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();
}
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 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 plot_toys()
{
TFile* f = new TFile("/project/atlas/users/tlenz/HWWStatisticsCode_v31/CPmix_kHWW2dot65m_0j_trainv31_v31_allsys_topLumi_toys.root");
RooWorkspace* w = (RooWorkspace*)f->Get("combined");
RooDataSet* data = (RooDataSet*)w->data("obsData");
RooDataSet* toydata = (RooDataSet*)w->data("toyData");
RooDataSet* toydata2 = (RooDataSet*)w->data("toyData4");
RooDataSet* thisAsimovData = (RooDataSet*)w->data("asimivData_profiled_for_epsilon_0"); //asimivData_profiled_for_epsilon_0
RooPlot* frame1 = w->var("obs_x_em_signalLike_0j_2012")->frame();
// RooPlot* frame2 = w->var("obs_x_em_mainControl_0j_2012")->frame();
// RooPlot* frame3 = w->var("obs_x_em_zbox_0j_2012")->frame();
data->plotOn(frame1, Name("data"), DataError(RooAbsData::Poisson), Cut("merged_cat==merged_cat::em_signalLike_0j_2012"));
//data->plotOn(frame2, Name("data"), DataError(RooAbsData::Poisson), Cut("merged_cat==merged_cat::em_mainControl_0j_2012"));
//data->plotOn(frame3, Name("data"), DataError(RooAbsData::Poisson), Cut("merged_cat==merged_cat::em_zbox_0j_2012"));
toydata->plotOn(frame1, Name("toy1"), LineColor(kRed), MarkerStyle(21), MarkerColor(kRed), DataError(RooAbsData::Poisson), Cut("merged_cat==merged_cat::em_signalLike_0j_2012"));
//toydata->plotOn(frame2, Name("toy1"), LineColor(kRed), MarkerStyle(21), MarkerColor(kRed), DataError(RooAbsData::Poisson), Cut("merged_cat==merged_cat::em_mainControl_0j_2012"));
//toydata->plotOn(frame3, Name("toy1"), LineColor(kRed), MarkerStyle(21), MarkerColor(kRed), DataError(RooAbsData::Poisson), Cut("merged_cat==merged_cat::em_zbox_0j_2012"));
toydata2->plotOn(frame1, Name("toy4"), LineColor(kGreen), MarkerStyle(21), MarkerColor(kGreen), DataError(RooAbsData::Poisson), Cut("merged_cat==merged_cat::em_signalLike_0j_2012"));
//toydata2->plotOn(frame2, Name("toy4"), LineColor(kGreen), MarkerStyle(21), MarkerColor(kGreen), DataError(RooAbsData::Poisson), Cut("merged_cat==merged_cat::em_mainControl_0j_2012"));
//toydata2->plotOn(frame3, Name("toy4"), LineColor(kGreen), MarkerStyle(21), MarkerColor(kGreen), DataError(RooAbsData::Poisson), Cut("merged_cat==merged_cat::em_zbox_0j_2012"));
thisAsimovData->plotOn(frame1, Name("asimov"), LineColor(kBlue), MarkerStyle(22), MarkerColor(kBlue), DataError(RooAbsData::Poisson), Cut("merged_cat==merged_cat::em_signalLike_0j_2012"));
//thisAsimovData->plotOn(frame2, Name("asimov"), LineColor(kBlue), MarkerStyle(22), MarkerColor(kBlue), DataError(RooAbsData::Poisson), Cut("merged_cat==merged_cat::em_mainControl_0j_2012"));
//thisAsimovData->plotOn(frame3, Name("asimov"), LineColor(kBlue), MarkerStyle(22), MarkerColor(kBlue), DataError(RooAbsData::Poisson), Cut("merged_cat==merged_cat::em_zbox_0j_2012"));
TCanvas* c = new TCanvas("c","c",1800,600);
//c->Divide(3,1);
/*c->cd(1);*/ /*gPad->SetLogy();*/ frame1->Draw();
//c->cd(2); frame2->Draw();
//draw legend
TLegend *leg = new TLegend(0.48, 0.60, 0.84, 0.80, NULL,"brNDC");
leg->SetBorderSize(0);
leg->SetTextFont(62);
leg->SetTextSize(0.04);
leg->SetLineColor(1);
leg->SetLineStyle(1);
leg->SetLineWidth(1);
leg->SetFillColor(0);
leg->SetFillStyle(1001);
leg->SetNColumns(1);
leg->AddEntry(frame1->findObject("data"),"data","pl");
leg->AddEntry(frame1->findObject("toy1"),"toy1","pl");
leg->AddEntry(frame1->findObject("toy4"),"toy4","pl");
leg->AddEntry(frame1->findObject("asimov"),"asimov","pl");
leg->Draw();
//c->cd(3); frame3->Draw();
c->Print("toys_linear.pdf");
}
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() ;
}
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") ;
}
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;
}
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 MakeSpinPlots::DrawSpinBackground(TString tag, TString mcName,bool signal){
bool drawSM = (smName!="" && smName!=mcName);
TCanvas cv;
double thisN = ws->data(mcName+"_Combined")->reduce(TString("evtcat==evtcat::")+tag)->sumEntries();
float norm = thisN; //607*lumi/12.*thisN/(totEB+totEE);
cout << norm <<endl;
if(signal) norm = ws->data(Form("Data_%s_%s_sigWeight",tag.Data(),mcName.Data()))->sumEntries();
RooPlot *frame = ws->var("cosT")->frame(0,1,5);
RooDataSet* bkgWeight = (RooDataSet*)ws->data(Form("Data_%s_%s_bkgWeight",tag.Data(),mcName.Data()));
RooDataSet* tmp = (RooDataSet*)ws->data("Data_Combined")->reduce(TString("((mass>115 && mass<120) || (mass>130 && mass<135)) && evtcat==evtcat::")+tag);
tmp->plotOn(frame,RooFit::Rescale(norm/tmp->sumEntries()));
cout << "b" <<endl;
ws->pdf(Form("%s_FIT_%s_cosTpdf",mcName.Data(),tag.Data()))->plotOn(frame,RooFit::LineColor(kGreen),RooFit::Normalization(norm/tmp->sumEntries()));
if(drawSM) ws->pdf(Form("%s_FIT_%s_cosTpdf",smName.Data(),tag.Data()))->plotOn(frame,RooFit::LineColor(kRed),RooFit::Normalization(norm/tmp->sumEntries()));
