void rf510_wsnamedsets() { // C r e a t e m o d e l a n d d a t a s e t // ----------------------------------------------- RooWorkspace* w = new RooWorkspace("w") ; fillWorkspace(*w) ; // Exploit convention encoded in named set "parameters" and "observables" // to use workspace contents w/o need for introspected RooAbsPdf* model = w->pdf("model") ; // Generate data from p.d.f. in given observables RooDataSet* data = model->generate(*w->set("observables"),1000) ; // Fit model to data model->fitTo(*data) ; // Plot fitted model and data on frame of first (only) observable RooPlot* frame = ((RooRealVar*)w->set("observables")->first())->frame() ; data->plotOn(frame) ; model->plotOn(frame) ; // Overlay plot with model with reference parameters as stored in snapshots w->loadSnapshot("reference_fit") ; model->plotOn(frame,LineColor(kRed)) ; w->loadSnapshot("reference_fit_bkgonly") ; model->plotOn(frame,LineColor(kRed),LineStyle(kDashed)) ; // Draw the frame on the canvas new TCanvas("rf510_wsnamedsets","rf503_wsnamedsets",600,600) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ; // Print workspace contents w->Print() ; // Workspace will remain in memory after macro finishes gDirectory->Add(w) ; }
RooAbsData * Tprime::GetPseudoData( void ) { // // Generate pseudo data, return a pointer. // Class member pointer data set to point to the dataset. // Caller does not take ownership. // static int n_toys = 0; // legend for printouts std::string legend = "[Tprime::GetPseudoData()]: "; delete data; // We will use ToyMCSampler to generate pseudo-data (and test statistic, eventually) // We are responsible for randomizing nuisances and global observables, // ToyMCSampler only generates observables (as of ROOT 5.30.00-rc1 and before) // MC sampler and test statistic if(n_toys == 0) { // on first entry // get B model config from workspace RooStats::ModelConfig * pBModel = (RooStats::ModelConfig *)pWs->obj("BModel"); pBModel->SetWorkspace(*pWs); // get parameter of interest set //const RooArgSet * poi = pSbModel->GetParametersOfInterest(); //RooStats::TestStatistic * pTestStatistic = new RooStats::ProfileLikelihoodTestStat(*pBModel->GetPdf()); //RooStats::ToyMCSampler toymcs(*pTestStatistic, 1); pTestStatistic = new RooStats::ProfileLikelihoodTestStat(*pBModel->GetPdf()); pToyMcSampler = new RooStats::ToyMCSampler(*pTestStatistic, 1); pToyMcSampler->SetPdf(*pBModel->GetPdf()); pToyMcSampler->SetObservables(*pBModel->GetObservables()); pToyMcSampler->SetParametersForTestStat(*pBModel->GetParametersOfInterest()); // just POI pToyMcSampler->SetGlobalObservables(*pBModel->GetGlobalObservables()); } // load parameter point pWs->loadSnapshot("parametersToGenerateData"); RooArgSet dummySet; data = pToyMcSampler->GenerateToyData(dummySet); std::cout << legend << "generated the following background-only pseudo-data:" << std::endl; data->Print(); // count number of generated toys ++n_toys; return data; }
vector<Double_t*> simFit(bool makeSoupFit_ = false, const string tnp_ = "etoTauMargLooseNoCracks70", const string category_ = "tauAntiEMVA", const string bin_ = "abseta<1.5", const float binCenter_ = 0.75, const float binWidth_ = 0.75, const float xLow_=60, const float xHigh_=120, bool SumW2_ = false, bool verbose_ = true){ vector<Double_t*> out; //return out; //TFile *test = new TFile( outFile->GetName(),"UPDATE"); // output file TFile *test = new TFile( Form("EtoTauPlotsFit_%s_%s_%f.root",tnp_.c_str(),category_.c_str(),binCenter_),"RECREATE"); test->mkdir(Form("bin%f",binCenter_)); TCanvas *c = new TCanvas("fitCanvas",Form("fitCanvas_%s_%s",tnp_.c_str(),bin_.c_str()),10,30,650,600); c->SetGrid(0,0); c->SetFillStyle(4000); c->SetFillColor(10); c->SetTicky(); c->SetObjectStat(0); TCanvas *c2 = new TCanvas("fitCanvasTemplate",Form("fitCanvasTemplate_%s_%s",tnp_.c_str(),bin_.c_str()),10,30,650,600); c2->SetGrid(0,0); c2->SetFillStyle(4000); c2->SetFillColor(10); c2->SetTicky(); c2->SetObjectStat(0); // input files TFile fsup("/data_CMS/cms/lbianchini/tagAndProbe/trees/38XWcut/testNewWriteFromPAT_soup.root"); TFile fbkg("/data_CMS/cms/lbianchini/tagAndProbe/trees/38XWcut/testNewWriteFromPAT_soup_bkg.root"); TFile fsgn("/data_CMS/cms/lbianchini/tagAndProbe/trees/38XWcut/testNewWriteFromPAT_soup_sgn.root"); TFile fdat("/data_CMS/cms/lbianchini/tagAndProbe/trees/38XWcut/testNewWriteFromPAT_Data.root"); // data from 2iter: //TFile fdat("/data_CMS/cms/lbianchini/35pb/testNewWriteFromPAT_Data.root"); //********************** signal only tree *************************/ TTree *fullTreeSgn = (TTree*)fsgn.Get((tnp_+"/fitter_tree").c_str()); TH1F* hSall = new TH1F("hSall","",1,0,150); TH1F* hSPall = new TH1F("hSPall","",1,0,150); TH1F* hS = new TH1F("hS","",1,0,150); TH1F* hSP = new TH1F("hSP","",1,0,150); fullTreeSgn->Draw("mass>>hS",Form("weight*(%s && mass>%f && mass<%f && mcTrue && signalPFChargedHadrCands<1.5)",bin_.c_str(),xLow_,xHigh_)); fullTreeSgn->Draw("mass>>hSall",Form("weight*(%s && mass>%f && mass<%f)",bin_.c_str(),xLow_,xHigh_)); float SGNtrue = hS->Integral(); float SGNall = hSall->Integral(); fullTreeSgn->Draw("mass>>hSP",Form("weight*(%s && %s>0 && mass>%f && mass<%f && mcTrue && signalPFChargedHadrCands<1.5 )",bin_.c_str(),category_.c_str(),xLow_,xHigh_)); fullTreeSgn->Draw("mass>>hSPall",Form("weight*(%s && %s>0 && mass>%f && mass<%f && signalPFChargedHadrCands<1.5 )",bin_.c_str(),category_.c_str(),xLow_,xHigh_)); float SGNtruePass = hSP->Integral(); float SGNallPass = hSPall->Integral(); //********************** background only tree *************************// TTree *fullTreeBkg = (TTree*)fbkg.Get((tnp_+"/fitter_tree").c_str()); TH1F* hB = new TH1F("hB","",1,0,150); TH1F* hBP = new TH1F("hBP","",1,0,150); fullTreeBkg->Draw("mass>>hB",Form("weight*(%s && mass>%f && mass<%f && signalPFChargedHadrCands<1.5 )",bin_.c_str(),xLow_,xHigh_)); float BKG = hB->Integral(); float BKGUnWeighted = hB->GetEntries(); fullTreeBkg->Draw("mass>>hBP",Form("weight*(%s && %s>0 && mass>%f && mass<%f && signalPFChargedHadrCands<1.5 )",bin_.c_str(),category_.c_str(),xLow_,xHigh_)); float BKGPass = hBP->Integral(); float BKGUnWeightedPass = hBP->GetEntries(); float BKGFail = BKG-BKGPass; cout << "*********** BKGFail " << BKGFail << endl; //********************** soup tree *************************// TTree *fullTreeSoup = (TTree*)fsup.Get((tnp_+"/fitter_tree").c_str()); //********************** data tree *************************// TTree *fullTreeData = (TTree*)fdat.Get((tnp_+"/fitter_tree").c_str()); //********************** workspace ***********************// RooWorkspace *w = new RooWorkspace("w","w"); // tree variables to be imported w->factory("mass[30,120]"); w->factory("weight[0,10000]"); w->factory("abseta[0,2.5]"); w->factory("pt[0,200]"); w->factory("mcTrue[0,1]"); w->factory("signalPFChargedHadrCands[0,10]"); w->factory((category_+"[0,1]").c_str()); // background pass pdf for MC w->factory("RooExponential::McBackgroundPdfP(mass,McCP[0,-10,10])"); // background fail pdf for MC w->factory("RooExponential::McBackgroundPdfF(mass,McCF[0,-10,10])"); // background pass pdf for Data w->factory("RooExponential::DataBackgroundPdfP(mass,DataCP[0,-10,10])"); // background fail pdf for Data w->factory("RooExponential::DataBackgroundPdfF(mass,DataCF[0,-10,10])"); // fit parameters for background w->factory("McEfficiency[0.04,0,1]"); w->factory("McNumSgn[0,1000000]"); w->factory("McNumBkgP[0,100000]"); w->factory("McNumBkgF[0,100000]"); w->factory("expr::McNumSgnP('McEfficiency*McNumSgn',McEfficiency,McNumSgn)"); w->factory("expr::McNumSgnF('(1-McEfficiency)*McNumSgn',McEfficiency,McNumSgn)"); w->factory("McPassing[pass=1,fail=0]"); // fit parameters for data w->factory("DataEfficiency[0.1,0,1]"); w->factory("DataNumSgn[0,1000000]"); w->factory("DataNumBkgP[0,1000000]"); w->factory("DataNumBkgF[0,10000]"); w->factory("expr::DataNumSgnP('DataEfficiency*DataNumSgn',DataEfficiency,DataNumSgn)"); w->factory("expr::DataNumSgnF('(1-DataEfficiency)*DataNumSgn',DataEfficiency,DataNumSgn)"); w->factory("DataPassing[pass=1,fail=0]"); RooRealVar *weight = w->var("weight"); RooRealVar *abseta = w->var("abseta"); RooRealVar *pt = w->var("pt"); RooRealVar *mass = w->var("mass"); mass->setRange(xLow_,xHigh_); RooRealVar *mcTrue = w->var("mcTrue"); RooRealVar *cut = w->var( category_.c_str() ); RooRealVar *signalPFChargedHadrCands = w->var("signalPFChargedHadrCands"); // build the template for the signal pass sample: RooDataSet templateP("templateP","dataset for signal-pass template", RooArgSet(*mass,*weight,*abseta,*pt,*cut,*mcTrue,*signalPFChargedHadrCands), Import( *fullTreeSgn ), /*WeightVar( *weight ),*/ Cut( Form("(mcTrue && %s>0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str()) ) ); // build the template for the signal fail sample: RooDataSet templateF("templateF","dataset for signal-fail template", RooArgSet(*mass,*weight,*abseta,*pt,*cut,*mcTrue,*signalPFChargedHadrCands), Import( *fullTreeSgn ), /*WeightVar( *weight ),*/ Cut( Form("(mcTrue && %s<0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str()) ) ); mass->setBins(24); RooDataHist templateHistP("templateHistP","",RooArgSet(*mass), templateP, 1.0); RooHistPdf TemplateSignalPdfP("TemplateSignalPdfP","",RooArgSet(*mass),templateHistP); w->import(TemplateSignalPdfP); mass->setBins(24); RooDataHist templateHistF("templateHistF","",RooArgSet(*mass),templateF,1.0); RooHistPdf TemplateSignalPdfF("TemplateSignalPdfF","",RooArgSet(*mass),templateHistF); w->import(TemplateSignalPdfF); mass->setBins(10000,"fft"); RooPlot* TemplateFrameP = mass->frame(Bins(24),Title("Template passing")); templateP.plotOn(TemplateFrameP); w->pdf("TemplateSignalPdfP")->plotOn(TemplateFrameP); RooPlot* TemplateFrameF = mass->frame(Bins(24),Title("Template failing")); templateF.plotOn(TemplateFrameF); w->pdf("TemplateSignalPdfF")->plotOn(TemplateFrameF); //w->factory("RooFFTConvPdf::McSignalPdfP(mass,TemplateSignalPdfP,RooTruthModel::McResolModP(mass))"); //w->factory("RooFFTConvPdf::McSignalPdfF(mass,TemplateSignalPdfF,RooTruthModel::McResolModF(mass))"); // FOR GREGORY: PROBLEM WHEN TRY TO USE THE PURE TEMPLATE => RooHistPdf McSignalPdfP("McSignalPdfP","McSignalPdfP",RooArgSet(*mass),templateHistP); RooHistPdf McSignalPdfF("McSignalPdfF","McSignalPdfF",RooArgSet(*mass),templateHistF); w->import(McSignalPdfP); w->import(McSignalPdfF); // FOR GREGORY: FOR DATA, CONVOLUTION IS OK => w->factory("RooFFTConvPdf::DataSignalPdfP(mass,TemplateSignalPdfP,RooGaussian::DataResolModP(mass,DataMeanResP[0.0,-5.,5.],DataSigmaResP[0.5,0.,10]))"); w->factory("RooFFTConvPdf::DataSignalPdfF(mass,TemplateSignalPdfF,RooGaussian::DataResolModF(mass,DataMeanResF[-5.,-10.,10.],DataSigmaResF[0.5,0.,10]))"); //w->factory("RooCBShape::DataSignalPdfF(mass,DataMeanF[91.2,88,95.],DataSigmaF[3,0.5,8],DataAlfaF[1.8,0.,10],DataNF[1.0,1e-06,10])"); //w->factory("RooFFTConvPdf::DataSignalPdfF(mass,RooVoigtian::DataVoigF(mass,DataMeanF[85,80,95],DataWidthF[2.49],DataSigmaF[3,0.5,10]),RooCBShape::DataResolModF(mass,DataMeanResF[0.5,0.,10.],DataSigmaResF[0.5,0.,10],DataAlphaResF[0.5,0.,10],DataNResF[1.0,1e-06,10]))"); //w->factory("SUM::DataSignalPdfF(fVBP[0.5,0,1]*RooBifurGauss::bifF(mass,DataMeanResF[91.2,80,95],sigmaLF[10,0.5,40],sigmaRF[0.]), RooVoigtian::voigF(mass, DataMeanResF, widthF[2.49], sigmaVoigF[5,0.1,10]) )" ); // composite model pass for MC w->factory("SUM::McModelP(McNumSgnP*McSignalPdfP,McNumBkgP*McBackgroundPdfP)"); w->factory("SUM::McModelF(McNumSgnF*McSignalPdfF,McNumBkgF*McBackgroundPdfF)"); // composite model pass for data w->factory("SUM::DataModelP(DataNumSgnP*DataSignalPdfP,DataNumBkgP*DataBackgroundPdfP)"); w->factory("SUM::DataModelF(DataNumSgnF*DataSignalPdfF,DataNumBkgF*DataBackgroundPdfF)"); // simultaneous fir for MC w->factory("SIMUL::McModel(McPassing,pass=McModelP,fail=McModelF)"); // simultaneous fir for data w->factory("SIMUL::DataModel(DataPassing,pass=DataModelP,fail=DataModelF)"); w->Print("V"); w->saveSnapshot("clean", w->allVars()); w->loadSnapshot("clean"); /****************** sim fit to soup **************************/ /////////////////////////////////////////////////////////////// TFile *f = new TFile("dummySoup.root","RECREATE"); TTree* cutTreeSoupP = fullTreeSoup->CopyTree(Form("(%s>0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str())); TTree* cutTreeSoupF = fullTreeSoup->CopyTree(Form("(%s<0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str())); RooDataSet McDataP("McDataP","dataset pass for the soup", RooArgSet(*mass), Import( *cutTreeSoupP ) ); RooDataSet McDataF("McDataF","dataset fail for the soup", RooArgSet(*mass), Import( *cutTreeSoupF ) ); RooDataHist McCombData("McCombData","combined data for the soup", RooArgSet(*mass), Index(*(w->cat("McPassing"))), Import("pass", *(McDataP.createHistogram("histoP",*mass)) ), Import("fail",*(McDataF.createHistogram("histoF",*mass)) ) ) ; RooPlot* McFrameP = 0; RooPlot* McFrameF = 0; RooRealVar* McEffFit = 0; if(makeSoupFit_){ cout << "**************** N bins in mass " << w->var("mass")->getBins() << endl; RooFitResult* ResMcCombinedFit = w->pdf("McModel")->fitTo(McCombData, Extended(1), Minos(1), Save(1), SumW2Error( SumW2_ ), Range(xLow_,xHigh_), NumCPU(4) /*, ExternalConstraints( *(w->pdf("ConstrainMcNumBkgF")) )*/ ); test->cd(Form("bin%f",binCenter_)); ResMcCombinedFit->Write("McFitResults_Combined"); RooArgSet McFitParam(ResMcCombinedFit->floatParsFinal()); McEffFit = (RooRealVar*)(&McFitParam["McEfficiency"]); RooRealVar* McNumSigFit = (RooRealVar*)(&McFitParam["McNumSgn"]); RooRealVar* McNumBkgPFit = (RooRealVar*)(&McFitParam["McNumBkgP"]); RooRealVar* McNumBkgFFit = (RooRealVar*)(&McFitParam["McNumBkgF"]); McFrameP = mass->frame(Bins(24),Title("MC: passing sample")); McCombData.plotOn(McFrameP,Cut("McPassing==McPassing::pass")); w->pdf("McModel")->plotOn(McFrameP,Slice(*(w->cat("McPassing")),"pass"), ProjWData(*(w->cat("McPassing")),McCombData), LineColor(kBlue),Range(xLow_,xHigh_)); w->pdf("McModel")->plotOn(McFrameP,Slice(*(w->cat("McPassing")),"pass"), ProjWData(*(w->cat("McPassing")),McCombData), Components("McSignalPdfP"), LineColor(kRed),Range(xLow_,xHigh_)); w->pdf("McModel")->plotOn(McFrameP,Slice(*(w->cat("McPassing")),"pass"), ProjWData(*(w->cat("McPassing")),McCombData), Components("McBackgroundPdfP"), LineColor(kGreen),Range(xLow_,xHigh_)); McFrameF = mass->frame(Bins(24),Title("MC: failing sample")); McCombData.plotOn(McFrameF,Cut("McPassing==McPassing::fail")); w->pdf("McModel")->plotOn(McFrameF,Slice(*(w->cat("McPassing")),"fail"), ProjWData(*(w->cat("McPassing")),McCombData), LineColor(kBlue),Range(xLow_,xHigh_)); w->pdf("McModel")->plotOn(McFrameF,Slice(*(w->cat("McPassing")),"fail"), ProjWData(*(w->cat("McPassing")),McCombData), Components("McSignalPdfF"), LineColor(kRed),Range(xLow_,xHigh_)); w->pdf("McModel")->plotOn(McFrameF,Slice(*(w->cat("McPassing")),"fail"), ProjWData(*(w->cat("McPassing")),McCombData), Components("McBackgroundPdfF"), LineColor(kGreen),Range(xLow_,xHigh_)); } /////////////////////////////////////////////////////////////// /****************** sim fit to data **************************/ /////////////////////////////////////////////////////////////// TFile *f2 = new TFile("dummyData.root","RECREATE"); TTree* cutTreeDataP = fullTreeData->CopyTree(Form("(%s>0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str())); TTree* cutTreeDataF = fullTreeData->CopyTree(Form("(%s<0.5 && %s && signalPFChargedHadrCands<1.5)",category_.c_str(),bin_.c_str())); RooDataSet DataDataP("DataDataP","dataset pass for the soup", RooArgSet(*mass), Import( *cutTreeDataP ) ); RooDataSet DataDataF("DataDataF","dataset fail for the soup", RooArgSet(*mass), Import( *cutTreeDataF ) ); RooDataHist DataCombData("DataCombData","combined