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wtagger_doFit_class.py
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wtagger_doFit_class.py
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#! /usr/bin/env python
import os
import glob
import math
import array
import ROOT
import ntpath
import sys
import subprocess
from subprocess import Popen
from optparse import OptionParser
from ROOT import gROOT, TPaveLabel, gStyle, gSystem, TGaxis, TStyle, TLatex, TString, TF1,TFile,TLine, TLegend, TH1D,TH2D,THStack,TChain, TCanvas, TMatrixDSym, TMath, TText, TPad, RooFit, RooArgSet, RooArgList, RooArgSet, RooAbsData, RooAbsPdf, RooAddPdf, RooWorkspace, RooExtendPdf,RooCBShape, RooLandau, RooFFTConvPdf, RooGaussian, RooBifurGauss, RooArgusBG, RooDataSet, RooExponential,RooBreitWigner, RooVoigtian, RooNovosibirsk, RooRealVar,RooFormulaVar, RooDataHist, RooHistPdf,RooCategory, RooChebychev, RooSimultaneous, RooGenericPdf,RooConstVar, RooKeysPdf, RooHistPdf, RooEffProd, RooProdPdf, TIter, kTRUE, kFALSE, kGray, kRed, kDashed, kGreen,kAzure, kOrange, kBlack,kBlue,kYellow,kCyan, kMagenta, kWhite
############################################
# Job steering #
############################################
def foo_callback(option, opt, value, parser):
setattr(parser.values, option.dest, value.split(','))
parser = OptionParser()
parser.add_option('-a', '--additioninformation',action="store",type="string",dest="additioninformation",default="EXO")
parser.add_option('--cprime', action="store",type="int",dest="cprime",default=10)
parser.add_option('--BRnew', action="store",type="int",dest="BRnew",default=0)
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="el")
parser.add_option('--fitwtagger', action='store_true', dest='fitwtagger', default=True, help='fit wtagger jet in ttbar control sample')
parser.add_option('--fitwtaggersim', action='store_true', dest='fitwtaggersim', default=False, help='fit wtagger jet in ttbar control sample with mu and el samples simultaneously')
parser.add_option('--inPath', action="store",type="string",dest="inPath",default="./")
parser.add_option('--category', action="store",type="string",dest="category",default="HP")
parser.add_option('--herwig', action="store",type="int",dest="herwig",default=0)
parser.add_option('--pTbin', action="callback",callback=foo_callback,type="string",dest="pTbin",default="")
parser.add_option('--shift', action="store", type="int",dest="shift",default=0)
parser.add_option('--smear', action="store", type="int",dest="smear",default=0)
parser.add_option('--tau2tau1cutHP', action="store", type="float",dest="tau2tau1cutHP",default=0.5)
parser.add_option('--tau2tau1cutLP', action="store", type="float",dest="tau2tau1cutLP",default=0.75)
(options, args) = parser.parse_args()
ROOT.gSystem.Load(options.inPath+"/PlotStyle/Util_cxx.so")
ROOT.gSystem.Load(options.inPath+"/PlotStyle/PlotUtils_cxx.so")
ROOT.gSystem.Load(options.inPath+"/PDFs/PdfDiagonalizer_cc.so")
ROOT.gSystem.Load(options.inPath+"/PDFs/HWWLVJRooPdfs_cxx.so")
ROOT.gSystem.Load(options.inPath+"/PDFs/MakePdf_cxx.so")
ROOT.gSystem.Load(options.inPath+"/BiasStudy/BiasUtils_cxx.so")
ROOT.gSystem.Load(options.inPath+"/FitUtils/FitUtils_cxx.so")
from ROOT import draw_error_band, draw_error_band_extendPdf, draw_error_band_Decor, draw_error_band_shape_Decor, Calc_error_extendPdf, Calc_error, RooErfExpPdf, RooAlpha, RooAlpha4ErfPowPdf, RooAlpha4ErfPow2Pdf, RooAlpha4ErfPowExpPdf, PdfDiagonalizer, RooPowPdf, RooPow2Pdf, RooErfPowExpPdf, RooErfPowPdf, RooErfPow2Pdf, RooQCDPdf, RooUser1Pdf, RooBWRunPdf, RooAnaExpNPdf,RooExpNPdf, RooAlpha4ExpNPdf, RooExpTailPdf, RooAlpha4ExpTailPdf, Roo2ExpPdf, RooAlpha42ExpPdf
from ROOT import MakeGeneralPdf, MakeExtendedModel, get_TTbar_mj_Model, get_STop_mj_Model, get_VV_mj_Model, get_WW_EWK_mj_Model, get_WJets_mj_Model, get_ggH_mj_Model, get_vbfH_mj_Model, get_TTbar_mlvj_Model, get_STop_mlvj_Model, get_VV_mlvj_Model, get_WW_EWK_mlvj_Model, get_WJets_mlvj_Model, get_ggH_mlvj_Model, get_vbfH_mlvj_Model, fix_Model, clone_Model
from ROOT import setTDRStyle, get_pull, draw_canvas, draw_canvas_with_pull, legend4Plot, GetDataPoissonInterval, GetLumi, draw_error_band_ws
from ROOT import fit_mj_single_MC, fit_mlvj_model_single_MC, fit_WJetsNormalization_in_Mj_signal_region, fit_mlvj_in_Mj_sideband, get_WJets_mlvj_correction_sb_lo_to_signal_region, get_mlvj_normalization_insignalregion, fit_genHMass, SystematicUncertaintyHiggs_2jetBin, SystematicUncertaintyHiggs_01jetBin, ScaleFactorTTbarControlSampleFit,DrawScaleFactorTTbarControlSample
from ROOT import *
gInterpreter.GenerateDictionary("std::map<std::string,std::string>", "map;string;string")
gInterpreter.GenerateDictionary("std::vector<std::string>", "vector;string")
###############################
## doFit Class Implemetation ##
###############################
class doFit_wj_and_wlvj:
## contructor taking channel, signal name, range in mj, label and a workspace
def __init__(self, in_channel,in_signal_sample, in_mj_min=40, in_mj_max=130, label="", input_workspace=None):
print " ";
print "#####################################################";
print "## Constructor of the fit object doFit_wj_and_wlvj ##";
print "#####################################################";
print " ";
RooAbsPdf.defaultIntegratorConfig().setEpsRel(1e-9);
RooAbsPdf.defaultIntegratorConfig().setEpsAbs(1e-9);
### set the channel type --> electron or muon
self.channel = in_channel;
### shapes to be used in mj
self.mj_shape = ROOT.std.map(ROOT.std.string,ROOT.std.string)();
self.mj_shape["TTbar"] = "2Gaus_ErfExp";
if self.channel == "mu":
self.mj_shape["STop"] = "ErfExpGaus_sp";
self.mj_shape["STop_fail"] = "Exp";
self.mj_shape["STop_extremefail"] = "Exp";
self.mj_shape["VV"] = "ErfExpGaus_sp";
self.mj_shape["VV_fail"] = "ExpGaus";
self.mj_shape["VV_extremefail"] = "Exp";
else:
self.mj_shape["STop"] = "ExpGaus";
self.mj_shape["STop_fail"] = "ExpGaus";
self.mj_shape["STop_extremefail"] = "Exp";
self.mj_shape["VV"] = "Gaus";
self.mj_shape["VV_fail"] = "Exp";
self.mj_shape["VV_extremefail"] = "Exp";
self.mj_shape["WJets0"] = "ErfExp";
self.mj_shape["WJets0_fail"] = "Exp";
self.mj_shape["WJets0_extremefail"] = "Exp";
self.mj_shape["bkg_data"] = "ErfExp_ttbar";
self.mj_shape["bkg_data_fail"] = "ErfExp_ttbar_failtau2tau1cut";
self.mj_shape["signal_data"] = "2Gaus_ttbar" ;
self.mj_shape["signal_data_fail"] = "GausChebychev_ttbar_failtau2tau1cut";
self.mj_shape["bkg_mc"] = "ErfExp_ttbar";
self.mj_shape["bkg_mc_fail"] = "ErfExp_ttbar_failtau2tau1cut";
self.mj_shape["signal_mc"] = "2Gaus_ttbar" ;
self.mj_shape["signal_mc_fail"] = "GausChebychev_ttbar_failtau2tau1cut";
self.mj_shape["data_extremefail"] = "Exp_ttbar_extremefailtau2tau1cut";
self.mj_shape["data_bkg_extremefail"] = "Exp_bkg_extremefailtau2tau1cut";
self.mj_shape["mc_extremefail"] = "Exp_ttbar_extremefailtau2tau1cut";
self.mj_shape["mc_bkg_extremefail"] = "Exp_bkg_extremefailtau2tau1cut";
### Set the mj binning for plots
self.BinWidth_mj = 5.;
#narrow the BinWidth_mj and BinWidth_mlvj by a factor of 5. Because Higgs-Combination-Tools will generate a binned sample, so need the bin width narrow. So, as a easy selution, we will increase the bin-width by a factor of 5 when ploting m_j m_WW
self.narrow_factor = 1.;
## set the range max properly in order to have a integer number of bins
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;
## declare the RooRealVar + binning
rrv_mass_j = RooRealVar("rrv_mass_j","pruned jet mass",(in_mj_min+in_mj_max)/2.,in_mj_min,in_mj_max,"GeV");
rrv_mass_j.setBins(nbins_mj);
## create the workspace and import the variable
if input_workspace is None:
self.workspace4fit_ = RooWorkspace("workspace4fit"+label+"_","Workspace4fit"+label+"_");
else:
self.workspace4fit_ = input_workspace;
getattr(self.workspace4fit_,"import")(rrv_mass_j);