cout << "c " <<bkgWeight <<endl;
bkgWeight->plotOn(frame,RooFit::Rescale(norm/bkgWeight->sumEntries()),RooFit::MarkerColor(kBlue) );
if(signal){
cout << "d" <<endl;
ws->data(Form("Data_%s_%s_sigWeight",tag.Data(),mcName.Data()))->plotOn(frame,RooFit::MarkerStyle(4));
}
cout << "d" <<endl;
frame->SetMaximum(frame->GetMaximum()*(signal?0.8:0.4)*norm/tmp->sumEntries());
frame->SetMinimum(-1*frame->GetMaximum());
TLegend l(0.6,0.2,0.95,0.45);
l.SetFillColor(0);
l.SetBorderSize(0);
l.SetHeader(tag);
l.AddEntry(frame->getObject(0),"Data m#in [115,120]#cup[130,135]","p");
l.AddEntry(frame->getObject(1),mcName,"l");
if(drawSM) l.AddEntry(frame->getObject(2),"SM Higgs","l");
l.AddEntry(frame->getObject(2+drawSM),"background weighted Data","p");
if(signal) l.AddEntry(frame->getObject(3+drawSM),"signal weighted Data","p");
cout << "e" <<endl;
frame->Draw();
l.Draw("SAME");
cv.SaveAs( basePath+Form("/cosThetaPlots/CosThetaDist_%s%s_%s_%s.png",outputTag.Data(),(signal ? "":"_BLIND"),mcName.Data(),tag.Data()) );
cv.SaveAs( basePath+Form("/cosThetaPlots/C/CosThetaDist_%s%s_%s_%s.C",outputTag.Data(),(signal ? "":"_BLIND"),mcName.Data(),tag.Data()) );
cv.SaveAs( basePath+Form("/cosThetaPlots/CosThetaDist_%s%s_%s_%s.pdf",outputTag.Data(),(signal ? "":"_BLIND"),mcName.Data(),tag.Data()) );
}
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 FitterUtils::PlotShape1D(RooDataSet& originDataSet, RooDataSet& genDataSet, RooAbsPdf& shape, string plotsfile, string canvName, RooRealVar& B_plus_M)
{
TFile f2(plotsfile.c_str(), "UPDATE");
RooPlot* plotGen = B_plus_M.frame(Binning(20));
genDataSet.plotOn(plotGen);
RooPlot* plotM = B_plus_M.frame();
originDataSet.plotOn(plotM);
shape.plotOn(plotM);
TCanvas canv(canvName.c_str(), canvName.c_str(), 800, 800);
canv.Divide(1,2);
canv.cd(1); plotGen->Draw();
canv.cd(2); plotM->Draw();
canv.Write();
f2.Close();
}
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 ;
}
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 FitterUtils::plot_fit_result(string plotsfile, RooAbsPdf &totPdf, RooDataSet dataGenTot)
{
//**************Prepare TFile to save the plots
TFile f2(plotsfile.c_str(), "UPDATE");
//**************Plot the results of the fit
RooArgSet *var_set = totPdf.getObservables(dataGenTot);
TIterator *iter = var_set->createIterator();
RooRealVar *var;
std::vector<RooPlot*> plots;
RooPlot* frame;
while((var = (RooRealVar*) iter->Next()))
{
frame = var->frame();
dataGenTot.plotOn(frame);
totPdf.plotOn(frame, Components("histPdfPartReco"), LineColor(kBlue));
totPdf.plotOn(frame, Components("histPdfSignalZeroGamma"), LineColor(kGreen));
totPdf.plotOn(frame, Components("histPdfSignalOneGamma"), LineColor(kMagenta));
totPdf.plotOn(frame, Components("histPdfSignalTwoGamma"), LineColor(kOrange));
totPdf.plotOn(frame, Components("histPdfJpsiLeak"), LineColor(14));
totPdf.plotOn(frame, Components("combPDF"), LineColor(kBlack));
totPdf.plotOn(frame, LineColor(kRed));
plots.push_back(frame);
}
if (!(plots.size())) return;
TCanvas cFit("cFit", "cFit", 600, 800);
cFit.Divide(1,2);
cFit.cd(1); plots[0]->Draw();
if (plots.size()>1){
cFit.cd(2); plots[1]->Draw();
}
cFit.Write();
f2.Close();
}
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 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 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 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) ;
}
void MakeSpinPlots::DrawSpinSubTotBackground(TString mcName,bool signal){
bool drawSM = (smName!="" && smName!=mcName);
TCanvas cv;
double thisN = ws->data(mcName+"_Combined")->sumEntries();
float norm = thisN;
if(signal) norm = ws->var(Form("Data_%s_FULLFIT_Nsig",mcName.Data()))->getVal();
RooPlot *frame = ws->var("cosT")->frame(0,1,10);
RooDataSet* tmp = (RooDataSet*)ws->data(Form("Data_Combined"))->reduce("(mass>115 && mass<120) || (mass>130 && mass<135)");
tmp->plotOn(frame,RooFit::Rescale(norm/tmp->sumEntries()));
ws->pdf(Form("%s_FIT_cosTpdf",mcName.Data()))->plotOn(frame,RooFit::LineColor(kGreen),RooFit::Normalization(norm/tmp->sumEntries()));
if(drawSM) ws->pdf(Form("%s_FIT_cosTpdf",smName.Data()))->plotOn(frame,RooFit::LineColor(kRed),RooFit::Normalization(norm/tmp->sumEntries()));
if(signal){
RooDataHist *h = (RooDataHist*)ws->data( Form("Data_%s_Combined_bkgSub_cosT",mcName.Data()) );
h->plotOn(frame,RooFit::MarkerStyle(4));
std::cout << "Nsig: " << h->sumEntries() << std::endl;
}
frame->SetMaximum(frame->GetMaximum()*(signal?2.:1.2)*norm/tmp->sumEntries());
frame->SetMinimum(-1*frame->GetMaximum());
TLegend l(0.6,0.2,0.95,0.45);
l.SetFillColor(0);
l.SetBorderSize(0);
l.SetHeader("Combined");
l.AddEntry(frame->getObject(0),"Data m#in [115,120]#cup[130,135]","p");
l.AddEntry(frame->getObject(1),mcName,"l");
if(drawSM) l.AddEntry(frame->getObject(2),"SM Higgs","l");
if(signal) l.AddEntry(frame->getObject(2+drawSM),"bkg-subtracted Data","p");
