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R2GGBBFitter_mtot.cc
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R2GGBBFitter_mtot.cc
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/** \macro H2GGFitter.cc
*
* $Id$
*
* Software developed for the CMS Detector at LHC
*
*
* Template Serguei Ganjour - CEA/IRFU/SPP, Saclay
*
*
* Macro is implementing the unbinned maximum-likelihood model for
* the Higgs to gamma gamma analysis. PDF model and RooDataSets
* are stored in the workspace which is feeded to HiggsAnalysis/CombinedLimit tools:
*
*/
// this one is for 4 body fit
#include "GaussExp.h"
#include "RooAbsPdf.h"
#include "RooRealProxy.h"
#include "RooCategoryProxy.h"
#include "RooAbsReal.h"
#include "RooAbsCategory.h"
//using namespace RooFit;
//using namespace RooStats ;
const Int_t NCAT = 2;
// declare the functions
void AddSigData(RooWorkspace*, Float_t);
void AddBkgData(RooWorkspace*);
void SigModelFit(RooWorkspace*, Float_t);
void MakePlots(RooWorkspace*, Float_t, RooFitResult* );
void MakeSigWS(RooWorkspace* w, const char* filename);
void MakeBkgWS(RooWorkspace* w, const char* filename);
void MakeDataCardonecat(RooWorkspace* w, const char* filename, const char* filename1);
void MakeDataCardREP(RooWorkspace* w, const char* filename, const char* filename1);
void MakeDataCardLnU(RooWorkspace* w, const char* filename, const char* filename1);
void SetParamNames(RooWorkspace*);
void SetConstantParams(const RooArgSet* params);
RooFitResult* fitresult[NCAT]; // container for the fit results
RooFitResult* BkgModelFitBernstein(RooWorkspace*, Bool_t);
const int minfit =150, maxfit=1200;
RooArgSet* defineVariables()
{
// define variables of the input ntuple
RooRealVar* mtot = new RooRealVar("mtot","M(#gamma#gamma jj)",150,1200,"GeV");
RooRealVar* mgg = new RooRealVar("mgg","M(#gamma#gamma)",100,180,"GeV");
RooRealVar* evWeight = new RooRealVar("evWeight","HqT x PUwei",0,100,"");
RooCategory* cut_based_ct = new RooCategory("cut_based_ct","event category 2") ;
//
cut_based_ct->defineType("cat4_0",0);
cut_based_ct->defineType("cat4_1",1);
//
RooArgSet* ntplVars = new RooArgSet(*mtot, *mgg, * cut_based_ct, *evWeight);
ntplVars->add(*cut_based_ct);
ntplVars->add(*mtot);
ntplVars->add(*mgg);
ntplVars->add(*evWeight);
return ntplVars;
}
void runfits(const Float_t mass=120, Int_t mode=1, Bool_t dobands = false)
{
style();
TString fileBaseName(TString::Format("hgg.mH%.1f_8TeV", mass));
TString fileBkgName(TString::Format("hgg.inputbkg_8TeV", mass));
TString card_name("models_mtot_exp.rs"); // fit model parameters
// declare a first WS
HLFactory hlf("HLFactory", card_name, false);
RooWorkspace* w = hlf.GetWs(); // Get models and variables
RooFitResult* fitresults;
// TString ssignal = "MiniTrees/OlivierAug13/v02_regkin_mggjj_0/Radion_m500_regression-m500_minimal.root";
// TString ddata = "MiniTrees/OlivierAug13/v02_regkin_mggjj_0/Data_regression-m500_minimal.root";
// TString ssignal = "MiniTrees/OlivierOc13/v15_base_mggjj_0/02013-10-30-Radion_m550_8TeV_nm_m550.root";
// TString ddata = "MiniTrees/OlivierOc13/v15_base_mggjj_0/02013-10-30-Data_m550.root";
//
TString ssignal = "MiniTrees/ChiaraNov13/v20/finalizedTrees_Radion_V07__fitToGGJJ__withKinFit/RadionSignal_m550.root";
TString ddata = "MiniTrees/ChiaraNov13/v20/finalizedTrees_Radion_V07__fitToGGJJ__withKinFit/Data.root";
//
cout<<"Signal: "<< ssignal<<endl;
cout<<"Data: "<< ddata<<endl;
AddSigData(w, mass,ssignal);
AddBkgData(w,ddata);
w->Print("v");
// construct the models to fit
SigModelFit(w, mass);
bool dobands=false;
fitresults = BkgModelFitBernstein(w, dobands);
// Construct points workspace
MakeSigWS(w, fileBaseName);
MakeBkgWS(w, fileBkgName);
MakePlots(w, mass, fitresults);
MakeDataCardonecat(w, fileBaseName, fileBkgName);
MakeDataCardREP(w, fileBaseName, fileBkgName);
MakeDataCardLnU(w, fileBaseName, fileBkgName);
cout<< "here"<<endl;
return;
} // close runfits
////////////////////////////////////////////////////////////////////
// we add the data to the workspace in categories
void AddSigData(RooWorkspace* w, Float_t mass, TString signalfile) {
const Int_t ncat = NCAT;
Float_t MASS(mass);
// Luminosity:
Float_t Lum = 19620.0; // pb-1
RooRealVar lumi("lumi","lumi",Lum);
w->import(lumi);
RooArgSet* ntplVars = defineVariables();
// file input
TFile sigFile(signalfile);
TTree* sigTree = (TTree*) sigFile.Get("TCVARS");
// common preselection cut
TString mainCut("1");
// one channel with right weights
RooDataSet sigScaled(
"sigScaled",
"dataset",
sigTree,
*ntplVars,
mainCut,
"evWeight");
RooDataSet* sigToFit[ncat];
TString cut0 = "&& mgg > 120 && mgg < 130 "; // " && 1>0";//
for (int c = 0; c < ncat; ++c) {
sigToFit[c] = (RooDataSet*) sigScaled.reduce(
*w->var("mtot"),
mainCut+TString::Format(" && cut_based_ct==%d",c)+cut0);
w->import(*sigToFit[c],Rename(TString::Format("Sig_cat%d",c)));
} // close ncat
// Create full signal data set without categorization
RooDataSet* sigToFitAll = (RooDataSet*) sigScaled.reduce(*w->var("mtot"),mainCut);
w->import(*sigToFitAll,Rename("Sig"));
// here we print the number of entries on the different categories
cout << "========= the number of entries on the different categories ==========" << endl;
cout << "---- one channel: " << sigScaled.sumEntries() << endl;
for (int c = 0; c < ncat; ++c) {
Float_t nExpEvt = sigToFit[c]->sumEntries();
cout << TString::Format("nEvt exp. cat%d : ",c) << nExpEvt
<< TString::Format(" eff x Acc cat%d : ",c)
<< "%"
<< endl;
}
cout << "======================================================================" << endl;
sigScaled.Print("v");
return;
} // end add signal function
///////////////////////////////////////////////////////////////////////////////////
// we add the data to the workspace in categories
void AddBkgData(RooWorkspace* w, TString datafile) {
const Int_t ncat = NCAT;
// common preselection cut
TString mainCut("1");
RooArgSet* ntplVars = defineVariables();
// weith data as with 1
RooRealVar weightVar("weightVar","",1,0,1000);
weightVar.setVal(1.);
TFile dataFile(datafile);
TTree* dataTree = (TTree*) dataFile.Get("TCVARS");
RooDataSet Data("Data","dataset",dataTree,*ntplVars,"","weightVar");
RooDataSet* dataToFit[ncat];
RooDataSet* dataToPlot[ncat];
TString cut0 = "&& mgg > 120 && mgg < 130 "; // " && 1>0";//
for (int c = 0; c < ncat; ++c) {
dataToFit[c] = (RooDataSet*) Data.reduce(
*w->var("mtot"),
TString::Format(" cut_based_ct==%d",c)+cut0);
dataToPlot[c] = (RooDataSet*) Data.reduce(
*w->var("mtot"),
TString::Format(" cut_based_ct==%d",c)
+TString::Format(" && (mtot > 2050)") + cut0 // blind
);
w->import(*dataToFit[c],Rename(TString::Format("Data_cat%d",c)));
w->import(*dataToPlot[c],Rename(TString::Format("Dataplot_cat%d",c)));
}
// Create full data set without categorization
RooDataSet* data = (RooDataSet*) Data.reduce(*w->var("mtot"));
w->import(*data, Rename("Data"));
data->Print("v");
return;
} // close add data ..