## Region definition --> 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;
## define ranges on mj
rrv_mass_j.setRange("sb_lo",self.mj_sideband_lo_min,self.mj_sideband_lo_max);
rrv_mass_j.setRange("signal_region",self.mj_signal_min,self.mj_signal_max);
rrv_mass_j.setRange("sb_hi",self.mj_sideband_hi_min,self.mj_sideband_hi_max);
rrv_mass_j.setRange("controlsample_fitting_range",40,130);
## directory where are the trees to be run
self.file_Directory = "trainingtrees_exo_%s/"%(self.channel);
## taking the root file for data and mc
self.file_data = ("ofile_data.root");
self.signal_sample = in_signal_sample;
self.file_signal = ("ofile_%s.root"%(self.signal_sample));
self.file_pseudodata = ("ofile_pseudodata4exo.root");
self.file_pseudodata_herwig = ("ofile_pseudodata4exo_herwig.root");
if self.channel != "em":
self.file_WJets0_mc = ("ofile_WJets_Pythia180.root");
else:
self.file_WJets0_mc = ("ofile_WJets_Pythia180.root");
self.file_WJets1_mc = ("ofile_WJets_Herwig.root");
self.file_VV_mc = ("ofile_VV.root");# WW+WZ
self.file_TTbar_mc = ("ofile_TTbar_Powheg.root"); ## powheg TTbar
self.file_TTbar_herwig = ("ofile_TTbar_mcanlo.root"); ## mc@nlo TTbar
self.file_TTbar_matchDn_mc = ("ofile_TTbar_matchDn.root"); ## madgraph matching down
self.file_TTbar_matchUp_mc = ("ofile_TTbar_matchUp.root"); ## madgraph matching up
self.file_TTbar_scaleDn_mc = ("ofile_TTbar_scaleDn.root"); ## madgraph qcd scale down
self.file_TTbar_scaleUp_mc = ("ofile_TTbar_scaleUp.root"); ## madgraph qcd scale up
self.file_TTbar_MG_mc = ("ofile_TTbar_MG.root"); ## madgraph ttbar
self.file_STop_mc = ("ofile_STop.root"); ##single Top
## Define the workin poit on the N-subjettines cut
self.wtagger_label = options.category;
if self.wtagger_label == "HP" :
if self.channel == "el" : self.wtagger_cut = options.tau2tau1cutHP ; self.wtagger_cut_min = 0. ;
if self.channel == "mu" : self.wtagger_cut = options.tau2tau1cutHP ; self.wtagger_cut_min = 0. ;
if self.channel == "em": self.wtagger_cut = options.tau2tau1cutHP ; self.wtagger_cut_min = 0. ;
if self.wtagger_label == "LP":
self.wtagger_cut = options.tau2tau1cutLP ; self.wtagger_cut_min = options.tau2tau1cutHP ;
if self.wtagger_label == "nocut":
self.wtagger_cut = 10000;
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["WW_EWK"] = 6;
self.color_palet["STop"] = 7;
self.color_palet["TTbar"] = 210;
self.color_palet["ggH"] = 1;
self.color_palet["vbfH"] = 12;
self.color_palet["Signal"] = 1;
self.color_palet["Uncertainty"] = 1;
self.color_palet["Other_Backgrounds"] = 1;
## Some basic cut vaule
self.vpt_cut = 200; ## hadronic and leptonic W cut
self.mass_lvj_max = 2000.; ## invariant mass of 3 body max
self.mass_lvj_min = 200.; ## invariant mass of 3 body min
self.pfMET_cut = 50; ## missing transverse energy
self.lpt_cut = 50; ## lepton pT
self.deltaPhi_METj_cut = 2.0;
## binning in the W jet pT for differential SF study in bins of pT
if options.pTbin != "" :
self.ca8_ungroomed_pt_min = int(options.pTbin[0]);
self.ca8_ungroomed_pt_max = int(options.pTbin[1]);
else:
self.ca8_ungroomed_pt_min = 200;
self.ca8_ungroomed_pt_max = 1000;
## tighter cut for the electron channel
if self.channel == "el" or self.channel == "em":
self.pfMET_cut = 80; self.lpt_cut = 90;
## out txt file with info about fit and event couting
self.file_ttbar_control_txt = "ttbar_control_%s_%s_wtaggercut%s.txt"%(self.signal_sample,self.channel,self.wtagger_label);
self.file_out_ttbar_control = open(self.file_ttbar_control_txt,"w");
### set the TDR Style
setTDRStyle();
############# -----------
def fit_mj_TTbar_controlsample(self,in_file_name,label=""):
##### Print the final result for the number of events passing the cut and before the cut + the efficiency for the W-tagging -> dataset yields in the signal region
self.workspace4fit_.var("rrv_number_dataset_signal_region_data"+label+"_"+self.channel+"_mj").Print()
self.workspace4fit_.var("rrv_number_dataset_signal_region_VV" +label+"_"+self.channel+"_mj").Print()
self.workspace4fit_.var("rrv_number_dataset_signal_region_WJets0"+label+"_"+self.channel+"_mj").Print()
self.workspace4fit_.var("rrv_number_dataset_signal_region_STop"+label+"_"+self.channel+"_mj").Print()
self.workspace4fit_.var("rrv_number_dataset_signal_region_TTbar"+label+"_"+self.channel+"_mj").Print()
number_dataset_signal_region_data_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_data"+label+"_"+self.channel+"_mj").getVal();
number_dataset_signal_region_error2_data_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_error2_data"+label+"_"+self.channel+"_mj").getVal();
print "event number of data in signal_region: %s +/- sqrt(%s)"%(number_dataset_signal_region_data_mj, number_dataset_signal_region_error2_data_mj);
self.file_out_ttbar_control.write("%s channel SF: \n"%(self.channel));
self.file_out_ttbar_control.write("event number of data in signal_region: %s +/- sqrt(%s)\n"%(number_dataset_signal_region_data_mj, number_dataset_signal_region_error2_data_mj));
number_dataset_signal_region_TotalMC_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_TotalMC"+label+"_"+self.channel+"_mj").getVal();
number_dataset_signal_region_error2_TotalMC_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_error2_TotalMC"+label+"_"+self.channel+"_mj").getVal();
print "event number of TotalMC %s in signal_region: %s +/- sqrt(%s) "%(label,number_dataset_signal_region_TotalMC_mj, number_dataset_signal_region_error2_TotalMC_mj);
self.file_out_ttbar_control.write("event number of TotalMC %s in signal_region: %s +/- sqrt(%s) \n"%(label,number_dataset_signal_region_TotalMC_mj, number_dataset_signal_region_error2_TotalMC_mj));
number_dataset_signal_region_before_cut_data_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_before_cut_data"+label+"_"+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"+label+"_"+self.channel+"_mj").getVal();
print "event number of data in signal_region before_cut: %s +/- sqrt(%s)"%(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 data in signal_region before_cut: %s +/- sqrt(%s)\n"%(number_dataset_signal_region_before_cut_data_mj, number_dataset_signal_region_before_cut_error2_data_mj));
number_dataset_signal_region_before_cut_TotalMC_mj = self.workspace4fit_.var("rrv_number_dataset_signal_region_before_cut_TotalMC"+label+"_"+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"+label+"_"+self.channel+"_mj").getVal();
print "event number of TotalMC %s in signal_region before_cut: %s +/- sqrt(%s) "%(label,number_dataset_signal_region_before_cut_TotalMC_mj, number_dataset_signal_region_before_cut_error2_TotalMC_mj);
self.file_out_ttbar_control.write("event number of TotalMC %s in signal_region before_cut: %s +/- sqrt(%s) \n"%(label,number_dataset_signal_region_before_cut_TotalMC_mj, number_dataset_signal_region_before_cut_error2_TotalMC_mj));
# wtagger_eff reweight: only reweight the efficiency difference between MC and data
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 "wtagger efficiency of %s channel"%(self.channel);
print "wtagger_eff_MC %s = %s "%(label,wtagger_eff_MC);
print "wtagger_eff_data = %s "%(wtagger_eff_data);
print "wtagger_eff_reweight %s = %s +/- %s"%(label,wtagger_eff_reweight, wtagger_eff_reweight_err);
self.file_out_ttbar_control.write("wtagger_eff_MC %s = %s \n"%(label,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 +/- %s\n"%(label,wtagger_eff_reweight, wtagger_eff_reweight_err));
##########################################
## To build the dataset to be fitted ####
##########################################
def get_mj_and_mlvj_dataset_TTbar_controlsample(self,in_file_name, label, jet_mass="ttb_ca8_mass_pr"):# to get the shape of m_lvj
# read in tree
fileIn_name = TString(options.inPath+"/"+self.file_Directory+in_file_name);
fileIn = TFile(fileIn_name.Data());
treeIn = fileIn.Get("otree");
rrv_mass_j = self.workspace4fit_.var("rrv_mass_j")
rrv_weight = RooRealVar("rrv_weight","rrv_weight",0. ,10000000.)
