Regular rather than matrix addition about pycraft HOT 5 OPEN

NumPy is generally only faster than pure Python for operations on large arrays. I don't remember the exact switching point, but most of the benchmarks I've seen don't show benefits for NumPy arrays over Python lists until you have on the order of a few thousand array elements.

I don't have a good sense of everywhere the cube_vertices() function is used in the code, but if it was refactored to take a list of blocks in a region and return the list of vertices for each block, rather than taking a single block, then a NumPy implementation might be faster.

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The change_sector function could also benefit from being matrix addition.
https://github.com/traverseda/pycraft/blob/master/pycraft/world.py#L302

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I notice that I'm confused, but re-implementing cube_vertices in numpy made it slower. If anyone knows why this is, I'm pretty curious.

Check it out on this branch if you curious:
https://github.com/TristanTrim/pycraft/blob/numpy_cube_vertices/pycraft/util.py

Original cube_vertices function:
Average time for moving a cube is 3.7735462188720705e-06 over 10000 tests
Numpy re-implementation:
Average time for moving a cube is 1.2723827362060548e-05 over 10000 tests
Numpy re-implementation without (necessary) array.reshape call:
Average time for moving a cube is 8.726072311401368e-06 over 10000 tests

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It seems to be because the costs of creating an array in numpy are a lot more then the costs of creating an array in plain python.

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if it was refactored to take a list of blocks in a region and return the list of vertices for each block

Yeah, this sound's good. I'm attempting it now, with some other refactoring.

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