void Raa3S_Workspace(const char* name_pbpb="chad_ws_fits/centFits/ws_PbPbData_262548_263757_0cent10_0.0pt50.0_0.0y2.4.root", const char* name_pp="chad_ws_fits/centFits/ws_PPData_262157_262328_-1cent1_0.0pt50.0_0.0y2.4.root", const char* name_out="fitresult_combo.root"){
//TFile File(filename);
//RooWorkspace * ws = test_combine(name_pbpb, name_pp);
TFile *f = new TFile("fitresult_combo_333.root") ;
RooWorkspace * ws1 = (RooWorkspace*) f->Get("wcombo");
//File.GetObject("wcombo", ws);
ws1->Print();
RooAbsData * data = ws1->data("data"); //dataOS, dataSS
// RooDataSet * US_data = (RooDataSet*) data->reduce( "QQsign == QQsign::PlusMinus");
// US_data->SetName("US_data");
// ws->import(* US_data);
// RooDataSet * hi_data = (RooDataSet*) US_data->reduce("dataCat == dataCat::hi");
// hi_data->SetName("hi_data");
// ws->import(* hi_data);
// hi_data->Print();
RooRealVar* raa3 = new RooRealVar("raa3","R_{AA}(#Upsilon (3S))",0.5,-1,1);
RooRealVar* leftEdge = new RooRealVar("leftEdge","leftEdge",0);
RooRealVar* rightEdge = new RooRealVar("rightEdge","rightEdge",1);
RooGenericPdf step("step", "step", "(@0 >= @1) && (@0 < @2)", RooArgList(*raa3, *leftEdge, *rightEdge));
ws1->import(step);
ws1->factory( "Uniform::flat(raa3)" );
//pp Luminosities, Taa and efficiency ratios Systematics
ws1->factory( "Taa_hi[5.662e-9]" );
ws1->factory( "Taa_kappa[1.062]" ); // was 1.057
ws1->factory( "expr::alpha_Taa('pow(Taa_kappa,beta_Taa)',Taa_kappa,beta_Taa[0,-5,5])" );
ws1->factory( "prod::Taa_nom(Taa_hi,alpha_Taa)" );
ws1->factory( "Gaussian::constr_Taa(beta_Taa,glob_Taa[0,-5,5],1)" );
ws1->factory( "lumipp_hi[5.4]" );
ws1->factory( "lumipp_kappa[1.037]" ); // was 1.06
ws1->factory( "expr::alpha_lumipp('pow(lumipp_kappa,beta_lumipp)',lumipp_kappa,beta_lumipp[0,-5,5])" );
ws1->factory( "prod::lumipp_nom(lumipp_hi,alpha_lumipp)" );
ws1->factory( "Gaussian::constr_lumipp(beta_lumipp,glob_lumipp[0,-5,5],1)" );
// ws->factory( "effRat1[1]" );
// ws->factory( "effRat2[1]" );
ws1->factory( "effRat3_hi[0.95]" );
ws1->factory( "effRat_kappa[1.054]" );
ws1->factory( "expr::alpha_effRat('pow(effRat_kappa,beta_effRat)',effRat_kappa,beta_effRat[0,-5,5])" );
// ws->factory( "prod::effRat1_nom(effRat1_hi,alpha_effRat)" );
ws1->factory( "Gaussian::constr_effRat(beta_effRat,glob_effRat[0,-5,5],1)" );
// ws->factory( "prod::effRat2_nom(effRat2_hi,alpha_effRat)" );
ws1->factory( "prod::effRat3_nom(effRat3_hi,alpha_effRat)" );
//
ws1->factory("Nmb_hi[1.161e9]");
ws1->factory("prod::denominator(Taa_nom,Nmb_hi)");
ws1->factory( "expr::lumiOverTaaNmbmodified('lumipp_nom/denominator',lumipp_nom,denominator)");
RooAbsReal *lumiOverTaaNmbmodified = ws1->function("lumiOverTaaNmbmodified"); //RooFormulaVar *lumiOverTaaNmbmodified = ws->function("lumiOverTaaNmbmodified");
//
// RooRealVar *raa1 = ws->var("raa1");
// RooRealVar* nsig1_pp = ws->var("nsig1_pp");
// RooRealVar* effRat1 = ws->function("effRat1_nom");
// RooRealVar *raa2 = ws->var("raa2");
// RooRealVar* nsig2_pp = ws->var("nsig2_pp");
// RooRealVar* effRat2 = ws->function("effRat2_nom");
RooRealVar* nsig3_pp = ws1->var("R_{#frac{3S}{1S}}_pp"); //RooRealVar* nsig3_pp = ws->var("N_{#Upsilon(3S)}_pp");
cout << nsig3_pp << endl;
RooAbsReal* effRat3 = ws1->function("effRat3_nom"); //RooRealVar* effRat3 = ws->function("effRat3_nom");
//
// RooFormulaVar nsig1_hi_modified("nsig1_hi_modified", "@0*@1*@3/@2", RooArgList(*raa1, *nsig1_pp, *lumiOverTaaNmbmodified, *effRat1));
// ws->import(nsig1_hi_modified);
// RooFormulaVar nsig2_hi_modified("nsig2_hi_modified", "@0*@1*@3/@2", RooArgList(*raa2, *nsig2_pp, *lumiOverTaaNmbmodified, *effRat2));
// ws->import(nsig2_hi_modified);
RooFormulaVar nsig3_hi_modified("nsig3_hi_modified", "@0*@1*@3/@2", RooArgList(*raa3, *nsig3_pp, *lumiOverTaaNmbmodified, *effRat3));
ws1->import(nsig3_hi_modified);
// // background yield with systematics
ws1->factory( "nbkg_hi_kappa[1.10]" );
ws1->factory( "expr::alpha_nbkg_hi('pow(nbkg_hi_kappa,beta_nbkg_hi)',nbkg_hi_kappa,beta_nbkg_hi[0,-5,5])" );
ws1->factory( "SUM::nbkg_hi_nom(alpha_nbkg_hi*bkgPdf_hi)" );
ws1->factory( "Gaussian::constr_nbkg_hi(beta_nbkg_hi,glob_nbkg_hi[0,-5,5],1)" );
RooAbsPdf* sig1S_hi = ws1->pdf("sig1S_hi"); //RooAbsPdf* sig1S_hi = ws->pdf("cbcb_hi");
RooAbsPdf* sig2S_hi = ws1->pdf("sig2S_hi");
RooAbsPdf* sig3S_hi = ws1->pdf("sig3S_hi");
RooAbsPdf* LSBackground_hi = ws1->pdf("nbkg_hi_nom");
RooRealVar* nsig1_hi = ws1->var("N_{#Upsilon(1S)}_hi");
RooRealVar* nsig2_hi = ws1->var("R_{#frac{2S}{1S}}_hi");
RooAbsReal* nsig3_hi = ws1->function("nsig3_hi_modified"); //RooFormulaVar* nsig3_hi = ws->function("nsig3_hi_modified");
cout << nsig1_hi << " " << nsig2_hi << " " << nsig3_pp << endl;
RooRealVar* norm_nbkg_hi = ws1->var("n_{Bkgd}_hi");
RooArgList pdfs_hi( *sig1S_hi,*sig2S_hi,*sig3S_hi, *LSBackground_hi);
RooArgList norms_hi(*nsig1_hi,*nsig2_hi,*nsig3_hi, *norm_nbkg_hi);
////////////////////////////////////////////////////////////////////////////////
ws1->factory( "nbkg_pp_kappa[1.03]" );
ws1->factory( "expr::alpha_nbkg_pp('pow(nbkg_pp_kappa,beta_nbkg_pp)',nbkg_pp_kappa,beta_nbkg_pp[0,-5,5])" );
ws1->factory( "SUM::nbkg_pp_nom(alpha_nbkg_pp*bkgPdf_pp)" );
ws1->factory( "Gaussian::constr_nbkg_pp(beta_nbkg_pp,glob_nbkg_pp[0,-5,5],1)" );
RooAbsPdf* sig1S_pp = ws1->pdf("sig1S_pp"); //RooAbsPdf* sig1S_pp = ws1->pdf("cbcb_pp");
RooAbsPdf* sig2S_pp = ws1->pdf("sig2S_pp");
RooAbsPdf* sig3S_pp = ws1->pdf("sig3S_pp");
RooAbsPdf* LSBackground_pp = ws1->pdf("nbkg_pp_nom");
RooRealVar* nsig1_pp = ws1->var("N_{#Upsilon(1S)}_pp");
RooRealVar* nsig2_pp = ws1->var("R_{#frac{2S}{1S}}_pp"); //RooRealVar* nsig2_pp = ws1->var("N_{#Upsilon(2S)}_pp");
// RooRealVar* nsig3_pp = ws1->var("N_{#Upsilon(3S)}_pp");
RooRealVar* norm_nbkg_pp = ws1->var("n_{Bkgd}_pp");
RooArgList pdfs_pp( *sig1S_pp,*sig2S_pp,*sig3S_pp, *LSBackground_pp);
RooArgList norms_pp( *nsig1_pp,*nsig2_pp,*nsig3_pp,*norm_nbkg_pp);
RooAddPdf model_num("model_num", "model_num", pdfs_hi,norms_hi);
ws1->import(model_num);
ws1->factory("PROD::model_hi(model_num, constr_nbkg_hi,constr_lumipp,constr_Taa,constr_effRat)");
RooAddPdf model_den("model_den", "model_den", pdfs_pp,norms_pp);
ws1->import(model_den);
ws1->factory("PROD::model_pp(model_den, constr_nbkg_pp)");
ws1->factory("SIMUL::joint(dataCat,hi=model_hi,pp=model_pp)");
/////////////////////////////////////////////////////////////////////
RooRealVar * pObs = ws1->var("invariantMass"); // get the pointer to the observable
RooArgSet obs("observables");
obs.add(*pObs);
obs.add( *ws1->cat("dataCat"));
// /////////////////////////////////////////////////////////////////////
ws1->var("glob_lumipp")->setConstant(true);
ws1->var("glob_Taa")->setConstant(true);
ws1->var("glob_effRat")->setConstant(true);
ws1->var("glob_nbkg_pp")->setConstant(true);
ws1->var("glob_nbkg_hi")->setConstant(true);
RooArgSet globalObs("global_obs");
globalObs.add( *ws1->var("glob_lumipp") );
globalObs.add( *ws1->var("glob_Taa") );
globalObs.add( *ws1->var("glob_effRat") );
globalObs.add( *ws1->var("glob_nbkg_hi") );
globalObs.add( *ws1->var("glob_nbkg_pp") );
cout << "66666" << endl;
// ws1->Print();
RooArgSet poi("poi");
poi.add( *ws1->var("raa3") );
cout << "77777" << endl;
// create set of nuisance parameters
RooArgSet nuis("nuis");
nuis.add( *ws1->var("beta_lumipp") );
nuis.add( *ws1->var("beta_nbkg_hi") );
nuis.add( *ws1->var("beta_nbkg_pp") );
nuis.add( *ws1->var("beta_Taa") );
nuis.add( *ws1->var("beta_effRat") );
cout << "88888" << endl;
ws1->var("#alpha_{CB}_hi")->setConstant(true);
ws1->var("#alpha_{CB}_pp")->setConstant(true);
ws1->var("#sigma_{CB1}_hi")->setConstant(true);
ws1->var("#sigma_{CB1}_pp")->setConstant(true);
ws1->var("#sigma_{CB2}/#sigma_{CB1}_hi")->setConstant(true);
ws1->var("#sigma_{CB2}/#sigma_{CB1}_pp")->setConstant(true);
//ws1->var("Centrality")->setConstant(true); //delete
ws1->var("N_{#varUpsilon(1S)}_hi")->setConstant(true);
ws1->var("N_{#varUpsilon(1S)}_pp")->setConstant(true);
//ws1->var("N_{#Upsilon(2S)}_hi")->setConstant(true);
//ws1->var("N_{#Upsilon(2S)}_pp")->setConstant(true);
//ws1->var("N_{#Upsilon(3S)}_pp")->setConstant(true);
ws1->var("R_{#frac{2S}{1S}}_hi")->setConstant(true); //new
ws1->var("R_{#frac{2S}{1S}}_pp")->setConstant(true); //new
ws1->var("R_{#frac{3S}{1S}}_hi")->setConstant(true); //new
ws1->var("R_{#frac{3S}{1S}}_pp")->setConstant(true); //new
ws1->var("Nmb_hi")->setConstant(true);
// ws1->var("QQsign")->setConstant(true);
ws1->var("Taa_hi")->setConstant(true);
ws1->var("Taa_kappa")->setConstant(true);
// ws1->var("beta_Taa")->setConstant(true);
// ws1->var("beta_effRat")->setConstant(true);
// ws1->var("beta_lumipp")->setConstant(true);
// ws1->var("beta_nbkg_hi")->setConstant(true);
// ws1->var("beta_nbkg_pp")->setConstant(true);
// ws1->var("dataCat")->setConstant(true);
ws1->var("decay_hi")->setConstant(true);
ws1->var("decay_pp")->setConstant(true);
ws1->var("effRat3_hi")->setConstant(true);
ws1->var("effRat_kappa")->setConstant(true);
// ws1->var("glob_Taa")->setConstant(true);
// ws1->var("glob_effRat")->setConstant(true);
// ws1->var("glob_lumipp")->setConstant(true);
// ws1->var("glob_nbkg_hi")->setConstant(true);
// ws1->var("glob_nbkg_pp")->setConstant(true);
// ws1->var("invariantMass")->setConstant(true);
ws1->var("leftEdge")->setConstant(true);
ws1->var("lumipp_hi")->setConstant(true);
ws1->var("lumipp_kappa")->setConstant(true);
ws1->var("m_{ #varUpsilon(1S)}_hi")->setConstant(true); //ws1->var("mass1S_hi")->setConstant(true);
ws1->var("m_{ #varUpsilon(1S)}_pp")->setConstant(true); //ws1->var("mass1S_pp")->setConstant(true);
ws1->var("muMinusPt")->setConstant(true);
ws1->var("muPlusPt")->setConstant(true);
ws1->var("n_{Bkgd}_hi")->setConstant(true);
ws1->var("n_{Bkgd}_pp")->setConstant(true);
ws1->var("nbkg_hi_kappa")->setConstant(true);
ws1->var("nbkg_pp_kappa")->setConstant(true);
//ws1->var("n_{CB}")->setConstant(true); //ws1->var("n_{CB}")->setConstant(true); //ws1->var("npow")->setConstant(true);
ws1->var("n_{CB}_hi")->setConstant(true); //ws1->var("n_{CB}")->setConstant(true); //ws1->var("npow")->setConstant(true);
ws1->var("n_{CB}_pp")->setConstant(true); //ws1->var("n_{CB}")->setConstant(true); //ws1->var("npow")->setConstant(true);
// ws1->var("raa3")->setConstant(true);
ws1->var("rightEdge")->setConstant(true);
ws1->var("sigmaFraction_hi")->setConstant(true);
ws1->var("sigmaFraction_pp")->setConstant(true);
ws1->var("turnOn_hi")->setConstant(true);
ws1->var("turnOn_pp")->setConstant(true);
ws1->var("dimuPt")->setConstant(true); //ws1->var("upsPt")->setConstant(true);
ws1->var("dimuRapidity")->setConstant(true); //ws1->var("upsRapidity")->setConstant(true);
ws1->var("vProb")->setConstant(true);
ws1->var("width_hi")->setConstant(true);
ws1->var("width_pp")->setConstant(true);
// ws1->var("x3raw")->setConstant(true);
// RooArgSet fixed_again("fixed_again");
// fixed_again.add( *ws1->var("leftEdge") );
// fixed_again.add( *ws1->var("rightEdge") );
// fixed_again.add( *ws1->var("Taa_hi") );
// fixed_again.add( *ws1->var("Nmb_hi") );
// fixed_again.add( *ws1->var("lumipp_hi") );
// fixed_again.add( *ws1->var("effRat1_hi") );
// fixed_again.add( *ws1->var("effRat2_hi") );
// fixed_again.add( *ws1->var("effRat3_hi") );
// fixed_again.add( *ws1->var("nsig3_pp") );
// fixed_again.add( *ws1->var("nsig1_pp") );
// fixed_again.add( *ws1->var("nbkg_hi") );
// fixed_again.add( *ws1->var("alpha") );
// fixed_again.add( *ws1->var("nbkg_kappa") );
// fixed_again.add( *ws1->var("Taa_kappa") );
// fixed_again.add( *ws1->var("lumipp_kappa") );
// fixed_again.add( *ws1->var("mean_hi") );
// fixed_again.add( *ws1->var("mean_pp") );
// fixed_again.add( *ws1->var("width_hi") );
// fixed_again.add( *ws1->var("turnOn_hi") );
// fixed_again.add( *ws1->var("bkg_a1_pp") );
// fixed_again.add( *ws1->var("bkg_a2_pp") );
// fixed_again.add( *ws1->var("decay_hi") );
// fixed_again.add( *ws1->var("raa1") );
// fixed_again.add( *ws1->var("raa2") );
// fixed_again.add( *ws1->var("nsig2_pp") );
// fixed_again.add( *ws1->var("sigma1") );
// fixed_again.add( *ws1->var("nbkg_pp") );
// fixed_again.add( *ws1->var("npow") );
// fixed_again.add( *ws1->var("muPlusPt") );
// fixed_again.add( *ws1->var("muMinusPt") );
// fixed_again.add( *ws1->var("mscale_hi") );
// fixed_again.add( *ws1->var("mscale_pp") );
//
// ws1->Print();
cout << "99999" << endl;
// create signal+background Model Config
RooStats::ModelConfig sbHypo("SbHypo");
sbHypo.SetWorkspace( *ws1 );
sbHypo.SetPdf( *ws1->pdf("joint") );
sbHypo.SetObservables( obs );
sbHypo.SetGlobalObservables( globalObs );
sbHypo.SetParametersOfInterest( poi );
sbHypo.SetNuisanceParameters( nuis );
sbHypo.SetPriorPdf( *ws1->pdf("step") ); // this is optional
// ws1->Print();
/////////////////////////////////////////////////////////////////////
RooAbsReal * pNll = sbHypo.GetPdf()->createNLL( *data,NumCPU(10) );
