| 1 |
dhidas |
1.1 |
from __future__ import division
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| 2 |
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| 3 |
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from HistGetter import *
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| 4 |
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from tdrStyle import *
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| 5 |
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from ROOT import *
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| 6 |
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from math import pow, exp, sqrt
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from copy import deepcopy
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from array import array
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| 10 |
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class QCDEstimator:
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| 11 |
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luminosity = 36.145#pb-1
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| 12 |
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mc_luminosity = 36.145#pb-1
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| 13 |
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luminosity_unit = 'pb-1'
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| 14 |
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scale = luminosity / mc_luminosity
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| 15 |
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jetBins = ['0jet', 'allJets', '1jet', '1orMoreJets', '2jets', '2orMoreJets', '3jets', '3orMoreJets', '4orMoreJets']
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| 16 |
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jetBinsLatex = {'0jet':'0 jet', 'allJets':'#geq 0 jets', '1jet':'1 jet', '1orMoreJets':'#geq 1 jet',
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| 17 |
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'2jets':'2 jets', '2orMoreJets':'#geq 2 jets', '3jets':'3 jets', '3orMoreJets':'#geq 3 jets',
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'4orMoreJets':'#geq 4 jets'}
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| 19 |
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binWidth = 0.01
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| 20 |
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rebin = 10
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| 21 |
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fitRangesClosureTest = [ ( 0.1, 0.9 ), ( 0.1, 1.0 ), ( 0.1, 1.1 ),
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( 0.2, 0.9 ), ( 0.2, 1.0 ), ( 0.2, 1.1 ),
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( 0.3, 0.9 ), ( 0.3, 1.0 ), ( 0.3, 1.1 )]
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| 24 |
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| 25 |
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fitRangesEstimation = [ ( 0.1, 1.1 ), ( 0.2, 1.1 )]#, ( 0.3, 1.1 )]
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| 26 |
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signalRegion = ( 0, 0.1 )
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| 27 |
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maxValue = 1.6
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| 28 |
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pfIsoHistogramPrefix = 'QCDest_PFIsolation_WithMETCutAndAsymJetCuts_'
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| 29 |
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pfIsoControlRegionHistogramPrefix = 'QCDest_PFIsolation_controlRegion2_WithMETCutAndAsymJetCuts_'
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relIsoHistogramPrefix = 'QCDest_CombRelIso_'
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| 31 |
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pfIsoResults = {}
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relIsoResults = {}
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allPfIsoResults = {}
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allRelIsoResults = {}
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useEntryAsData = 'data'
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constrainFit = False
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numberOfFreeParameters = 0
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| 39 |
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| 40 |
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currentFitRange = ( 0.1, 1.6 )
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| 41 |
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currentFitFuntion = 'gaus'
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| 42 |
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currentJetBin = jetBins[-1]
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| 43 |
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| 44 |
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outputFormat = 'pdf'
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| 45 |
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outputFolder = ''
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| 46 |
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def __init__( self, files ):
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self.files = files
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HistGetter.samplefiles = files
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self.histograms = {}
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self.histGetter = HistGetter()
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self.histGetter.setStyle()
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self.getHistograms()
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self.applyStyleAndCreateStack()
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for bin in self.jetBins:
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self.pfIsoResults[bin] = {'actualNumberOfQCDEvents': 0, 'estimatedNumberOfQCDEvents':0,
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'fitFunction': None, 'fitParameters': {}, 'numberOfAllDataEvents':0,
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| 59 |
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'numberOfAllMCEvents':0}
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| 60 |
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self.relIsoResults[bin] = {'actualNumberOfQCDEvents': 0, 'estimatedNumberOfQCDEvents':0,
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'fitFunction': None, 'fitParameters': {}, 'numberOfAllDataEvents':0,
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'numberOfAllMCEvents':0}
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| 63 |
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def getHistograms( self ):
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relIsoHists = [self.relIsoHistogramPrefix + jetbin for jetbin in self.jetBins]
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pfIsoHists = [self.pfIsoHistogramPrefix + jetbin for jetbin in self.jetBins]
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pfIsoControlHists = [self.pfIsoControlRegionHistogramPrefix + jetbin for jetbin in self.jetBins]
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allHists = relIsoHists
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allHists.extend( pfIsoHists )
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allHists.extend( pfIsoControlHists )
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HistGetter.hists = allHists
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| 73 |
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self.histograms = self.histGetter.getHistsFromFiles()
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self.histograms = self.histGetter.addSampleSum( self.histograms )
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def applyStyleAndCreateStack( self ):
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samplesOfInterest = ['data', 'qcd', 'zjets', 'wjets', 'singleTop', 'ttbar', 'allMC']
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colors = {'ttbar' : kRed + 1,
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'wjets' : kGreen - 3,
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'zjets' : kAzure - 2,
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'qcd' : kYellow,
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'singleTop' : kMagenta}
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| 83 |
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| 84 |
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mcStack = {}
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| 85 |
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| 86 |
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for sample in samplesOfInterest:#sample
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for histname in self.histograms[sample].keys():
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self.histograms[sample][histname].Rebin( self.rebin )
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if not sample in ['data', 'allMC']:
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| 90 |
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self.histograms[sample][histname].Scale( self.scale )
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| 91 |
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self.histograms[sample][histname].SetFillStyle( 1001 )
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| 92 |
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self.histograms[sample][histname].SetFillColor( colors[sample] )
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| 93 |
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if not mcStack.has_key( histname ):
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mcStack[histname] = THStack( "MC_" + histname, "MC_" + histname );
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mcStack[histname].Add( self.histograms[sample][histname] )
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else:
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self.histograms[sample][histname].SetMarkerStyle( 8 );
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| 98 |
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| 99 |
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| 100 |
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self.histograms['MCStack'] = mcStack
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| 101 |
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self.setStyle()
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| 102 |
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print "=" * 40
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| 103 |
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print "data integrated luminosity:", self.luminosity, self.luminosity_unit
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| 104 |
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print "MC integrated luminosity:", self.mc_luminosity, self.luminosity_unit
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| 105 |
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print "MC scale factor: ", self.scale
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| 106 |
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print '=' * 40
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| 107 |
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| 108 |
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| 109 |
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def setStyle( self ):
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| 110 |
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tdrstyle = setTDRStyle();
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| 111 |
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tdrstyle.SetPadRightMargin( 0.05 )#originally was 0.02, too narrow!