### dataset of m_j 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) );
### dataset of m_j 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) );
### dataset of m_j failed tau2tau1 cut :
rdataset_failtau2tau1cut_mj = RooDataSet("rdataset"+label+"_failtau2tau1cut_"+self.channel+"_mj","rdataset"+label+"_failtau2tau1cut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) );
rdataset4fit_failtau2tau1cut_mj = RooDataSet("rdataset4fit"+label+"_failtau2tau1cut_"+self.channel+"_mj","rdataset4fit"+label+"_failtau2tau1cut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) );
### dataset of m_j extreme failed tau2tau1 cut: >0.75
rdataset_extremefailtau2tau1cut_mj = RooDataSet("rdataset"+label+"_extremefailtau2tau1cut_"+self.channel+"_mj","rdataset"+label+"_failtau2tau1cut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) );
rdataset4fit_extremefailtau2tau1cut_mj = RooDataSet("rdataset4fit"+label+"_extremefailtau2tau1cut_"+self.channel+"_mj","rdataset4fit"+label+"_failtau2tau1cut_"+self.channel+"_mj",RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight) );
### create the category for the fit
if TString(label).Contains("herwig"):
if self.workspace4fit_.cat("category_p_f"+"_herwig_"+self.channel):
category_p_f = self.workspace4fit_.cat("category_p_f"+"_herwig_"+self.channel);
else:
category_p_f = RooCategory("category_p_f"+"_herwig_"+self.channel,"category_p_f"+"_herwig_"+self.channel);
category_p_f.defineType("pass");
category_p_f.defineType("fail");
getattr(self.workspace4fit_,"import")(category_p_f);
else:
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
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
rand = ROOT.TRandom3() ;
rand.SetSeed();
for i in range(treeIn.GetEntries()):
if i % 100000 == 0: print "iEntry: ",i
treeIn.GetEntry(i);
if i==0: tmp_scale_to_lumi = treeIn.wSampleWeight ## weigth for xs for the mc
discriminantCut = 0;
wtagger = getattr(treeIn,"ttb_ca8_tau2tau1");
if wtagger < options.tau2tau1cutHP:
discriminantCut = 2;
elif wtagger > options.tau2tau1cutHP and wtagger < options.tau2tau1cutLP:
discriminantCut = 1;
elif wtagger > options.tau2tau1cutLP :
discriminantCut = 0;
tmp_jet_mass = 0. ;
if options.shift == 1 and not TString(label).Contains("data"):
tmp_jet_mass = getattr(treeIn, jet_mass) + self.mean_shift;
elif options.smear == 1 and not TString(label).Contains("data"):
tmp_jet_mass = getattr(treeIn, jet_mass)*(1+rand.Gaus(0,self.sigma_scale-1));
else:
tmp_jet_mass = getattr(treeIn, jet_mass);
tmp_event_weight = getattr(treeIn,"totalEventWeight");
tmp_event_weight4fit = getattr(treeIn,"eff_and_pu_Weight");
tmp_event_weight4fit = tmp_event_weight4fit*getattr(treeIn,"wSampleWeight")/tmp_scale_to_lumi
if not TString(label).Contains("data"):
tmp_event_weight = tmp_event_weight*getattr(treeIn,"btag_weight");
tmp_event_weight4fit = tmp_event_weight4fit*getattr(treeIn,"btag_weight");
else:
tmp_event_weight = 1.;
tmp_event_weight4fit = 1.;
### Cut for the HP category
if discriminantCut ==2 and getattr(treeIn,"mass_lvj_type0_met") < self.mass_lvj_max and getattr(treeIn,"mass_lvj_type0_met") > self.mass_lvj_min and (getattr(treeIn,"ttb_nak5_same_csvm") > 0 or getattr(treeIn,"ttb_nak5_oppoveto_csvm") > 0) and getattr(treeIn,"isttbar") > 0 and getattr(treeIn,"v_pt") > self.vpt_cut and getattr(treeIn,"l_pt") >= self.lpt_cut and getattr(treeIn,"pfMET") > self.pfMET_cut and getattr(treeIn,"ttb_ca8_ungroomed_pt") > 200 and tmp_jet_mass>rrv_mass_j.getMin() and tmp_jet_mass<rrv_mass_j.getMax() and getattr(treeIn,"ttb_ca8_ungroomed_pt") > self.ca8_ungroomed_pt_min and getattr(treeIn,"ttb_ca8_ungroomed_pt") < self.ca8_ungroomed_pt_max and getattr(treeIn,"ttb_dR_ca8_bjet_closer") < 2:
if TString(label).Contains("herwig") and not TString(label).Contains("data") :
tmp_event_weight = tmp_event_weight*treeIn.event_weight;
rrv_mass_j.setVal(tmp_jet_mass);
rdataset_mj.add(RooArgSet(rrv_mass_j),tmp_event_weight);
rdataset4fit_mj.add(RooArgSet( rrv_mass_j ),tmp_event_weight4fit);
if tmp_jet_mass >= self.mj_sideband_lo_min and tmp_jet_mass < self.mj_sideband_lo_max:
hnum_4region.Fill(-1,tmp_event_weight );
if tmp_jet_mass >= self.mj_signal_min and tmp_jet_mass < self.mj_signal_max:
hnum_2region.Fill(1,tmp_event_weight);
if tmp_jet_mass >= self.mj_signal_min and tmp_jet_mass < self.mj_signal_max :
hnum_4region.Fill(0,tmp_event_weight);
hnum_4region_error2.Fill(0,tmp_event_weight*tmp_event_weight);
if tmp_jet_mass >= self.mj_sideband_hi_min and tmp_jet_mass < self.mj_sideband_hi_max:
hnum_4region.Fill(1,tmp_event_weight);
hnum_4region.Fill(2,tmp_event_weight);
category_p_f.setLabel("pass");
combData_p_f.add(RooArgSet(rrv_mass_j,category_p_f),tmp_event_weight);
### Cut for the Total category
if getattr(treeIn,"mass_lvj_type0_met") < self.mass_lvj_max and getattr(treeIn,"mass_lvj_type0_met") > self.mass_lvj_min and (getattr(treeIn,"ttb_nak5_same_csvm") > 0 or getattr(treeIn,"ttb_nak5_oppoveto_csvm") > 0) and getattr(treeIn,"isttbar") > 0 and getattr(treeIn,"v_pt") > self.vpt_cut and getattr(treeIn,"l_pt") >= self.lpt_cut and getattr(treeIn,"pfMET") > self.pfMET_cut and getattr(treeIn,"ttb_ca8_ungroomed_pt") > 200 and tmp_jet_mass>rrv_mass_j.getMin() and tmp_jet_mass<rrv_mass_j.getMax() and getattr(treeIn,"ttb_ca8_ungroomed_pt") > self.ca8_ungroomed_pt_min and getattr(treeIn,"ttb_ca8_ungroomed_pt") < self.ca8_ungroomed_pt_max and getattr(treeIn,"ttb_dR_ca8_bjet_closer") < 2:
if TString(label).Contains("herwig") and not TString(label).Contains("data") :
tmp_event_weight = tmp_event_weight*treeIn.event_weight;
rrv_mass_j.setVal(tmp_jet_mass);
if tmp_jet_mass >=self.mj_signal_min and tmp_jet_mass <self.mj_signal_max :
hnum_4region_before_cut.Fill(0,tmp_event_weight);
hnum_4region_before_cut_error2.Fill(0,tmp_event_weight*tmp_event_weight);
rdataset_beforetau2tau1cut_mj.add(RooArgSet(rrv_mass_j),tmp_event_weight);
rdataset4fit_beforetau2tau1cut_mj.add(RooArgSet(rrv_mass_j),tmp_event_weight4fit);
### 1-HP category
if (discriminantCut==1 or discriminantCut==0) and getattr(treeIn,"mass_lvj_type0_met") < self.mass_lvj_max and getattr(treeIn,"mass_lvj_type0_met") > self.mass_lvj_min and (getattr(treeIn,"ttb_nak5_same_csvm") > 0 or getattr(treeIn,"ttb_nak5_oppoveto_csvm") > 0) and getattr(treeIn,"isttbar") > 0 and getattr(treeIn,"v_pt") > self.vpt_cut and getattr(treeIn,"l_pt") >= self.lpt_cut and getattr(treeIn,"pfMET") > self.pfMET_cut and getattr(treeIn,"ttb_ca8_ungroomed_pt") > 200 and tmp_jet_mass>rrv_mass_j.getMin() and tmp_jet_mass<rrv_mass_j.getMax() and getattr(treeIn,"ttb_ca8_ungroomed_pt") > self.ca8_ungroomed_pt_min and getattr(treeIn,"ttb_ca8_ungroomed_pt") < self.ca8_ungroomed_pt_max and getattr(treeIn,"ttb_dR_ca8_bjet_closer") < 2:
