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

EMMEL Thomas Thomas.EMMEL at 3ds.com
Mon Jan 10 03:09:17 EST 2011

```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.
The brute force method would look like this:

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
def bruteForceSearch(points, point):

minpt = min([(vec2Norm(pt, point), pt, i)
for i, pt in enumerate(points)], key=itemgetter(0))
return sqrt(minpt[0]), minpt[1], minpt[2]

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
def vec2Norm(pt1,pt2):
xDis = pt1[0]-pt2[0]
yDis = pt1[1]-pt2[1]
zDis = pt1[2]-pt2[2]
return xDis*xDis+yDis*yDis+zDis*zDis

I have a more clever method but it still takes a lot of time in the vec2norm-function.
If you like I can attach a running example.

To Ben:

> Don't know how much of an impact it would have, but those timeit statements
> for array creation include the import process, which are going to be
> different for each module and are probably not indicative of the speed of
> array creation.

No, the timeit statements counts the time for the statement in the first argument only,
the import-thing isn't included in the time.

Thomas

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