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| 112 |
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tdrStyle.SetStatH( 0.2 );
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| 113 |
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tdrStyle.SetOptStat( 0 );#off title
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| 114 |
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tdrStyle.SetOptFit( 0 );#off title
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| 115 |
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tdrStyle.cd();
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| 116 |
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gROOT.ForceStyle();
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| 117 |
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gStyle.SetTitleH( 0.1 );
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| 118 |
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gStyle.SetStatH( 0.22 ); #0.24);
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| 119 |
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gStyle.SetStatW( 0.22 ); #0.26);
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| 120 |
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gStyle.SetOptStat( 1 ); #on stat
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| 121 |
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gStyle.SetLineScalePS( 2 ); #D=3
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| 122 |
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gStyle.SetOptFit( 112 );
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| 123 |
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| 124 |
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| 125 |
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def doClosureTests( self, function = 'gaus' ):
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self.useEntryAsData = 'allMC'
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| 127 |
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self.currentFitFuntion = function
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| 128 |
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| 129 |
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for fitRange in self.fitRangesClosureTest:
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| 130 |
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self.currentFitRange = fitRange
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| 131 |
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for bin in self.jetBins:
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| 132 |
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self.currentJetBin = bin
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| 133 |
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self.EstimateJetBin( bin )
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| 134 |
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self.plot( self.pfIsoHistogramPrefix + bin, self.pfIsoResults[bin] )
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| 135 |
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self.plot( self.relIsoHistogramPrefix + bin, self.relIsoResults[bin] )
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| 136 |
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self.allPfIsoResults['%1.1f-%1.1f' % fitRange] = deepcopy( self.pfIsoResults )
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| 137 |
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self.allRelIsoResults['%1.1f-%1.1f' % fitRange] = deepcopy( self.relIsoResults )
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| 138 |
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self.plotClosureTest( self.pfIsoHistogramPrefix, self.allPfIsoResults )
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| 139 |
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self.plotClosureTest( self.relIsoHistogramPrefix, self.allRelIsoResults )
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| 140 |
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| 141 |
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| 142 |
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def doEstimate( self, function = 'gaus' ):
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| 143 |
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self.useEntryAsData = 'data'
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| 144 |
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self.currentFitFuntion = function
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| 145 |
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| 146 |
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for fitRange in self.fitRangesEstimation:
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| 147 |
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self.currentFitRange = fitRange
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| 148 |
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for bin in self.jetBins:
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self.currentJetBin = bin
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| 150 |