cout << "111111" << endl;
RooMinuit(*pNll).migrad(); // minimize likelihood wrt all parameters before making plots
cout << "444444" << endl;
RooPlot *framepoi = ((RooRealVar *)poi.first())->frame(Bins(10),Range(0.,0.2),Title("LL and profileLL in raa3"));
cout << "222222" << endl;
pNll->plotOn(framepoi,ShiftToZero());
cout << "333333" << endl;
RooAbsReal * pProfile = pNll->createProfile( globalObs ); // do not profile global observables
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
pProfile->plotOn(framepoi,LineColor(kRed));
framepoi->SetMinimum(0);
framepoi->SetMaximum(3);
TCanvas *cpoi = new TCanvas();
cpoi->cd(); framepoi->Draw();
cpoi->SaveAs("cpoi.pdf");
((RooRealVar *)poi.first())->setMin(0.);
RooArgSet * pPoiAndNuisance = new RooArgSet("poiAndNuisance");
// pPoiAndNuisance->add(*sbHypo.GetNuisanceParameters());
// pPoiAndNuisance->add(*sbHypo.GetParametersOfInterest());
pPoiAndNuisance->add( nuis );
pPoiAndNuisance->add( poi );
sbHypo.SetSnapshot(*pPoiAndNuisance);
RooPlot* xframeSB = pObs->frame(Title("SBhypo"));
data->plotOn(xframeSB,Cut("dataCat==dataCat::hi"));
RooAbsPdf *pdfSB = sbHypo.GetPdf();
RooCategory *dataCat = ws1->cat("dataCat");
pdfSB->plotOn(xframeSB,Slice(*dataCat,"hi"),ProjWData(*dataCat,*data));
TCanvas *c1 = new TCanvas();
c1->cd(); xframeSB->Draw();
c1->SaveAs("c1.pdf");
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
ws1->import( sbHypo );
/////////////////////////////////////////////////////////////////////
RooStats::ModelConfig bHypo = sbHypo;
bHypo.SetName("BHypo");
bHypo.SetWorkspace(*ws1);
pNll = bHypo.GetPdf()->createNLL( *data,NumCPU(2) );
RooArgSet poiAndGlobalObs("poiAndGlobalObs");
poiAndGlobalObs.add( poi );
poiAndGlobalObs.add( globalObs );
pProfile = pNll->createProfile( poiAndGlobalObs ); // do not profile POI and global observables
((RooRealVar *)poi.first())->setVal( 0 ); // set raa3=0 here
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet( "poiAndNuisance" );
pPoiAndNuisance->add( nuis );
pPoiAndNuisance->add( poi );
bHypo.SetSnapshot(*pPoiAndNuisance);
RooPlot* xframeB = pObs->frame(Title("Bhypo"));
data->plotOn(xframeB,Cut("dataCat==dataCat::hi"));
RooAbsPdf *pdfB = bHypo.GetPdf();
pdfB->plotOn(xframeB,Slice(*dataCat,"hi"),ProjWData(*dataCat,*data));
TCanvas *c2 = new TCanvas();
c2->cd(); xframeB->Draw();
c2->SaveAs("c2.pdf");
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// import model config into workspace
bHypo.SetWorkspace(*ws1);
ws1->import( bHypo );
/////////////////////////////////////////////////////////////////////
ws1->Print();
bHypo.Print();
sbHypo.Print();
// save workspace to file
ws1 -> SaveAs(name_out);
return;
}
void LL(){
//y0 = 0.000135096401209 sigma_y0 = 0.000103896581837 x0 = 0.000446013873443 sigma_x0 =1.81384394011e-06
//0.014108652249 0.0168368471049 0.0219755396247 0.000120423865262 1.5575931164 1.55759310722 3.41637854038
//0.072569437325 0.084063541977 0.0376693978906 0.000284216132439 0.51908074913 0.519080758095 1.12037749267
// double d = 0.014108652249;
// double sd = 0.0168368471049;
// double mc = 0.0219755396247;
// double smc = 0.000120423865262;
// double r0 = d/mc;
double d = 0.072569437325;
double sd = 0.084063541977;
double mc = 0.0376693978906;
double smc = 0.00028421613243;
double r0 = d/mc;
RooRealVar x("x","x",mc*0.9,mc*1.1);
RooRealVar x0("x0","x0",mc);
RooRealVar sx("sx","sx",smc);
RooRealVar r("r","r",r0,0.,5.);
RooRealVar y0("y0","y0",d);
RooRealVar sy("sy","sy",sd);
RooProduct rx("rx","rx",RooArgList(r,x));
RooGaussian g1("g1","g1",x,x0,sx);
RooGaussian g2("g2","g2",rx,y0,sy);
RooProdPdf LL("LL","LL",g1,g2);
RooArgSet obs(x0,y0); //observables
RooArgSet poi(r); //parameters of interest
RooDataSet data("data", "data", obs);
data.add(obs); //actually add the data
RooFitResult* res = LL.fitTo(data,RooFit::Minos(poi),RooFit::Save(),RooFit::Hesse(false));
if(res->status()==0) {
r.Print();
x.Print();
cout << r.getErrorLo() << " " << r.getErrorHi() << endl;
} else {
cout << "Likelihood maximization failed" << endl;
}
RooAbsReal* nll = LL.createNLL(data);
RooPlot* frame = r.frame();
RooAbsReal* pll = nll->createProfile(poi);
pll->plotOn(frame);//,RooFit::LineColor(ROOT::kRed));
frame->Draw();
r.setVal(0.);
cout << pll->getVal() << endl;
return;
}
/*
* Prepares the workspace to be used by the hypothesis test calculator
*/
void workspace_preparer(char *signal_file_name, char *signal_hist_name_in_file, char *background_file_name, char *background_hist_name_in_file, char *data_file_name, char *data_hist_name_in_file, char *config_file) {
// Include the config_reader class.
TString path = gSystem->GetIncludePath();
path.Append(" -I/home/max/cern/cls/mario");
gSystem->SetIncludePath(path);
gROOT->LoadMacro("config_reader.cxx");
// RooWorkspace used to store values.
RooWorkspace * pWs = new RooWorkspace("ws");
// Create a config_reader (see source for details) to read the config
// file.
config_reader reader(config_file, pWs);
// Read MR and RR bounds from the config file.
double MR_lower = reader.find_double("MR_lower");
double MR_upper = reader.find_double("MR_upper");
double RR_lower = reader.find_double("RR_lower");
double RR_upper = reader.find_double("RR_upper");
double MR_initial = (MR_lower + MR_upper)/2;
double RR_initial = (RR_lower + RR_upper)/2;
// Define the Razor Variables
RooRealVar MR = RooRealVar("MR", "MR", MR_initial, MR_lower, MR_upper);
RooRealVar RR = RooRealVar("RSQ", "RSQ", RR_initial, RR_lower, RR_upper);
// Argument lists
RooArgList pdf_arg_list(MR, RR, "input_args_list");
RooArgSet pdf_arg_set(MR, RR, "input_pdf_args_set");
/***********************************************************************/
/* PART 1: IMPORTING SIGNAL AND BACKGROUND HISTOGRAMS */
/***********************************************************************/
/*
* Get the signal's unextended pdf by converting the TH2D in the file
* into a RooHistPdf
*/
TFile *signal_file = new TFile(signal_file_name);
TH2D *signal_hist = (TH2D *)signal_file->Get(signal_hist_name_in_file);
RooDataHist *signal_RooDataHist = new RooDataHist("signal_roodatahist",
"signal_roodatahist",
pdf_arg_list,
signal_hist);
RooHistPdf *unextended_sig_pdf = new RooHistPdf("unextended_sig_pdf",
"unextended_sig_pdf",
pdf_arg_set,
*signal_RooDataHist);
/*
* Repeat this process for the background.
*/
TFile *background_file = new TFile(background_file_name);
TH2D *background_hist =
(TH2D *)background_file->Get(background_hist_name_in_file);
RooDataHist *background_RooDataHist =
new RooDataHist("background_roodatahist", "background_roodatahist",
pdf_arg_list, background_hist);
RooHistPdf *unextended_bkg_pdf = new RooHistPdf("unextended_bkg_pdf",
"unextended_bkg_pdf",
pdf_arg_set,
*background_RooDataHist);
/*
* Now, we want to create the bprime variable, which represents the
* integral over the background-only sample. We will perform the
* integral automatically (that's why this is the only nuisance
* parameter declared in this file - its value can be determined from
* the input histograms).
*/
ostringstream bprime_string;
ostringstream bprime_pdf_string;
bprime_string << "bprime[" << background_hist->Integral() << ", 0, 999999999]";
bprime_pdf_string << "Poisson::bprime_pdf(bprime, " << background_hist->Integral() << ")";
pWs->factory(bprime_string.str().c_str());
pWs->factory(bprime_pdf_string.str().c_str());
/*
* This simple command will create all values from the config file
* with 'make:' at the beginning and a delimiter at the end (see config
* _reader if you don't know what a delimiter is). In other
* words, the luminosity, efficiency, transfer factors, and their pdfs
* are created from this command. The declarations are contained in the
* config file to be changed easily without having to modify this code.
*/
reader.factory_all();
/*
* Now, we want to create the extended pdfs from the unextended pdfs, as
* well as from the S and B values we manufactured in the config file.
* S and B are the values by which the signal and background pdfs,
* respectively, are extended. Recall that they were put in the
* workspace in the reader.facotry_all() command.
*/
RooAbsReal *S = pWs->function("S");
RooAbsReal *B = pWs->function("B");
RooExtendPdf *signalpart = new RooExtendPdf("signalpart", "signalpart",
*unextended_sig_pdf, *S);
RooExtendPdf *backgroundpart =
new RooExtendPdf("backgroundpart", "backgroundpart",
*unextended_bkg_pdf, *B);
RooArgList *pdf_list = new RooArgList(*signalpart, *backgroundpart,
"list");
// Add the signal and background pdfs to make a TotalPdf
RooAddPdf *TotalPdf = new RooAddPdf("TotalPdf", "TotalPdf", *pdf_list);
RooArgList *pdf_prod_list = new RooArgList(*TotalPdf,
*pWs->pdf("lumi_pdf"),
*pWs->pdf("eff_pdf"),
*pWs->pdf("rho_pdf"),
*pWs->pdf("bprime_pdf"));
// This creates the final model pdf.
RooProdPdf *model = new RooProdPdf("model", "model", *pdf_prod_list);
/*
* Up until now, we have been using the workspace pWs to contain all of
* our values. Now, all of our values that we require are in use in the
* RooProdPdf called "model". So, we need to import "model" into a
* RooWorkspace. To avoid recopying values into the rooworkspace, when
* the values may already be present (which can cause problems), we will
* simply create a new RooWorkspace to avoid confusion and problems. The
* new RooWorkspace is created here.
*/
RooWorkspace *newworkspace = new RooWorkspace("newws");
newworkspace->import(*model);
// Immediately delete pWs, so we don't accidentally use it again.
delete pWs;
// Show off the newworkspace
newworkspace->Print();
// observables
RooArgSet obs(*newworkspace->var("MR"), *newworkspace->var("RSQ"), "obs");
// global observables
RooArgSet globalObs(*newworkspace->var("nom_lumi"), *newworkspace->var("nom_eff"), *newworkspace->var("nom_rho"));
//fix global observables to their nominal values
newworkspace->var("nom_lumi")->setConstant();
newworkspace->var("nom_eff")->setConstant();
newworkspace->var("nom_rho")->setConstant();
//Set Parameters of interest
RooArgSet poi(*newworkspace->var("sigma"), "poi");
//Set Nuisnaces
RooArgSet nuis(*newworkspace->var("prime_lumi"), *newworkspace->var("prime_eff"), *newworkspace->var("prime_rho"), *newworkspace->var("bprime"));
// priors (for Bayesian calculation)
newworkspace->factory("Uniform::prior_signal(sigma)"); // for parameter of interest
newworkspace->factory("Uniform::prior_bg_b(bprime)"); // for data driven nuisance parameter
newworkspace->factory("PROD::prior(prior_signal,prior_bg_b)"); // total prior
//Observed data is pulled from histogram.
//TFile *data_file = new TFile(data_file_name);
TFile *data_file = new TFile(data_file_name);
TH2D *data_hist = (TH2D *)data_file->Get(data_hist_name_in_file);
RooDataHist *pData = new RooDataHist("data", "data", obs, data_hist);
newworkspace->import(*pData);
// Now, we will draw our data from a RooDataHist.
/*TFile *data_file = new TFile(data_file_name);
TTree *data_tree = (TTree *) data_file->Get(data_hist_name_in_file);
RooDataSet *pData = new RooDataSet("data", "data", data_tree, obs);
newworkspace->import(*pData);*/
// Craft the signal+background model
ModelConfig * pSbModel = new ModelConfig("SbModel");
pSbModel->SetWorkspace(*newworkspace);
pSbModel->SetPdf(*newworkspace->pdf("model"));
pSbModel->SetPriorPdf(*newworkspace->pdf("prior"));
pSbModel->SetParametersOfInterest(poi);
pSbModel->SetNuisanceParameters(nuis);
pSbModel->SetObservables(obs);
pSbModel->SetGlobalObservables(globalObs);
// set all but obs, poi and nuisance to const
SetConstants(newworkspace, pSbModel);
newworkspace->import(*pSbModel);
// background-only model
// use the same PDF as s+b, with sig=0
// POI value under the background hypothesis
// (We will set the value to 0 later)
Double_t poiValueForBModel = 0.0;
ModelConfig* pBModel = new ModelConfig(*(RooStats::ModelConfig *)newworkspace->obj("SbModel"));
pBModel->SetName("BModel");
pBModel->SetWorkspace(*newworkspace);
newworkspace->import(*pBModel);
// find global maximum with the signal+background model
// with conditional MLEs for nuisance parameters
// and save the parameter point snapshot in the Workspace
// - safer to keep a default name because some RooStats calculators
// will anticipate it
RooAbsReal * pNll = pSbModel->GetPdf()->createNLL(*pData);
RooAbsReal * pProfile = pNll->createProfile(RooArgSet());
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
RooArgSet * pPoiAndNuisance = new RooArgSet();
if(pSbModel->GetNuisanceParameters())
pPoiAndNuisance->add(*pSbModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pSbModel->GetParametersOfInterest());
cout << "\nWill save these parameter points that correspond to the fit to data" << endl;
pPoiAndNuisance->Print("v");
pSbModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// Find a parameter point for generating pseudo-data
// with the background-only data.
