Bioinformatics

UPDATE 7-10-08: + add example for scatter plot + link to ken-ichi === Figuring how to make a client side image map from a matplotlib image has stumped me more than once. Andrew Dalke does have a working example. Below, I have the minimal example. It's simple once you get the steps right: just use mpl's transform() to convert the data into the image's coordinate system. Then flip the y-axis as required by the imagemap, then do the normal imagemap stuff and save the html. The only real gotcha, is to make sure to put the dpi in the call to savefig(). import pylab import sys import random name = 'imap' # make some fake data xs = range(15) ys = [random.choice(xs) for i in range(len(xs))] xys = zip(xs, ys) # can also use : f = pylab.subplot(121) f, = pylab.plot(xs, ys, 'ro') dpi = f.figure.get_dpi() height = f.figure.get_figheight() * dpi # convert the x,y coords into image coords. transform = f.get_transform() icoords = transform.transform(xys) # the minimal ...

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')