Hi, I've just come across Travis Oliphant's array version of the game of life here https://gist.github.com/3353411. It's a really nice example of how to efficiently find neighbours using numpy and family. However, I wanted to apply these ideas to higher order arrays (eg GRID being three dimensional rather than two dimensional). My previous attempts at this include the (sorry for the ensuing horror!) neighbours = np.array( [ \ # Lower row... x[:-2, :-2, :-2], x[:-2, :-2, 1:-1], x[:-2, :-2, 2: ], x[:-2, 1:-1,:-2], x[:-2, 1:-1, 1:-1], x[:-2, 1:-1, 2:], x[:-2, 2:,:-2], x[:-2, 2:, 1:-1], x[:-2, 2:, 2:], # Middle row x[1:-1, :-2, :-2], x[1:-1, :-2, 1:-1], x[1:-1, :-2, 2: ], x[1:-1, 1:-1,:-2], x[1:-1, 1:-1, 2:], x[1:-1, 2:,:-2], x[1:-1, 2:, 1:-1], x[1:-1, 2:, 2:], # Top row x[2:, :-2, :-2], x[2:, :-2, 1:-1], x[2:, :-2, 2: ], x[2:, 1:-1,:-2], x[2:, 1:-1, 1:-1], x[2:, 1:-1, 2:], x[2:, 2:,:-2], x[2:, 2:, 1:-1], x[2:, 2:, 2:] ] ) I think this works (it's been a while! ;D), but it's a bit messy and the boundary condition is a bit crude. I was wondering whether there's some other nice way of doing it, like demonstrated in the above link. Also, what if you want to have a larger neighbourhood (say 5x5x2 instead of 3x3x3)? While I appreciate these index tricks, I also find them quite mind boggling! Thanks! Jose
participants (3)
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Cera, Tim
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Jose Gomez-Dans
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Sebastian Berg