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self.EstimateJetBin( bin )
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| 151 |
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self.plot( self.pfIsoHistogramPrefix + bin, self.pfIsoResults[bin] )
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| 152 |
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self.plot( self.relIsoHistogramPrefix + bin, self.relIsoResults[bin] )
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| 153 |
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self.allPfIsoResults['%1.1f-%1.1f' % fitRange] = deepcopy( self.pfIsoResults )
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| 154 |
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self.allRelIsoResults['%1.1f-%1.1f' % fitRange] = deepcopy( self.relIsoResults )
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| 155 |
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| 156 |
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def EstimateJetBin( self, jetbin ):
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| 157 |
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# fitRange = self.currentFitRange
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| 158 |
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function = self.currentFitFuntion
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| 159 |
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| 160 |
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QCD = self.histograms['qcd']
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| 161 |
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data = self.histograms[self.useEntryAsData]
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| 162 |
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allMC = self.histograms['allMC']
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| 163 |
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pfIsoHist = self.pfIsoHistogramPrefix + jetbin
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| 164 |
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relIsoHist = self.relIsoHistogramPrefix + jetbin
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| 165 |
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| 166 |
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self.pfIsoResults[jetbin]['actualNumberOfQCDEvents'] = QCD[pfIsoHist].GetBinContent(1)
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| 167 |
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self.pfIsoResults[jetbin]['numberOfAllDataEvents'] = data[pfIsoHist].GetBinContent(1)
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| 168 |
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self.pfIsoResults[jetbin]['numberOfAllMCEvents'] = allMC[pfIsoHist].GetBinContent(1)
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| 169 |
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| 170 |
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pfIsoFit = self.doFit( data[pfIsoHist] )
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| 171 |
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self.pfIsoResults[jetbin]['fitFunction'] = pfIsoFit
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| 172 |
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self.pfIsoResults[jetbin]['fitParameters'] = self.getFitParameters( pfIsoFit )
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| 173 |
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estimate = 0
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| 174 |
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if pfIsoFit:
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| 175 |
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estimate = pfIsoFit.Integral( self.signalRegion[0], self.signalRegion[1] ) / ( self.binWidth * self.rebin )
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| 176 |
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| 177 |
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self.pfIsoResults[jetbin]['estimatedNumberOfQCDEvents'] = estimate
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| 178 |
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| 179 |
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#----------------------------------------------------------------------------------------------------------------------
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| 180 |
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self.relIsoResults[jetbin]['actualNumberOfQCDEvents'] = QCD[relIsoHist].GetBinContent(1)
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| 181 |
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self.relIsoResults[jetbin]['numberOfAllDataEvents'] = data[relIsoHist].GetBinContent(1)
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| 182 |
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self.relIsoResults[jetbin]['numberOfAllMCEvents'] = allMC[relIsoHist].GetBinContent(1)
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| 183 |
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| 184 |
