file = TFile(output + ".root")
runPileup = None
ipoint = 0
for key in file.GetListOfKeys():
kname = key.GetName()
if (kname.find('pileup_') < 0): continue
runnb = int(kname.split('_')[1])
h = file.Get(kname)
avgpileup = h.GetMean()
rmspileup = h.GetRMS()
evol.SetPoint(ipoint, runnb, avgpileup)
evol.SetPointError(ipoint, 0, 0, rmspileup, rmspileup)
ipoint = ipoint + 1
if (runPileup is None):
runPileup = h.Clone('runPileup')
formatPlot(runPileup, 1, 1, 1, 20, 0, True, True, 1, 1, 1)
file.Close()
setStyle()
c = getNewCanvas("evolc", "evolc", False)
c.SetWindowSize(1200, 600)
c.Divide(2, 1)
c.cd(1)
evol.Draw('ap')
c.cd(2)
runPileup.DrawNormalized('hist')
c.SaveAs(output + "_summary.C")
c.SaveAs(output + "_summary.root")
print "Average pileup: " + str(runPileup.GetMean()) + ' +/- ' + str(
runPileup.GetMeanError())
file = TFile(output+".root")
runPileup=None
ipoint=0
for key in file.GetListOfKeys() :
kname=key.GetName()
if(kname.find('pileup_')<0): continue
runnb=int(kname.split('_')[1])
h=file.Get(kname)
avgpileup=h.GetMean()
rmspileup=h.GetRMS()
evol.SetPoint(ipoint,runnb,avgpileup)
evol.SetPointError(ipoint,0,0,rmspileup,rmspileup)
ipoint=ipoint+1
if(runPileup is None) :
runPileup = h.Clone('runPileup')
formatPlot( runPileup, 1,1,1,20,0,True,True,1,1,1)
file.Close()
setStyle()
c = getNewCanvas("evolc","evolc",False)
c.SetWindowSize(1200,600)
c.Divide(2,1)
c.cd(1)
evol.Draw('ap')
c.cd(2)
runPileup.DrawNormalized('hist')
c.SaveAs(output + "_summary.C")
c.SaveAs(output + "_summary.root")
print "Average pileup: " + str(runPileup.GetMean()) + ' +/- ' + str(runPileup.GetMeanError())
cnv3 =getNewCanvas('fractfitc','fracfitc',False)
cnv3.SetWindowSize(1500,500)
cnv3.Divide(3,1)
fracFitter=[]
icnv=0
for c in cats:
prefix=''
if(c!='all') : prefix=c+'_'
icnv = icnv+1
#inclusive distributions
cnv.cd(icnv)
spimpose = ROOT.TList()
mcmodelH=plotF.Get('localAnalysis/'+c+'/'+prefix+'avgwrongmodelmlj')
mcmodelH.SetTitle('MC model')
formatPlot(mcmodelH,8,9,2,1,0,True,False,8,8,8)
datamodelH =plotF.Get('localAnalysis/'+c+'/'+prefix+'datawrongmodelmlj')
datamodelH.SetTitle('data model')
formatPlot(datamodelH,1,9,2,1,0,True,False,1,1,1)
spimpose.Add(mcmodelH)
spimpose.Add(datamodelH)
stack = ROOT.TList()
mcWrongH=plotF.Get('localAnalysis/'+c+'/'+prefix+'avgwrongmlj')
formatPlot(mcWrongH,809,1,1,0,1001,True,False,1,809,809)
mcWrongH.SetTitle('Wrong assignments')
mcCorrectH=plotF.Get('localAnalysis/'+c+'/'+prefix+'avgcorrectmlj')
formatPlot(mcCorrectH,614,1,1,0,1001,True,False,1,614,614)
mcCorrectH.SetTitle('Correct assignments')
stack .Add(mcWrongH)
stack .Add(mcCorrectH)
def runOverSamples(samplesDB, integratedLumi=1.0, inputDir='data', outputDir='data/plots', getFromDir='', forceNormalization=False ) :
#open the file which describes the sample
jsonFile = open(samplesDB,'r')
procList=json.load(jsonFile,encoding='utf-8').items()
stackplots=[]
spimposeplots=[]
dataplots=[]
plottitles=[]
generalLabel='CMS preliminary, #sqrt{s}=7 TeV, #int L=%3.1f fb^{-1}' % (integratedLumi/1000)
samplehtml="<tr><th colspan=\"3\"><large>Samples used in analysis</large></th></tr>"
#run over sample