if TString(label).Contains("herwig") and not TString(label).Contains("data") :
tmp_event_weight = tmp_event_weight*treeIn.event_weight;
rrv_mass_j.setVal(tmp_jet_mass);
rdataset_failtau2tau1cut_mj.add(RooArgSet(rrv_mass_j),tmp_event_weight);
rdataset4fit_failtau2tau1cut_mj.add( RooArgSet( rrv_mass_j ), tmp_event_weight4fit );
category_p_f.setLabel("fail");
combData_p_f.add(RooArgSet(rrv_mass_j,category_p_f),tmp_event_weight);
### extreme fail category
if discriminantCut==0 and getattr(treeIn,"mass_lvj_type0_met") < self.mass_lvj_max and getattr(treeIn,"mass_lvj_type0_met") > self.mass_lvj_min and (getattr(treeIn,"ttb_nak5_same_csvm") > 0 or getattr(treeIn,"ttb_nak5_oppoveto_csvm") > 0) and getattr(treeIn,"isttbar") > 0 and getattr(treeIn,"v_pt") > self.vpt_cut and getattr(treeIn,"l_pt") >= self.lpt_cut and getattr(treeIn,"pfMET") > self.pfMET_cut and getattr(treeIn,"ttb_ca8_ungroomed_pt") > 200 and tmp_jet_mass>rrv_mass_j.getMin() and tmp_jet_mass<rrv_mass_j.getMax() and getattr(treeIn,"ttb_ca8_ungroomed_pt") > self.ca8_ungroomed_pt_min and getattr(treeIn,"ttb_ca8_ungroomed_pt") < self.ca8_ungroomed_pt_max and getattr(treeIn,"ttb_dR_ca8_jet_closer") < 2:
if TString(label).Contains("herwig") and not TString(label).Contains("data") :
tmp_event_weight = tmp_event_weight*treeIn.event_weight;
rdataset_extremefailtau2tau1cut_mj.add(RooArgSet(rrv_mass_j),tmp_event_weight);
rdataset4fit_extremefailtau2tau1cut_mj.add( RooArgSet( rrv_mass_j ), tmp_event_weight4fit );
rrv_scale_to_lumi = RooRealVar("rrv_scale_to_lumi"+label+"_"+self.channel,"rrv_scale_to_lumi"+label+"_"+self.channel,rdataset_mj.sumEntries()/rdataset4fit_mj.sumEntries());
rrv_scale_to_lumi_failtau2tau1cut = RooRealVar("rrv_scale_to_lumi"+label+"_failtau2tau1cut_"+self.channel,"rrv_scale_to_lumi"+label+"_failtau2tau1cut_"+self.channel,rdataset_failtau2tau1cut_mj.sumEntries()/rdataset4fit_failtau2tau1cut_mj.sumEntries());
rrv_scale_to_lumi_extremefailtau2tau1cut = RooRealVar("rrv_scale_to_lumi"+label+"_extremefailtau2tau1cut_"+self.channel,"rrv_scale_to_lumi"+label+"_extremefailtau2tau1cut_"+self.channel,rdataset_extremefailtau2tau1cut_mj.sumEntries()/rdataset4fit_extremefailtau2tau1cut_mj.sumEntries());
getattr(self.workspace4fit_,"import")(rrv_scale_to_lumi);
getattr(self.workspace4fit_,"import")(rrv_scale_to_lumi_failtau2tau1cut);
getattr(self.workspace4fit_,"import")(rrv_scale_to_lumi_extremefailtau2tau1cut);
#prepare m_j dataset
rrv_number_dataset_sb_lo_mj = RooRealVar("rrv_number_dataset_sb_lo"+label+"_"+self.channel+"_mj","rrv_number_dataset_sb_lo"+label+"_"+self.channel+"_mj",hnum_4region.GetBinContent(1));
rrv_number_dataset_signal_region_mj = RooRealVar("rrv_number_dataset_signal_region"+label+"_"+self.channel+"_mj","rrv_number_dataset_signal_region"+label+"_"+self.channel+"_mj",hnum_4region.GetBinContent(2));
rrv_number_dataset_signal_region_error2_mj = RooRealVar("rrv_number_dataset_signal_region_error2"+label+"_"+self.channel+"_mj","rrv_number_dataset_signal_region_error2"+label+"_"+self.channel+"_mj",hnum_4region_error2.GetBinContent(2));
rrv_number_dataset_signal_region_before_cut_mj = RooRealVar("rrv_number_dataset_signal_region_before_cut"+label+"_"+self.channel+"_mj","rrv_number_dataset_signal_region_before_cut"+label+"_"+self.channel+"_mj",hnum_4region_before_cut.GetBinContent(2));
rrv_number_dataset_signal_region_before_cut_error2_mj = RooRealVar("rrv_number_dataset_signal_region_before_cut_error2"+label+"_"+self.channel+"_mj","rrv_number_dataset_signal_region_before_cut_error2"+label+"_"+self.channel+"_mj",hnum_4region_before_cut_error2.GetBinContent(2));
rrv_number_dataset_sb_hi_mj = RooRealVar("rrv_number_dataset_sb_hi"+label+"_"+self.channel+"_mj","rrv_number_dataset_sb_hi"+label+"_"+self.channel+"_mj",hnum_4region.GetBinContent(3));
getattr(self.workspace4fit_,"import")(rrv_number_dataset_sb_lo_mj);
getattr(self.workspace4fit_,"import")(rrv_number_dataset_signal_region_mj);
getattr(self.workspace4fit_,"import")(rrv_number_dataset_signal_region_error2_mj);
getattr(self.workspace4fit_,"import")(rrv_number_dataset_signal_region_before_cut_mj);
getattr(self.workspace4fit_,"import")(rrv_number_dataset_signal_region_before_cut_error2_mj);
getattr(self.workspace4fit_,"import")(rrv_number_dataset_sb_hi_mj);
getattr(self.workspace4fit_,"import")(combData_p_f);
print "N_rdataset_mj: "
getattr(self.workspace4fit_,"import")(rdataset_mj);
getattr(self.workspace4fit_,"import")(rdataset4fit_mj)
getattr(self.workspace4fit_,"import")(rdataset_beforetau2tau1cut_mj);
getattr(self.workspace4fit_,"import")(rdataset4fit_beforetau2tau1cut_mj)
getattr(self.workspace4fit_,"import")(rdataset_failtau2tau1cut_mj);
getattr(self.workspace4fit_,"import")(rdataset4fit_failtau2tau1cut_mj);
getattr(self.workspace4fit_,"import")(rdataset_extremefailtau2tau1cut_mj);
getattr(self.workspace4fit_,"import")(rdataset4fit_extremefailtau2tau1cut_mj)
rdataset_mj.Print();
rdataset4fit_mj.Print();
rdataset_failtau2tau1cut_mj.Print();
rdataset4fit_failtau2tau1cut_mj.Print();
rdataset_extremefailtau2tau1cut_mj.Print();
rdataset4fit_extremefailtau2tau1cut_mj.Print();
rrv_number_dataset_sb_lo_mj.Print();
rrv_number_dataset_signal_region_mj.Print();
rrv_number_dataset_signal_region_error2_mj.Print();
rrv_number_dataset_signal_region_before_cut_mj.Print();
rrv_number_dataset_signal_region_before_cut_error2_mj.Print();
rrv_number_dataset_sb_hi_mj.Print();
rdataset_mj.Print();
rdataset_beforetau2tau1cut_mj.Print();
rdataset_failtau2tau1cut_mj.Print();
rdataset_extremefailtau2tau1cut_mj.Print();
rrv_number_dataset_signal_region_mj.Print();
rrv_number_dataset_signal_region_error2_mj.Print();
rrv_number_dataset_signal_region_before_cut_mj.Print();
rrv_number_dataset_signal_region_before_cut_error2_mj.Print();
combData_p_f.Print("v");
#### defines two different way to fit depending on pythia or herwig analysis
def fit_TTbar_controlsample(self, isherwig=0, ttbarMC=0):
if isherwig ==0 :
print "fit_TTbar_controlsample --> Pythia samples"
rrv_mass_j = self.workspace4fit_.var("rrv_mass_j");
print "##################################################"
print "############### Single Top DataSet ###############"
print "##################################################"
self.get_mj_and_mlvj_dataset_TTbar_controlsample(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,1);
fit_mj_single_MC(self.workspace4fit_,self.file_STop_mc,"_STop_failtau2tau1cut",self.mj_shape["STop_fail"],self.channel,self.wtagger_label,1);
fit_mj_single_MC(self.workspace4fit_,self.file_STop_mc,"_STop_extremefailtau2tau1cut",self.mj_shape["STop_extremefail"],self.channel,self.wtagger_label,1);