data for the soup", RooArgSet(*mass), Index(*(w->cat("DataPassing"))), Import("pass",*(DataDataP.createHistogram("histoDataP",*mass))),Import("fail",*(DataDataF.createHistogram("histoDataF",*mass)))) ; RooFitResult* ResDataCombinedFit = w->pdf("DataModel")->fitTo(DataCombData, Extended(1), Minos(1), Save(1), SumW2Error( SumW2_ ), Range(xLow_,xHigh_), NumCPU(4)); test->cd(Form("bin%f",binCenter_)); ResDataCombinedFit->Write("DataFitResults_Combined"); RooArgSet DataFitParam(ResDataCombinedFit->floatParsFinal()); RooRealVar* DataEffFit = (RooRealVar*)(&DataFitParam["DataEfficiency"]); RooRealVar* DataNumSigFit = (RooRealVar*)(&DataFitParam["DataNumSgn"]); RooRealVar* DataNumBkgPFit = (RooRealVar*)(&DataFitParam["DataNumBkgP"]); RooRealVar* DataNumBkgFFit = (RooRealVar*)(&DataFitParam["DataNumBkgF"]); RooPlot* DataFrameP = mass->frame(Bins(24),Title("Data: passing sample")); DataCombData.plotOn(DataFrameP,Cut("DataPassing==DataPassing::pass")); w->pdf("DataModel")->plotOn(DataFrameP,Slice(*(w->cat("DataPassing")),"pass"), ProjWData(*(w->cat("DataPassing")),DataCombData), LineColor(kBlue),Range(xLow_,xHigh_)); w->pdf("DataModel")->plotOn(DataFrameP,Slice(*(w->cat("DataPassing")),"pass"), ProjWData(*(w->cat("DataPassing")),DataCombData), Components("DataSignalPdfP"), LineColor(kRed),Range(xLow_,xHigh_)); w->pdf("DataModel")->plotOn(DataFrameP,Slice(*(w->cat("DataPassing")),"pass"), ProjWData(*(w->cat("DataPassing")),DataCombData), Components("DataBackgroundPdfP"), LineColor(kGreen),LineStyle(kDashed),Range(xLow_,xHigh_)); RooPlot* DataFrameF = mass->frame(Bins(24),Title("Data: failing sample")); DataCombData.plotOn(DataFrameF,Cut("DataPassing==DataPassing::fail")); w->pdf("DataModel")->plotOn(DataFrameF,Slice(*(w->cat("DataPassing")),"fail"), ProjWData(*(w->cat("DataPassing")),DataCombData), LineColor(kBlue),Range(xLow_,xHigh_)); w->pdf("DataModel")->plotOn(DataFrameF,Slice(*(w->cat("DataPassing")),"fail"), ProjWData(*(w->cat("DataPassing")),DataCombData), Components("DataSignalPdfF"), LineColor(kRed),Range(xLow_,xHigh_)); w->pdf("DataModel")->plotOn(DataFrameF,Slice(*(w->cat("DataPassing")),"fail"), ProjWData(*(w->cat("DataPassing")),DataCombData), Components("DataBackgroundPdfF"), LineColor(kGreen),LineStyle(kDashed),Range(xLow_,xHigh_)); /////////////////////////////////////////////////////////////// if(makeSoupFit_) c->Divide(2,2); else c->Divide(2,1); c->cd(1); DataFrameP->Draw(); c->cd(2); DataFrameF->Draw(); if(makeSoupFit_){ c->cd(3); McFrameP->Draw(); c->cd(4); McFrameF->Draw(); } c->Draw(); test->cd(Form("bin%f",binCenter_)); c->Write(); c2->Divide(2,1); c2->cd(1); TemplateFrameP->Draw(); c2->cd(2); TemplateFrameF->Draw(); c2->Draw(); test->cd(Form("bin%f",binCenter_)); c2->Write(); // MINOS errors, otherwise HESSE quadratic errors float McErrorLo = 0; float McErrorHi = 0; if(makeSoupFit_){ McErrorLo = McEffFit->getErrorLo()<0 ? McEffFit->getErrorLo() : (-1)*McEffFit->getError(); McErrorHi = McEffFit->getErrorHi()>0 ? McEffFit->getErrorHi() : McEffFit->getError(); } float DataErrorLo = DataEffFit->getErrorLo()<0 ? DataEffFit->getErrorLo() : (-1)*DataEffFit->getError(); float DataErrorHi = DataEffFit->getErrorHi()>0 ? DataEffFit->getErrorHi() : DataEffFit->getError(); float BinomialError = TMath::Sqrt(SGNtruePass/SGNtrue*(1-SGNtruePass/SGNtrue)/SGNtrue); Double_t* truthMC = new Double_t[6]; Double_t* tnpMC = new Double_t[6]; Double_t* tnpData = new Double_t[6]; truthMC[0] = binCenter_; truthMC[1] = binWidth_; truthMC[2] = binWidth_; truthMC[3] = SGNtruePass/SGNtrue; truthMC[4] = BinomialError; truthMC[5] = BinomialError; if(makeSoupFit_){ tnpMC[0] = binCenter_; tnpMC[1] = binWidth_; tnpMC[2] = binWidth_; tnpMC[3] = McEffFit->getVal(); tnpMC[4] = (-1)*McErrorLo; tnpMC[5] = McErrorHi; } tnpData[0] = binCenter_; tnpData[1] = binWidth_; tnpData[2] = binWidth_; tnpData[3] = DataEffFit->getVal(); tnpData[4] = (-1)*DataErrorLo; tnpData[5] = DataErrorHi; out.push_back(truthMC); out.push_back(tnpData); if(makeSoupFit_) out.push_back(tnpMC); test->Close(); //delete c; delete c2; if(verbose_) cout << "returning from bin " << bin_ << endl; return out; }
void compute_p0(const char* inFileName, const char* wsName = "combined", const char* modelConfigName = "ModelConfig", const char* dataName = "obsData", const char* asimov1DataName = "asimovData_1", const char* conditional1Snapshot = "conditionalGlobs_1", const char* nominalSnapshot = "nominalGlobs", string smass = "130", string folder = "test") { double mass; stringstream massStr; massStr << smass; massStr >> mass; double mu_profile_value = 1; // mu value to profile the obs data at wbefore generating the expected bool doConditional = 1; // do conditional expected data bool remakeData = 0; // handle unphysical pdf cases in H->ZZ->4l bool doUncap = 1; // uncap p0 bool doInj = 0; // setup the poi for injection study (zero is faster if you're not) bool doObs = 1; // compute median significance bool doMedian = 1; // compute observed significance TStopwatch timer; timer.Start(); TFile f(inFileName); RooWorkspace* ws = (RooWorkspace*)f.Get(wsName); if (!ws) { cout << "ERROR::Workspace: " << wsName << " doesn't exist!" << endl; return; } ModelConfig* mc = (ModelConfig*)ws->obj(modelConfigName); if (!mc) { cout << "ERROR::ModelConfig: " << modelConfigName << " doesn't exist!" << endl; return; } RooDataSet* data = (RooDataSet*)ws->data(dataName); if (!data) { cout << "ERROR::Dataset: " << dataName << " doesn't exist!" << endl; return; } mc->GetNuisanceParameters()->Print("v"); ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2"); ROOT::Math::MinimizerOptions::SetDefaultStrategy(0); ROOT::Math::MinimizerOptions::SetDefaultPrintLevel(1); cout << "Setting max function calls" << endl; ws->loadSnapshot("conditionalNuis_0"); RooArgSet nuis(*mc->GetNuisanceParameters()); RooRealVar* mu = (RooRealVar*)mc->GetParametersOfInterest()->first(); RooAbsPdf* pdf = mc->GetPdf(); string condSnapshot(conditional1Snapshot); RooArgSet nuis_tmp2 = *mc->GetNuisanceParameters(); RooNLLVar* obs_nll = doObs ? (RooNLLVar*)pdf->createNLL(*data, Constrain(nuis_tmp2)) : NULL; RooDataSet* asimovData1 = (RooDataSet*)ws->data(asimov1DataName); RooRealVar* emb = (RooRealVar*)mc->GetNuisanceParameters()->find("ATLAS_EMB"); if (!asimovData1 || (string(inFileName).find("ic10") != string::npos && emb)) { if (emb) emb->setVal(0.7); cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl; string mu_str, mu_prof_str; asimovData1 = makeAsimovData(mc, doConditional, ws, obs_nll, 1, &mu_str, &mu_prof_str, mu_profile_value, true); condSnapshot="conditionalGlobs"+mu_prof_str; } if (!doUncap) mu->setRange(0, 40); else mu->setRange(-40, 40); RooAbsPdf* pdf = mc->GetPdf(); RooArgSet nuis_tmp1 = *mc->GetNuisanceParameters(); RooNLLVar* asimov_nll = (RooNLLVar*)pdf->createNLL(*asimovData1, Constrain(nuis_tmp1)); //do asimov mu->setVal(1); mu->setConstant(0); if (!doInj) mu->setConstant(1); int status,sign; double med_sig=0,obs_sig=0,asimov_q0=0,obs_q0=0; if (doMedian) { ws->loadSnapshot(condSnapshot.c_str()); if (doInj) ws->loadSnapshot("conditionalNuis_inj"); else ws->loadSnapshot("conditionalNuis_1"); mc->GetGlobalObservables()->Print("v"); mu->setVal(0); mu->setConstant(1); status = minimize(asimov_nll, ws); if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(asimov_nll, ws); if (status >= 0) cout << "Success!" << endl; } double asimov_nll_cond = asimov_nll->getVal(); mu->setVal(1); if (doInj) ws->loadSnapshot("conditionalNuis_inj"); else ws->loadSnapshot("conditionalNuis_1"); if (doInj) mu->setConstant(0); status = minimize(asimov_nll, ws); if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(asimov_nll, ws); if (status >= 0) cout << "Success!" << endl; } double asimov_nll_min = asimov_nll->getVal(); asimov_q0 = 2*(asimov_nll_cond - asimov_nll_min); if (doUncap && mu->getVal() < 0) asimov_q0 = -asimov_q0; sign = int(asimov_q0 != 0 ? asimov_q0/fabs(asimov_q0) : 0); med_sig = sign*sqrt(fabs(asimov_q0)); ws->loadSnapshot(nominalSnapshot); } if (doObs) { ws->loadSnapshot("conditionalNuis_0"); mu->setVal(0); mu->setConstant(1); status = minimize(obs_nll, ws); if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(obs_nll, ws); if (status >= 0) cout << "Success!" << endl; } double obs_nll_cond = obs_nll->getVal(); mu->setConstant(0); status = minimize(obs_nll, ws); if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(obs_nll, ws); if (status >= 0) cout << "Success!" << endl; } double obs_nll_min = obs_nll->getVal(); obs_q0 = 2*(obs_nll_cond - obs_nll_min); if (doUncap && mu->getVal() < 0) obs_q0 = -obs_q0; sign = int(obs_q0 == 0 ? 0 : obs_q0 / fabs(obs_q0)); if (!doUncap && (obs_q0 < 0 && obs_q0 > -0.1 || mu->getVal() < 0.001)) obs_sig = 0; else obs_sig = sign*sqrt(fabs(obs_q0)); } // Report results cout << "obs: " << obs_sig << endl; cout << "Observed significance: " << obs_sig << endl; cout << "Corresponding to a p-value of " << (1-ROOT::Math::gaussian_cdf( obs_sig )) << endl; if (med_sig) { cout << "Median test stat val: " << asimov_q0 << endl; cout << "Median significance: " << med_sig << endl; } f.Close(); stringstream fileName; fileName << "root-files/" << folder << "/" << mass << ".root"; system(("mkdir -vp root-files/" + folder).c_str()); TFile f2(fileName.str().c_str(),"recreate"); TH1D* h_hypo = new TH1D("hypo","hypo",2,0,2); h_hypo->SetBinContent(1, obs_sig); h_hypo->SetBinContent(2, med_sig); f2.Write(); f2.Close(); timer.Stop(); timer.Print(); }
void computeLimits( const char* ACTag, // ACTag: where to look for combined workspaces in Limits/CombinedWorkspaces/ bool doSyst= false, double CL=0.95, // Confidence Level for limits computation int calculatorType = 2, int testStatType = 2, bool useCLs = false ) // define calc type and test stat type: // // calculatorType = 0 Freq calculator // calculatorType = 1 Hybrid calculator // calculatorType = 2 Asymptotic calculator // calculatorType = 3 Asymptotic calculator using nominal Asimov data sets (not using fitted parameter values but nominal ones) // // testStatType = 0 LEP // = 1 Tevatron // = 2 Profile Likelihood two sided // = 3 Profile Likelihood one sided (i.e. = 0 if mu < mu_hat) // = 4 Profile Likelihood signed ( pll = -pll if mu < mu_hat) // = 5 Max Likelihood Estimate as test statistic // = 6 Number of observed event as test statistic // // 0,3,true for frequentist CLs; 2,3,true for asymptotic CLs; 0,2,false for FC { // list of files vector<TString> theFiles = combFileList(ACTag, doSyst ? "wSyst" : "woSyst"); if ( theFiles.empty() ) { cout << "# [Error]: No combined workspaces found" << endl; return; } // File to save results ostringstream strs; strs << CL; string str = strs.str(); TString sCL(str); sCL.Remove(0,sCL.First('.')