VHbb/python/HistoMaker.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

class HistoMaker:
    def __init__(self, path, config, region, optionsList,rescale=1,which_weightF='weightF'):
        self.path = path
        self.config = config
        self.optionsList = optionsList
        self.rescale = rescale
        self.which_weightF=which_weightF
        self.region = region
        self.lumi=0.

    def getScale(self,job,subsample=-1):
        anaTag=self.config.get('Analysis','tag')
        input = TFile.Open(self.path+'/'+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(self.lumi)*xsec*sf/(0.46502*CountWithPU.GetBinContent(1)+0.53498*CountWithPU2011B.GetBinContent(1))*self.rescale/float(job.split)
        elif anaTag == '8TeV':
            theScale = float(self.lumi)*xsec*sf/(CountWithPU.GetBinContent(1))*self.rescale/float(job.split)
        return theScale 


    def getHistoFromTree(self,job,subsample=-1):
        if self.lumi == 0: raise Exception("You're trying to plot with no lumi")
         
        hTreeList=[]
        groupList=[]


        plot_path = self.config.get('Directories','plotpath')

        # define treeCut
        if job.type != 'DATA':
            if type(self.region)==str:
                cutcut=self.config.get('Cuts',self.region)
            elif type(self.region)==list:
                #replace vars with other vars in the cutstring (used in DC writer)
                cutcut=self.config.get('Cuts',self.region[0])
                cutcut=cutcut.replace(self.region[1],self.region[2])
                #print cutcut
            if subsample>-1:
                treeCut='%s & %s & EventForTraining == 0'%(cutcut,job.subcuts[subsample])        
            else:
                treeCut='%s & EventForTraining == 0'%(cutcut)
        elif job.type == 'DATA':
            cutcut=self.config.get('Cuts',self.region)
            treeCut='%s'%(cutcut)

        # get and skim the Trees
        output=TFile.Open(plot_path+'/tmp_plotCache_%s_%s.root'%(self.region,job.identifier),'recreate')
        input = TFile.Open(self.path+'/'+job.getpath(),'read')
        Tree = input.Get(job.tree)
        output.cd()
        CuttedTree=Tree.CopyTree(treeCut)
    
        # get all Histos at once
        weightF=self.config.get('Weights',self.which_weightF)
        for options in self.optionsList:
            if subsample>-1:
                name=job.subnames[subsample]
                group=job.group[subsample]
            else:
                name=job.name
                group=job.group
            treeVar=options[0]
            name=options[1]
            nBins=int(options[3])
            xMin=float(options[4])
            xMax=float(options[5])

            if job.type != 'DATA':
                if CuttedTree.GetEntries():
                    output.cd() 
                    CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax), weightF, "goff,e")
                    full=True
                else:
                    full=False
            elif job.type == 'DATA':
                if options[11] == 'blind':
                    output.cd()
                    CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),treeVar+'<0', "goff,e")
                else:
                    output.cd()
                    CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),'1', "goff,e")
                full = True
            if full:
                hTree = ROOT.gDirectory.Get(name)
            else:
                output.cd()
                hTree = ROOT.TH1F('%s'%name,'%s'%name,nBins,xMin,xMax)
                hTree.Sumw2()
            if job.type != 'DATA':
                ScaleFactor = self.getScale(job,subsample)
                if ScaleFactor != 0:
                    hTree.Scale(ScaleFactor)
            #print '\t-->import %s\t Integral: %s'%(job.name,hTree.Integral())
            hTree.SetDirectory(0)
            input.Close()
            hTreeList.append(hTree)
            groupList.append(group)
            
