void FitterUtils::PlotShape2D(RooDataSet& originDataSet, RooDataSet& genDataSet, RooAbsPdf& shape, string plotsfile, string canvName, RooRealVar& B_plus_M, RooRealVar& misPT)
{
//**************Prepare TFile to save the plots
TFile f2(plotsfile.c_str(), "UPDATE");
//**************Plot Signal Zero Gamma
TH2F* th2fKey = (TH2F*)shape.createHistogram("th2Shape", B_plus_M, Binning(20), YVar(misPT, Binning(20)));
cout<<genDataSet.sumEntries()<<endl;
TH2F* th2fGen = (TH2F*)genDataSet.createHistogram("th2fGen", B_plus_M, Binning(20), YVar(misPT, Binning(20)));
RooPlot* plotM = B_plus_M.frame();
originDataSet.plotOn(plotM);
shape.plotOn(plotM);
RooPlot* plotMisPT = misPT.frame();
originDataSet.plotOn(plotMisPT);
shape.plotOn(plotMisPT);
TCanvas canv(canvName.c_str(), canvName.c_str(), 800, 800);
canv.Divide(2,2);
canv.cd(1); th2fGen->Draw("lego");
canv.cd(2); th2fKey->Draw("surf");
canv.cd(3); plotM->Draw();
canv.cd(4); plotMisPT->Draw();
canv.Write();
f2.Close();
}
void TestNonCentral(){
RooWorkspace w("w");
// k <2, must use sum
w.factory("NonCentralChiSquare::nc(x[0,50],k[1.99,0,5],lambda[5])");
// kk > 2 can use bessel
w.factory("NonCentralChiSquare::ncc(x,kk[2.01,0,5],lambda)");
// kk > 2, force sum
w.factory("NonCentralChiSquare::nccc(x,kk,lambda)");
((RooNonCentralChiSquare*)w.pdf("nccc"))->SetForceSum(true);
// a normal "central" chi-square for comparision when lambda->0
w.factory("ChiSquarePdf::cs(x,k)");
//w.var("kk")->setVal(4.); // test a large kk
RooDataSet* ncdata = w.pdf("nc")->generate(*w.var("x"),100);
RooDataSet* csdata = w.pdf("cs")->generate(*w.var("x"),100);
RooPlot* plot = w.var("x")->frame();
ncdata->plotOn(plot,MarkerColor(kRed));
csdata->plotOn(plot,MarkerColor(kBlue));
w.pdf("nc")->plotOn(plot,LineColor(kRed));
w.pdf("ncc")->plotOn(plot,LineColor(kGreen));
w.pdf("nccc")->plotOn(plot,LineColor(kYellow),LineStyle(kDashed));
w.pdf("cs")->plotOn(plot,LineColor(kBlue),LineStyle(kDotted));
plot->Draw();
}
int main(){
RooMsgService::instance().setGlobalKillBelow(ERROR);
TFile *bkgFile = TFile::Open("comb_svn/hgg.inputbkgdata_8TeV_MVA.root");
RooWorkspace *bkgWS = (RooWorkspace*)bkgFile->Get("cms_hgg_workspace");
RooRealVar *mass = (RooRealVar*)bkgWS->var("CMS_hgg_mass");
RooDataSet *data = (RooDataSet*)bkgWS->data("data_mass_cat0");
RooRealVar *p1 = new RooRealVar("p1","p1",-2.,-10.,0.);
RooRealVar *p2 = new RooRealVar("p2","p2",-0.001,-0.01,0.01);
RooRealVar *p3 = new RooRealVar("p3","p3",-0.0001,-0.01,0.01);
//RooPowerLawSum *pow1 = new RooPowerLawSum("pow","pow",*mass,RooArgList(*p1));
//RooPowerLawSum *pow2 = new RooPowerLawSum("pow","pow",*mass,RooArgList(*p1,*p2));
//RooPowerLawSum *pow3 = new RooPowerLawSum("pow","pow",*mass,RooArgList(*p1,*p2,*p3));
RooExponentialSum *pow1 = new RooExponentialSum("pow1","pow1",*mass,RooArgList(*p1));
RooExponentialSum *pow2 = new RooExponentialSum("pow2","pow2",*mass,RooArgList(*p1,*p2));
RooExponentialSum *pow3 = new RooExponentialSum("pow3","pow3",*mass,RooArgList(*p1,*p2,*p3));
TCanvas *canv = new TCanvas();
RooPlot *frame = mass->frame();
data->plotOn(frame);
cout << "bus" << endl;
pow1->fitTo(*data,PrintEvalErrors(-1),PrintLevel(-1),Warnings(-1),Verbose(-1));
cout << "bus" << endl;
pow1->plotOn(frame);
pow2->fitTo(*data);
pow2->plotOn(frame,LineColor(kRed),LineStyle(kDashed));
pow3->fitTo(*data);
pow3->plotOn(frame,LineColor(kGreen),LineStyle(7));
frame->Draw();
canv->Print("test.pdf");
TFile *outFile = new TFile("test.root","RECREATE");
TTree *tree = new TTree("tree","tree");
vector<double> mu;
mu.push_back(1.);
mu.push_back(2.);
mu.push_back(3.);
mu.push_back(4.);
vector<string> label;
label.push_back("pol");
label.push_back("pow");
label.push_back("lau");
label.push_back("exp");
tree->Branch("mu",&mu);
tree->Branch("label",&label);
tree->Fill();
outFile->cd();
tree->Write();
outFile->Close();
return 0;
}
RooDataHist * genHistFromModelPdf(const char * name, RooAbsPdf *model, RooRealVar *var, double ScaleLumi, int range, int rebin, int seed ) {
double genEvents = model->expectedEvents(*var);
TRandom3 *rndm = new TRandom3();
rndm->SetSeed(seed);
double nEvt = rndm->PoissonD( genEvents) ;
int intEvt = ( (nEvt- (int)nEvt) >= 0.5) ? (int)nEvt +1 : int(nEvt);
RooDataSet * data = model->generate(*var , intEvt );
cout<< " expected events for " << name << " = "<< genEvents << endl;
cout<< " data->numEntries() for name " << name << " == " << data->numEntries()<< endl;
// cout<< " nEvt from PoissonD for" << name << " == " << nEvt<< endl;