// Save the parameter point snapshot in the Workspace
pNll = pBModel->GetPdf()->createNLL(*pData);
pProfile = pNll->createProfile(poi);
((RooRealVar *)poi.first())->setVal(poiValueForBModel);
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet();
if(pBModel->GetNuisanceParameters())
pPoiAndNuisance->add(*pBModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pBModel->GetParametersOfInterest());
cout << "\nShould use these parameter points to generate pseudo data for bkg only" << endl;
pPoiAndNuisance->Print("v");
pBModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// save workspace to file
newworkspace->writeToFile("ws_twobin.root");
// clean up
delete newworkspace;
delete pData;
delete pSbModel;
delete pBModel;
} // ----- end of tutorial ----------------------------------------
// implementation
void TwoBinInstructional( void ){
// let's time this example
TStopwatch t;
t.Start();
// set RooFit random seed for reproducible results
RooRandom::randomGenerator()->SetSeed(4357);
// make model
RooWorkspace * pWs = new RooWorkspace("ws");
// derived from data
pWs->factory("xsec[0.2,0,2]"); // POI
pWs->factory("bg_b[10,0,50]"); // data driven nuisance
// predefined nuisances
pWs->factory("lumi[100,0,1000]");
pWs->factory("eff_a[0.2,0,1]");
pWs->factory("eff_b[0.05,0,1]");
pWs->factory("tau[0,1]");
pWs->factory("xsec_bg_a[0.05]"); // constant
pWs->var("xsec_bg_a")->setConstant(1);
// channel a (signal): lumi*xsec*eff_a + lumi*bg_a + tau*bg_b
pWs->factory("prod::sig_a(lumi,xsec,eff_a)");
pWs->factory("prod::bg_a(lumi,xsec_bg_a)");
pWs->factory("prod::tau_bg_b(tau, bg_b)");
pWs->factory("Poisson::pdf_a(na[14,0,100],sum::mu_a(sig_a,bg_a,tau_bg_b))");
// channel b (control): lumi*xsec*eff_b + bg_b
pWs->factory("prod::sig_b(lumi,xsec,eff_b)");
pWs->factory("Poisson::pdf_b(nb[11,0,100],sum::mu_b(sig_b,bg_b))");
// nuisance constraint terms (systematics)
pWs->factory("Lognormal::l_lumi(lumi,nom_lumi[100,0,1000],sum::kappa_lumi(1,d_lumi[0.1]))");
pWs->factory("Lognormal::l_eff_a(eff_a,nom_eff_a[0.20,0,1],sum::kappa_eff_a(1,d_eff_a[0.05]))");
pWs->factory("Lognormal::l_eff_b(eff_b,nom_eff_b[0.05,0,1],sum::kappa_eff_b(1,d_eff_b[0.05]))");
pWs->factory("Lognormal::l_tau(tau,nom_tau[0.50,0,1],sum::kappa_tau(1,d_tau[0.05]))");
//pWs->factory("Lognormal::l_bg_a(bg_a,nom_bg_a[0.05,0,1],sum::kappa_bg_a(1,d_bg_a[0.10]))");
// complete model PDF
pWs->factory("PROD::model(pdf_a,pdf_b,l_lumi,l_eff_a,l_eff_b,l_tau)");
// Now create sets of variables. Note that we could use the factory to
// create sets but in that case many of the sets would be duplicated
// when the ModelConfig objects are imported into the workspace. So,
// we create the sets outside the workspace, and only the needed ones
// will be automatically imported by ModelConfigs
// observables
RooArgSet obs(*pWs->var("na"), *pWs->var("nb"), "obs");
// global observables
RooArgSet globalObs(*pWs->var("nom_lumi"), *pWs->var("nom_eff_a"), *pWs->var("nom_eff_b"),
*pWs->var("nom_tau"),
"global_obs");
// parameters of interest
RooArgSet poi(*pWs->var("xsec"), "poi");
// nuisance parameters
RooArgSet nuis(*pWs->var("lumi"), *pWs->var("eff_a"), *pWs->var("eff_b"), *pWs->var("tau"), "nuis");
// priors (for Bayesian calculation)
pWs->factory("Uniform::prior_xsec(xsec)"); // for parameter of interest
pWs->factory("Uniform::prior_bg_b(bg_b)"); // for data driven nuisance parameter
pWs->factory("PROD::prior(prior_xsec,prior_bg_b)"); // total prior
// create data
pWs->var("na")->setVal(14);
pWs->var("nb")->setVal(11);
RooDataSet * pData = new RooDataSet("data","",obs);
pData->add(obs);
pWs->import(*pData);
//pData->Print();
// signal+background model
ModelConfig * pSbModel = new ModelConfig("SbModel");
pSbModel->SetWorkspace(*pWs);
pSbModel->SetPdf(*pWs->pdf("model"));
pSbModel->SetPriorPdf(*pWs->pdf("prior"));
pSbModel->SetParametersOfInterest(poi);
pSbModel->SetNuisanceParameters(nuis);
pSbModel->SetObservables(obs);
pSbModel->SetGlobalObservables(globalObs);
// set all but obs, poi and nuisance to const
SetConstants(pWs, pSbModel);
pWs->import(*pSbModel);
// background-only model
// use the same PDF as s+b, with xsec=0
// POI value under the background hypothesis
Double_t poiValueForBModel = 0.0;
ModelConfig* pBModel = new ModelConfig(*(RooStats::ModelConfig *)pWs->obj("SbModel"));
pBModel->SetName("BModel");
pBModel->SetWorkspace(*pWs);
pWs->import(*pBModel);
// find global maximum with the signal+background model
// with conditional MLEs for nuisance parameters
// and save the parameter point snapshot in the Workspace
// - safer to keep a default name because some RooStats calculators
// will anticipate it
RooAbsReal * pNll = pSbModel->GetPdf()->createNLL(*pData);
RooAbsReal * pProfile = pNll->createProfile(RooArgSet());
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
RooArgSet * pPoiAndNuisance = new RooArgSet();
if(pSbModel->GetNuisanceParameters())
pPoiAndNuisance->add(*pSbModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pSbModel->GetParametersOfInterest());
cout << "\nWill save these parameter points that correspond to the fit to data" << endl;
pPoiAndNuisance->Print("v");
pSbModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// Find a parameter point for generating pseudo-data
// with the background-only data.
// Save the parameter point snapshot in the Workspace
pNll = pBModel->GetPdf()->createNLL(*pData);
pProfile = pNll->createProfile(poi);
((RooRealVar *)poi.first())->setVal(poiValueForBModel);
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet();
if(pBModel->GetNuisanceParameters())
pPoiAndNuisance->add(*pBModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pBModel->GetParametersOfInterest());
cout << "\nShould use these parameter points to generate pseudo data for bkg only" << endl;
pPoiAndNuisance->Print("v");
pBModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// inspect workspace
pWs->Print();
// save workspace to file
pWs->writeToFile("ws_twobin.root");
// clean up
delete pWs;
delete pData;
delete pSbModel;
delete pBModel;
} // ----- end of tutorial ----------------------------------------
RooWorkspace* makeInvertedANFit(TTree* tree, float forceSigma=-1, bool constrainMu=false, float forceMu=-1) {
RooWorkspace *ws = new RooWorkspace("ws","");
std::vector< TString (*)(TString, RooRealVar&, RooWorkspace&) > bkgPdfList;
bkgPdfList.push_back(makeSingleExp);
bkgPdfList.push_back(makeDoubleExp);
#if DEBUG==0
//bkgPdfList.push_back(makeTripleExp);
bkgPdfList.push_back(makeModExp);
bkgPdfList.push_back(makeSinglePow);
bkgPdfList.push_back(makeDoublePow);
bkgPdfList.push_back(makePoly2);
bkgPdfList.push_back(makePoly3);
#endif
RooRealVar mgg("mgg","m_{#gamma#gamma}",103,160,"GeV");
mgg.setBins(38);
mgg.setRange("sideband_low", 103,120);
mgg.setRange("sideband_high",131,160);
mgg.setRange("signal",120,131);
RooRealVar MR("MR","",0,3000,"GeV");
MR.setBins(60);
RooRealVar Rsq("t1Rsq","",0,1,"GeV");
Rsq.setBins(20);
RooRealVar hem1_M("hem1_M","",-1,2000,"GeV");
hem1_M.setBins(40);
RooRealVar hem2_M("hem2_M","",-1,2000,"GeV");
hem2_M.setBins(40);
RooRealVar ptgg("ptgg","p_{T}^{#gamma#gamma}",0,500,"GeV");
ptgg.setBins(50);
RooDataSet data("data","",tree,RooArgSet(mgg,MR,Rsq,hem1_M,hem2_M,ptgg));
RooDataSet* blind_data = (RooDataSet*)data.reduce("mgg<121 || mgg>130");
std::vector<TString> tags;
//fit many different background models
for(auto func = bkgPdfList.begin(); func != bkgPdfList.end(); func++) {
TString tag = (*func)("bonly",mgg,*ws);
tags.push_back(tag);
ws->pdf("bonly_"+tag+"_ext")->fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE),RooFit::Range("sideband_low,sideband_high"));
RooFitResult* bres = ws->pdf("bonly_"+tag+"_ext")->fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE),RooFit::Range("sideband_low,sideband_high"));
bres->SetName(tag+"_bonly_fitres");
ws->import(*bres);
//make blinded fit
RooPlot *fmgg_b = mgg.frame();
blind_data->plotOn(fmgg_b,RooFit::Range("sideband_low,sideband_high"));
TBox blindBox(121,fmgg_b->GetMinimum()-(fmgg_b->GetMaximum()-fmgg_b->GetMinimum())*0.015,130,fmgg_b->GetMaximum());
blindBox.SetFillColor(kGray);
fmgg_b->addObject(&blindBox);
ws->pdf("bonly_"+tag+"_ext")->plotOn(fmgg_b,RooFit::LineColor(kRed),RooFit::Range("Full"),RooFit::NormRange("sideband_low,sideband_high"));
fmgg_b->SetName(tag+"_blinded_frame");
ws->import(*fmgg_b);
delete fmgg_b;
//set all the parameters constant
RooArgSet* vars = ws->pdf("bonly_"+tag)->getVariables();
RooFIter iter = vars->fwdIterator();
RooAbsArg* a;
while( (a = iter.next()) ){
if(string(a->GetName()).compare("mgg")==0) continue;
static_cast<RooRealVar*>(a)->setConstant(kTRUE);
}
//make the background portion of the s+b fit
(*func)("b",mgg,*ws);
RooRealVar sigma(tag+"_s_sigma","",5,0,100);
if(forceSigma!=-1) {
sigma.setVal(forceSigma);
sigma.setConstant(true);
}
RooRealVar mu(tag+"_s_mu","",126,120,132);
if(forceMu!=-1) {
mu.setVal(forceMu);
mu.setConstant(true);
}
RooGaussian sig(tag+"_sig_model","",mgg,mu,sigma);
RooRealVar Nsig(tag+"_sb_Ns","",5,0,100);
RooRealVar Nbkg(tag+"_sb_Nb","",100,0,100000);
RooRealVar HiggsMass("HiggsMass","",125.1);
RooRealVar HiggsMassError("HiggsMassError","",0.24);
RooGaussian HiggsMassConstraint("HiggsMassConstraint","",mu,HiggsMass,HiggsMassError);
RooAddPdf fitModel(tag+"_sb_model","",RooArgList( *ws->pdf("b_"+tag), sig ),RooArgList(Nbkg,Nsig));
RooFitResult* sbres;
RooAbsReal* nll;
if(constrainMu) {
fitModel.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
sbres = fitModel.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
nll = fitModel.createNLL(data,RooFit::NumCPU(4),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
} else {
fitModel.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE));
sbres = fitModel.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE));
nll = fitModel.createNLL(data,RooFit::NumCPU(4),RooFit::Extended(kTRUE));
}
sbres->SetName(tag+"_sb_fitres");
ws->import(*sbres);
ws->import(fitModel);
RooPlot *fmgg = mgg.frame();
data.plotOn(fmgg);
fitModel.plotOn(fmgg);
ws->pdf("b_"+tag+"_ext")->plotOn(fmgg,RooFit::LineColor(kRed),RooFit::Range("Full"),RooFit::NormRange("Full"));
fmgg->SetName(tag+"_frame");
ws->import(*fmgg);
delete fmgg;
RooMinuit(*nll).migrad();
RooPlot *fNs = Nsig.frame(0,25);
fNs->SetName(tag+"_Nsig_pll");
RooAbsReal *pll = nll->createProfile(Nsig);
//nll->plotOn(fNs,RooFit::ShiftToZero(),RooFit::LineColor(kRed));
pll->plotOn(fNs);
ws->import(*fNs);
delete fNs;
RooPlot *fmu = mu.frame(125,132);
fmu->SetName(tag+"_mu_pll");
RooAbsReal *pll_mu = nll->createProfile(mu);
pll_mu->plotOn(fmu);
ws->import(*fmu);
delete fmu;
}
RooArgSet weights("weights");
RooArgSet pdfs_bonly("pdfs_bonly");
RooArgSet pdfs_b("pdfs_b");
RooRealVar minAIC("minAIC","",1E10);
//compute AIC stuff
for(auto t = tags.begin(); t!=tags.end(); t++) {
RooAbsPdf *p_bonly = ws->pdf("bonly_"+*t);
RooAbsPdf *p_b = ws->pdf("b_"+*t);
RooFitResult *sb = (RooFitResult*)ws->obj(*t+"_bonly_fitres");
RooRealVar k(*t+"_b_k","",p_bonly->getParameters(RooArgSet(mgg))->getSize());
RooRealVar nll(*t+"_b_minNll","",sb->minNll());
RooRealVar Npts(*t+"_b_N","",blind_data->sumEntries());
RooFormulaVar AIC(*t+"_b_AIC","2*@0+2*@1+2*@1*(@1+1)/(@2-@1-1)",RooArgSet(nll,k,Npts));
ws->import(AIC);
if(AIC.getVal() < minAIC.getVal()) {
minAIC.setVal(AIC.getVal());
}
//aicExpSum+=TMath::Exp(-0.5*AIC.getVal()); //we will need this precomputed for the next step
pdfs_bonly.add(*p_bonly);
pdfs_b.add(*p_b);
}
ws->import(minAIC);
//compute the AIC weight
float aicExpSum=0;
for(auto t = tags.begin(); t!=tags.end(); t++) {
RooFormulaVar *AIC = (RooFormulaVar*)ws->obj(*t+"_b_AIC");
aicExpSum+=TMath::Exp(-0.5*(AIC->getVal()-minAIC.getVal())); //we will need this precomputed for the next step
}
std::cout << "aicExpSum: " << aicExpSum << std::endl;
for(auto t = tags.begin(); t!=tags.end(); t++) {
RooFormulaVar *AIC = (RooFormulaVar*)ws->obj(*t+"_b_AIC");
RooRealVar *AICw = new RooRealVar(*t+"_b_AICWeight","",TMath::Exp(-0.5*(AIC->getVal()-minAIC.getVal()))/aicExpSum);
if( TMath::IsNaN(AICw->getVal()) ) {AICw->setVal(0);}
ws->import(*AICw);
std::cout << *t << ": " << AIC->getVal()-minAIC.getVal() << " " << AICw->getVal() << std::endl;
weights.add(*AICw);
}
RooAddPdf bonly_AIC("bonly_AIC","",pdfs_bonly,weights);
RooAddPdf b_AIC("b_AIC","",pdfs_b,weights);
//b_AIC.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE),RooFit::Range("sideband_low,sideband_high"));
//RooFitResult* bres = b_AIC.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE),RooFit::Range("sideband_low,sideband_high"));
//bres->SetName("AIC_b_fitres");
//ws->import(*bres);
//make blinded fit
RooPlot *fmgg_b = mgg.frame(RooFit::Range("sideband_low,sideband_high"));
blind_data->plotOn(fmgg_b,RooFit::Range("sideband_low,sideband_high"));
TBox blindBox(121,fmgg_b->GetMinimum()-(fmgg_b->GetMaximum()-fmgg_b->GetMinimum())*0.015,130,fmgg_b->GetMaximum());
blindBox.SetFillColor(kGray);
fmgg_b->addObject(&blindBox);
bonly_AIC.plotOn(fmgg_b,RooFit::LineColor(kRed),RooFit::Range("Full"),RooFit::NormRange("sideband_low,sideband_high"));
fmgg_b->SetName("AIC_blinded_frame");
ws->import(*fmgg_b);
delete fmgg_b;
#if 1
RooRealVar sigma("AIC_s_sigma","",5,0,100);
if(forceSigma!=-1) {
sigma.setVal(forceSigma);
sigma.setConstant(true);
}
RooRealVar mu("AIC_s_mu","",126,120,132);
if(forceMu!=-1) {
mu.setVal(forceMu);
mu.setConstant(true);
}
RooGaussian sig("AIC_sig_model","",mgg,mu,sigma);
RooRealVar Nsig("AIC_sb_Ns","",5,0,100);
RooRealVar Nbkg("AIC_sb_Nb","",100,0,100000);
RooRealVar HiggsMass("HiggsMass","",125.1);
RooRealVar HiggsMassError("HiggsMassError","",0.24);
RooGaussian HiggsMassConstraint("HiggsMassConstraint","",mu,HiggsMass,HiggsMassError);
RooAddPdf fitModel("AIC_sb_model","",RooArgList( b_AIC, sig ),RooArgList(Nbkg,Nsig));
RooFitResult* sbres;
RooAbsReal *nll;
if(constrainMu) {
fitModel.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
sbres = fitModel.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
nll = fitModel.createNLL(data,RooFit::NumCPU(4),RooFit::Extended(kTRUE),RooFit::ExternalConstraints(RooArgSet(HiggsMassConstraint)));
} else {
fitModel.fitTo(data,RooFit::Strategy(0),RooFit::Extended(kTRUE));
sbres = fitModel.fitTo(data,RooFit::Strategy(2),RooFit::Save(kTRUE),RooFit::Extended(kTRUE));
nll = fitModel.createNLL(data,RooFit::NumCPU(4),RooFit::Extended(kTRUE));
}
assert(nll!=0);
sbres->SetName("AIC_sb_fitres");
ws->import(*sbres);
ws->import(fitModel);
RooPlot *fmgg = mgg.frame();
data.plotOn(fmgg);
fitModel.plotOn(fmgg);
ws->pdf("b_AIC")->plotOn(fmgg,RooFit::LineColor(kRed),RooFit::Range("Full"),RooFit::NormRange("Full"));
fmgg->SetName("AIC_frame");
ws->import(*fmgg);
delete fmgg;
RooMinuit(*nll).migrad();
RooPlot *fNs = Nsig.frame(0,25);
fNs->SetName("AIC_Nsig_pll");
RooAbsReal *pll = nll->createProfile(Nsig);
//nll->plotOn(fNs,RooFit::ShiftToZero(),RooFit::LineColor(kRed));
pll->plotOn(fNs);
ws->import(*fNs);
delete fNs;
RooPlot *fmu = mu.frame(125,132);
fmu->SetName("AIC_mu_pll");
RooAbsReal *pll_mu = nll->createProfile(mu);
pll_mu->plotOn(fmu);
ws->import(*fmu);
delete fmu;
std::cout << "min AIC: " << minAIC.getVal() << std::endl;
for(auto t = tags.begin(); t!=tags.end(); t++) {
RooFormulaVar *AIC = (RooFormulaVar*)ws->obj(*t+"_b_AIC");
RooRealVar *AICw = ws->var(*t+"_b_AICWeight");
RooRealVar* k = ws->var(*t+"_b_k");
printf("%s & %0.0f & %0.2f & %0.2f \\\\\n",t->Data(),k->getVal(),AIC->getVal()-minAIC.getVal(),AICw->getVal());
//std::cout << k->getVal() << " " << AIC->getVal()-minAIC.getVal() << " " << AICw->getVal() << std::endl;
}
#endif
return ws;
}
void OneSidedFrequentistUpperLimitWithBands(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData") {
double confidenceLevel=0.95;
int nPointsToScan = 20;
int nToyMC = 200;
// -------------------------------------------------------
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
const char* filename = "";
if (!strcmp(infile,"")) {
filename = "results/example_combined_GaussExample_model.root";
bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
// if file does not exists generate with histfactory
if (!fileExist) {
#ifdef _WIN32
cout << "HistFactory file cannot be generated on Windows - exit" << endl;
return;
#endif
// Normally this would be run on the command line
cout <<"will run standard hist2workspace example"<<endl;
gROOT->ProcessLine(".! prepareHistFactory .");
gROOT->ProcessLine(".! hist2workspace config/example.xml");
cout <<"\n\n---------------------"<<endl;
cout <<"Done creating example input"<<endl;
cout <<"---------------------\n\n"<<endl;
}
}
else
filename = infile;
// Try to open the file
TFile *file = TFile::Open(filename);
// if input file was specified byt not found, quit
if(!file ){
cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
return;
}
// -------------------------------------------------------
// Now get the data and workspace
// get the workspace out of the file
RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
if(!w){
cout <<"workspace not found" << endl;
return;
}
// get the modelConfig out of the file
ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName);
// get the modelConfig out of the file
RooAbsData* data = w->data(dataName);
// make sure ingredients are found
if(!data || !mc){
w->Print();
cout << "data or ModelConfig was not found" <<endl;
return;
}
// -------------------------------------------------------
// Now get the POI for convenience
// you may want to adjust the range of your POI
RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first();
/* firstPOI->setMin(0);*/
/* firstPOI->setMax(10);*/
// --------------------------------------------
// Create and use the FeldmanCousins tool
// to find and plot the 95% confidence interval
// on the parameter of interest as specified
// in the model config
// REMEMBER, we will change the test statistic
// so this is NOT a Feldman-Cousins interval
FeldmanCousins fc(*data,*mc);
fc.SetConfidenceLevel(confidenceLevel);
/* fc.AdditionalNToysFactor(0.25); // degrade/improve sampling that defines confidence belt*/
/* fc.UseAdaptiveSampling(true); // speed it up a bit, don't use for expected limits*/
fc.SetNBins(nPointsToScan); // set how many points per parameter of interest to scan
fc.CreateConfBelt(true); // save the information in the belt for plotting
// -------------------------------------------------------
// Feldman-Cousins is a unified limit by definition
// but the tool takes care of a few things for us like which values
// of the nuisance parameters should be used to generate toys.