frame->Draw();
l.Draw("SAME");
cv.SaveAs( basePath+Form("/cosThetaPlots/CosThetaDist_SimpleSub_%s%s_%s.png",outputTag.Data(),(signal ? "":"_BLIND"),mcName.Data()) );
cv.SaveAs( basePath+Form("/cosThetaPlots/C/CosThetaDist_SimpleSub_%s%s_%s.C",outputTag.Data(),(signal ? "":"_BLIND"),mcName.Data()) );
cv.SaveAs( basePath+Form("/cosThetaPlots/CosThetaDist_SimpleSub_%s%s_%s.pdf",outputTag.Data(),(signal ? "":"_BLIND"),mcName.Data()) );
}
void rf509_wsinteractive()
{
// C r e a t e a n d f i l l w o r k s p a c e
// ------------------------------------------------
// Create a workspace named 'w' that exports its contents to
// a same-name C++ namespace in CINT 'namespace w'.
RooWorkspace* w = new RooWorkspace("w",kTRUE) ;
// Fill workspace with p.d.f. and data in a separate function
fillWorkspace(*w) ;
// Print workspace contents
w->Print() ;
// U s e w o r k s p a c e c o n t e n t s t h r o u g h C I N T C + + n a m e s p a c e
// -------------------------------------------------------------------------------------------------
// Use the name space prefix operator to access the workspace contents
RooDataSet* d = w::model.generate(w::x,1000) ;
RooFitResult* r = w::model.fitTo(*d) ;
RooPlot* frame = w::x.frame() ;
d->plotOn(frame) ;
// NB: The 'w::' prefix can be omitted if namespace w is imported in local namespace
// in the usual C++ way
using namespace w;
model.plotOn(frame) ;
model.plotOn(frame,Components(bkg),LineStyle(kDashed)) ;
// Draw the frame on the canvas
new TCanvas("rf509_wsinteractive","rf509_wsinteractive",600,600) ;
gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
}
void FitterUtilsSimultaneousExpOfPolyTimesX::plot_kemu_fit_result(string plotsfile, RooAbsPdf &totKemuPdf, RooDataSet const& dataGenKemu)
{
//**************Prepare TFile to save the plots
TFile f2(plotsfile.c_str(), "UPDATE");
//**************Plot the results of the fit
RooArgSet *var_set = totKemuPdf.getObservables(dataGenKemu);
TIterator *iter = var_set->createIterator();
RooRealVar *var;
std::vector<RooPlot*> plots;
RooPlot* frame;
while((var = (RooRealVar*) iter->Next()))
{
frame = var->frame();
dataGenKemu.plotOn(frame);
totKemuPdf.plotOn(frame, LineColor(kRed));
plots.push_back(frame);
}
if (!(plots.size())) return;
TCanvas cFit("cKemuFit", "cKemuFit", 600, 800);
cFit.Divide(1,2);
cFit.cd(1); plots[0]->Draw();
if (plots.size()>1){
cFit.cd(2); plots[1]->Draw();
}
cFit.Write();
f2.Close();
}
void rf109_chi2residpull()
{
// S e t u p m o d e l
// ---------------------
// Create observables
RooRealVar x("x","x",-10,10) ;
// Create Gaussian
RooRealVar sigma("sigma","sigma",3,0.1,10) ;
RooRealVar mean("mean","mean",0,-10,10) ;
RooGaussian gauss("gauss","gauss",x,RooConst(0),sigma) ;
// Generate a sample of 1000 events with sigma=3
RooDataSet* data = gauss.generate(x,10000) ;
// Change sigma to 3.15
sigma=3.15 ;
// P l o t d a t a a n d s l i g h t l y d i s t o r t e d m o d e l
// ---------------------------------------------------------------------------
// Overlay projection of gauss with sigma=3.15 on data with sigma=3.0
RooPlot* frame1 = x.frame(Title("Data with distorted Gaussian pdf"),Bins(40)) ;
data->plotOn(frame1,DataError(RooAbsData::SumW2)) ;
gauss.plotOn(frame1) ;
// C a l c u l a t e c h i ^ 2
// ------------------------------
// Show the chi^2 of the curve w.r.t. the histogram
// If multiple curves or datasets live in the frame you can specify
// the name of the relevant curve and/or dataset in chiSquare()
cout << "chi^2 = " << frame1->chiSquare() << endl ;
// S h o w r e s i d u a l a n d p u l l d i s t s
// -------------------------------------------------------
// Construct a histogram with the residuals of the data w.r.t. the curve
RooHist* hresid = frame1->residHist() ;
// Construct a histogram with the pulls of the data w.r.t the curve
RooHist* hpull = frame1->pullHist() ;
// Create a new frame to draw the residual distribution and add the distribution to the frame
RooPlot* frame2 = x.frame(Title("Residual Distribution")) ;
frame2->addPlotable(hresid,"P") ;
// Create a new frame to draw the pull distribution and add the distribution to the frame
RooPlot* frame3 = x.frame(Title("Pull Distribution")) ;
frame3->addPlotable(hpull,"P") ;
TCanvas* c = new TCanvas("rf109_chi2residpull","rf109_chi2residpull",900,300) ;
c->Divide(3) ;
c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
}
void rf703_effpdfprod()
{
// 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) ;
// Make pdf
RooRealVar tau("tau","tau",-1.54,-4,-0.1) ;
RooExponential model("model","model",t,tau) ;
// D e f i n e e f f i c i e n c y f u n c t i o n
// ---------------------------------------------------
// Use error function to simulate turn-on slope
RooFormulaVar eff("eff","0.5*(TMath::Erf((t-1)/0.5)+1)",t) ;
// D e f i n e d e c a y p d f w i t h e f f i c i e n c y
// ---------------------------------------------------------------