/////////////////////////////////////////////////////////////////////
// we make the fit model
void SigModelFit(RooWorkspace* w, Float_t mass) {
const Int_t ncat = NCAT;
Float_t MASS(mass);
RooDataSet* sigToFit[ncat];
RooAbsPdf* mtotSig[ncat];
// fit range
Float_t minMassFit(minfit),maxMassFit(maxfit);
for (int c = 0; c < ncat; ++c) {
// import sig and data from workspace
sigToFit[c] = (RooDataSet*) w->data(TString::Format("Sig_cat%d",c));
mtotSig[c] = (RooAbsPdf*) w->pdf(TString::Format("mtotSig_cat%d",c));
RooRealVar* peak = w->var(TString::Format("mtot_sig_m0_cat%d",c));
peak->setVal(MASS);
cout << "OK up to now..." <<MASS<< endl;
// Fit model as M(x|y) to D(x,y)
mtotSig[c]->fitTo(*sigToFit[c],Range(minMassFit,maxMassFit),SumW2Error(kTRUE));
// IMPORTANT: fix all pdf parameters to constant
w->defineSet(TString::Format("SigPdfParam_cat%d",c),
RooArgSet(
*w->var(TString::Format("mtot_sig_m0_cat%d",c)),
*w->var(TString::Format("mtot_sig_alpha_cat%d",c)),
*w->var(TString::Format("mtot_sig_n_cat%d",c)),
*w->var(TString::Format("mtot_sig_gsigma_cat%d",c)),
*w->var(TString::Format("mtot_sig_frac_cat%d",c))) );
SetConstantParams(w->set(TString::Format("SigPdfParam_cat%d",c)));
} // close for ncat
} // close signal model fit
////////////////////////////////////////////////////////////
// BKG model berestein 3
RooFitResult* BkgModelFitBernstein(RooWorkspace* w, Bool_t dobands) {
const Int_t ncat = NCAT;
std::vector<TString> catdesc;
catdesc.push_back("2 btag");
catdesc.push_back("1 btag");
//******************************************//
// Fit background with model pdfs
//******************************************//
// retrieve pdfs and datasets from workspace to fit with pdf models
RooDataSet* data[ncat];
RooDataSet* dataplot[ncat];
//RooBernstein* mtotBkg[ncat];
RooPlot* plotmtotBkg[ncat];
RooDataSet* sigToFit[ncat]; // the signal
RooAbsPdf* mtotSig[ncat]; // polinomial of 4* order for signal
Float_t minMassFit(minfit),maxMassFit(maxfit);
// Fit data with background pdf for data limit
RooRealVar* mtot = w->var("mtot");
mtot->setUnit("GeV");
//
TLatex *text = new TLatex();
text->SetNDC();
text->SetTextSize(0.04);
//
int order=-2;
//
for (int c = 0; c < ncat; ++c) {
data[c] = (RooDataSet*) w->data(TString::Format("Data_cat%d",c));
cout << "!!!!!!!!!!!!!" << endl;
/*
w->factory(TString::Format("mtot_bkg_8TeV_norm_cat%d[1.0,0.0,100000]",c)); // is is on the .rs
*/
////////////////////////////////////
// these are the parameters for exponential fit
// one slope by category - not squared slopes
//((RooRealVar*) w->var(TString::Format("mtot_bkg_8TeV_slope3_cat%d",c)))->setConstant(true);
//cout << "---------------- Parameter 3 set to const" << endl;
cout<<"here 0 "<< c<<endl;
RooFormulaVar *p1mod = new RooFormulaVar( // parameter 0 is the mean of the Gaussian core,
TString::Format("p1mod_cat%d",c),"","@0",*w->var(TString::Format("mtot_bkg_8TeV_slope1_cat%d",c)));
RooFormulaVar *p2mod = new RooFormulaVar( // parameter 1 is the std dev of the Gaussian
TString::Format("p2mod_cat%d",c),"","@0",*w->var(TString::Format("mtot_bkg_8TeV_slope2_cat%d",c)));
RooFormulaVar *p3mod = new RooFormulaVar( // parameter 2 is the number of std dev from the mean where the exponential fall off starts on the high side of the Gaussian. Parameter number 2, incidentally, is identical to theexponent of the exponential, by requiring analytical continuity at the joining point
TString::Format("p3mod_cat%d",c),"","@0",*w->var(TString::Format("mtot_bkg_8TeV_slope3_cat%d",c)));
RooAbsPdf* mtotBkgTmp0 = 0; // declare a empty pdf
// adding pdf's, using the variables
mtotBkgTmp0 = new GaussExp( // fill the pdf with the floating parameters
TString::Format("mggBkgTmp0_cat%d",c),
"", *mtot,
*p1mod, *p2mod, *p3mod);
/*
RooAbsPdf* mtotBkgTmp0 = new RooGenericPdf( // if exp function
TString::Format("DijetBackground_%d",c),
"exp(-@0/(@1*@1+@2*@2*@0))",
RooArgList(*mtot, *p1mod, *p2mod));
*/
w->factory(TString::Format("mtot_bkg_8TeV_cat%d_norm[1.0,0.0,100000]",c));
RooExtendPdf mtotBkgTmp( // we copy the pdf? normalized
TString::Format("mtotBkg_cat%d",c),
"",*mtotBkgTmp0,
*w->var(TString::Format("mtot_bkg_8TeV_cat%d_norm",c)));
fitresult[c] = mtotBkgTmp.fitTo( // fit with normalized pdf,and return values
*data[c], // bkg
Strategy(1), // MINUIT strategy
Minos(kFALSE), // interpretation on the errors, nonlinearities
Range(minMassFit,maxMassFit),
SumW2Error(kTRUE),
Save(kTRUE));
w->import(mtotBkgTmp); //store the normalized pdf on wp
//************************************************//
// Plot mtot background fit results per categories
//************************************************//
TCanvas* ctmp = new TCanvas("ctmp","mtot Background Categories",0,0,501,501);
Int_t nBinsMass(80);
plotmtotBkg[c] = mtot->frame(nBinsMass);
dataplot[c] = (RooDataSet*) w->data(TString::Format("Dataplot_cat%d",c));
data[c]->plotOn(plotmtotBkg[c],LineColor(kWhite),MarkerColor(kWhite));
mtotBkgTmp.plotOn(
plotmtotBkg[c],
LineColor(kBlue),
Range("fitrange"),NormRange("fitrange"));
// dataplot[c]->plotOn(plotmtotBkg[c]); // blind
plotmtotBkg[c]->Draw();
cout << "!!!!!!!!!!!!!!!!!" << endl;
cout << "!!!!!!!!!!!!!!!!!" << endl; // now we fit the gaussian on signal
// we draw signal on the same
/*
sigToFit[c] = (RooDataSet*) w->data(TString::Format("Sig_cat%d",c));
double norm = 0.01*sigToFit[c]->sumEntries(); //
mtotSig[c] = (RooAbsPdf*) w->pdf(TString::Format("mtotSig_cat%d",c));
// we are not constructing signal pdf, this is constructed on sig to fit function...