// so let's just change the test statistic and realize this is
// no longer "Feldman-Cousins" but is a fully frequentist Neyman-Construction.
/* ProfileLikelihoodTestStatModified onesided(*mc->GetPdf());*/
/* fc.GetTestStatSampler()->SetTestStatistic(&onesided);*/
/* ((ToyMCSampler*) fc.GetTestStatSampler())->SetGenerateBinned(true); */
ToyMCSampler* toymcsampler = (ToyMCSampler*) fc.GetTestStatSampler();
ProfileLikelihoodTestStat* testStat = dynamic_cast<ProfileLikelihoodTestStat*>(toymcsampler->GetTestStatistic());
testStat->SetOneSided(true);
// Since this tool needs to throw toy MC the PDF needs to be
// extended or the tool needs to know how many entries in a dataset
// per pseudo experiment.
// In the 'number counting form' where the entries in the dataset
// are counts, and not values of discriminating variables, the
// datasets typically only have one entry and the PDF is not
// extended.
if(!mc->GetPdf()->canBeExtended()){
if(data->numEntries()==1)
fc.FluctuateNumDataEntries(false);
else
cout <<"Not sure what to do about this model" <<endl;
}
// We can use PROOF to speed things along in parallel
// However, the test statistic has to be installed on the workers
// so either turn off PROOF or include the modified test statistic
// in your `$ROOTSYS/roofit/roostats/inc` directory,
// add the additional line to the LinkDef.h file,
// and recompile root.
if (useProof) {
ProofConfig pc(*w, nworkers, "", false);
toymcsampler->SetProofConfig(&pc); // enable proof
}
if(mc->GetGlobalObservables()){
cout << "will use global observables for unconditional ensemble"<<endl;
mc->GetGlobalObservables()->Print();
toymcsampler->SetGlobalObservables(*mc->GetGlobalObservables());
}
// Now get the interval
PointSetInterval* interval = fc.GetInterval();
ConfidenceBelt* belt = fc.GetConfidenceBelt();
// print out the interval on the first Parameter of Interest
cout << "\n95% interval on " <<firstPOI->GetName()<<" is : ["<<
interval->LowerLimit(*firstPOI) << ", "<<
interval->UpperLimit(*firstPOI) <<"] "<<endl;
// get observed UL and value of test statistic evaluated there
RooArgSet tmpPOI(*firstPOI);
double observedUL = interval->UpperLimit(*firstPOI);
firstPOI->setVal(observedUL);
double obsTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*data,tmpPOI);
// Ask the calculator which points were scanned
RooDataSet* parameterScan = (RooDataSet*) fc.GetPointsToScan();
RooArgSet* tmpPoint;
// make a histogram of parameter vs. threshold
TH1F* histOfThresholds = new TH1F("histOfThresholds","",
parameterScan->numEntries(),
firstPOI->getMin(),
firstPOI->getMax());
histOfThresholds->GetXaxis()->SetTitle(firstPOI->GetName());
histOfThresholds->GetYaxis()->SetTitle("Threshold");
// loop through the points that were tested and ask confidence belt
// what the upper/lower thresholds were.
// For FeldmanCousins, the lower cut off is always 0
for(Int_t i=0; i<parameterScan->numEntries(); ++i){
tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp");
//cout <<"get threshold"<<endl;
double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
double poiVal = tmpPoint->getRealValue(firstPOI->GetName()) ;
histOfThresholds->Fill(poiVal,arMax);
}
TCanvas* c1 = new TCanvas();
c1->Divide(2);
c1->cd(1);
histOfThresholds->SetMinimum(0);
histOfThresholds->Draw();
c1->cd(2);
// -------------------------------------------------------
// Now we generate the expected bands and power-constraint
// First: find parameter point for mu=0, with conditional MLEs for nuisance parameters
RooAbsReal* nll = mc->GetPdf()->createNLL(*data);
RooAbsReal* profile = nll->createProfile(*mc->GetParametersOfInterest());
firstPOI->setVal(0.);
profile->getVal(); // this will do fit and set nuisance parameters to profiled values
RooArgSet* poiAndNuisance = new RooArgSet();
if(mc->GetNuisanceParameters())
poiAndNuisance->add(*mc->GetNuisanceParameters());
poiAndNuisance->add(*mc->GetParametersOfInterest());
w->saveSnapshot("paramsToGenerateData",*poiAndNuisance);
RooArgSet* paramsToGenerateData = (RooArgSet*) poiAndNuisance->snapshot();
cout << "\nWill use these parameter points to generate pseudo data for bkg only" << endl;
paramsToGenerateData->Print("v");
RooArgSet unconditionalObs;
unconditionalObs.add(*mc->GetObservables());
unconditionalObs.add(*mc->GetGlobalObservables()); // comment this out for the original conditional ensemble
double CLb=0;
double CLbinclusive=0;
// Now we generate background only and find distribution of upper limits
TH1F* histOfUL = new TH1F("histOfUL","",100,0,firstPOI->getMax());
histOfUL->GetXaxis()->SetTitle("Upper Limit (background only)");
histOfUL->GetYaxis()->SetTitle("Entries");
for(int imc=0; imc<nToyMC; ++imc){
// set parameters back to values for generating pseudo data
// cout << "\n get current nuis, set vals, print again" << endl;
w->loadSnapshot("paramsToGenerateData");
// poiAndNuisance->Print("v");
RooDataSet* toyData = 0;
// now generate a toy dataset
if(!mc->GetPdf()->canBeExtended()){
if(data->numEntries()==1)
toyData = mc->GetPdf()->generate(*mc->GetObservables(),1);
else
cout <<"Not sure what to do about this model" <<endl;
} else{
// cout << "generating extended dataset"<<endl;
toyData = mc->GetPdf()->generate(*mc->GetObservables(),Extended());
}
// generate global observables
// need to be careful for simpdf
// RooDataSet* globalData = mc->GetPdf()->generate(*mc->GetGlobalObservables(),1);
RooSimultaneous* simPdf = dynamic_cast<RooSimultaneous*>(mc->GetPdf());
if(!simPdf){
RooDataSet *one = mc->GetPdf()->generate(*mc->GetGlobalObservables(), 1);
const RooArgSet *values = one->get();
RooArgSet *allVars = mc->GetPdf()->getVariables();
*allVars = *values;
delete allVars;
delete values;
delete one;
} else {
//try fix for sim pdf
TIterator* iter = simPdf->indexCat().typeIterator() ;
RooCatType* tt = NULL;
while((tt=(RooCatType*) iter->Next())) {
// Get pdf associated with state from simpdf
RooAbsPdf* pdftmp = simPdf->getPdf(tt->GetName()) ;
// Generate only global variables defined by the pdf associated with this state
RooArgSet* globtmp = pdftmp->getObservables(*mc->GetGlobalObservables()) ;
RooDataSet* tmp = pdftmp->generate(*globtmp,1) ;
// Transfer values to output placeholder
*globtmp = *tmp->get(0) ;
// Cleanup
delete globtmp ;
delete tmp ;
}
}
// globalData->Print("v");
// unconditionalObs = *globalData->get();
// mc->GetGlobalObservables()->Print("v");
// delete globalData;
// cout << "toy data = " << endl;
// toyData->get()->Print("v");
// get test stat at observed UL in observed data
firstPOI->setVal(observedUL);
double toyTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);
// toyData->get()->Print("v");
// cout <<"obsTSatObsUL " <<obsTSatObsUL << "toyTS " << toyTSatObsUL << endl;
if(obsTSatObsUL < toyTSatObsUL) // not sure about <= part yet
CLb+= (1.)/nToyMC;
if(obsTSatObsUL <= toyTSatObsUL) // not sure about <= part yet
CLbinclusive+= (1.)/nToyMC;
// loop over points in belt to find upper limit for this toy data
double thisUL = 0;
for(Int_t i=0; i<parameterScan->numEntries(); ++i){
tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp");
double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
firstPOI->setVal( tmpPoint->getRealValue(firstPOI->GetName()) );
// double thisTS = profile->getVal();
double thisTS = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);
// cout << "poi = " << firstPOI->getVal()
// << " max is " << arMax << " this profile = " << thisTS << endl;
// cout << "thisTS = " << thisTS<<endl;
if(thisTS<=arMax){
thisUL = firstPOI->getVal();
} else{
break;
}
}
/*
// loop over points in belt to find upper limit for this toy data
double thisUL = 0;
for(Int_t i=0; i<histOfThresholds->GetNbinsX(); ++i){
tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp");
cout <<"---------------- "<<i<<endl;
tmpPoint->Print("v");
cout << "from hist " << histOfThresholds->GetBinCenter(i+1) <<endl;
double arMax = histOfThresholds->GetBinContent(i+1);
// cout << " threhold from Hist = aMax " << arMax<<endl;
// double arMax2 = belt->GetAcceptanceRegionMax(*tmpPoint);
// cout << "from scan arMax2 = "<< arMax2 << endl; // not the same due to TH1F not TH1D
// cout << "scan - hist" << arMax2-arMax << endl;
firstPOI->setVal( histOfThresholds->GetBinCenter(i+1));
// double thisTS = profile->getVal();
double thisTS = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);
// cout << "poi = " << firstPOI->getVal()
// << " max is " << arMax << " this profile = " << thisTS << endl;
// cout << "thisTS = " << thisTS<<endl;
// NOTE: need to add a small epsilon term for single precision vs. double precision
if(thisTS<=arMax + 1e-7){
thisUL = firstPOI->getVal();
} else{
break;
}
}
*/
histOfUL->Fill(thisUL);
// for few events, data is often the same, and UL is often the same
// cout << "thisUL = " << thisUL<<endl;
delete toyData;
}
histOfUL->Draw();
c1->SaveAs("one-sided_upper_limit_output.pdf");
// if you want to see a plot of the sampling distribution for a particular scan point:
/*
SamplingDistPlot sampPlot;
int indexInScan = 0;
tmpPoint = (RooArgSet*) parameterScan->get(indexInScan)->clone("temp");
firstPOI->setVal( tmpPoint->getRealValue(firstPOI->GetName()) );
toymcsampler->SetParametersForTestStat(tmpPOI);
SamplingDistribution* samp = toymcsampler->GetSamplingDistribution(*tmpPoint);
sampPlot.AddSamplingDistribution(samp);
sampPlot.Draw();
*/
// Now find bands and power constraint
Double_t* bins = histOfUL->GetIntegral();
TH1F* cumulative = (TH1F*) histOfUL->Clone("cumulative");
cumulative->SetContent(bins);
double band2sigDown, band1sigDown, bandMedian, band1sigUp,band2sigUp;
for(int i=1; i<=cumulative->GetNbinsX(); ++i){
if(bins[i]<RooStats::SignificanceToPValue(2))
band2sigDown=cumulative->GetBinCenter(i);
if(bins[i]<RooStats::SignificanceToPValue(1))
band1sigDown=cumulative->GetBinCenter(i);
if(bins[i]<0.5)
bandMedian=cumulative->GetBinCenter(i);
if(bins[i]<RooStats::SignificanceToPValue(-1))
band1sigUp=cumulative->GetBinCenter(i);
if(bins[i]<RooStats::SignificanceToPValue(-2))
band2sigUp=cumulative->GetBinCenter(i);
}
cout << "-2 sigma band " << band2sigDown << endl;
cout << "-1 sigma band " << band1sigDown << " [Power Constraint)]" << endl;
cout << "median of band " << bandMedian << endl;
cout << "+1 sigma band " << band1sigUp << endl;
cout << "+2 sigma band " << band2sigUp << endl;
// print out the interval on the first Parameter of Interest
cout << "\nobserved 95% upper-limit "<< interval->UpperLimit(*firstPOI) <<endl;
cout << "CLb strict [P(toy>obs|0)] for observed 95% upper-limit "<< CLb <<endl;
cout << "CLb inclusive [P(toy>=obs|0)] for observed 95% upper-limit "<< CLbinclusive <<endl;
delete profile;
delete nll;
}
int DiagnosisMacro(int Nbins = 10, int Nsigma = 10, int CPUused = 1, TString Filename = "FIT_DATA_Psi2SJpsi_PPPrompt_Bkg_SecondOrderChebychev_pt65300_rap016_cent0200_262620_263757.root", TString Outputdir = "./")
//Nbins: Number of points for which to calculate profile likelihood. Time required is about (1/CPU) minutes per point per parameter. 0 means do plain likelihood only
//Nsigma: The range in which the scan is performed (value-Nsigma*sigma, value+Nsigma*sigma)
//CPUused: anything larger than 1 causes weird fit results on my laptop, runs fine on lxplus with more (16)
{
// R e a d w o r k s p a c e f r o m f i l e
// -----------------------------------------------
// Open input file with workspace
//Filename = "FIT_DATA_Psi2SJpsi_PP_Jpsi_DoubleCrystalBall_Psi2S_DoubleCrystalBall_Bkg_Chebychev2_pt6590_rap016_cent0200.root";
//Filename = "FIT_DATA_Psi2SJpsi_PbPb_Jpsi_DoubleCrystalBall_Psi2S_DoubleCrystalBall_Bkg_Chebychev1_pt6590_rap016_cent0200.root";
TFile *f = new TFile(Filename);
// Retrieve workspace from file
RooWorkspace* w = (RooWorkspace*)f->Get("workspace");
// Retrieve x,model and data from workspace
RooRealVar* x = w->var("invMass");
RooAbsPdf* model = w->pdf("simPdf_syst");
if (model == 0) {
model = w->pdf("simPdf");
}
if (model == 0) {
model = w->pdf("pdfMASS_Tot_PP");
}
if (model == 0) {
model = w->pdf("pdfMASS_Tot_PbPb");
}
if (model == 0) {
cout << "[ERROR] pdf failed to load from the workspace" << endl;
return false;
}
RooAbsData* data = w->data("dOS_DATA");
if (data == 0) {
data = w->data("dOS_DATA_PP");
}
if (data == 0) {
data = w->data("dOS_DATA_PbPb");
}
if (data == 0) {
cout << "[ERROR] data failed to load from the workspace" << endl;
return false;
}
// Print structure of composite p.d.f.