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relIsoFit = self.doFit( data[relIsoHist] )
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| 185 |
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self.relIsoResults[jetbin]['fitFunction'] = relIsoFit
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| 186 |
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self.relIsoResults[jetbin]['fitParameters'] = self.getFitParameters( relIsoFit )
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| 187 |
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| 188 |
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estimate = relIsoFit.Integral( self.signalRegion[0], self.signalRegion[1] ) / ( self.binWidth * self.rebin )
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| 189 |
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self.relIsoResults[jetbin]['estimatedNumberOfQCDEvents'] = estimate
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| 190 |
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| 191 |
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| 192 |
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| 193 |
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def doFit( self, histogram ):
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| 194 |
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function = self.currentFitFuntion
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| 195 |
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fitRange = self.currentFitRange
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| 196 |
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| 197 |
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if not self.constrainFit:
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| 198 |
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histogram.Fit( function, "Q0", "ah", fitRange[0], fitRange[1] )
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| 199 |
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| 200 |
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else:
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| 201 |
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ff = TF1( function, function, 0, 1 );
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| 202 |
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self.numberOfFreeParameters = ff.GetNumberFreeParameters();
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| 203 |
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| 204 |
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return histogram.GetFunction( function )
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| 205 |
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| 206 |
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def plot( self, histname, results ):
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| 207 |
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data = self.histograms[self.useEntryAsData][histname]
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| 208 |
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mcStack = self.histograms['MCStack'][histname]
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| 209 |
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fitFunction = results['fitFunction']
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| 210 |
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if not fitFunction:
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| 211 |
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print 'no fitfunction found'
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| 212 |
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return;
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| 213 |
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| 214 |
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data.GetXaxis().SetRangeUser( 0, self.maxValue - 0.01 );
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| 215 |
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fitFunction.SetLineColor( kRed );
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| 216 |
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fitFunction.SetLineWidth( 2 )
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| 217 |
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| 218 |
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fitFunction2 = fitFunction.Clone()
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| 219 |
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fitFunction2.SetLineColor( kBlue );
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| 220 |
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fitFunction2.SetRange( self.signalRegion[0], self.signalRegion[1] );
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| 221 |
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| 222 |
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fitFunction3 = fitFunction.Clone()
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| 223 |
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fitFunction3.SetLineColor( kBlue );
