/
calculateDistanceOnNGroups.py
executable file
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/
calculateDistanceOnNGroups.py
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#!/usr/bin/env python
from sys import *
from albertcommon import *
from matrixTranspose import matrix_transpose
from getopt import getopt
from scipy.stats.stats import pearsonr,spearmanr
from os.path import basename
def printUsageAndExit(programName):
print >> stderr,"%s [options] filename [grp1Label=]grp1Selector [grp2Label=]grp2Selector [ ... [grpNLabel=]grpNSelector ]" %(basename(programName))
print >> stderr,"Options:"
print >> stderr,"--method [*pearson|spearman]"
print >> stderr,"--linkage [*average|single|complete]"
exit(1)
#A[0]=A1[...]
#A[1]=A2[...]
#A[n]=An[...]
def single_linkage(A,B,distfunc):
minDist=None
for a in A:
for b in B:
if minDist==None:
minDist=distfunc(a,b)
else:
minDist=min(minDist,distfunc(a,b))
return minDist
def complete_linkage(A,B,distfunc):
maxDist=None
for a in A:
for b in B:
if maxDist==None:
maxDist=distfunc(a,b)
else:
maxDist=max(maxDist,distfunc(a,b))
return maxDist
def average_linkage(A,B,distfunc):
sumDist=0
lenA=len(A)
lenB=len(B)
#print >> stderr,A
#print >> stderr,B
for a in A:
for b in B:
#print >> stderr,a,b
sumDist+=distfunc(a,b)
return 1.0/(lenA*lenB)*sumDist
def pearsonDist(a,b):
return 1-pearsonr(a,b)[0]
def spearmanDist(a,b):
return 1-spearmanr(a,b)[0]
if __name__=='__main__':
programName=argv[0]
opts,args=getopt(argv[1:],'',['method=','linkage='])
groupLabelSelectors=[]
try:
filename=args[0]
groupLabelSelectors=args[1:]
if len(groupLabelSelectors)<2:
print >> stderr,"less than two groups to compare, abort"
raise Exception("less than two groups to compare")
for i in range(0,len(groupLabelSelectors)):
splits=groupLabelSelectors[i].split("=")
if len(splits)>1:
groupLabelSelectors[i]=[splits[0],splits[1]]
else:
groupLabelSelectors[i]=[splits[0],splits[0]]
except:
printUsageAndExit(programName)
headerRow=1
startRow=2
fs="\t"
header,prestarts=getHeader(filename,headerRow,startRow,fs)
groupData=[]
for i in range(0,len(groupLabelSelectors)):
groupLabelSelectors[i][1]=getCol0ListFromCol1ListStringAdv(header,groupLabelSelectors[i][1])
groupData.append([])
#grp1Selector=getCol0ListFromCol1ListStringAdv(header,grp1Selector)
#grp2Selector=getCol0ListFromCol1ListStringAdv(header,grp2Selector)
method=pearsonDist
linkage=average_linkage
for o,v in opts:
if o=='--method':
if v=="spearman":
method=spearmanDist
elif v=="pearson":
method=pearsonDist
elif o=='--linkage':
if v=="average":
linkage=average_linkage
elif v=="complete":
linkage=complete_linkage
elif v=="single":
linkage=single_linkage
#StrListToFloatList(L)
#selectListByIndexVector(L,I)
fil=open(filename)
lino=0
for lin in fil:
lino+=1
if lino<startRow:
continue
lin=lin.rstrip("\r\n")
fields=lin.split(fs)
#A.append(StrListToFloatList(selectListByIndexVector(fields,grp1Selector)))
#B.append(StrListToFloatList(selectListByIndexVector(fields,grp2Selector)))
for groupDataMatrix,groupLabelSelector in zip(groupData,groupLabelSelectors):
groupLabel,groupSelectors=groupLabelSelector
groupDataMatrix.append(StrListToFloatList(selectListByIndexVector(fields,groupSelectors)))
fil.close()
#now transpose
#A=matrix_transpose(A,len(A),len(A[0]))[0] #get the data only, discards the nrow and ncol
#B=matrix_transpose(B,len(B),len(B[0]))[0]
for i in range(0,len(groupData)):
groupData[i]=matrix_transpose(groupData[i],len(groupData[i]),len(groupData[i][0]))[0]
#now calculate distance
#dist=linkage(A,B,method)
for i in range(0,len(groupLabelSelectors)):
for j in range(i,len(groupLabelSelectors)):
ALabel=groupLabelSelectors[i][0]
BLabel=groupLabelSelectors[j][0]
if i==j:
print >> stdout,"%s\t%s\t%f" %(ALabel,BLabel,0.0)
continue
A=groupData[i]
B=groupData[j]
print >> stderr,"Calculate Distance Between",ALabel,"and",BLabel,
dist=linkage(A,B,method)
print >> stderr," dist=%f" %(dist)
print >> stdout,"%s\t%s\t%f" %(ALabel,BLabel,dist)
#print >> stdout,dist