Beispiel #1
0
def getCons(path, tfName):
    for infile in glob.glob(os.path.join(path, "*.wigFix")):
	(wigpath,wigfilename) = os.path.split(infile)
	chrom = wigfilename.split('.')[0]
	consName = '_'.join(wigfilename.split('.')[1:3])
	#print chrom, tfName, consName
	with open(infile,'rt') as wigFile:
	    wig = csv.reader(wigFile,delimiter='\t')
	    stepDict, startDict, valuesDict = countWig.getFixStart(wig,consName)#'phyloP30wayEuarchontoglires')
	    start = startDict[consName][chrom]
	    arrayDict = countWig.buildFixHist(chrom,stepDict,startDict,valuesDict,consName)
	    cursor = mcollection.find({"tf_name": tfName, 
				"motif_genomic_regions_info.chr": chrom})
	    for test in cursor:
	        motifStart, motifEnd = test["motif_genomic_regions_info"]["start"], test["motif_genomic_regions_info"]["end"]
	    	avg = 0
		#print motifStart, motifEnd
		startlist = [start[i] for i in xrange(len(start)-1) if (motifStart > start[i] and motifStart < start[i+1]) or (motifEnd > start[i] and motifEnd < start[i+1])] 
		if len(startlist) > 0:
		    print startlist
		#print start[-1]
		startlist.append(start[-1])
	    	for i in xrange(len(startlist)):
		    #print arrayDict[start[i]]
		 #   if avg != 0 and motifEnd < start[i]:
		  #  	break ##fall into range and break out
		    if avg != 0:
			if motifEnd > startlist[i]:##cases of partial overlap need to renormalize over two fragments
		    	    xs, xvals, sums = arrayDict[startlist[i]]
		    	    avg = avg * (startlist[i] - motifStart) + (countWig.queryHist(xs, 
				xvals, sums, motifStart, motifEnd)[0] * (motifEnd - startlist[i] + 1)) / (motifEnd - motifStart + 1)	
			else:
			    break
		    elif avg == 0:
		   	xs, xvals, sums = arrayDict[startlist[i]]
		   	avg = countWig.queryHist(xs, xvals, sums, motifStart, motifEnd)[0]
	    	if avg > 0:
	            print avg, motifStart, motifEnd
	    	    mcollection.update({"_id":test["_id"]},{"$set":{"motif_cons_info":{consName: avg}}}, upsert = True)
		else:
		    mcollection.update({"_id":test["_id"]},{"$set":{"motif_cons_info":{consName: avg}}}, upsert = True)
	    	#mcollection.save(test)
    return 0