VHbb/python/gethistofromtree.py

from samplesclass import sample
from printcolor import printc
import pickle
import ROOT 
from ROOT import TFile, TTree
import ROOT
from array import array
from BetterConfigParser import BetterConfigParser
import sys


#load config
config = BetterConfigParser()
config.read('./config')

#get locations:
Wdir=config.get('Directories','Wdir')
anaTag=config.get('Analysis','tag')


def getScale(job,rescale,subsample=-1):
    input = TFile.Open(job.getpath())
    CountWithPU = input.Get("CountWithPU")
    CountWithPU2011B = input.Get("CountWithPU2011B")
    #print lumi*xsecs[i]/hist.GetBinContent(1)
    
    if subsample>-1:
        xsec=float(job.xsec[subsample])
        sf=float(job.sf[subsample])
    else:
        xsec=float(job.xsec)
        sf=float(job.sf)
    
    
    theScale = 1.
    if anaTag == '7TeV':
        theScale = float(job.lumi)*xsec*sf/(0.46502*CountWithPU.GetBinContent(1)+0.53498*CountWithPU2011B.GetBinContent(1))*rescale/float(job.split)
    elif anaTag == '8TeV':
        theScale = float(job.lumi)*xsec*sf/(CountWithPU.GetBinContent(1))*rescale/float(job.split)
    return theScale 

def getHistoFromTree(job,options,rescale=1,subsample=-1):
    treeVar=options[0]
    if subsample>-1:
        name=job.subnames[subsample]
        group=job.group[subsample]
    else:
        name=job.name
        group=job.group

    #title=job.plotname()
    nBins=int(options[3])
    xMin=float(options[4])
    xMax=float(options[5])

    if job.type != 'DATA':
        cutcut=config.get('Cuts',options[7])
        if subsample>0:
            treeCut='%s & %s & EventForTraining == 0'%(cutcut,job.subcuts[subsample])        
        else:
            treeCut='%s & EventForTraining == 0'%(cutcut)

    elif job.type == 'DATA':
        cutcut=config.get('Cuts',options[8])
        treeCut='%s & EventForTraining == 0'%(cutcut)


    input = TFile.Open(job.getpath(),'read')

    Tree = input.Get(job.tree)
    #Tree=tmpTree.CloneTree()
    #Tree.SetDirectory(0)
    
    #Tree=tmpTree.Clone()
    weightF=config.get('Weights','weightF')
    #hTree = ROOT.TH1F('%s'%name,'%s'%title,nBins,xMin,xMax)
    #hTree.SetDirectory(0)
    #hTree.Sumw2()
    #print 'drawing...'
    if job.type != 'DATA':
        #print treeCut
        #print job.name
        if Tree.GetEntries():
            Tree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),'(%s)*(%s)' %(treeCut,weightF), "goff,e")
            full=True
        else:
            full=False
    elif job.type == 'DATA':
    
        if len(options)>10:
            if options[11] == 'blind':
                treeCut = treeCut + '&'+treeVar+'<0'
    
    
        Tree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),treeCut, "goff,e")
        full = True
    if full:
        hTree = ROOT.gDirectory.Get(name)
    else:
        hTree = ROOT.TH1F('%s'%name,'%s'%name,nBins,xMin,xMax)
        hTree.Sumw2()
    #print job.name + ' Sumw2', hTree.GetEntries()

    if job.type != 'DATA':
        ScaleFactor = getScale(job,rescale,subsample)
        if ScaleFactor != 0:
            hTree.Scale(ScaleFactor)
            
    print '\t-->import %s\t Integral: %s'%(job.name,hTree.Integral())
            
    hTree.SetDirectory(0)
    input.Close()  
    
              
    return hTree, group
    

######################


def orderandadd(histos,typs,setup):
#ORDER AND ADD TOGETHER

    ordnung=[]
    ordnungtyp=[]
    num=[0]*len(setup)
    for i in range(0,len(setup)):
        for j in range(0,len(histos)):
            if typs[j] in setup[i]:
                num[i]+=1
                ordnung.append(histos[j])
                ordnungtyp.append(typs[j])

    del histos
    del typs

    histos=ordnung
    typs=ordnungtyp

    for k in range(0,len(num)):
        for m in range(0,num[k]):
            if m > 0:
            
                #add
                histos[k].Add(histos[k+1],1)
                printc('magenta','','\t--> added %s to %s'%(typs[k],typs[k+1]))
                del histos[k+1]
                del typs[k+1]

    del histos[len(setup):]
    del typs[len(setup):]