//cout<< " cast of nEvt for" << name << " == " << intEvt<< endl;
RooAbsData *binned_data = data->binnedClone();
TH1 * toy_hist = binned_data->createHistogram( name, *var, Binning(range/rebin ) );
for(int i = 1; i <= toy_hist->GetNbinsX(); ++i) {
toy_hist->SetBinError( i, sqrt( toy_hist->GetBinContent(i)) );
if(toy_hist->GetBinContent(i) == 0.00) {
cout<< " WARNING: histo " << name << " has 0 enter in bin number " << i << endl;
}
if(toy_hist->GetBinContent(i) < 0.1) {
toy_hist->SetBinContent(i, 0.0);
toy_hist->SetBinError(i, 0.0);
cout<< " WARNING: setting value 0.0 to histo " << name << " for bin number " << i << endl;
}
}
RooDataHist * toy_rooHist = new RooDataHist(name, name , RooArgList(*var), toy_hist );
return toy_rooHist;
}
void AnalyzeToy::extract_signal()
{
calculate_yield();
MakeSpinSPlot splotter(toyData);
splotter.addSpecies("signal",ws->pdf("model_signal_mass"),signalYield);
splotter.addSpecies("background",ws->pdf("model_bkg_mass"),backgroundYield);
splotter.addVariable(ws->var("mass"));
splotter.calculate();
RooDataSet *sweights = splotter.getSWeightDataSet();
sweights->SetName("sweights");
RooRealVar weight("weight","",-5.,5.);
RooArgSet event;
event.add(*mass);
event.add(*cosT);
event.add(weight);
extractedData = new RooDataSet("extractedData","",event,WeightVar("weight"));
Long64_t nEntries = toyData->numEntries();
for(int i=0;i<nEntries;i++)
{
double weight_double=0;
weight_double += sweights->get(i)->getRealValue("signal_sw");
// weight_double += sweights->get(i)->getRealValue("background_sw");
mass->setVal(toyData->get(i)->getRealValue("mass"));
cosT->setVal(toyData->get(i)->getRealValue("cosT"));
extractedData->add(event,weight_double);
}
delete toyData;
}
void trig_pt()
{
TCanvas *myCan=new TCanvas("myCan","myCan");
myCan->SetGrid();
TFile *f_MC= new TFile("TnP_WptCutToTrig_MCptminus.root","read");
RooDataSet *datasetMC = (RooDataSet*)f_MC->Get("tpTree/Tnp_WptCut_to_Mu15_eta2p1_pt/cnt_eff");
cout<<"ntry: "<<datasetMC->numEntries()<<endl;
double XMC[Nbin],XMCerrL[Nbin],XMCerrH[Nbin],YMC[Nbin],YMCerrLo[Nbin],YMCerrHi[Nbin];
for(int i(0); i<datasetMC->numEntries();i++)
{
const RooArgSet &pointMC=*datasetMC->get(i);
RooRealVar &ptMC=pointMC["pt"],&effMC = pointMC["efficiency"];
XMC[i]=ptMC.getVal();
XMCerrL[i]=-ptMC.getAsymErrorLo();
XMCerrH[i]=ptMC.getAsymErrorHi();
YMC[i]=effMC.getVal();
YMCerrLo[i]=-effMC.getAsymErrorLo();
YMCerrHi[i]=effMC.getAsymErrorHi();
}
grMC=new TGraphAsymmErrors(11,XMC,YMC,XMCerrL,XMCerrH,YMCerrLo,YMCerrHi);
grMC->SetLineColor(kRed);
grMC->SetMarkerColor(kRed);
grMC->Draw("AP");
//grMC->Draw("psame");
myCan->SaveAs("trig_McMinus_pt.png");
myCan->SaveAs("trig_McMinus_pt.eps");
}
void Plot(RooAbsPdf *iGen,RooAbsPdf *iFit,int iN,int iNEvents,RooRealVar &iVar,RooRealVar &iSig,RooRealVar &iMean,
RooRealVar &iScale,RooRealVar &iRes,RooDataSet *iData=0) {
TRandom1 *lRand = new TRandom1(0xDEADBEEF);
RooDataSet * lSignal = iGen->generate(iVar,iNEvents);
if(iData == 0) {
iFit->fitTo(*lSignal,Strategy(1));
if(iMean.getError() < 0.05) iFit->fitTo(*lSignal,Strategy(2));
} else {
iFit->fitTo(*iData,Strategy(1));
if(iMean.getError() < 0.05) iFit->fitTo(*iData,Strategy(2));
}
iVar.setBins(30);
RooPlot *lFrame1 = iVar.frame(RooFit::Title("XXX")) ;
if(iData == 0) lSignal->plotOn(lFrame1);
if(iData != 0) iData->plotOn(lFrame1);
iFit->plotOn(lFrame1);
TCanvas *iC =new TCanvas("A","A",800,600);
iC->cd(); lFrame1->Draw();
iC->SaveAs("Crap.png");
if(iData != 0) {
RooPlot *lFrame2 = iVar.frame(RooFit::Title("XXX")) ;
iData->plotOn(lFrame2);
iGen->plotOn(lFrame2);
TCanvas *iC1 =new TCanvas("B","B",800,600);
iC1->cd(); lFrame2->Draw();
iC1->SaveAs("Crap.png");
}
}
void glbToId_eta()
{
TCanvas *myCan=new TCanvas("myCan","myCan");
myCan->SetGrid();
TFile *f_MC= new TFile("TnP_GlbToID_MCetaplus_WptTight2012_eta.root","read");
RooDataSet *datasetMC = (RooDataSet*)f_MC->Get("tpTree/WptTight2012_eta/fit_eff");
//RooDataSet *datasetMC = (RooDataSet*)f_MC->Get("tpTree/WptTight2012_eta/cnt_eff");
cout<<"ntry: "<<datasetMC->numEntries()<<endl;
double XMC[Nbin],XMCerrL[Nbin],XMCerrH[Nbin],YMC[Nbin],YMCerrLo[Nbin],YMCerrHi[Nbin];
for(int i(0); i<datasetMC->numEntries();i++)
{
const RooArgSet &pointMC=*datasetMC->get(i);
RooRealVar &etaMC=pointMC["eta"],&effMC = pointMC["efficiency"];
XMC[i]=etaMC.getVal();
XMCerrL[i]=-etaMC.getAsymErrorLo();
XMCerrH[i]=etaMC.getAsymErrorHi();
YMC[i]=effMC.getVal();
YMCerrLo[i]=-effMC.getAsymErrorLo();
