void DefineSet(RooWorkspace &w, const string &set_name, const vector<string> &var_names){ if(var_names.size()==0){ w.defineSet(set_name.c_str(), ""); }else{ string cat_names = var_names.at(0); for(size_t ivar = 1; ivar < var_names.size(); ++ivar){ cat_names += ("," + var_names.at(ivar)); } w.defineSet(set_name.c_str(), cat_names.c_str()); } }
void makeDataset( TString fname, TString tname, TString outfname ) { RooWorkspace *w = new RooWorkspace("w","w"); w->factory( "Dst_M[1950.,2070.]" ); w->factory( "D0_M[1810.,1920.]" ); w->factory( "D0_LTIME_ps[0.00,5.]" ); RooArgSet *observables = new RooArgSet(); observables->add( *w->var("Dst_M") ); observables->add( *w->var("D0_M") ); observables->add( *w->var("D0_LTIME_ps") ); w->defineSet("observables", *observables); w->var("Dst_M")->setBins(240); w->var("D0_M")->setBins(220); w->var("D0_LTIME_ps")->setBins(200); double Dst_M = -999.; double D0_M = -999.; double D0_LTIME_ps = -999.; TFile *tf = TFile::Open(fname); TTree *tree = (TTree*)tf->Get(tname); tree->SetBranchAddress( "Dst_M", &Dst_M ); tree->SetBranchAddress( "D0_M" , &D0_M ); tree->SetBranchAddress( "D0_LTIME_ps", &D0_LTIME_ps ); RooDataSet *data = new RooDataSet("Data","Data",*observables); RooDataHist *dataH = new RooDataHist("DataHist","Data",*observables); for ( int ev=0; ev<tree->GetEntries(); ev++) { tree->GetEntry(ev); if ( ev%10000 == 0 ) cout << ev << " / " << tree->GetEntries() << endl; if ( Dst_M < w->var("Dst_M")->getMin() || Dst_M > w->var("Dst_M")->getMax() ) continue; if ( D0_M < w->var("D0_M")->getMin() || D0_M > w->var("D0_M")->getMax() ) continue; if ( D0_LTIME_ps < w->var("D0_LTIME_ps")->getMin() || D0_LTIME_ps > w->var("D0_LTIME_ps")->getMax() ) continue; w->var("Dst_M")->setVal(Dst_M); w->var("D0_M")->setVal(D0_M); w->var("D0_LTIME_ps")->setVal(D0_LTIME_ps); data->add( *observables ); dataH->add( *observables ); } tf->Close(); w->import(*data); w->import(*dataH); w->writeToFile(outfname); }
int main(int argc, char* argv[]) { doofit::builder::EasyPdf *epdf = new doofit::builder::EasyPdf(); epdf->Var("sig_yield"); epdf->Var("sig_yield").setVal(153000); epdf->Var("sig_yield").setConstant(false); //decay time epdf->Var("obsTime"); epdf->Var("obsTime").SetTitle("t_{#kern[-0.2]{B}_{#kern[-0.1]{ d}}^{#kern[-0.1]{ 0}}}"); epdf->Var("obsTime").setUnit("ps"); epdf->Var("obsTime").setRange(0.,16.); // tag, respectively the initial state of the produced B meson epdf->Cat("obsTag"); epdf->Cat("obsTag").defineType("B_S",1); epdf->Cat("obsTag").defineType("Bbar_S",-1); //finalstate epdf->Cat("catFinalState"); epdf->Cat("catFinalState").defineType("f",1); epdf->Cat("catFinalState").defineType("fbar",-1); epdf->Var("obsEtaOS"); epdf->Var("obsEtaOS").setRange(0.0,0.5); std::vector<double> knots; knots.push_back(0.07); knots.push_back(0.10); knots.push_back(0.138); knots.push_back(0.16); knots.push_back(0.23); knots.push_back(0.28); knots.push_back(0.35); knots.push_back(0.42); knots.push_back(0.44); knots.push_back(0.48); knots.push_back(0.5); // empty arg list for coefficients RooArgList* list = new RooArgList(); // create first coefficient RooRealVar* coeff_first = &(epdf->Var("parCSpline1")); coeff_first->setRange(0,10000); coeff_first->setVal(1); coeff_first->setConstant(false); list->add( *coeff_first ); for (unsigned int i=1; i <= knots.size(); ++i){ std::string number = boost::lexical_cast<std::string>(i); RooRealVar* coeff = &(epdf->Var("parCSpline"+number)); coeff->setRange(0,10000); coeff->setVal(1); coeff->setConstant(false); list->add( *coeff ); } // create last coefficient RooRealVar* coeff_last = &(epdf->Var("parCSpline"+boost::lexical_cast<std::string>(knots.size()))); coeff_last->setRange(0,10000); coeff_last->setVal(1); coeff_last->setConstant(false); list->add( *coeff_last ); list->Print(); doofit::roofit::pdfs::DooCubicSplinePdf splinePdf("splinePdf",epdf->Var("obsEtaOS"),knots,*list,0,0.5); //doofit::roofit::pdfs::DooCubicSplinePdf* splinePdf = new doofit::roofit::pdfs::DooCubicSplinePdf("splinePdf", epdf->Var("obsEtaOS"), knots, *list,0,0.5); //Koeffizienten DecRateCoeff *coeff_c = new DecRateCoeff("coef_cos","coef_cos",DecRateCoeff::CPOdd,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("C_f"),epdf->Var("C_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Var("obsEtaOS"),epdf->Var("asym_prod"),epdf->Var("asym_det"),epdf->Var("asym_tageff")); DecRateCoeff *coeff_s = new DecRateCoeff("coef_sin","coef_sin",DecRateCoeff::CPOdd,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("S_f"),epdf->Var("S_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Var("obsEtaOS"),epdf->Var("asym_prod"),epdf->Var("asym_det"),epdf->Var("asym_tageff")); DecRateCoeff *coeff_sh = new DecRateCoeff("coef_sinh","coef_sinh",DecRateCoeff::CPEven,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("f1_f"),epdf->Var("f1_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Var("obsEtaOS"),epdf->Var("asym_prod"),epdf->Var("asym_det"),epdf->Var("asym_tageff")); DecRateCoeff *coeff_ch = new DecRateCoeff("coef_cosh","coef_cosh",DecRateCoeff::CPEven,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("f0_f"),epdf->Var("f0_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Var("obsEtaOS"),epdf->Var("asym_prod"),epdf->Var("asym_det"),epdf->Var("asym_tageff")); epdf->AddRealToStore(coeff_ch); epdf->AddRealToStore(coeff_sh); epdf->AddRealToStore(coeff_c); epdf->AddRealToStore(coeff_s); ///////////////////Generiere PDF's///////////////////// //Zeit epdf->GaussModel("resTimeGauss",epdf->Var("obsTime"),epdf->Var("allTimeResMean"),epdf->Var("allTimeReso")); epdf->BDecay("pdfSigTime",epdf->Var("obsTime"),epdf->Var("tau"),epdf->Var("dgamma"),epdf->Real("coef_cosh"),epdf->Real("coef_sinh"),epdf->Real("coef_cos"),epdf->Real("coef_sin"),epdf->Var("deltaM"),epdf->Model("resTimeGauss")); //Zusammenfassen der Parameter in einem RooArgSet RooArgSet Observables; Observables.add(RooArgSet( epdf->Var("obsTime"),epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("obsEtaOS"))); epdf->Extend("pdfExtend", epdf->Pdf("pdfSigTime"),epdf->Real("sig_yield")); //Multipliziere Signal und Untergrund PDF mit ihrer jeweiligen Zerfalls PDF// //Untergrund * Zerfall /*epdf->Product("pdf_bkg", RooArgSet(epdf->Pdf("pdf_bkg_mass_expo"), epdf->Pdf("pdf_bkg_mass_time"))); //Signal * Zerfall epdf->Product("pdf_sig", RooArgSet(epdf->Pdf("pdf_sig_mass_gauss"),epdf->Pdf("pdfSigTime"))); //Addiere PDF's epdf->Add("pdf_total", RooArgSet(epdf->Pdf("pdf_sig_mass_gauss*pdf_sig_time_decay"), epdf->Pdf("pdf_bkg_mass*pdf_bkg_time_decay")), RooArgSet(epdf->Var("bkg_Yield"),epdf->Var("sig_Yield")));*/ RooWorkspace ws; ws.import(epdf->Pdf("pdfExtend")); ws.defineSet("Observables",Observables, true); ws.Print(); doofit::config::CommonConfig cfg_com("common"); cfg_com.InitializeOptions(argc, argv); doofit::toy::ToyFactoryStdConfig cfg_tfac("toyfac"); cfg_tfac.InitializeOptions(cfg_com); doofit::toy::ToyStudyStdConfig cfg_tstudy("toystudy"); cfg_tstudy.InitializeOptions(cfg_tfac); // set a previously defined workspace to get PDF from (not mandatory, but convenient) cfg_tfac.set_workspace(&ws); // Check for a set --help flag and if so, print help and exit gracefully // (recommended). cfg_com.CheckHelpFlagAndPrintHelp(); // More custom code, e.g. to set options internally. // Not required as configuration via command line/config file is enough. cfg_com.PrintAll(); // Print overview of all options (optional) // cfg_com.PrintAll(); // Initialize the toy factory module with the config objects and start // generating toy samples. doofit::toy::ToyFactoryStd tfac(cfg_com, cfg_tfac); doofit::toy::ToyStudyStd tstudy(cfg_com, cfg_tstudy); RooDataSet* data = tfac.Generate(); data->Print(); epdf->Pdf("pdfExtend").getParameters(data)->readFromFile("/home/chasenberg/Repository/bachelor-template/ToyStudy/dootoycp-parameter_spline.txt"); epdf->Pdf("pdfExtend").getParameters(data)->writeToFile("/home/chasenberg/Repository/bachelor-template/ToyStudy/dootoycp-parameter_spline.txt.new"); //epdf->Pdf("pdfExtend").fitTo(*data); //epdf->Pdf("pdfExtend").getParameters(data)->writeToFile("/home/chasenberg/Repository/bachelor-template/ToyStudy/dootoycp-fit-result.txt"); RooFitResult* fit_result = epdf->Pdf("pdfExtend").fitTo(*data, RooFit::Save(true)); tstudy.StoreFitResult(fit_result); /*using namespace doofit::plotting; PlotConfig cfg_plot("cfg_plot"); cfg_plot.InitializeOptions(); cfg_plot.set_plot_directory("/net/lhcb-tank/home/chasenberg/Ergebnis/dootoycp_spline-lhcb/time/"); // plot PDF and directly specify components Plot myplot(cfg_plot, epdf->Var("obsTime"), *data, RooArgList(epdf->Pdf("pdfExtend"))); myplot.PlotItLogNoLogY(); PlotConfig cfg_plotEta("cfg_plotEta"); cfg_plotEta.InitializeOptions(); cfg_plotEta.set_plot_directory("/net/lhcb-tank/home/chasenberg/Ergebnis/dootoycp_spline-lhcb/eta/"); // plot PDF and directly specify components Plot myplotEta(cfg_plotEta, epdf->Var("obsEtaOS"), *data, RooArgList(splinePdf)); myplotEta.PlotIt();*/ }
void IntervalExamples() { // Time this macro TStopwatch t; t.Start(); // set RooFit random seed for reproducible results RooRandom::randomGenerator()->SetSeed(3001); // make a simple model via the workspace factory RooWorkspace* wspace = new RooWorkspace(); wspace->factory("Gaussian::normal(x[-10,10],mu[-1,1],sigma[1])"); wspace->defineSet("poi","mu"); wspace->defineSet("obs","x"); // specify components of model for statistical tools ModelConfig* modelConfig = new ModelConfig("Example G(x|mu,1)"); modelConfig->SetWorkspace(*wspace); modelConfig->SetPdf( *wspace->pdf("normal") ); modelConfig->SetParametersOfInterest( *wspace->set("poi") ); modelConfig->SetObservables( *wspace->set("obs") ); // create a toy dataset RooDataSet* data = wspace->pdf("normal")->generate(*wspace->set("obs"),100); data->Print(); // for convenience later on RooRealVar* x = wspace->var("x"); RooRealVar* mu = wspace->var("mu"); // set confidence level double confidenceLevel = 0.95; // example use profile likelihood calculator ProfileLikelihoodCalculator plc(*data, *modelConfig); plc.SetConfidenceLevel( confidenceLevel); LikelihoodInterval* plInt = plc.GetInterval(); // example use of Feldman-Cousins FeldmanCousins fc(*data, *modelConfig); fc.SetConfidenceLevel( confidenceLevel); fc.SetNBins(100); // number of points to test per parameter fc.UseAdaptiveSampling(true); // make it go faster // Here, we consider only ensembles with 100 events // The PDF could be extended and this could be removed fc.FluctuateNumDataEntries(false); // Proof // ProofConfig pc(*wspace, 4, "workers=4", kFALSE); // proof-lite //ProofConfig pc(w, 8, "localhost"); // proof cluster at "localhost" // ToyMCSampler* toymcsampler = (ToyMCSampler*) fc.GetTestStatSampler(); // toymcsampler->SetProofConfig(&pc); // enable proof PointSetInterval* interval = (PointSetInterval*) fc.GetInterval(); // example use of BayesianCalculator // now we also need to specify a prior in the ModelConfig wspace->factory("Uniform::prior(mu)"); modelConfig->SetPriorPdf(*wspace->pdf("prior")); // example usage of BayesianCalculator BayesianCalculator bc(*data, *modelConfig); bc.SetConfidenceLevel( confidenceLevel); SimpleInterval* bcInt = bc.GetInterval(); // example use of MCMCInterval MCMCCalculator mc(*data, *modelConfig); mc.SetConfidenceLevel( confidenceLevel); // special options mc.SetNumBins(200); // bins used internally for representing posterior mc.SetNumBurnInSteps(500); // first N steps to be ignored as burn-in mc.SetNumIters(100000); // how long to run chain mc.SetLeftSideTailFraction(0.5); // for central interval MCMCInterval* mcInt = mc.GetInterval(); // for this example we know the expected intervals double expectedLL = data->mean(*x) + ROOT::Math::normal_quantile( (1-confidenceLevel)/2,1) / sqrt(data->numEntries()); double expectedUL = data->mean(*x) + ROOT::Math::normal_quantile_c((1-confidenceLevel)/2,1) / sqrt(data->numEntries()) ; // Use the intervals std::cout << "expected interval is [" << expectedLL << ", " << expectedUL << "]" << endl; cout << "plc interval is [" << plInt->LowerLimit(*mu) << ", " << plInt->UpperLimit(*mu) << "]" << endl; std::cout << "fc interval is ["<< interval->LowerLimit(*mu) << " , " << interval->UpperLimit(*mu) << "]" << endl; cout << "bc interval is [" << bcInt->LowerLimit() << ", " << bcInt->UpperLimit() << "]" << endl; cout << "mc interval is [" << mcInt->LowerLimit(*mu) << ", " << mcInt->UpperLimit(*mu) << "]" << endl; mu->setVal(0); cout << "is mu=0 in the interval? " << plInt->IsInInterval(RooArgSet(*mu)) << endl; // make a reasonable style gStyle->SetCanvasColor(0); gStyle->SetCanvasBorderMode(0); gStyle->SetPadBorderMode(0); gStyle->SetPadColor(0); gStyle->SetCanvasColor(0); gStyle->SetTitleFillColor(0); gStyle->SetFillColor(0); gStyle->SetFrameFillColor(0); gStyle->SetStatColor(0); // some plots TCanvas* canvas = new TCanvas("canvas"); canvas->Divide(2,2); // plot the data canvas->cd(1); RooPlot* frame = x->frame(); data->plotOn(frame); data->statOn(frame); frame->Draw(); // plot the profile likelihood canvas->cd(2); LikelihoodIntervalPlot plot(plInt); plot.Draw(); // plot the MCMC interval canvas->cd(3); MCMCIntervalPlot* mcPlot = new MCMCIntervalPlot(*mcInt); mcPlot->SetLineColor(kGreen); mcPlot->SetLineWidth(2); mcPlot->Draw(); canvas->cd(4); RooPlot * bcPlot = bc.GetPosteriorPlot(); bcPlot->Draw(); canvas->Update(); t.Stop(); t.Print(); }
void fillWorkspace(RooWorkspace& w) { // C r e a t e m o d e l // ----------------------- // Declare observable x RooRealVar x("x","x",0,10) ; // Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters RooRealVar mean("mean","mean of gaussians",5,0,10) ; RooRealVar sigma1("sigma1","width of gaussians",0.5) ; RooRealVar sigma2("sigma2","width of gaussians",1) ; RooGaussian sig1("sig1","Signal component 1",x,mean,sigma1) ; RooGaussian sig2("sig2","Signal component 2",x,mean,sigma2) ; // Build Chebychev polynomial p.d.f. RooRealVar a0("a0","a0",0.5,0.,1.) ; RooRealVar a1("a1","a1",-0.2,0.,1.) ; RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ; // Sum the signal components into a composite signal p.d.f. RooRealVar sig1frac("sig1frac","fraction of component 1 in signal",0.8,0.,1.) ; RooAddPdf sig("sig","Signal",RooArgList(sig1,sig2),sig1frac) ; // Sum the composite signal and background RooRealVar bkgfrac("bkgfrac","fraction of background",0.5,0.,1.) ; RooAddPdf model("model","g1+g2+a",RooArgList(bkg,sig),bkgfrac) ; // Import model into p.d.f. w.import(model) ; // E n c o d e d e f i n i t i o n o f p a r a m e t e r s i n w o r k s p a c e // --------------------------------------------------------------------------------------- // Define named sets "parameters" and "observables", which list which variables should be considered // parameters and observables by the users convention // // Variables appearing in sets _must_ live in the workspace already, or the autoImport flag // of defineSet must be set to import them on the fly. Named sets contain only references // to the original variables, therefore the value of observables in named sets already // reflect their 'current' value RooArgSet* params = (RooArgSet*) model.getParameters(x) ; w.defineSet("parameters",*params) ; w.defineSet("observables",x) ; // E n c o d e r e f e r e n c e v a l u e f o r p a r a m e t e r s i n w o r k s p a c e // --------------------------------------------------------------------------------------------------- // Define a parameter 'snapshot' in the p.d.f. // Unlike a named set, a parameter snapshot stores an independent set of values for // a given set of variables in the workspace. The values can be stored and reloaded // into the workspace variable objects using the loadSnapshot() and saveSnapshot() // methods. A snapshot saves the value of each variable, any errors that are stored // with it as well as the 'Constant' flag that is used in fits to determine if a // parameter is kept fixed or not. // Do a dummy fit to a (supposedly) reference dataset here and store the results // of that fit into a snapshot RooDataSet* refData = model.generate(x,10000) ; model.fitTo(*refData,PrintLevel(-1)) ; // The kTRUE flag imports the values of the objects in (*params) into the workspace // If not set, the present values of the workspace parameters objects are stored w.saveSnapshot("reference_fit",*params,kTRUE) ; // Make another fit with the signal componentforced to zero // and save those parameters too bkgfrac.setVal(1) ; bkgfrac.setConstant(kTRUE) ; bkgfrac.removeError() ; model.fitTo(*refData,PrintLevel(-1)) ; w.saveSnapshot("reference_fit_bkgonly",*params,kTRUE) ; }