mtotSig[c] ->plotOn(
plotmtotBkg[c],
Normalization(norm,RooAbsPdf::NumEvent),
DrawOption("F"),
LineColor(kRed),FillStyle(1001),FillColor(19));
mtotSig[c]->plotOn(
plotmtotBkg[c],
Normalization(norm,RooAbsPdf::NumEvent),LineColor(kRed));
*/
//plotmtotBkg[c]->SetTitle("CMS preliminary 19.702/fb");
plotmtotBkg[c]->SetMinimum(0.0);
plotmtotBkg[c]->SetMaximum(20*plotmtotBkg[c]->GetMaximum());
plotmtotBkg[c]->GetXaxis()->SetTitle("M_{#gamma#gamma jj} (GeV)");
if (dobands) {
RooAbsPdf *cpdf; cpdf = mtotBkgTmp0;
TGraphAsymmErrors *onesigma = new TGraphAsymmErrors();
TGraphAsymmErrors *twosigma = new TGraphAsymmErrors();
RooRealVar *nlim = new RooRealVar(TString::Format("nlim%d",c),"",0.0,0.0,10.0);
nlim->removeRange();
RooCurve *nomcurve = dynamic_cast<RooCurve*>(plotmtotBkg[c]->getObject(1));
for (int i=1; i<(plotmtotBkg[c]->GetXaxis()->GetNbins()+1); ++i) {
double lowedge = plotmtotBkg[c]->GetXaxis()->GetBinLowEdge(i);
double upedge = plotmtotBkg[c]->GetXaxis()->GetBinUpEdge(i);
double center = plotmtotBkg[c]->GetXaxis()->GetBinCenter(i);
double nombkg = nomcurve->interpolate(center);
nlim->setVal(nombkg);
mtot->setRange("errRange",lowedge,upedge);
RooAbsPdf *epdf = 0;
epdf = new RooExtendPdf("epdf","",*cpdf,*nlim,"errRange");
RooAbsReal *nll = epdf->createNLL(*(data[c]),Extended());
RooMinimizer minim(*nll);
minim.setStrategy(0);
double clone = 1.0 - 2.0*RooStats::SignificanceToPValue(1.0);
double cltwo = 1.0 - 2.0*RooStats::SignificanceToPValue(2.0);
minim.migrad();
minim.minos(*nlim);
// printf("errlo = %5f, errhi = %5f\n",nlim->getErrorLo(),nlim->getErrorHi());
onesigma->SetPoint(i-1,center,nombkg);
onesigma->SetPointError(i-1,0.,0.,-nlim->getErrorLo(),nlim->getErrorHi());
minim.setErrorLevel(0.5*pow(ROOT::Math::normal_quantile(1-0.5*(1-cltwo),1.0), 2));
// the 0.5 is because qmu is -2*NLL
minim.migrad();
minim.minos(*nlim);
twosigma->SetPoint(i-1,center,nombkg);
twosigma->SetPointError(i-1,0.,0.,-nlim->getErrorLo(),nlim->getErrorHi());
delete nll;
delete epdf;
} // close for each bin
mtot->setRange("errRange",minMassFit,maxMassFit);
twosigma->SetLineColor(kGreen);
twosigma->SetFillColor(kGreen);
twosigma->SetMarkerColor(kGreen);
twosigma->Draw("L3 SAME");
onesigma->SetLineColor(kYellow);
onesigma->SetFillColor(kYellow);
onesigma->SetMarkerColor(kYellow);
onesigma->Draw("L3 SAME");
plotmtotBkg[c]->Draw("SAME");
} else plotmtotBkg[c]->Draw("SAME"); // close dobands
plotmtotBkg[c]->GetYaxis()->SetRangeUser(0.0000001,100); // this one works
//plotmtotBkg[c]->Draw("AC");
ctmp->SetLogy(1);
//ctmp->SetGrid(0);
cout << "!!!!!!!!!!!!!!!!!" << endl;
//////////////////////////////////////////////////////////////////
TPaveText *pt = new TPaveText(0.2,0.93,0.8,0.99, "brNDC");
// pt->SetName("title");
pt->SetBorderSize(0);
pt->SetFillColor(0);
// pt->SetShadowColor(kWhite);
pt->AddText(" CMS Preliminary L = 19.7 fb^{-1} #sqrt{s} = 8 TeV ");
pt->SetTextSize(0.04);
pt->Draw();
////////////////////////////////////////////////////////////////////
TLegend *legmc = new TLegend(0.62,0.75,0.99,0.99);
// legmc->AddEntry(plotmtotBkg[c]->getObject(2),"Data ",""); //"LPE" blind
legmc->AddEntry(plotmtotBkg[c]->getObject(1),"Exponential fit","L");
if(dobands)legmc->AddEntry(twosigma,"two sigma ","F");
if(dobands)legmc->AddEntry(onesigma,"one sigma","F");
//legmc->SetHeader("WP4 550 GeV");
legmc->SetBorderSize(0);
legmc->SetFillStyle(0);
legmc->Draw();
TLatex *lat2 = new TLatex(363.0+30,0.85*plotmtotBkg[c]->GetMaximum(),catdesc.at(c));
lat2->Draw();
ctmp->SaveAs(TString::Format("databkgoversig_cat%d.pdf",c));
cout<<"here 2 "<< c<<endl;
ctmp->SaveAs(TString::Format("databkgoversig_cat%d.png",c));
cout<<"here 3 "<< c<<endl;
//ctmp->SaveAs(TString::Format("databkgoversig_cat%d.C",c));
} // close to each category
cout<<"here out loop"<<endl;
return fitresult;
} // close berestein 3
///////////////////////////////////////////////////////////////
void MakeSigWS(RooWorkspace* w, const char* fileBaseName) {
TString wsDir = "workspaces/";
const Int_t ncat = NCAT;
//**********************************************************************//
// Write pdfs and datasets into the workspace
// for statistical tests.
