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wtagSFfits_N2DDT.py
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wtagSFfits_N2DDT.py
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from optparse import OptionParser
import ROOT
import sys
import os
import time
import math
import CMS_lumi, tdrstyle
from ROOT import *
parser = OptionParser()
parser.add_option('-b', action='store_true', dest='noX', default=False, help='no X11 windows')
parser.add_option('-c', '--channel',action="store",type="string",dest="channel",default="em")
parser.add_option('--HP', action="store", type="float",dest="tau2tau1cutHP",default=0.45)
parser.add_option('--LP', action="store", type="float",dest="tau2tau1cutLP",default=0.75)
parser.add_option('--sample', action="store",type="string",dest="sample",default="powheg")
parser.add_option('--fitTT', action='store_true', dest='fitTT', default=False, help='Only do ttbar fits')
parser.add_option('--fitMC', action='store_true', dest='fitMC', default=False, help='Only do MC fits')
parser.add_option('--doBinned',dest="doBinnedFit", default=False, action="store_true", help="Do binned fit")
parser.add_option('--76X',dest="use76X", default=False, action="store_true", help="Use 76X samples")
parser.add_option('--useDDT',dest="useDDT", default=False, action="store_true", help="Use DDT tagger")
parser.add_option('--useN2DDT',dest="useN2DDT", default=False, action="store_true", help="Use N_2^DDT tagger")
parser.add_option('--usePuppiSD',dest="usePuppiSD", default=False, action="store_true", help="Use PUPPI+softdrop")
(options, args) = parser.parse_args()
#Added these 2 lines - Michael
ROOT.gSystem.Load("/cvmfs/cms.cern.ch/slc6_amd64_gcc491/lcg/roofit/5.34.22-cms3/lib/libRooFitCore.so")
ROOT.gSystem.Load("/cvmfs/cms.cern.ch/slc6_amd64_gcc491/lcg/roofit/5.34.22-cms3/lib/libRooFit.so")
ROOT.gSystem.Load(".//PlotStyle/Util_cxx.so")
ROOT.gSystem.Load(".//PlotStyle/PlotUtils_cxx.so")
ROOT.gSystem.Load(".//PDFs/PdfDiagonalizer_cc.so")
ROOT.gSystem.Load(".//PDFs/HWWLVJRooPdfs_cxx.so")
ROOT.gSystem.Load(".//PDFs/MakePdf_cxx.so")
ROOT.gSystem.Load(".//BiasStudy/BiasUtils_cxx.so")
ROOT.gSystem.Load(".//FitUtils/FitUtils_cxx.so")
tdrstyle.setTDRStyle()
CMS_lumi.lumi_13TeV = "35.8 fb^{-1}"
if options.use76X: CMS_lumi.lumi_13TeV = "35.8 fb^{-1}"
CMS_lumi.writeExtraText = 1
CMS_lumi.extraText = "Preliminary"
CMS_lumi.lumi_sqrtS = "13 TeV" # used with iPeriod = 0, e.g. for simulation-only plots (default is an empty string)
iPos = 11
if( iPos==0 ): CMS_lumi.relPosX = 0.12
iPeriod = 4
gInterpreter.GenerateDictionary("std::map<std::string,std::string>", "map;string;string")
gStyle.SetOptTitle(0)
RooMsgService.instance().setGlobalKillBelow(RooFit.FATAL)
if options.noX: gROOT.SetBatch(True)
def getLegend():
legend = TLegend(0.6010112,0.7183362,0.8202143,0.919833)
legend.SetTextSize(0.032)
legend.SetLineColor(0)
legend.SetShadowColor(0)
legend.SetLineStyle(1)
legend.SetLineWidth(1)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetMargin(0.35)
return legend
def getPavetext():
# addInfo = TPaveText(0.18,0.3,0.25,0.4,"NDC")
addInfo = TPaveText(0.18,0.74,0.25,0.84,"NDC")
addInfo.SetFillColor(0)
addInfo.SetLineColor(0)
addInfo.SetFillStyle(0)
addInfo.SetBorderSize(0)
addInfo.SetTextFont(42)
addInfo.SetTextSize(0.030)
addInfo.SetTextAlign(12)
return addInfo
def drawFrameGetChi2(variable,fitResult,dataset,pdfModel,isData):
wpForPlotting ="%.2f"%options.tau2tau1cutHP
wpForPlotting = wpForPlotting.replace(".","v")
postfix=""
if options.usePuppiSD: postfix = "_PuppiSD"
if options.useDDT: postfix = "_PuppiSD_DDT"
if options.useN2DDT: postfix = "_PuppiSD_N2DDT"
title = "Pruned jet mass (GeV)"
if options.usePuppiSD or options.useDDT or options.useN2DDT: title = "PUPPI Softdrop Jet Mass (GeV)"
frame = variable.frame()
if(isData): dataset.plotOn(frame,RooFit.DataError(RooAbsData.Poisson),RooFit.Name(dataset.GetName ()))
else: dataset.plotOn(frame,RooFit.DataError(RooAbsData.SumW2),RooFit.Name(dataset.GetName ()))
pdfModel.plotOn(frame,RooFit.VisualizeError(fitResult,1), RooFit.Name("Fit error"),RooFit.FillColor(kGray),RooFit.LineColor(kGray)) #,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.LineColor(kBlack),RooFit.Name(pdfModel.GetName())) #,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
chi2 = frame.chiSquare(pdfModel.GetName(), dataset.GetName ())
pdfModel.plotOn(frame,RooFit.Name( "Gaussian 2" ),RooFit.Components("gaus2*"),RooFit.LineStyle(kSolid),RooFit.LineColor(kBlue+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.Name( "ErfExp comp." ),RooFit.Components("model_bkg*"),RooFit.LineStyle(9),RooFit.LineColor(kBlue+2),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.Name( "Gaussian 1" ),RooFit.Components("gaus1*"),RooFit.LineStyle(kDashed),RooFit.LineColor(kRed+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.Name( "Chebyshev comp." ),RooFit.Components("cheb*"),RooFit.LineStyle(kDashed),RooFit.LineColor(kBlue+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
if(isData): dataset.plotOn(frame,RooFit.DataError(RooAbsData.Poisson),RooFit.Name(dataset.GetName ()))
else: dataset.plotOn(frame,RooFit.DataError(RooAbsData.SumW2),RooFit.Name(dataset.GetName ()))
c1 =TCanvas("data_fail","",800,800)
frame.GetYaxis().SetTitleSize(0.05)
frame.GetYaxis().SetTitleOffset(0.90)
frame.SetName("mjjFit")
frame.GetYaxis().SetTitle("Events")
frame.GetXaxis().SetTitle(title)
frame.Draw()
frame.GetYaxis().SetRangeUser(0,1500)
frame.SetMinimum(0.)