YMCerrHi[i]=effMC.getAsymErrorHi();
}
grMC=new TGraphAsymmErrors(Nbin,XMC,YMC,XMCerrL,XMCerrH,YMCerrLo,YMCerrHi);
grMC->SetLineColor(kRed);
grMC->SetMarkerColor(kRed);
grMC->Draw("AP");
//grMC->Draw("psame");
myCan->SaveAs("glbToId_MCplus_eta.png");
myCan->SaveAs("glbToId_MCplus_eta.eps");
}
void test01()
{
// S e t u p m o d e l
// ---------------------
TFile *openFile = new TFile("/tmp/kyolee/dimuonTree_upsiMiniTree_pA5tev_14nb_Run210498-210909_trigBit1_allTriggers0_pt4.root");
TTree* tree = (TTree*)openFile->Get("UpsilonTree");
// Declare variables with associated name, title, initial value and allowed range
RooRealVar mass("invariantMass","M_{#mu#mu} [GeV/c]",9.4,7,14) ;
RooRealVar muPlusPt("muPlusPt","muPlusPt",0,40) ;
RooRealVar muMinusPt("muMinusPt","muMinusPt",0,40) ;
RooRealVar mean("mean","mean of gaussian",9.4,8,11) ;
RooRealVar sigma("sigma","width of gaussian",1,0.1,10) ;
RooDataSet* data = new RooDataSet("data", "data", RooArgSet(mass,muPlusPt,muMinusPt), Import(*tree), Cut("muPlusPt>4 && muMinusPt>4"));
data->Print();
// Build gaussian p.d.f in terms of x,mean and sigma
RooGaussian gauss("gauss","gaussian PDF",mass,mean,sigma) ;
// Construct plot frame in 'x'
RooPlot* xframe = mass.frame(Title("Gaussian p.d.f.")) ;
data->plotOn(xframe);
/*
// P l o t m o d e l a n d c h a n g e p a r a m e t e r v a l u e s
// ---------------------------------------------------------------------------
// Plot gauss in frame (i.e. in x)
gauss.plotOn(xframe) ;
// Change the value of sigma to 3
sigma.setVal(3) ;
// Plot gauss in frame (i.e. in x) and draw frame on canvas
gauss.plotOn(xframe,LineColor(kRed)) ;
*/
// F i t m o d e l t o d a t a
// -----------------------------
// Fit pdf to data
gauss.fitTo(*data,Range(9,10)) ;
gauss.plotOn(xframe) ;
// Print values of mean and sigma (that now reflect fitted values and errors)
mean.Print() ;
sigma.Print() ;
// Draw all frames on a canvas
TCanvas* c = new TCanvas("rf101_basics","rf101_basics",800,400) ;
c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.6) ; xframe->Draw() ;
}
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);
}
void rf314_paramfitrange()
{
// D e f i n e o b s e r v a b l e s a n d d e c a y p d f
// ---------------------------------------------------------------
// Declare observables
RooRealVar t("t","t",0,5) ;
RooRealVar tmin("tmin","tmin",0,0,5) ;
// Make parameterized range in t : [tmin,5]
t.setRange(tmin,RooConst(t.getMax())) ;
// Make pdf
RooRealVar tau("tau","tau",-1.54,-10,-0.1) ;
RooExponential model("model","model",t,tau) ;
// C r e a t e i n p u t d a t a
// ------------------------------------
// Generate complete dataset without acceptance cuts (for reference)
RooDataSet* dall = model.generate(t,10000) ;
// Generate a (fake) prototype dataset for acceptance limit values
RooDataSet* tmp = RooGaussian("gmin","gmin",tmin,RooConst(0),RooConst(0.5)).generate(tmin,5000) ;
// Generate dataset with t values that observe (t>tmin)
RooDataSet* dacc = model.generate(t,ProtoData(*tmp)) ;
// F i t p d f t o d a t a i n a c c e p t a n c e r e g i o n
// -----------------------------------------------------------------------
RooFitResult* r = model.fitTo(*dacc,Save()) ;
// P l o t f i t t e d p d f o n f u l l a n d a c c e p t e d d a t a
// ---------------------------------------------------------------------------------
// Make plot frame, add datasets and overlay model
RooPlot* frame = t.frame(Title("Fit to data with per-event acceptance")) ;
dall->plotOn(frame,MarkerColor(kRed),LineColor(kRed)) ;
model.plotOn(frame) ;
dacc->plotOn(frame) ;
// Print fit results to demonstrate absence of bias
r->Print("v") ;
new TCanvas("rf314_paramranges","rf314_paramranges",600,600) ;
gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
return ;
}
void reweight_diphotonpt_1d(TH1F*weight_diphopt,TH1F*weight_diphopto,TH2F*weight_diphoptn,TH2F*weight_diphopt2o,TH2F*weight_diphoptnr, TH2F* weight_diphopt2,RooDataSet **dset, RooDataSet *dsetdestination){
if (!(*dset)) return;
if (!(dsetdestination)) return;
///numerator and denominator
TH1F *hnum = new TH1F("hnum","hnum",n_diphoptbins_forreweighting,diphoptbins_forreweighting);
TH1F *hden = new TH1F("hden","hden",n_diphoptbins_forreweighting,diphoptbins_forreweighting);
hnum->Sumw2();
hden->Sumw2();
// RooAbsData->get*() Load a given row of data
//getRealValue Get value of a RooAbsReal stored in set with given name. If none is found, value of defVal is returned.