//**********************************************************************//
RooAbsPdf* mtotSigPdf[ncat];
RooWorkspace *wAll = new RooWorkspace("w_all","w_all");
// import pdfs from previous fit calculation before to save
for (int c = 0; c < ncat; ++c) {
mtotSigPdf[c] = (RooAbsPdf*) w->pdf(TString::Format("mtotSig_cat%d",c));
wAll->import(*w->pdf(TString::Format("mtotSig_cat%d",c))); // import datasets from previous fit calculation
}
// (2) Systematics on energy scale and resolution
// 1,1,1 statistical to be treated on the datacard
wAll->factory("CMS_hgg_sig_m0_absShift[1,1,1]");
// wAll->factory("CMS_hgg_sig_m0_absShift_cat1[1,1,1]");
// wAll->factory("CMS_hgg_sig_m0_absShift_cat2[1,1,1]"); //CHANGE
//
wAll->factory("prod::CMS_hgg_sig_m0_cat0(mtot_sig_m0_cat0, CMS_hgg_sig_m0_absShift)");
wAll->factory("prod::CMS_hgg_sig_m0_cat1(mtot_sig_m0_cat1, CMS_hgg_sig_m0_absShift)");
// (3) Systematics on resolution
// 1,1,1 statistical to be treated on the datacard
wAll->factory("CMS_hgg_sig_sigmaScale[1,1,1]");
//
wAll->factory("prod::CMS_hgg_sig_sigma_cat0(mtot_sig_sigma_cat0, CMS_hgg_sig_sigmaScale)");
wAll->factory("prod::CMS_hgg_sig_sigma_cat1(mtot_sig_sigma_cat1, CMS_hgg_sig_sigmaScale)");
//
wAll->factory("prod::CMS_hgg_sig_gsigma_cat0(mtot_sig_gsigma_cat0, CMS_hgg_sig_sigmaScale)");
wAll->factory("prod::CMS_hgg_sig_gsigma_cat1(mtot_sig_gsigma_cat1, CMS_hgg_sig_sigmaScale)");
// (4) do reparametrization of signal
for (int c = 0; c < ncat; ++c) wAll->factory(
TString::Format("EDIT::CMS_hgg_sig_cat%d(mtotSig_cat%d,",c,c) +
TString::Format(" mtot_sig_m0_cat%d=CMS_hgg_sig_m0_cat%d, ", c,c) +
TString::Format(" mtot_sig_sigma_cat%d=CMS_hgg_sig_sigma_cat%d, ", c,c) +
TString::Format(" mtot_sig_gsigma_cat%d=CMS_hgg_sig_gsigma_cat%d)", c,c)
);
TString filename(wsDir+TString(fileBaseName)+".inputsig.root");
wAll->writeToFile(filename);
cout << "Write signal workspace in: " << filename << " file" << endl;
return;
} // close make signal WP
////////////////////////////////////////////////////////////////
void MakeBkgWS(RooWorkspace* w, const char* fileBaseName) {
TString wsDir = "workspaces/";
const Int_t ncat = NCAT;
//**********************************************************************//
// Write pdfs and datasets into the workspace
// for statistical tests.
//**********************************************************************//
RooDataSet* data[ncat];
RooAbsPdf* mtotBkgPdf[ncat];
// (1) import everything functions before to save to a file
RooWorkspace *wAll = new RooWorkspace("w_all","w_all");
for (int c = 0; c < ncat; ++c) {
data[c] = (RooDataSet*) w->data(TString::Format("Data_cat%d",c));
mtotBkgPdf[c] = (RooAbsPdf*) w->pdf(TString::Format("mtotBkg_cat%d",c));
wAll->import(*data[c], Rename(TString::Format("data_obs_cat%d",c)));
wAll->import(*w->pdf(TString::Format("mtotBkg_cat%d",c)));
wAll->factory(
TString::Format("CMS_hgg_bkg_8TeV_cat%d_norm[%g,0.0,100000.0]",
c, wAll->var(TString::Format("mtot_bkg_8TeV_cat%d_norm",c))->getVal()));
wAll->factory(
TString::Format("CMS_hgg_bkg_8TeV_slope1_cat%d[%g,-500,500]",
c, wAll->var(TString::Format("mtot_bkg_8TeV_slope1_cat%d",c))->getVal()));
wAll->factory(
TString::Format("CMS_hgg_bkg_8TeV_slope2_cat%d[%g,-100,100]",
c, wAll->var(TString::Format("mtot_bkg_8TeV_slope2_cat%d",c))->getVal()));
// wAll->factory(
// TString::Format("CMS_hgg_bkg_8TeV_slope3_cat%d[%g,-100,100]",
// c, wAll->var(TString::Format("mtot_bkg_8TeV_slope3_cat%d",c))->getVal()));
}
// (2) do reparametrization of background
for (int c = 0; c < ncat; ++c){
wAll->factory(
TString::Format("EDIT::CMS_hgg_bkg_8TeV_cat%d(mtotBkg_cat%d,",c,c) +
TString::Format(" mtot_bkg_8TeV_cat%d_norm=CMS_hgg_bkg_8TeV_cat%d_norm,", c,c)+
TString::Format(" mtot_bkg_8TeV_slope1_cat%d=CMS_hgg_bkg_8TeV_slope1_cat%d,", c,c)+
TString::Format(" mtot_bkg_8TeV_slope2_cat%d=CMS_hgg_bkg_8TeV_slope2_cat%d)", c,c)//+
// TString::Format(" mtot_bkg_8TeV_slope3_cat%d=CMS_hgg_bkg_8TeV_slope3_cat%d)", c,c)
);
} // close for cat
TString filename(wsDir+TString(fileBaseName)+".root");
wAll->writeToFile(filename);
cout << "Write background workspace in: " << filename << " file" << endl;
std::cout << "observation ";
for (int c = 0; c < ncat; ++c) {
std::cout << " " << wAll->data(TString::Format("data_obs_cat%d",c))->sumEntries();
}
std::cout << std::endl;
return;
} // close make BKG workspace
////////////////////////////////////////////////////////////////////
void SetConstantParams(const RooArgSet* params) {
// set constant parameters for signal fit, ... NO IDEA !!!!