legend = getLegend()
if(isData): legend.AddEntry(frame.findObject(dataset.GetName ()),"CMS data","lpe")
else: legend.AddEntry(frame.findObject(dataset.GetName ()),"Total MC","lpe")
legend.AddEntry(frame.findObject(pdfModel.GetName()),"Sim. fit","l")
if frame.findObject("Gaussian 1"):
legend.AddEntry(frame.findObject("Gaussian 1"),frame.findObject("Gaussian 1").GetName(),"l")
if frame.findObject("Chebyshev comp."):
legend.AddEntry(frame.findObject("Chebyshev comp."),frame.findObject("Chebyshev comp.").GetName(),"l")
if frame.findObject("Gaussian 2"):
legend.AddEntry(frame.findObject("Gaussian 2"),frame.findObject("Gaussian 2").GetName(),"l")
if frame.findObject("ErfExp comp."):
legend.AddEntry(frame.findObject("ErfExp comp."),frame.findObject("ErfExp comp.").GetName(),"l")
legend.Draw("same")
CMS_lumi.CMS_lumi(c1, iPeriod, iPos)
addInfo = getPavetext()
addInfo.AddText("#chi^{2}/nDOF = %.3f"%chi2)
addInfo.Draw()
c1.Update()
cname = pdfModel.GetName()
c1.SaveAs("plots/%s_%s.pdf"%(cname,options.sample))
c1.SaveAs("plots/%s_%s.root"%(cname,options.sample))
return chi2
def drawDataAndMC(variable,fitResult,dataset,pdfModel,isData,variable2,fitResult2,dataset2,pdfModel2,isData2):
wpForPlotting ="%.2f"%options.tau2tau1cutHP
wpForPlotting = wpForPlotting.replace(".","v")
postfix=""
if options.usePuppiSD: postfix = "_PuppiSD"
if options.useDDT: postfix = "_PuppiSD_DDT"
if options.useN2DDT: postfix = "_PuppiSD_N2DDT"
title = "Pruned jet mass (GeV)"
if options.usePuppiSD or options.useDDT or options.useN2DDT: title = "PUPPI Softdrop Jet mass (GeV)"
frame = variable.frame()
if(isData): dataset.plotOn(frame,RooFit.DataError(RooAbsData.Poisson),RooFit.Name(dataset.GetName ()))
else: dataset.plotOn(frame,RooFit.DataError(RooAbsData.SumW2),RooFit.Name(dataset.GetName ()))
# pdfModel.plotOn(frame,RooFit.VisualizeError(fitResult,1), RooFit.Name("Fit error"),RooFit.FillColor(kGray),RooFit.LineColor(kGray)) #,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.LineColor(kBlack),RooFit.Name(pdfModel.GetName())) #,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
chi2 = frame.chiSquare(pdfModel.GetName(), dataset.GetName ())
pdfModel.plotOn(frame,RooFit.Name( "Gaussian 2" ),RooFit.Components("gaus2*"),RooFit.LineStyle(kSolid),RooFit.LineColor(kBlue+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.Name( "ErfExp Data comp." ),RooFit.Components("model_bkg*"),RooFit.LineStyle(9),RooFit.LineColor(kBlue+2),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.Name( "Gaussian Data comp." ),RooFit.Components("gaus1*"),RooFit.LineStyle(kDashed),RooFit.LineColor(kRed+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel.plotOn(frame,RooFit.Name( "Chebyshev comp." ),RooFit.Components("cheb*"),RooFit.LineStyle(kDashed),RooFit.LineColor(kBlue+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
if(isData): dataset.plotOn(frame,RooFit.DataError(RooAbsData.Poisson),RooFit.Name(dataset.GetName ()))
else: dataset.plotOn(frame,RooFit.DataError(RooAbsData.SumW2),RooFit.Name(dataset.GetName ()))
if(isData2): dataset2.plotOn(frame,RooFit.DataError(RooAbsData.Poisson),RooFit.Name(dataset2.GetName ()),RooFit.MarkerColor(kBlue),RooFit.LineColor(kBlue))
else: dataset2.plotOn(frame,RooFit.DataError(RooAbsData.SumW2),RooFit.Name(dataset2.GetName ()),RooFit.MarkerColor(kBlue),RooFit.LineColor(kBlue))
# pdfModel2.plotOn(frame,RooFit.VisualizeError(fitResult2,1), RooFit.Name("Fit error"),RooFit.FillColor(kGray),RooFit.LineColor(kGray)) #,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel2.plotOn(frame,RooFit.LineColor(kGreen),RooFit.Name(pdfModel2.GetName())) #,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
chi2_2 = frame.chiSquare(pdfModel2.GetName(), dataset2.GetName ())
pdfModel2.plotOn(frame,RooFit.Name( "Gaussian 2 MC" ),RooFit.Components("gaus2*"),RooFit.LineStyle(kSolid),RooFit.LineColor(kBlue+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel2.plotOn(frame,RooFit.Name( "ErfExp MC comp." ),RooFit.Components("model_bkg*"),RooFit.LineStyle(9),RooFit.LineColor(kMagenta+2),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel2.plotOn(frame,RooFit.Name( "Gaussian MC comp." ),RooFit.Components("gaus1*"),RooFit.LineStyle(kDashed),RooFit.LineColor(kOrange-3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
pdfModel2.plotOn(frame,RooFit.Name( "Chebyshev comp. MC" ),RooFit.Components("cheb*"),RooFit.LineStyle(kDashed),RooFit.LineColor(kBlue+3),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
if(isData2): dataset2.plotOn(frame,RooFit.DataError(RooAbsData.Poisson),RooFit.Name(dataset2.GetName ()),RooFit.MarkerColor(kGreen+3),RooFit.LineColor(kGreen+3))
else: dataset2.plotOn(frame,RooFit.DataError(RooAbsData.SumW2),RooFit.Name(dataset2.GetName ()),RooFit.MarkerColor(kGreen+3),RooFit.LineColor(kGreen+3))
c1 =TCanvas("data_fail","",800,800)
frame.GetYaxis().SetTitleSize(0.05)
frame.GetYaxis().SetTitleOffset(0.90)
frame.SetName("mjjFit")
frame.GetYaxis().SetTitle("Events")
frame.GetXaxis().SetTitle(title)
frame.Draw()
frame.SetMinimum(0.)
legend = getLegend()
if(isData): legend.AddEntry(frame.findObject(dataset.GetName ()),"CMS data","lpe")
else: legend.AddEntry(frame.findObject(dataset.GetName ()),"Total MC","lpe")
legend.AddEntry(frame.findObject(dataset2.GetName ()),"Total MC","lpe")
legend.AddEntry(frame.findObject(pdfModel.GetName()),"Sim. Data fit","l")
legend.AddEntry(frame.findObject(pdfModel2.GetName()),"Sim. MC fit","l")
if frame.findObject("Gaussian Data comp."):
legend.AddEntry(frame.findObject("Gaussian Data comp."),frame.findObject("Gaussian Data comp.").GetName(),"l")
legend.AddEntry(frame.findObject("Gaussian MC comp."),frame.findObject("Gaussian MC comp.").GetName(),"l")
if frame.findObject("Chebyshev comp."):
legend.AddEntry(frame.findObject("Chebyshev comp."),frame.findObject("Chebyshev comp.").GetName(),"l")
if frame.findObject("Gaussian 2"):
legend.AddEntry(frame.findObject("Gaussian Data comp."),frame.findObject("Gaussian Data comp.").GetName(),"l")
legend.AddEntry(frame.findObject("Gaussian MC comp."),frame.findObject("Gaussian MC comp.").GetName(),"l")
if frame.findObject("ErfExp Data comp."):
legend.AddEntry(frame.findObject("ErfExp Data comp."),frame.findObject("ErfExp Data comp.").GetName(),"l")
legend.AddEntry(frame.findObject("ErfExp MC comp."),frame.findObject("ErfExp MC comp.").GetName(),"l")
legend.Draw("same")
CMS_lumi.CMS_lumi(c1, iPeriod, iPos)
addInfo = getPavetext()
addInfo.AddText("Data #chi^{2}/nDOF = %.3f"%chi2)
addInfo.AddText("MC #chi^{2}/nDOF = %.3f"%chi2_2)
addInfo.Draw()
c1.Update()
cname = pdfModel.GetName()
c1.SaveAs("plots/CombinedPlot_%s.pdf"%(cname))
c1.SaveAs("plots/CombinedPlot_%s.root"%(cname))
return chi2
def getSF():
print "Getting W-tagging SF for cut " ,options.tau2tau1cutHP
if options.useDDT:
options.usePuppiSD = True
options.use76X = True
boostedW_fitter_sim = doWtagFits()
def doFitsToMatchedTT():
workspace4fit_ = RooWorkspace("workspace4fit_","workspace4fit_")