+1); string limitsFileName = string("csv/") + "cLimits_" + string(sCL) + "_" + string(ACTag); if ( calculatorType == 0 ) limitsFileName = limitsFileName + "_Freq"; else if ( calculatorType == 1 ) limitsFileName = limitsFileName + "_Hybr"; else if ( calculatorType == 2 ) limitsFileName = limitsFileName + "_Asym"; else if ( calculatorType == 3 ) limitsFileName = limitsFileName + "_AsymAsi"; if ( testStatType == 0 ) limitsFileName = limitsFileName + "_LEP"; else if ( testStatType == 1 ) limitsFileName = limitsFileName + "_Tev"; else if ( testStatType == 2 ) limitsFileName = limitsFileName + "_2SPL"; else if ( testStatType == 3 ) limitsFileName = limitsFileName + "_1SPL"; else if ( testStatType == 4 ) limitsFileName = limitsFileName + "_SPL"; else if ( testStatType == 5 ) limitsFileName = limitsFileName + "_MaxL"; else if ( testStatType == 6 ) limitsFileName = limitsFileName + "_NOE"; if ( doSyst ) limitsFileName = limitsFileName + "_wSyst.csv"; else limitsFileName = limitsFileName + "_woSyst.csv"; ofstream file(limitsFileName.c_str()); file << CL << endl; // Confidence interval computation int cnt=1; for (vector<TString>::const_iterator it=theFiles.begin(); it!=theFiles.end(); it++) { cout << ">>>>>>> Computing " << CL*100 << "% " << "limits for analysis bin " << cnt << endl; cout << "Using combined PbPb-PP workspace " << cnt << " / " << theFiles.size() << ": " << *it << endl; anabin thebin = binFromFile(*it); // if (thebin != anabin(1.6,2.4,3,30,40,80)) continue; if (!usebatch) { pair<double,double> lims = runLimit_RaaNS_Workspace(*it, "RFrac2Svs1S_PbPbvsPP_P", "simPdf", "workspace", "dOS_DATA", ACTag, CL, calculatorType, testStatType, useCLs); file << thebin.rapbin().low() << ", " << thebin.rapbin().high() << ", " << thebin.ptbin().low() << ", " << thebin.ptbin().high() << ", " << thebin.centbin().low() << ", " << thebin.centbin().high() << ", " << lims.first << ", " << lims.second << endl; } else { TString exports; exports += Form("export it=%s; ",it->Data()); exports += Form("export ACTag=%s; ",ACTag); exports += Form("export CL=%f; ",CL); exports += Form("export calculatorType=%i; ",calculatorType); exports += Form("export testStatType=%i; ",testStatType); exports += Form("export useCLs=%i; ",useCLs); exports += Form("export pwd_=%s; ", gSystem->pwd()); if (calculatorType==0) { // special case of frequentist limits: submit more jobs TFile *f = TFile::Open(*it); RooWorkspace *ws = (RooWorkspace*) f->Get("workspace"); ws->loadSnapshot("SbHypo_poiAndNuisance_snapshot"); RooRealVar *theVar = (RooRealVar*) ws->var("RFrac2Svs1S_PbPbvsPP_P"); double val = theVar->getVal(); double err = theVar->getError(); f->Close(); if (ws) delete ws; double nsigma = sqrt(2)*TMath::ErfcInverse(1-CL); // double poimin = max(0.,0.5*(val - nsigma*err)); // double poimax = 1.5*(val + nsigma*err); double poimin = max(0.,val + 0.9*nsigma*err); double poimax = val + 1.1*nsigma*err; int npoints = 5; double dpoi = (poimax-poimin)/npoints; for (int ipoi=0; ipoi<npoints; ipoi++) for (int irnd=0; irnd<50; irnd++) { TString exports2 = exports + Form("export poival=%f; ",poimin+ipoi*dpoi); exports2 += Form("export rndseed=%i; ",irnd+1); TString command("qsub -k oe -q cms@llrt3 -p -500 "); // -p option: lower the priority since we're submitting many jobs... command += Form("-N limits_bin%i_ipoi%i_%i ",cnt,ipoi,irnd+1); command += "-V "; command += Form("-o %s ", gSystem->pwd()); command += Form("-v it,ACTag,CL,calculatorType,testStatType,useCLs,pwd_,poival,rndseed "); command += "runbatch_limits_4WS.sh"; TString command_full = exports2 + command; cout << command_full.Data() << endl; int njobs = atoi(exec("qstat -u $USER cms@llrt3 | wc -l").c_str()); int njobs_queue = atoi(exec("qstat cms@llrt3 | grep \" Q \" | wc -l").c_str()); while (njobs_queue>0 && njobs >= maxjobs) { system("sleep 60"); njobs = atoi(exec("qstat -u $USER cms@llrt3 | wc -l").c_str()); njobs_queue = atoi(exec("qstat cms@llrt3 | grep \" Q \" | wc -l").c_str()); } system(command_full.Data()); } } else { exports += Form("export poival=%f; ",-1.); exports += Form("export rndseed=%i; ",-1); TString command("qsub -k oe -q cms@llrt3 -l nodes=1:ppn=23 "); command += Form("-N limits_bin%i ",cnt); command += "-V "; command += Form("-o %s ", gSystem->pwd()); command += Form("-v it,ACTag,CL,calculatorType,testStatType,useCLs,pwd_,poival,rndseed "); command += "runbatch_limits_4WS.sh"; TString command_full = exports + command; cout << command_full.Data() << endl; system(command_full.Data()); } system("sleep 1"); } cnt++; } // loop on the files file.close(); cout << "Closed " << limitsFileName << endl << endl; }
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; }