for proc in procList :
#run over processes
iprocess=0
for desc in proc[1] :
iprocess=iprocess+1
procplots=[]
procplotstitles=[]
isdata = getByLabel(desc,'isdata',False)
forceDataNorm = getByLabel(desc,'forceDataNorm',False)
spimpose = getByLabel(desc,'spimpose',False)
color = rootConst(getByLabel(desc,'color',1))
line = getByLabel(desc,'line', 1)
lwidth = getByLabel(desc,'lwidth', 1)
fill = getByLabel(desc,'fill',1001)
marker = getByLabel(desc,'marker',20)
lcolor = rootConst(getByLabel(desc,'lcolor',color))
fcolor = rootConst(getByLabel(desc,'fcolor',color))
mcolor = rootConst(getByLabel(desc,'fcolor',color))
tag = getByLabel(desc,'tag','')
samplehtml+="<tr><th colspan=\"3\">"+tag+" ("
if(isdata) : samplehtml += "data"
else : samplehtml +="mc"
samplehtml+=") </th></tr>\n"
#run over items in process
data = getByLabel(desc,'data')
normto = getByLabel(desc,'normto',-1)
for d in data :
#get the plots
plots=getControlPlots(d,isdata,inputDir,getFromDir)
dtag=getByLabel(d,'dtag')
if(plots==None):continue
if(len(plots)==0): continue
#compute the normalization
weight=1
absNorm=False
sfactor = getByLabel(d,'sfactor',1)
xsec = getByLabel(d,'xsec',-1)
br = getByLabel(d,'br',[])
brprod=1.0
if(xsec>0 or forceDataNorm) :
for ibr in br : brprod = brprod*ibr
weight = integratedLumi*sfactor*xsec*brprod
samplehtml+="<tr><td>"+dtag+"</td>"
samplehtml+="<td>"+str(xsec) + "x" +str(brprod) + "x" + str(sfactor) + "</td>"
samplehtml+="<td><small>" + getByLabel(d,'dset','n/a') + "</small></td></tr>\n"
#book keep the result
iplot=0
for p in plots.items():
#create the base plot for this sample if non existing
if(len(procplots)<=iplot):
procplotstitles.append( p[1].GetTitle() )
# newname=p[1].GetName()+'_'+str(iprocess)
newname=p[1].GetName()
newplot=p[1].Clone( newname )
newplot.Reset('ICE')
newplot.SetTitle(tag)
formatPlot(newplot, color,line,lwidth,marker,fill,True,True,lcolor,fcolor,mcolor)
procplots.append( newplot )
#apply new normalization
p[1].Scale(weight)
#add to base plot
#print ' Normalizing plots from ' + dtag + ' with abs=' + str(absNorm) + ' and weight=' + str(weight)
procplots[iplot].Add(p[1])
iplot=iplot+1
#overall normalization
if(normto>0) :
#print ' Normalizing final yields to: ' + str(normto)
for p in procplots:
total=p.Integral()
if(total>0): p.Scale(normto/total)
#store the final result
if(len(procplots)==0): continue
plottitles=procplotstitles
if(not isdata) :
if(not spimpose) : stackplots.append(procplots)
else : spimposeplots.append(procplots)
else : dataplots.append(procplots)
#save copy in plotter.root
pOut = TFile.Open(outputDir+"/plotter.root","UPDATE")
pOut.cd()
#pDir = pOut.mkdir( 'proc_'+str(iprocess) )
pDir = pOut.mkdir( tag.replace('/','-') )
pDir.cd()
for i in xrange(0,len(procplots)): procplots[i].Write()
pOut.Close()
print 'Output plots available @ ' + outputDir + '/plotter.root'
showControlPlots(stackplots,spimposeplots,dataplots,plottitles,generalLabel,outputDir,samplehtml,forceNormalization)
cnv3.SetWindowSize(1500, 500)
cnv3.Divide(3, 1)
fracFitter = []
icnv = 0
for c in cats:
prefix = ''
if (c != 'all'): prefix = c + '_'
icnv = icnv + 1
#inclusive distributions
