def getHistograms( self ):
relIsoHists = [self.relIsoHistogramPrefix + jetbin for jetbin in self.jetBins]
pfIsoHists = [self.pfIsoHistogramPrefix + jetbin for jetbin in self.jetBins]
pfIsoControlHists = [self.pfIsoControlRegionHistogramPrefix + jetbin for jetbin in self.jetBins]
allHists = relIsoHists
allHists.extend( pfIsoHists )
allHists.extend( pfIsoControlHists )
#print allHists
# HistGetter.hists = allHists
self.histograms = HistGetter.getHistsFromFiles(allHists, self.files);
self.histograms = HistGetter.addSampleSum( self.histograms )
def printCutFlow(hist, analysis):
files = inputFiles.files
hist_1mBtag = 'TTbarPlusMetAnalysis/' + analysis + '/Ref selection/MET/patMETsPFlow/MET_1orMoreBtag'
hist_2mBtag = 'TTbarPlusMetAnalysis/' + analysis + '/Ref selection/MET/patMETsPFlow/MET_2orMoreBtags'
hists = [hist, #due to b-tag scale factors these are not as simple any more
hist_1mBtag,
hist_2mBtag
]
hists = HistGetter.getHistsFromFiles(hists, files, bJetBins=[])
hists = HistGetter.addSampleSum(hists)
# histname3 = 'EventCount/TTbarEplusJetsSelection_2orMoreBtags'
header = "| Step | TTJet | W+jets | DY + Jets | single top | QCD | Sum MC | Data |"
row = " | %s | %d | %d | %d | %d | %d| %d | %d | "
print header
numbers = getEventNumbers(hists, hist, hist_1mBtag, hist_2mBtag)# + '_0orMoreBtag')
for step in range(len(cuts)):
nums = numbers[step]
sumMC = nums['ttbar'] + nums['wjets'] + nums['zjets'] + nums['singleTop'] + nums['qcd']
print row % (cuts[step], nums['ttbar'], nums['wjets'], nums['zjets'], nums['singleTop'], nums['qcd'], sumMC, nums['data'])
class QCDEstimator:
luminosity = 36.145#pb-1
mc_luminosity = 36.145#pb-1
luminosity_unit = 'pb-1'
scale = luminosity / mc_luminosity
jetBins = ['0jet', 'allJets', '1jet', '1orMoreJets', '2jets', '2orMoreJets', '3jets', '3orMoreJets', '4orMoreJets']
jetBinsLatex = {'0jet':'0 jet', 'allJets':'#geq 0 jets', '1jet':'1 jet', '1orMoreJets':'#geq 1 jet',
'2jets':'2 jets', '2orMoreJets':'#geq 2 jets', '3jets':'3 jets', '3orMoreJets':'#geq 3 jets',
'4orMoreJets':'#geq 4 jets'}
binWidth = 0.01
rebin = 10
fitRangesClosureTest = [ ( 0.1, 0.9 ), ( 0.1, 1.0 ), ( 0.1, 1.1 ),
( 0.2, 0.9 ), ( 0.2, 1.0 ), ( 0.2, 1.1 ),
( 0.3, 0.9 ), ( 0.3, 1.0 ), ( 0.3, 1.1 )]
fitRangesEstimation = [ ( 0.1, 1.1 ), ( 0.2, 1.1 )]#, ( 0.3, 1.1 )]
signalRegion = ( 0, 0.1 )
maxValue = 1.6
pfIsoHistogramPrefix = 'QCDest_PFIsolation_WithMETCutAndAsymJetCuts_'
pfIsoControlRegionHistogramPrefix = 'QCDest_PFIsolation_controlRegion2_WithMETCutAndAsymJetCuts_'
relIsoHistogramPrefix = 'QCDest_CombRelIso_'
pfIsoResults = {}
relIsoResults = {}
allPfIsoResults = {}
allRelIsoResults = {}
useEntryAsData = 'data'
constrainFit = False
numberOfFreeParameters = 0
currentFitRange = ( 0.1, 1.6 )
currentFitFuntion = 'gaus'
currentJetBin = jetBins[-1]
outputFormat = 'pdf'
outputFolder = ''
def __init__( self, files ):
self.files = files
HistGetter.samplefiles = files
self.histograms = {}
self.histGetter = HistGetter()
self.histGetter.setStyle()
self.getHistograms()
self.applyStyleAndCreateStack()
for bin in self.jetBins:
self.pfIsoResults[bin] = {'actualNumberOfQCDEvents': 0, 'estimatedNumberOfQCDEvents':0,
'fitFunction': None, 'fitParameters': {}, 'numberOfAllDataEvents':0,
'numberOfAllMCEvents':0}
self.relIsoResults[bin] = {'actualNumberOfQCDEvents': 0, 'estimatedNumberOfQCDEvents':0,
'fitFunction': None, 'fitParameters': {}, 'numberOfAllDataEvents':0,
'numberOfAllMCEvents':0}
def getHistograms( self ):
relIsoHists = [self.relIsoHistogramPrefix + jetbin for jetbin in self.jetBins]