for (int i=0; i<(*dset)->numEntries(); i++){
hden->Fill(fabs((*dset)->get(i)->getRealValue("roodiphopt")),(*dset)->store()->weight(i));
}
for (int i=0; i<dsetdestination->numEntries(); i++){
hnum->Fill(fabs(dsetdestination->get(i)->getRealValue("roodiphopt")),dsetdestination->store()->weight(i));
}
//normalize to one
hnum->Scale(1.0/hnum->Integral());
hden->Scale(1.0/hden->Integral());
hnum->Divide(hden);
TH1F *h = hnum;
RooDataSet *newdset = new RooDataSet(**dset,Form("%s_diphoptrew",(*dset)->GetName()));
newdset->reset();
assert(newdset);
for (int i=0; i<(*dset)->numEntries(); i++){
RooArgSet args = *((*dset)->get(i));
float oldw = (*dset)->store()->weight(i);
float diphopt = fabs((*dset)->get(i)->getRealValue("roodiphopt"));
float neww = oldw*h->GetBinContent(h->FindBin(fabs(diphopt)));
newdset->add(args,neww);
if(debug){
weight_diphopt->Fill(neww);
weight_diphopto->Fill(oldw);
weight_diphopt2o->Fill((h->FindBin(fabs(diphopt))),oldw);
weight_diphoptn->Fill((h->FindBin(fabs(diphopt))),neww);
if(oldw!=0)weight_diphoptnr->Fill(h->FindBin(fabs(diphopt)),oldw/neww);
else {weight_diphoptnr->Fill(-10,1);}//cout << "dipho weight old 0" << endl;}
if(oldw!=0)weight_diphopt2->Fill(diphopt,oldw/neww);
else {weight_diphopt2->Fill(-10,1);}//cout << "dipho weight old 0" << endl;}
}
}
newdset->SetName((*dset)->GetName());
newdset->SetTitle((*dset)->GetTitle());
delete hnum; delete hden;
RooDataSet *old_dset = *dset;
*dset=newdset;
std::cout << "Diphoton Pt 1d rew: norm from " << old_dset->sumEntries() << " to " << newdset->sumEntries() << std::endl;
delete old_dset;
};
void rf602_chi2fit()
{
// S e t u p 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) ;
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) ;
// C r e a t e b i n n e d d a t a s e t
// -----------------------------------------
RooDataSet* d = model.generate(x,10000) ;
RooDataHist* dh = d->binnedClone() ;
// Construct a chi^2 of the data and the model.
// When a p.d.f. is used in a chi^2 fit, the probability density scaled
// by the number of events in the dataset to obtain the fit function
// If model is an extended p.d.f, the expected number events is used
// instead of the observed number of events.
model.chi2FitTo(*dh) ;
// NB: It is also possible to fit a RooAbsReal function to a RooDataHist
// using chi2FitTo().
// Note that entries with zero bins are _not_ allowed
// for a proper chi^2 calculation and will give error
// messages
RooDataSet* dsmall = (RooDataSet*) d->reduce(EventRange(1,100)) ;
RooDataHist* dhsmall = dsmall->binnedClone() ;
RooChi2Var chi2_lowstat("chi2_lowstat","chi2",model,*dhsmall) ;
cout << chi2_lowstat.getVal() << endl ;
}
pair<double,double> datEvents(RooWorkspace *work, int m_hyp, int cat, bool spin=false){
vector<double> result;
RooDataSet *data = (RooDataSet*)work->data(Form("data_mass_cat%d",cat));
double evs = data->numEntries();
double evsPerGev;
if (!spin) evsPerGev = data->sumEntries(Form("CMS_hgg_mass>=%4.1f && CMS_hgg_mass<%4.1f",double(m_hyp)-0.5,double(m_hyp)+0.5));
else evsPerGev = data->sumEntries(Form("mass>=%4.1f && mass<%4.1f",double(m_hyp)-0.5,double(m_hyp)+0.5));
return pair<double,double>(evs,evsPerGev);
}
void rf903_numintcache(Int_t mode=0)
{
// Mode = 0 : Run plain fit (slow)
// Mode = 1 : Generate workspace with pre-calculated integral and store it on file (prepare for accelerated running)
// Mode = 2 : Run fit from previously stored workspace including cached integrals (fast, requires run in mode=1 first)
// C r e a t e , s a v e o r l o a d w o r k s p a c e w i t h p . d . f .