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 runQ(const char* inFileName, const char* wsName = "combined", const char* modelConfigName = "ModelConfig", const char* dataName = "obsData", const char* asimov0DataName = "asimovData_0", const char* conditional0Snapshot = "conditionalGlobs_0", const char* asimov1DataName = "asimovData_1", const char* conditional1Snapshot = "conditionalGlobs_1", const char* nominalSnapshot = "nominalGlobs", string smass = "130", string folder = "test") { double mass; stringstream massStr; massStr << smass; massStr >> mass; bool errFast = 0; bool goFast = 1; bool remakeData = 1; bool doRightSided = 1; bool doInj = 0; bool doObs = 1; bool doMedian = 1; TStopwatch timer; timer.Start(); TFile f(inFileName); RooWorkspace* ws = (RooWorkspace*)f.Get(wsName); if (!ws) { cout << "ERROR::Workspace: " << wsName << " doesn't exist!" << endl; return; } ModelConfig* mc = (ModelConfig*)ws->obj(modelConfigName); if (!mc) { cout << "ERROR::ModelConfig: " << modelConfigName << " doesn't exist!" << endl; return; } RooDataSet* data = (RooDataSet*)ws->data(dataName); if (!data) { cout << "ERROR::Dataset: " << dataName << " doesn't exist!" << endl; return; } mc->GetNuisanceParameters()->Print("v"); RooNLLVar::SetIgnoreZeroEntries(1); ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2"); ROOT::Math::MinimizerOptions::SetDefaultStrategy(0); ROOT::Math::MinimizerOptions::SetDefaultPrintLevel(1); cout << "Setting max function calls" << endl; //ROOT::Math::MinimizerOptions::SetDefaultMaxFunctionCalls(20000); RooMinimizer::SetMaxFunctionCalls(10000); ws->loadSnapshot("conditionalNuis_0"); RooArgSet nuis(*mc->GetNuisanceParameters()); RooRealVar* mu = (RooRealVar*)mc->GetParametersOfInterest()->first(); if (string(mc->GetPdf()->ClassName()) == "RooSimultaneous" && remakeData) { RooSimultaneous* simPdf = (RooSimultaneous*)mc->GetPdf(); double min_mu; data = makeData(data, simPdf, mc->GetObservables(), mu, mass, min_mu); } RooDataSet* asimovData0 = (RooDataSet*)ws->data(asimov0DataName); if (!asimovData0) { cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl; makeAsimovData(mc, true, ws, mc->GetPdf(), data, 1); ws->Print(); asimovData0 = (RooDataSet*)ws->data("asimovData_0"); } RooDataSet* asimovData1 = (RooDataSet*)ws->data(asimov1DataName); if (!asimovData1) { cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl; makeAsimovData(mc, true, ws, mc->GetPdf(), data, 0); ws->Print(); asimovData1 = (RooDataSet*)ws->data("asimovData_1"); } if (!doRightSided) mu->setRange(0, 40); else mu->setRange(-40, 40); bool old = false; if (old) { mu->setVal(0); RooArgSet poi(*mu); ProfileLikelihoodTestStat_modified asimov_testStat_sig(*mc->GetPdf()); asimov_testStat_sig.SetRightSided(doRightSided); asimov_testStat_sig.SetNuis(&nuis); if (!doInj) asimov_testStat_sig.SetDoAsimov(true, 1); asimov_testStat_sig.SetWorkspace(ws); ProfileLikelihoodTestStat_modified testStat(*mc->GetPdf()); testStat.SetRightSided(doRightSided); testStat.SetNuis(&nuis); testStat.SetWorkspace(ws); //RooMinimizerFcn::SetOverrideEverything(true); double med_sig = 0; double med_testStat_val = 0; //gRandom->SetSeed(1); //RooRandom::randomGenerator()->SetSeed(1); RooNLLVar::SetIgnoreZeroEntries(1); if (asimov1DataName != "" && doMedian) { mu->setVal(0); if (!doInj) mu->setRange(0, 2); ws->loadSnapshot("conditionalNuis_0"); asimov_testStat_sig.SetLoadUncondSnapshot("conditionalNuis_1"); if (string(conditional1Snapshot) != "") ws->loadSnapshot(conditional1Snapshot); med_testStat_val = 2*asimov_testStat_sig.Evaluate(*asimovData1, poi); if (med_testStat_val < 0 && !doInj) { mu->setVal(0); med_testStat_val = 2*asimov_testStat_sig.Evaluate(*asimovData1, poi); // just try again } int sign = med_testStat_val != 0 ? med_testStat_val/fabs(med_testStat_val) : 0; med_sig = sign*sqrt(fabs(med_testStat_val)); if (string(nominalSnapshot) != "") ws->loadSnapshot(nominalSnapshot); if (!doRightSided) mu->setRange(0, 40); else mu->setRange(-40, 40); } RooNLLVar::SetIgnoreZeroEntries(0); //gRandom->SetSeed(1); //RooRandom::randomGenerator()->SetSeed(1); //RooMinimizerFcn::SetOverrideEverything(false); cout << "med test stat: " << med_testStat_val << endl; ws->loadSnapshot("nominalGlobs"); ws->loadSnapshot("conditionalNuis_0"); mu->setVal(0); testStat.SetWorkspace(ws); testStat.SetLoadUncondSnapshot("ucmles"); double obsTestStat_val = doObs ? 2*testStat.Evaluate(*data, poi) : 0; cout << "obs test stat: " << obsTestStat_val << endl; // obsTestStat_val = 2*testStat.Evaluate(*data, poi); // cout << "obs test stat: " << obsTestStat_val << endl; // obsTestStat_val = 2*testStat.Evaluate(*data, poi); // cout << "obs test stat: " << obsTestStat_val << endl; double obs_sig; int sign = obsTestStat_val == 0 ? 0 : obsTestStat_val / fabs(obsTestStat_val); if (!doRightSided && (obsTestStat_val < 0 && obsTestStat_val > -0.1 || mu->getVal() < 0.001)) obs_sig = 0; else obs_sig = sign*sqrt(fabs(obsTestStat_val)); if (obs_sig != obs_sig) //nan, do by hand { cout << "Obs test stat gave nan: try by hand" << endl; mu->setVal(0); mu->setConstant(1); mc->GetPdf()->fitTo(*data, Hesse(0), Minos(0), PrintLevel(-1), Constrain(*mc->GetNuisanceParameters())); mu->setConstant(0); double L_0 = mc->GetPdf()->getVal(); //mu->setVal(0); //mu->setConstant(1); mc->GetPdf()->fitTo(*data, Hesse(0), Minos(0), PrintLevel(-1), Constrain(*mc->GetNuisanceParameters())); //mu->setConstant(0); double L_muhat = mc->GetPdf()->getVal(); cout << "L_0: " << L_0 << ", L_muhat: " << L_muhat << endl; obs_sig = sqrt(-2*TMath::Log(L_0/L_muhat)); //still