void hggfitmceerr(double nommass=123., double tgtr=1., int ijob=0) { //gSystem->cd("/scratch/bendavid/root/bare/fitplotsJun10test/"); int seed = 65539+ijob+1; TString dirname = "/scratch/bendavid/root/bare/hggfiteerrtestall_large2/"; gSystem->mkdir(dirname,true); gSystem->cd(dirname); //nommass=150.; // gSystem->cd("/scratch/bendavid/root/bare/fitplotsJun8_150_2x/"); gRandom->SetSeed(seed); RooRandom::randomGenerator()->SetSeed(seed); // TFile *fin = TFile::Open("/home/mingyang/cms/hist_approval/hgg-2013Moriond/merged/hgg-2013Moriond_s12-h150gg-gf-v7a_noskim.root"); // TDirectory *hdir = (TDirectory*)fin->FindObjectAny("PhotonTreeWriterPresel"); // TTree *htree = (TTree*)hdir->Get("hPhotonTree"); // TFile *fdin = TFile::Open("/home/mingyang/cms/hist/hgg-2013Moriond/merged/hgg-2013Moriond_r12_ABCD.root"); // TDirectory *ddir = (TDirectory*)fdin->FindObjectAny("PhotonTreeWriterPresel"); // TTree *dtree = (TTree*)ddir->Get("hPhotonTree"); //TCut selcut = "(ph1.pt > (mass/3.0) && ph2.pt > (mass/4.0) && mass>100. && mass<180. && ph1.idmva>-0.2 && ph2.idmva>-0.2)"; TCut selcut = "(ph1.pt > (mass/3.0) && ph2.pt > (mass/4.0) && mass>100. && mass<180. && ph1.idmva>-0.2 && ph2.idmva>-0.2)"; //TCut selweight = "xsecweight(procidx)*puweight(numPU,procidx)"; TCut selweight = "xsecweight(procidx)*mcweight*kfact(procidx,ph1.ispromptgen,ph2.ispromptgen)"; TCut sigFcut = "(procidx==0 || procidx==3)"; TCut sigVcut = "(procidx==1 || procidx==2)"; TCut bkgPPcut = "(procidx==4)"; TCut bkgPFcut = "(procidx==5 || procidx==6)"; TCut bkgFFcut = "(procidx==7 || procidx==8)"; TCut bkgcut = "(procidx>3)"; TCut bkgcutnoq = "(procidx>3 && procidx<7)"; TCut prescalenone = "(1==1)"; TCut evenevents = "(evt%2==0)"; TCut oddevents = "(evt%2==1)"; TCut prescale10 = "(evt%10==0)"; TCut prescale25 = "(evt%25==0)"; TCut prescale50 = "(evt%50==0)"; TCut prescale100 = "(evt%100==0)"; TCut fcut = prescale50; float xsecs[50]; //TCut selcutsingle = "ph.pt>25. && ph.isbarrel && ph.ispromptgen"; //TCut selcutsingle = "ph.pt>25.&& ph.ispromptgen"; TCut selcutsingle = "ph.genpt>16.&& ph.ispromptgen"; TCut selweightsingle = "xsecweight(procidx)"; // TChain *tree = new TChain("RunLumiSelectionMod/MCProcessSelectionMod/HLTModP/GoodPVFilterMod/PhotonMvaMod/JetPub/JetCorrectionMod/SeparatePileUpMod/ElectronIDMod/MuonIDMod/PhotonPairSelectorPresel/PhotonTreeWriterPresel/hPhotonTreeSingle"); // tree->Add("/home/mingyang/cms/hist/hgg-2013Final8TeV/merged/hgg-2013Final8TeV_s12-diphoj-v7n_noskim.root"); TChain *tree = new TChain("RunLumiSelectionMod/MCProcessSelectionMod/HLTModP/GoodPVFilterMod/PhotonMvaMod/PhotonIDModPresel/PhotonTreeWriterSingle/hPhotonTreeSingle"); tree->Add("/home/mingyang/cms/hist/hgg-2013Final8TeV_reg_trans/merged/hgg-2013Final8TeV_reg_trans_s12-pj20_40-2em-v7n_noskim.root"); tree->Add("/home/mingyang/cms/hist/hgg-2013Final8TeV_reg_trans/merged/hgg-2013Final8TeV_reg_trans_s12-pj40-2em-v7n_noskim.root"); xsecs[0] = 0.001835*81930.0; xsecs[1] = 0.05387*8884.0; initweights(tree,xsecs,1.); double weightscale = xsecweights[1]; xsecweights[0] /= weightscale; xsecweights[1] /= weightscale; tree->SetCacheSize(64*1024*1024); RooRealVar energy("energy","ph.e",0); RooRealVar sceta("sceta","ph.sceta",0.); RooRealVar idmva("idmva","ph.idmva",0.,-1.,1.); RooRealVar eerr("eerr","(ph.isbarrel + 0.5*!ph.isbarrel)*ph.eerr/ph.e",0.); RooRealVar evt("evt","evt",0.); RooArgList vars; vars.add(energy); vars.add(sceta); //vars.add(idmva); RooArgList condvars(vars); vars.add(eerr); RooArgList condvarsid(vars); vars.add(idmva); vars.add(evt); // RooPowerLaw("testpow","",pt1,pt2); // return; // new TCanvas; // tree->Draw("mass>>htmpall(80,100.,180.)",bkgcut*selcut*selweight,"HIST"); // // new TCanvas; // tree->Draw("mass>>htmpallid(80,100.,180.)",idcut*bkgcut*selcut*selweight,"HIST"); // // new TCanvas; // tree->Draw("mass>>htmp(80,100.,180.)",bkgcutnoq*selcut*selweight,"HIST"); // // // return; //RooRealVar weightvar("weightvar","(ph1.pt > (mass/3.0) && ph2.pt > (mass/4.0) && mass>100. && mass<180. && ph1.idmva>-0.2 && ph2.idmva>-0.2 && evt%100!=0)",1.); RooRealVar weightvar("weightvar","",1.); //RooRealVar weightvar("weightvar","(ph1.pt > (mass/3.0) && ph2.pt > (mass/4.0) && mass>100. && mass<180. && ph1.idmva>-0.2 && ph2.idmva>-0.2)",1.); weightvar.SetTitle(selcutsingle*selweightsingle); RooDataSet *hdataSingle = RooTreeConvert::CreateDataSet("hdataSingle",tree,vars,weightvar); int ngauseerr = 4; //int nparmseerr = 3*ngauseerr + 2; //int nparmseerr = 3*ngauseerr + 2; int nparmseerr = 5*ngauseerr; RooArgList tgtseerr; RooGBRFunction funceerr("funceerr","",condvars,nparmseerr); int iparmeerr = 0; RooArgList eerrgauspdfs; RooArgList eerrgauscoeffs; double stepeerr = 0.07/double(std::max(1,ngauseerr-1)); // RooRealVar *gmeanvar = new RooRealVar(TString::Format("gmeanvar_eerr_%i",0),"",0.007+stepeerr*0); // RooRealVar *gsigmavar = new RooRealVar(TString::Format("gsigmavar_eerr_%i",0),"",0.01); // // //RooRealVar *gsigmaRvar = new RooRealVar(TString::Format("gsigmaRvar_eerr_%i",0),"",0.02); // // // // //if (0==0) gmeanvar->setVal(0.007); // // // gmeanvar->setConstant(false); // gsigmavar->setConstant(false); // // //gsigmaRvar->setConstant(false); // // // // // RooGBRTarget *gmean = new RooGBRTarget(TString::Format("gmean_eerr_%i",0),"",funceerr,iparmeerr++,*gmeanvar); // RooGBRTarget *gsigma = new RooGBRTarget(TString::Format("gsigma_eerr_%i",0),"",funceerr,iparmeerr++,*gsigmavar); // // //RooGBRTarget *gsigmaR = new RooGBRTarget(TString::Format("gsigmaR_eerr_%i",0),"",funceerr,iparmeerr++,*gsigmaRvar); // // // RooRealConstraint *gmeanlim = new RooRealConstraint(TString::Format("gmeanlim_eerr_%i",0),"",*gmean,0.,0.5); // RooRealConstraint *gsigmalim = new RooRealConstraint(TString::Format("gsigmalim_eerr_%i",0),"",*gsigma,1e-7,0.5); // //RooRealConstraint *gsigmaRlim = new RooRealConstraint(TString::Format("gsigmaRlim_eerr_%i",0),"",*gsigmaR,1e-7,0.2); // // tgtseerr.add(*gmean); // tgtseerr.add(*gsigma); for (int igaus=0; igaus<ngauseerr; ++igaus) { RooRealVar *gmeanvar = new RooRealVar(TString::Format("gmeanvar_eerr_%i",igaus),"",0.007+stepeerr*igaus); RooRealVar *gsigmavar = new RooRealVar(TString::Format("gsigmavar_eerr_%i",igaus),"",0.01); RooRealVar *galphavar = new RooRealVar(TString::Format("galphavar_eerr_%i",igaus),"",1.0); RooRealVar *gnvar = new RooRealVar(TString::Format("gnvar_eerr_%i",igaus),"",2.); RooRealVar *gfracvar = new RooRealVar(TString::Format("gfracvar_eerr_%i",igaus),"",1.0); //if (igaus==0) gmeanvar->setVal(0.007); gmeanvar->setConstant(false); gsigmavar->setConstant(false); galphavar->setConstant(false); gnvar->setConstant(false); gfracvar->setConstant(false); RooGBRTarget *gmean = new RooGBRTarget(TString::Format("gmean_eerr_%i",igaus),"",funceerr,iparmeerr++,*gmeanvar); RooGBRTarget *gsigma = new RooGBRTarget(TString::Format("gsigma_eerr_%i",igaus),"",funceerr,iparmeerr++,*gsigmavar); RooGBRTarget *galpha = new RooGBRTarget(TString::Format("galpha_eerr_%i",igaus),"",funceerr,iparmeerr++,*galphavar); RooGBRTarget *gn = new RooGBRTarget(TString::Format("gn_eerr_%i",igaus),"",funceerr,iparmeerr++,*gnvar); RooGBRTarget *gfrac = new RooGBRTarget(TString::Format("gfrac_eerr_%i",igaus),"",funceerr,iparmeerr++,*gfracvar); RooRealConstraint *gmeanlim = new RooRealConstraint(TString::Format("gmeanlim_eerr_%i",igaus),"",*gmean,0.,0.5); RooRealConstraint *gsigmalim = new RooRealConstraint(TString::Format("gsigmalim_eerr_%i",igaus),"",*gsigma,1e-5,0.1); RooRealConstraint *galphalim = new RooRealConstraint(TString::Format("galphalim_eerr_%i",igaus),"",*galpha,0.05,8.); RooRealConstraint *gnlim = new RooRealConstraint(TString::Format("gnlim_eerr_%i",igaus),"",*gn,1.01,5000.); //RooRealConstraint *gfraclim = new RooRealConstraint(TString::Format("gfraclim_eerr_%i",igaus),"",*gfrac,0.,1.); RooAbsReal *gfraclim = new RooProduct(TString::Format("gfraclim_eerr_%i",igaus),"",RooArgList(*gfrac,*gfrac)); if (igaus==0) { gfraclim = new RooConstVar(TString::Format("gfraclimconst_eerr_%i",igaus),"",1.); } else { tgtseerr.add(*gfrac); } //RooGaussianFast *gpdf = new RooGaussianFast(TString::Format("gdf_eerr_%i",igaus),"",eerr,*gmeanlim,*gsigmalim); //RooBifurGauss *gpdf = new RooBifurGauss(TString::Format("gdf_eerr_%i",igaus),"",eerr,*gmeanlim,*gsigmalim,*galphalim); if (igaus==0) { RooRevCBFast *gpdf = new RooRevCBFast(TString::Format("gdf_eerr_%i",igaus),"",eerr,*gmeanlim,*gsigmalim,*galphalim, *gnlim); tgtseerr.add(*gmean); tgtseerr.add(*gsigma); tgtseerr.add(*galpha); tgtseerr.add(*gn); eerrgauspdfs.add(*gpdf); } else { RooGaussianFast *gpdf = new RooGaussianFast(TString::Format("gdf_eerr_%i",igaus),"",eerr,*gmeanlim,*gsigmalim); tgtseerr.add(*gmean); tgtseerr.add(*gsigma); eerrgauspdfs.add(*gpdf); } eerrgauscoeffs.add(*gfraclim); } RooCondAddPdf eerrpdf("eerrpdf","",eerrgauspdfs,eerrgauscoeffs); RooAbsPdf *pdf0 = static_cast<RooAbsPdf*>(eerrgauspdfs.at(0)); int ngaus = 6; int nparms = 4*ngaus; RooArgList tgtsid; RooGBRFunction funcid("funcid","",condvarsid,nparms); RooArgList gauspdfs; RooArgList gauscoeffs; double step = 0.5/double(std::max(1,ngaus-1)); int iparm = 0; for (int igaus=0; igaus<ngaus; ++igaus) { RooRealVar *gmeanvar = new RooRealVar(TString::Format("gmeanvar_%i",igaus),"",-0.2+step*igaus); RooRealVar *gsigmavar = new RooRealVar(TString::Format("gsigmavar_%i",igaus),"",0.1); RooRealVar *gsigmaRvar = new RooRealVar(TString::Format("gsigmaRvar_%i",igaus),"",0.1); RooRealVar *gfracvar = new RooRealVar(TString::Format("gfracvar_%i",igaus),"",1.0); gmeanvar->setConstant(false); gsigmavar->setConstant(false); gsigmaRvar->setConstant(false); gfracvar->setConstant(false); RooGBRTarget *gmean = new RooGBRTarget(TString::Format("gmean_%i",igaus),"",funcid,iparm++,*gmeanvar); RooGBRTarget *gsigma = new RooGBRTarget(TString::Format("gsigma_%i",igaus),"",funcid,iparm++,*gsigmavar); RooGBRTarget *gsigmaR = new RooGBRTarget(TString::Format("gsigmaR_%i",igaus),"",funcid,iparm++,*gsigmaRvar); RooGBRTarget *gfrac = new RooGBRTarget(TString::Format("gfrac_%i",igaus),"",funcid,iparm++,*gfracvar); RooRealConstraint *gmeanlim = new RooRealConstraint(TString::Format("gmeanlim_%i",igaus),"",*gmean,-1.,1.); RooRealConstraint *gsigmalim = new RooRealConstraint(TString::Format("gsigmalim_%i",igaus),"",*gsigma,1e-4,2.); RooRealConstraint *gsigmaRlim = new RooRealConstraint(TString::Format("gsigmaRlim_%i",igaus),"",*gsigmaR,1e-4,2.); //RooRealConstraint *gfraclim = new RooRealConstraint(TString::Format("gfraclim_%i",igaus),"",*gfrac,0.,1.); RooAbsReal *gfraclim = new RooProduct(TString::Format("gfraclim_%i",igaus),"",RooArgList(*gfrac,*gfrac)); if (igaus==0) { gfraclim = new RooConstVar(TString::Format("gfraclimconst_%i",igaus),"",1.); } else { tgtsid.add(*gfrac); } RooGaussianFast *gpdf = new RooGaussianFast(TString::Format("gdf_%i",igaus),"",idmva,*gmeanlim,*gsigmalim); //RooBifurGauss *gpdf = new RooBifurGauss(TString::Format("gdf_%i",igaus),"",idmva,*gmeanlim,*gsigmalim,*gsigmaRlim); gauspdfs.add(*gpdf); gauscoeffs.add(*gfraclim); tgtsid.add(*gmean); tgtsid.add(*gsigma); //tgtsid.add(*gsigmaR); //tgtsid.add(*gfrac); } RooCondAddPdf idpdf("idpdf","",gauspdfs,gauscoeffs); RooConstVar etermconst("etermconst","",0.); RooAbsReal &eterm = etermconst; RooRealVar dummy("dummy","",1.0); std::vector<RooAbsData*> vdata; vdata.push_back(hdataSingle); std::vector<RooAbsReal*> vpdf; vpdf.push_back(&eerrpdf); //vpdf.push_back(pdf0); std::vector<RooAbsReal*> vpdfid; vpdfid.push_back(&idpdf); RooHybridBDTAutoPdf bdtpdf("bdtpdf","",funceerr,tgtseerr,eterm,dummy,vdata,vpdf); bdtpdf.SetPrescaleInit(100); bdtpdf.SetMinCutSignificance(5.0); bdtpdf.SetShrinkage(0.1); bdtpdf.SetMinWeightTotal(200.); bdtpdf.SetMaxNodes(200); bdtpdf.TrainForest(1e6); RooHybridBDTAutoPdf bdtpdfid("bdtpdfid","",funcid,tgtsid,eterm,dummy,vdata,vpdfid); bdtpdfid.SetPrescaleInit(100); bdtpdfid.SetMinCutSignificance(5.0); bdtpdfid.SetShrinkage(0.1); bdtpdfid.SetMinWeightTotal(200.); bdtpdfid.SetMaxNodes(200); bdtpdfid.TrainForest(1e6); RooAbsReal *finalcdferr = eerrpdf.createCDF(eerr); RooFormulaVar transerr("transerr","","sqrt(2.)*TMath::ErfInverse(2.*@0-1.)",*finalcdferr); RooAbsReal *finalcdfid = idpdf.createCDF(idmva); RooFormulaVar transid("transid","","sqrt(2.)*TMath::ErfInverse(2.*@0-1.)",*finalcdfid); RooWorkspace *wsout = new RooWorkspace("wsfiteerr"); wsout->import(*hdataSingle); wsout->import(eerrpdf,RecycleConflictNodes()); wsout->import(idpdf,RecycleConflictNodes()); // wsout->import(transerr,RecycleConflictNodes()); // wsout->import(transid,RecycleConflictNodes()); wsout->defineSet("datavars",vars,true); wsout->writeToFile("hggfiteerr.root"); RooRealVar *cdfidvar = (RooRealVar*)hdataSingle->addColumn(*finalcdfid); RooRealVar *transidvar = (RooRealVar*)hdataSingle->addColumn(transid); RooGaussianFast unormpdfid("unormpdfid","",*transidvar,RooConst(0.),RooConst(1.)); RooRealVar *cdferrvar = (RooRealVar*)hdataSingle->addColumn(*finalcdferr); RooRealVar *transerrvar = (RooRealVar*)hdataSingle->addColumn(transerr); RooGaussianFast unormpdferr("unormpdferr","",*transerrvar,RooConst(0.),RooConst(1.)); //RooDataSet *testdata = (RooDataSet*)hdataSingle->reduce("abs(sceta)>1.3 && abs(sceta)<1.4"); RooDataSet *testdata = hdataSingle; new TCanvas; RooPlot *eerrplot = eerr.frame(0.,0.1,200); testdata->plotOn(eerrplot); eerrpdf.plotOn(eerrplot,ProjWData(*testdata)); eerrplot->Draw(); new TCanvas; RooPlot *transplot = transerrvar->frame(-5.,5.,100); hdataSingle->plotOn(transplot); unormpdferr.plotOn(transplot); transplot->Draw(); //return; new TCanvas; RooPlot *cdfploterr = cdferrvar->frame(0.,1.,100); hdataSingle->plotOn(cdfploterr); //unormpdf.plotOn(transplot); cdfploterr->Draw(); //return; new TCanvas; RooPlot *idplot = idmva.frame(-1.,1.,200); testdata->plotOn(idplot); idpdf.plotOn(idplot,ProjWData(*testdata)); idplot->Draw(); new TCanvas; RooPlot *transplotid = transidvar->frame(-5.,5.,100); testdata->plotOn(transplotid); unormpdfid.plotOn(transplotid); transplotid->Draw(); //return; new TCanvas; RooPlot *cdfplotid = cdfidvar->frame(0.,1.,100); testdata->plotOn(cdfplotid); //unormpdf.plotOn(transplot); cdfplotid->Draw(); //return; TH1 *herrid = testdata->createHistogram("herrid",eerr,Binning(30,0.,0.1), YVar(idmva,Binning(30,-0.5,0.6))); TH1 *herre = testdata->createHistogram("herre",energy,Binning(30,0.,200.), YVar(eerr,Binning(30,0.,0.1))); TH1 *hideta = testdata->createHistogram("hideta",sceta,Binning(40,-2.5,2.5), YVar(idmva,Binning(30,-0.5,0.6))); TH1 *herridtrans = testdata->createHistogram("herridtrans",*transerrvar,Binning(30,-5.,5.), YVar(*transidvar,Binning(30,-5.,5.))); TH1 *herrtranse = testdata->createHistogram("herrtranse",energy,Binning(30,0.,200.), YVar(*transerrvar,Binning(30,-5.,5.))); TH1 *hidtranseta = testdata->createHistogram("hidtranseta",sceta,Binning(40,-2.5,2.5), YVar(*transidvar,Binning(30,-5.,5.))); new TCanvas; herrid->Draw("COLZ"); new TCanvas; herre->Draw("COLZ"); new TCanvas; hideta->Draw("COLZ"); new TCanvas; herridtrans->Draw("COLZ"); new TCanvas; herrtranse->Draw("COLZ"); new TCanvas; hidtranseta->Draw("COLZ"); // new TCanvas; // RooRealVar *meanvar = (RooRealVar*)hdataSingle->addColumn(eerrmeanlim); // RooPlot *meanplot = meanvar->frame(0.,0.1,200); // hdataSingle->plotOn(meanplot); // meanplot->Draw(); return; }