pfIsoHists = [self.pfIsoHistogramPrefix + jetbin for jetbin in self.jetBins]
pfIsoControlHists = [self.pfIsoControlRegionHistogramPrefix + jetbin for jetbin in self.jetBins]
allHists = relIsoHists
allHists.extend( pfIsoHists )
allHists.extend( pfIsoControlHists )
HistGetter.hists = allHists
self.histograms = self.histGetter.getHistsFromFiles()
self.histograms = self.histGetter.addSampleSum( self.histograms )
def applyStyleAndCreateStack( self ):
samplesOfInterest = ['data', 'qcd', 'zjets', 'wjets', 'singleTop', 'ttbar', 'allMC']
colors = {'ttbar' : kRed + 1,
'wjets' : kGreen - 3,
'zjets' : kAzure - 2,
'qcd' : kYellow,
'singleTop' : kMagenta}
mcStack = {}
for sample in samplesOfInterest:#sample
for histname in self.histograms[sample].keys():
self.histograms[sample][histname].Rebin( self.rebin )
if not sample in ['data', 'allMC']:
self.histograms[sample][histname].Scale( self.scale )
self.histograms[sample][histname].SetFillStyle( 1001 )
self.histograms[sample][histname].SetFillColor( colors[sample] )
if not mcStack.has_key( histname ):
mcStack[histname] = THStack( "MC_" + histname, "MC_" + histname );
mcStack[histname].Add( self.histograms[sample][histname] )
else:
self.histograms[sample][histname].SetMarkerStyle( 8 );
self.histograms['MCStack'] = mcStack
self.setStyle()
print "=" * 40
print "data integrated luminosity:", self.luminosity, self.luminosity_unit
print "MC integrated luminosity:", self.mc_luminosity, self.luminosity_unit
print "MC scale factor: ", self.scale
print '=' * 40
def setStyle( self ):
tdrstyle = setTDRStyle();
tdrstyle.SetPadRightMargin( 0.05 )#originally was 0.02, too narrow!
tdrStyle.SetStatH( 0.2 );
tdrStyle.SetOptStat( 0 );#off title
tdrStyle.SetOptFit( 0 );#off title
tdrStyle.cd();
gROOT.ForceStyle();
gStyle.SetTitleH( 0.1 );
gStyle.SetStatH( 0.22 ); #0.24);
gStyle.SetStatW( 0.22 ); #0.26);
gStyle.SetOptStat( 1 ); #on stat
gStyle.SetLineScalePS( 2 ); #D=3
gStyle.SetOptFit( 112 );
def doClosureTests( self, function = 'gaus' ):
self.useEntryAsData = 'allMC'
self.currentFitFuntion = function
for fitRange in self.fitRangesClosureTest:
self.currentFitRange = fitRange
for bin in self.jetBins:
self.currentJetBin = bin
self.EstimateJetBin( bin )
self.plot( self.pfIsoHistogramPrefix + bin, self.pfIsoResults[bin] )
self.plot( self.relIsoHistogramPrefix + bin, self.relIsoResults[bin] )
self.allPfIsoResults['%1.1f-%1.1f' % fitRange] = deepcopy( self.pfIsoResults )
self.allRelIsoResults['%1.1f-%1.1f' % fitRange] = deepcopy( self.relIsoResults )
self.plotClosureTest( self.pfIsoHistogramPrefix, self.allPfIsoResults )
self.plotClosureTest( self.relIsoHistogramPrefix, self.allRelIsoResults )
def doEstimate( self, function = 'gaus' ):
self.useEntryAsData = 'data'
self.currentFitFuntion = function
for fitRange in self.fitRangesEstimation:
self.currentFitRange = fitRange
for bin in self.jetBins:
self.currentJetBin = bin
self.EstimateJetBin( bin )
self.plot( self.pfIsoHistogramPrefix + bin, self.pfIsoResults[bin] )
self.plot( self.relIsoHistogramPrefix + bin, self.relIsoResults[bin] )
self.allPfIsoResults['%1.1f-%1.1f' % fitRange] = deepcopy( self.pfIsoResults )
self.allRelIsoResults['%1.1f-%1.1f' % fitRange] = deepcopy( self.relIsoResults )
def EstimateJetBin( self, jetbin ):
# fitRange = self.currentFitRange
function = self.currentFitFuntion
QCD = self.histograms['qcd']
data = self.histograms[self.useEntryAsData]
allMC = self.histograms['allMC']
pfIsoHist = self.pfIsoHistogramPrefix + jetbin
relIsoHist = self.relIsoHistogramPrefix + jetbin
