#
#  Copyright (c) 2009, Novartis Institutes for BioMedical Research Inc.
#  All rights reserved.
# 
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met: 
#
#     * Redistributions of source code must retain the above copyright 
#       notice, this list of conditions and the following disclaimer.
#     * Redistributions in binary form must reproduce the above
#       copyright notice, this list of conditions and the following 
#       disclaimer in the documentation and/or other materials provided 
#       with the distribution.
#     * Neither the name of Novartis Institutes for BioMedical Research Inc. 
#       nor the names of its contributors may be used to endorse or promote 
#       products derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# Created by Greg Landrum and Anna Vulpetti, March 2009
from rdkit.ML.Cluster import Butina
from rdkit import DataStructs
import sys,cPickle

# sims is the list of similarity thresholds used to generate clusters
sims=[.9,.8,.7,.6]
smis=[]
uniq=[]
uFps=[] 

for fileN in sys.argv[1:]:
    inF = file(sys.argv[1],'r')
    cols = cPickle.load(inF)
    fps = cPickle.load(inF)

    for row in fps:
        nm,smi,fp = row[:3]
        if smi not in smis:
            try:
                fpIdx = uFps.index(fp)
            except ValueError:
                fpIdx=len(uFps)
                uFps.append(fp)
            uniq.append([fp,nm,smi,'FP_%d'%fpIdx]+row[3:])
            smis.append(smi)
            

def distFunc(a,b):
    return 1.-DataStructs.DiceSimilarity(a[0],b[0])

for sim in sims:
    clusters=Butina.ClusterData(uniq,len(uniq),1.-sim,False,distFunc)
    print >>sys.stderr,'Sim: %.2f, nClusters: %d'%(sim,len(clusters))
    for i,cluster in enumerate(clusters):
        for pt in cluster:
            uniq[pt].append(str(i+1))
    cols.append('cluster_thresh_%d'%(int(100*sim)))
print ' '.join(cols)
for row in uniq:
    print ' '.join(row[1:])