ttMC_fitter = initialiseFits("em", options.sample, 40, 130, workspace4fit_)
ttMC_fitter.get_mj_dataset(ttMC_fitter.file_TTbar_mc,"_TTbar_realW")
ttMC_fitter.get_mj_dataset(ttMC_fitter.file_TTbar_mc,"_TTbar_fakeW")
print ttMC_fitter.file_TTbar_mc
print "_TTbar_realW"
print ttMC_fitter.mj_shape["TTbar_realW"]
print ttMC_fitter.channel
print ttMC_fitter.wtagger_label
fit_mj_single_MC(ttMC_fitter.workspace4fit_,ttMC_fitter.file_TTbar_mc,"_TTbar_realW",ttMC_fitter.mj_shape["TTbar_realW"],ttMC_fitter.channel,ttMC_fitter.wtagger_label)
fit_mj_single_MC(ttMC_fitter.workspace4fit_,ttMC_fitter.file_TTbar_mc,"_TTbar_realW_failN2DDTcut",ttMC_fitter.mj_shape["TTbar_realW_fail"],ttMC_fitter.channel,ttMC_fitter.wtagger_label)
fit_mj_single_MC(ttMC_fitter.workspace4fit_,ttMC_fitter.file_TTbar_mc,"_TTbar_fakeW",ttMC_fitter.mj_shape["TTbar_fakeW"],ttMC_fitter.channel,ttMC_fitter.wtagger_label)
fit_mj_single_MC(ttMC_fitter.workspace4fit_,ttMC_fitter.file_TTbar_mc,"_TTbar_fakeW_failN2DDTcut",ttMC_fitter.mj_shape["TTbar_fakeW_fail"],ttMC_fitter.channel,ttMC_fitter.wtagger_label)
print "Finished fitting matched tt MC! Plots can be found in plots_*_MCfits. Printing workspace:"
workspace4fit_.Print()
def doFitsToMC():
workspace4fit_ = RooWorkspace("workspace4fit_","workspace4fit_")
boostedW_fitter_em = initialiseFits("em", options.sample, 40, 130, workspace4fit_)
boostedW_fitter_em.get_datasets_fit_minor_bkg()
print "Finished fitting MC! Plots can be found in plots_*_MCfits. Printing workspace:"
workspace4fit_.Print()
def GetWtagScalefactors(workspace,fitter):
#------------- Calculate scalefactors -------------
### Efficiency in data and MC
rrv_eff_MC_em = workspace.var("eff_ttbar_TotalMC_em_mj")
rrv_mean_MC_em = workspace.var("rrv_mean1_gaus_ttbar_TotalMC_em_mj")
rrv_sigma_MC_em = workspace.var("rrv_sigma1_gaus_ttbar_TotalMC_em_mj")
rrv_eff_data_em = workspace.var("eff_ttbar_data_em_mj")
rrv_mean_data_em = workspace.var("rrv_mean1_gaus_ttbar_data_em_mj")
rrv_sigma_data_em = workspace.var("rrv_sigma1_gaus_ttbar_data_em_mj")
## GET HP SCALEFACTOR AND UNCERTIANTIES
wtagger_sf_em = rrv_eff_data_em.getVal()/rrv_eff_MC_em.getVal()
wtagger_sf_em_err = wtagger_sf_em * ( (rrv_eff_data_em.getError()/rrv_eff_data_em.getVal() )**2 + (rrv_eff_MC_em.getError()/rrv_eff_MC_em.getVal())**2 )**0.5
wtagger_mean_shift_em = rrv_mean_data_em.getVal()-rrv_mean_MC_em.getVal()
wtagger_mean_shift_err_em = (rrv_mean_data_em.getError()**2 + rrv_mean_MC_em.getError()**2)**0.5
wtagger_sigma_enlarge_em = rrv_sigma_data_em.getVal()/rrv_sigma_MC_em.getVal()
wtagger_sigma_enlarge_err_em = ((rrv_sigma_data_em.getError()/rrv_sigma_data_em.getVal())**2 + (rrv_sigma_MC_em.getError()/rrv_sigma_MC_em.getVal())**2 )**0.5* wtagger_sigma_enlarge_em
mean_sf_error = (rrv_mean_data_em.getVal()/rrv_mean_MC_em.getVal()) * ( (rrv_mean_data_em.getError()/rrv_mean_data_em.getVal())**2 + (rrv_mean_MC_em.getError() /rrv_mean_MC_em.getVal())**2 )**0.5
sigma_sf_error = (rrv_sigma_data_em.getVal()/rrv_sigma_MC_em.getVal()) * ( (rrv_sigma_data_em.getError()/rrv_sigma_data_em.getVal())**2 + (rrv_sigma_MC_em.getError()/rrv_sigma_MC_em.getVal())**2 )**0.5
eff_sf_error = (rrv_eff_data_em.getVal()/rrv_eff_MC_em.getVal()) * ( (rrv_eff_data_em.getError()/rrv_eff_data_em.getVal())**2 + (rrv_eff_MC_em.getError() /rrv_eff_MC_em.getVal())**2 )**0.5
## GET EXTREME FAIL NUMBERS IN ORDER TO COMPUTE LP SF:
rrv_number_ttbar_TotalMC_extremefailN2DDTcut_em_mj = workspace.var("rrv_number_ttbar_TotalMC_extremefailN2DDTcut_em_mj")
rrv_number_ttbar_data_extremefailN2DDTcut_em_mj = workspace.var("rrv_number_ttbar_data_extremefailN2DDTcut_em_mj")
rrv_number_total_ttbar_TotalMC_em = workspace.var("rrv_number_ttbar_TotalMC_beforetau2tau1cut_em_mj")
rrv_number_total_ttbar_data_em = workspace.var("rrv_number_ttbar_data_beforetau2tau1cut_em_mj")
eff_MC_em_extremefail = rrv_number_ttbar_TotalMC_extremefailN2DDTcut_em_mj.getVal() / rrv_number_total_ttbar_TotalMC_em.getVal()
eff_data_em_extremefail = rrv_number_ttbar_data_extremefailN2DDTcut_em_mj.getVal() / rrv_number_total_ttbar_data_em.getVal()
eff_MC_em_extremefail_error = 0.1
# eff_MC_em_extremefail_error = eff_MC_em_extremefail * ( (rrv_number_ttbar_TotalMC_extremefailN2DDTcut_em_mj.getError()/rrv_number_ttbar_TotalMC_extremefailN2DDTcut_em_mj.getVal() )**2 + (rrv_number_total_ttbar_TotalMC_em.getError()/rrv_number_total_ttbar_TotalMC_em.getVal())**2 )**0.5
eff_data_em_extremefail_error = 0.1
# eff_data_em_extremefail_error = eff_data_em_extremefail * ( (rrv_number_ttbar_data_extremefailN2DDTcut_em_mj.getError()/rrv_number_ttbar_data_extremefailN2DDTcut_em_mj.getVal() )**2 + (rrv_number_total_ttbar_data_em.getError() /rrv_number_total_ttbar_data_em.getVal() )**2 )**0.5
eff_SF_extremefail = 0.1
eff_SF_extremefail_error = 0.1
# eff_SF_extremefail = eff_data_em_extremefail/eff_MC_em_extremefail
# eff_SF_extremefail_error = eff_SF_extremefail * ( (eff_data_em_extremefail_error/eff_data_em_extremefail)**2 + (eff_MC_em_extremefail_error/eff_MC_em_extremefail)**2 )**0.5
# w/ extreme fail
eff_MC_em_LP = 1.-rrv_eff_MC_em.getVal() - eff_MC_em_extremefail
eff_data_em_LP = 1.-rrv_eff_data_em.getVal() - eff_data_em_extremefail
eff_MC_em_LP_err = TMath.Sqrt( rrv_eff_MC_em.getError() **2 + eff_MC_em_extremefail_error**2 )
eff_data_em_LP_err = TMath.Sqrt( rrv_eff_data_em.getError()**2 + eff_data_em_extremefail_error**2 )
wtagger_sf_em_LP = eff_data_em_LP / eff_MC_em_LP
wtagger_sf_em_LP_err = wtagger_sf_em_LP * ( (eff_data_em_LP_err/eff_data_em_LP)**2 + (eff_MC_em_LP_err/eff_MC_em_LP)**2 )**0.5
# w/o extreme fail
tmpq_eff_MC_em_LP = 1. - rrv_eff_MC_em.getVal()
tmpq_eff_data_em_LP = 1. - rrv_eff_data_em.getVal()
tmpq_eff_MC_em_LP_err = TMath.Sqrt( rrv_eff_MC_em.getError() **2)
tmpq_eff_data_em_LP_err = TMath.Sqrt( rrv_eff_data_em.getError()**2)
pureq_wtagger_sf_em_LP = tmpq_eff_data_em_LP / tmpq_eff_MC_em_LP
pureq_wtagger_sf_em_LP_err = pureq_wtagger_sf_em_LP*TMath.Sqrt( (tmpq_eff_data_em_LP_err/tmpq_eff_data_em_LP)**2 + (tmpq_eff_MC_em_LP_err/tmpq_eff_MC_em_LP)**2 )
#------------- Print results -------------
print "--------------------------------------------------------------------------------------------"
print " HP "
print "--------------------------------------------------------------------------------------------"
print "W-tagging SF : %0.3f +/- %0.3f (rel. error = %0.1f percent)" %(wtagger_sf_em, wtagger_sf_em_err,(wtagger_sf_em_err/wtagger_sf_em*100))
print "Mass shift [GeV] : %0.3f +/- %0.3f" %(wtagger_mean_shift_em, wtagger_mean_shift_err_em)
print "Mass resolution SF: %0.3f +/- %0.3f" %(wtagger_sigma_enlarge_em, wtagger_sigma_enlarge_err_em)
print ""
print "Parameter Data Simulation Data/Simulation"
print " < m > %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(rrv_mean_data_em.getVal(),rrv_mean_data_em.getError(),rrv_mean_MC_em.getVal(),rrv_mean_MC_em.getError() , rrv_mean_data_em.getVal()/rrv_mean_MC_em.getVal() , mean_sf_error)
