for each array A?

A:

You might be interested in Numpy.count_nonzero.
For example, consider the input array of one’s [ones(N), ones(N)] (or a matrix of 0’s and 1’s).
s = [ones(N), ones(N)]
np.count_nonzero(s)
# array([2, 2])

s[0:1, 0:1]
# [[ True True]
# [ True True]]

np.count_nonzero(s[0:1, 0:1])
# array([2, 2])

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