TIterator* iter(params->createIterator());
for (TObject *a = iter->Next(); a != 0; a = iter->Next()) {
RooRealVar *rrv = dynamic_cast<RooRealVar *>(a);
if (rrv) { rrv->setConstant(true); std::cout << " " << rrv->GetName(); }
}
} // close set const parameters
////////////////////////////////////////////////////////////////////////
void MakePlots(RooWorkspace* w, Float_t Mass, RooFitResult* fitresults) {
const Int_t ncat = NCAT;
std::vector<TString> catdesc;
catdesc.push_back("2 btag");
catdesc.push_back("1 btag");
RooDataSet* signalAll = (RooDataSet*) w->data("Sig");
// blinded dataset
RooDataSet* sigToFit[ncat];
RooAbsPdf* mtotGaussSig[ncat];
RooAbsPdf* mtotCBSig[ncat];
RooAbsPdf* mtotSig[ncat];
for (int c = 0; c < ncat; ++c) {
sigToFit[c] = (RooDataSet*) w->data(TString::Format("Sig_cat%d",c));
mtotGaussSig[c] = (RooAbsPdf*) w->pdf(TString::Format("mtotGaussSig_cat%d",c));
mtotCBSig[c] = (RooAbsPdf*) w->pdf(TString::Format("mtotCBSig_cat%d",c));
mtotSig[c] = (RooAbsPdf*) w->pdf(TString::Format("mtotSig_cat%d",c));
} // close categories
RooRealVar* mtot = w->var("mtot");
mtot->setUnit("GeV");
RooAbsPdf* mtotGaussSigAll = w->pdf("mtotGaussSig");
RooAbsPdf* mtotCBSigAll = w->pdf("mtotCBSig");
RooAbsPdf* mtotSigAll = w->pdf("mtotSig");
RooAbsPdf* mtotBkgAll = w->pdf("mtotBkg_cat1");
//****************************//
// Plot mtot Fit results
//****************************//
Float_t minMassFit(minfit),maxMassFit(maxfit);
Float_t MASS(Mass);
Int_t nBinsMass(93);
RooPlot* plotmtotAll = mtot->frame(Range(minMassFit,maxMassFit),Bins(nBinsMass));
/* signalAll->plotOn(plotmtotAll);
gStyle->SetOptTitle(0);
mtotSigAll->plotOn(plotmtotAll);
mtotSigAll->plotOn(
plotmtotAll,Components("mtotGaussSig"),
LineStyle(kDashed),LineColor(kGreen));
//mtotSigAll->plotOn(
//plotmtotAll,Components("mtotCBSig"),
//LineStyle(kDashed),LineColor(kRed));
mtotSigAll->paramOn(
plotmtotAll,
ShowConstants(true),
Layout(0.15,0.55,0.9),
Format("NEU",AutoPrecision(2)));
plotmtotAll->getAttText()->SetTextSize(0.03);
*/
TCanvas* c1 = new TCanvas("c1","mtot",0,0,501,501);
c1->cd(1);
plotmtotAll->Draw();
//****************************//
// Plot Signal Categories
//****************************//
TLatex *text = new TLatex();
text->SetNDC();
text->SetTextSize(0.04);
RooPlot* plotmtot[ncat];
for (int c = 0; c < ncat; ++c) {
plotmtot[c] = mtot->frame(Range(minMassFit,maxMassFit),Bins(nBinsMass));
sigToFit[c]->plotOn(plotmtot[c],LineColor(kWhite),MarkerColor(kWhite));
mtotSig[c] ->plotOn(plotmtot[c]);
mtotSig[c] ->plotOn(
plotmtot[c],
Components(TString::Format("mtotGaussSig_cat%d",c)),
LineStyle(kDashed),LineColor(kGreen));
mtotSig[c] ->plotOn(
plotmtot[c],
Components(TString::Format("mtotCBSig_cat%d",c)),
LineStyle(kDashed),LineColor(kRed));
mtotSig[c] ->paramOn(plotmtot[c]);
sigToFit[c] ->plotOn(plotmtot[c]);
TCanvas* dummy = new TCanvas("dummy", "dummy",0, 0, 400, 400);
TH1F *hist = new TH1F("hist", "hist", 400, minMassFit, maxMassFit);
//plotmtot[c]->SetTitle("CMS preliminary 19.702/fb ");
plotmtot[c]->SetMinimum(0.0);
plotmtot[c]->SetMaximum(1.40*plotmtot[c]->GetMaximum());
plotmtot[c]->GetXaxis()->SetTitle("M_{#gamma#gamma jj} (GeV)");
TCanvas* ctmp = new TCanvas("ctmp","Background Categories",0,0,501,501);
plotmtot[c]->Draw();
plotmtot[c]->Draw("SAME");
TLegend *legmc = new TLegend(0.62,0.75,0.99,0.99);
legmc->AddEntry(plotmtot[c]->getObject(5),"Simulation","LPE");
legmc->AddEntry(plotmtot[c]->getObject(1),"Parametric Model","L");
legmc->AddEntry(plotmtot[c]->getObject(3),"Crystal Ball ","L");
legmc->AddEntry(plotmtot[c]->getObject(2),"Gaussian ","L");
legmc->SetHeader(" ");
legmc->SetBorderSize(0);
legmc->SetFillStyle(0);
legmc->Draw();
//////////////////////////////////////////////////////////////////
TPaveText *pt = new TPaveText(0.2,0.93,0.8,0.99, "brNDC");
// pt->SetName("title");
pt->SetBorderSize(0);
pt->SetFillColor(0);
// pt->SetShadowColor(kWhite);
pt->AddText(" CMS Preliminary L = 19.7 fb^{-1} #sqrt{s} = 8 TeV ");
pt->SetTextSize(0.04);
pt->Draw();
////////////////////////////////////////////////////////////////////
// float effS = effSigma(hist);
TLatex *lat = new TLatex(
minMassFit+1.5,0.85*plotmtot[c]->GetMaximum(),
" M_{X} = 550 GeV");
lat->Draw();
TLatex *lat2 = new TLatex(
minMassFit+2.5,0.75*plotmtot[c]->GetMaximum(),catdesc.at(c));
//lat2->Draw();
ctmp->SaveAs(TString::Format("sigmodel_cat%d.pdf",c));
ctmp->SaveAs(TString::Format("sigmodel_cat%d.png",c));
//ctmp->SaveAs(TString::Format("sigmodel_cat%d.C",c));
} // close categories
return;
} // close makeplots
///////////////////////////////////////////////////////////
// declare histos or what -> NOT USED
Double_t effSigma(TH1 *hist) {
TAxis *xaxis = hist->GetXaxis();
Int_t nb = xaxis->GetNbins();
if(nb < 10) {
std::cout << "effsigma: Not a valid histo. nbins = " << nb << std::endl;
return 0.;
}
Double_t bwid = xaxis->GetBinWidth(1);
if(bwid == 0) {
std::cout << "effsigma: Not a valid histo. bwid = " << bwid << std::endl;
return 0.;
}
Double_t xmax = xaxis->GetXmax();
Double_t xmin = xaxis->GetXmin();
Double_t ave = hist->GetMean();
Double_t rms = hist->GetRMS();
Double_t total=0.;
for(Int_t i=0; i<nb+2; i++) {
total+=hist->GetBinContent(i);
}
if(total < 100.) {
std::cout << "effsigma: Too few entries " << total << std::endl;
return 0.;
}
Int_t ierr=0;
Int_t ismin=999;
Double_t rlim=0.683*total;
Int_t nrms=rms/(bwid); // Set scan size to +/- rms
if(nrms > nb/10) nrms=nb/10; // Could be tuned...