        return hTreeList, groupList
        

######################
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
    print typs
    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:	1.3
Committed:	Tue Oct 2 13:19:45 2012 UTC (12 years, 7 months ago) by peller
Content type:	text/x-python
Branch:	MAIN
Changes since 1.2:	+5 -3 lines
Log Message:	bugfix
#	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			from BetterConfigParser import BetterConfigParser
9			import sys
10
11			class HistoMaker:
12	peller	1.2	def __init__(self, path, config, region, optionsList,rescale=1,which_weightF='weightF'):
13	peller	1.1	self.path = path
14			self.config = config
15			self.optionsList = optionsList
16			self.rescale = rescale
17			self.which_weightF=which_weightF
18	peller	1.2	self.region = region
19	peller	1.3	self.lumi=0.
20	peller	1.1
21			def getScale(self,job,subsample=-1):
22			anaTag=self.config.get('Analysis','tag')
23			input = TFile.Open(self.path+'/'+job.getpath())
24			CountWithPU = input.Get("CountWithPU")
25			CountWithPU2011B = input.Get("CountWithPU2011B")
26			#print lumi*xsecs[i]/hist.GetBinContent(1)
27			if subsample>-1:
28			xsec=float(job.xsec[subsample])
29			sf=float(job.sf[subsample])
30			else:
31			xsec=float(job.xsec)
32			sf=float(job.sf)
33			theScale = 1.
34			if anaTag == '7TeV':
35	peller	1.3	theScale = float(self.lumi)xsecsf/(0.46502CountWithPU.GetBinContent(1)+0.53498CountWithPU2011B.GetBinContent(1))*self.rescale/float(job.split)
36	peller	1.1	elif anaTag == '8TeV':
37	peller	1.3	theScale = float(self.lumi)xsecsf/(CountWithPU.GetBinContent(1))*self.rescale/float(job.split)
38	peller	1.1	return theScale
39
40
41			def getHistoFromTree(self,job,subsample=-1):
42	peller	1.3	if self.lumi == 0: raise Exception("You're trying to plot with no lumi")
43
44	peller	1.1	hTreeList=[]
45			groupList=[]
46
47	peller	1.2
48			plot_path = self.config.get('Directories','plotpath')
49
50			# define treeCut
51	peller	1.1	if job.type != 'DATA':
52	peller	1.2	if type(self.region)==str:
53			cutcut=self.config.get('Cuts',self.region)
54			elif type(self.region)==list:
55			#replace vars with other vars in the cutstring (used in DC writer)
56			cutcut=self.config.get('Cuts',self.region[0])
57			cutcut=cutcut.replace(self.region[1],self.region[2])
58			#print cutcut
59	peller	1.1	if subsample>-1:
60			treeCut='%s & %s & EventForTraining == 0'%(cutcut,job.subcuts[subsample])
61			else:
62			treeCut='%s & EventForTraining == 0'%(cutcut)
63			elif job.type == 'DATA':
64	peller	1.2	cutcut=self.config.get('Cuts',self.region)
65	peller	1.1	treeCut='%s'%(cutcut)
66	peller	1.2
67			# get and skim the Trees
68			output=TFile.Open(plot_path+'/tmp_plotCache_%s_%s.root'%(self.region,job.identifier),'recreate')
69	peller	1.1	input = TFile.Open(self.path+'/'+job.getpath(),'read')
70			Tree = input.Get(job.tree)
71	peller	1.2	output.cd()
72			CuttedTree=Tree.CopyTree(treeCut)
73
74			# get all Histos at once
75	peller	1.1	weightF=self.config.get('Weights',self.which_weightF)
76			for options in self.optionsList:
77			if subsample>-1:
78			name=job.subnames[subsample]
79			group=job.group[subsample]
80			else:
81			name=job.name
82			group=job.group
83			treeVar=options[0]
84			name=options[1]
85			nBins=int(options[3])
86			xMin=float(options[4])
87			xMax=float(options[5])
88
89			if job.type != 'DATA':
90			if CuttedTree.GetEntries():
91			output.cd()
92			CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax), weightF, "goff,e")
93			full=True
94			else:
95			full=False
96			elif job.type == 'DATA':
97			if options[11] == 'blind':
98			output.cd()
99			CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),treeVar+'<0', "goff,e")
100			else:
101			output.cd()
102			CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),'1', "goff,e")
103			full = True
104			if full:
105			hTree = ROOT.gDirectory.Get(name)
106			else:
107			output.cd()
108			hTree = ROOT.TH1F('%s'%name,'%s'%name,nBins,xMin,xMax)
109			hTree.Sumw2()
110			if job.type != 'DATA':
111			ScaleFactor = self.getScale(job,subsample)
112			if ScaleFactor != 0:
113			hTree.Scale(ScaleFactor)
114			#print '\t-->import %s\t Integral: %s'%(job.name,hTree.Integral())
115			hTree.SetDirectory(0)
116			input.Close()
117			hTreeList.append(hTree)
118			groupList.append(group)
119
120			return hTreeList, groupList
121
122
123			######################
124			def orderandadd(histos,typs,setup):
125			#ORDER AND ADD TOGETHER
126			ordnung=[]
127			ordnungtyp=[]
128			num=[0]*len(setup)
129			for i in range(0,len(setup)):
130			for j in range(0,len(histos)):
131			if typs[j] in setup[i]:
132			num[i]+=1
133			ordnung.append(histos[j])
134			ordnungtyp.append(typs[j])
135			del histos
136			del typs
137			histos=ordnung
138			typs=ordnungtyp
139			print typs
140			for k in range(0,len(num)):
141			for m in range(0,num[k]):
142			if m > 0:
143			#add
144			histos[k].Add(histos[k+1],1)
145			printc('magenta','','\t--> added %s to %s'%(typs[k],typs[k+1]))
146			del histos[k+1]
147			del typs[k+1]
148			del histos[len(setup):]
149			del typs[len(setup):]
150			return histos, typs