k-means clustering in scipy
it's fairly simple to do clustering of points with similar z-values in scipy: import numpy import matplotlib matplotlib.use('Agg') from scipy.cluster.vq import * import pylab pylab.close() # generate some random xy points and # give them some striation so there will be "real" groups. xy = numpy.random.rand(30,2) xy[3:8,1] -= .9 xy[22:28,1] += .9 # make some z vlues z = numpy.sin(xy[:,1]-0.2*xy[:,1]) # whiten them z = whiten(z) # let scipy do its magic (k==3 groups) res, idx = kmeans2(numpy.array(zip(xy[:,0],xy[:,1],z)),3) # convert groups to rbg 3-tuples. colors = ([([0,0,0],[1,0,0],[0,0,1])[i] for i in idx]) # show sizes and colors. each color belongs in diff cluster. pylab.scatter(xy[:,0],xy[:,1],s=20*z+9, c=colors) pylab.savefig('/var/www/tmp/clust.png')