nan if (obs_sig != obs_sig && fabs(L_0 - L_muhat) < 0.000001) obs_sig = 0; } cout << "obs: " << obs_sig << endl; cout << "Observed significance: " << obs_sig << endl; if (med_sig) { cout << "Median test stat val: " << med_testStat_val << endl; cout << "Median significance: " << med_sig << endl; } f.Close(); stringstream fileName; fileName << "root_files/" << folder << "/" << mass << ".root"; system(("mkdir -vp root_files/" + folder).c_str()); TFile f2(fileName.str().c_str(),"recreate"); // stringstream fileName; // fileName << "results_sig/" << mass << ".root"; // system("mkdir results_sig"); // TFile f(fileName.str().c_str(),"recreate"); TH1D* h_hypo = new TH1D("hypo","hypo",2,0,2); h_hypo->SetBinContent(1, obs_sig); h_hypo->SetBinContent(2, med_sig); f2.Write(); f2.Close(); //mc->GetPdf()->fitTo(*data, PrintLevel(0)); timer.Stop(); timer.Print(); } else { RooAbsPdf* pdf = mc->GetPdf(); RooArgSet nuis_tmp1 = *mc->GetNuisanceParameters(); RooNLLVar* asimov_nll0 = (RooNLLVar*)pdf->createNLL(*asimovData0, Constrain(nuis_tmp1)); RooArgSet nuis_tmp2 = *mc->GetNuisanceParameters(); RooNLLVar* asimov_nll1 = (RooNLLVar*)pdf->createNLL(*asimovData1, Constrain(nuis_tmp2)); RooArgSet nuis_tmp3 = *mc->GetNuisanceParameters(); RooNLLVar* obs_nll = (RooNLLVar*)pdf->createNLL(*data, Constrain(nuis_tmp3)); //do asimov int status; //get sigma_b ws->loadSnapshot(conditional0Snapshot); status = ws->loadSnapshot("conditionalNuis_0"); if (status != 0 && goFast) errFast = 1; mu->setVal(0); mu->setConstant(1); status = goFast ? 0 : minimize(asimov_nll0, ws); if (status < 0) { cout << "Retrying" << endl; //ws->loadSnapshot("conditionalNuis_0"); status = minimize(asimov_nll0, ws); if (status >= 0) cout << "Success!" << endl; } double asimov0_nll0 = asimov_nll0->getVal(); mu->setVal(1); ws->loadSnapshot("conditionalNuis_1"); status = minimize(asimov_nll0, ws); if (status < 0) { cout << "Retrying" << endl; //ws->loadSnapshot("conditionalNuis_0"); status = minimize(asimov_nll0, ws); if (status >= 0) cout << "Success!" << endl; } double asimov0_nll1 = asimov_nll0->getVal(); double asimov0_q = 2*(asimov0_nll1 - asimov0_nll0); double sigma_b = sqrt(1./asimov0_q); ws->loadSnapshot(nominalSnapshot); //get sigma_sb ws->loadSnapshot(conditional1Snapshot); ws->loadSnapshot("conditionalNuis_0"); mu->setVal(0); mu->setConstant(1); status = minimize(asimov_nll1, ws); if (status < 0) { cout << "Retrying" << endl; //ws->loadSnapshot("conditionalNuis_0"); status = minimize(asimov_nll1, ws); if (status >= 0) cout << "Success!" << endl; } double asimov1_nll0 = asimov_nll1->getVal(); mu->setVal(1); status = ws->loadSnapshot("conditionalNuis_1"); if (status != 0 && goFast) errFast = 1; status = goFast ? 0 : minimize(asimov_nll1, ws); if (status < 0) { cout << "Retrying" << endl; //ws->loadSnapshot("conditionalNuis_0"); status = minimize(asimov_nll1, ws); if (status >= 0) cout << "Success!" << endl; } double asimov1_nll1 = asimov_nll1->getVal(); double asimov1_q = 2*(asimov1_nll1 - asimov1_nll0); double sigma_sb = sqrt(-1./asimov1_q); ws->loadSnapshot(nominalSnapshot); //do obs mu->setVal(0); status = ws->loadSnapshot("conditionalNuis_0"); if (status != 0 && goFast) errFast = 1; mu->setConstant(1); status = goFast ? 0 : minimize(obs_nll, ws); if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(obs_nll, ws); if (status >= 0) cout << "Success!" << endl; } double obs_nll0 = obs_nll->getVal(); status = ws->loadSnapshot("conditionalNuis_1"); if (status != 0 && goFast) errFast = 1; mu->setVal(1); status = goFast ? 0 : minimize(obs_nll, ws); if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(obs_nll, ws); if (status >= 0) cout << "Success!" << endl; } double obs_nll1 = obs_nll->getVal(); double obs_q = 2*(obs_nll1 - obs_nll0); double Zobs = (1./sigma_b/sigma_b - obs_q) / (2./sigma_b); double Zexp = (1./sigma_b/sigma_b - asimov1_q) / (2./sigma_b); double pb_obs = 1-ROOT::Math::gaussian_cdf(Zobs); double pb_exp = 1-ROOT::Math::gaussian_cdf(Zexp); cout << "asimov0_q = " << asimov0_q << endl; cout << "asimov1_q = " << asimov1_q << endl; cout << "obs_q = " << obs_q << endl; cout << "sigma_b = " << sigma_b << endl; cout << "sigma_sb = " << sigma_sb << endl; cout << "Z obs = " << Zobs << endl; cout << "Z exp = " << Zexp << endl; f.Close(); stringstream fileName; fileName << "root_files/" << folder << "/" << mass << ".root"; system(("mkdir -vp root_files/" + folder).c_str()); TFile f2(fileName.str().c_str(),"recreate"); TH1D* h_hypo = new TH1D("hypo_tev","hypo_tev",2,0,2); h_hypo->SetBinContent(1, pb_obs); h_hypo->SetBinContent(2, pb_exp); f2.Write(); f2.Close(); stringstream fileName3; fileName3 << "root_files/" << folder << "_llr/" << mass << ".root"; system(("mkdir -vp root_files/" + folder + "_llr").c_str()); TFile f3(fileName3.str().c_str(),"recreate"); TH1D* h_hypo3 = new TH1D("hypo_llr","hypo_llr",7,0,7); h_hypo3->SetBinContent(1, -obs_q); h_hypo3->SetBinContent(2, -asimov1_q); h_hypo3->SetBinContent(3, -asimov0_q); h_hypo3->SetBinContent(4, -asimov0_q-2*2/sigma_b); h_hypo3->SetBinContent(5, -asimov0_q-1*2/sigma_b); h_hypo3->SetBinContent(6, -asimov0_q+1*2/sigma_b); h_hypo3->SetBinContent(7, -asimov0_q+2*2/sigma_b); f3.Write(); f3.Close(); timer.Stop(); timer.Print(); } }