On Saturday, January 14, 2012, Thiago Franco de Moraes < totonixsame@gmail.com> wrote:

Hi all,

I have the following problem:

Given a array with dimension Nx3, where N is generally greater than 1.000.000, for each item in this array I have to calculate its density, Where its density is the number of items from the same array with distance less than a given r. The items are the rows from the array.

I was not able to think a solution to this using one or two functions of Numpy. Then I wrote this code http://pastebin.com/iQV0bMNy . The problem it so slow. So I tried to implement it in Cython, here the result http://pastebin.com/zTywzjyM , but it is very slow yet.

Is there a better and faster way of doing that? Is there something in my Cython implementation I can do to perform better?

Thanks!

Have you looked at scipy.spatial.KDTree? It can efficiently load up a data structure that lets you easily determine the spatial relationship between datapoints. Ben Root

Den 14.01.2012 21:52, skrev Thiago Franco de Moraes:

Is there a better and faster way of doing that? Is there something in my Cython implementation I can do to perform better?

You need to use a kd-tree to make the computation run in O(n log n) time instead of O(n**2). scipy.spatial.cKDTree is very fast. Sturla