// -----------------------------------------------------------------------------------
// Make/load workspace, exit here in mode 1
RooWorkspace* w1 = getWorkspace(mode) ;
if (mode==1) {
// Show workspace that was created
w1->Print() ;
// Show plot of cached integral values
RooDataHist* hhcache = (RooDataHist*) w1->expensiveObjectCache().getObj(1) ;
if (hhcache) {
new TCanvas("rf903_numintcache","rf903_numintcache",600,600) ;
hhcache->createHistogram("a")->Draw() ;
}
else {
Error("rf903_numintcache","Cached histogram is not existing in workspace");
}
return ;
}
// U s e p . d . f . f r o m w o r k s p a c e f o r g e n e r a t i o n a n d f i t t i n g
// -----------------------------------------------------------------------------------
// This is always slow (need to find maximum function value empirically in 3D space)
RooDataSet* d = w1->pdf("model")->generate(RooArgSet(*w1->var("x"),*w1->var("y"),*w1->var("z")),1000) ;
// This is slow in mode 0, but fast in mode 1
w1->pdf("model")->fitTo(*d,Verbose(kTRUE),Timer(kTRUE)) ;
// Projection on x (always slow as 2D integral over Y,Z at fitted value of a is not cached)
RooPlot* framex = w1->var("x")->frame(Title("Projection of 3D model on X")) ;
d->plotOn(framex) ;
w1->pdf("model")->plotOn(framex) ;
// Draw x projection on canvas
new TCanvas("rf903_numintcache","rf903_numintcache",600,600) ;
framex->Draw() ;
// Make workspace available on command line after macro finishes
gDirectory->Add(w1) ;
return ;
}
Roo2DKeysPdf *SmoothKeys(TH2F *h)
{
RooDataSet *dataset = th22dataset(h);
RooRealVar *v1 = (RooRealVar *) dataset->get()->find("constTerm");
RooRealVar *v2 = (RooRealVar *) dataset->get()->find("alpha");
Roo2DKeysPdf *myPdf = new Roo2DKeysPdf("mypdf","", *v1, *v2, *dataset);
return myPdf;
}
void read() {
// Open File
//TFile *datafile = TFile::Open("~/Documents/uni/LHCb_CharmSummerProj/Gedcode/baryon-lifetimes-2015/data/run-II-data/datafileLambda_TAUmin200fs_max2200fs_Mmin2216_max2356.root");
//TFile *datafile = TFile::Open("~/Documents/uni/LHCb_CharmSummerProj/Gedcode/baryon-lifetimes-2015/data/run-II-data/datafileLambda_TAUmin200fs_max2200fs_Mmin2216_max2356_CutIPCHI2lt3.root");
TFile *datafile = TFile::Open("~/Documents/uni/LHCb_CharmSummerProj/learning_root/DataSetLambda_TAUmin0002_max0022.root");
//TFile *datafile = TFile::Open("~/Documents/uni/LHCb_CharmSummerProj/learning_root/DataSetfromGed_fit_data_wSWeight.root");
// Define dataset
RooDataSet* ds = (RooDataSet*)datafile->Get("ds") ;
// Define TAU variable, get limits.
RooRealVar Lambda_cplus_TAU("Lambda_cplus_TAU","Lambda_cplus_TAU",0.0002 ,0.0022 ,"ns") ; //real range of interest is [0.00025, 0.002], this is defined later.
double highestTAU;
double lowestTAU;
ds->getRange(Lambda_cplus_TAU, lowestTAU, highestTAU);
// Define Mass variable, get limits.
RooRealVar Lambda_cplus_M("Lambda_cplus_M","Lambda_cplus_M",2216 ,2356, "GeV") ;
double highestM;
double lowestM;
ds->RooAbsData::getRange(Lambda_cplus_M, lowestM, highestM) ;
// Define IPCHI2 variable
RooRealVar Lambda_cplus_IPCHI2_OWNPV("Lambda_cplus_IPCHI2_OWNPV","Lambda_cplus_IPCHI2_OWNPV",-100 ,100) ;
double highestIPCHI2_OWNPV;
double lowestIPCHI2_OWNPV;
ds->RooAbsData::getRange(Lambda_cplus_IPCHI2_OWNPV, lowestIPCHI2_OWNPV, highestIPCHI2_OWNPV) ;
//Lambda_cplus_TAU.setRange("R1",0.00018, 0.0012);
// Print to Screen
cout<<endl<<endl<<"************info************"<<endl;
cout<< "Lowest lifetime value in dataset = "<<lowestTAU<<endl;
cout<< "Highest lifetime value in dataset = "<<highestTAU<<endl;
cout<< "Lowest mass value in dataset (should be: 2216)= "<<lowestM<<endl;
cout<< "Highest mass value in dataset (should be: 2356)= "<<highestM<<endl;
cout<< "Lowest IPCHI2_OWNPV value in dataset = "<<lowestIPCHI2_OWNPV<<endl;
cout<< "Highest IPCHI2_OWNPV value in dataset = "<<highestIPCHI2_OWNPV<<endl;
cout<<"number of events: "<<endl ;
ds->Print(); //number of events in dataset
//rmodel->Print(); //results
//cout<<"Chi squared ="<<chi2<<endl ;
}
double effRmsFromSigmaCB(double meanCB, double meanCB_err, double sigmaCB, double sigmaCB_err) {
RooRealVar mass("zmass","m(e^{+}e^{-})",75,105,"GeV/c^{2}");
float zwidth=2.4952;
RooRealVar bwMean("m_{Z}","BW Mean", 91.1876, "GeV/c^{2}");
RooRealVar bwWidth("#Gamma_{Z}", "BW Width", zwidth, "GeV/c^{2}");
RooRealVar cbBias ("#Deltam_{CB}", "CB Bias", 0.00, -10, 10, "GeV/c^{2}"); // typical value
RooRealVar cbSigma("#sigma_{CB}", "CB Width", 1.5, 0.8, 5.0, "GeV/c^{2}");
RooRealVar cbCut ("a_{CB}","CB Cut", 1.0, 1.0, 3.0); // typical value
RooRealVar cbPower("n_{CB}","CB Order", 2.5, 0.1, 20.0); // typical value
float mean_lo = meanCB - meanCB_err;
float mean_hi = meanCB + meanCB_err;
float sigma_lo = sigmaCB - sigmaCB_err;
float sigma_hi = sigmaCB + sigmaCB_err;