### Build WJet fit pass and fail distributions
print "###########################################"
print "############### WJets Pythia ##############"
print "###########################################"
self.get_mj_and_mlvj_dataset_TTbar_controlsample(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,1);
fit_mj_single_MC(self.workspace4fit_,self.file_WJets0_mc,"_WJets0_failtau2tau1cut",self.mj_shape["WJets0_fail"],self.channel,self.wtagger_label,1);
fit_mj_single_MC(self.workspace4fit_,self.file_WJets0_mc,"_WJets0_extremefailtau2tau1cut",self.mj_shape["WJets0_extremefail"],self.channel,self.wtagger_label,1);
### Build VV fit pass and fail distributions
print "#########################################"
print "############### VV Pythia ###############"
print "#########################################"
self.get_mj_and_mlvj_dataset_TTbar_controlsample(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,1);
fit_mj_single_MC(self.workspace4fit_,self.file_VV_mc,"_VV_failtau2tau1cut",self.mj_shape["VV_fail"],self.channel,self.wtagger_label,1);
fit_mj_single_MC(self.workspace4fit_,self.file_VV_mc,"_VV_extremefailtau2tau1cut",self.mj_shape["VV_extremefail"],self.channel,self.wtagger_label,1);
print "#########################################"
print "############# TTbar Powheg ##############"
print "#########################################"
self.get_mj_and_mlvj_dataset_TTbar_controlsample(self.file_TTbar_mc,"_TTbar");
print "################################################"
print "############## Pseudo Data Powheg ##############"
print "################################################"
self.get_mj_and_mlvj_dataset_TTbar_controlsample(self.file_pseudodata,"_TotalMC");
print "#################################"
print "############# Data ##############"
print "#################################"
self.get_mj_and_mlvj_dataset_TTbar_controlsample(self.file_data,"_data");
self.fit_mj_TTbar_controlsample(self.file_data);
self.constrainslist_data = ROOT.std.vector(ROOT.std.string)();
self.constrainslist_mc = ROOT.std.vector(ROOT.std.string)();
ScaleFactorTTbarControlSampleFit(self.workspace4fit_,self.mj_shape,self.color_palet,self.constrainslist_data,self.constrainslist_mc,"",self.channel,self.wtagger_label,self.ca8_ungroomed_pt_min,self.ca8_ungroomed_pt_max);
else:
print "###############################################"
print "############# Single Top DataSet ##############"
print "###############################################"
self.get_mj_and_mlvj_dataset_TTbar_controlsample(self.file_STop_mc,"_STop_herwig");
fit_mj_single_MC(self.workspace4fit_,self.file_STop_mc,"_STop_herwig",self.mj_shape["STop"],self.channel,self.wtagger_label,1);
fit_mj_single_MC(self.workspace4fit_,self.file_STop_mc,"_STop_herwig_failtau2tau1cut",self.mj_shape["STop_fail"],self.channel,self.wtagger_label,1);
fit_mj_single_MC(self.workspace4fit_,self.file_STop_mc,"_STop_extremefailtau2tau1cut",self.mj_shape["STop_extremefail"],self.channel,self.wtagger_label,1);
print "##########################################"
print "############## WJets Herwig ##############"
print "##########################################"
self.get_mj_and_mlvj_dataset_TTbar_controlsample(self.file_WJets1_mc,"_WJets0_herwig");
fit_mj_single_MC(self.workspace4fit_,self.file_WJets0_mc,"_WJets0_herwig",self.mj_shape["WJets0"],self.channel,self.wtagger_label,1);
fit_mj_single_MC(self.workspace4fit_,self.file_WJets0_mc,"_WJets0_herwig_failtau2tau1cut",self.mj_shape["WJets0_fail"],self.channel,self.wtagger_label,1);
fit_mj_single_MC(self.workspace4fit_,self.file_WJets0_mc,"_WJets0_herwig_extremefailtau2tau1cut",self.mj_shape["WJets0_extremefail"],self.channel,self.wtagger_label,1);
print "##########################################"
print "################ VV Pythia ###############"
print "##########################################"
self.get_mj_and_mlvj_dataset_TTbar_controlsample(self.file_VV_mc,"_VV_herwig");
fit_mj_single_MC(self.workspace4fit_,self.file_VV_mc,"_VV_herwig",self.mj_shape["VV"],self.channel,self.wtagger_label,1);
fit_mj_single_MC(self.workspace4fit_,self.file_VV_mc,"_VV_herwig_failtau2tau1cut",self.mj_shape["VV_fail"],self.channel,self.wtagger_label,1);
fit_mj_single_MC(self.workspace4fit_,self.file_VV_mc,"_VV_herwig_extremefailtau2tau1cut",self.mj_shape["VV_extremefail"],self.channel,self.wtagger_label,1);
self.fit_mj_single_MC(self.file_VV_mc,"_VV_herwig_extremefailtau2tau1cut","Exp","_TTbar_controlsample"); ## Exp
print "##########################################"
print "################ TTbar Herwig ############"
print "##########################################"
self.get_mj_and_mlvj_dataset_TTbar_controlsample(self.file_TTbar_herwig,"_TTbar_herwig");
print "##############################################"
print "############# Pseudo Data Herwig #############"
print "##############################################"
self.get_mj_and_mlvj_dataset_TTbar_controlsample(self.file_pseudodata_herwig,"_TotalMC_herwig");
print "##################################"
print "############## Data ##############"
print "##################################"
self.get_mj_and_mlvj_dataset_TTbar_controlsample(self.file_data,"_data_herwig");
self.fit_mj_TTbar_controlsample(self.file_data,"_herwig");
self.constrainslist_data = ROOT.std.vector(ROOT.std.string)();
self.constrainslist_mc = ROOT.std.vector(ROOT.std.string)();
ScaleFactorTTbarControlSampleFit(self.workspace4fit_,self.mj_shape,self.color_palet,self.constrainslist_data,self.constrainslist_mc,"",self.channel,self.wtagger_label,self.ca8_ungroomed_pt_min,self.ca8_ungroomed_pt_max);
class doFit_wj_and_wlvj_simultaneous:
def __init__(self,isherwig=0):
label = "";
if isherwig==1 : label = "_herwig" ;
self.workspace4fit_ = RooWorkspace("workspace4fit"+label+"_","workspace4fit"+label+"_"); ## create the workspace
self.boostedW_fitter_el = doFit_wj_and_wlvj("el","ggH600",40,130,label, self.workspace4fit_); ## single object analysis for electrons
self.boostedW_fitter_mu = doFit_wj_and_wlvj("mu","ggH600",40,130,label, self.workspace4fit_); ## single object analysis for muons
self.boostedW_fitter_el.fit_TTbar_controlsample(isherwig); ## run the electron analysis
self.boostedW_fitter_mu.fit_TTbar_controlsample(isherwig); ## run the muon analysis
self.workspace4fit_.data("rdataset_data"+label+"_mu_mj").Print();
self.workspace4fit_.data("rdataset_data"+label+"_el_mj").Print();
#### Define simultaneusly 4 category
sample_type = RooCategory("sample_type"+label,"sample_type"+label);
sample_type.defineType("mu_pass");
sample_type.defineType("mu_fail");
sample_type.defineType("el_pass");
sample_type.defineType("el_fail");
rrv_weight = RooRealVar("rrv_weight","rrv_weight",0. ,10000000.)