model->Print("t");
/*
// P l o t m o d e l
// ---------------------------------------------------------
// Plot data and PDF overlaid
RooPlot* xframe = x->frame(Title("J/psi Model and Data"));
data->plotOn(xframe);
model->plotOn(xframe);
// Draw the frame on the canvas
TCanvas* c2 = new TCanvas("PlotModel", "PlotModel", 1000, 1000);
gPad->SetLeftMargin(0.15);
xframe->GetYaxis()->SetTitleOffset(2.0);
xframe->Draw();//*/
///// Check parameters
RooArgSet* paramSet1 = model->getDependents(data);
paramSet1->Print("v"); // Just check
RooArgSet* paramSet2 = model->getParameters(data);
paramSet2->Print("v");
int Nparams = paramSet2->getSize();
cout << "Number of parameters: " << Nparams<<endl<<endl;
// C o n s t r u c t p l a i n l i k e l i h o o d
// ---------------------------------------------------
// Construct unbinned likelihood
RooAbsReal* nll = model->createNLL(*data, NumCPU(CPUused));
// Minimize likelihood w.r.t all parameters before making plots
RooMinuit(*nll).migrad();
//////////////////////////////////////////////////////
/////////////////// L O O P O V E R P A R A M E T E R S
/////////////////////////////////////////////////////
/// Set up loop over parameters
TString ParamName;
double ParamValue;
double ParamError;
double ParamLimitLow;
double ParamLimitHigh;
double FitRangeLow;
double FitRangeHigh;
RooRealVar* vParam;
int counter = 0;
// Loop start
TIterator* iter = paramSet2->createIterator();
TObject* var = iter->Next();
while (var != 0) {
counter++;
ParamName = var->GetName();
vParam = w->var(ParamName);
ParamValue = vParam->getVal();
ParamError = vParam->getError();
ParamLimitLow = vParam->getMin();
ParamLimitHigh = vParam->getMax();
cout << ParamName << " has value " << ParamValue << " with error: " << ParamError << " and limits: " << ParamLimitLow << " to " << ParamLimitHigh << endl << endl;
if (ParamError == 0) { //Skipping fixed parameters
cout << "Parameter was fixed, skipping its fitting" << endl;
cout << endl << "DONE WITH " << counter << " PARAMETER OUT OF " << Nparams << endl << endl;
var = iter->Next();
continue;
}
// determining fit range: Nsigma sigma on each side unless it would be outside of parameter limits
if ((ParamValue - Nsigma * ParamError) > ParamLimitLow) {
FitRangeLow = (ParamValue - Nsigma * ParamError);
}
else {
FitRangeLow = ParamLimitLow;
}
if ((ParamValue + Nsigma * ParamError) < ParamLimitHigh) {
FitRangeHigh = (ParamValue + Nsigma * ParamError);
}
else {
FitRangeHigh = ParamLimitHigh;
}
// P l o t p l a i n l i k e l i h o o d a n d C o n s t r u c t p r o f i l e l i k e l i h o o d
// ---------------------------------------------------
RooPlot* frame1;
RooAbsReal* pll=NULL;
if (Nbins != 0) {
frame1 = vParam->frame(Bins(Nbins), Range(FitRangeLow, FitRangeHigh), Title(TString::Format("LL and profileLL in %s", ParamName.Data())));
nll->plotOn(frame1, ShiftToZero());
pll = nll->createProfile(*vParam);
// Plot the profile likelihood
pll->plotOn(frame1, LineColor(kRed), RooFit::Precision(-1));
}
else { //Skip profile likelihood
frame1 = vParam->frame(Bins(10), Range(FitRangeLow, FitRangeHigh), Title(TString::Format("LL and profileLL in %s", ParamName.Data())));
nll->plotOn(frame1, ShiftToZero());
}
// D r a w a n d s a v e p l o t s
// -----------------------------------------------------------------------
// Adjust frame maximum for visual clarity
frame1->SetMinimum(0);
frame1->SetMaximum(20);
TCanvas* c = new TCanvas("CLikelihoodResult", "CLikelihoodResult", 800, 600);
c->cd(1);
gPad->SetLeftMargin(0.15);
frame1->GetYaxis()->SetTitleOffset(1.4);
frame1->Draw();
TLegend* leg = new TLegend(0.70, 0.70, 0.95, 0.88, "");
leg->SetFillColor(kWhite);
leg->SetBorderSize(0);
leg->SetTextSize(0.035);
TLegendEntry *le1 = leg->AddEntry(nll, "Plain likelihood", "l");
le1->SetLineColor(kBlue);
le1->SetLineWidth(3);
TLegendEntry *le2 = leg->AddEntry(pll, "Profile likelihood", "l");
le2->SetLineColor(kRed);
le2->SetLineWidth(3);
leg->Draw("same");
//Save plot
TString StrippedName = TString(Filename(Filename.Last('/')+1,Filename.Length()));
StrippedName = StrippedName.ReplaceAll(".root","");
cout << StrippedName << endl;
gSystem->mkdir(Form("%s/root/%s", Outputdir.Data(), StrippedName.Data()), kTRUE);
c->SaveAs(Form("%s/root/%s/Likelihood_scan_%s.root", Outputdir.Data(), StrippedName.Data(), ParamName.Data()));
gSystem->mkdir(Form("%s/pdf/%s", Outputdir.Data(), StrippedName.Data()), kTRUE);
c->SaveAs(Form("%s/pdf/%s/Likelihood_scan_%s.pdf", Outputdir.Data(), StrippedName.Data(), ParamName.Data()));
gSystem->mkdir(Form("%s/png/%s", Outputdir.Data(), StrippedName.Data()), kTRUE);
c->SaveAs(Form("%s/png/%s/Likelihood_scan_%s.png", Outputdir.Data(), StrippedName.Data(), ParamName.Data()));
delete c;
delete frame1;
if (pll) delete pll;
cout << endl << "DONE WITH " << counter << " PARAMETER OUT OF " << Nparams << endl << endl;
//if (counter == 2){ break; } //Exit - for testing
var = iter->Next();
} // End of the loop
return true;
}
void new_RA4(){
// let's time this challenging example
TStopwatch t;
t.Start();
// set RooFit random seed for reproducible results
RooRandom::randomGenerator()->SetSeed(4357);
// make model
RooWorkspace* wspace = new RooWorkspace("wspace");
wspace->factory("Gaussian::sigCons(prime_SigEff[0,-5,5], nom_SigEff[0,-5,5], 1)");
wspace->factory("expr::SigEff('1.0*pow(1.20,@0)',prime_SigEff)"); // // 1+-20%, 1.20=exp(20%)
wspace->factory("Poisson::on(non[0,50], sum::splusb(prod::SigUnc(s[0,0,50],SigEff),mainb[8.8,0,50],dilep[0.9,0,20],tau[2.3,0,20],QCD[0.,0,10],MC[0.1,0,4]))");
wspace->factory("Gaussian::mcCons(prime_rho[0,-5,5], nom_rho[0,-5,5], 1)");
wspace->factory("expr::rho('1.0*pow(1.39,@0)',prime_rho)"); // // 1+-39%
wspace->factory("Poisson::off(noff[0,200], prod::rhob(mainb,rho,mu_plus_e[0.74,0.01,10],1.08))");
wspace->factory("Gaussian::mcCons2(mu_plus_enom[0.74,0.01,4], mu_plus_e, sigmatwo[.05])");
wspace->factory("Gaussian::dilep_pred(dilep_nom[0.9,0,20], dilep, sigma3[2.2])");
wspace->factory("Gaussian::tau_pred(tau_nom[2.3,0,20], tau, sigma4[0.5])");
wspace->factory("Gaussian::QCD_pred(QCD_nom[0.0,0,10], QCD, sigma5[1.0])");
wspace->factory("Gaussian::MC_pred(MC_nom[0.1,0.01,4], MC, sigma7[0.14])");
wspace->factory("PROD::model(on,off,mcCons,mcCons2,sigCons,dilep_pred,tau_pred,QCD_pred,MC_pred)");
RooArgSet obs(*wspace->var("non"), *wspace->var("noff"), *wspace->var("mu_plus_enom"), *wspace->var("dilep_nom"), *wspace->var("tau_nom"), "obs");
obs.add(*wspace->var("QCD_nom")); obs.add(*wspace->var("MC_nom"));
RooArgSet globalObs(*wspace->var("nom_SigEff"), *wspace->var("nom_rho"), "global_obs");
// fix global observables to their nominal values
wspace->var("nom_SigEff")->setConstant();
wspace->var("nom_rho")->setConstant();
RooArgSet poi(*wspace->var("s"), "poi");
RooArgSet nuis(*wspace->var("mainb"), *wspace->var("prime_rho"), *wspace->var("prime_SigEff"), *wspace->var("mu_plus_e"), *wspace->var("dilep"), *wspace->var("tau"), "nuis");
nuis.add(*wspace->var("QCD")); nuis.add(*wspace->var("MC"));
wspace->factory("Uniform::prior_poi({s})");
wspace->factory("Uniform::prior_nuis({mainb,mu_plus_e,dilep,tau,QCD,MC})");
wspace->factory("PROD::prior(prior_poi,prior_nuis)");
wspace->var("non")->setVal(8); //observed
//wspace->var("non")->setVal(12); //expected observation
wspace->var("noff")->setVal(7); //observed events in control region
wspace->var("mu_plus_enom")->setVal(0.74);
wspace->var("dilep_nom")->setVal(0.9);
wspace->var("tau_nom")->setVal(2.3);
wspace->var("QCD")->setVal(0.0);
wspace->var("MC")->setVal(0.1);
RooDataSet * data = new RooDataSet("data","",obs);
data->add(obs);
wspace->import(*data);
/////////////////////////////////////////////////////
// Now the statistical tests
// model config
ModelConfig* pSbModel = new ModelConfig("SbModel");
pSbModel->SetWorkspace(*wspace);
pSbModel->SetPdf(*wspace->pdf("model"));
pSbModel->SetPriorPdf(*wspace->pdf("prior"));
pSbModel->SetParametersOfInterest(poi);
pSbModel->SetNuisanceParameters(nuis);
pSbModel->SetObservables(obs);
pSbModel->SetGlobalObservables(globalObs);
wspace->import(*pSbModel);
// set all but obs, poi and nuisance to const
SetConstants(wspace, pSbModel);
wspace->import(*pSbModel);
Double_t poiValueForBModel = 0.0;
ModelConfig* pBModel = new ModelConfig(*(RooStats::ModelConfig *)wspace->obj("SbModel"));
pBModel->SetName("BModel");
pBModel->SetWorkspace(*wspace);
wspace->import(*pBModel);
RooAbsReal * pNll = pSbModel->GetPdf()->createNLL(*data);
RooAbsReal * pProfile = pNll->createProfile(RooArgSet());
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
RooArgSet * pPoiAndNuisance = new RooArgSet();
//if(pSbModel->GetNuisanceParameters())
// pPoiAndNuisance->add(*pSbModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pSbModel->GetParametersOfInterest());
cout << "\nWill save these parameter points that correspond to the fit to data" << endl;
pPoiAndNuisance->Print("v");
pSbModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
pNll = pBModel->GetPdf()->createNLL(*data);
pProfile = pNll->createProfile(poi);
((RooRealVar *)poi.first())->setVal(poiValueForBModel);
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet();
//if(pBModel->GetNuisanceParameters())
// pPoiAndNuisance->add(*pBModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pBModel->GetParametersOfInterest());
cout << "\nShould use these parameter points to generate pseudo data for bkg only" << endl;
pPoiAndNuisance->Print("v");
pBModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// inspect workspace
wspace->Print();
// save workspace to file
wspace->writeToFile("tight.root");
//wspace->writeToFile("tight_median.root");
// clean up
delete wspace;
delete data;
delete pSbModel;
delete pBModel;
}
void MakeWorkspace( void ){
//
// this function implements a RooFit model for a counting experiment
//
// create workspace
RooWorkspace * pWs = new RooWorkspace("myWS");
// observable: number of events
pWs->factory( "n[0.0]" );
// integrated luminosity with systematics
pWs->factory( "lumi_nom[5000.0, 4000.0, 6000.0]" );
pWs->factory( "lumi_kappa[1.045]" );
pWs->factory( "cexpr::alpha_lumi('pow(lumi_kappa,beta_lumi)',lumi_kappa,beta_lumi[0,-5,5])" );
pWs->factory( "prod::lumi(lumi_nom,alpha_lumi)" );
pWs->factory( "Gaussian::constr_lumi(beta_lumi,glob_lumi[0,-5,5],1)" );
// cross section - parameter of interest
pWs->factory( "xsec[0.001,0.0,0.1]" );
// selection efficiency * acceptance with systematics
pWs->factory( "efficiency_nom[0.1, 0.05, 0.15]" );
pWs->factory( "efficiency_kappa[1.10]" );
pWs->factory( "cexpr::alpha_efficiency('pow(efficiency_kappa,beta_efficiency)',efficiency_kappa,beta_efficiency[0,-5,5])" );
pWs->factory( "prod::efficiency(efficiency_nom,alpha_efficiency)" );
pWs->factory( "Gaussian::constr_efficiency(beta_efficiency,glob_efficiency[0,-5,5],1)" );
// signal yield
pWs->factory( "prod::nsig(lumi,xsec,efficiency)" );
// background yield with systematics
pWs->factory( "nbkg_nom[10.0, 5.0, 15.0]" );
pWs->factory( "nbkg_kappa[1.10]" );
pWs->factory( "cexpr::alpha_nbkg('pow(nbkg_kappa,beta_nbkg)',nbkg_kappa,beta_nbkg[0,-5,5])" );
pWs->factory( "prod::nbkg(nbkg_nom,alpha_lumi,alpha_nbkg)" );
pWs->factory( "Gaussian::constr_nbkg(beta_nbkg,glob_nbkg[0,-5,5],1)" );
// full event yield
pWs->factory("sum::yield(nsig,nbkg)");
// Core model: Poisson probability with mean signal+bkg
pWs->factory( "Poisson::model_core(n,yield)" );
// define Bayesian prior PDF for POI
pWs->factory( "Uniform::prior(xsec)" );
// model with systematics
pWs->factory( "PROD::model(model_core,constr_lumi,constr_efficiency,constr_nbkg)" );
// create set of observables (will need it for datasets and ModelConfig later)
RooRealVar * pObs = pWs->var("n"); // get the pointer to the observable
RooArgSet obs("observables");
obs.add(*pObs);
// create the dataset
pObs->setVal(11); // this is your observed data: we counted ten events
RooDataSet * data = new RooDataSet("data", "data", obs);
data->add( *pObs );
// import dataset into workspace
pWs->import(*data);
// create set of global observables (need to be defined as constants)
pWs->var("glob_lumi")->setConstant(true);
pWs->var("glob_efficiency")->setConstant(true);
pWs->var("glob_nbkg")->setConstant(true);
RooArgSet globalObs("global_obs");
globalObs.add( *pWs->var("glob_lumi") );
globalObs.add( *pWs->var("glob_efficiency") );
globalObs.add( *pWs->var("glob_nbkg") );
// create set of parameters of interest (POI)
RooArgSet poi("poi");
poi.add( *pWs->var("xsec") );
// create set of nuisance parameters
RooArgSet nuis("nuis");
nuis.add( *pWs->var("beta_lumi") );
nuis.add( *pWs->var("beta_efficiency") );
nuis.add( *pWs->var("beta_nbkg") );
// create signal+background Model Config
RooStats::ModelConfig sbHypo("SbHypo");
sbHypo.SetWorkspace( *pWs );
sbHypo.SetPdf( *pWs->pdf("model") );
sbHypo.SetObservables( obs );
sbHypo.SetGlobalObservables( globalObs );
sbHypo.SetParametersOfInterest( poi );
sbHypo.SetNuisanceParameters( nuis );
sbHypo.SetPriorPdf( *pWs->pdf("prior") ); // this is optional
// fix all other variables in model:
// everything except observables, POI, and nuisance parameters
// must be constant
pWs->var("lumi_nom")->setConstant(true);
pWs->var("efficiency_nom")->setConstant(true);
pWs->var("nbkg_nom")->setConstant(true);
pWs->var("lumi_kappa")->setConstant(true);
pWs->var("efficiency_kappa")->setConstant(true);
pWs->var("nbkg_kappa")->setConstant(true);
RooArgSet fixed("fixed");
fixed.add( *pWs->var("lumi_nom") );
fixed.add( *pWs->var("efficiency_nom") );
fixed.add( *pWs->var("nbkg_nom") );
fixed.add( *pWs->var("lumi_kappa") );
fixed.add( *pWs->var("efficiency_kappa") );
fixed.add( *pWs->var("nbkg_kappa") );
// set parameter snapshot that corresponds to the best fit to data
RooAbsReal * pNll = sbHypo.GetPdf()->createNLL( *data );
RooAbsReal * pProfile = pNll->createProfile( globalObs ); // do not profile global observables
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
RooArgSet * pPoiAndNuisance = new RooArgSet("poiAndNuisance");
pPoiAndNuisance->add(*sbHypo.GetNuisanceParameters());
pPoiAndNuisance->add(*sbHypo.GetParametersOfInterest());
sbHypo.SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// import S+B ModelConfig into workspace
pWs->import( sbHypo );
// create background-only Model Config from the S+B one
RooStats::ModelConfig bHypo = sbHypo;
bHypo.SetName("BHypo");
bHypo.SetWorkspace(*pWs);
// set parameter snapshot for bHypo, setting xsec=0
// it is useful to understand how this block of code works
// but you can also use it as a recipe to make a parameter snapshot
pNll = bHypo.GetPdf()->createNLL( *data );
RooArgSet poiAndGlobalObs("poiAndGlobalObs");
poiAndGlobalObs.add( poi );
poiAndGlobalObs.add( globalObs );
pProfile = pNll->createProfile( poiAndGlobalObs ); // do not profile POI and global observables
((RooRealVar *)poi.first())->setVal( 0 ); // set xsec=0 here
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet( "poiAndNuisance" );
pPoiAndNuisance->add( nuis );
pPoiAndNuisance->add( poi );
bHypo.SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// import model config into workspace
pWs->import( bHypo );
// print out the workspace contents
pWs->Print();
// save workspace to file
pWs -> SaveAs("workspace.root");
return;
}
void build_hbb_workspace1( const char* infile = "outputfiles/input-file.txt", const char* outfile = "outputfiles/ws.root" ) {
//-------------------------------------------------------------------------
//-- Create workspace and other RooStats things.