self.pfIsoResults[jetbin]['actualNumberOfQCDEvents'] = QCD[pfIsoHist].GetBinContent(1)
self.pfIsoResults[jetbin]['numberOfAllDataEvents'] = data[pfIsoHist].GetBinContent(1)
self.pfIsoResults[jetbin]['numberOfAllMCEvents'] = allMC[pfIsoHist].GetBinContent(1)
pfIsoFit = self.doFit( data[pfIsoHist] )
self.pfIsoResults[jetbin]['fitFunction'] = pfIsoFit
self.pfIsoResults[jetbin]['fitParameters'] = self.getFitParameters( pfIsoFit )
estimate = 0
if pfIsoFit:
estimate = pfIsoFit.Integral( self.signalRegion[0], self.signalRegion[1] ) / ( self.binWidth * self.rebin )
self.pfIsoResults[jetbin]['estimatedNumberOfQCDEvents'] = estimate
#----------------------------------------------------------------------------------------------------------------------
self.relIsoResults[jetbin]['actualNumberOfQCDEvents'] = QCD[relIsoHist].GetBinContent(1)
self.relIsoResults[jetbin]['numberOfAllDataEvents'] = data[relIsoHist].GetBinContent(1)
self.relIsoResults[jetbin]['numberOfAllMCEvents'] = allMC[relIsoHist].GetBinContent(1)
relIsoFit = self.doFit( data[relIsoHist] )
self.relIsoResults[jetbin]['fitFunction'] = relIsoFit
self.relIsoResults[jetbin]['fitParameters'] = self.getFitParameters( relIsoFit )
estimate = relIsoFit.Integral( self.signalRegion[0], self.signalRegion[1] ) / ( self.binWidth * self.rebin )
self.relIsoResults[jetbin]['estimatedNumberOfQCDEvents'] = estimate
def doFit( self, histogram ):
function = self.currentFitFuntion
fitRange = self.currentFitRange
if not self.constrainFit:
histogram.Fit( function, "Q0", "ah", fitRange[0], fitRange[1] )
else:
ff = TF1( function, function, 0, 1 );
self.numberOfFreeParameters = ff.GetNumberFreeParameters();
return histogram.GetFunction( function )
def plot( self, histname, results ):
data = self.histograms[self.useEntryAsData][histname]
mcStack = self.histograms['MCStack'][histname]
fitFunction = results['fitFunction']
if not fitFunction:
print 'no fitfunction found'
return;
data.GetXaxis().SetRangeUser( 0, self.maxValue - 0.01 );
fitFunction.SetLineColor( kRed );
fitFunction.SetLineWidth( 2 )
fitFunction2 = fitFunction.Clone()
fitFunction2.SetLineColor( kBlue );
fitFunction2.SetRange( self.signalRegion[0], self.signalRegion[1] );
fitFunction3 = fitFunction.Clone()
fitFunction3.SetLineColor( kBlue );
fitFunction3.SetLineStyle( kDashed );
fitFunction3.SetRange( self.signalRegion[1], self.currentFitRange[0] );
canvas = TCanvas( "c1", "Iso fit", 1920, 1080 )
data.Draw();
max = 0
if mcStack.GetMaximum() > data.GetBinContent( 1 ):
max = mcStack.GetMaximum()*1.1
else:
max = data.GetBinContent( 1 ) * 1.1
data.GetYaxis().SetRangeUser( 0, max );
data.SetXTitle( "Relative Isolation" );
data.SetYTitle( "Events/0.1" );
# draw mc
mcStack.Draw( "hist same" );
data.Draw( "ae same" );
fitFunction.Draw( "same" );
fitFunction2.Draw( "same" );
fitFunction3.Draw( "same" );
label = self.add_cms_label( self.currentJetBin )
label.Draw()
legend = self.add_legend( histname )
# legend.Draw()
if self.currentFitFuntion == "pol1":
out = "%s_fit_linear_from_0%.0f_%s" % ( histname, self.currentFitRange[0] * 10.0, self.useEntryAsData );
else:
out = "%s_fit_%s_from_%1.1f_to_%1.1f_%s" % ( histname, self.currentFitFuntion, self.currentFitRange[0],
self.currentFitRange[1], self.useEntryAsData );
# if self.outputFormat == 'pdf':
# canvas.SaveAs( '%s.eps' % out );
# gROOT.ProcessLine( ".!ps2pdf -dEPSCrop %s.eps" % out );
# gROOT.ProcessLine( ".!rm -f %s.eps" % out );
# else:
canvas.SaveAs( '%s/%s.%s' % ( self.outputFolder, out, self.outputFormat ) )
canvas.Close(); #crucial!