void runBATCalculator() { // Definiton of a RooWorkspace containing the statistics model. Later the // information for BATCalculator is retrieved from the workspace. This is // certainly a bit of overhead but better from an educative point of view. cout << "preparing the RooWorkspace object" << endl; RooWorkspace* myWS = new RooWorkspace("myWS", true); // combined prior for signal contribution myWS->factory("Product::signal({sigma_s[0,20],L[5,15],epsilon[0,1]})"); myWS->factory("N_bkg[0,3]"); // define prior functions // uniform prior for signal crosssection myWS->factory("Uniform::prior_sigma_s(sigma_s)"); // (truncated) prior for efficiency myWS->factory("Gaussian::prior_epsilon(epsilon,0.51,0.0765)"); // (truncated) Gaussian prior for luminosity myWS->factory("Gaussian::prior_L(L,10,1)"); // (truncated) Gaussian prior for bkg crosssection myWS->factory("Gaussian::prior_N_bkg(N_bkg,0.52,0.156)"); // Poisson distribution with mean signal+bkg myWS->factory("Poisson::model(n[0,300],sum(signal,N_bkg))"); // define the global prior function myWS->factory("PROD::prior(prior_sigma_s,prior_epsilon,prior_L,prior_N_bkg)"); // Definition of observables and parameters of interest myWS->defineSet("obsSet", "n"); myWS->defineSet("poiSet", "sigma_s"); myWS->defineSet("nuisanceSet", "N_bkg,L,epsilon"); // ->model complete (Additional information can be found in the // RooStats manual) // feel free to vary the parameters, but don't forget to choose reasonable ranges for the // variables. Currently the Bayesian methods will often not work well if the variable ranges // are either too short (for obvious reasons) or too large (for technical reasons). // A ModelConfig object is used to associate parts of your workspace with their statistical // meaning (it is also possible to initialize BATCalculator directly with elements from the // workspace but if you are sharing your workspace with others or if you want to use several // different methods the use of ModelConfig will most often turn out to be the better choice.) // setup the ModelConfig object cout << "preparing the ModelConfig object" << endl; ModelConfig modelconfig("modelconfig", "ModelConfig for this example"); modelconfig.SetWorkspace(*myWS); modelconfig.SetPdf(*(myWS->pdf("model"))); modelconfig.SetParametersOfInterest(*(myWS->set("poiSet"))); modelconfig.SetPriorPdf(*(myWS->pdf("prior"))); modelconfig.SetNuisanceParameters(*(myWS->set("nuisanceSet"))); modelconfig.SetObservables(*(myWS->set("obsSet"))); // use BATCalculator to the derive credibility intervals as a function of the observed number of // events in the hypothetical experiment // define vector with tested numbers of events TVectorD obsEvents; // define vectors which will be filled with the lower and upper limits for each tested number // of observed events TVectorD BATul; TVectorD BATll; // fix upper limit of tested observed number of events int obslimit = 10; obsEvents.ResizeTo(obslimit); BATul.ResizeTo(obslimit); BATll.ResizeTo(obslimit); cout << "starting the calculation of Bayesian credibility intervals with BATCalculator" << endl; // loop over observed number of events in the hypothetical experiment for (int obs = 1; obs <= obslimit; obs++) { obsEvents[obs - 1] = (static_cast<double>(obs)); // prepare data input for the the observed number of events // adjust number of observed events in the workspace. This is communicated to ModelConfig! myWS->var("n")->setVal(obs); // create data RooDataSet data("data", "", *(modelconfig.GetObservables())); data.add( *(modelconfig.GetObservables())); // prepare BATCalulator BATCalculator batcalc(data, modelconfig); // give the BATCalculator a unique name (always a good idea in ROOT) TString namestring = "mybatc_"; namestring += obs; batcalc.SetName(namestring); // fix amount of posterior probability in the calculated interval. // the name confidence level is incorrect here batcalc.SetConfidenceLevel(0.90); // fix length of the Markov chain. (in general: the longer the Markov chain the more // precise will be the results) batcalc.SetnMCMC(20000); // retrieve SimpleInterval object containing the information about the interval (this // triggers the actual calculations) SimpleInterval* interval = batcalc.GetInterval1D("sigma_s"); std::cout << "BATCalculator: 90% credibility interval: [ " << interval->LowerLimit() << " - " << interval->UpperLimit() << " ] or 95% credibility upper limit\n"; // add the interval borders for the current number of observed events to the vectors // containing the lower and upper limits BATll[obs - 1] = interval->LowerLimit(); BATul[obs - 1] = interval->UpperLimit(); // clean up for next loop element batcalc.CleanCalculatorForNewData(); delete interval; } cout << "all limits calculated" << endl; // summarize the results in a plot TGraph* grBATll = new TGraph(obsEvents, BATll); grBATll->SetLineColor(kGreen); grBATll->SetLineWidth(200); grBATll->SetFillStyle(3001); grBATll->SetFillColor(kGreen); TGraph* grBATul = new TGraph(obsEvents, BATul); grBATul->SetLineColor(kGreen); grBATul->SetLineWidth(-200); grBATul->SetFillStyle(3001); grBATul->SetFillColor(kGreen); // create and draw multigraph TMultiGraph* mg = new TMultiGraph("BayesianLimitsBATCalculator", "BayesianLimitsBATCalculator"); mg->SetTitle("example of Bayesian credibility intervals derived with BATCAlculator "); mg->Add(grBATll); mg->Add(grBATul); mg->Draw("AC"); mg->GetXaxis()->SetTitle ("# observed events"); mg->GetYaxis()->SetTitle("limits on signal S (size of test: 0.1)"); mg->Draw("AC"); }
void retrieve_input_from_histo(RooWorkspace &w){ // Variable definition /* */ /********* GLOBAL VARIABLES ********************/ /* Default --- double Bkg_norm_factor_obs = 0.5 ; // Normalizzation factor for background (N_Data_CR / N_Co_CR) double err_Norm_factor_obs = 0.05; double S_tot = 1000.; // Total events in SR for AmBe double B_tot = 10000.; // Total events in SR for Co60 double Acceptance_SR_obs = 0.9; // Cut Acceptance for Signal double err_Acceptance_SR = 0.01; double N_tot_theory = 5.; // Total Expected Signal Events for a given mass and Xsec. */ double Bkg_norm_factor_obs = 0.0378 ; // Normalizzation factor for background (N_Data_CR / N_Co_CR) double err_Norm_factor_obs = 0.0013; double S_tot = 123552; // Total events in ALL REGIONS for AmBe (--> the Eff_i are the efficiencies relative to the total) double B_tot = 24318; // Total events in SR for Co60 double Acceptance_SR_obs = 0.9; // Cut Acceptance for Signal double err_Acceptance_SR = 0.05; double N_tot_theory = 10.; // Total Expected Signal Events for a given mass and Xsec. Set to 10 because gives mu in range [0.,100] /**************************************************/ //---- retrieving global variables ----// TFile *histos = TFile::Open("h_for_limits.root"); TH1D *h_ambe_SR = (TH1D*)histos->Get("ambe_SR"); TH1D *h_co60_SR = (TH1D*)histos->Get("co60_SR"); TH1D *h_DM_SR = (TH1D*)histos->Get("DM_SR"); //----- Model for Global Variables -----// w.factory("N_tot_theory[10]"); w.factory("Acceptance_SR[0.9,0.0,1]"); w.factory("mu[1.,0.,100]"); w.factory("Bkg_norm_factor[0.7, 0.0,10.0]"); w.factory("S_tot[1000.0]"); w.factory("Gaussian:costraint_bkg_norm(Bkg_norm_factor_obs[0,10], Bkg_norm_factor, err_Norm_factor[1])"); // approximation (should be kind of complicated cauchy....) w.factory("Gaussian:costarint_sig_acc(Acceptance_SR_obs[0,1], Acceptance_SR, err_Acceptance_SR[0.01])");// this has the problem of boundaries acceptance cannot be larger than 1. --->Lognormal??? RooArgSet nuissanceParameter ;//(*w.var("Bkg_norm_factor"),*w.var("Acceptance_SR")); RooArgSet g_obs(*w.var("Bkg_norm_factor_obs"), *w.var("Acceptance_SR_obs")); RooArgSet obs; //----- Setting Value to Global Variables ------// w.var("N_tot_theory")->setVal(N_tot_theory); // Total expected Signal events w.var("Bkg_norm_factor_obs")->setVal(Bkg_norm_factor_obs); // Ratio between data in CR and Co in CR w.var("err_Norm_factor")->setVal(err_Norm_factor_obs); // Set error on norm factor w.var("Acceptance_SR_obs")->setVal(Acceptance_SR_obs); w.var("err_Acceptance_SR")->setVal(err_Acceptance_SR); w.var("S_tot")->setVal(S_tot); // Total events of AmBe in SR w.var("N_tot_theory")->setConstant(true); w.var("S_tot")->setConstant(true); w.var("Bkg_norm_factor_obs")->setConstant(true); w.var("err_Norm_factor")->setConstant(true); w.var("Acceptance_SR_obs")->setConstant(true); w.var("err_Acceptance_SR")->setConstant(true); //-------- Setting Value of NP to Observed!! ---------------// w.var("Acceptance_SR")->setVal(w.var("Acceptance_SR_obs")->getValV()); w.var("Acceptance_SR")->setMax(w.var("Acceptance_SR_obs")->getValV() + w.var("err_Acceptance_SR")->getValV() * 10.);// Set to 10 sigma. w.var("Bkg_norm_factor")->setMax(w.var("Bkg_norm_factor_obs")->getValV() + w.var("err_Norm_factor")->getValV()*10.); //set to 10 sigma. w.var("Bkg_norm_factor")->setVal(w.var("Bkg_norm_factor_obs")->getValV());// Set to observed value!!! //--- Set NP to constant!!! ONLY FOR TEST!! w.var("Acceptance_SR")->setConstant(true); w.var("Bkg_norm_factor")->setConstant(true); TString total_model = "PROD:model("; //bin-auxiliary measure variables double S_bin = 0.; double B_bin =0.; double nobs_bin =0.; /***************** LOOP OVER BINS *******************/ for (int bin_itr = 1; bin_itr <= h_ambe_SR->GetNbinsX() ; bin_itr++){ //retrieve bin info /* Default S_bin = 500.; // Observed events in bin_i for AmBe B_bin = 100.; // Observed events in bin_i for Co60 nobs_bin = 45.; // Observed events in bin_i for DM data */ S_bin = h_ambe_SR->GetBinContent(bin_itr); // Observed events in bin_i for AmBe B_bin = h_co60_SR->GetBinContent(bin_itr); // Observed events in bin_i for Co60 nobs_bin = h_DM_SR->GetBinContent(bin_itr); // Observed events in bin_i for DM data TString bin_name(TString::Itoa(bin_itr, 10)); w.factory("prod:N_s_"+bin_name+"(N_tot_theory, Acceptance_SR, Eff_AmBe_bin_"+bin_name+"[0.01,0.0,1], mu)"); w.factory("prod:N_b_"+bin_name+"(N_Co_SR_bin_"+bin_name+"[100.0,0.0,1000.0], Bkg_norm_factor)"); w.factory("sum:nexp_"+bin_name+"(N_s_"+bin_name+", N_b_"+bin_name+")"); // Poisson of (n | s+b) w.factory("Poisson:pdf_"+bin_name+"(nobs_"+bin_name+"[0.0,1000.0],nexp_"+bin_name+")"); // Poisson constraint --- Stat uncertainty on the bin content w.factory("prod:S_"+bin_name+"_exp(S_tot, Eff_AmBe_bin_"+bin_name+")"); // put 1000. to right AmBe amount w.factory("Poisson:AmBe_bin_"+bin_name+"(S_"+bin_name+"[500,0.0,50000.0],S_"+bin_name+"_exp)"); // change 50 for multiple bin, put 1000. to right AmBe amount w.factory("Poisson:Co_SR_bin_"+bin_name+"(B_"+bin_name+"[0.0,1000.0], N_Co_SR_bin_"+bin_name+")"); //change 100 for multiple bin, put 1000. to right Co tot amount in SR w.factory("PROD:model_"+bin_name+"(pdf_"+bin_name+",AmBe_bin_"+bin_name+",Co_SR_bin_"+bin_name+")"); // Set input variables -- Global OBSERVABLESerr_Norm_factor w.var("S_"+bin_name)->setMax(w.var("S_tot")->getValV()); // the range of the poisson needs to be defined w.var("S_"+bin_name)->setVal(S_bin); // Events in bin i for AmBe w.var("B_"+bin_name)->setMax(B_tot); // set to TOT Co in SR w.var("B_"+bin_name)->setVal(B_bin); // Set observed event in SR Co w.var("S_"+bin_name)->setConstant(true); w.var("B_"+bin_name)->setConstant(true); // Set range and value for nuissance parameters w.var("N_Co_SR_bin_"+bin_name)->setMax(B_bin + sqrt(B_bin)*10.); //set to 10 sigma. w.var("N_Co_SR_bin_"+bin_name)->setVal(B_bin); // set to observed value!! w.var("Eff_AmBe_bin_"+bin_name)->setVal(S_bin/ S_tot ); // Set to observed value!!! w.var("Eff_AmBe_bin_"+bin_name)->setMax( (S_bin + sqrt(S_bin)*10.) / S_tot ); // Set to observed value!!! if(w.var("Eff_AmBe_bin_"+bin_name)->getMax() > 1.) cout << "WARNING!!! Eff_AmBe_bin_"+bin_name+" efficiency > 1. " << endl; // Set NP to constant w.var("N_Co_SR_bin_"+bin_name)->setConstant(true); w.var("Eff_AmBe_bin_"+bin_name)->setConstant(true); //Set Observed events! w.var("nobs_"+bin_name)->setVal(nobs_bin); // Do not Add NP !!! // nuissanceParameter.add(*w.var("N_Co_SR_bin_"+bin_name)); // nuissanceParameter.add(*w.var("Eff_AmBe_bin_"+bin_name)); g_obs.add(*w.var("S_"+bin_name)); g_obs.add(*w.var("B_"+bin_name)); obs.add(*w.var("nobs_"+bin_name)); total_model.Append("model_"+bin_name+", "); } /************************************************************************/ total_model.Append("costraint_bkg_norm, costarint_sig_acc)"); w.factory(total_model); w.defineSet("nuissance_parameter",nuissanceParameter); w.defineSet("observables",obs); w.defineSet("g_observables",g_obs); histos->Close(); // RooArgSet set = w.allPdfs(); // set.Print(); // Set input Observables // w.var("nobs_"+bin_name+"")->setVal(40); /* // various printing w.pdf("AmBe_bin_i")->Print("all"); // Building the model ModelConfig mc("ModelConfig",&w); mc.SetPdf(*w.pdf("model")); mc.SetParametersOfInterest(*w.var("mu")); //mc.SetPriorPdf(*w.pdf("prior_s")); // Setting nuissance parameter RooArgSet nuissanceParameter (*w.var("Bkg_norm_factor"),*w.var("Acceptance_SR")); nuissanceParameter.add(*w.var("N_Co_SR_bin_i")); nuissanceParameter.add(*w.var("Eff_AmBe_bin_i")); mc.SetNuisanceParameters(nuissanceParameter); // need now to set the global observable RooArgSet g_obs(*w.var("S_i"), *w.var("B_i")); // g_obs.add(*w.var("N_tot_theory")); // g_obs.add(*w.var("S_tot")); g_obs.add(*w.var("Bkg_norm_factor_obs")); // g_obs.add(*w.var("err_Norm_factor")); g_obs.add(*w.var("Acceptance_SR_obs")); // g_obs.add(*w.var("err_Acceptance_SR")); mc.SetGlobalObservables(g_obs); RooArgSet observables(*w.var("nobs_i")); //observables.add(variousbins...); mc.SetObservables(observables); // this is needed for the hypothesis tests mc.SetSnapshot(*w.var("mu")); // make data set with the number of observed events RooDataSet data("data","", observables); data.add(observables); // import data set in workspace and save it in a file w.import(data); // import model in the workspace w.import(mc); w.writeToFile("CountingModel.root", true); */ }