Double_t widmin=9999999.;
for(Int_t iscan=-nrms;iscan<nrms+1;iscan++) { // Scan window centre
Int_t ibm=(ave-xmin)/bwid+1+iscan;
Double_t x=(ibm-0.5)*bwid+xmin;
Double_t xj=x;
Double_t xk=x;
Int_t jbm=ibm;
Int_t kbm=ibm;
Double_t bin=hist->GetBinContent(ibm);
total=bin;
for(Int_t j=1;j<nb;j++){
if(jbm < nb) {jbm++; xj+=bwid; bin=hist->GetBinContent(jbm); total+=bin; if(total > rlim) break;} else ierr=1;
if(kbm > 0) {kbm--; xk-=bwid; bin=hist->GetBinContent(kbm); total+=bin; if(total > rlim) break; } else ierr=1;
}
Double_t dxf=(total-rlim)*bwid/bin;
Double_t wid=(xj-xk+bwid-dxf)*0.5;
if(wid < widmin) { widmin=wid; ismin=iscan; }
} // Scan window centre
if(ismin == nrms || ismin == -nrms) ierr=3;
if(ierr != 0) std::cout << "effsigma: Error of type " << ierr << std::endl;
return widmin;
} // close effSigma
//////////////////////////////////////////////////
// with reparametrization of BKG
void MakeDataCardREP(RooWorkspace* w, const char* fileBaseName, const char* fileBkgName) {
TString cardDir = "datacards/";
const Int_t ncat = NCAT;
RooDataSet* data[ncat];
RooDataSet* sigToFit[ncat];
for (int c = 0; c < ncat; ++c) {
data[c] = (RooDataSet*) w->data(TString::Format("Data_cat%d",c));
sigToFit[c] = (RooDataSet*) w->data(TString::Format("Sig_cat%d",c));
}
RooRealVar* lumi = w->var("lumi");
cout << "======== Expected Events Number =====================" << endl;
cout << ".........Measured Data for L = " << lumi->getVal() << " pb-1 ............................" << endl;
cout << "#Events data: " << w->data("Data")->sumEntries() << endl;
for (int c = 0; c < ncat; ++c) {
cout << TString::Format("#Events data cat%d: ",c) << data[c]->sumEntries() << endl;
}
cout << ".........Expected Signal for L = " << lumi->getVal() << " pb-1 ............................" << endl;
cout << "#Events Signal: " << w->data("Data")->sumEntries() << endl;
Float_t siglikeErr[ncat];
for (int c = 0; c < ncat; ++c) {
cout << TString::Format("#Events Signal cat%d: ",c) << sigToFit[c]->sumEntries() << endl;
siglikeErr[c]=0.6*sigToFit[c]->sumEntries();
}
cout << "====================================================" << endl;
TString filename(cardDir+TString(fileBaseName)+"rep.txt");
ofstream outFile(filename);
outFile << "#CMS-HGG DataCard for Unbinned Limit Setting, " << lumi->getVal() << " pb-1 " << endl;
outFile << "#Run with: combine -d hgg.mH130.0.shapes-Unbinned.txt -U -m 130 -H ProfileLikelihood -M MarkovChainMC --rMin=0 --rMax=20.0 -b 3000 -i 50000 --optimizeSim=1 --tries 30" << endl;
outFile << "# Lumi = " << lumi->getVal() << " pb-1" << endl;
outFile << "imax "<<ncat << endl;
outFile << "jmax 1" << endl;
outFile << "kmax *" << endl;
outFile << "---------------" << endl;
cout<<"here"<<endl;
outFile << "# the name after w_all is the name of the rooextpdf we want to use, we have both saved" << endl;
outFile << "# BKG" << endl;
outFile << "shapes data_obs cat0 " << TString(fileBkgName)+".root" << " w_all:data_obs_cat0" << endl;
outFile << "shapes data_obs cat1 "<< TString(fileBkgName)+".root" << " w_all:data_obs_cat1" << endl;
outFile << "############## shape with reparametrization" << endl;
outFile << "shapes mtotBkg cat0 " << TString(fileBkgName)+".root" << " w_all:CMS_hgg_bkg_8TeV_cat0" << endl;
outFile << "shapes mtotBkg cat1 "<< TString(fileBkgName)+".root" << " w_all:CMS_hgg_bkg_8TeV_cat1" << endl;
outFile << "# signal" << endl;
outFile << "shapes mtotSig cat0 " << TString(fileBaseName)+".inputsig.root" << " w_all:CMS_hgg_sig_cat0" << endl;
outFile << "shapes mtotSig cat1 " << TString(fileBaseName)+".inputsig.root" << " w_all:CMS_hgg_sig_cat1" << endl;
outFile << "---------------" << endl;
/////////////////////////////////////
/////////////////////////////////////
outFile << "bin cat0 cat1 " << endl;
outFile << "observation "
<< data[0]->sumEntries() << " "
<< data[1]->sumEntries() << " "
<< endl;
outFile << "------------------------------" << endl;
outFile << "bin cat0 cat0 cat1 cat1" << endl;
outFile << "process mtotSig mtotBkg mtotSig mtotBkg" << endl;
outFile << "process 0 1 0 1" << endl;
outFile << "rate "
<< " " << sigToFit[0]->sumEntries() << " " << 1
<< " " << sigToFit[1]->sumEntries() << " " << 1
<< " " << endl;
outFile << "--------------------------------" << endl;
outFile << "lumi_8TeV lnN "
<< "1.022 - "
<< "1.022 - " << endl;
outFile << "############## jet" << endl;
outFile << "Mjj_acceptance lnN "
<< "1.015 - "
<< "1.015 - "
<<"# JER and JES " << endl;
outFile << "btag_eff lnN "
<< "1.06 - "
<< "1.03 - "
<<"# b tag efficiency uncertainty" << endl;
outFile << "############## photon " << endl;
outFile << "CMS_hgg_eff_g lnN "
<< "1.010 - "
<< "1.010 - "
<< "# photon selection accep." << endl;
outFile << "DiphoTrigger lnN "
<< "1.01 - "
<< "1.01 - "
<< "# Trigger efficiency" << endl;
outFile << "############## for mtot fit" << endl;
outFile << "maa_acceptance lnN "
<< "1.005 - "
<< "1.005 - "
<< "# photon energy resolution" << endl;
outFile << "############## normalization floating" << endl;
outFile << "# Parametric shape uncertainties, entered by hand. they act on both higgs/signal " << endl;
outFile << "CMS_hgg_sig_m0_absShift param 1 0.006 # displacement of the dipho mean" << endl;
outFile << "CMS_hgg_sig_sigmaScale param 1 0.11 # optimistic estimative of resolution uncertainty " << endl;
outFile << "############## for mtot fit - slopes" << endl;
outFile << "############## with reparametrization" << endl;
outFile << "CMS_hgg_bkg_8TeV_cat0_norm flatParam # Normalization uncertainty on background slope" << endl;
