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pythonnumpypath-finding

Python numpy replacing values that are in certain pattern

I am trying to 'avoid walls' using an A* star (A-Star) algorithm. My array look like this:

[1, 1, 1, 0, 0, 0, 1, 1, 1],
[1, 0, 0, 0, 0, 0, 1, 1, 1],
[1, 0, 0, 0, 1, 1, 1, 1, 1],
[1, 0, 0, 0, 1, 1, 1, 1, 1],
[1, 1, 0, 0, 0, 1, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 1, 1, 1]

I can only walk on 0 (zeroes) and 1 (ones) are the walls. I want my AI to walk on the center of the the path, assuming that there is enough room to walk. AI can walk diagonally.

for example instead of [1, 1, 1, 0, 0, 0, 1, 1, 1],(First array) since there is enough room not to block the path how can I replace it with [1, 1, 1, 1, 0, 1, 1, 1, 1],

Afterthought:

The optimal path here if we will walk on center is [4 3 2 2 3 4].

Also, what if we are given the shortest path possible for this case it would be [3 3 3 3 4 4] if we are going from (3, 0) to (4, 5). If we just don't want walls in our path like having a single element before the wall, how can we arrive to [3 3 2 2 3 4] if we allow start and finish to touch walls?

Edit: Ali_Sh answer is what I am initially looking for and is the accepted answer.

Solution

  • If a be the main array, indices of the middle 0 in each row can be achieved by:

    cond = np.where(a == 0)
unique = np.unique(cond[0], return_index=True, return_counts=True)
ind = unique[1] + unique[2] // 2
cols = cond[1][ind]  # --> [4 3 2 2 3 4]

    and it can be used to substitute 1 values in a ones array with the main array shape:

    one = np.ones(shape=a.shape)
one[np.arange(len(one)), cols] = 0

    which will:

    [[1. 1. 1. 1. 0. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 0. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 0. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 0. 1. 1. 1. 1.]]