int main(int argc, char* argv[]) { doofit::builder::EasyPdf *epdf = new doofit::builder::EasyPdf(); epdf->Var("sig_yield"); epdf->Var("sig_yield").setVal(153000); epdf->Var("sig_yield").setConstant(false); //decay time epdf->Var("obsTime"); epdf->Var("obsTime").SetTitle("t_{#kern[-0.2]{B}_{#kern[-0.1]{ d}}^{#kern[-0.1]{ 0}}}"); epdf->Var("obsTime").setUnit("ps"); epdf->Var("obsTime").setRange(0.,16.); // tag, respectively the initial state of the produced B meson epdf->Cat("obsTag"); epdf->Cat("obsTag").defineType("B_S",1); epdf->Cat("obsTag").defineType("Bbar_S",-1); //finalstate epdf->Cat("catFinalState"); epdf->Cat("catFinalState").defineType("f",1); epdf->Cat("catFinalState").defineType("fbar",-1); epdf->Var("obsEtaOS"); epdf->Var("obsEtaOS").setRange(0.0,0.5); std::vector<double> knots; knots.push_back(0.07); knots.push_back(0.10); knots.push_back(0.138); knots.push_back(0.16); knots.push_back(0.23); knots.push_back(0.28); knots.push_back(0.35); knots.push_back(0.42); knots.push_back(0.44); knots.push_back(0.48); knots.push_back(0.5); // empty arg list for coefficients RooArgList* list = new RooArgList(); // create first coefficient RooRealVar* coeff_first = &(epdf->Var("parCSpline1")); coeff_first->setRange(0,10000); coeff_first->setVal(1); coeff_first->setConstant(false); list->add( *coeff_first ); for (unsigned int i=1; i <= knots.size(); ++i){ std::string number = boost::lexical_cast<std::string>(i); RooRealVar* coeff = &(epdf->Var("parCSpline"+number)); coeff->setRange(0,10000); coeff->setVal(1); coeff->setConstant(false); list->add( *coeff ); } // create last coefficient RooRealVar* coeff_last = &(epdf->Var("parCSpline"+boost::lexical_cast<std::string>(knots.size()))); coeff_last->setRange(0,10000); coeff_last->setVal(1); coeff_last->setConstant(false); list->add( *coeff_last ); list->Print(); // define Eta PDF doofit::roofit::pdfs::DooCubicSplinePdf splinePdf("splinePdf",epdf->Var("obsEtaOS"),knots,*list,0,0.5); //Berechne die Tagging Assymetrie epdf->Var("p0"); epdf->Var("p0").setVal(0.369); epdf->Var("p0").setConstant(true); epdf->Var("p1"); epdf->Var("p1").setVal(0.952); epdf->Var("p1").setConstant(true); epdf->Var("delta_p0"); epdf->Var("delta_p0").setVal(0.019); epdf->Var("delta_p0").setConstant(true); epdf->Var("delta_p1"); epdf->Var("delta_p1").setVal(-0.012); epdf->Var("delta_p1").setConstant(true); epdf->Var("etamean"); epdf->Var("etamean").setVal(0.365); epdf->Var("etamean").setConstant(true); epdf->Formula("omega","@0 +@1/2 +(@2+@3/2)*(@4-@5)", RooArgList(epdf->Var("p0"),epdf->Var("delta_p0"),epdf->Var("p1"),epdf->Var("delta_p1"),epdf->Var("obsEtaOS"),epdf->Var("etamean"))); epdf->Formula("omegabar","@0 -@1/2 +(@2-@3/2)*(@4-@5)", RooArgList(epdf->Var("p0"),epdf->Var("delta_p0"),epdf->Var("p1"),epdf->Var("delta_p1"),epdf->Var("obsEtaOS"),epdf->Var("etamean"))); //Koeffizienten DecRateCoeff *coeff_c = new DecRateCoeff("coef_cos","coef_cos",DecRateCoeff::CPOdd,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("C_f"),epdf->Var("C_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Real("omega"),epdf->Real("omegabar"),epdf->Var("asym_prod"),epdf->Var("asym_det"),epdf->Var("asym_tageff")); DecRateCoeff *coeff_s = new DecRateCoeff("coef_sin","coef_sin",DecRateCoeff::CPOdd,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("S_f"),epdf->Var("S_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Real("omega"),epdf->Real("omegabar"),epdf->Var("asym_prod"),epdf->Var("asym_det"),epdf->Var("asym_tageff")); DecRateCoeff *coeff_sh = new DecRateCoeff("coef_sinh","coef_sinh",DecRateCoeff::CPEven,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("f1_f"),epdf->Var("f1_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Real("omega"),epdf->Real("omegabar"),epdf->Var("asym_prod"),epdf->Var("asym_det"),epdf->Var("asym_tageff")); DecRateCoeff *coeff_ch = new DecRateCoeff("coef_cosh","coef_cosh",DecRateCoeff::CPEven,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("f0_f"),epdf->Var("f0_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Real("omega"),epdf->Real("omegabar"),epdf->Var("asym_prod"),epdf->Var("asym_det"),epdf->Var("asym_tageff")); epdf->AddRealToStore(coeff_ch); epdf->AddRealToStore(coeff_sh); epdf->AddRealToStore(coeff_c); epdf->AddRealToStore(coeff_s); ///////////////////Generiere PDF's///////////////////// //Zeit epdf->GaussModel("resTimeGauss",epdf->Var("obsTime"),epdf->Var("allTimeResMean"),epdf->Var("allTimeReso")); epdf->BDecay("pdfSigTime",epdf->Var("obsTime"),epdf->Var("tau"),epdf->Var("dgamma"),epdf->Real("coef_cosh"),epdf->Real("coef_sinh"),epdf->Real("coef_cos"),epdf->Real("coef_sin"),epdf->Var("deltaM"),epdf->Model("resTimeGauss")); //Zusammenfassen der Parameter in einem RooArgSet RooArgSet Observables; Observables.add(RooArgSet( epdf->Var("obsTime"),epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("obsEtaOS"))); epdf->Extend("pdfExtend", epdf->Pdf("pdfSigTime"),epdf->Real("sig_yield")); RooWorkspace ws; ws.import(epdf->Pdf("pdfExtend")); ws.defineSet("Observables",Observables, true); ws.Print(); doofit::config::CommonConfig cfg_com("common"); cfg_com.InitializeOptions(argc, argv); doofit::toy::ToyFactoryStdConfig cfg_tfac("toyfac"); cfg_tfac.InitializeOptions(cfg_com); doofit::toy::ToyStudyStdConfig cfg_tstudy("toystudy"); cfg_tstudy.InitializeOptions(cfg_tfac); // set a previously defined workspace to get PDF from (not mandatory, but convenient) cfg_tfac.set_workspace(&ws); cfg_com.CheckHelpFlagAndPrintHelp(); // Initialize the toy factory module with the config objects and start // generating toy samples. doofit::toy::ToyFactoryStd tfac(cfg_com, cfg_tfac); doofit::toy::ToyStudyStd tstudy(cfg_com, cfg_tstudy); //Generate data RooDataSet* data = tfac.Generate(); data->Print(); epdf->Pdf("pdfExtend").getParameters(data)->readFromFile("/home/chasenberg/Repository/bachelor-template/ToyStudy/dootoycp-parameter.txt"); epdf->Pdf("pdfExtend").getParameters(data)->writeToFile("/home/chasenberg/Repository/bachelor-template/ToyStudy/dootoycp-parameter.txt.new"); //FIT-PDF-Koeffizienten epdf->Var("asym_prodFit"); epdf->Var("asym_prodFit").setVal(-0.0108); epdf->Var("asym_prodFit").setConstant(false); DecRateCoeff *coeff_cFit = new DecRateCoeff("coef_cosFit","coef_cosFit",DecRateCoeff::CPOdd,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("C_f"),epdf->Var("C_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Real("omega"),epdf->Real("omegabar"),epdf->Var("asym_prodFit"),epdf->Var("asym_det"),epdf->Var("asym_tageff")); DecRateCoeff *coeff_sFit = new DecRateCoeff("coef_sinFit","coef_sinFit",DecRateCoeff::CPOdd,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("S_f"),epdf->Var("S_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Real("omega"),epdf->Real("omegabar"),epdf->Var("asym_prodFit"),epdf->Var("asym_det"),epdf->Var("asym_tageff")); DecRateCoeff *coeff_shFit = new DecRateCoeff("coef_sinhFit","coef_sinhFit",DecRateCoeff::CPEven,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("f1_f"),epdf->Var("f1_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Real("omega"),epdf->Real("omegabar"),epdf->Var("asym_prodFit"),epdf->Var("asym_det"),epdf->Var("asym_tageff")); DecRateCoeff *coeff_chFit = new DecRateCoeff("coef_coshFit","coef_coshFit",DecRateCoeff::CPEven,epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("f0_f"),epdf->Var("f0_fbar"),epdf->Var("obsEtaOS"),splinePdf,epdf->Var("tageff"),epdf->Real("omega"),epdf->Real("omegabar"),epdf->Var("asym_prodFit"),epdf->Var("asym_det"),epdf->Var("asym_tageff")); epdf->AddRealToStore(coeff_chFit); epdf->AddRealToStore(coeff_shFit); epdf->AddRealToStore(coeff_cFit); epdf->AddRealToStore(coeff_sFit); ///////////////////Generiere PDF's///////////////////// //Zeit epdf->BDecay("pdfSigTimeFit",epdf->Var("obsTime"),epdf->Var("tau"),epdf->Var("dgamma"),epdf->Real("coef_coshFit"),epdf->Real("coef_sinhFit"),epdf->Real("coef_cosFit"),epdf->Real("coef_sinFit"),epdf->Var("deltaM"),epdf->Model("resTimeGauss")); //Zusammenfassen der Parameter in einem RooArgSet RooArgSet ObservablesFit; ObservablesFit.add(RooArgSet( epdf->Var("obsTime"),epdf->Cat("catFinalState"),epdf->Cat("obsTag"),epdf->Var("obsEtaOS"))); epdf->Extend("pdfExtendFit", epdf->Pdf("pdfSigTimeFit"),epdf->Real("sig_yield")); RooFitResult* fit_result = epdf->Pdf("pdfExtendFit").fitTo(*data, RooFit::Save(true)); tstudy.StoreFitResult(fit_result); //epdf->Pdf("pdfExtendFit").getParameters(data)->readFromFile("/home/chasenberg/Repository/bachelor-template/ToyStudy/dootoycp-parameter.txt"); //epdf->Pdf("pdfExtendFit").getParameters(data)->writeToFile("/home/chasenberg/Repository/bachelor-template/ToyStudy/dootoycp-parameter.txt.new"); //Plotten auf lhcb /*using namespace doofit::plotting; PlotConfig cfg_plot("cfg_plot"); cfg_plot.InitializeOptions(); cfg_plot.set_plot_directory("/net/storage03/data/users/chasenberg/ergebnis/dootoycp_float-lhcb/dgamma/time/"); // plot PDF and directly specify components Plot myplot(cfg_plot, epdf->Var("obsTime"), *data, RooArgList(epdf->Pdf("pdfExtend"))); myplot.PlotItLogNoLogY(); PlotConfig cfg_plotEta("cfg_plotEta"); cfg_plotEta.InitializeOptions(); cfg_plotEta.set_plot_directory("/net/storage03/data/users/chasenberg/ergebnis/dootoycp_float-lhcb/dgamma/eta/"); // plot PDF and directly specify components Plot myplotEta(cfg_plotEta, epdf->Var("obsEtaOS"), *data, RooArgList(splinePdf)); myplotEta.PlotIt();*/ }