print " #sigma %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(rrv_sigma_data_em.getVal(),rrv_sigma_data_em.getError(),rrv_sigma_MC_em.getVal(),rrv_sigma_MC_em.getError(), rrv_sigma_data_em.getVal()/rrv_sigma_MC_em.getVal(), sigma_sf_error)
print ""
print "HP W-tag eff+SF %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(rrv_eff_data_em.getVal(),rrv_eff_data_em.getError(),rrv_eff_MC_em.getVal(),rrv_eff_MC_em.getError() , rrv_eff_data_em.getVal()/rrv_eff_MC_em.getVal() , eff_sf_error)
print "--------------------------------------------------------------------------------------------"
print " EXTREME FAIL "
print "--------------------------------------------------------------------------------------------"
print "Parameter Data Simulation Data/Simulation"
print "Extreme fail eff+SF %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(eff_data_em_extremefail,eff_data_em_extremefail_error,eff_MC_em_extremefail,eff_MC_em_extremefail_error, eff_SF_extremefail, eff_SF_extremefail_error)
print "--------------------------------------------------------------------------------------------"
print " LP "
print "--------------------------------------------------------------------------------------------"
print "Parameter Data Simulation Data/Simulation"
print "LP W-tag eff+SF ( w/ext fail) %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(eff_data_em_LP,eff_data_em_LP_err,eff_MC_em_LP,eff_MC_em_LP_err,wtagger_sf_em_LP,wtagger_sf_em_LP_err)
print "LP W-tag eff+SF (wo/ext fail) %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(tmpq_eff_data_em_LP,tmpq_eff_data_em_LP_err,tmpq_eff_MC_em_LP,tmpq_eff_MC_em_LP_err,pureq_wtagger_sf_em_LP,pureq_wtagger_sf_em_LP_err)
print "--------------------------------------------------------------------------------------------"
print "--------------------------------------------------------------------------------------------"
print ""; print "";
#Write results to file
fitter.file_out_ttbar_control.write("\n HP ")
fitter.file_out_ttbar_control.write("\n-----------------------------------------------------------------------------------------------------------------------------")
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\nW-tagging SF : %0.3f +/- %0.3f (rel. error = %.1f percent)" %(wtagger_sf_em, wtagger_sf_em_err, (wtagger_sf_em_err/wtagger_sf_em*100)))
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\nMass shift [GeV] : %0.3f +/- %0.3f" %(wtagger_mean_shift_em, wtagger_mean_shift_err_em))
fitter.file_out_ttbar_control.write("\nMass resolution SF: %0.3f +/- %0.3f" %(wtagger_sigma_enlarge_em, wtagger_sigma_enlarge_err_em))
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\nParameter Data Simulation Data/Simulation")
fitter.file_out_ttbar_control.write("\n < m > %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(rrv_mean_data_em.getVal(),rrv_mean_data_em.getError(),rrv_mean_MC_em.getVal(),rrv_mean_MC_em.getError() , rrv_mean_data_em.getVal()/rrv_mean_MC_em.getVal() , mean_sf_error))
fitter.file_out_ttbar_control.write("\n #sigma %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(rrv_sigma_data_em.getVal(),rrv_sigma_data_em.getError(),rrv_sigma_MC_em.getVal(),rrv_sigma_MC_em.getError(), rrv_sigma_data_em.getVal()/rrv_sigma_MC_em.getVal(), sigma_sf_error))
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\nHP W-tag eff+SF %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(rrv_eff_data_em.getVal(),rrv_eff_data_em.getError(),rrv_eff_MC_em.getVal(),rrv_eff_MC_em.getError(), rrv_eff_data_em.getVal()/rrv_eff_MC_em.getVal(), eff_sf_error))
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\n-----------------------------------------------------------------------------------------------------------------------------")
fitter.file_out_ttbar_control.write("\n EXTREME FAIL ")
fitter.file_out_ttbar_control.write("\n-----------------------------------------------------------------------------------------------------------------------------")
fitter.file_out_ttbar_control.write("\nParameter Data Simulation Data/Simulation")
fitter.file_out_ttbar_control.write("\nExtreme fail eff+SF %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(eff_data_em_extremefail,eff_data_em_extremefail_error,eff_MC_em_extremefail,eff_MC_em_extremefail_error, eff_SF_extremefail, eff_SF_extremefail_error))
fitter.file_out_ttbar_control.write("\n-----------------------------------------------------------------------------------------------------------------------------")
fitter.file_out_ttbar_control.write("\n LP ")
fitter.file_out_ttbar_control.write("\n-----------------------------------------------------------------------------------------------------------------------------")
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\nParameter Data Simulation Data/Simulation")
fitter.file_out_ttbar_control.write("\nLP W-tag eff+SF ( w/ext fail) %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(eff_data_em_LP,eff_data_em_LP_err,eff_MC_em_LP,eff_MC_em_LP_err,wtagger_sf_em_LP,wtagger_sf_em_LP_err))
fitter.file_out_ttbar_control.write("\nLP W-tag eff+SF (wo/ext fail) %0.3f +/- %0.3f %0.3f +/- %0.3f %0.3f +/- %0.3f" %(tmpq_eff_data_em_LP,tmpq_eff_data_em_LP_err,tmpq_eff_MC_em_LP,tmpq_eff_MC_em_LP_err,pureq_wtagger_sf_em_LP,pureq_wtagger_sf_em_LP_err))
fitter.file_out_ttbar_control.write("\n")
fitter.file_out_ttbar_control.write("\n-----------------------------------------------------------------------------------------------------------------------------")
class doWtagFits:
def __init__(self):
self.workspace4fit_ = RooWorkspace("workspace4fit_","workspace4fit_") # create workspace
self.boostedW_fitter_em = initialiseFits("em", options.sample, 40, 130, self.workspace4fit_) # Define all shapes to be used for Mj, define regions (SB,signal) and input files.
self.boostedW_fitter_em.get_datasets_fit_minor_bkg() # Loop over intrees to create datasets om Mj and fit the single MCs.
print "Printing workspace:"; self.workspace4fit_.Print(); print ""
self.boostedW_fitter_em.get_sim_fit_components()
#Defining categories
sample_type = RooCategory("sample_type","sample_type")
sample_type.defineType("em_pass")
sample_type.defineType("em_fail")
#Importing fit variables
rrv_mass_j = self.workspace4fit_.var("rrv_mass_j")
rrv_weight = RooRealVar("rrv_weight","rrv_weight",0. ,10000000.)
rdataset_data_em_mj = self.workspace4fit_.data("rdataset_data_em_mj")
rdataset_data_em_mj_fail = self.workspace4fit_.data("rdataset_data_failN2DDTcut_em_mj")
#For binned fit (shorter computing time, more presise when no SumW2Error is used!)
if options.doBinnedFit:
#Converting to RooDataHist
rdatahist_data_em_mj = RooDataHist(rdataset_data_em_mj.binnedClone())
rdatahist_data_em_mj_fail = RooDataHist(rdataset_data_em_mj_fail.binnedClone())
#Converting back to RooDataSet
rdataset_data_em_mj_2 = rdataset_data_em_mj.emptyClone()
for i in range(0,rdatahist_data_em_mj.numEntries()):