outFile << "CMS_hgg_bkg_8TeV_cat1_norm flatParam # Normalization uncertainty on background slope" << endl;
outFile << "CMS_hgg_bkg_8TeV_slope1_cat0 flatParam # Mean and absolute uncertainty on background slope" << endl;
outFile << "CMS_hgg_bkg_8TeV_slope1_cat1 flatParam # Mean and absolute uncertainty on background slope" << endl;
outFile << "CMS_hgg_bkg_8TeV_slope2_cat0 flatParam # Mean and absolute uncertainty on background slope" << endl;
outFile << "CMS_hgg_bkg_8TeV_slope2_cat1 flatParam # Mean and absolute uncertainty on background slope" << endl;
outFile.close();
cout << "Write data card in: " << filename << " file" << endl;
return;
} // close write datacard with rep
//////////////////////////////////////////////////
// with reparametrization of BKG
void MakeDataCardLnU(RooWorkspace* w, const char* fileBaseName, const char* fileBkgName) {
TString cardDir = "datacards/";
const Int_t ncat = NCAT;
RooDataSet* data[ncat];
RooDataSet* sigToFit[ncat];
RooAbsPdf* datashape[ncat];
for (int c = 0; c < ncat; ++c) {
data[c] = (RooDataSet*) w->data(TString::Format("Data_cat%d",c));
sigToFit[c] = (RooDataSet*) w->data(TString::Format("Sig_cat%d",c));
datashape[c] = (RooAbsPdf*) w->data(TString::Format("mtotBkg_ca%d",c));
}
RooRealVar* lumi = w->var("lumi");
cout << "======== Expected Events Number =====================" << endl;
cout << ".........Measured Data for L = " << lumi->getVal() << " pb-1 ............................" << endl;
cout << "#Events data: " << w->data("Data")->sumEntries() << endl;
for (int c = 0; c < ncat; ++c) {
cout << TString::Format("#Events data cat%d: ",c) << data[c]->sumEntries() << endl;
}
// cout << "#Events expected: " << endl;
// for (int c = 0; c < ncat; ++c) {
// cout << TString::Format("#Events expected cat%d: ",c) << datashape[c]->getAnalyticalIntegral()
// << endl;
// }
cout << ".........Expected Signal for L = " << lumi->getVal() << " pb-1 ............................" << endl;
cout << "#Events Signal: " << w->data("Data")->sumEntries() << endl;
Float_t siglikeErr[ncat];
for (int c = 0; c < ncat; ++c) {
cout << TString::Format("#Events Signal cat%d: ",c) << sigToFit[c]->sumEntries() << endl;
siglikeErr[c]=0.6*sigToFit[c]->sumEntries();
}
cout << "====================================================" << endl;
TString filename(cardDir+TString(fileBaseName)+"lnu.txt");
ofstream outFile(filename);
outFile << "#CMS-HGG DataCard for Unbinned Limit Setting, " << lumi->getVal() << " pb-1 " << endl;
outFile << "#Run with: combine hgg.mH500.0_8TeV.txt -M Asymptotic" << endl;
outFile << "# Lumi = " << lumi->getVal() << " pb-1" << endl;
outFile << "imax "<<ncat << endl;
outFile << "jmax 1" << endl;
outFile << "kmax *" << endl;
outFile << "---------------" << endl;
cout<<"here"<<endl;
outFile << "# the name after w_all is the name of the rooextpdf we want to use, we have both saved" << endl;
outFile << "# BKG" << endl;
outFile << "shapes data_obs cat0 " << TString(fileBkgName)+".root" << " w_all:data_obs_cat0" << endl;
outFile << "shapes data_obs cat1 "<< TString(fileBkgName)+".root" << " w_all:data_obs_cat1" << endl;
outFile << "############## without reparametrization" << endl;
outFile << "shapes mtotBkg cat0 " << TString(fileBkgName)+".root" << " w_all:mtotBkg_cat0" << endl;
outFile << "shapes mtotBkg cat1 "<< TString(fileBkgName)+".root" << " w_all:mtotBkg_cat1" << endl;
outFile << "---------------" << endl;
/////////////////////////////////////
/////////////////////////////////////
outFile << "bin cat0 cat1 " << endl;
outFile << "observation "
<< data[0]->sumEntries() << " "
<< data[1]->sumEntries() << " "
<< endl;
outFile << "------------------------------" << endl;
outFile << "bin cat0 cat0 cat1 cat1" << endl;
outFile << "process mtotSig mtotBkg mtotSig mtotBkg" << endl;
outFile << "process 0 1 0 1" << endl;
outFile << "rate "
<< " " << sigToFit[0]->sumEntries() << " " << data[0]->sumEntries()
<< " " << sigToFit[1]->sumEntries() << " " << data[1]->sumEntries()
<< " " << endl;
outFile << "--------------------------------" << endl;
outFile << "lumi_8TeV lnN "
<< "1.022 - "
<< "1.022 - " << endl;
outFile << "############## jet" << endl;
outFile << "Mjj_acceptance lnN "
<< "1.015 - "
<< "1.015 - "
<<"# JER and JES " << endl;
outFile << "btag_eff lnN "
<< "1.06 - "
<< "1.03 - "
<<"# b tag efficiency uncertainty" << endl;
outFile << "############## photon " << endl;
outFile << "CMS_hgg_eff_g lnN "
<< "1.010 - "
<< "1.010 - "
<< "# photon selection accep." << endl;
outFile << "DiphoTrigger lnN "
<< "1.01 - "
<< "1.01 - "
<< "# Trigger efficiency" << endl;
outFile << "############## for mtot fit" << endl;
outFile << "maa_acceptance lnN "
<< "1.005 - "
<< "1.005 - "
<< "# photon energy resolution" << endl;
outFile << "############## normalization floating" << endl;
outFile << "mtotBkg lnU "
<< "- 2 "
<< "- 2 "
<< "# k means vary between 1/k and k times the value in the workspace" << endl;
outFile << "# Parametric shape uncertainties, entered by hand. they act on both higgs/signal " << endl;
outFile << "CMS_hgg_sig_m0_absShift param 1 0.006 # displacement of the dipho mean" << endl;
outFile << "CMS_hgg_sig_sigmaScale param 1 0.11 # optimistic estimative of resolution uncertainty " << endl;
outFile << "############## for mtot fit - slopes" << endl;
outFile << "############## without reparametrization" << endl;
outFile << "mtot_bkg_8TeV_slope1_cat0 flatParam # Mean and absolute uncertainty on background slope" << endl;
