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# Created by Greg Landrum and Anna Vulpetti, March 2009
from __future__ import print_function
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('Sim: %.2f, nClusters: %d'%(sim,len(clusters)), file=sys.stderr)
    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:]))
