def clusterModels(modelList):
	from EnsembleMatch.distmat import DistanceMatrix
	from chimera import match
	# First we need to construct a full distance matrix, including
	# possibly identical elements
	fulldm = DistanceMatrix(len(modelList))
	sameAs = {}
	atoms = [ match._coordArray(m.sortedAtoms()) for m in modelList ]
	for i in range(len(modelList)):
		mi = modelList[i]
		if mi in sameAs:
			continue
		ai = atoms[i]
		for j in range(i + 1, len(modelList)):
			aj = atoms[j]
			m = match.Match(ai, aj)
			if m.rms <= 0:
				# Detect identical elements and track them
				mj = modelList[j]
				sameAs[mj] = mi
			fulldm.set(i, j, m.rms)
	# Now we reduce the distance matrix by removing identical elements
	if not sameAs:
		# No identical elements, just use the whole thing
		dm = fulldm
		models = modelList
	else:
		models = []
		indexMap = []
		for i, mi in enumerate(modelList):
			if mi in sameAs:
				# Skip identical element
				continue
			models.append(mi)
			indexMap.append(i)
		dm = DistanceMatrix(len(models))
		for i in range(len(models)):
			im = indexMap[i]
			for j in range(i + 1, len(models)):
				jm = indexMap[j]
				dm.set(i, j, fulldm.get(im, jm))
	# Next we run the clustering algorithm
	from EnsembleMatch.nmrclust import NMRClust
	nmrc = NMRClust(dm)
	# Next reformat the results into something useful.
	# "clusterInfo" is a list of whose elements are 2-tuples of
	# (cluster representative model, list of models in cluster)
	clusterInfo = []
	for cid, c in enumerate(nmrc.clusters):
		members = c.members()
		mList = []
		for member in members:
			m = models[member]
			m.clusterId = cid
			m.clusterRep = 0
			mList.append(m)
		rep = nmrc.representative(c)
		m = models[rep]
		m.clusterRep = len(members)
		clusterInfo.append((m, mList))
	for mj, mi in sameAs.iteritems():
		mj.clusterId = mi.clusterId
		mj.clusterRep = 0
		rep, members = clusterInfo[mi.clusterId]
		rep.clusterRep += 1
		members.append(mj)
	return clusterInfo

# Here's an example on how you can use the function above.
# Run this script with:
#	chimera --nogui --silent cluster.py
# where cluster.py is the name of this file
if __name__ == "chimeraOpenSandbox":
	import chimera
	# Use following line to fetch from RCSB
	models = chimera.openModels.open("1n9u", type="PDBID")
	# Use following line for your own data file
	# models = chimera.openModels.open("1N9U.pdb", type="PDB")
	ci = clusterModels(models)
	print len(models), "models ->", len(ci), "clusters"
	for rep, members in ci:
		print rep.oslIdent(), "(%d member)" % len(members)
		for m in members:
			print '\t', m.oslIdent()