Thank you so much for the suggestion, Paulo! Selecting 2D points in a list by creating an array 'mask' of booleans and then using arr[mask, :] is indeed really fast compared to using numpy.apply_along_axis(), in my case (simple "larger than" tests on individual coordinates). I had not realized that you could do "arr[mask, :]": this works great! EOL PS: here are the speed tests I've done on the selection of 2D points from a list, with the following results: filter0: 107.2 s filter1: 0.3 s filter2: 9.7 s filter3: 0.6 s obtained with: #!/usr/bin/env python import numpy def filter0(points): """ Returns only those points that match the filter. """ def filter(p): return (p[0] > 0.5) and (p[1] < 0.5) return points[numpy.apply_along_axis(filter, axis = 1, arr = points)] def filter1(points): """ Returns only those points that match the filter. """ mask = (points[:, 0] > 0.5) & (points[:, 1] < 0.5) return points[mask, :] def filter2(points): """ Returns only those points that match the filter. """ return numpy.array([p for p in points if ((p[0] > 0.5) and p[1] < 0.5)]) def filter3(points): """ Returns only those points that match the filter. """ mask = (points[:, 0] > 0.5) points = points[mask, :] mask = points[:, 1] < 0.5 return points[mask, :] if __name__ == '__main__': import timeit # We generate many random points: NUM_PTS = 1000000 points = numpy.random.random((NUM_PTS, 2)) # We make sure that all the filters give the same result: #print "Initial points:" #print points #print "Filtered points:" #print filter0(points) #print filter1(points) #print filter2(points) #print filter3(points) for filter_num in range(4): func_name = "filter%d" % filter_num t = timeit.Timer("%s(points)" % func_name, "from __main__ import %s, points" % func_name) print "%s: %.1f s" % (func_name, t.timeit(number = 3))

Date: Mon, 12 Jan 2009 11:33:08 -0300 From: "Paulo J. S. Silva" Subject: Re: [Numpy-discussion] Fast function application on list of 2D points? To: Discussion of Numerical Python Message-ID: <1231770788.6170.3.camel@trinity> Content-Type: text/plain; charset="UTF-8"

Why you don't create a mask to select only the points in array that satisfies the condition on x and y coordinates. For example the code below applies filter only to the values that have x coordinate bigger than 0.7 and y coordinate smaller than 0.3:

mask = numpy.logical_and(points[:,0] > 0.7, points[:,1] < 0.3) points = numpy.apply_along_axis(filter, axis = 1, arr = points[mask,:])

best,

Paulo

Em Seg, 2009-01-12 ?s 15:21 +0100, Eric LEBIGOT escreveu:

...

Hello,

What is the fastest way of applying a function on a list of 2D points? More specifically, I have a list of 2D points, and some do not meet some criteria and must be rejected. Even more specifically, the filter only lets through points whose x coordinate satisfies some condition, _and_ whose y coordinates satisfies another condition (maybe is there room for optimization, here?).

Currently, I use

points = numpy.apply_along_axis(filter, axis = 1, arr = points)

but this creates a bottleneck in my program (array arr may contains 1 million points, for instance).

Is there anything that could be faster?

Any suggestion would be much appreciated!

EOL