int nstep_mean = 10;
int nstep_sigma = 10;
float step_mean = 2*meanCB_err/float(nstep_mean);
float step_sigma = 2*sigmaCB_err/float(nstep_sigma);
double max_rms = 0.0;
double min_rms = 999.;
RooMsgService::instance().setGlobalKillBelow(RooFit::WARNING);
int nsteps=0;
for(float mean=mean_lo; mean<mean_hi; mean+=step_mean) {
for(float sigma=sigma_lo; sigma<sigma_hi; sigma+=step_sigma) {
cbBias.setVal(meanCB);
cbSigma.setVal(sigma);
RooBreitWigner bw("bw", "bw", mass, bwMean, bwWidth);
RooCBShape cball("cball", "Crystal Ball", mass, cbBias, cbSigma, cbCut, cbPower);
RooFFTConvPdf BWxCB("BWxCB", "bw X crystal ball", mass, bw, cball);
RooDataSet *gendata = BWxCB.generate(mass,1000);
TH1D *genh = (TH1D*) gendata->createHistogram("genh",mass,RooFit::Binning(100,75,105));
math::HistogramTools ht(genh);
double rms = ht.effectiveSigma();
rms = sqrt(pow(rms,2)-pow(zwidth,2));
if(rms > max_rms) max_rms=rms;
if(rms < min_rms) min_rms=rms;
delete genh;
nsteps++;
}
}
return (max_rms-min_rms)/sqrt(12)/sqrt(nsteps);
}
void rf304_uncorrprod()
{
// C r e a t e c o m p o n e n t p d f s i n x a n d y
// ----------------------------------------------------------------
// Create two p.d.f.s gaussx(x,meanx,sigmax) gaussy(y,meany,sigmay) and its variables
RooRealVar x("x","x",-5,5) ;
RooRealVar y("y","y",-5,5) ;
RooRealVar meanx("mean1","mean of gaussian x",2) ;
RooRealVar meany("mean2","mean of gaussian y",-2) ;
RooRealVar sigmax("sigmax","width of gaussian x",1) ;
RooRealVar sigmay("sigmay","width of gaussian y",5) ;
RooGaussian gaussx("gaussx","gaussian PDF",x,meanx,sigmax) ;
RooGaussian gaussy("gaussy","gaussian PDF",y,meany,sigmay) ;
// C o n s t r u c t u n c o r r e l a t e d p r o d u c t p d f
// -------------------------------------------------------------------
// Multiply gaussx and gaussy into a two-dimensional p.d.f. gaussxy
RooProdPdf gaussxy("gaussxy","gaussx*gaussy",RooArgList(gaussx,gaussy)) ;
// S a m p l e p d f , p l o t p r o j e c t i o n o n x a n d y
// ---------------------------------------------------------------------------
// Generate 10000 events in x and y from gaussxy
RooDataSet *data = gaussxy.generate(RooArgSet(x,y),10000) ;
// Plot x distribution of data and projection of gaussxy on x = Int(dy) gaussxy(x,y)
RooPlot* xframe = x.frame(Title("X projection of gauss(x)*gauss(y)")) ;
data->plotOn(xframe) ;
gaussxy.plotOn(xframe) ;
// Plot x distribution of data and projection of gaussxy on y = Int(dx) gaussxy(x,y)
RooPlot* yframe = y.frame(Title("Y projection of gauss(x)*gauss(y)")) ;
data->plotOn(yframe) ;
gaussxy.plotOn(yframe) ;
// Make canvas and draw RooPlots
TCanvas *c = new TCanvas("rf304_uncorrprod","rf304_uncorrprod",800, 400);
c->Divide(2);
c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
c->cd(2) ; gPad->SetLeftMargin(0.15) ; yframe->GetYaxis()->SetTitleOffset(1.4) ; yframe->Draw() ;
}
///////////////////////////////////////////////////////////////////////////////
//weightDS
///////////////////////////////////////////////////////////////////////////////
RooDataSet* weightDS(RooDataSet* set, double wgt){
RooRealVar wt("wt","wt",wgt);
set->addColumn(wt);
RooDataSet* wSet = new RooDataSet(set->GetName(),set->GetTitle(),set,*set->get(),0,wt.GetName()) ;
std::cout << "Returning weighted d.s." << std::endl;
std::cout << std::endl;
wSet->Print();
return wSet;
}
void sc_wptCut_P_et()
{
TCanvas *myCan=new TCanvas("myCan","myCan");
myCan->SetGrid();
/************************
TFile *f_RD= new TFile("TnP_Z_Trigger_RDpt.root","read");
RooDataSet *dataset = (RooDataSet*)f_RD->Get("tpTree/Track_To_TightCombRelIso_Mu15_eta2p1_pt/fit_eff");
cout<<"ntry: "<<dataset->numEntries()<<endl;
double X[11],XerrL[11],XerrH[11],Y[11],YerrLo[11],YerrHi[11];
for(int i(0); i<dataset->numEntries();i++)
{
const RooArgSet &point=*dataset->get(i);
RooRealVar &pt=point["pt"],&eff = point["efficiency"];
X[i]=pt.getVal();
XerrL[i]=-pt.getAsymErrorLo();
XerrH[i]=pt.getAsymErrorHi();
Y[i]=eff.getVal();
YerrLo[i]=-eff.getAsymErrorLo();
YerrHi[i]=eff.getAsymErrorHi();
}
gr=new TGraphAsymmErrors(11,X,Y,XerrL,XerrH,YerrLo,YerrHi);
gr->Draw("AP");
***************************/
TFile *f_MC= new TFile("efficiency-mc-SCToPfElectron_et_P.root","read");
RooDataSet *datasetMC = (RooDataSet*)f_MC->Get("SuperClusterToPFElectron/SCtoWptCut_efficiency/cnt_eff");
//RooDataSet *datasetMC = (RooDataSet*)f_MC->Get("tpTree/Track_with_TightCombRelIso_to_Mu15_eta2p1_pt/fit_eff");
cout<<"ntry: "<<datasetMC->numEntries()<<endl;
double XMC[binSize],XMCerrL[binSize],XMCerrH[binSize],YMC[binSize],YMCerrLo[binSize],YMCerrHi[binSize];
for(int i(0); i<datasetMC->numEntries();i++)
{
const RooArgSet &pointMC=*datasetMC->get(i);