// Multiply pdf(t) with efficiency in t
RooEffProd modelEff("modelEff","model with efficiency",model,eff) ;
// P l o t e f f i c i e n c y , p d f
// ----------------------------------------
RooPlot* frame1 = t.frame(Title("Efficiency")) ;
eff.plotOn(frame1,LineColor(kRed)) ;
RooPlot* frame2 = t.frame(Title("Pdf with and without efficiency")) ;
model.plotOn(frame2,LineStyle(kDashed)) ;
modelEff.plotOn(frame2) ;
// G e n e r a t e t o y d a t a , f i t m o d e l E f f t o d a t a
// ------------------------------------------------------------------------------
// Generate events. If the input pdf has an internal generator, the internal generator
// is used and an accept/reject sampling on the efficiency is applied.
RooDataSet* data = modelEff.generate(t,10000) ;
// Fit pdf. The normalization integral is calculated numerically.
modelEff.fitTo(*data) ;
// Plot generated data and overlay fitted pdf
RooPlot* frame3 = t.frame(Title("Fitted pdf with efficiency")) ;
data->plotOn(frame3) ;
modelEff.plotOn(frame3) ;
TCanvas* c = new TCanvas("rf703_effpdfprod","rf703_effpdfprod",1200,400) ;
c->Divide(3) ;
c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
}
void rf103_interprfuncs()
{
/////////////////////////////////////////////////////////
// G e n e r i c i n t e r p r e t e d p . d . f . //
/////////////////////////////////////////////////////////
// Declare observable x
RooRealVar x("x","x",-20,20) ;
// C o n s t r u c t g e n e r i c p d f f r o m i n t e r p r e t e d e x p r e s s i o n
// -------------------------------------------------------------------------------------------------
// To construct a proper p.d.f, the formula expression is explicitly normalized internally by dividing
// it by a numeric integral of the expresssion over x in the range [-20,20]
//
RooRealVar alpha("alpha","alpha",5,0.1,10) ;
RooGenericPdf genpdf("genpdf","genpdf","(1+0.1*abs(x)+sin(sqrt(abs(x*alpha+0.1))))",RooArgSet(x,alpha)) ;
// S a m p l e , f i t a n d p l o t g e n e r i c p d f
// ---------------------------------------------------------------
// Generate a toy dataset from the interpreted p.d.f
RooDataSet* data = genpdf.generate(x,10000) ;
// Fit the interpreted p.d.f to the generated data
genpdf.fitTo(*data) ;
// Make a plot of the data and the p.d.f overlaid
RooPlot* xframe = x.frame(Title("Interpreted expression pdf")) ;
data->plotOn(xframe) ;
genpdf.plotOn(xframe) ;
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// S t a n d a r d p . d . f a d j u s t w i t h i n t e r p r e t e d h e l p e r f u n c t i o n //
// //
// Make a gauss(x,sqrt(mean2),sigma) from a standard RooGaussian //
// //
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// C o n s t r u c t s t a n d a r d p d f w i t h f o r m u l a r e p l a c i n g p a r a m e t e r
// ------------------------------------------------------------------------------------------------------------
// Construct parameter mean2 and sigma
RooRealVar mean2("mean2","mean^2",10,0,200) ;
RooRealVar sigma("sigma","sigma",3,0.1,10) ;
// Construct interpreted function mean = sqrt(mean^2)
RooFormulaVar mean("mean","mean","sqrt(mean2)",mean2) ;
// Construct a gaussian g2(x,sqrt(mean2),sigma) ;
RooGaussian g2("g2","h2",x,mean,sigma) ;
// G e n e r a t e t o y d a t a
// ---------------------------------
// Construct a separate gaussian g1(x,10,3) to generate a toy Gaussian dataset with mean 10 and width 3
RooGaussian g1("g1","g1",x,RooConst(10),RooConst(3)) ;
RooDataSet* data2 = g1.generate(x,1000) ;
// F i t a n d p l o t t a i l o r e d s t a n d a r d p d f
// -------------------------------------------------------------------
// Fit g2 to data from g1
RooFitResult* r = g2.fitTo(*data2,Save()) ;
r->Print() ;
// Plot data on frame and overlay projection of g2
RooPlot* xframe2 = x.frame(Title("Tailored Gaussian pdf")) ;
data2->plotOn(xframe2) ;
g2.plotOn(xframe2) ;
// Draw all frames on a canvas
TCanvas* c = new TCanvas("rf103_interprfuncs","rf103_interprfuncs",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) ; xframe2->GetYaxis()->SetTitleOffset(1.4) ; xframe2->Draw() ;
}
RooAddPdf fitZToMuMuGammaMassUnbinned(
const char *filename = "ZMuMuGammaMass_36.1ipb_EE.txt",
// const char *filename = "ZMuMuGammaMass_2.9ipb_EB.txt",
// const char *filename = "ZMuMuGammaMass_2.9ipb_EE.txt",
// const char *filename = "ZMuMuGammaMass_Zmumu_Spring10_EB.txt",
// const char *filename = "DimuonMass_data_Nov4ReReco.txt",
const char* plotOpt = "NEU",
const int nbins = 60)
{
gROOT->ProcessLine(".L tdrstyle.C");
setTDRStyle();
gStyle->SetPadRightMargin(0.05);
double minMass = 60;
double maxMass = 120;
RooRealVar mass("mass","m(#mu#mu#gamma)", minMass, maxMass,"GeV/c^{2}");
// Read data set
RooDataSet *data = RooDataSet::read(filename,RooArgSet(mass));
// RooDataSet *dataB = RooDataSet::read(filenameB,RooArgSet(mass));
// Build p.d.f.