cnv.cd(icnv)
spimpose = ROOT.TList()
mcmodelH = plotF.Get('localAnalysis/' + c + '/' + prefix +
'avgwrongmodelmlj')
mcmodelH.SetTitle('MC model')
formatPlot(mcmodelH, 8, 9, 2, 1, 0, True, False, 8, 8, 8)
datamodelH = plotF.Get('localAnalysis/' + c + '/' + prefix +
'datawrongmodelmlj')
datamodelH.SetTitle('data model')
formatPlot(datamodelH, 1, 9, 2, 1, 0, True, False, 1, 1, 1)
spimpose.Add(mcmodelH)
spimpose.Add(datamodelH)
stack = ROOT.TList()
mcWrongH = plotF.Get('localAnalysis/' + c + '/' + prefix + 'avgwrongmlj')
formatPlot(mcWrongH, 809, 1, 1, 0, 1001, True, False, 1, 809, 809)
mcWrongH.SetTitle('Wrong assignments')
mcCorrectH = plotF.Get('localAnalysis/' + c + '/' + prefix +
'avgcorrectmlj')
formatPlot(mcCorrectH, 614, 1, 1, 0, 1001, True, False, 1, 614, 614)
mcCorrectH.SetTitle('Correct assignments')
dataZMassRegionCtr = h.Clone(evcat+'_datactr')
dataZMassRegionCtr.SetDirectory(0)
else :
dataZMassRegionCtr.Add(h)
else:
otherKeys.append(kname)
if(otherZMassRegionCtr is None):
otherZMassRegionCtr = h.Clone(evcat+'_otherctr')
otherZMassRegionCtr.SetDirectory(0)
else :
otherZMassRegionCtr.Add(h)
f.Close()
#re-scale factors
topRescaleFactor = ROOT.TH1F(evcat+'kttbar',eventCategoryTitles[ievcat-1]+';Selection step; N_{out}/N_{in}',len(rescaleRegions),0,len(rescaleRegions))
formatPlot(topRescaleFactor,1,1,1,20+ievcat,0,True,True,1,0,1)
otherRescaleFactor = ROOT.TH1F(evcat+'kothers',eventCategoryTitles[ievcat-1]+';Selection step; N_{out}/N_{in}',len(rescaleRegions),0,len(rescaleRegions))
formatPlot(otherRescaleFactor,1,1,1,20+ievcat,0,True,True,1,0,1)
ipoint=0
for reg in rescaleRegions:
ipoint=ipoint+1
topYields=[]
otherYields=[]
for cat in reg[1] :
for i in xrange(1,topZMassRegionCtr.GetXaxis().GetNbins()):
ibinLabel= topZMassRegionCtr.GetXaxis().GetBinLabel(i)
if( ibinLabel != cat) : continue
topYieldsVal = topZMassRegionCtr.GetBinContent(i)
def runOverSamples(samplesDB, integratedLumi=1.0, inputDir='data', outputDir='data/plots', getFromDir='', forceNormalization=False ) :
#open the file which describes the sample
jsonFile = open(samplesDB,'r')
procList=json.load(jsonFile,encoding='utf-8').items()
stackplots=[]
spimposeplots=[]
dataplots=[]
plottitles=[]
generalLabel='CMS preliminary, #sqrt{s}=7 TeV, #int L=%3.1f fb^{-1}' % (integratedLumi/1000)
samplehtml="<tr><th colspan=\"3\"><large>Samples used in analysis</large></th></tr>"
#run over sample
for proc in procList :
#run over processes
iprocess=0
for desc in proc[1] :
iprocess=iprocess+1
procplots=[]
procplotstitles=[]
isdata = getByLabel(desc,'isdata',False)
forceDataNorm = getByLabel(desc,'forceDataNorm',False)
spimpose = getByLabel(desc,'spimpose',False)
color = rootConst(getByLabel(desc,'color',1))
line = getByLabel(desc,'line', 1)
lwidth = getByLabel(desc,'lwidth', 1)
fill = getByLabel(desc,'fill',1001)
marker = getByLabel(desc,'marker',20)
lcolor = rootConst(getByLabel(desc,'lcolor',color))
fcolor = rootConst(getByLabel(desc,'fcolor',color))
mcolor = rootConst(getByLabel(desc,'fcolor',color))
tag = getByLabel(desc,'tag','')
samplehtml+="<tr><th colspan=\"3\">"+tag+" ("
if(isdata) : samplehtml += "data"
