[FIXED] python matrix traversal expanding outwards concentric squares

Issue

Let's say I have a 5x5 matrix as follows:

0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0

Based on some location, I am given an index in both the (x,y) coordinate where I want to start building concentric squares outwards of values. A few examples below:

3 3 3 3 3
3 2 2 2 2
3 2 1 1 2
3 2 1 1 2
3 2 2 2 2

2 2 2 3 4
1 1 2 3 4
1 1 2 3 4
2 2 2 3 4
3 3 3 3 4

Is there a more automated / function / libraries that can do this easily instead of hard-coding these sort of values?

Solution

Not sure of the exact logic, but assuming x, y the coordinates of the top left corner of the square of 1s, you could craft a vertical and horizontal array with numpy.where and combine them with broadcasting and numpy.maximum:

x, y = 2, 2
N = 5

Xs = np.arange(N)-x
Ys = np.arange(N)-y

out = np.maximum(np.where(Xs>0, Xs, 1-Xs)[:, None],
                 np.where(Ys>0, Ys, 1-Ys)
                )

Output:

array([[3, 3, 3, 3, 3],
       [3, 2, 2, 2, 2],
       [3, 2, 1, 1, 2],
       [3, 2, 1, 1, 2],
       [3, 2, 2, 2, 2]])

Output for x = 1 ; y = 0 ; N = 6:

array([[2, 2, 2, 3, 4, 5],
       [1, 1, 2, 3, 4, 5],
       [1, 1, 2, 3, 4, 5],
       [2, 2, 2, 3, 4, 5],
       [3, 3, 3, 3, 4, 5],
       [4, 4, 4, 4, 4, 5]])

Intermediates (x = 5 ; y = 5 ; N = 5):

# Xs
array([-2, -1,  0,  1,  2])

# np.where(Xs>0, Xs, 1-Xs)[:, None]
array([[3],
       [2],
       [1],
       [1],
       [2]])

# Ys
array([-2, -1,  0,  1,  2])

# np.where(Ys>0, Ys, 1-Ys)
array([3, 2, 1, 1, 2])

Answered By - mozway