printf("\n\n Creating workspace.\n\n") ;
RooWorkspace workspace("ws") ;
workspace.autoImportClassCode(true) ;
globalObservables = new RooArgSet("globalObservables");
allNuisances = new RooArgSet("allNuisances");
allNuisancePdfs = new RooArgSet("allNuisancePdfs");
RooArgSet* observedParametersList = new RooArgSet("observables") ;
//-------------------------------------------------------------------------
printf("\n\n Reading input file: %s\n\n", infile ) ;
float fileVal ;
char pname[1000] ;
char formula[1000] ;
sprintf( pname, "bins_of_met" ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
int bins_of_met = TMath::Nint( fileVal ) ;
//-- save bins_of_met in the workspace for convenience.
RooRealVar bom( "bins_of_met", "bins_of_met", bins_of_met, 0., 1000. ) ;
bom.setConstant(kTRUE) ;
workspace.import(bom) ;
//-- save bins_of_nb in the workspace for convenience.
RooRealVar bonb( "bins_of_nb", "bins_of_nb", bins_of_nb, 0., 1000. ) ;
bonb.setConstant(kTRUE) ;
workspace.import(bonb) ;
RooRealVar* rv_N_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooRealVar* rv_N_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooRealVar* rv_smc_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooRealVar* rv_smc_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_Rsigsb_corr[bins_of_nb][max_bins_of_met] ;
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "N_%db_msig_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_N_msig[nbi][mbi] = new RooRealVar( pname, pname, 0., 1.e6 ) ;
rv_N_msig[nbi][mbi] -> setVal( TMath::Nint(fileVal) ) ;
rv_N_msig[nbi][mbi] -> setConstant( kTRUE ) ;
observedParametersList -> add( *rv_N_msig[nbi][mbi] ) ;
sprintf( pname, "N_%db_msb_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_N_msb[nbi][mbi] = new RooRealVar( pname, pname, 0., 1.e6 ) ;
rv_N_msb[nbi][mbi] -> setVal( TMath::Nint(fileVal) ) ;
rv_N_msb[nbi][mbi] -> setConstant( kTRUE ) ;
observedParametersList -> add( *rv_N_msb[nbi][mbi] ) ;
sprintf( pname, "smc_%db_msig_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_smc_msig[nbi][mbi] = new RooRealVar( pname, pname, 0., 1.e6 ) ;
rv_smc_msig[nbi][mbi] -> setVal( TMath::Nint(fileVal) ) ;
rv_smc_msig[nbi][mbi] -> setConstant( kTRUE ) ;
sprintf( pname, "smc_%db_msb_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_smc_msb[nbi][mbi] = new RooRealVar( pname, pname, 0., 1.e6 ) ;
rv_smc_msb[nbi][mbi] -> setVal( TMath::Nint(fileVal) ) ;
rv_smc_msb[nbi][mbi] -> setConstant( kTRUE ) ;
float corrVal, corrSyst ;
sprintf( pname, "Rsigsb_syst_%db_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, corrSyst ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
sprintf( pname, "Rsigsb_corr_%db_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, corrVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_Rsigsb_corr[nbi][mbi] = makeLognormalConstraint( pname, corrVal, corrSyst ) ;
} // mbi.
} // nbi.
//-- Finished reading input from file.
//-------------------------------------------------------------------------
printf("\n\n Creating and importing dataset into workspace.\n\n") ;
RooDataSet* dsObserved = new RooDataSet("hbb_observed_rds", "hbb observed data values", *observedParametersList ) ;
dsObserved -> add( *observedParametersList ) ;
workspace.import( *dsObserved ) ;
//-------------------------------------------------------------------------
//-- Define all floats.
printf("\n\n Defining all unconstrained floats (Ratios, signal strength).\n\n") ;
double R_msigmsb_initialval(0.15) ;
RooRealVar* rv_R_msigmsb[50] ;
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "R_msigmsb_met%d", mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_R_msigmsb[mbi] = new RooRealVar( pname, pname, R_msigmsb_initialval, 0., 3. ) ;
rv_R_msigmsb[mbi] -> setConstant( kFALSE ) ;
rv_R_msigmsb[mbi] -> Print() ;
} // mbi.
printf("\n") ;
sprintf( pname, "sig_strength" ) ;
RooRealVar* rv_sig_strength = new RooRealVar( pname, pname, 1.0, 0., 10. ) ;
rv_sig_strength -> setConstant(kFALSE) ;
rv_sig_strength -> Print() ;
printf(" %s\n\n", pname ) ;
//-------------------------------------------------------------------------
//-- Define all mu parameters.
printf("\n\n Defining mu parameters.\n\n") ;
RooAbsReal* rv_mu_bg_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_mu_bg_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_mu_sig_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_mu_sig_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "mu_bg_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_bg_msb[nbi][mbi] = new RooRealVar( pname, pname, rv_N_msb[nbi][mbi] -> getVal(), 0., 1.e6 ) ;
rv_mu_bg_msb[nbi][mbi] -> Print() ;
sprintf( formula, "@0 * @1 * @2" ) ;
sprintf( pname, "mu_bg_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_bg_msig[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_Rsigsb_corr[nbi][mbi], *rv_R_msigmsb[mbi], *rv_mu_bg_msb[nbi][mbi] ) ) ;
rv_mu_bg_msig[nbi][mbi] -> Print() ;
sprintf( formula, "@0 * @1" ) ;
sprintf( pname, "mu_sig_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_sig_msig[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_sig_strength, *rv_smc_msig[nbi][mbi] ) ) ;
rv_mu_sig_msig[nbi][mbi] -> Print() ;
sprintf( formula, "@0 * @1" ) ;
sprintf( pname, "mu_sig_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_sig_msb[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_sig_strength, *rv_smc_msb[nbi][mbi] ) ) ;
rv_mu_sig_msb[nbi][mbi] -> Print() ;
} // mbi.
} // nbi.
//-- Finished defining mu parameters.
//-------------------------------------------------------------------------
//-- Defining small n's
printf("\n\n Defining small n's.\n\n") ;
RooAbsReal* rv_n_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_n_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( formula, "@0 + @1" ) ;
sprintf( pname, "n_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_n_msig[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_mu_sig_msig[nbi][mbi], *rv_mu_bg_msig[nbi][mbi] ) ) ;
rv_n_msig[nbi][mbi] -> Print() ;
workspace.import( *rv_n_msig[nbi][mbi] ) ;
sprintf( pname, "n_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_n_msb[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_mu_sig_msb[nbi][mbi], *rv_mu_bg_msb[nbi][mbi] ) ) ;
rv_n_msb[nbi][mbi] -> Print() ;
workspace.import( *rv_n_msb[nbi][mbi] ) ;
} // mbi.
} // nbi.
//-------------------------------------------------------------------------
//-- Define the Poisson pdfs for the observables.
printf("\n\n Defining Poisson pdfs for the observables.\n\n") ;
RooAbsReal* rv_pdf_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_pdf_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooArgSet pdflist ;
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "pdf_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_pdf_msig[nbi][mbi] = new RooPoisson( pname, pname, *rv_N_msig[nbi][mbi], *rv_n_msig[nbi][mbi] ) ;
rv_pdf_msig[nbi][mbi] -> Print() ;
pdflist.add( *rv_pdf_msig[nbi][mbi] ) ;
sprintf( pname, "pdf_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_pdf_msb[nbi][mbi] = new RooPoisson( pname, pname, *rv_N_msb[nbi][mbi], *rv_n_msb[nbi][mbi] ) ;
rv_pdf_msb[nbi][mbi] -> Print() ;
pdflist.add( *rv_pdf_msb[nbi][mbi] ) ;
} // mbi.
} // nbi.
//-------------------------------------------------------------------------
//-- Build the likelihood.
printf("\n\n Building the likelihood.\n\n") ;
pdflist.add( *allNuisancePdfs ) ;
pdflist.Print() ;
printf("\n") ;
RooProdPdf* likelihood = new RooProdPdf( "likelihood", "hbb likelihood", pdflist ) ;
likelihood->Print() ;
//-------------------------------------------------------------------------
// printf("\n\n Running a test fit.\n\n") ;
// dsObserved -> Print() ;
// dsObserved -> printMultiline(cout, 1, kTRUE, "") ;
// printf("\n\n =============================================\n\n") ;
// likelihood -> fitTo( *dsObserved, PrintLevel(3), Hesse(0), Minos(0) ) ;
// printf("\n\n =============================================\n\n") ;
//-- Set up RooStats models.
printf("\n\n Setting up S+B model.\n\n") ;
RooArgSet poi( *rv_sig_strength, "poi" ) ;
RooUniform signal_prior( "signal_prior", "signal_prior", *rv_sig_strength ) ;
ModelConfig sbModel ("SbModel");
sbModel.SetWorkspace( workspace ) ;
sbModel.SetPdf( *likelihood ) ;
sbModel.SetParametersOfInterest( poi );
sbModel.SetPriorPdf(signal_prior);
sbModel.SetObservables( *observedParametersList );
sbModel.SetNuisanceParameters( *allNuisances );
sbModel.SetGlobalObservables( *globalObservables );
workspace.Print() ;
printf("\n\n Doing fit for S+B model.\n" ) ; fflush(stdout) ;
RooAbsReal* pNll = sbModel.GetPdf()->createNLL(*dsObserved);
RooAbsReal* pProfile = pNll->createProfile(RooArgSet());
pProfile->getVal();
RooArgSet* pPoiAndNuisance = new RooArgSet();
pPoiAndNuisance->add(*sbModel.GetParametersOfInterest());
if(sbModel.GetNuisanceParameters()) pPoiAndNuisance->add(*sbModel.GetNuisanceParameters());
printf("\n\n Will save these parameter points that correspond to the fit to data.\n\n") ; fflush(stdout) ;
pPoiAndNuisance->Print("v");
sbModel.SetSnapshot(*pPoiAndNuisance);
workspace.import (sbModel);
delete pProfile ;
delete pNll ;
delete pPoiAndNuisance ;
printf("\n\n Setting up BG-only model.\n\n") ;
ModelConfig bModel (*(RooStats::ModelConfig *)workspace.obj("SbModel"));
bModel.SetName("BModel");
bModel.SetWorkspace(workspace);
printf("\n\n Doing fit for BG-only model.\n" ) ; fflush(stdout) ;
pNll = bModel.GetPdf()->createNLL(*dsObserved);
pProfile = pNll->createProfile(*bModel.GetParametersOfInterest());
((RooRealVar *)(bModel.GetParametersOfInterest()->first()))->setVal(0.);
pProfile->getVal();
pPoiAndNuisance = new RooArgSet();
pPoiAndNuisance->add(*bModel.GetParametersOfInterest());
if(bModel.GetNuisanceParameters()) pPoiAndNuisance->add(*bModel.GetNuisanceParameters());
printf("\n\n Should use these parameter points to generate pseudo data for bkg only.\n\n") ; fflush(stdout) ;
pPoiAndNuisance->Print("v");
bModel.SetSnapshot(*pPoiAndNuisance);
workspace.import (bModel);
delete pProfile ;
delete pNll ;
delete pPoiAndNuisance ;
workspace.Print() ;
printf("\n\n Saving workspace in : %s\n\n", outfile ) ;
gSystem->Exec(" mkdir -p outputfiles " ) ;
workspace.writeToFile( outfile ) ;
} // build_hbb_workspace1.
void OneSidedFrequentistUpperLimitWithBands_intermediate(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData"){
double confidenceLevel=0.95;
// degrade/improve number of pseudo-experiments used to define the confidence belt.
// value of 1 corresponds to default number of toys in the tail, which is 50/(1-confidenceLevel)
double additionalToysFac = 1.;
int nPointsToScan = 30; // number of steps in the parameter of interest
int nToyMC = 100; // number of toys used to define the expected limit and band
TStopwatch t;
t.Start();
/////////////////////////////////////////////////////////////
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
////////////////////////////////////////////////////////////
const char* filename = "";
if (!strcmp(infile,""))
filename = "results/example_combined_GaussExample_model.root";
else
filename = infile;
// Check if example input file exists
TFile *file = TFile::Open(filename);
// if input file was specified byt not found, quit
if(!file && strcmp(infile,"")){
cout <<"file not found" << endl;
return;
}
// if default file not found, try to create it
if(!file ){
// Normally this would be run on the command line
cout <<"will run standard hist2workspace example"<<endl;
gROOT->ProcessLine(".! prepareHistFactory .");
gROOT->ProcessLine(".! hist2workspace config/example.xml");
cout <<"\n\n---------------------"<<endl;
cout <<"Done creating example input"<<endl;
cout <<"---------------------\n\n"<<endl;
}
// now try to access the file again
file = TFile::Open(filename);
if(!file){
// if it is still not there, then we can't continue
cout << "Not able to run hist2workspace to create example input" <<endl;
return;
}
/////////////////////////////////////////////////////////////
// Now get the data and workspace
////////////////////////////////////////////////////////////
// get the workspace out of the file
RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
if(!w){
cout <<"workspace not found" << endl;
return;
}
// get the modelConfig out of the file
ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName);
// get the modelConfig out of the file
RooAbsData* data = w->data(dataName);
// make sure ingredients are found
if(!data || !mc){
w->Print();
cout << "data or ModelConfig was not found" <<endl;
return;
}
cout << "Found data and ModelConfig:" <<endl;
mc->Print();
/////////////////////////////////////////////////////////////
// Now get the POI for convenience
// you may want to adjust the range of your POI
////////////////////////////////////////////////////////////
RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first();
// firstPOI->setMin(0);
// firstPOI->setMax(10);
/////////////////////////////////////////////
// create and use the FeldmanCousins tool
// to find and plot the 95% confidence interval
// on the parameter of interest as specified
// in the model config
// REMEMBER, we will change the test statistic
// so this is NOT a Feldman-Cousins interval
FeldmanCousins fc(*data,*mc);
fc.SetConfidenceLevel(confidenceLevel);
fc.AdditionalNToysFactor(additionalToysFac); // improve sampling that defines confidence belt
// fc.UseAdaptiveSampling(true); // speed it up a bit, but don't use for expectd limits
fc.SetNBins(nPointsToScan); // set how many points per parameter of interest to scan
fc.CreateConfBelt(true); // save the information in the belt for plotting
/////////////////////////////////////////////
// Feldman-Cousins is a unified limit by definition
// but the tool takes care of a few things for us like which values
// of the nuisance parameters should be used to generate toys.
// so let's just change the test statistic and realize this is
// no longer "Feldman-Cousins" but is a fully frequentist Neyman-Construction.