else : samplehtml +="mc"
samplehtml+=") </th></tr>\n"
#run over items in process
data = getByLabel(desc,'data')
normto = getByLabel(desc,'normto',-1)
for d in data :
#get the plots
plots=getControlPlots(d,isdata,inputDir,getFromDir)
dtag=getByLabel(d,'dtag')
if(plots==None):continue
if(len(plots)==0): continue
#compute the normalization
weight=1
absNorm=False
sfactor = getByLabel(d,'sfactor',1)
xsec = getByLabel(d,'xsec',-1)
br = getByLabel(d,'br',[])
brprod=1.0
if(xsec>0 or forceDataNorm) :
for ibr in br : brprod = brprod*ibr
weight = integratedLumi*sfactor*xsec*brprod
samplehtml+="<tr><td>"+dtag+"</td>"
samplehtml+="<td>"+str(xsec) + "x" +str(brprod) + "x" + str(sfactor) + "</td>"
samplehtml+="<td><small>" + getByLabel(d,'dset','n/a') + "</small></td></tr>\n"
#book keep the result
iplot=0
for p in plots.items():
#create the base plot for this sample if non existing
if(len(procplots)<=iplot):
procplotstitles.append( p[1].GetTitle() )
# newname=p[1].GetName()+'_'+str(iprocess)
newname=p[1].GetName()
newplot=p[1].Clone( newname )
newplot.Reset('ICE')
newplot.SetTitle(tag)
formatPlot(newplot, color,line,lwidth,marker,fill,True,True,lcolor,fcolor,mcolor)
procplots.append( newplot )
#apply new normalization
p[1].Scale(weight)
#add to base plot
#print ' Normalizing plots from ' + dtag + ' with abs=' + str(absNorm) + ' and weight=' + str(weight)
procplots[iplot].Add(p[1])
iplot=iplot+1
#overall normalization
if(normto>0) :
#print ' Normalizing final yields to: ' + str(normto)
for p in procplots:
total=p.Integral()
if(total>0): p.Scale(normto/total)
#store the final result
if(len(procplots)==0): continue
plottitles=procplotstitles
if(not isdata) :
if(not spimpose) : stackplots.append(procplots)
else : spimposeplots.append(procplots)
else : dataplots.append(procplots)
#save copy in plotter.root
pOut = TFile.Open(outputDir+"/plotter.root","UPDATE")
pOut.cd()
#pDir = pOut.mkdir( 'proc_'+str(iprocess) )
pDir = pOut.mkdir( tag.replace('/','-') )
pDir.cd()
for i in xrange(0,len(procplots)): procplots[i].Write()
pOut.Close()
print 'Output plots available @ ' + outputDir + '/plotter.root'
showControlPlots(stackplots,spimposeplots,dataplots,plottitles,generalLabel,outputDir,samplehtml,forceNormalization)
dataZMassRegionCtr.Add(h)
else:
otherKeys.append(kname)
if (otherZMassRegionCtr is None):
otherZMassRegionCtr = h.Clone(evcat + '_otherctr')
otherZMassRegionCtr.SetDirectory(0)
else:
otherZMassRegionCtr.Add(h)
f.Close()
#re-scale factors
topRescaleFactor = ROOT.TH1F(
evcat + 'kttbar',
eventCategoryTitles[ievcat - 1] + ';Selection step; N_{out}/N_{in}',
len(rescaleRegions), 0, len(rescaleRegions))
formatPlot(topRescaleFactor, 1, 1, 1, 20 + ievcat, 0, True, True, 1, 0, 1)
otherRescaleFactor = ROOT.TH1F(
evcat + 'kothers',
eventCategoryTitles[ievcat - 1] + ';Selection step; N_{out}/N_{in}',
len(rescaleRegions), 0, len(rescaleRegions))
formatPlot(otherRescaleFactor, 1, 1, 1, 20 + ievcat, 0, True, True, 1, 0,
1)
ipoint = 0
for reg in rescaleRegions:
ipoint = ipoint + 1
topYields = []
otherYields = []
for cat in reg[1]:
for i in xrange(1, topZMassRegionCtr.GetXaxis().GetNbins()):