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| 224 |
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fitFunction3.SetLineStyle( kDashed );
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| 225 |
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fitFunction3.SetRange( self.signalRegion[1], self.currentFitRange[0] );
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| 226 |
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| 227 |
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canvas = TCanvas( "c1", "Iso fit", 1920, 1080 )
|
| 228 |
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data.Draw();
|
| 229 |
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| 230 |
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max = 0
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| 231 |
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if mcStack.GetMaximum() > data.GetBinContent( 1 ):
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| 232 |
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max = mcStack.GetMaximum()*1.1
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| 233 |
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else:
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| 234 |
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max = data.GetBinContent( 1 ) * 1.1
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| 235 |
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| 236 |
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data.GetYaxis().SetRangeUser( 0, max );
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| 237 |
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data.SetXTitle( "Relative Isolation" );
|
| 238 |
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data.SetYTitle( "Events/0.1" );
|
| 239 |
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# draw mc
|
| 240 |
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mcStack.Draw( "hist same" );
|
| 241 |
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data.Draw( "ae same" );
|
| 242 |
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| 243 |
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fitFunction.Draw( "same" );
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| 244 |
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fitFunction2.Draw( "same" );
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| 245 |
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fitFunction3.Draw( "same" );
|
| 246 |
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|
| 247 |
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label = self.add_cms_label( self.currentJetBin )
|
| 248 |
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label.Draw()
|
| 249 |
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|
| 250 |
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legend = self.add_legend( histname )
|
| 251 |
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# legend.Draw()
|
| 252 |
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|
| 253 |
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if self.currentFitFuntion == "pol1":
|
| 254 |
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out = "%s_fit_linear_from_0%.0f_%s" % ( histname, self.currentFitRange[0] * 10.0, self.useEntryAsData );
|
| 255 |
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else:
|
| 256 |
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out = "%s_fit_%s_from_%1.1f_to_%1.1f_%s" % ( histname, self.currentFitFuntion, self.currentFitRange[0],
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| 257 |
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self.currentFitRange[1], self.useEntryAsData );
|
| 258 |
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# if self.outputFormat == 'pdf':
|
| 259 |
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# canvas.SaveAs( '%s.eps' % out );
|
| 260 |
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# gROOT.ProcessLine( ".!ps2pdf -dEPSCrop %s.eps" % out );
|
| 261 |
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# gROOT.ProcessLine( ".!rm -f %s.eps" % out );
|
| 262 |
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# else:
|
| 263 |
|
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canvas.SaveAs( '%s/%s.%s' % ( self.outputFolder, out, self.outputFormat ) )
|
| 264 |
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|
| 265 |
|
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canvas.Close(); #crucial!
|
| 266 |
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|
| 267 |
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|
| 268 |
|
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def plotClosureTest( self, histname, results ):
|
| 269 |
|
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c2 = TCanvas( "c2", "QCD estimates", 1080, 1080 );
|
| 270 |
|
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x = array( 'd', [2, 3, 4] )
|
| 271 |
|
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jetBinsOfInterest = [#'1jet',
|
| 272 |
|
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'2jets', '3jets', '4orMoreJets']
|
| 273 |
|
