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Date: Wed, 03 Oct 2012 18:30:24 -0400
From: Benjamin Jessup <bsj@abzinc.com>
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Subject: fastest data structure for retrieving objects identified by (x,y) tuple?
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Xref: csiph.com comp.lang.python:30701

I have a group of objects identified by unique (x,y) pairs and I want to 
find out an object's "neighbors" in a matrix of size 2400 x 2400.
        #############
        #obj#   #   #
        #############
        #   #   #obj#      3 x 3 Example
        #############
        #   #   #   #
        #############
There is either a neighbor, or a null value. I always know the (x,y) 
pair to check the neighbors of, so is doing,
 >> obj = grid[x][y] #lists, doesn't scale with num of objects
or,
 >> obj = grid.get((x,y),None) #dictionary, scales with num of objects
the fastest? I can't seem to find a conclusion by testing each alone, 
then in the full environment. Is it that, depending on the number of 
objects, each has an advantage?

I know the fastest way to retrieve them would be to have them store 
pointers to their neighbors, then use those for retrieval. When large 
numbers of objects are changing their (x,y) pairs, rebuilding the 
pointers is too slow.