# [Numpy-discussion] speed of numpy.ndarray compared toNumeric.array

EMMEL Thomas Thomas.EMMEL at 3ds.com
Mon Jan 10 04:04:48 EST 2011

```> On Mon, Jan 10, 2011 at 5:09 PM, EMMEL Thomas <Thomas.EMMEL at 3ds.com>
> wrote:
> > To John:
> >
> >> Did you try larger arrays/tuples? I would guess that makes a
> significant
> >> difference.
> >
> > No I didn't, due to the fact that these values are coordinates in 3D
> (x,y,z).
> > In fact I work with a list/array/tuple of arrays with 100000 to 1M of
> elements or more.
> > What I need to do is to calculate the distance of each of these
> elements (coordinates)
> > to a given coordinate and filter for the nearest.
>
> Note that for this exact problem, there are much better methods than
> brute force (O(N^2) for N vectors), through e.g. kd-trees, which work
> very well in low-dimension. This will matter much more than numeric vs
> numpy
>
> cheers,
>
> David

David,

Yes, of course and my real implementation uses exactly these methods,
but there are still issues with the arrays.
Example:
If I would use brute-force it will take ~5000s for a particular example to
find all points in a list of points. Theoretically it should be possible to
come to O(N*log(N)) with would mean ~2s in my case.
My method need ~28s with tuples, but it takes ~30s with Numeric arrays
and ~60s and more with numpy.ndarrays!
I just use the brute-force method since it delivers the most reusable results
for performance testing, the other methods are a bit dependent on the
distribution of points in space.

Thomas

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