Patchwork quilt in Python

Here's a fun python function to make a plot of [x,y] coordinate data as a patchwork quilt with user-defined 'complexity'. It initially segments the data into 'numsegs' number of clusters by a k-means algorithm. It then takes each segment and creates 'numclass' sub-clusters based on the euclidean distance from the centroid of the cluster. Finally, it plots each subcluster a different colour and prints the result as a png file.

inputs:
'data' is a Nx2 numpy array of [x,y] points
'numsegs' and 'numclass' are integer scalars. The greater these numbers the greater the 'complexity' of the output and the longer the processing time

	import numpy as np
	from scipy.cluster.vq import kmeans,vq
	from pylab import *
	def patchwork(data,numsegs,numclass):
	# computing K-Means with K = numsegs
	centroids,_ = kmeans(data,numsegs)
	# assign each sample to a cluster
	idx,_ = vq(data,centroids)
	# loop through number of classes
	for k in range(numsegs):
	# get data in kth segment
	datXY=data[idx==k,:]
	# get distances of each point from centroid
	dat1=np.sqrt((datXY[:,0]-centroids[k,0])**2 + (datXY[:,1]-centroids[k,1])**2)
	# get numclass new centroids within kth segment
	cents,_ = kmeans(dat1,numclass)
	# assign each to subcluster
	i,_ = vq(dat1,cents)
	# loop through and plot each
	for p in range(numclass):
	plot(datXY[i==p,0],datXY[i==p,1],'.')
	savefig("outputs"+str(numsegs)+'_'+str(numclass)+'_res.png')
	close()

view raw patchwork.py hosted with ❤ by GitHub

Some example outputs in increasing complexity:

numsegs=10, numclass=5

numsegs=15, numclass=15

numsegs=20, numclass=50