Re: [Numpy-discussion] Calculating density based on distance
On Saturday, January 14, 2012, Thiago Franco de Moraes totonixs...@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 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Calculating density based on distance
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 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Calculating density based on distance
No dia Sábado, 14 de Janeiro de 2012, Benjamin rootben.r...@ou.edu escreveu: On Saturday, January 14, 2012, Thiago Franco de Moraes totonixs...@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 Thanks, Ben, I'm going to do that. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