void makeModel(RooWorkspace& w) { TFile *_file0 = TFile::Open("plots/htotal_root_ZprimeRecomass.root"); TH1F *Histo = (TH1F*)_file0->Get("htotaldata"); RooRealVar invm("invm","invm",200.,4000.); RooDataHist* data = new RooDataHist("data","data",invm,Import(*Histo)) ; // TTree* tree = new TTree("simple","data from ascii file"); // Long64_t nlines = tree->ReadFile("list_mll_200_2016.txt","x1:x2:x3:invm:x5:x6"); // Long64_t nlines = tree->ReadFile("a.txt","x1:x2:x3:invm:x5:x6"); // printf(" found %lld pointsn",nlines); // tree->Write(); // tree->GetEntries(); RooRealVar mass("mass","mass", 300., 200., 1600.); RooRealVar nsig("nsig","Number of signal events", 0., 5000.); RooRealVar nbkg("nbkg","Number of background events", 0., 300000.); w.import(mass); w.import(nsig); w.import(nbkg); // RooRealVar invm("invm","Invariant mass", 200., 4000.); // RooDataSet* data = new RooDataSet("data", "Data", invm, RooFit::Import(*tree)); data->Print("v"); w.import(invm); w.import(*data); w.factory("expr::sigma('invm*(0.01292 + 0.00001835 * invm - 0.0000000002733 * invm*invm)',invm)"); w.factory("expr::width('0.03*invm',invm)"); w.factory("CEXPR::bkgpdf('exp(24.9327 - 2.39287e-03*invm + 3.19926e-07*invm*invm - 3.38799e-11*invm*invm*invm)*pow(invm,-3.3634)',invm)"); w.factory("Voigtian::sigpdf(invm,mass,width,sigma)"); w.factory("SUM::model(nbkg*bkgpdf, nsig*sigpdf)"); RooAbsPdf* sigpdf = w.pdf("sigpdf"); RooAbsPdf* bkgpdf = w.pdf("bkgpdf"); RooAbsPdf* model = w.pdf("model"); RooStats::ModelConfig* mc = new ModelConfig("mc",&w); mc->SetPdf(*w.pdf("model")); mc->SetParametersOfInterest(*w.var("nsig")); mc->SetObservables(*w.var("invm")); w.defineSet("nuisParams","nbkg"); mc->SetNuisanceParameters(*w.set("nuisParams")); w.var("mass")->setConstant(true); w.import(*mc); w.Print("tree"); w.writeToFile("MyModel_workspace.root"); TCanvas* c1 = new TCanvas("c1","Control Plots", 900, 700); RooPlot* plot = w.var("invm")->frame(); w.data("data")->plotOn(plot); w.pdf("model")->plotOn(plot); w.pdf("model")->plotOn(plot, Components("bkgpdf"),LineStyle(kDashed)); w.pdf("model")->plotOn(plot, Components("sigpdf"),LineColor(kRed)); plot->Draw(); return; }
int main(int argc, char* argv[]) { doofit::builder::EasyPdf *epdf = new doofit::builder::EasyPdf(); //Observable epdf->Var("obsMass").setVal(5280); epdf->Var("obsMass").setRange(5200.,5500.); epdf->Var("obsTime").setVal(1.3); epdf->Var("obsTime").setRange(0.3, 18.3); //Zusammenfassen der Parameter in einem RooArgSet RooArgSet Observables; Observables.add(RooArgSet(epdf->Var("obsMass"), epdf->Var("obsTime"))); //Anlegen der Parameter /*epdf->Var("pdf_sig_mass_mean").setRange(5230, 5330); epdf->Var("pdf_sig_mass_mean").setVal(5280); epdf->Var("pdf_sig_mass_width").setRange(1.0, 15); epdf->Var("pdf_sig_mass_width").setVal(10); epdf->Var("pdf_bkg_mass_lambda").setRange(-0.0074, 0.0); epdf->Var("pdf_bkg_mass_lambda").setVal(-0.001); epdf->Var("sig_Yield").setRange(0.0, 10000); epdf->Var("sig_Yield").setVal(5000); epdf->Var("bkg_Yield").setRange(0.0, 10000); epdf->Var("bkg_Yield").setVal(5000);*/ ///////////////////Generiere PDF's///////////////////// //Signal PDF's// epdf->DoubleGaussianScaled("pdf_sig_mass_gauss", epdf->Var("obsMass"), epdf->Var("pdf_sig_mass_mean"), epdf-> Var("pdf_sig_mass_width"), epdf->Var("scale"), epdf->Var("fraction"), "Sigma"); //Untergrund PDF epdf->Exponential("pdf_bkg_mass_expo", epdf->Var("obsMass"),epdf->Var("pdf_bkg_mass_lambda")); //Zerfalls PDF's// //Resolution Model epdf->GaussModel("pdf_resolution", epdf->Var("obsTime"), epdf->Var("pdf_res_mean"), epdf->Var("obsTimeErr")); //Zerfalls PDF des Untergrunds epdf->Decay("pdf_bkg_time_decay", epdf->Var("obsTime"),epdf->Var("pdf_bkg_time_tau"), epdf->Model("pdf_resolution")); //Zerfall PDF des Signals epdf->Decay("pdf_sig_time_decay", epdf->Var("obsTime"),epdf->Var("pdf_sig_time_tau"), epdf->Model("pdf_resolution")); //Multipliziere Signal und Untergrund PDF mit ihrer jeweiligen Zerfalls PDF// //Untergrund * Zerfall epdf->Product("pdf_bkg_mass*pdf_bkg_time_decay", RooArgSet(epdf->Pdf("pdf_bkg_mass_expo"), epdf->Pdf("pdf_bkg_time_decay"))); //Signal * Zerfall epdf->Product("pdf_sig_mass_gauss*pdf_sig_time_decay", RooArgSet(epdf->Pdf("pdf_sig_mass_gauss"),epdf->Pdf("pdf_sig_time_decay"))); //Addiere PDF's epdf->Add("pdf_total", RooArgSet(epdf->Pdf("pdf_sig_mass_gauss*pdf_sig_time_decay"), epdf->Pdf("pdf_bkg_mass*pdf_bkg_time_decay")), RooArgSet(epdf->Var("bkg_Yield"),epdf->Var("sig_Yield"))); RooWorkspace ws; ws.import(epdf->Pdf("pdf_total")); ws.defineSet("Observables",Observables, true); ws.Print(); doofit::config::CommonConfig cfg_com("common"); cfg_com.InitializeOptions(argc, argv); doofit::toy::ToyFactoryStdConfig cfg_tfac("toyfac"); cfg_tfac.InitializeOptions(cfg_com); doofit::toy::ToyStudyStdConfig cfg_tstudy("toystudy"); cfg_tstudy.InitializeOptions(cfg_tfac); // set a previously defined workspace to get PDF from (not mandatory, but convenient) cfg_tfac.set_workspace(&ws); // Check for a set --help flag and if so, print help and exit gracefully // (recommended). cfg_com.CheckHelpFlagAndPrintHelp(); // More custom code, e.g. to set options internally. // Not required as configuration via command line/config file is enough. // Print overview of all options (optional) // cfg_com.PrintAll(); // Initialize the toy factory module with the config objects and start // generating toy samples. doofit::toy::ToyFactoryStd tfac(cfg_com, cfg_tfac); doofit::toy::ToyStudyStd tstudy(cfg_com, cfg_tstudy); //for(int i=0;i<100;i++) //{ RooDataSet* data = tfac.Generate(); epdf->Pdf("pdf_total").getParameters(data)->readFromFile("/home/chasenberg/Repository/bachelor-template/ToyStudy/parameters_generate2.txt"); epdf->Pdf("pdf_total").getParameters(data)->writeToFile("/home/chasenberg/Repository/bachelor-template/ToyStudy/parameters_generate2.txt.new"); // fitting on the generated dataset is your responsibility RooFitResult* fit_result = epdf->Pdf("pdf_total").fitTo(*data, RooFit::Save(true)); /*using namespace doofit::plotting; PlotConfig cfg_plot("cfg_plot"); cfg_plot.InitializeOptions(); // plot PDF and directly specify component //Plot myplot(cfg_plot, epdf->Var("obsTime"), *data, RooArgList(epdf->Pdf("pdf_total"))); Plot myplot(cfg_plot, epdf->Var("obsMass"), *data, RooArgList(epdf->Pdf("pdf_total"))); myplot.PlotIt();*/ //Speichern der Ergebnisse tstudy.StoreFitResult(fit_result); //} /*tstudy.FinishFitResultSaving(); tstudy.ReadFitResults(); tstudy.EvaluateFitResults(); tstudy.PlotEvaluatedParameters();*/ }
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; }