rdataset_data_em_mj_2.add(rdatahist_data_em_mj.get(i),rdatahist_data_em_mj.weight())
rdataset_data_em_mj_fail_2 = rdataset_data_em_mj_fail.emptyClone()
for i in range(0,rdatahist_data_em_mj_fail.numEntries()):
rdataset_data_em_mj_fail_2.add(rdatahist_data_em_mj_fail.get(i),rdatahist_data_em_mj_fail.weight())
#Combined dataset
combData_data = RooDataSet("combData_data","combData_data",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight),RooFit.Index(sample_type),RooFit.Import("em_pass",rdataset_data_em_mj_2),RooFit.Import("em_fail",rdataset_data_em_mj_fail_2) )
#For unbinned fit
else:
combData_data = RooDataSet("combData_data","combData_data",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight),RooFit.Index(sample_type),RooFit.Import("em_pass",rdataset_data_em_mj),RooFit.Import("em_fail",rdataset_data_em_mj_fail) )
#-------------IMPORT MC-------------
#Importing MC datasets
rdataset_TotalMC_em_mj = self.workspace4fit_.data("rdataset_TotalMC_em_mj")
rdataset_TotalMC_em_mj_fail = self.workspace4fit_.data("rdataset_TotalMC_failN2DDTcut_em_mj")
if options.doBinnedFit:
#Converting to RooDataHist
rdatahist_TotalMC_em_mj = RooDataHist(rdataset_TotalMC_em_mj.binnedClone())
rdatahist_TotalMC_em_mj_fail = RooDataHist(rdataset_TotalMC_em_mj_fail.binnedClone())
#Converting back to RooDataSet
rdataset_TotalMC_em_mj_2 = rdataset_TotalMC_em_mj.emptyClone()
for i in range(0,rdatahist_TotalMC_em_mj.numEntries()):
rdataset_TotalMC_em_mj_2.add(rdatahist_TotalMC_em_mj.get(i),rdatahist_TotalMC_em_mj.weight())
rdataset_TotalMC_em_mj_fail_2 = rdataset_TotalMC_em_mj_fail.emptyClone()
for i in range(0,rdatahist_TotalMC_em_mj_fail.numEntries()):
rdataset_TotalMC_em_mj_fail_2.add(rdatahist_TotalMC_em_mj_fail.get(i),rdatahist_TotalMC_em_mj_fail.weight())
#Combined MC dataset
combData_TotalMC = RooDataSet("combData_TotalMC","combData_TotalMC",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight),RooFit.Index(sample_type),RooFit.Import("em_pass",rdataset_TotalMC_em_mj_2),RooFit.Import("em_fail",rdataset_TotalMC_em_mj_fail_2) )
else:
combData_TotalMC = RooDataSet("combData_TotalMC","combData_TotalMC",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight),RooFit.Index(sample_type),RooFit.Import("em_pass",rdataset_TotalMC_em_mj),RooFit.Import("em_fail",rdataset_TotalMC_em_mj_fail) )
#-------------Define and perform fit to data-------------
#Import pdf from single fits and define the simultaneous total pdf
model_data_em = self.workspace4fit_.pdf("model_data_em")
model_data_fail_em = self.workspace4fit_.pdf("model_data_failN2DDTcut_em")
simPdf_data = RooSimultaneous("simPdf_data_em","simPdf_data_em",sample_type)
simPdf_data.addPdf(model_data_em,"em_pass")
simPdf_data.addPdf(model_data_fail_em,"em_fail")
#Import Gaussian constraints to propagate error to likelihood
constrainslist_data_em = ROOT.std.vector(ROOT.std.string)()
for i in range(self.boostedW_fitter_em.constrainslist_data.size()):
constrainslist_data_em.push_back(self.boostedW_fitter_em.constrainslist_data.at(i))
print self.boostedW_fitter_em.constrainslist_data.at(i)
pdfconstrainslist_data_em = RooArgSet("pdfconstrainslist_data_em")
for i in range(constrainslist_data_em.size()):
pdfconstrainslist_data_em.add(self.workspace4fit_.pdf(constrainslist_data_em.at(i)) )
pdfconstrainslist_data_em.Print()
# Perform simoultaneous fit to data
if options.doBinnedFit:
rfresult_data = simPdf_data.fitTo(combData_data,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_data_em))#, RooFit.SumW2Error(kTRUE))
rfresult_data = simPdf_data.fitTo(combData_data,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_data_em))#, RooFit.SumW2Error(kTRUE))
else:
rfresult_data = simPdf_data.fitTo(combData_data,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_data_em), RooFit.SumW2Error(kTRUE))
rfresult_data = simPdf_data.fitTo(combData_data,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_data_em), RooFit.SumW2Error(kTRUE))
#Draw
isData = True
chi2FailData = drawFrameGetChi2(rrv_mass_j,rfresult_data,rdataset_data_em_mj_fail,model_data_fail_em,isData)
chi2PassData = drawFrameGetChi2(rrv_mass_j,rfresult_data,rdataset_data_em_mj,model_data_em,isData)
#Print final data fit results
print "FIT parameters (DATA) :"; print ""
print "CHI2 PASS = %.3f CHI2 FAIL = %.3f" %(chi2PassData,chi2FailData)
print ""; print rfresult_data.Print("v"); print ""
#-------------Define and perform fit to MC-------------
# fit TotalMC --> define the simultaneous total pdf
model_TotalMC_em = self.workspace4fit_.pdf("model_TotalMC_em")
model_TotalMC_fail_em = self.workspace4fit_.pdf("model_TotalMC_failN2DDTcut_em")
simPdf_TotalMC = RooSimultaneous("simPdf_TotalMC_em","simPdf_TotalMC_em",sample_type)
simPdf_TotalMC.addPdf(model_TotalMC_em,"em_pass")
simPdf_TotalMC.addPdf(model_TotalMC_fail_em,"em_fail")
#Import Gaussian constraints for fixed paramters to propagate error to likelihood
constrainslist_TotalMC_em = ROOT.std.vector(ROOT.std.string)()
for i in range(self.boostedW_fitter_em.constrainslist_mc.size()):
constrainslist_TotalMC_em.push_back(self.boostedW_fitter_em.constrainslist_mc.at(i))
pdfconstrainslist_TotalMC_em = RooArgSet("pdfconstrainslist_TotalMC_em")
for i in range(constrainslist_TotalMC_em.size()):
pdfconstrainslist_TotalMC_em.add(self.workspace4fit_.pdf(constrainslist_TotalMC_em[i]) )
# Perform simoultaneous fit to MC
if options.doBinnedFit:
rfresult_TotalMC = simPdf_TotalMC.fitTo(combData_TotalMC,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_TotalMC_em))#, RooFit.SumW2Error(kTRUE))--> Removing due to unexected behaviour. See https://root.cern.ch/phpBB3/viewtopic.php?t=16917, https://root.cern.ch/phpBB3/viewtopic.php?t=16917
rfresult_TotalMC = simPdf_TotalMC.fitTo(combData_TotalMC,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_TotalMC_em))#, RooFit.SumW2Error(kTRUE))
else:
rfresult_TotalMC = simPdf_TotalMC.fitTo(combData_TotalMC,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_TotalMC_em), RooFit.SumW2Error(kTRUE))
rfresult_TotalMC = simPdf_TotalMC.fitTo(combData_TotalMC,RooFit.Save(kTRUE),RooFit.Verbose(kFALSE), RooFit.Minimizer("Minuit2"),RooFit.ExternalConstraints(pdfconstrainslist_TotalMC_em), RooFit.SumW2Error(kTRUE))
isData = False
chi2FailMC = drawFrameGetChi2(rrv_mass_j,rfresult_TotalMC,rdataset_TotalMC_em_mj_fail,model_TotalMC_fail_em,isData)
chi2PassMC = drawFrameGetChi2(rrv_mass_j,rfresult_TotalMC,rdataset_TotalMC_em_mj,model_TotalMC_em,isData)
#Print final MC fit results
print "FIT Par. (MC) :"; print ""
print "CHI2 PASS = %.3f CHI2 FAIL = %.3f" %(chi2PassMC,chi2FailMC)
print ""; print rfresult_TotalMC.Print("v"); print ""
drawDataAndMC(rrv_mass_j,rfresult_data,rdataset_data_em_mj_fail,model_data_fail_em,True,rrv_mass_j,rfresult_TotalMC,rdataset_TotalMC_em_mj_fail,model_TotalMC_fail_em,False)
drawDataAndMC(rrv_mass_j,rfresult_data,rdataset_data_em_mj,model_data_em,True,rrv_mass_j,rfresult_TotalMC,rdataset_TotalMC_em_mj,model_TotalMC_em,False)