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function = self.currentFitFuntion
|
| 274 |
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|
| 275 |
|
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gStyle.SetMarkerSize( 1.7 );
|
| 276 |
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gStyle.SetMarkerStyle( 20 );
|
| 277 |
|
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c2.SetTopMargin( 0.1 );
|
| 278 |
|
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# c2.SetLeftMargin( 0.12 );
|
| 279 |
|
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c2.SetRightMargin( 0.35 );
|
| 280 |
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|
| 281 |
|
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y = {}
|
| 282 |
|
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for fitRange in self.fitRangesClosureTest:
|
| 283 |
|
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range = '%1.1f-%1.1f' % fitRange
|
| 284 |
|
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y[range] = []
|
| 285 |
|
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for bin in jetBinsOfInterest:
|
| 286 |
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|
| 287 |
|
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est = results[range][bin]['estimatedNumberOfQCDEvents']
|
| 288 |
|
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true = results[range][bin]['actualNumberOfQCDEvents']
|
| 289 |
|
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variation = 0
|
| 290 |
|
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if not true == 0:
|
| 291 |
|
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variation = ( est - true ) / true
|
| 292 |
|
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y[range].append( variation )
|
| 293 |
|
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nbins = 3
|
| 294 |
|
|
gr1 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[0]] ) )
|
| 295 |
|
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gr2 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[1]] ) )
|
| 296 |
|
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gr3 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[2]] ) )
|
| 297 |
|
|
gr4 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[3]] ) )
|
| 298 |
|
|
gr5 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[4]] ) )
|
| 299 |
|
|
gr6 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[5]] ) )
|
| 300 |
|
|
gr7 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[6]] ) )
|
| 301 |
|
|
gr8 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[7]] ) )
|
| 302 |
|
|
gr9 = TGraph( nbins, x, array( 'd', y['%1.1f-%1.1f' % self.fitRangesClosureTest[8]] ) )
|
| 303 |
|
|
|
| 304 |
|
|
gr1.SetMarkerColor( kGreen + 1 );
|
| 305 |
|
|
gr2.SetMarkerColor( kGreen + 2 );
|
| 306 |
|
|
gr3.SetMarkerColor( kGreen + 3 );
|
| 307 |
|
|
gr4.SetMarkerColor( kAzure + 7 );
|
| 308 |
|
|
gr5.SetMarkerColor( kAzure - 3 );
|
| 309 |
|
|
gr6.SetMarkerColor( kBlue );
|
| 310 |
|
|
gr7.SetMarkerColor( kOrange );
|
| 311 |
|
|
gr8.SetMarkerColor( kOrange - 1 );
|
| 312 |
|
|
gr9.SetMarkerColor( kOrange - 6 );
|
| 313 |
|
|
|
| 314 |
|
|
gStyle.SetTitleW( 0.9 );
|
| 315 |
|
|
gStyle.SetTitleH( 0.05 )
|
| 316 |
|
|
|
| 317 |
|
|
h = None
|
| 318 |
|
|
if function == "gaus":
|
| 319 |
|
|
h = TH1D( "h", "Variation of QCD estimates with fit range (Gaussian)", 4, 0.5, 4.5 );
|
| 320 |
|
|
elif function == "pol3":
|
| 321 |
|
|
h = TH1D( "h", "Variation of QCD estimates with fit range (Pol3)", 4, 0.5, 4.5 );
|
| 322 |
|
|
elif function == "pol1":
|
| 323 |
|
|
h = TH1D( "h", "Variation of QCD estimates with fit range (Pol1)", 4, 0.5, 4.5 );
|
| 324 |
|
|
elif function == "landau":
|
| 325 |
|
|
h = TH1D( "h", "Variation of QCD estimates with fit range (Landau)", 4, 0.5, 4.5 );
|
| 326 |
|
|
|
| 327 |
|
|
h.SetStats( kFALSE ); # no statistics
|
| 328 |
|
|
h.Draw();
|
| 329 |
|
|
h.SetYTitle( "Deviation = (N_{QCD,est}-N_{QCD,true})/N_{QCD,true}" );
|
| 330 |
|
|
h.GetYaxis().SetRangeUser( -1, 1 );
|
| 331 |
|
|
h.GetXaxis().SetRangeUser( 1.5, 5.5 );
|
| 332 |
|
|
# h.GetXaxis().SetBinLabel( 1, "1j" );
|
| 333 |
|
|
h.GetXaxis().SetBinLabel( 2, "2j" );
|
| 334 |
|
|
h.GetXaxis().SetBinLabel( 3, "3j" );
|
| 335 |
|
|
h.GetXaxis().SetBinLabel( 4, "#geq4j" );
|
| 336 |
|
|
h.GetXaxis().SetLabelSize( 0.07 );
|
| 337 |
|
|
h.GetYaxis().SetTitleOffset( 1.3 );
|
| 338 |
|
|
|
| 339 |
|
|
gr1.Draw( "P" );
|
| 340 |
|
|
gr2.Draw( "P" ); #to superimpose graphs, do not re-draw axis
|
| 341 |
|
|
gr3.Draw( "P" );
|
| 342 |
|
|
gr4.Draw( "P" );
|
| 343 |
|
|
gr5.Draw( "P" );
|
| 344 |
|
|
gr6.Draw( "P" );
|
| 345 |
|
|
gr7.Draw( "P" );
|
| 346 |
|
|
gr8.Draw( "P" );
|
| 347 |
|
|
gr9.Draw( "P" );
|
| 348 |
|
|
|
| 349 |
|
|
c2.SetGrid( 1, 1 );
|
| 350 |
|
|
|
| 351 |
|
|
leg = TLegend( 0.65, 0.1, 0.98, 0.9 );
|
| 352 |
|
|
leg.SetFillColor( 0 );
|
| 353 |
|
|
leg.AddEntry( gr1, "Free: 0.1-0.9", "p" );
|
| 354 |
|
|
leg.AddEntry( gr2, "Free: 0.1-1.0", "p" );
|
| 355 |
|
|
leg.AddEntry( gr3, "Free: 0.1-1.1", "p" );
|
| 356 |
|
|
leg.AddEntry( gr4, "Free: 0.2-0.9", "p" );
|
| 357 |
|
|
leg.AddEntry( gr5, "Free: 0.2-1.0", "p" );
|
| 358 |
|
|