#### take the datasets
rdataset_data_mu_mj = self.workspace4fit_.data("rdataset_data"+label+"_mu_mj");
rdataset_data_el_mj = self.workspace4fit_.data("rdataset_data"+label+"_el_mj");
rdataset_data_mu_mj_fail = self.workspace4fit_.data("rdataset_data"+label+"_failtau2tau1cut_mu_mj");
rdataset_data_el_mj_fail = self.workspace4fit_.data("rdataset_data"+label+"_failtau2tau1cut_el_mj");
rrv_mass_j = self.workspace4fit_.var("rrv_mass_j");
## combined dataset fill
combData_data = RooDataSet("combData_data"+label,"combData_data"+label,RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight),RooFit.Index(sample_type),RooFit.Import("mu_pass",rdataset_data_mu_mj),RooFit.Import("el_pass",rdataset_data_el_mj),RooFit.Import("mu_fail",rdataset_data_mu_mj_fail),RooFit.Import("el_fail",rdataset_data_el_mj_fail) );
combData_data.Print();
rdataset_TotalMC_mu_mj = self.workspace4fit_.data("rdataset_TotalMC"+label+"_mu_mj");
rdataset_TotalMC_el_mj = self.workspace4fit_.data("rdataset_TotalMC"+label+"_el_mj");
rdataset_TotalMC_mu_mj_fail = self.workspace4fit_.data("rdataset_TotalMC"+label+"_failtau2tau1cut_mu_mj");
rdataset_TotalMC_el_mj_fail = self.workspace4fit_.data("rdataset_TotalMC"+label+"_failtau2tau1cut_el_mj");
combData_TotalMC = RooDataSet("combData_TotalMC"+label,"combData_TotalMC"+label,RooArgSet(rrv_mass_j,rrv_weight),RooFit.WeightVar(rrv_weight),RooFit.Index(sample_type),RooFit.Import("mu_pass",rdataset_TotalMC_mu_mj),RooFit.Import("el_pass",rdataset_TotalMC_el_mj),RooFit.Import("mu_fail",rdataset_TotalMC_mu_mj_fail),RooFit.Import("el_fail",rdataset_TotalMC_el_mj_fail) );
combData_TotalMC.Print();
# fit data --> import the pdf from the single fits and define the simultaneous total pdf
model_data_mu = self.workspace4fit_.pdf("model_data"+label+"_mu");
model_data_fail_mu = self.workspace4fit_.pdf("model_data"+label+"_failtau2tau1cut_mu");
model_data_el = self.workspace4fit_.pdf("model_data"+label+"_el");
model_data_fail_el = self.workspace4fit_.pdf("model_data"+label+"_failtau2tau1cut_el");
simPdf_data = RooSimultaneous("simPdf_data_em"+label,"simPdf_data_em"+label,sample_type);
simPdf_data.addPdf(model_data_mu,"mu_pass");
simPdf_data.addPdf(model_data_el,"el_pass");
simPdf_data.addPdf(model_data_fail_mu,"mu_fail");
simPdf_data.addPdf(model_data_fail_el,"el_fail");
constrainslist_data_em = ROOT.std.vector(ROOT.std.string)();
for i in range(self.boostedW_fitter_el.constrainslist_data.size()):
constrainslist_data_em.push_back(self.boostedW_fitter_el.constrainslist_data.at(i));
for i in range(self.boostedW_fitter_mu.constrainslist_data.size()):
constrainslist_data_em.push_back(self.boostedW_fitter_mu.constrainslist_data.at(i));
pdfconstrainslist_data_em = RooArgSet("pdfconstrainslist_data_em"+label);
for i in range(constrainslist_data_em.size()):
self.workspace4fit_.pdf(constrainslist_data_em.at(i)).Print();
pdfconstrainslist_data_em.add(self.workspace4fit_.pdf(constrainslist_data_em.at(i)) );
pdfconstrainslist_data_em.Print();
rfresult_data = simPdf_data.fitTo(combData_data,RooFit.Save(kTRUE),RooFit.ExternalConstraints(pdfconstrainslist_data_em));
rfresult_data = simPdf_data.fitTo(combData_data,RooFit.Save(kTRUE),RooFit.ExternalConstraints(pdfconstrainslist_data_em));
# fit TotalMC --> define the simultaneous total pdf
model_TotalMC_mu = self.workspace4fit_.pdf("model_TotalMC"+label+"_mu");
model_TotalMC_fail_mu = self.workspace4fit_.pdf("model_TotalMC"+label+"_failtau2tau1cut_mu");
model_TotalMC_el = self.workspace4fit_.pdf("model_TotalMC"+label+"_el");
model_TotalMC_fail_el = self.workspace4fit_.pdf("model_TotalMC"+label+"_failtau2tau1cut_el");
simPdf_TotalMC = RooSimultaneous("simPdf_TotalMC_em"+label,"simPdf_TotalMC_em"+label,sample_type);
simPdf_TotalMC.addPdf(model_TotalMC_mu,"mu_pass");
simPdf_TotalMC.addPdf(model_TotalMC_el,"el_pass");
simPdf_TotalMC.addPdf(model_TotalMC_fail_mu,"mu_fail");
simPdf_TotalMC.addPdf(model_TotalMC_fail_el,"el_fail");
constrainslist_TotalMC_em = ROOT.std.vector(ROOT.std.string)();
for i in range(self.boostedW_fitter_el.constrainslist_mc.size()):
constrainslist_TotalMC_em.push_back(self.boostedW_fitter_el.constrainslist_mc.at(i));
for i in range(self.boostedW_fitter_mu.constrainslist_mc.size()):
constrainslist_TotalMC_em.push_back(self.boostedW_fitter_mu.constrainslist_mc.at(i));
pdfconstrainslist_TotalMC_em = RooArgSet("pdfconstrainslist_TotalMC_em"+label);
for i in range(constrainslist_TotalMC_em.size()):
self.workspace4fit_.pdf(constrainslist_TotalMC_em[i]).Print();
pdfconstrainslist_TotalMC_em.add(self.workspace4fit_.pdf(constrainslist_TotalMC_em[i]) );
pdfconstrainslist_TotalMC_em.Print();
rfresult_TotalMC = simPdf_TotalMC.fitTo(combData_TotalMC,RooFit.Save(kTRUE),RooFit.ExternalConstraints(pdfconstrainslist_TotalMC_em));
rfresult_TotalMC = simPdf_TotalMC.fitTo(combData_TotalMC,RooFit.Save(kTRUE),RooFit.ExternalConstraints(pdfconstrainslist_TotalMC_em));
## draw the plots
DrawScaleFactorTTbarControlSample(self.workspace4fit_,self.boostedW_fitter_mu.color_palet,label,"mu",self.boostedW_fitter_mu.wtagger_label,self.boostedW_fitter_mu.ca8_ungroomed_pt_min,self.boostedW_fitter_mu.ca8_ungroomed_pt_max);
DrawScaleFactorTTbarControlSample(self.workspace4fit_,self.boostedW_fitter_el.color_palet,label,"el",self.boostedW_fitter_el.wtagger_label,self.boostedW_fitter_el.ca8_ungroomed_pt_min,self.boostedW_fitter_el.ca8_ungroomed_pt_max);
rfresult_TotalMC.Print();
rfresult_data.Print();
### take the efficienty value from the fits in both data nad mc
rrv_eff_MC_el = self.workspace4fit_.var("eff_ttbar_TotalMC"+label+"_el_mj");
rrv_eff_MC_mu = self.workspace4fit_.var("eff_ttbar_TotalMC"+label+"_mu_mj");
rrv_mean_MC_el = self.workspace4fit_.var("rrv_mean1_gaus_ttbar_TotalMC"+label+"_el_mj");
rrv_sigma_MC_el = self.workspace4fit_.var("rrv_sigma1_gaus_ttbar_TotalMC"+label+"_el_mj");
rrv_eff_data_el = self.workspace4fit_.var("eff_ttbar_data"+label+"_el_mj");
rrv_eff_data_mu = self.workspace4fit_.var("eff_ttbar_data"+label+"_mu_mj");
rrv_mean_data_el = self.workspace4fit_.var("rrv_mean1_gaus_ttbar_data"+label+"_el_mj");
rrv_sigma_data_el = self.workspace4fit_.var("rrv_sigma1_gaus_ttbar_data"+label+"_el_mj");