# draw the final fit results
DrawScaleFactorTTbarControlSample(self.workspace4fit_,self.boostedW_fitter_em.color_palet,"","em",self.boostedW_fitter_em.wtagger_label,self.boostedW_fitter_em.AK8_pt_min,self.boostedW_fitter_em.AK8_pt_max,options.sample)
# Get W-tagging scalefactor and efficiencies
GetWtagScalefactors(self.workspace4fit_,self.boostedW_fitter_em)
# wpForPlotting ="%.2f"%options.tau2tau1cutHP
# wpForPlotting = wpForPlotting.replace(".","v")
# pf=""
# if options.usePuppiSD: pf = "_PuppiSD"
# if options.useDDT: pf = "_PuppiSD_DDT"
# postfix = "%s_%s" %(pf,wpForPlotting)
# cmd = "mv plots plots%s" %postfix
# print cmd
# os.system(cmd)
# cmd = "mv WtaggingSF.txt plots%s" %postfix
# print cmd
# os.system(cmd)
self.workspace4fit_.Print()
sys.exit()
wout = ROOT.TFile.Open("Workspace.root","RECREATE")
wout.cd()
self.workspace4fit_.Write()
# Delete workspace
# del self.workspace4fit_
class initialiseFits:
# Constructor: Input is channel (mu,ele,em), range in mj and a workspace
def __init__(self, in_channel, in_sample, in_mj_min=40, in_mj_max=130, input_workspace=None):
RooAbsPdf.defaultIntegratorConfig().setEpsRel(1e-9)
RooAbsPdf.defaultIntegratorConfig().setEpsAbs(1e-9)
# Set channel
self.channel = in_channel
print "CHANNEL = %s" %in_channel
print "Using Tau21 HP cut of " ,options.tau2tau1cutHP
# Map of shapes to be used for the various fits (defined in PDFs/MakePdf.cxx)
self.mj_shape = ROOT.std.map(ROOT.std.string,ROOT.std.string)()
# Fit functions for matched tt MC
# self.mj_shape["TTbar_realW"] = "ExpGaus"
self.mj_shape["TTbar_realW"] = "GausErfExp_ttbar" #before "2Gaus_ttbar"
# self.mj_shape["TTbar_realW_fail"] = "GausExp_failN2DDTcut"
self.mj_shape["TTbar_realW_fail"] = "GausErfExp_ttbar_failN2DDTcut" #before "GausChebychev_ttbar_failN2DDTcut"
self.mj_shape["TTbar_fakeW"] = "ErfExp_ttbar"
# self.mj_shape["TTbar_fakeW_fail"] = "Exp"
self.mj_shape["TTbar_fakeW_fail"] = "ErfExp_ttbar_failN2DDTcut"
if (options.useDDT):
self.mj_shape["TTbar_realW_fail"] = "GausChebychev_ttbar_failN2DDTcut"
# Fit functions for minor backgrounds
self.mj_shape["VV"] = "ExpGaus"
self.mj_shape["VV_fail"] = "ExpGaus"
# self.mj_shape["WJets0"] = "ErfExp"
self.mj_shape["WJets0"] = "Exp"
# self.mj_shape["WJets0_fail"] = "ErfExp"
self.mj_shape["WJets0_fail"] = "Exp"
self.mj_shape["QCD"] = "ErfExp"
self.mj_shape["QCD_fail"] = "ErfExp"
self.mj_shape["STop"] = "ExpGaus_sp"
# self.mj_shape["STop"] = "ErfExpGaus_sp"
self.mj_shape["STop_fail"] = "ExpGaus"
if (options.usePuppiSD):
self.mj_shape["STop_fail"] = "ErfExpGaus_sp"
if (options.useN2DDT):
# self.mj_shape["STop_fail"] = "ErfExpGaus_sp"
# self.mj_shape["STop_fail"] = "ExpGaus"
self.mj_shape["STop_fail"] = "ExpGaus_sp"
if (options.tau2tau1cutHP==0.60):
self.mj_shape["VV_fail"] = "Exp"
self.mj_shape["VV"] = "Exp"
self.mj_shape["WJets0_fail"] = "Exp"
if (options.useDDT):
self.mj_shape["VV"] = "ExpGaus"
self.mj_shape["VV_fail"] = "ErfExpGaus_sp"
self.mj_shape["STop_fail"] = "ErfExpGaus_sp"
self.mj_shape["STop"] = "ExpGaus"
# Fit functions used in simultaneous fit of pass and fail categories
self.mj_shape["bkg_mc_fail"] = "ErfExp_ttbar_failN2DDTcut"
# self.mj_shape["bkg_mc_fail"] = "Exp"
self.mj_shape["bkg_data_fail"] = "ErfExp_ttbar_failN2DDTcut"
# self.mj_shape["bkg_data_fail"] = "Exp"
# self.mj_shape["signal_mc_fail"] = "GausExp_failN2DDTcut" #Before GausChebychev_ttbar_failN2DDTcut
# self.mj_shape["signal_data_fail"] = "GausExp_failN2DDTcut"
self.mj_shape["signal_mc_fail"] = "GausErfExp_ttbar_failN2DDTcut" #Before GausChebychev_ttbar_failN2DDTcut
self.mj_shape["signal_data_fail"] = "GausErfExp_ttbar_failN2DDTcut"
self.mj_shape["bkg_data"] = "ErfExp_ttbar"
self.mj_shape["bkg_mc"] = "ErfExp_ttbar"
# self.mj_shape["signal_data"] = "ExpGaus"
self.mj_shape["signal_data"] = "GausErfExp_ttbar" #Before 2Gaus_ttbar
# self.mj_shape["signal_mc"] = "ExpGaus"
self.mj_shape["signal_mc"] = "GausErfExp_ttbar"
if (options.useDDT):
self.mj_shape["signal_mc_fail"] = "GausChebychev_ttbar_failN2DDTcut"
self.mj_shape["signal_data_fail"] = "GausChebychev_ttbar_failN2DDTcut"
# # #TESTS USING OLD METHOD, EG ADDING TWO ADDITIONAL FIT FUNCTIONS
# self.mj_shape["signal_data"] = "2Gaus_ttbar"
# self.mj_shape["signal_mc"] = "2Gaus_ttbar"
# self.mj_shape["signal_mc_fail"] = "GausChebychev_ttbar_failN2DDTcut"
# self.mj_shape["signal_data_fail"] = "GausChebychev_ttbar_failN2DDTcut"
#Set lumi
self.Lumi=35.8
# self.Lumi=2198. #74
if options.use76X: self.Lumi=35.8 #76
self.BinWidth_mj = 5.
self.narrow_factor = 1.
self.BinWidth_mj = self.BinWidth_mj/self.narrow_factor
nbins_mj = int( (in_mj_max - in_mj_min) / self.BinWidth_mj )
in_mj_max = in_mj_min+nbins_mj*self.BinWidth_mj
jetMass = "Pruned jet mass"
if options.usePuppiSD: jetMass = "PUPPI Softdrop Jet Mass"
if options.useN2DDT: jetMass = "PUPPI Softdrop Jet Mass"
rrv_mass_j = RooRealVar("rrv_mass_j", jetMass ,(in_mj_min+in_mj_max)/2.,in_mj_min,in_mj_max,"GeV")
rrv_mass_j.setBins(nbins_mj)
# Create workspace and import fit variable
if input_workspace is None:
self.workspace4fit_ = RooWorkspace("workspace4fit_","Workspace4fit_")
else:
self.workspace4fit_ = input_workspace
getattr(self.workspace4fit_,"import")(rrv_mass_j)
# Signal region between 65 and 105 GeV
self.mj_sideband_lo_min = in_mj_min
self.mj_sideband_lo_max = 65
self.mj_signal_min = 65
self.mj_signal_max = 105
self.mj_sideband_hi_min = 105
self.mj_sideband_hi_max = in_mj_max
# Setting ranges...
rrv_mass_j.setRange("sb_lo",self.mj_sideband_lo_min,self.mj_sideband_lo_max) # 30-65 GeV
rrv_mass_j.setRange("signal_region",self.mj_signal_min,self.mj_signal_max) # 65-105 GeV
rrv_mass_j.setRange("sb_hi",self.mj_sideband_hi_min,self.mj_sideband_hi_max) # 105-135 GeV
rrv_mass_j.setRange("controlsample_fitting_range",40,130) # ---> what is this????
# Directory and input files
self.file_Directory = "/eos/uscms/store/user/mkrohn/WTaggerSF/Trees_N2DDT/v12.02/mu_Tight/"
# self.file_Directory = "$HOME/EXOVVAnalysisRunII/AnalysisOutput/Wtag_80X/WWTree_%s/"%(self.channel) #For 80X!!!!
# self.file_Directory = "$HOME/EXOVVAnalysisRunII/AnalysisOutput/Wtag/PRUNED/WWTree_%s/"%(self.channel)
# if options.usePuppiSD :
# self.file_Directory = "$HOME/EXOVVAnalysisRunII/AnalysisOutput/Wtag_80X/WWTree_%s/"%(self.channel)
# if options.use76X:
# self.file_Directory = "$HOME/EXOVVAnalysisRunII/AnalysisOutput/Wtag/PUPPISD_newcorr/WWTree_%s/"%(self.channel)
postfix = ""
if options.use76X: postfix ="_76X"
self.file_data = ("Data_2Trans.root")
self.file_WJets0_mc = ("WJets_2Trans.root")
self.file_VV_mc = ("VV_2Trans.root")
self.file_QCD_mc = ("QCD.root")
self.file_STop_mc = ("ST_2Trans.root")
self.file_TTbar_mc = ("TT_2Trans.root")
self.file_pseudodata = ("PsuedoData_2Trans.root") #Important! ROOT tree containing all backgrounds added together (tt+singleT+VV+Wjets). Used for fit to total MC
# self.file_pseudodata = ("pseudodata_weighted.root")
# Define Tau21 WP
self.wtagger_label = "HP"
self.wtagger_cut = options.tau2tau1cutHP
self.wtagger_cut_min = 0.