////////////////////////////////////////////////
// Parameters //
////////////////////////////////////////////////
// Signal p.d.f. parameters
// Parameters for a Gaussian and a Crystal Ball Lineshape
RooRealVar cbBias ("#Deltam_{CB}", "CB Bias", 0.05, -2, 2,"GeV/c^{2}");
RooRealVar cbSigma("#sigma_{CB}","CB Width", 1.38, 0.01, 10.0,"GeV/c^{2}");
RooRealVar cbCut ("a_{CB}","CB Cut", 1.5, 0.1, 2.0);
RooRealVar cbPower("n_{CB}","CB Power", 1.3, 0.1, 20.0);
// cbSigma.setConstant(kTRUE);
// cbCut.setConstant(kTRUE);
// cbPower.setConstant(kTRUE);
// Parameters for Breit-Wigner
RooRealVar bwMean("m_{Z}","BW Mean", 91.1876, "GeV/c^{2}");
RooRealVar bwWidth("#Gamma_{Z}", "BW Width", 2.4952, "GeV/c^{2}");
// Keep Breit-Wigner parameters fixed to the PDG values
// bwMean.setConstant(kTRUE);
// bwWidth.setConstant(kTRUE);
// Background p.d.f. parameters
// Parameters for exponential
RooRealVar expRate("#lambda_{exp}", "Exponential Rate", -0.119, -10, 1);
// expRate.setConstant(kTRUE);
// fraction of signal
// RooRealVar frac("frac", "Signal Fraction", 0.1,0.,0.3.);
/* RooRealVar nsig("N_{S}", "#signal events", 9000, 0.,10000.);
RooRealVar nbkg("N_{B}", "#background events", 1000,2,10000.);*/
RooRealVar nsig("N_{S}", "#signal events", 29300, 0.1, 100000.);
RooRealVar nbkg("N_{B}", "#background events", 0, 0., 10000.);
// nbkg.setConstant(kTRUE);
////////////////////////////////////////////////
// P.D.F.s //
////////////////////////////////////////////////
// Di-photon mass signal p.d.f.
RooBreitWigner bw("bw", "bw", mass, bwMean, bwWidth);
// RooGaussian signal("signal", "A Gaussian Lineshape", mass, m0, sigma);
RooCBShape cball("cball", "A Crystal Ball Lineshape", mass, cbBias, cbSigma, cbCut, cbPower);
mass.setBins(100000, "fft");
RooFFTConvPdf BWxCB("BWxCB","bw (X) crystall ball", mass, bw, cball);
// Di-photon mass background p.d.f.
RooExponential bg("bg","bkgd exp", mass, expRate);
// Di-photon mass model p.d.f.