rrv_eff_MC_el.Print() ; rrv_eff_MC_mu.Print();
rrv_eff_data_el.Print() ; rrv_eff_data_mu.Print();
rrv_mean_MC_el.Print() ; rrv_mean_data_el.Print();
rrv_sigma_MC_el.Print() ; rrv_sigma_data_el.Print();
## compute the scale factors and uncertainty for HP
pure_wtagger_sf_el = rrv_eff_data_el.getVal()/rrv_eff_MC_el.getVal();
pure_wtagger_sf_mu = rrv_eff_data_mu.getVal()/rrv_eff_MC_mu.getVal();
pure_wtagger_mean_shift_el = rrv_mean_data_el.getVal()-rrv_mean_MC_el.getVal();
pure_wtagger_sigma_enlarge_el = rrv_sigma_data_el.getVal()/rrv_sigma_MC_el.getVal();
pure_wtagger_sf_el_err = ((rrv_eff_data_el.getError()/rrv_eff_data_el.getVal())**2 + (rrv_eff_MC_el.getError()/rrv_eff_MC_el.getVal())**2 )**0.5* pure_wtagger_sf_el;
pure_wtagger_sf_mu_err = ((rrv_eff_data_mu.getError()/rrv_eff_data_mu.getVal())**2 + (rrv_eff_MC_mu.getError()/rrv_eff_MC_mu.getVal())**2 )**0.5* pure_wtagger_sf_mu;
pure_wtagger_mean_shift_err_el = (rrv_mean_data_el.getError()**2 + rrv_mean_MC_el.getError()**2)**0.5;
pure_wtagger_sigma_enlarge_err_el = ((rrv_sigma_data_el.getError()/rrv_sigma_data_el.getVal())**2 + (rrv_sigma_MC_el.getError()/rrv_sigma_MC_el.getVal())**2 )**0.5* pure_wtagger_sigma_enlarge_el;
print "Pure W-tagger SF of el %s : %0.3f +/- %0.3f"%(label,pure_wtagger_sf_el, pure_wtagger_sf_el_err);
print "Pure W-tagger SF of mu %s : %0.3f +/- %0.3f"%(label,pure_wtagger_sf_mu, pure_wtagger_sf_mu_err);
print "Pure W-tagger mean shift el %s : %0.3f +/- %0.3f"%(label,pure_wtagger_mean_shift_el, pure_wtagger_mean_shift_err_el);
print "Pure W-tagger sigma enlarge el %s : %0.3f +/- %0.3f"%(label,pure_wtagger_sigma_enlarge_el, pure_wtagger_sigma_enlarge_err_el);
self.boostedW_fitter_el.file_out_ttbar_control.write( "\n***************************************************" )
self.boostedW_fitter_el.file_out_ttbar_control.write( "\nPure W-tagger SF of el %s : %0.3f +/- %0.3f"%(label,pure_wtagger_sf_el, pure_wtagger_sf_el_err));
self.boostedW_fitter_el.file_out_ttbar_control.write( "\nPure W-tagger SF of mu %s : %0.3f +/- %0.3f"%(label,pure_wtagger_sf_mu, pure_wtagger_sf_mu_err));
self.boostedW_fitter_el.file_out_ttbar_control.write( "\nPure W-tagger mean shift el %s : %0.3f +/- %0.3f"%(label,pure_wtagger_mean_shift_el, pure_wtagger_mean_shift_err_el));
self.boostedW_fitter_el.file_out_ttbar_control.write( "\nPure W-tagger sigma enlarge el %s : %0.3f +/- %0.3f"%(label,pure_wtagger_sigma_enlarge_el, pure_wtagger_sigma_enlarge_err_el));
## take the extreme fail informations
rrv_number_ttbar_TotalMC_extremefailtau2tau1cut_el_mj = self.workspace4fit_.var("rrv_number_ttbar_TotalMC"+label+"_extremefailtau2tau1cut_el_mj");
rrv_number_ttbar_TotalMC_extremefailtau2tau1cut_mu_mj = self.workspace4fit_.var("rrv_number_ttbar_TotalMC"+label+"_extremefailtau2tau1cut_mu_mj");
rrv_number_ttbar_data_extremefailtau2tau1cut_el_mj = self.workspace4fit_.var("rrv_number_ttbar_data"+label+"_extremefailtau2tau1cut_el_mj");
rrv_number_ttbar_data_extremefailtau2tau1cut_mu_mj = self.workspace4fit_.var("rrv_number_ttbar_data"+label+"_extremefailtau2tau1cut_mu_mj");
rrv_number_ttbar_TotalMC_extremefailtau2tau1cut_el_mj.Print();
rrv_number_ttbar_TotalMC_extremefailtau2tau1cut_mu_mj.Print();
rrv_number_ttbar_data_extremefailtau2tau1cut_el_mj.Print();
rrv_number_ttbar_data_extremefailtau2tau1cut_mu_mj.Print();
rrv_number_total_ttbar_TotalMC_el = self.workspace4fit_.var("rrv_number_total_ttbar_TotalMC"+label+"_el_mj");
rrv_number_total_ttbar_TotalMC_mu = self.workspace4fit_.var("rrv_number_total_ttbar_TotalMC"+label+"_mu_mj");
rrv_number_total_ttbar_data_el = self.workspace4fit_.var("rrv_number_total_ttbar_data"+label+"_el_mj");
rrv_number_total_ttbar_data_mu = self.workspace4fit_.var("rrv_number_total_ttbar_data"+label+"_mu_mj");
rrv_number_total_ttbar_TotalMC_el.Print();
rrv_number_total_ttbar_TotalMC_mu.Print();
rrv_number_total_ttbar_data_el.Print();
rrv_number_total_ttbar_data_mu.Print();
print "el TotalMC Eff of extremefail %s: %0.6f"%(label, rrv_number_ttbar_TotalMC_extremefailtau2tau1cut_el_mj.getVal() / rrv_number_total_ttbar_TotalMC_el.getVal());
print "mu TotalMC Eff of extremefail %s: %0.6f"%(label, rrv_number_ttbar_TotalMC_extremefailtau2tau1cut_mu_mj.getVal() / rrv_number_total_ttbar_TotalMC_mu.getVal());
print "el data Eff of extremefail %s: %0.6f"%(label, rrv_number_ttbar_data_extremefailtau2tau1cut_el_mj.getVal() / rrv_number_total_ttbar_data_el.getVal());
print "mu data Eff of extremefail %s: %0.6f"%(label, rrv_number_ttbar_data_extremefailtau2tau1cut_mu_mj.getVal() / rrv_number_total_ttbar_data_mu.getVal());
self.boostedW_fitter_el.file_out_ttbar_control.write("\nel TotalMC Eff of extremefail %s: %0.6f"%(label, rrv_number_ttbar_TotalMC_extremefailtau2tau1cut_el_mj.getVal() / rrv_number_total_ttbar_TotalMC_el.getVal()));
self.boostedW_fitter_el.file_out_ttbar_control.write("\nel data Eff of extremefail %s: %0.6f"%(label, rrv_number_ttbar_data_extremefailtau2tau1cut_el_mj.getVal() / rrv_number_total_ttbar_data_el.getVal()));
self.boostedW_fitter_mu.file_out_ttbar_control.write("\nmu TotalMC Eff of extremefail %s: %0.6f"%(label, rrv_number_ttbar_TotalMC_extremefailtau2tau1cut_mu_mj.getVal() / rrv_number_total_ttbar_TotalMC_mu.getVal()));
self.boostedW_fitter_mu.file_out_ttbar_control.write("\nmu data Eff of extremefail %s: %0.6f"%(label, rrv_number_ttbar_data_extremefailtau2tau1cut_mu_mj.getVal() / rrv_number_total_ttbar_data_mu.getVal()));
tmp_eff_MC_el_extremefail = rrv_number_ttbar_TotalMC_extremefailtau2tau1cut_el_mj.getVal() / rrv_number_total_ttbar_TotalMC_el.getVal();
tmp_eff_MC_mu_extremefail = rrv_number_ttbar_TotalMC_extremefailtau2tau1cut_mu_mj.getVal() / rrv_number_total_ttbar_TotalMC_mu.getVal();
tmp_eff_data_el_extremefail= rrv_number_ttbar_data_extremefailtau2tau1cut_el_mj.getVal() / rrv_number_total_ttbar_data_el.getVal();
tmp_eff_data_mu_extremefail= rrv_number_ttbar_data_extremefailtau2tau1cut_mu_mj.getVal() / rrv_number_total_ttbar_data_mu.getVal();
tmp_eff_MC_el_extremefail_error =tmp_eff_MC_el_extremefail* TMath.Sqrt( (rrv_number_ttbar_TotalMC_extremefailtau2tau1cut_el_mj.getError()/rrv_number_ttbar_TotalMC_extremefailtau2tau1cut_el_mj.getVal() )**2+ (rrv_number_total_ttbar_TotalMC_el.getError()/rrv_number_total_ttbar_TotalMC_el.getVal() )**2 );