if options.usePuppiSD:
postfix = postfix + "_PuppiSD"
if options.useDDT:
postfix = postfix + "_PuppiSD_DDT"
if options.useN2DDT:
postfix = postfix + "_PuppiSD_N2DDT"
# Define label used for plots and choosing fit paramters in PDFs/MakePdf.cxx
wp = "%.2f" %options.tau2tau1cutHP
wp = wp.replace(".","v")
self.wtagger_label = self.wtagger_label + "%s%s%s"%(wp,in_sample,postfix)
#Color pallett for plots
self.color_palet = ROOT.std.map(ROOT.std.string, int) ()
self.color_palet["data"] = 1
self.color_palet["WJets"] = 2
self.color_palet["VV"] = 4
self.color_palet["QCD"] = 5
self.color_palet["STop"] = 7
self.color_palet["TTbar"] = 210
self.color_palet["TTbar_realW"] = 210
self.color_palet["TTbar_fakeW"] = 419
self.color_palet["Signal"] = 1
self.color_palet["Uncertainty"] = 1
self.color_palet["Other_Backgrounds"] = 1
# Cuts (dont need these cuts if they are already implemented in ROOT tree)
self.vpt_cut = 200 # hadronic and leptonic W cut
self.mass_lvj_max = 5000. # invariant mass of 3 body max
self.mass_lvj_min = 0. # invariant mass of 3 body min
self.pfMET_cut = 40. # missing transverse energy
self.lpt_cut = 53. # lepton pT
self.AK8_pt_min = 200
self.AK8_pt_max = 5000
if self.channel == "el":
self.pfMET_cut = 80
self.lpt_cut = 120
# Out .txt file with final SF numbers
self.file_ttbar_control_txt = "WtaggingSF.txt"
self.file_out_ttbar_control = open(self.file_ttbar_control_txt,"w")
setTDRStyle()
def get_datasets_fit_minor_bkg(self):
rrv_mass_j = self.workspace4fit_.var("rrv_mass_j")
# Build single-t fit pass and fail distributions
print "##################################################"
print "############### Single Top DataSet ###############"
print "##################################################"
print ""
self.get_mj_dataset(self.file_STop_mc,"_STop")
fit_mj_single_MC(self.workspace4fit_,self.file_STop_mc,"_STop" ,self.mj_shape["STop"],self.channel,self.wtagger_label) #Start value and range of parameters defined in PDFs/MakePDF.cxx
fit_mj_single_MC(self.workspace4fit_,self.file_STop_mc,"_STop_failN2DDTcut" ,self.mj_shape["STop_fail"],self.channel,self.wtagger_label)
### Build WJet fit pass and fail distributions
print "###########################################"
print "############### WJets Pythia ##############"
print "###########################################"
print ""
self.get_mj_dataset(self.file_WJets0_mc,"_WJets0")
fit_mj_single_MC(self.workspace4fit_,self.file_WJets0_mc,"_WJets0",self.mj_shape["WJets0"],self.channel,self.wtagger_label)
fit_mj_single_MC(self.workspace4fit_,self.file_WJets0_mc,"_WJets0_failN2DDTcut",self.mj_shape["WJets0_fail"],self.channel,self.wtagger_label)
# Build VV fit pass and fail distributions
print "#########################################"
print "############### VV Pythia ###############"
print "#########################################"
print ""
print ""
# self.get_mj_dataset(self.file_VV_mc,"_VV")
# fit_mj_single_MC(self.workspace4fit_,self.file_VV_mc,"_VV",self.mj_shape["VV"],self.channel,self.wtagger_label)
# fit_mj_single_MC(self.workspace4fit_,self.file_VV_mc,"_VV_failN2DDTcut",self.mj_shape["VV_fail"],self.channel,self.wtagger_label)
# # Build QCD fit pass and fail distributions
# print "#########################################"
# print "################## QCD ##################"
# print "#########################################"
# print ""
# print ""
# self.get_mj_dataset(self.file_VV_mc,"_QCD")
# fit_mj_single_MC(self.workspace4fit_,self.file_VV_mc,"_QCD",self.mj_shape["QCD"],self.channel,self.wtagger_label)
# fit_mj_single_MC(self.workspace4fit_,self.file_VV_mc,"_QCD_failN2DDTcut",self.mj_shape["QCD_fail"],self.channel,self.wtagger_label)
if options.fitMC:
return
# Get dataset for ttbar
print "#########################################"
print "################ TTbar %s################"%options.sample
print "#########################################"
print ""
self.get_mj_dataset(self.file_TTbar_mc,"_TTbar")
self.get_mj_dataset(self.file_TTbar_mc,"_TTbar_realW")
self.get_mj_dataset(self.file_TTbar_mc,"_TTbar_fakeW")
# Get dataset used for fit to total MC
print "################################################"
print "############## Pseudo Data Powheg ##############"
print "################################################"
print ""
self.get_mj_dataset(self.file_pseudodata,"_TotalMC")
print "#################################"
print "############# Data ##############"
print "#################################"
print ""
print ""
self.get_mj_dataset(self.file_data,"_data")
# print "Saving workspace in myworkspace.root! To save time when debugging use option --WS myworkspace.root to avoid recreating workspace every time"
# filename = "myworkspace.root"
# self.workspace4fit_.writeToFile(filename)
def get_sim_fit_components(self):
# self.print_yields()
self.constrainslist_data = ROOT.std.vector(ROOT.std.string)()
self.constrainslist_mc = ROOT.std.vector(ROOT.std.string)()
#Construct pass/fail models (fix minor backgrounds, create sim. fit total PDFS)
ScaleFactorTTbarControlSampleFit(self.workspace4fit_,self.mj_shape,self.color_palet,self.constrainslist_data,self.constrainslist_mc,"",self.channel,self.wtagger_label,self.AK8_pt_min,self.AK8_pt_max)
#Get data/MC scalefactors
rrv_scale_number = self.workspace4fit_.var("rrv_scale_number_TTbar_STop_VV_WJets").getVal()
rrv_scale_number_fail = self.workspace4fit_.var("rrv_scale_number_TTbar_STop_VV_WJets_fail").getVal()
#Print data/MC scalefactors
print " Pass MC / all data = %.3f" %(rrv_scale_number)
print " Fail MC / all data = %.3f" %(rrv_scale_number_fail)
def print_yields(self):
# Print dataset yields in the signal region
print ""
print ""
print ""
self.workspace4fit_.var("rrv_number_dataset_signal_region_data_" +self.channel+"_mj").Print()
# self.workspace4fit_.var("rrv_number_dataset_signal_region_VV_" +self.channel+"_mj").Print()
self.workspace4fit_.var("rrv_number_dataset_signal_region_WJets0_" +self.channel+"_mj").Print()
# self.workspace4fit_.var("rrv_number_dataset_signal_region_QCD_" +self.channel+"_mj").Print()
self.workspace4fit_.var("rrv_number_dataset_signal_region_STop_" +self.channel+"_mj").Print()
self.workspace4fit_.var("rrv_number_dataset_signal_region_TTbar_" +self.channel+"_mj").Print()
print ""
print ""
print ""
number_dataset_signal_region_data_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_data_"+self.channel+"_mj").getVal()
number_dataset_signal_region_error2_data_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_error2_data_"+self.channel+"_mj").getVal()
number_dataset_signal_region_TotalMC_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_TotalMC_"+self.channel+"_mj").getVal()
number_dataset_signal_region_error2_TotalMC_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_error2_TotalMC_"+self.channel+"_mj").getVal()
number_dataset_signal_region_before_cut_data_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_before_cut_data_"+self.channel+"_mj").getVal()
number_dataset_signal_region_before_cut_error2_data_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_before_cut_error2_data_"+self.channel+"_mj").getVal()
number_dataset_signal_region_before_cut_TotalMC_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_before_cut_TotalMC_"+self.channel+"_mj").getVal()
number_dataset_signal_region_before_cut_error2_TotalMC_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_before_cut_error2_TotalMC_"+self.channel+"_mj").getVal()
wtagger_eff_MC = number_dataset_signal_region_TotalMC_mj/number_dataset_signal_region_before_cut_TotalMC_mj
wtagger_eff_data = number_dataset_signal_region_data_mj/number_dataset_signal_region_before_cut_data_mj
wtagger_eff_reweight = wtagger_eff_data/wtagger_eff_MC
wtagger_eff_reweight_err = wtagger_eff_reweight*TMath.Sqrt(number_dataset_signal_region_error2_data_mj/number_dataset_signal_region_data_mj/number_dataset_signal_region_data_mj + number_dataset_signal_region_error2_TotalMC_mj/number_dataset_signal_region_TotalMC_mj/number_dataset_signal_region_TotalMC_mj +number_dataset_signal_region_before_cut_error2_data_mj/number_dataset_signal_region_before_cut_data_mj/number_dataset_signal_region_data_mj + number_dataset_signal_region_before_cut_error2_TotalMC_mj/number_dataset_signal_region_before_cut_TotalMC_mj/number_dataset_signal_region_before_cut_TotalMC_mj)
print ""
print "Nr. data events in signal_region : %s +/- sqrt(%s)"%(number_dataset_signal_region_data_mj, number_dataset_signal_region_error2_data_mj**.5)
print ""
print "Nr. MC events in signal_region : %s +/- sqrt(%s)"%(number_dataset_signal_region_TotalMC_mj, number_dataset_signal_region_error2_TotalMC_mj)
print ""
print "Nr. dataevents in signalregion before cut on tau21: %s +/- sqrt(%s)"%(number_dataset_signal_region_before_cut_data_mj, number_dataset_signal_region_before_cut_error2_data_mj)
print ""
print "Nr. MC events in signalregion before cut on tau21 : %s +/- sqrt(%s) "%(number_dataset_signal_region_before_cut_TotalMC_mj, number_dataset_signal_region_before_cut_error2_TotalMC_mj)
print ""
print "W-tagging efficiency (pre-fit):"
print "W-tagging eff. MC = %.3f "%(wtagger_eff_MC)
print "W-tagging eff. data = %.3f "%(wtagger_eff_data)
print "W-tagging SF = %.3f +/- %.3f"%(wtagger_eff_reweight, wtagger_eff_reweight_err)
print ""
print ""
self.file_out_ttbar_control.write("%s channel SF: \n"%(self.channel))
self.file_out_ttbar_control.write("Nr. events in signal_region : %s +/- sqrt(%s)\n"%(number_dataset_signal_region_data_mj, number_dataset_signal_region_error2_data_mj))
self.file_out_ttbar_control.write("Nr. TotalMC in signal_region : %s +/- sqrt(%s) \n"%(number_dataset_signal_region_TotalMC_mj, number_dataset_signal_region_error2_TotalMC_mj))
self.file_out_ttbar_control.write("event number of data in signalregion before_cut : %s +/- sqrt(%s)\n"%(number_dataset_signal_region_before_cut_data_mj, number_dataset_signal_region_before_cut_error2_data_mj))
self.file_out_ttbar_control.write("event number of TotalMC in signal_region before_cut: %s +/- sqrt(%s) \n"%(number_dataset_signal_region_before_cut_TotalMC_mj, number_dataset_signal_region_before_cut_error2_TotalMC_mj))
self.file_out_ttbar_control.write("wtagger_eff_MC = %s \n"%(wtagger_eff_MC ))
self.file_out_ttbar_control.write("wtagger_eff_data = %s \n"%(wtagger_eff_data ))
self.file_out_ttbar_control.write("wtagger_eff_reweight = %s +/- %s\n"%(wtagger_eff_reweight, wtagger_eff_reweight_err))
# Loop over trees
def get_mj_dataset(self,in_file_name, label, jet_mass="Whadr_pruned"):
if options.usePuppiSD or options.useDDT or options.useN2DDT:
jet_mass="AK8Puppijet0_msd_TheaCorr"
print "Using mass variable " ,jet_mass
fileIn_name = TString(self.file_Directory+in_file_name)
print "Using file " ,fileIn_name
fileIn = TFile(fileIn_name.Data())
treeIn = fileIn.Get("myTree")
rrv_mass_j = self.workspace4fit_.var("rrv_mass_j")
rrv_weight = RooRealVar("rrv_weight","rrv_weight",0. ,10000000.)