RooAddPdf model("model", "signal + background mass model", RooArgList(BWxCB, bg), RooArgList(nsig, nbkg));
TStopwatch t ;
t.Start() ;
model.fitTo(*data,FitOptions("mh"),Optimize(0),Timer(1));
// signal->fitTo(*data,FitOptions("mh"),Optimize(0),Timer(1));
t.Print() ;
TCanvas *c = new TCanvas("c","Unbinned Invariant Mass Fit", 0,0,800,600);
// Plot the fit results
RooPlot* plot = mass.frame(Range(minMass,maxMass),Bins(nbins));
// Plot 1
// dataB->plotOn(plot, MarkerColor(kRed), LineColor(kRed));
data->plotOn(plot);
// model.plotOn(plot);
model.plotOn(plot);
//model.paramOn(plot, Format(plotOpt, AutoPrecision(1)), Parameters(RooArgSet(nsig, nbkg, m0, sigma)));
model.paramOn(plot,
Format(plotOpt, AutoPrecision(2) ),
Parameters(RooArgSet(cbBias,
cbSigma,
cbCut,
cbPower,
bwMean,
bwWidth,
expRate,
nsig,
nbkg)),
Layout(.67, 0.97, 0.97),
ShowConstants(kTRUE) );
// model.plotOn(plot, Components("signal"), LineStyle(kDashed), LineColor(kRed));
model.plotOn(plot, Components("bg"), LineStyle(kDashed), LineColor(kRed));
plot->Draw();
// TLatex * tex = new TLatex(0.2,0.8,"CMS preliminary");
// tex->SetNDC();
// tex->SetTextFont(42);
// tex->SetLineWidth(2);
// tex->Draw();
// tex->DrawLatex(0.2, 0.725, "7 TeV Data, L = 258 pb^{-1}");
//
// float fsig_peak = NormalizedIntegral(model,
// mass,
// cbBias.getVal() - 2.5*cbSigma.getVal(),
// cbBias.getVal() + 2.5*cbSigma.getVal()
// );
// float fbkg_peak = NormalizedIntegral(bg,
// mass,
// m0.getVal() - 2.5*sigma.getVal(),
// m0.getVal() + 2.5*sigma.getVal()
// );
/* double nsigVal = fsig_peak * nsig.getVal();
double nsigErr = fsig_peak * nsig.getError();
double nsigErrRel = nsigErr / nsigVal;*/
// double nbkgVal = fbkg_peak * nbkg.getVal();
// double nbkgErr = fbkg_peak * nbkg.getError();
// double nbkgErrRel = nbkgErr / nbkgVal;
// cout << "nsig " << nsigVal << " +/- " << nsigErr << endl;
// cout << "S/B_{#pm2.5#sigma} " << nsigVal/nbkgVal << " +/- "
// << (nsigVal/nbkgVal)*sqrt(nsigErrRel*nsigErrRel + nbkgErrRel*nbkgErrRel)
// << endl;
// tex->DrawLatex(0.2, 0.6, Form("N_{S} = %.0f#pm%.0f", nsigVal, nsigErr) );
// tex->DrawLatex(0.2, 0.525, Form("S/B_{#pm2.5#sigma} = %.1f", nsigVal/nbkgVal) );
// tex->DrawLatex(0.2, 0.45, Form("#frac{S}{#sqrt{B}}_{#pm2.5#sigma} = %.1f", nsigVal/sqrt(nbkgVal)));
// leg = new TLegend(0.65,0.6,0.9,0.75);
// leg->SetFillColor(kWhite);
// leg->SetLineColor(kWhite);
// leg->SetShadowColor(kWhite);
// leg->SetTextFont(42);
// TLegendEntry * ldata = leg->AddEntry(data, "Opposite Sign");
// TLegendEntry * ldataB = leg->AddEntry(dataB, "Same Sign");
// ldata->SetMarkerStyle(20);
// ldataB->SetMarkerStyle(20);
// ldataB->SetMarkerColor(kRed);
// leg->Draw();
return model;
}
void rf105_funcbinding()
{
// B i n d T M a t h : : E r f C f u n c t i o n
// ---------------------------------------------------
// Bind one-dimensional TMath::Erf function as RooAbsReal function
RooRealVar x("x","x",-3,3) ;
RooAbsReal* errorFunc = bindFunction("erf",TMath::Erf,x) ;
// Print erf definition
errorFunc->Print() ;
// Plot erf on frame
RooPlot* frame1 = x.frame(Title("TMath::Erf bound as RooFit function")) ;
errorFunc->plotOn(frame1) ;
// B i n d R O O T : : M a t h : : b e t a _ p d f C f u n c t i o n
// -----------------------------------------------------------------------
// Bind pdf ROOT::Math::Beta with three variables as RooAbsPdf function
RooRealVar x2("x2","x2",0,0.999) ;
RooRealVar a("a","a",5,0,10) ;
RooRealVar b("b","b",2,0,10) ;
RooAbsPdf* beta = bindPdf("beta",ROOT::Math::beta_pdf,x2,a,b) ;
// Perf beta definition
beta->Print() ;
// Generate some events and fit
RooDataSet* data = beta->generate(x2,10000) ;
beta->fitTo(*data) ;
// Plot data and pdf on frame
RooPlot* frame2 = x2.frame(Title("ROOT::Math::Beta bound as RooFit pdf")) ;
data->plotOn(frame2) ;
beta->plotOn(frame2) ;
// B i n d R O O T T F 1 a s R o o F i t f u n c t i o n
// ---------------------------------------------------------------
// Create a ROOT TF1 function
TF1 *fa1 = new TF1("fa1","sin(x)/x",0,10);
// Create an observable
RooRealVar x3("x3","x3",0.01,20) ;
// Create binding of TF1 object to above observable
RooAbsReal* rfa1 = bindFunction(fa1,x3) ;