    return histos, typs


Revision:	1.7
Committed:	Thu Aug 2 16:03:52 2012 UTC (12 years, 9 months ago) by peller
Content type:	text/x-python
Branch:	MAIN
Changes since 1.6:	+30 -9 lines
Log Message:	removed flavour splitting, fixed training readin problem
#	User	Rev	Content
1	peller	1.1	from samplesclass import sample
2			from printcolor import printc
3			import pickle
4			import ROOT
5			from ROOT import TFile, TTree
6			import ROOT
7			from array import array
8	nmohr	1.3	from BetterConfigParser import BetterConfigParser
9	peller	1.1	import sys
10
11
12			#load config
13	nmohr	1.3	config = BetterConfigParser()
14	peller	1.1	config.read('./config')
15
16			#get locations:
17			Wdir=config.get('Directories','Wdir')
18	nmohr	1.6	anaTag=config.get('Analysis','tag')
19	peller	1.1
20
21
22	peller	1.7	def getScale(job,rescale,subsample=-1):
23	peller	1.1	input = TFile.Open(job.getpath())
24			CountWithPU = input.Get("CountWithPU")
25			CountWithPU2011B = input.Get("CountWithPU2011B")
26			#print lumi*xsecs[i]/hist.GetBinContent(1)
27	peller	1.7
28			if subsample>-1:
29			xsec=float(job.xsec[subsample])
30			sf=float(job.sf[subsample])
31			else:
32			xsec=float(job.xsec)
33			sf=float(job.sf)
34
35
36	nmohr	1.6	theScale = 1.
37			if anaTag == '7TeV':
38	peller	1.7	theScale = float(job.lumi)xsecsf/(0.46502CountWithPU.GetBinContent(1)+0.53498CountWithPU2011B.GetBinContent(1))*rescale/float(job.split)
39	nmohr	1.6	elif anaTag == '8TeV':
40	peller	1.7	theScale = float(job.lumi)xsecsf/(CountWithPU.GetBinContent(1))*rescale/float(job.split)
41	nmohr	1.6	return theScale
42	peller	1.1
43	peller	1.7	def getHistoFromTree(job,options,rescale=1,subsample=-1):
44	peller	1.1	treeVar=options[0]
45	peller	1.7	if subsample>-1:
46			name=job.subnames[subsample]
47			group=job.group[subsample]
48			else:
49			name=job.name
50			group=job.group
51
52	peller	1.1	#title=job.plotname()
53			nBins=int(options[3])
54			xMin=float(options[4])
55			xMax=float(options[5])
56
57			if job.type != 'DATA':
58			cutcut=config.get('Cuts',options[7])
59	peller	1.7	if subsample>0:
60			treeCut='%s & %s & EventForTraining == 0'%(cutcut,job.subcuts[subsample])
61			else:
62			treeCut='%s & EventForTraining == 0'%(cutcut)
63	peller	1.1
64			elif job.type == 'DATA':
65	peller	1.5	cutcut=config.get('Cuts',options[8])
66			treeCut='%s & EventForTraining == 0'%(cutcut)
67
68	peller	1.1
69			input = TFile.Open(job.getpath(),'read')
70
71			Tree = input.Get(job.tree)
72			#Tree=tmpTree.CloneTree()
73			#Tree.SetDirectory(0)
74
75			#Tree=tmpTree.Clone()
76			weightF=config.get('Weights','weightF')
77			#hTree = ROOT.TH1F('%s'%name,'%s'%title,nBins,xMin,xMax)
78			#hTree.SetDirectory(0)
79			#hTree.Sumw2()
80			#print 'drawing...'
81			if job.type != 'DATA':
82			#print treeCut
83			#print job.name
84			if Tree.GetEntries():
85			Tree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),'(%s)*(%s)' %(treeCut,weightF), "goff,e")
86			full=True
87			else:
88			full=False
89			elif job.type == 'DATA':
90
91			if len(options)>10:
92			if options[11] == 'blind':
93			treeCut = treeCut + '&'+treeVar+'<0'
94
95
96			Tree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),treeCut, "goff,e")
97			full = True
98			if full:
99			hTree = ROOT.gDirectory.Get(name)
100			else:
101			hTree = ROOT.TH1F('%s'%name,'%s'%name,nBins,xMin,xMax)
102			hTree.Sumw2()
103			#print job.name + ' Sumw2', hTree.GetEntries()
104
105			if job.type != 'DATA':
106	peller	1.7	ScaleFactor = getScale(job,rescale,subsample)
107	peller	1.1	if ScaleFactor != 0:
108			hTree.Scale(ScaleFactor)
109
110			print '\t-->import %s\t Integral: %s'%(job.name,hTree.Integral())
111
112			hTree.SetDirectory(0)
113	peller	1.7	input.Close()
114
115
116
117			return hTree, group
118	peller	1.1
119
120			######################
121
122
123
124			def orderandadd(histos,typs,setup):
125			#ORDER AND ADD TOGETHER
126
127			ordnung=[]
128			ordnungtyp=[]
129			num=[0]*len(setup)
130			for i in range(0,len(setup)):
131			for j in range(0,len(histos)):
132	peller	1.2	if typs[j] in setup[i]:
133	peller	1.1	num[i]+=1
134			ordnung.append(histos[j])
135			ordnungtyp.append(typs[j])
136
137			del histos
138			del typs
139
140			histos=ordnung
141			typs=ordnungtyp
142
143			for k in range(0,len(num)):
144			for m in range(0,num[k]):
145			if m > 0:
146
147			#add
148			histos[k].Add(histos[k+1],1)
149	peller	1.2	printc('magenta','','\t--> added %s to %s'%(typs[k],typs[k+1]))
150	peller	1.1	del histos[k+1]
151			del typs[k+1]
152
153			del histos[len(setup):]
154			del typs[len(setup):]
155
156			return histos, typs
157
158