def plotClosureTest( self, histname, results ):
c2 = TCanvas( "c2", "QCD estimates", 1080, 1080 );
x = array( 'd', [2, 3, 4] )
jetBinsOfInterest = [#'1jet',
'2jets', '3jets', '4orMoreJets']
function = self.currentFitFuntion
gStyle.SetMarkerSize( 1.7 );
gStyle.SetMarkerStyle( 20 );
c2.SetTopMargin( 0.1 );
# c2.SetLeftMargin( 0.12 );
c2.SetRightMargin( 0.35 );
y = {}
for fitRange in self.fitRangesClosureTest:
range = '%1.1f-%1.1f' % fitRange
y[range] = []
for bin in jetBinsOfInterest:
est = results[range][bin]['estimatedNumberOfQCDEvents']
true = results[range][bin]['actualNumberOfQCDEvents']
variation = 0
if not true == 0:
variation = ( est - true ) / true
y[range].append( variation )
nbins = 3
gr1 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[0]] ) )
gr2 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[1]] ) )
gr3 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[2]] ) )
gr4 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[3]] ) )
gr5 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[4]] ) )
gr6 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[5]] ) )
gr7 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[6]] ) )
gr8 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[7]] ) )
gr9 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[8]] ) )
gr1.SetMarkerColor( kGreen + 1 );
gr2.SetMarkerColor( kGreen + 2 );
gr3.SetMarkerColor( kGreen + 3 );
gr4.SetMarkerColor( kAzure + 7 );
gr5.SetMarkerColor( kAzure - 3 );
gr6.SetMarkerColor( kBlue );
gr7.SetMarkerColor( kOrange );
gr8.SetMarkerColor( kOrange - 1 );
gr9.SetMarkerColor( kOrange - 6 );
gStyle.SetTitleW( 0.9 );
gStyle.SetTitleH( 0.05 )
h = None
if function == "gaus":
h = TH1D( "h", "Variation of QCD estimates with fit range (Gaussian)", 4, 0.5, 4.5 );
elif function == "pol3":
h = TH1D( "h", "Variation of QCD estimates with fit range (Pol3)", 4, 0.5, 4.5 );
elif function == "pol1":
h = TH1D( "h", "Variation of QCD estimates with fit range (Pol1)", 4, 0.5, 4.5 );
elif function == "landau":
h = TH1D( "h", "Variation of QCD estimates with fit range (Landau)", 4, 0.5, 4.5 );
h.SetStats( kFALSE ); # no statistics
h.Draw();
h.SetYTitle( "Deviation = (N_{QCD,est}-N_{QCD,true})/N_{QCD,true}" );
h.GetYaxis().SetRangeUser( -1, 1 );
h.GetXaxis().SetRangeUser( 1.5, 5.5 );
# h.GetXaxis().SetBinLabel( 1, "1j" );
h.GetXaxis().SetBinLabel( 2, "2j" );
h.GetXaxis().SetBinLabel( 3, "3j" );
h.GetXaxis().SetBinLabel( 4, "#geq4j" );
h.GetXaxis().SetLabelSize( 0.07 );
h.GetYaxis().SetTitleOffset( 1.3 );
gr1.Draw( "P" );
gr2.Draw( "P" ); #to superimpose graphs, do not re-draw axis
gr3.Draw( "P" );
gr4.Draw( "P" );
gr5.Draw( "P" );
gr6.Draw( "P" );
gr7.Draw( "P" );
gr8.Draw( "P" );
gr9.Draw( "P" );
c2.SetGrid( 1, 1 );
leg = TLegend( 0.65, 0.1, 0.98, 0.9 );
leg.SetFillColor( 0 );
leg.AddEntry( gr1, "Free: 0.1-0.9", "p" );
leg.AddEntry( gr2, "Free: 0.1-1.0", "p" );
leg.AddEntry( gr3, "Free: 0.1-1.1", "p" );