// ProfileLikelihoodTestStatModified onesided(*mc->GetPdf());
// fc.GetTestStatSampler()->SetTestStatistic(&onesided);
// ((ToyMCSampler*) fc.GetTestStatSampler())->SetGenerateBinned(true);
ToyMCSampler* toymcsampler = (ToyMCSampler*) fc.GetTestStatSampler();
ProfileLikelihoodTestStat* testStat = dynamic_cast<ProfileLikelihoodTestStat*>(toymcsampler->GetTestStatistic());
testStat->SetOneSided(true);
// test speedups:
testStat->SetReuseNLL(true);
// toymcsampler->setUseMultiGen(true); // not fully validated
// Since this tool needs to throw toy MC the PDF needs to be
// extended or the tool needs to know how many entries in a dataset
// per pseudo experiment.
// In the 'number counting form' where the entries in the dataset
// are counts, and not values of discriminating variables, the
// datasets typically only have one entry and the PDF is not
// extended.
if(!mc->GetPdf()->canBeExtended()){
if(data->numEntries()==1)
fc.FluctuateNumDataEntries(false);
else
cout <<"Not sure what to do about this model" <<endl;
}
// We can use PROOF to speed things along in parallel
ProofConfig pc(*w, 4, "",false);
if(mc->GetGlobalObservables()){
cout << "will use global observables for unconditional ensemble"<<endl;
mc->GetGlobalObservables()->Print();
toymcsampler->SetGlobalObservables(*mc->GetGlobalObservables());
}
toymcsampler->SetProofConfig(&pc); // enable proof
// Now get the interval
PointSetInterval* interval = fc.GetInterval();
ConfidenceBelt* belt = fc.GetConfidenceBelt();
// print out the iterval on the first Parameter of Interest
cout << "\n95% interval on " <<firstPOI->GetName()<<" is : ["<<
interval->LowerLimit(*firstPOI) << ", "<<
interval->UpperLimit(*firstPOI) <<"] "<<endl;
// get observed UL and value of test statistic evaluated there
RooArgSet tmpPOI(*firstPOI);
double observedUL = interval->UpperLimit(*firstPOI);
firstPOI->setVal(observedUL);
double obsTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*data,tmpPOI);
// Ask the calculator which points were scanned
RooDataSet* parameterScan = (RooDataSet*) fc.GetPointsToScan();
RooArgSet* tmpPoint;
// make a histogram of parameter vs. threshold
TH1F* histOfThresholds = new TH1F("histOfThresholds","",
parameterScan->numEntries(),
firstPOI->getMin(),
firstPOI->getMax());
histOfThresholds->GetXaxis()->SetTitle(firstPOI->GetName());
histOfThresholds->GetYaxis()->SetTitle("Threshold");
// loop through the points that were tested and ask confidence belt
// what the upper/lower thresholds were.
// For FeldmanCousins, the lower cut off is always 0
for(Int_t i=0; i<parameterScan->numEntries(); ++i){
tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp");
double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
double poiVal = tmpPoint->getRealValue(firstPOI->GetName()) ;
histOfThresholds->Fill(poiVal,arMax);
}
TCanvas* c1 = new TCanvas();
c1->Divide(2);
c1->cd(1);
histOfThresholds->SetMinimum(0);
histOfThresholds->Draw();
c1->cd(2);
/////////////////////////////////////////////////////////////
// Now we generate the expected bands and power-constriant
////////////////////////////////////////////////////////////
// First: find parameter point for mu=0, with conditional MLEs for nuisance parameters
RooAbsReal* nll = mc->GetPdf()->createNLL(*data);
RooAbsReal* profile = nll->createProfile(*mc->GetParametersOfInterest());
firstPOI->setVal(0.);
profile->getVal(); // this will do fit and set nuisance parameters to profiled values
RooArgSet* poiAndNuisance = new RooArgSet();
if(mc->GetNuisanceParameters())
poiAndNuisance->add(*mc->GetNuisanceParameters());
poiAndNuisance->add(*mc->GetParametersOfInterest());
w->saveSnapshot("paramsToGenerateData",*poiAndNuisance);
RooArgSet* paramsToGenerateData = (RooArgSet*) poiAndNuisance->snapshot();
cout << "\nWill use these parameter points to generate pseudo data for bkg only" << endl;
paramsToGenerateData->Print("v");
double CLb=0;
double CLbinclusive=0;
// Now we generate background only and find distribution of upper limits
TH1F* histOfUL = new TH1F("histOfUL","",100,0,firstPOI->getMax());
histOfUL->GetXaxis()->SetTitle("Upper Limit (background only)");
histOfUL->GetYaxis()->SetTitle("Entries");
for(int imc=0; imc<nToyMC; ++imc){
// set parameters back to values for generating pseudo data
w->loadSnapshot("paramsToGenerateData");
// in 5.30 there is a nicer way to generate toy data & randomize global obs
RooAbsData* toyData = toymcsampler->GenerateToyData(*paramsToGenerateData);
// get test stat at observed UL in observed data
firstPOI->setVal(observedUL);
double toyTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);
// toyData->get()->Print("v");
// cout <<"obsTSatObsUL " <<obsTSatObsUL << "toyTS " << toyTSatObsUL << endl;
if(obsTSatObsUL < toyTSatObsUL) // (should be checked)
CLb+= (1.)/nToyMC;
if(obsTSatObsUL <= toyTSatObsUL) // (should be checked)
CLbinclusive+= (1.)/nToyMC;
// loop over points in belt to find upper limit for this toy data
double thisUL = 0;
for(Int_t i=0; i<parameterScan->numEntries(); ++i){
tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp");
double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
firstPOI->setVal( tmpPoint->getRealValue(firstPOI->GetName()) );
double thisTS = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);
if(thisTS<=arMax){
thisUL = firstPOI->getVal();
} else{
break;
}
}
histOfUL->Fill(thisUL);
delete toyData;
}
histOfUL->Draw();
c1->SaveAs("one-sided_upper_limit_output.pdf");
// if you want to see a plot of the sampling distribution for a particular scan point:
// Now find bands and power constraint
Double_t* bins = histOfUL->GetIntegral();
TH1F* cumulative = (TH1F*) histOfUL->Clone("cumulative");
cumulative->SetContent(bins);
double band2sigDown=0, band1sigDown=0, bandMedian=0, band1sigUp=0,band2sigUp=0;
for(int i=1; i<=cumulative->GetNbinsX(); ++i){
if(bins[i]<RooStats::SignificanceToPValue(2))
band2sigDown=cumulative->GetBinCenter(i);
if(bins[i]<RooStats::SignificanceToPValue(1))
band1sigDown=cumulative->GetBinCenter(i);
if(bins[i]<0.5)
bandMedian=cumulative->GetBinCenter(i);
if(bins[i]<RooStats::SignificanceToPValue(-1))
band1sigUp=cumulative->GetBinCenter(i);
if(bins[i]<RooStats::SignificanceToPValue(-2))
band2sigUp=cumulative->GetBinCenter(i);
}
t.Stop();
t.Print();
cout << "-2 sigma band " << band2sigDown << endl;
cout << "-1 sigma band " << band1sigDown << endl;
cout << "median of band " << bandMedian << " [Power Constriant)]" << endl;
cout << "+1 sigma band " << band1sigUp << endl;
cout << "+2 sigma band " << band2sigUp << endl;
// print out the iterval on the first Parameter of Interest
cout << "\nobserved 95% upper-limit "<< interval->UpperLimit(*firstPOI) <<endl;
cout << "CLb strict [P(toy>obs|0)] for observed 95% upper-limit "<< CLb <<endl;
cout << "CLb inclusive [P(toy>=obs|0)] for observed 95% upper-limit "<< CLbinclusive <<endl;
delete profile;
delete nll;
}
void combinedWorkspace_4WS(const char* name_pbpb_pass="******", const char* name_pbpb_fail="fitresult_pbpb_fail.root", const char* name_pp_pass="******", const char* name_pp_fail="fitresult_pp_fail.root", const char* name_out="fitresult_combo.root", const float systval = 0., const char* subDirName ="wsTest", int nCPU=2){
// subdir: Directory to save workspaces under currentPATH/CombinedWorkspaces/subDir/
// set things silent
gErrorIgnoreLevel=kError;
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR);
bool dosyst = (systval > 0.);
TString nameOut(name_out);
RooWorkspace * ws = test_combine_4WS(name_pbpb_pass, name_pp_pass, name_pbpb_fail, name_pp_fail, false, nCPU);
RooAbsData * data = ws->data("dOS_DATA");
RooRealVar* RFrac2Svs1S_PbPbvsPP_P = ws->var("RFrac2Svs1S_PbPbvsPP_P");
RooRealVar* leftEdge = new RooRealVar("leftEdge","leftEdge",-10);
RooRealVar* rightEdge = new RooRealVar("rightEdge","rightEdge",10);
RooGenericPdf step("step", "step", "(@0 >= @1) && (@0 < @2)", RooArgList(*RFrac2Svs1S_PbPbvsPP_P, *leftEdge, *rightEdge));
ws->import(step);
ws->factory( "Uniform::flat(RFrac2Svs1S_PbPbvsPP_P)" );
// systematics
if (dosyst) {
ws->factory( Form("kappa_syst[%f]",systval) );
ws->factory( "expr::alpha_syst('kappa_syst*beta_syst',kappa_syst,beta_syst[0,-5,5])" );
ws->factory( "Gaussian::constr_syst(beta_syst,glob_syst[0,-5,5],1)" );
// add systematics into the double ratio
ws->factory( "expr::RFrac2Svs1S_PbPbvsPP_P_syst('@0+@1',RFrac2Svs1S_PbPbvsPP_P,alpha_syst)" );
// build the pbpb pdf
RooRealVar* effjpsi_pp_P = (RooRealVar*)ws->var("effjpsi_pp_P");
RooRealVar* effpsip_pp_P = (RooRealVar*)ws->var("effpsip_pp_P");
RooRealVar* effjpsi_pp_NP = (RooRealVar*)ws->var("effjpsi_pp_NP");
Double_t Npsi2SPbPbPass = npsip_pbpb_pass_from_doubleratio_prompt(ws, RooArgList(*effjpsi_pp_P,*effpsip_pp_P,*effjpsi_pp_NP),true); // Create and import N_Psi2S_PbPb_pass_syst
ws->factory( "SUM::pdfMASS_Tot_PbPb_pass_syst(N_Jpsi_PbPb_pass * pdfMASS_Jpsi_PbPb_pass, N_Psi2S_PbPb_pass_syst * pdfMASS_Psi2S_PbPb_pass, N_Bkg_PbPb_pass * pdfMASS_Bkg_PbPb_pass)" );
ws->factory( "PROD::pdfMASS_Tot_PbPb_pass_constr(pdfMASS_Tot_PbPb_pass_syst,constr_syst)" );
// build the combined pdf
ws->factory("SIMUL::simPdf_syst_noconstr(sample,PbPb_pass=pdfMASS_Tot_PbPb_pass_syst,PbPb_fail=pdfMASS_Tot_PbPb_fail,PP_pass=pdfMASS_Tot_PP_pass,PP_fail=pdfMASS_Tot_PP_fail)");
RooSimultaneous *simPdf = (RooSimultaneous*) ws->pdf("simPdf_syst_noconstr");
RooGaussian *constr_syst = (RooGaussian*) ws->pdf("constr_syst");
RooProdPdf *simPdf_constr = new RooProdPdf("simPdf_syst","simPdf_syst",RooArgSet(*simPdf,*constr_syst));
ws->import(*simPdf_constr);
} else {
ws->factory("SIMUL::simPdf_syst(sample,PbPb_pass=pdfMASS_Tot_PbPb_pass,PbPb_fail=pdfMASS_Tot_PbPb_fail,PP_pass=pdfMASS_Tot_PP_pass,PP_fail=pdfMASS_Tot_PP_fail)");
}
ws->Print();
if (dosyst) ws->var("beta_syst")->setConstant(kFALSE);
/////////////////////////////////////////////////////////////////////
RooRealVar * pObs = ws->var("invMass"); // get the pointer to the observable
RooArgSet obs("observables");
obs.add(*pObs);
obs.add( *ws->cat("sample"));
// /////////////////////////////////////////////////////////////////////
if (dosyst) ws->var("glob_syst")->setConstant(true);
RooArgSet globalObs("global_obs");
if (dosyst) globalObs.add( *ws->var("glob_syst") );
// ws->Print();
RooArgSet poi("poi");
poi.add( *ws->var("RFrac2Svs1S_PbPbvsPP_P") );
// create set of nuisance parameters
RooArgSet nuis("nuis");
if (dosyst) nuis.add( *ws->var("beta_syst") );
// set parameters constant
RooArgSet allVars = ws->allVars();
TIterator* it = allVars.createIterator();
RooRealVar *theVar = (RooRealVar*) it->Next();
while (theVar) {
TString varname(theVar->GetName());
// if (varname != "RFrac2Svs1S_PbPbvsPP"
// && varname != "invMass"
// && varname != "sample"
// )
// theVar->setConstant();
if ( varname.Contains("f_Jpsi_PP") || varname.Contains("f_Jpsi_PbPb") ||
varname.Contains("rSigma21_Jpsi_PP") ||
varname.Contains("m_Jpsi_PP") || varname.Contains("m_Jpsi_PbPb") ||
varname.Contains("sigma1_Jpsi_PP") || varname.Contains("sigma1_Jpsi_PbPb") ||
(varname.Contains("lambda")) ||
(varname.Contains("_fail") && !varname.Contains("RFrac2Svs1S")))
{
theVar->setConstant();
}
if (varname=="glob_syst"
|| varname=="beta_syst"
) {
cout << varname << endl;
theVar->setConstant(!dosyst);
}
theVar = (RooRealVar*) it->Next();
}
// create signal+background Model Config
RooStats::ModelConfig sbHypo("SbHypo");
sbHypo.SetWorkspace( *ws );
sbHypo.SetPdf( *ws->pdf("simPdf_syst") );
sbHypo.SetObservables( obs );
sbHypo.SetGlobalObservables( globalObs );
sbHypo.SetParametersOfInterest( poi );
sbHypo.SetNuisanceParameters( nuis );
sbHypo.SetPriorPdf( *ws->pdf("step") ); // this is optional
/////////////////////////////////////////////////////////////////////
RooAbsReal * pNll = sbHypo.GetPdf()->createNLL( *data,NumCPU(nCPU) );
RooMinuit(*pNll).migrad(); // minimize likelihood wrt all parameters before making plots
if (controlPlots)
{
RooPlot *framepoi = ((RooRealVar *)poi.first())->frame(Bins(10),Range(0.,1),Title("LL and profileLL in RFrac2Svs1S_PbPbvsPP_P"));
pNll->plotOn(framepoi,ShiftToZero());
framepoi->SetMinimum(0);
framepoi->SetMaximum(10);
TCanvas *cpoi = new TCanvas();
cpoi->cd(); framepoi->Draw();
cpoi->SaveAs("cpoi.pdf");
}
((RooRealVar *)poi.first())->setMin(0.);
RooArgSet * pPoiAndNuisance = new RooArgSet("poiAndNuisance");
pPoiAndNuisance->add( nuis );
pPoiAndNuisance->add( poi );
sbHypo.SetSnapshot(*pPoiAndNuisance);
if (controlPlots)
{
RooPlot* xframeSB_PP_pass = pObs->frame(Title("SBhypo_PP_pass"));
data->plotOn(xframeSB_PP_pass,Cut("sample==sample::PP_pass"));
RooAbsPdf *pdfSB_PP_pass = sbHypo.GetPdf();
RooCategory *sample = ws->cat("sample");
pdfSB_PP_pass->plotOn(xframeSB_PP_pass,Slice(*sample,"PP_pass"),ProjWData(*sample,*data));
TCanvas *c1 = new TCanvas();
c1->cd(); xframeSB_PP_pass->Draw();
c1->SaveAs("c1.pdf");
RooPlot* xframeSB_PP_fail = pObs->frame(Title("SBhypo_PP_fail"));
data->plotOn(xframeSB_PP_fail,Cut("sample==sample::PP_fail"));
RooAbsPdf *pdfSB_PP_fail = sbHypo.GetPdf();
pdfSB_PP_fail->plotOn(xframeSB_PP_fail,Slice(*sample,"PP_fail"),ProjWData(*sample,*data));
TCanvas *c2 = new TCanvas();
c2->cd(); xframeSB_PP_fail->Draw();
c2->SaveAs("c1.pdf");
RooPlot* xframeB_PbPb_pass = pObs->frame(Title("SBhypo_PbPb_pass"));
data->plotOn(xframeB_PbPb_pass,Cut("sample==sample::PbPb_pass"));
RooAbsPdf *pdfB_PbPb_pass = sbHypo.GetPdf();
pdfB_PbPb_pass->plotOn(xframeB_PbPb_pass,Slice(*sample,"PbPb_pass"),ProjWData(*sample,*data));
TCanvas *c3 = new TCanvas();
c3->cd(); xframeB_PbPb_pass->Draw();
c3->SetLogy();
c3->SaveAs("c2.pdf");