leg.AddEntry( gr6, "Free: 0.2-1.1", "p" );
|
| 359 |
|
|
leg.AddEntry( gr7, "Free: 0.3-0.9", "p" );
|
| 360 |
|
|
leg.AddEntry( gr8, "Free: 0.3-1.0", "p" );
|
| 361 |
|
|
leg.AddEntry( gr9, "Free: 0.3-1.1", "p" );
|
| 362 |
|
|
|
| 363 |
|
|
leg.Draw();
|
| 364 |
|
|
|
| 365 |
|
|
c2.SaveAs( "%s/%s_qcd_estimate_%s.%s" % ( self.outputFolder, histname, function, self.outputFormat ) )
|
| 366 |
|
|
|
| 367 |
|
|
setRange = h.GetYaxis().SetRangeUser
|
| 368 |
|
|
saveAs = c2.SaveAs
|
| 369 |
|
|
for limit in [1, 2, 3, 6, 8]:
|
| 370 |
|
|
setRange( -limit, limit );
|
| 371 |
|
|
saveAs( "%s/%s_qcd_estimate_%s_zoom_%d.%s" % ( self.outputFolder, histname, function, limit, self.outputFormat ) );
|
| 372 |
|
|
|
| 373 |
|
|
def getFitParameters( self, fitFunction ):
|
| 374 |
|
|
fitParameters = {'chi2':-1, 'numberOfdegreesOfFreedom': 0, 'constrain1': 0, 'constrain2': 0,
|
| 375 |
|
|
'constrain3': 0, 'constrain4': 0}
|
| 376 |
|
|
if fitFunction:
|
| 377 |
|
|
fitParameters['chi2'] = fitFunction.GetChisquare()
|
| 378 |
|
|
fitParameters['numberOfdegreesOfFreedom'] = fitFunction.GetNDF()
|
| 379 |
|
|
return fitParameters
|
| 380 |
|
|
|
| 381 |
|
|
def doConstrainedFit( self, histogram, function = 'gaus', limits = ( 0.1, 1.6 ) ):
|
| 382 |
|
|
fitFunction = None
|
| 383 |
|
|
if function == 'gaus':
|
| 384 |
|
|
fitFunction = TF1( "gaus", "gaus", 0, 2 );
|
| 385 |
|
|
elif function == 'pol3':
|
| 386 |
|
|
fitFunction = TF1( "pol3", "[0] * ( 1 + [1]*x + [2]*x^2 + [3]*x^3 )", 0, 2 );
|
| 387 |
|
|
elif function == 'landau':
|
| 388 |
|
|
fitFunction = TF1( "landau", "landau", 0, 2 )
|
| 389 |
|
|
|
| 390 |
|
|
|
| 391 |
|
|
myFitResult = data.Fit( function, "Q0", "ah", limits[0], limits[1] );
|
| 392 |
|
|
|
| 393 |
|
|
def add_cms_label( self, njet = "" ):
|
| 394 |
|
|
|
| 395 |
|
|
mytext = TPaveText( 0.3, 0.8, 0.6, 0.93, "NDC" );
|
| 396 |
|
|
mytext.AddText( "CMS Preliminary" );
|
| 397 |
|
|
mytext.AddText( "%.1f pb^{-1} at #sqrt{s} = 7 TeV" % self.luminosity );
|
| 398 |
|
|
if njet != "":
|
| 399 |
|
|
mytext.AddText( "e+jets, %s" % self.jetBinsLatex[njet] )
|
| 400 |
|
|
mytext.SetFillStyle( 0 );
|
| 401 |
|
|
mytext.SetBorderSize( 0 );
|
| 402 |
|
|
mytext.SetTextFont( 42 );
|
| 403 |
|
|
mytext.SetTextAlign( 13 );
|
| 404 |
|
|
return mytext
|
| 405 |
|
|
|
| 406 |
|
|
def add_legend( self, histname ):
|
| 407 |
|
|
function = self.currentFitFuntion
|
| 408 |
|
|
|
| 409 |
|
|
tt = self.histograms['ttbar'][histname]
|
| 410 |
|
|
wj = self.histograms['wjets'][histname]
|
| 411 |
|
|
zj = self.histograms['zjets'][histname]
|
| 412 |
|
|
data = self.histograms['data'][histname]
|
| 413 |
|
|
QCD = self.histograms['qcd'][histname]
|
| 414 |
|
|
stop = self.histograms['singleTop'][histname]
|
| 415 |
|
|
|
| 416 |
|
|
leg = TLegend( 0.64, 0.4, 0.9, 0.9 );
|
| 417 |
|
|
leg.SetFillStyle( 0 );
|
| 418 |
|
|
leg.SetBorderSize( 0 );
|
| 419 |
|
|
leg.SetTextFont( 42 );
|
| 420 |
|
|
|
| 421 |
|
|
# Here I define coloured lines for use in the legend
|
| 422 |
|
|
blue = TF1( "blue", "pol0", 0, 1 );
|
| 423 |
|
|
red = TF1( "red", "pol0", 0, 1 );
|
| 424 |
|
|
|
| 425 |
|
|
blue.SetLineColor( kBlue );
|
| 426 |
|
|
red.SetLineColor( kRed );
|
| 427 |
|
|
|
| 428 |
|
|
red.SetLineWidth( 2 );
|
| 429 |
|
|
blue.SetLineWidth( 2 );
|
| 430 |
|
|
|
| 431 |
|
|
blue.SetLineStyle( kDashed );
|
| 432 |
|
|
|
| 433 |
|
|
# Add entry to legend
|
| 434 |
|
|
if self.useEntryAsData == 'data':
|
| 435 |
|
|
leg.AddEntry( data, "Data", "LP" );
|
| 436 |
|
|
else:
|
| 437 |
|
|
leg.AddEntry( data, "All MC events", "LP" );
|
| 438 |
|
|
if function == "pol1":
|
| 439 |
|
|
leg.AddEntry( red, "Linear Fit", "l" );
|
| 440 |
|
|
elif function == "expo":
|
| 441 |
|
|
leg.AddEntry( red, "Exponenetial Fit", "l" );
|
| 442 |
|
|
elif function == "gaus":
|
| 443 |
|
|
leg.AddEntry( red, "Gaussian Fit", "l" );
|
| 444 |
|
|
|
| 445 |
|
|
leg.AddEntry( blue, "Extrapolation", "l" );
|
| 446 |
|
|
leg.AddEntry( tt, "t#bar{t}", "F" );
|
| 447 |
|
|
leg.AddEntry( stop, "Single-Top", "F" );
|
| 448 |
|
|
leg.AddEntry( wj, "W#rightarrowl#nu", "F" );
|
| 449 |
|
|
leg.AddEntry( zj, "Z/#gamma*#rightarrowl^{+}l^{-}", "F" );
|
| 450 |
|
|
leg.AddEntry( QCD, "QCD & #gamma+jets", "F" );
|
| 451 |
|
|
leg.Draw()
|
| 452 |
|
|
return ( leg, red, blue )
|
| 453 |
|
|
|
| 454 |
|
|
def printResults( self, results ):
|
| 455 |
|
|
self.printJetBinResults( results, '3jets' )
|
| 456 |
|
|
print '=' * 60
|
| 457 |
|
|
self.printJetBinResults( results, '3orMoreJets' )
|
| 458 |
|
|
print '=' * 60
|
| 459 |
|
|
self.printJetBinResults( results, '4orMoreJets' )
|
| 460 |
|
|
print '=' * 60
|
| 461 |
|
|
|
| 462 |
|
|
|
| 463 |
|
|
def printJetBinResults( self, results, jetBin ):
|
| 464 |
|
|
estimate = 0
|
| 465 |
|
|
estimate2 = 0
|
| 466 |
|
|
predicted = results[results.keys()[0]][jetBin]['actualNumberOfQCDEvents']
|
| 467 |
|
|
allData = results[results.keys()[0]][jetBin]['numberOfAllDataEvents']
|
| 468 |
|
|
allMC = results[results.keys()[0]][jetBin]['numberOfAllMCEvents']
|
| 469 |
|
|
|
| 470 |
|
|
if jetBin == '4orMoreJets':
|
| 471 |
|
|
print 'Estimation for >= 4 jets'
|
| 472 |
|
|
elif jetBin == '3orMoreJets':
|
| 473 |
|
|