RooRealVar &ptMC=pointMC["probe_sc_et"],&effMC = pointMC["efficiency"];
XMC[i]=ptMC.getVal();
XMCerrL[i]=-ptMC.getAsymErrorLo();
XMCerrH[i]=ptMC.getAsymErrorHi();
YMC[i]=effMC.getVal();
YMCerrLo[i]=-effMC.getAsymErrorLo();
YMCerrHi[i]=effMC.getAsymErrorHi();
}
grMC=new TGraphAsymmErrors(binSize,XMC,YMC,XMCerrL,XMCerrH,YMCerrLo,YMCerrHi);
grMC->SetLineColor(kRed);
grMC->SetMarkerColor(kRed);
//myCan->SetLogx();
grMC->Draw("AP");
//grMC->Draw("psame");
TLine *myLine=new TLine(25,0,25,1);
myLine->Draw("same");
myCan->SaveAs("sc_wptCut_P_et.png");
myCan->SaveAs("sc_wptCut_P_et.eps");
}
void rf208_convolution()
{
// S e t u p c o m p o n e n t p d f s
// ---------------------------------------
// Construct observable
RooRealVar t("t","t",-10,30) ;
// Construct landau(t,ml,sl) ;
RooRealVar ml("ml","mean landau",5.,-20,20) ;
RooRealVar sl("sl","sigma landau",1,0.1,10) ;
RooLandau landau("lx","lx",t,ml,sl) ;
// Construct gauss(t,mg,sg)
RooRealVar mg("mg","mg",0) ;
RooRealVar sg("sg","sg",2,0.1,10) ;
RooGaussian gauss("gauss","gauss",t,mg,sg) ;
// C o n s t r u c t c o n v o l u t i o n p d f
// ---------------------------------------
// Set #bins to be used for FFT sampling to 10000
t.setBins(10000,"cache") ;
// Construct landau (x) gauss
RooFFTConvPdf lxg("lxg","landau (X) gauss",t,landau,gauss) ;
// S a m p l e , f i t a n d p l o t c o n v o l u t e d p d f
// ----------------------------------------------------------------------
// Sample 1000 events in x from gxlx
RooDataSet* data = lxg.generate(t,10000) ;
// Fit gxlx to data
lxg.fitTo(*data) ;
// Plot data, landau pdf, landau (X) gauss pdf
RooPlot* frame = t.frame(Title("landau (x) gauss convolution")) ;
data->plotOn(frame) ;
lxg.plotOn(frame) ;
landau.plotOn(frame,LineStyle(kDashed)) ;
// Draw frame on canvas
new TCanvas("rf208_convolution","rf208_convolution",600,600) ;
gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
}
void rf301_composition()
{
// S e t u p c o m p o s e d m o d e l g a u s s ( x , m ( y ) , s )
// -----------------------------------------------------------------------
// Create observables
RooRealVar x("x","x",-5,5) ;
RooRealVar y("y","y",-5,5) ;
// Create function f(y) = a0 + a1*y
RooRealVar a0("a0","a0",-0.5,-5,5) ;
RooRealVar a1("a1","a1",-0.5,-1,1) ;
RooPolyVar fy("fy","fy",y,RooArgSet(a0,a1)) ;
// Creat gauss(x,f(y),s)
RooRealVar sigma("sigma","width of gaussian",0.5) ;
RooGaussian model("model","Gaussian with shifting mean",x,fy,sigma) ;
// S a m p l e d a t a , p l o t d a t a a n d p d f o n x a n d y
// ---------------------------------------------------------------------------------
// Generate 10000 events in x and y from model
RooDataSet *data = model.generate(RooArgSet(x,y),10000) ;
// Plot x distribution of data and projection of model on x = Int(dy) model(x,y)
RooPlot* xframe = x.frame() ;
data->plotOn(xframe) ;
model.plotOn(xframe) ;
// Plot x distribution of data and projection of model on y = Int(dx) model(x,y)
RooPlot* yframe = y.frame() ;
data->plotOn(yframe) ;
model.plotOn(yframe) ;
// Make two-dimensional plot in x vs y
TH1* hh_model = model.createHistogram("hh_model",x,Binning(50),YVar(y,Binning(50))) ;
hh_model->SetLineColor(kBlue) ;
// Make canvas and draw RooPlots
TCanvas *c = new TCanvas("rf301_composition","rf301_composition",1200, 400);
c->Divide(3);
c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
c->cd(2) ; gPad->SetLeftMargin(0.15) ; yframe->GetYaxis()->SetTitleOffset(1.4) ; yframe->Draw() ;
c->cd(3) ; gPad->SetLeftMargin(0.20) ; hh_model->GetZaxis()->SetTitleOffset(2.5) ; hh_model->Draw("surf") ;
}
//
// single measurement (LM0 or LM1)
//
void RA4Single (StatMethod method, double* sig, double* bkg) {
// RooWorkspace* wspace = createWorkspace();
RA4WorkSpace ra4WSpace("wspace",true,true,true);
ra4WSpace.addChannel(RA4WorkSpace::MuChannel);
ra4WSpace.finalize();
double lm0_mc[4] = { 1.09, 7.68, 3.78, 21.13 };
double lm1_mc[4] = { 0.05 , 0.34 , 1.12 , 3.43 };
double* lm_mc = sig ? sig : lm0_mc;
double bkg_mc[4] = { 14.73, 18.20, 8.48, 10.98 };
ra4WSpace.setBackground(RA4WorkSpace::MuChannel,bkg_mc[0],bkg_mc[1],bkg_mc[2],bkg_mc[3]);
ra4WSpace.setSignal(RA4WorkSpace::MuChannel,lm_mc[0],lm_mc[1],lm_mc[2],lm_mc[3],1.,1.,1.,1.);
// setBackgrounds(wspace,bkg);
// setSignal(wspace,lm_mc);
RooWorkspace* wspace = ra4WSpace.workspace();
// wspace->Print("v");
// RooArgSet allVars = wspace->allVars();
// // allVars.printLatex(std::cout,1);
// TIterator* it = allVars.createIterator();
// RooRealVar* var;
// while ( var=(RooRealVar*)it->Next() ) {
// var->Print("v");
// var->printValue(std::cout);
// }
////////////////////////////////////////////////////////////
// Generate toy data
// generate toy data assuming current value of the parameters
// import into workspace.