// Print rfa1 definition
rfa1->Print() ;
// Make plot frame in observable, plot TF1 binding function
RooPlot* frame3 = x3.frame(Title("TF1 bound as RooFit function")) ;
rfa1->plotOn(frame3) ;
TCanvas* c = new TCanvas("rf105_funcbinding","rf105_funcbinding",1200,400) ;
c->Divide(3) ;
c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.6) ; frame1->Draw() ;
c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.6) ; frame2->Draw() ;
c->cd(3) ; gPad->SetLeftMargin(0.15) ; frame3->GetYaxis()->SetTitleOffset(1.6) ; frame3->Draw() ;
}
void RooToyMCFit(){
stringstream out;
out.str("");
out << "toyMC_" << _sigma_over_tau << "_" << _purity << "_" << mistag_rate << ".root";
TFile* file = TFile::Open(out.str().c_str());
TTree* tree = (TTree*)file->Get("ToyTree");
tree->Print();
// TFile* file = TFile::Open("ToyData/toyMC_453_0.912888_0.3_0.8_0.root");
// TFile* file = TFile::Open("ToyData/toyMC_10000_0.9_0.3_0.8_0.root");
// TTree* tree = (TTree*)file->Get("ToyTree1");
RooRealVar tau("tau","tau",_tau,"ps"); tau.setConstant(kTRUE);
RooRealVar dm("dm","dm",_dm,"ps^{-1}"); dm.setConstant(kTRUE);
RooAbsReal* sin2beta;
RooAbsReal* cos2beta;
if(softlimit){
RooRealVar x("x","x",_cos2beta,-3.,30000.); if(constBeta) x.setConstant(kTRUE);
RooRealVar y("y","y",_sin2beta,-3.,30000.); if(constBeta) y.setConstant(kTRUE);
cos2beta = new RooFormulaVar("cos2beta","cos2beta","@0/sqrt(@0*@0+@1*@1)",RooArgSet(x,y));
sin2beta = new RooFormulaVar("sin2beta","sin2beta","@0/sqrt(@0*@0+@1*@1)",RooArgSet(y,x));
// sin2beta = new RooFormulaVar("sin2beta","sin2beta","2/TMath::Pi()*TMath::ATan(@0)",RooArgSet(y));
// cos2beta = new RooFormulaVar("cos2beta","cos2beta","2/TMath::Pi()*TMath::ATan(@0)",RooArgSet(x));
}
else{
sin2beta = new RooRealVar("sin2beta","sin2beta",_sin2beta,-3.,3.); if(constBeta) ((RooRealVar*)sin2beta)->setConstant(kTRUE);
cos2beta = new RooRealVar("cos2beta","cos2beta",_cos2beta,-3.,3.); if(constBeta) ((RooRealVar*)cos2beta)->setConstant(kTRUE);
}
RooRealVar dt("dt","#Deltat",-5.,5.,"ps");
RooRealVar avgMisgat("avgMisgat","avgMisgat",mistag_rate,0.0,0.5); if(constMistag) avgMisgat.setConstant(kTRUE);
RooRealVar delMisgat("delMisgat","delMisgat",0); delMisgat.setConstant(kTRUE);
RooRealVar mu("mu","mu",0); mu.setConstant(kTRUE);
tau.Print();
dm.Print();
sin2beta->Print();
cos2beta->Print();
avgMisgat.Print();
RooRealVar moment("moment","moment",0.); moment.setConstant(kTRUE);
RooRealVar parity("parity","parity",-1.); parity.setConstant(kTRUE);
RooRealVar* K[8];
RooAbsReal* Kb[8];
RooArgList Kset;
RooRealVar* C[8];
RooRealVar* S[8];
RooFormulaVar* a1[2][8];
RooFormulaVar* b1[2][8];
RooFormulaVar* a2[2][8];
RooFormulaVar* b2[2][8];
for(int i=0; i<8; i++){
out.str("");
out << "K" << i+1;
K[i] = new RooRealVar(out.str().c_str(),out.str().c_str(),_K[i],0.,1.); Kset.add(*K[i]); if(constK) K[i]->setConstant(kTRUE);
K[i]->Print();
if(i!=7){
out.str("");
out << "Kb" << i+1;
Kb[i] = new RooRealVar(out.str().c_str(),out.str().c_str(),_Kb[i],0.,1.); Kset.add(*Kb[i]); if(constK) ((RooRealVar*)Kb[i])->setConstant(kTRUE);
Kb[i]->Print();
}
out.str("");
out << "C" << i+1;
C[i] = new RooRealVar(out.str().c_str(),out.str().c_str(),_C[i]); C[i]->setConstant(kTRUE);
C[i]->Print();
out.str("");
out << "S" << i+1;
S[i] = new RooRealVar(out.str().c_str(),out.str().c_str(),_S[i]); S[i]->setConstant(kTRUE);
S[i]->Print();
}
Kb[7] = new RooFormulaVar("K8b","K8b","1-@0-@1-@2-@3-@4-@5-@6-@7-@8-@9-@10-@11-@12-@13-@14",Kset);
for(int i=0; i<8; i++){
out.str("");
out << "a10_" << i+1;
a1[0][i] = new RooFormulaVar(out.str().c_str(),out.str().c_str(),"-(@0-@1)/(@0+@1)",RooArgList(*K[i],*Kb[i]));
a1[0][i]->Print();
out.str("");
out << "a11_" << i+1;
a1[1][i] = new RooFormulaVar(out.str().c_str(),out.str().c_str(),"(@0-@1)/(@0+@1)",RooArgList(*K[i],*Kb[i]));
a1[1][i]->Print();
out.str("");
out << "a20_" << i+1;
a2[0][i] = new RooFormulaVar(out.str().c_str(),out.str().c_str(),"(@0-@1)/(@0+@1)",RooArgList(*K[i],*Kb[i]));
a2[0][i]->Print();
out.str("");
out << "a21_" << i+1;
a2[1][i] = new RooFormulaVar(out.str().c_str(),out.str().c_str(),"-(@0-@1)/(@0+@1)",RooArgList(*K[i],*Kb[i]));