print 'Estimation for >= 3 jets'
|
| 474 |
|
|
elif jetBin == '3jets':
|
| 475 |
|
|
print 'Estimation for == 3 jets'
|
| 476 |
|
|
print 'predicted number of QCD events', predicted
|
| 477 |
|
|
for fitRange in self.fitRangesEstimation:
|
| 478 |
|
|
range = '%1.1f-%1.1f' % fitRange
|
| 479 |
|
|
est = results[range][jetBin]['estimatedNumberOfQCDEvents']
|
| 480 |
|
|
true = results[range][jetBin]['actualNumberOfQCDEvents']
|
| 481 |
|
|
variation = ( est - true ) / true
|
| 482 |
|
|
estimate += est
|
| 483 |
|
|
estimate2 += est * est
|
| 484 |
|
|
|
| 485 |
|
|
|
| 486 |
|
|
print
|
| 487 |
|
|
print 'estimated number of QCD events'
|
| 488 |
|
|
print 'for range', range, ': ',
|
| 489 |
|
|
print est
|
| 490 |
|
|
print
|
| 491 |
|
|
mean = estimate / len( self.fitRangesEstimation )
|
| 492 |
|
|
mean2 = estimate2 / len( self.fitRangesEstimation )
|
| 493 |
|
|
error = sqrt( ( mean2 - mean * mean ) / len( self.fitRangesEstimation ) )
|
| 494 |
|
|
print 'average estimate', mean, '+-', error
|
| 495 |
|
|
weight = estimate / len( self.fitRangesEstimation ) / predicted
|
| 496 |
|
|
print 'average weight factor', weight
|
| 497 |
|
|
print 'Total number of data in signal bin(<0.1)', allData
|
| 498 |
|
|
print 'Total number of MC in signal bin(<0.1) before reweighting QCD', allMC
|
| 499 |
|
|
print 'Total number of MC in signal bin(<0.1) after reweighting QCD', ( allMC - predicted ) + predicted * weight
|
| 500 |
|
|
|
| 501 |
|
|
def plotControlRegionComparison( self ):
|
| 502 |
|
|
for bin in self.jetBins:
|
| 503 |
|
|
hist = self.pfIsoHistogramPrefix + bin
|
| 504 |
|
|
histControl = self.pfIsoControlRegionHistogramPrefix + bin
|
| 505 |
|
|
QCD_control = self.histograms['qcd'][histControl]
|
| 506 |
|
|
QCD = self.histograms['qcd'][hist]
|
| 507 |
|
|
nQCD_Control = QCD_control.Integral()
|
| 508 |
|
|
nQCD = QCD.Integral()
|
| 509 |
|
|
if nQCD_Control > 0:
|
| 510 |
|
|
QCD_control.Scale( 1 / nQCD_Control )
|
| 511 |
|
|
if nQCD > 0:
|
| 512 |
|
|
QCD.Scale( 1 / nQCD )
|
| 513 |
|
|
QCD_control.SetFillStyle( 3004 )
|
| 514 |
|
|
QCD.GetXaxis().SetRangeUser( 0., self.maxValue - 0.01 )
|
| 515 |
|
|
|
| 516 |
|
|
max = 0
|
| 517 |
|
|
if QCD.GetMaximum() > QCD_control.GetMaximum():
|
| 518 |
|
|
max = QCD.GetMaximum()*1.1
|
| 519 |
|
|
else:
|
| 520 |
|
|
max = QCD_control.GetMaximum()*1.1
|
| 521 |
|
|
|
| 522 |
|
|
QCD.GetYaxis().SetRangeUser( 0., max )
|
| 523 |
|
|
|
| 524 |
|
|
canvas = TCanvas( "c1", "Shape comparision", 1920, 1080 )
|
| 525 |
|
|
QCD.Draw()
|
| 526 |
|
|
QCD_control.Draw( 'same' )
|
| 527 |
|
|
label = self.add_cms_label( bin )
|
| 528 |
|
|
label.Draw()
|
| 529 |
|
|
|
| 530 |
|
|
leg = TLegend( 0.64, 0.4, 0.9, 0.9 );
|
| 531 |
|
|
leg.SetFillStyle( 0 );
|
| 532 |
|
|
leg.SetBorderSize( 0 );
|
| 533 |
|
|
leg.SetTextFont( 42 );
|
| 534 |
|
|
leg.AddEntry( QCD_control, 'QCD control region' )
|
| 535 |
|
|
leg.AddEntry( QCD, 'QCD standard selection' )
|
| 536 |
|
|
leg.Draw()
|
| 537 |
|
|
|
| 538 |
|
|
canvas.SaveAs( '%s/%s_shapeComparison.%s' % ( self.outputFolder, hist, self.outputFormat ) )
|
| 539 |
|
|
|
| 540 |
|
|
if __name__ == '__main__':
|
| 541 |
|
|
gROOT.SetBatch( True )
|
| 542 |
|
|
gROOT.ProcessLine( 'gErrorIgnoreLevel = 1001;' )
|
| 543 |
|
|
|
| 544 |
|
|
path = '/storage/results/outputfiles/Preapproval2/'
|
| 545 |
|
|
files = {'data': path + "data_36.145pb_PFElectron_PF2PATJets_PFMET.root",
|
| 546 |
|
|
'ttbar' : path + "ttjet_36.145pb_PFElectron_PF2PATJets_PFMET.root",
|
| 547 |
|
|
'wjets' : path + "wj_36.145pb_PFElectron_PF2PATJets_PFMET.root",
|
| 548 |
|
|
'zjets' : path + "zj_36.145pb_PFElectron_PF2PATJets_PFMET.root",
|
| 549 |
|
|
'bce1' : path + "bce1_36.145pb_PFElectron_PF2PATJets_PFMET.root",
|
| 550 |
|
|
'bce2' : path + "bce2_36.145pb_PFElectron_PF2PATJets_PFMET.root",
|
| 551 |
|
|
'bce3' : path + "bce3_36.145pb_PFElectron_PF2PATJets_PFMET.root",
|
| 552 |
|
|
'enri1' : path + "enri1_36.145pb_PFElectron_PF2PATJets_PFMET.root",
|
| 553 |
|
|
'enri2' : path + "enri2_36.145pb_PFElectron_PF2PATJets_PFMET.root",
|
| 554 |
|
|
'enri3' : path + "enri3_36.145pb_PFElectron_PF2PATJets_PFMET.root",
|
| 555 |
|
|
'pj1' : path + "pj1_36.145pb_PFElectron_PF2PATJets_PFMET.root",
|
| 556 |
|
|
'pj2' : path + "pj2_36.145pb_PFElectron_PF2PATJets_PFMET.root",
|
| 557 |
|
|
'pj3' : path + "pj3_36.145pb_PFElectron_PF2PATJets_PFMET.root",
|
| 558 |
|
|
'tW' : path + "tW_36.145pb_PFElectron_PF2PATJets_PFMET.root",
|
| 559 |
|
|
'tchan' : path + "tchan_36.145pb_PFElectron_PF2PATJets_PFMET.root"}
|
| 560 |
|
|
q = QCDEstimator( files )
|
| 561 |
|
|
QCDEstimator.outputFolder = '/storage/results/QCD_Estimate/Preapproval2'
|
| 562 |
|
|
QCDEstimator.outputFormat = 'pdf'
|
| 563 |
|
|
|
| 564 |
|
|
q.doEstimate( 'gaus' )
|
| 565 |
|
|
print '=' * 60
|
| 566 |
|
|
print 'ParticleFlowIsolation results'
|
| 567 |
|
|
q.printResults( q.allPfIsoResults )
|
| 568 |
|
|
# print 'Relative isolation results'
|
| 569 |
|
|
# q.printResults( q.allRelIsoResults )
|
| 570 |
|
|
# print '=' * 60
|
| 571 |
|
|
|
| 572 |
|
|
|
| 573 |
|
|
print 'Starting closure tests'
|
| 574 |
|
|
q.doClosureTests( 'gaus' )
|
| 575 |
|
|
q.plotControlRegionComparison()
|
| 576 |
|
|
|