// add Verbose() to see how it's being generated
// wspace->var("s")->setVal(0.);
// RooDataSet* data = wspace->pdf("model")->generate(*wspace->set("obs"),1);
// data->Print("v");
// // wspace->import(*data);
// wspace->var("s")->setVal(lm_mc[3]);
RooDataSet* data = new RooDataSet("data","data",*wspace->set("obs"));
data->add(*wspace->set("obs"));
data->Print("v");
MyLimit limit = computeLimit(wspace,data,method,true);
std::cout << "Limit [ " << limit.lowerLimit << " , "
<< limit.upperLimit << " ] ; isIn = " << limit.isInInterval << std::endl;
}
void rs602_HLFactoryCombinationexample() {
using namespace RooStats;
using namespace RooFit;
// create a card
TString card_name("HLFavtoryCombinationexample.rs");
ofstream ofile(card_name);
ofile << "// The simplest card for combination\n\n"
<< "gauss1 = Gaussian(x[0,100],mean1[50,0,100],4);\n"
<< "flat1 = Polynomial(x,0);\n"
<< "sb_model1 = SUM(nsig1[120,0,300]*gauss1 , nbkg1[100,0,1000]*flat1);\n"
<< "gauss2 = Gaussian(x,mean2[80,0,100],5);\n"
<< "flat2 = Polynomial(x,0);\n"
<< "sb_model2 = SUM(nsig2[90,0,400]*gauss2 , nbkg2[80,0,1000]*flat2);\n";
ofile.close();
HLFactory hlf("HLFavtoryCombinationexample",
card_name,
false);
hlf.AddChannel("model1","sb_model1","flat1");
hlf.AddChannel("model2","sb_model2","flat2");
RooAbsPdf* pdf=hlf.GetTotSigBkgPdf();
RooCategory* thecat = hlf.GetTotCategory();
RooRealVar* x= static_cast<RooRealVar*>(hlf.GetWs()->arg("x"));
RooDataSet* data = pdf->generate(RooArgSet(*x,*thecat),Extended());
// --- Perform extended ML fit of composite PDF to toy data ---
pdf->fitTo(*data) ;
// --- Plot toy data and composite PDF overlaid ---
RooPlot* xframe = x->frame() ;
data->plotOn(xframe);
thecat->setIndex(0);
pdf->plotOn(xframe,Slice(*thecat),ProjWData(*thecat,*data)) ;
thecat->setIndex(1);
pdf->plotOn(xframe,Slice(*thecat),ProjWData(*thecat,*data)) ;
gROOT->SetStyle("Plain");
xframe->Draw();
}
void AnalyzeToy::generate_toy()
{
RooDataSet *toySignal;
toySignal = ws->pdf("model_signal_mass")->generate(*genSpec_sig_mass);
toySignal->merge(ws->pdf("model_signal0_cosT")->generate(*cosT,nSignal_gen,AutoBinned(false)));
RooDataSet *toyBkg;
toyBkg= ws->pdf("model_bkg_mass")->generate(*genSpec_bkg_mass);
toyBkg->merge(ws->pdf("model_bkg_cosT")->generate(*genSpec_bkg_cosT));
toyData = new RooDataSet("toyData","",RooArgSet(*mass, *cosT));
toyData->append(*toySignal);
toyData->append(*toyBkg);
delete toySignal;
delete toyBkg;
}
int main() {
std::vector<float> masses;
masses.push_back( 300. );
masses.push_back( 400. );
masses.push_back( 500. );
masses.push_back( 750. );
masses.push_back( 1000. );
masses.push_back( 1250. );
masses.push_back( 1500. );
masses.push_back( 2000. );
masses.push_back( 2500. );
masses.push_back( 3000. );
masses.push_back( 5000. );
for( unsigned i=0; i<masses.size(); ++i ) {
std::cout << "-> Starting mass: " << masses[i] << std::endl;
TFile* file = TFile::Open( Form("toySignal_m%.0f.root", masses[i]), "recreate" );
file->cd();
float thisMass = masses[i];
RooRealVar x("mass", "mass", thisMass, 0.2*thisMass, 2.*thisMass );
// Crystal-Ball
RooRealVar mean( "mean", "mean", thisMass );
RooRealVar sigma( "sigma", "sigma", 0.015*thisMass );
RooRealVar alpha( "alpha", "alpha", 1.2 );
RooRealVar n( "n", "n", 0.81 );
RooCBShape cb( "cb", "cb", x, mean, sigma, alpha, n );
// Generate a toy dataset from the interpreted p.d.f
RooDataSet* data = cb.generate(x,10000) ;
data->SetName("data");
data->Write();
file->Close();
}
return 0;
}
void rfExExpo(){
// Declare Eponential Variables
RooRealVar x("x","x",0,10);
RooRealVar lambda("lambda","lambda",-3,-10,0);
// Create an Exponential PDF with variables
RooExponential expo("expo","eponential PDF",x,lambda);
// Generate a dataset of 1000 and plot it, then plot the PDF
RooDataSet* data = expo.generate(x,1000);
RooPlot* xframe = x.frame(Title("Exponential p.d.f."));
data->plotOn(xframe);
expo.plotOn(xframe);
// Draw the plot on a TCanvas
TCanvas* c = new TCanvas("rfExExpo","rfExExpo",800,400);
xframe->Draw();
}
void plotFromWorkspace() {
TFile* file = new TFile("card_m125_1JetIncl_XX_workspace.root");
RooWorkspace* w = (RooWorkspace*)file->Get("w");
RooRealVar* m = (RooRealVar*)w->var("CMS_hmumu_mass");
RooRealVar* e = (RooRealVar*)w->var("CMS_hmumu_merr");
RooDataSet* d = (RooDataSet*)w->data("data_pseudo");
RooAbsPdf * b = w->pdf("bkg_mass_merr_1JetIncl_XX_pdf");
RooAbsPdf * s = w->pdf("sig_mass_merr_ggH_1JetIncl_XX_pdf");
RooPlot* frame = m->frame();
d->plotOn(frame);
b->plotOn(frame);
//s->plotOn(frame, RooFit::ProjWData(*e, *d), RooFit::LineColor(kOrange+1));
//s->plotOn(frame, RooFit::LineColor(kOrange+1));
frame->Draw();
}
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) ;
}