leg.AddEntry( gr4, "Free: 0.2-0.9", "p" );
leg.AddEntry( gr5, "Free: 0.2-1.0", "p" );
leg.AddEntry( gr6, "Free: 0.2-1.1", "p" );
leg.AddEntry( gr7, "Free: 0.3-0.9", "p" );
leg.AddEntry( gr8, "Free: 0.3-1.0", "p" );
leg.AddEntry( gr9, "Free: 0.3-1.1", "p" );
leg.Draw();
c2.SaveAs( "%s/%s_qcd_estimate_%s.%s" % ( self.outputFolder, histname, function, self.outputFormat ) )
setRange = h.GetYaxis().SetRangeUser
saveAs = c2.SaveAs
for limit in [1, 2, 3, 6, 8]:
setRange( -limit, limit );
saveAs( "%s/%s_qcd_estimate_%s_zoom_%d.%s" % ( self.outputFolder, histname, function, limit, self.outputFormat ) );
def getFitParameters( self, fitFunction ):
fitParameters = {'chi2':-1, 'numberOfdegreesOfFreedom': 0, 'constrain1': 0, 'constrain2': 0,
'constrain3': 0, 'constrain4': 0}
if fitFunction:
fitParameters['chi2'] = fitFunction.GetChisquare()
fitParameters['numberOfdegreesOfFreedom'] = fitFunction.GetNDF()
return fitParameters
def doConstrainedFit( self, histogram, function = 'gaus', limits = ( 0.1, 1.6 ) ):
fitFunction = None
if function == 'gaus':
fitFunction = TF1( "gaus", "gaus", 0, 2 );
elif function == 'pol3':
fitFunction = TF1( "pol3", "[0] * ( 1 + [1]*x + [2]*x^2 + [3]*x^3 )", 0, 2 );
elif function == 'landau':
fitFunction = TF1( "landau", "landau", 0, 2 )
myFitResult = data.Fit( function, "Q0", "ah", limits[0], limits[1] );
def add_cms_label( self, njet = "" ):
mytext = TPaveText( 0.3, 0.8, 0.6, 0.93, "NDC" );
mytext.AddText( "CMS Preliminary" );
mytext.AddText( "%.1f pb^{-1} at #sqrt{s} = 7 TeV" % self.luminosity );
if njet != "":
mytext.AddText( "e+jets, %s" % self.jetBinsLatex[njet] )
mytext.SetFillStyle( 0 );
mytext.SetBorderSize( 0 );
mytext.SetTextFont( 42 );
mytext.SetTextAlign( 13 );
return mytext
def add_legend( self, histname ):
function = self.currentFitFuntion
tt = self.histograms['ttbar'][histname]
wj = self.histograms['wjets'][histname]
zj = self.histograms['zjets'][histname]
data = self.histograms['data'][histname]
QCD = self.histograms['qcd'][histname]
stop = self.histograms['singleTop'][histname]
leg = TLegend( 0.64, 0.4, 0.9, 0.9 );
leg.SetFillStyle( 0 );
leg.SetBorderSize( 0 );
leg.SetTextFont( 42 );
# Here I define coloured lines for use in the legend
blue = TF1( "blue", "pol0", 0, 1 );
red = TF1( "red", "pol0", 0, 1 );
blue.SetLineColor( kBlue );
red.SetLineColor( kRed );
red.SetLineWidth( 2 );
blue.SetLineWidth( 2 );
blue.SetLineStyle( kDashed );
# Add entry to legend
if self.useEntryAsData == 'data':
leg.AddEntry( data, "Data", "LP" );
else:
leg.AddEntry( data, "All MC events", "LP" );
if function == "pol1":
leg.AddEntry( red, "Linear Fit", "l" );
elif function == "expo":
leg.AddEntry( red, "Exponenetial Fit", "l" );
elif function == "gaus":
leg.AddEntry( red, "Gaussian Fit", "l" );
leg.AddEntry( blue, "Extrapolation", "l" );
leg.AddEntry( tt, "t#bar{t}", "F" );
leg.AddEntry( stop, "Single-Top", "F" );
leg.AddEntry( wj, "W#rightarrowl#nu", "F" );