outFile << "mtot_bkg_8TeV_slope1_cat1 flatParam # Mean and absolute uncertainty on background slope" << endl;
outFile << "mtot_bkg_8TeV_slope2_cat0 flatParam # Mean and absolute uncertainty on background slope" << endl;
outFile << "mtot_bkg_8TeV_slope2_cat1 flatParam # Mean and absolute uncertainty on background slope" << endl;
outFile.close();
cout << "Write data card in: " << filename << " file" << endl;
return;
} // close write datacard with LnU
void MakeDataCardonecat(RooWorkspace* w, const char* fileBaseName, const char* fileBkgName) {
TString cardDir = "datacards/";
const Int_t ncat = NCAT;
RooDataSet* data[ncat];
RooDataSet* sigToFit[ncat];
for (int c = 0; c < ncat; ++c) {
data[c] = (RooDataSet*) w->data(TString::Format("Data_cat%d",c));
sigToFit[c] = (RooDataSet*) w->data(TString::Format("Sig_cat%d",c));
}
RooRealVar* lumi = w->var("lumi");
cout << "======== Expected Events Number =====================" << endl;
cout << ".........Measured Data for L = " << lumi->getVal() << " pb-1 ............................" << endl;
cout << "#Events data: " << w->data("Data")->sumEntries() << endl;
for (int c = 0; c < ncat; ++c) {
cout << TString::Format("#Events data cat%d: ",c) << data[c]->sumEntries() << endl;
}
cout << ".........Expected Signal for L = " << lumi->getVal() << " pb-1 ............................" << endl;
cout << "#Events Signal: " << w->data("Data")->sumEntries() << endl;
Float_t siglikeErr[ncat];
for (int c = 0; c < ncat; ++c) {
cout << TString::Format("#Events Signal cat%d: ",c) << sigToFit[c]->sumEntries() << endl;
siglikeErr[c]=0.6*sigToFit[c]->sumEntries();
}
cout << "====================================================" << endl;
TString filename(cardDir+TString(fileBaseName)+"onecat.txt");
ofstream outFile(filename);
outFile << "#CMS-HGG DataCard for Unbinned Limit Setting, " << lumi->getVal() << " pb-1 " << endl;
outFile << "#Run with: combine -d hgg.mH130.0.shapes-Unbinned.txt -U -m 130 -H ProfileLikelihood -M MarkovChainMC --rMin=0 --rMax=20.0 -b 3000 -i 50000 --optimizeSim=1 --tries 30" << endl;
outFile << "# Lumi = " << lumi->getVal() << " pb-1" << endl;
outFile << "imax 1" << endl;
outFile << "jmax 1" << endl;
outFile << "kmax *" << endl;
outFile << "---------------" << endl;
cout<<"here"<<endl;
outFile << "# the name after w_all is the name of the rooextpdf we want to use, we have both saved" << endl;
outFile << "# BKG" << endl;
outFile << "shapes data_obs cat0 " << TString(fileBkgName)+".root" << " w_all:data_obs_cat0" << endl;
outFile << "shapes data_obs cat1 "<< TString(fileBkgName)+".root" << " w_all:data_obs_cat1" << endl;
outFile << "############## shape with reparametrization" << endl;
outFile << "shapes mtotBkg cat0 " << TString(fileBkgName)+".root" << " w_all:CMS_hgg_bkg_8TeV_cat0" << endl;
outFile << "shapes mtotBkg cat1 "<< TString(fileBkgName)+".root" << " w_all:CMS_hgg_bkg_8TeV_cat1" << endl;
outFile << "# signal" << endl;
outFile << "shapes mtotSig cat0 " << TString(fileBaseName)+".inputsig.root" << " w_all:CMS_hgg_sig_cat0" << endl;
outFile << "shapes mtotSig cat1 " << TString(fileBaseName)+".inputsig.root" << " w_all:CMS_hgg_sig_cat1" << endl;
outFile << "---------------" << endl;
/////////////////////////////////////
/////////////////////////////////////
outFile << "bin cat0 " << endl;
outFile << "observation "
<< data[0]->sumEntries() << " "
<< endl;
outFile << "------------------------------" << endl;
outFile << "bin cat0 cat0 " << endl;
outFile << "process mtotSig mtotBkg" << endl;
outFile << "process 0 1 " << endl;
outFile << "rate "
<< " " << sigToFit[0]->sumEntries() << " " << 1
<< " " << endl;
outFile << "--------------------------------" << endl;
outFile << "lumi_8TeV lnN "
<< "1.022 - "
<< endl;
outFile << "############## jet" << endl;
outFile << "Mjj_acceptance lnN "
<< "1.015 - "
<<"# JER and JES " << endl;
outFile << "btag_eff lnN "
<< "1.06 - "
<<"# b tag efficiency uncertainty" << endl;
outFile << "############## photon " << endl;
outFile << "CMS_hgg_eff_g lnN "
<< "1.010 - "
<< "# photon selection accep." << endl;
outFile << "DiphoTrigger lnN "
<< "1.01 - "
<< "# Trigger efficiency" << endl;
outFile << "############## for mtot fit" << endl;
outFile << "maa_acceptance lnN "
<< "1.005 - "
<< "1.005 - "
<< "# photon energy resolution" << endl;
outFile << "############## normalization floating" << endl;
outFile << "# Parametric shape uncertainties, entered by hand. they act on both higgs/signal " << endl;
outFile << "CMS_hgg_sig_m0_absShift param 1 0.006 # displacement of the dipho mean" << endl;
outFile << "CMS_hgg_sig_sigmaScale param 1 0.11 # optimistic estimative of resolution uncertainty " << endl;
outFile << "############## for mtot fit - slopes" << endl;
outFile << "############## with reparametrization" << endl;
outFile << "CMS_hgg_bkg_8TeV_cat0_norm flatParam # Normalization uncertainty on background slope" << endl;
outFile << "CMS_hgg_bkg_8TeV_slope1_cat0 flatParam # Mean and absolute uncertainty on background slope" << endl;
outFile << "CMS_hgg_bkg_8TeV_slope2_cat0 flatParam # Mean and absolute uncertainty on background slope" << endl;
outFile.close();
cout << "Write data card in: " << filename << " file" << endl;
return;
} // close write datacard
void style(){
TStyle *defaultStyle = new TStyle("defaultStyle","Default Style");
// defaultStyle->SetOptStat(0000);
// defaultStyle->SetOptFit(000);
// defaultStyle->SetPalette(1);
/////// pad ////////////
defaultStyle->SetPadBorderMode(1);