tmp_eff_MC_mu_extremefail_error =tmp_eff_MC_mu_extremefail* TMath.Sqrt( (rrv_number_ttbar_TotalMC_extremefailtau2tau1cut_mu_mj.getError()/rrv_number_ttbar_TotalMC_extremefailtau2tau1cut_mu_mj.getVal() )**2+ (rrv_number_total_ttbar_TotalMC_mu.getError()/rrv_number_total_ttbar_TotalMC_mu.getVal() )**2 );
tmp_eff_data_el_extremefail_error =tmp_eff_data_el_extremefail* TMath.Sqrt( (rrv_number_ttbar_data_extremefailtau2tau1cut_el_mj.getError()/rrv_number_ttbar_data_extremefailtau2tau1cut_el_mj.getVal() )**2+ (rrv_number_total_ttbar_data_el.getError()/rrv_number_total_ttbar_data_el.getVal() )**2 );
tmp_eff_data_mu_extremefail_error =tmp_eff_data_mu_extremefail* TMath.Sqrt( (rrv_number_ttbar_data_extremefailtau2tau1cut_mu_mj.getError()/rrv_number_ttbar_data_extremefailtau2tau1cut_mu_mj.getVal() )**2+ (rrv_number_total_ttbar_data_mu.getError()/rrv_number_total_ttbar_data_mu.getVal() )**2 );
print "eff_MC_el_extremefail_error %s: %f"%(label,tmp_eff_MC_el_extremefail_error);
print "eff_MC_mu_extremefail_error %s: %f"%(label,tmp_eff_MC_mu_extremefail_error);
print "eff_data_el_extremefail_error %s: %f"%(label,tmp_eff_data_el_extremefail_error);
print "eff_data_mu_extremefail_error %s: %f"%(label,tmp_eff_data_mu_extremefail_error);
self.boostedW_fitter_el.file_out_ttbar_control.write("\neff_MC_el_extremefail_error %s: %f"%(label,tmp_eff_MC_el_extremefail_error));
self.boostedW_fitter_el.file_out_ttbar_control.write("\neff_data_el_extremefail_error %s: %f"%(label,tmp_eff_data_el_extremefail_error));
self.boostedW_fitter_mu.file_out_ttbar_control.write("\neff_MC_mu_extremefail_error %s: %f"%(label,tmp_eff_MC_mu_extremefail_error));
self.boostedW_fitter_mu.file_out_ttbar_control.write("\neff_data_mu_extremefail_error %s: %f"%(label,tmp_eff_data_mu_extremefail_error));
## Low purity scale factors LP
tmp_eff_MC_el_LP = 1.-rrv_eff_MC_el.getVal()-tmp_eff_MC_el_extremefail;
tmp_eff_MC_mu_LP = 1.-rrv_eff_MC_mu.getVal()-tmp_eff_MC_mu_extremefail;
tmp_eff_data_el_LP =1.-rrv_eff_data_el.getVal()-tmp_eff_data_el_extremefail;
tmp_eff_data_mu_LP =1.-rrv_eff_data_mu.getVal()-tmp_eff_data_mu_extremefail;
tmp_eff_MC_el_LP_err = TMath.Sqrt( rrv_eff_MC_el.getError()**2 + tmp_eff_MC_el_extremefail_error**2 );
tmp_eff_MC_mu_LP_err = TMath.Sqrt( rrv_eff_MC_mu.getError()**2 + tmp_eff_MC_mu_extremefail_error**2 );
tmp_eff_data_el_LP_err = TMath.Sqrt( rrv_eff_data_el.getError()**2 + tmp_eff_data_el_extremefail_error**2 );
tmp_eff_data_mu_LP_err = TMath.Sqrt( rrv_eff_data_mu.getError()**2 + tmp_eff_data_mu_extremefail_error**2 );
print "LP Eff of el data %s: %0.3f +/- %0.3f"%(label,tmp_eff_data_el_LP, tmp_eff_data_el_LP_err);
print "LP Eff of el MC %s: %0.3f +/- %0.3f"%(label,tmp_eff_MC_el_LP, tmp_eff_MC_el_LP_err);
print "LP Eff of mu data %s: %0.3f +/- %0.3f"%(label,tmp_eff_data_mu_LP, tmp_eff_data_mu_LP_err);
print "LP Eff of mu MC %s: %0.3f +/- %0.3f"%(label,tmp_eff_MC_mu_LP, tmp_eff_MC_mu_LP_err);
self.boostedW_fitter_el.file_out_ttbar_control.write("\nLP Eff of el data %s: %f +/- %f"%(label,tmp_eff_data_el_LP, tmp_eff_data_el_LP_err));
self.boostedW_fitter_el.file_out_ttbar_control.write("\nLP Eff of el MC %s: %f +/- %f"%(label,tmp_eff_MC_el_LP, tmp_eff_MC_el_LP_err));
self.boostedW_fitter_mu.file_out_ttbar_control.write("\nLP Eff of mu data %s: %f +/- %f"%(label,tmp_eff_data_mu_LP, tmp_eff_data_mu_LP_err));
self.boostedW_fitter_mu.file_out_ttbar_control.write("\nLP Eff of mu MC %s: %f +/- %f"%(label,tmp_eff_MC_mu_LP, tmp_eff_MC_mu_LP_err));
pure_wtagger_sf_el_LP = tmp_eff_data_el_LP / tmp_eff_MC_el_LP;
pure_wtagger_sf_mu_LP = tmp_eff_data_mu_LP / tmp_eff_MC_mu_LP;
pure_wtagger_sf_el_LP_err = pure_wtagger_sf_el_LP*TMath.Sqrt( (tmp_eff_data_el_LP_err/tmp_eff_data_el_LP)**2 + (tmp_eff_MC_el_LP_err/tmp_eff_MC_el_LP)**2 );
pure_wtagger_sf_mu_LP_err = pure_wtagger_sf_mu_LP*TMath.Sqrt( (tmp_eff_data_mu_LP_err/tmp_eff_data_mu_LP)**2 + (tmp_eff_MC_mu_LP_err/tmp_eff_MC_mu_LP)**2 );
print "Pure W-tagger LP SF of el %s: %0.3f +/- %0.3f"%(label,pure_wtagger_sf_el_LP, pure_wtagger_sf_el_LP_err);
print "Pure W-tagger LP SF of mu %s: %0.3f +/- %0.3f"%(label,pure_wtagger_sf_mu_LP, pure_wtagger_sf_mu_LP_err);
self.boostedW_fitter_el.file_out_ttbar_control.write("\nPure W-tagger LP SF of el %s: %f +/- %f"%(label,pure_wtagger_sf_el_LP, pure_wtagger_sf_el_LP_err));
self.boostedW_fitter_mu.file_out_ttbar_control.write("\nPure W-tagger LP SF of mu %s: %f +/- %f"%(label,pure_wtagger_sf_mu_LP, pure_wtagger_sf_mu_LP_err));
### function to call single channel fits
def control_sample(channel="mu",isherwig=0, ttbarMC=0):
print "control sample "+channel;
if isherwig == 0:
## create the object and do the single for pythia
boostedW_fitter = doFit_wj_and_wlvj(channel,"ggH600",40,130);
boostedW_fitter.fit_TTbar_controlsample(isherwig);
elif isherwig == 1:
## create the object and do the single for herwig
boostedW_fitter_herwig = doFit_wj_and_wlvj(channel,"ggH600",40,130,"_herwig");
boostedW_fitter_herwig.fit_TTbar_controlsample(isherwig);
elif isherwig == 2:
## do Pythia and herwig analysis at the same time
boostedW_fitter = doFit_wj_and_wlvj(channel,"ggH600",40,130);
boostedW_fitter.fit_TTbar_controlsample(0);
boostedW_fitter_herwig = doFit_wj_and_wlvj(channel,"ggH600",40,130,"_herwig");
boostedW_fitter_herwig.fit_TTbar_controlsample(1);
### function to call simultaneous channel fits
def control_sample_simultaneous():
print "control_sample_simultaneous";
if options.herwig == 0 :
boostedW_fitter_sim = doFit_wj_and_wlvj_simultaneous(options.herwig);
elif options.herwig == 1 :
boostedW_fitter_sim_herwig = doFit_wj_and_wlvj_simultaneous(options.herwig);
elif options.herwig == 2:
boostedW_fitter_sim = doFit_wj_and_wlvj_simultaneous();
boostedW_fitter_sim = doFit_wj_and_wlvj_simultaneous(options.herwig);
### main code
if __name__ == '__main__':
channel = options.channel;
if options.fitwtaggersim:
print 'fitwtagger for el+mu sample'
control_sample_simultaneous();
elif options.fitwtagger:
print 'fitwtagger for %s sample'%(channel)
control_sample(channel,options.herwig);