leg.AddEntry( zj, "Z/#gamma*#rightarrowl^{+}l^{-}", "F" );
leg.AddEntry( QCD, "QCD & #gamma+jets", "F" );
leg.Draw()
return ( leg, red, blue )
def printResults( self, results ):
self.printJetBinResults( results, '3jets' )
print '=' * 60
self.printJetBinResults( results, '3orMoreJets' )
print '=' * 60
self.printJetBinResults( results, '4orMoreJets' )
print '=' * 60
def printJetBinResults( self, results, jetBin ):
estimate = 0
estimate2 = 0
predicted = results[results.keys()[0]][jetBin]['actualNumberOfQCDEvents']
allData = results[results.keys()[0]][jetBin]['numberOfAllDataEvents']
allMC = results[results.keys()[0]][jetBin]['numberOfAllMCEvents']
if jetBin == '4orMoreJets':
print 'Estimation for >= 4 jets'
elif jetBin == '3orMoreJets':
print 'Estimation for >= 3 jets'
elif jetBin == '3jets':
print 'Estimation for == 3 jets'
print 'predicted number of QCD events', predicted
for fitRange in self.fitRangesEstimation:
range = '%1.1f-%1.1f' % fitRange
est = results[range][jetBin]['estimatedNumberOfQCDEvents']
true = results[range][jetBin]['actualNumberOfQCDEvents']
variation = ( est - true ) / true
estimate += est
estimate2 += est * est
print
print 'estimated number of QCD events'
print 'for range', range, ': ',
print est
print
mean = estimate / len( self.fitRangesEstimation )
mean2 = estimate2 / len( self.fitRangesEstimation )
error = sqrt( ( mean2 - mean * mean ) / len( self.fitRangesEstimation ) )
print 'average estimate', mean, '+-', error
weight = estimate / len( self.fitRangesEstimation ) / predicted
print 'average weight factor', weight
print 'Total number of data in signal bin(<0.1)', allData
print 'Total number of MC in signal bin(<0.1) before reweighting QCD', allMC
print 'Total number of MC in signal bin(<0.1) after reweighting QCD', ( allMC - predicted ) + predicted * weight
def plotControlRegionComparison( self ):
for bin in self.jetBins:
hist = self.pfIsoHistogramPrefix + bin
histControl = self.pfIsoControlRegionHistogramPrefix + bin
QCD_control = self.histograms['qcd'][histControl]
QCD = self.histograms['qcd'][hist]
nQCD_Control = QCD_control.Integral()
nQCD = QCD.Integral()
if nQCD_Control > 0:
QCD_control.Scale( 1 / nQCD_Control )
if nQCD > 0:
QCD.Scale( 1 / nQCD )
QCD_control.SetFillStyle( 3004 )
QCD.GetXaxis().SetRangeUser( 0., self.maxValue - 0.01 )
max = 0
if QCD.GetMaximum() > QCD_control.GetMaximum():
max = QCD.GetMaximum()*1.1
else:
max = QCD_control.GetMaximum()*1.1
QCD.GetYaxis().SetRangeUser( 0., max )
canvas = TCanvas( "c1", "Shape comparision", 1920, 1080 )
QCD.Draw()
QCD_control.Draw( 'same' )
label = self.add_cms_label( bin )
label.Draw()
leg = TLegend( 0.64, 0.4, 0.9, 0.9 );
leg.SetFillStyle( 0 );
leg.SetBorderSize( 0 );
leg.SetTextFont( 42 );
leg.AddEntry( QCD_control, 'QCD control region' )
leg.AddEntry( QCD, 'QCD standard selection' )
leg.Draw()
canvas.SaveAs( '%s/%s_shapeComparison.%s' % ( self.outputFolder, hist, self.outputFormat ) )