RooPlot* xframeB_PbPb_fail = pObs->frame(Title("SBhypo_PbPb_fail"));
data->plotOn(xframeB_PbPb_fail,Cut("sample==sample::PbPb_fail"));
RooAbsPdf *pdfB_PbPb_fail = sbHypo.GetPdf();
pdfB_PbPb_fail->plotOn(xframeB_PbPb_fail,Slice(*sample,"PbPb_fail"),ProjWData(*sample,*data));
TCanvas *c4 = new TCanvas();
c4->cd(); xframeB_PbPb_fail->Draw();
c4->SetLogy();
c4->SaveAs("c2.pdf");
}
delete pNll;
delete pPoiAndNuisance;
ws->import( sbHypo );
/////////////////////////////////////////////////////////////////////
RooStats::ModelConfig bHypo = sbHypo;
bHypo.SetName("BHypo");
bHypo.SetWorkspace(*ws);
pNll = bHypo.GetPdf()->createNLL( *data,NumCPU(nCPU) );
// RooMinuit(*pNll).migrad(); // minimize likelihood wrt all parameters before making plots
RooArgSet poiAndGlobalObs("poiAndGlobalObs");
poiAndGlobalObs.add( poi );
poiAndGlobalObs.add( globalObs );
RooAbsReal * pProfile = pNll->createProfile( poiAndGlobalObs ); // do not profile POI and global observables
((RooRealVar *)poi.first())->setVal( 0 ); // set RFrac2Svs1S_PbPbvsPP=0 here
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet( "poiAndNuisance" );
pPoiAndNuisance->add( nuis );
pPoiAndNuisance->add( poi );
bHypo.SetSnapshot(*pPoiAndNuisance);
delete pNll;
delete pPoiAndNuisance;
// import model config into workspace
bHypo.SetWorkspace(*ws);
ws->import( bHypo );
/////////////////////////////////////////////////////////////////////
ws->Print();
bHypo.Print();
sbHypo.Print();
// save workspace to file
string mainDIR = gSystem->ExpandPathName(gSystem->pwd());
string wsDIR = mainDIR + "/CombinedWorkspaces/";
string ssubDirName="";
if (subDirName) ssubDirName.append(subDirName);
string subDIR = wsDIR + ssubDirName;
void * dirp = gSystem->OpenDirectory(wsDIR.c_str());
if (dirp) gSystem->FreeDirectory(dirp);
else gSystem->mkdir(wsDIR.c_str(), kTRUE);
void * dirq = gSystem->OpenDirectory(subDIR.c_str());
if (dirq) gSystem->FreeDirectory(dirq);
else gSystem->mkdir(subDIR.c_str(), kTRUE);
const char* saveName = Form("%s/%s",subDIR.c_str(),nameOut.Data());
ws->writeToFile(saveName);
}
void plot_pll(TString fname="monoh_withsm_SRCR_bg11.7_bgslop-0.0_nsig0.0.root")
{
SetAtlasStyle();
TFile* file = TFile::Open(fname);
RooWorkspace* wspace = (RooWorkspace*) file->Get("wspace");
cout << "\n\ncheck that eff and reco terms included in BSM component to make fiducial cross-section" <<endl;
wspace->function("nsig")->Print();
RooRealVar* reco = wspace->var("reco");
if( wspace->function("nsig")->dependsOn(*reco) ) {
cout << "all good." <<endl;
} else {
cout << "need to rerun fit_withsm using DO_FIDUCIAL_LIMIT true" <<endl;
return;
}
/*
// DANGER
// TEST WITH EXAGGERATED UNCERTAINTY
wspace->var("unc_theory")->setMax(1);
wspace->var("unc_theory")->setVal(1);
wspace->var("unc_theory")->Print();
*/
// this was for making plot about decoupling/recoupling approach
TCanvas* tc = new TCanvas("tc","",400,400);
RooPlot *frame = wspace->var("xsec_bsm")->frame();
RooAbsPdf* pdfc = wspace->pdf("jointModeld");
RooAbsData* data = wspace->data("data");
RooAbsReal *nllJoint = pdfc->createNLL(*data, RooFit::Constrained()); // slice with fixed xsec_bsm
RooAbsReal *profileJoint = nllJoint->createProfile(*wspace->var("xsec_bsm"));
wspace->allVars().Print("v");
pdfc->fitTo(*data);
wspace->allVars().Print("v");
wspace->var("xsec_bsm")->Print();
double nllmin = 2*nllJoint->getVal();
wspace->var("xsec_bsm")->setVal(0);
double nll0 = 2*nllJoint->getVal();
cout << Form("nllmin = %f, nll0 = %f, Z=%f", nllmin, nll0, sqrt(nll0-nllmin)) << endl;
nllJoint->plotOn(frame, RooFit::LineColor(kGreen), RooFit::LineStyle(kDotted), RooFit::ShiftToZero(), RooFit::Name("nll_statonly")); // no error
profileJoint->plotOn(frame,RooFit::Name("pll") );
wspace->var("xsec_sm")->Print();
wspace->var("theory")->Print();
wspace->var("theory")->setConstant();
profileJoint->plotOn(frame, RooFit::LineColor(kRed), RooFit::LineStyle(kDashed), RooFit::Name("pll_smfixed") );
frame->GetXaxis()->SetTitle("#sigma_{BSM, fid} [fb]");
frame->GetYaxis()->SetTitle("-log #lambda ( #sigma_{BSM, fid} )");
double temp = frame->GetYaxis()->GetTitleOffset();
frame->GetYaxis()->SetTitleOffset( 1.1* temp );
frame->SetMinimum(1e-7);
frame->SetMaximum(4);
// Legend
double x1,y1,x2,y2;
GetX1Y1X2Y2(tc,x1,y1,x2,y2);
TLegend *legend_sr=FastLegend(x2-0.75,y2-0.3,x2-0.25,y2-0.5,0.045);
legend_sr->AddEntry(frame->findObject("pll"),"with #sigma_{SM} uncertainty","L");
legend_sr->AddEntry(frame->findObject("pll_smfixed"),"with #sigma_{SM} constant","L");
legend_sr->AddEntry(frame->findObject("nll_statonly"),"no systematics","L");
frame->Draw();
legend_sr->Draw("SAME");
// descriptive text
vector<TString> pavetext11;
pavetext11.push_back("#bf{#it{ATLAS Internal}}");
pavetext11.push_back("#sqrt{#it{s}} = 8 TeV #scale[0.6]{#int}Ldt = 20.3 fb^{-1}");
pavetext11.push_back("#it{H}+#it{E}_{T}^{miss} , #it{H #rightarrow #gamma#gamma}, #it{m}_{#it{H}} = 125.4 GeV");
TPaveText* text11=CreatePaveText(x2-0.75,y2-0.25,x2-0.25,y2-0.05,pavetext11,0.045);
text11->Draw();
tc->SaveAs("pll.pdf");
/*
wspace->var("xsec_bsm")->setConstant(true);
wspace->var("eff" )->setConstant(true);
wspace->var("mh" )->setConstant(true);
wspace->var("sigma_h" )->setConstant(true);
wspace->var("lumi" )->setConstant(true);
wspace->var("xsec_sm" )->setVal(v_xsec_sm);
wspace->var("eff" )->setVal(1.0);
wspace->var("lumi" )->setVal(v_lumi);
TH1* nllHist = profileJoint->createHistogram("xsec_bsm",100);
TFile* out = new TFile("nllHist.root","REPLACE");
nllHist->Write()
out->Write();
out->Close();
*/
}
void LHFit()
{
double lorange = 100.;
double hirange = 140.;
// Import data
TFile *file = new TFile("/atlas/data18a/yupan/HZZ4l2012/MiniTree/MiniTree_2013Moriond/v03/MC12a/JHUPythia8_AU2CTEQ6L1_ggH125_Spin0p_ZZ4lep.root");
TTree *tree = (TTree*)file->Get("tree_incl_4mu");
// Define variables
float m4l = 0;
float m34 = 0;
float m4lerr = 0;
float wgt = 0;
// Get the number of entries in the tree
int nevents = tree->GetEntries();
tree->SetBranchStatus("*",0);
tree->SetBranchStatus("m4l_unconstrained",1);
tree->SetBranchStatus("mZ2_unconstrained",1);
tree->SetBranchStatus("m4lerr_unconstrained",1);
tree->SetBranchStatus("weight_corr",1);
tree->SetBranchAddress("m4l_unconstrained",&m4l);
tree->SetBranchAddress("mZ2_unconstrained",&m34);
tree->SetBranchAddress("m4lerr_unconstrained",&m4lerr);
tree->SetBranchAddress("weight_corr",&wgt);
///////////////
// Build pdfs
//////////////
RooRealVar* mass = new RooRealVar("m4l","mass",lorange,hirange,"GeV");
RooRealVar* mZ2 = new RooRealVar("m34","mZ2",10.,60.,"GeV");
RooRealVar merr("m4lerr","mass err",0.1,5.0,"GeV");
RooRealVar weight("weight","weight",0,10);
float totalwgt(0);
for (Int_t i=0; i<nevents; i++) {
tree->GetEntry(i);
totalwgt+=wgt;
}
std::cout<<"total weight = "<<totalwgt<<std::endl;
//////////////////
// Create ND dataset
/////////////////
RooDataSet signal("signal","signal",RooArgSet(*mass,*mZ2,merr,weight),"weight");
std::cout<<"Reading in events from signal minitree"<<std::endl;
for (int i=0; i<nevents; i++) {
tree->GetEntry(i);
mass->setVal(m4l);
mZ2->setVal(m34);
merr.setVal(m4lerr);
weight.setVal(wgt/totalwgt);
signal.add(RooArgSet(*mass,*mZ2,merr),wgt/totalwgt);
}
// Create 1D kernel estimation for signal mass
RooKeysPdf kestmass("kestmass","kestmass",*mass,signal);
// Create histogram of 1D kernel estimation pdf for mass
TH1* hm = kestmass.createHistogram("hm",*mass);
// Create mass pdf from the 1D kernel histogram
RooDataHist* dmass = new RooDataHist("dmass","dmass",RooArgSet(*mass),hm);
RooHistPdf* masspdf = new RooHistPdf("masspdf","masspdf",*mass,*dmass,2);
//////////////////////////////
// Construct plain likelihood
/////////////////////////////
// construct unbinned likelihood
RooAbsReal *nll = masspdf->createNLL(signal,NumCPU(2));
// Minimize likelihood w.r.t all parameters before making plots
RooMinuit(*nll).migrad();
// Plot likelihood scan mass
RooPlot* frame1 = mass->frame(Bins(10),Title("LL and profileLL in mass"));
// Construct profile likelihood in mass
RooAbsReal *pll_mass = nll->createProfile(*mass);
// Plot the profile likelihood in mass
pll_mass->plotOn(frame1,LineColor(kRed));
TCanvas *c = new TCanvas("c","c",800,600);
gPad->SetLeftMargin(0.15);
frame1->GetYaxis()->SetTitleOffset(1.4);
frame1->Draw();
c->SaveAs("testProLL.png");
}
void rf605_profilell()
{
// C r e a t e m o d e l a n d d a t a s e t
// -----------------------------------------------
// Observable
RooRealVar x("x","x",-20,20) ;
// Model (intentional strong correlations)
RooRealVar mean("mean","mean of g1 and g2",0,-10,10) ;
RooRealVar sigma_g1("sigma_g1","width of g1",3) ;
RooGaussian g1("g1","g1",x,mean,sigma_g1) ;
RooRealVar sigma_g2("sigma_g2","width of g2",4,3.0,6.0) ;
RooGaussian g2("g2","g2",x,mean,sigma_g2) ;
RooRealVar frac("frac","frac",0.5,0.0,1.0) ;
RooAddPdf model("model","model",RooArgList(g1,g2),frac) ;
// Generate 1000 events
RooDataSet* data = model.generate(x,1000) ;
// C o n s t r u c t p l a i n l i k e l i h o o d
// ---------------------------------------------------
// Construct unbinned likelihood
RooAbsReal* nll = model.createNLL(*data,NumCPU(2)) ;
// Minimize likelihood w.r.t all parameters before making plots
RooMinuit(*nll).migrad() ;
// Plot likelihood scan frac
RooPlot* frame1 = frac.frame(Bins(10),Range(0.01,0.95),Title("LL and profileLL in frac")) ;
nll->plotOn(frame1,ShiftToZero()) ;
// Plot likelihood scan in sigma_g2
RooPlot* frame2 = sigma_g2.frame(Bins(10),Range(3.3,5.0),Title("LL and profileLL in sigma_g2")) ;
nll->plotOn(frame2,ShiftToZero()) ;
// C o n s t r u c t p r o f i l e l i k e l i h o o d i n f r a c
// -----------------------------------------------------------------------
// The profile likelihood estimator on nll for frac will minimize nll w.r.t
// all floating parameters except frac for each evaluation
RooAbsReal* pll_frac = nll->createProfile(frac) ;
// Plot the profile likelihood in frac
pll_frac->plotOn(frame1,LineColor(kRed)) ;
// Adjust frame maximum for visual clarity
frame1->SetMinimum(0) ;
frame1->SetMaximum(3) ;
// C o n s t r u c t p r o f i l e l i k e l i h o o d i n s i g m a _ g 2
// -------------------------------------------------------------------------------
// The profile likelihood estimator on nll for sigma_g2 will minimize nll
// w.r.t all floating parameters except sigma_g2 for each evaluation
RooAbsReal* pll_sigmag2 = nll->createProfile(sigma_g2) ;
// Plot the profile likelihood in sigma_g2
pll_sigmag2->plotOn(frame2,LineColor(kRed)) ;
// Adjust frame maximum for visual clarity
frame2->SetMinimum(0) ;
frame2->SetMaximum(3) ;
// Make canvas and draw RooPlots
TCanvas *c = new TCanvas("rf605_profilell","rf605_profilell",800, 400);
c->Divide(2);
c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
delete pll_frac ;
delete pll_sigmag2 ;
delete nll ;
}
Int_t Tprime::SetParameterPoints( std::string sbModelName,
std::string bModelName ) {
//
// Fit the data with S+B model.
// Make a snapshot of the S+B parameter point.
// Profile with POI=0.
// Make a snapshot of the B parameter point
// (B model is the S+B model with POI=0
//
Double_t poi_value_for_b_model = 0.0;
// get S+B model config from workspace
RooStats::ModelConfig * pSbModel = (RooStats::ModelConfig *)pWs->obj(sbModelName.c_str());
pSbModel->SetWorkspace(*pWs);
// get parameter of interest set
const RooArgSet * poi = pSbModel->GetParametersOfInterest();
// get B model config from workspace
RooStats::ModelConfig * pBModel = (RooStats::ModelConfig *)pWs->obj(bModelName.c_str());
pBModel->SetWorkspace(*pWs);
// make sure that data has been loaded
if (!data) return -1;
// find parameter point for global maximum with the S+B model,
// with conditional MLEs for nuisance parameters
// and save the parameter point snapshot in the Workspace
RooAbsReal * nll = pSbModel->GetPdf()->createNLL(*data);
RooAbsReal * profile = nll->createProfile(RooArgSet());
profile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
RooArgSet * poiAndNuisance = new RooArgSet();
if(pSbModel->GetNuisanceParameters())
poiAndNuisance->add(*pSbModel->GetNuisanceParameters());
poiAndNuisance->add(*pSbModel->GetParametersOfInterest());
pWs->defineSet("SPlusBModelParameters", *poiAndNuisance);
pWs->saveSnapshot("SPlusBFitParameters",*poiAndNuisance);
pSbModel->SetSnapshot(*poi);
RooArgSet * sbModelFitParams = (RooArgSet *)poiAndNuisance->snapshot();
cout << "\nWill save these parameter points that correspond to the fit to data" << endl;
sbModelFitParams->Print("v");
delete profile;
delete nll;
delete poiAndNuisance;
delete sbModelFitParams;
//
// Find a parameter point for generating pseudo-data
// with the background-only data.
// Save the parameter point snapshot in the Workspace
nll = pBModel->GetPdf()->createNLL(*data);
profile = nll->createProfile(*poi);
((RooRealVar *)poi->first())->setVal(poi_value_for_b_model);
profile->getVal(); // this will do fit and set nuisance parameters to profiled values
poiAndNuisance = new RooArgSet();
if(pBModel->GetNuisanceParameters())
poiAndNuisance->add(*pBModel->GetNuisanceParameters());
poiAndNuisance->add(*pBModel->GetParametersOfInterest());
pWs->defineSet("parameterPointToGenerateData", *poiAndNuisance);
pWs->saveSnapshot("parametersToGenerateData",*poiAndNuisance);
pBModel->SetSnapshot(*poi);
RooArgSet * paramsToGenerateData = (RooArgSet *)poiAndNuisance->snapshot();
cout << "\nShould use these parameter points to generate pseudo data for bkg only" << endl;
paramsToGenerateData->Print("v");
delete profile;
delete nll;
delete poiAndNuisance;
delete paramsToGenerateData;
return 0;
}