# Mj dataset before tau2tau1 cut : Passed
rdataset_mj = RooDataSet("rdataset" +label+"_"+self.channel+"_mj","rdataset" +label+"_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rdataset4fit_mj = RooDataSet("rdataset4fit" +label+"_"+self.channel+"_mj","rdataset4fit"+label+"_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rrv_number_pass = RooRealVar("rrv_number_ttbar"+label+"_passtau2tau1cut_em_mj","rrv_number_ttbar"+label+"_passtau2tau1cut_em_mj",0.,10000000.) #LUCA
# Mj dataset before tau2tau1 cut : Total
rdataset_beforetau2tau1cut_mj = RooDataSet("rdataset" +label+"_beforetau2tau1cut_"+self.channel+"_mj","rdataset" +label+"_beforetau2tau1cut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rdataset4fit_beforetau2tau1cut_mj = RooDataSet("rdataset4fit" +label+"_beforetau2tau1cut_"+self.channel+"_mj","rdataset4fit"+label+"_beforetau2tau1cut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rrv_number_before = RooRealVar("rrv_number_ttbar"+label+"_beforetau2tau1cut_em_mj","rrv_number_ttbar"+label+"_beforetau2tau1cut_em_mj",0.,10000000.) #LUCA
### Mj dataset failed tau2tau1 cut :
rdataset_failN2DDTcut_mj = RooDataSet("rdataset" +label+"_failN2DDTcut_"+self.channel+"_mj","rdataset" +label+"_failN2DDTcut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rdataset4fit_failN2DDTcut_mj = RooDataSet("rdataset4fit" +label+"_failN2DDTcut_"+self.channel+"_mj","rdataset4fit"+label+"_failN2DDTcut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rrv_number_fail = RooRealVar("rrv_number_ttbar"+label+"_failN2DDTcut_em_mj","rrv_number_ttbar"+label+"_failN2DDTcut_em_mj",0.,10000000.) #LUCA
### Mj dataset extreme failed tau2tau1 cut: > 0.75
rdataset_extremefailN2DDTcut_mj = RooDataSet("rdataset" +label+"_extremefailN2DDTcut_"+self.channel+"_mj","rdataset" +label+"_extremefailN2DDTcut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rdataset4fit_extremefailN2DDTcut_mj = RooDataSet("rdataset4fit"+label+"_extremefailN2DDTcut_"+self.channel+"_mj","rdataset4fit" +label+"_extremefailN2DDTcut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) )
rrv_number_extremefail = RooRealVar("rrv_number_ttbar"+label+"_extremefailN2DDTcut_em_mj","rrv_number_ttbar"+label+"_extremefailN2DDTcut_em_mj",0.,10000000.) #LUCA
# category_cut = RooCategory("category_cut"+"_"+self.channel,"category_cut"+"_"+self.channel) #---->Think this can be removed!!!!
# category_cut.defineType("cut",1)
# category_cut.defineType("beforecut",2)
# combData4cut = RooDataSet("combData4cut"+"_"+self.channel,"combData4cut"+"_"+self.channel,RooArgSet(rrv_mass_j, category_cut, rrv_weight),RooFit.WeightVar(rrv_weight) )
# Define categories
if self.workspace4fit_.cat("category_p_f"+"_"+self.channel):
category_p_f = self.workspace4fit_.cat("category_p_f"+"_"+self.channel)
else:
category_p_f = RooCategory("category_p_f"+"_"+self.channel,"category_p_f"+"_"+self.channel)
category_p_f.defineType("pass")
category_p_f.defineType("fail")
getattr(self.workspace4fit_,"import")(category_p_f)
combData_p_f = RooDataSet("combData_p_f"+label+"_"+self.channel,"combData_p_f"+label+"_"+self.channel,RooArgSet(rrv_mass_j, category_p_f, rrv_weight),RooFit.WeightVar(rrv_weight))
print "N entries: ", treeIn.GetEntries()
hnum_4region = TH1D("hnum_4region" +label+"_"+self.channel,"hnum_4region" +label+"_"+self.channel,4, -1.5, 2.5) # m_j -1: sb_lo; 0:signal_region; 1: sb_hi; 2:total
hnum_4region_error2 = TH1D("hnum_4region_error2"+label+"_"+self.channel,"hnum_4region_error2" +label+"_"+self.channel,4, -1.5, 2.5) # m_j -1: sb_lo; 0:signal_region; 1: sb_hi; 2:total
hnum_4region_before_cut = TH1D("hnum_4region_before_cut" +label+"_"+self.channel,"hnum_4region_before_cut" +label+"_"+self.channel,4,-1.5,2.5);# m_j -1: sb_lo; 0:signal_region; 1: sb_hi; 2:total
hnum_4region_before_cut_error2 = TH1D("hnum_4region_before_cut_error2" +label+"_"+self.channel,"hnum_4region_before_cut_error2"+label+"_"+self.channel,4,-1.5,2.5);# m_j -1: sb_lo; 0:signal_region; 1: sb_hi; 2:total
hnum_2region = TH1D("hnum_2region" +label+"_"+self.channel,"hnum_2region" +label+"_"+self.channel,2,-0.5,1.5);# m_lvj 0: signal_region; 1: total --> There is only 1 and that is SIGNAL REGION?!
hnum_2region_error2 = TH1D("hnum_2region_error2"+label+"_"+self.channel,"hnum_2region_error2" +label+"_"+self.channel,2,-0.5,1.5);# m_lvj 0: signal_region; 1: total
#-------------------------------------------------------------------------------------------
# Loop over tree entries
tmp_scale_to_lumi = 1
i = 0
for i in range(treeIn.GetEntries()):
if i % 5000 == 0: print "iEntry: ",i
treeIn.GetEntry(i)
if TString(label).Contains("realW") and not getattr(treeIn,"LeadingAK8Jet_MatchedHadW"): #Is a real W, meaning both daughters of W is within jet cone!!
continue
if TString(label).Contains("fakeW") and getattr(treeIn,"LeadingAK8Jet_MatchedHadW"):
continue
# if treeIn.AK8Puppijet0_N2DDT_26Per > 0.:
# continue
if treeIn.AK8CHSjet0_pt > 400:
continue
if treeIn.AK8CHSjet0_pt < 300:
continue
# if treeIn.AK8CHSjet0_pt < 350 or TMath.log(treeIn.AK8Puppijet0_msd_TheaCorr*treeIn.AK8Puppijet0_msd_TheaCorr/(treeIn.AK8CHSjet0_pt*treeIn.AK8CHSjet0_pt)) > -2:
# continue
wtagger = getattr(treeIn,"AK8Puppijet0_tau21")
if options.useN2DDT:
wtagger = getattr(treeIn,"AK8Puppijet0_N2DDT_5Per")
# wtagger = getattr(treeIn,"AK8Puppijet0_doublecsv")
if options.usePuppiSD:
wtagger = getattr(treeIn,"AK8Puppijet0_tau21")