a2[1][i]->Print();
out.str("");
out << "b10_" << i+1;
b1[0][i] = new RooFormulaVar(out.str().c_str(),out.str().c_str(),"2.*(@2*@4+@3*@5)*TMath::Sqrt(@0*@1)/(@0+@1)",RooArgList(*K[i],*Kb[i],*C[i],*S[i],*sin2beta,*cos2beta));
b1[0][i]->Print();
out.str("");
out << "b11_" << i+1;
b1[1][i] = new RooFormulaVar(out.str().c_str(),out.str().c_str(),"2.*(@2*@4-@3*@5)*TMath::Sqrt(@0*@1)/(@0+@1)",RooArgList(*K[i],*Kb[i],*C[i],*S[i],*sin2beta,*cos2beta));
b1[1][i]->Print();
out.str("");
out << "b20_" << i+1;
b2[0][i] = new RooFormulaVar(out.str().c_str(),out.str().c_str(),"-2.*(@2*@4+@3*@5)*TMath::Sqrt(@0*@1)/(@0+@1)",RooArgList(*K[i],*Kb[i],*C[i],*S[i],*sin2beta,*cos2beta));
b2[0][i]->Print();
out.str("");
out << "b21_" << i+1;
b2[1][i] = new RooFormulaVar(out.str().c_str(),out.str().c_str(),"-2.*(@2*@4-@3*@5)*TMath::Sqrt(@0*@1)/(@0+@1)",RooArgList(*K[i],*Kb[i],*C[i],*S[i],*sin2beta,*cos2beta));
b2[1][i]->Print();
cout << "bin = " << i+1 << endl;
cout << " a11 = " << a1[0][i]->getVal() << " b11 = " << b1[0][i]->getVal() << endl;
cout << " a12 = " << a1[1][i]->getVal() << " b12 = " << b1[1][i]->getVal() << endl;
cout << " a21 = " << a2[0][i]->getVal() << " b21 = " << b2[0][i]->getVal() << endl;
cout << " a22 = " << a2[1][i]->getVal() << " b22 = " << b2[1][i]->getVal() << endl;
}
RooRealVar* dgamma = new RooRealVar("dgamma","dgamma",0.); dgamma->setConstant(kTRUE);
RooRealVar* f0 = new RooRealVar("f0","f0",1.); f0->setConstant(kTRUE);
RooRealVar* f1 = new RooRealVar("f1","f1",0.); f1->setConstant(kTRUE);
RooCategory tag("tag","tag");
tag.defineType("B0",1);
tag.defineType("anti-B0",-1);
RooCategory bin("bin","bin");
bin.defineType("1",1);
bin.defineType("2",2);
bin.defineType("3",3);
bin.defineType("4",4);
bin.defineType("5",5);
bin.defineType("6",6);
bin.defineType("7",7);
bin.defineType("8",8);
bin.defineType("-1",-1);
bin.defineType("-2",-2);
bin.defineType("-3",-3);
bin.defineType("-4",-4);
bin.defineType("-5",-5);
bin.defineType("-6",-6);
bin.defineType("-7",-7);
bin.defineType("-8",-8);
RooSuperCategory bintag("bintag","bintag",RooArgSet(bin,tag));
tree->Print();
RooDataSet d("data","data",tree,RooArgSet(dt,bin,tag));
cout << "DataSet is ready." << endl;
d.Print();
RooRealVar mean("mean","mean",0.,"ps"); mean.setConstant(kTRUE);
RooRealVar sigma("sigma","sigma",_sigma_over_tau*_tau,0.,1.5*_tau,"ps"); if(constSigma) sigma.setConstant(kTRUE);
RooGaussModel rf("rf","rf",dt,mean,sigma);
// RooTruthModel rf("rf","rf",dt);
RooGaussian rfpdf("rfpdf","rfpdf",dt,mean,sigma);
cout << "Preparing PDFs..." << endl;
// RooRealVar* fsigs1[8];
// RooRealVar* fsigs1b[8];
// RooRealVar* fsigs2[8];
// RooRealVar* fsigs2b[8];
RooBDecay* sigpdfs1[8];
RooBDecay* sigpdfs1b[8];
RooBDecay* sigpdfs2[8];
RooBDecay* sigpdfs2b[8];
RooAddPdf* PDFs1[8];
RooAddPdf* PDFs1b[8];
RooAddPdf* PDFs2[8];
RooAddPdf* PDFs2b[8];
RooAddPdf* pdfs1[8];
RooAddPdf* pdfs1b[8];
RooAddPdf* pdfs2[8];
RooAddPdf* pdfs2b[8];
RooSimultaneous pdf("pdf","pdf",bintag);
RooRealVar fsig("fsig","fsig",_purity,0.,1.); if(constFSig) fsig.setConstant(kTRUE);
fsig.Print();
for(int j=0; j<8; j++){
// out.str("");
// out << "fsig1" << j+1;
// fsigs1[j] = new RooRealVar(out.str().c_str(),out.str().c_str(),_purity,0.,1.); if(constFSig) fsigs1[j]->setConstant(kTRUE);
// out.str("");
// out << "fsig1b" << j+1;
// fsigs1b[j] = new RooRealVar(out.str().c_str(),out.str().c_str(),_purity,0.,1.); if(constFSig) fsigs1b[j]->setConstant(kTRUE);
// out.str("");
// out << "fsig2" << j+1;
// fsigs2[j] = new RooRealVar(out.str().c_str(),out.str().c_str(),_purity,0.,1.); if(constFSig) fsigs2[j]->setConstant(kTRUE);
// out.str("");
// out << "fsig2b" << j+1;
// fsigs2b[j] = new RooRealVar(out.str().c_str(),out.str().c_str(),_purity,0.,1.); if(constFSig) fsigs2b[j]->setConstant(kTRUE);
out.str("");
out << "sigpdf1" << j+1;
sigpdfs1[j] = new RooBDecay(out.str().c_str(),out.str().c_str(),dt,tau,*dgamma,*f0,*f1,*a1[0][j],*b1[0][j],dm,rf,RooBDecay::DoubleSided);
out.str("");
out << "sigpdf1b" << j+1;
sigpdfs1b[j] = new RooBDecay(out.str().c_str(),out.str().c_str(),dt,tau,*dgamma,*f0,*f1,*a1[1][j],*b1[1][j],dm,rf,RooBDecay::DoubleSided);
out.str("");
out << "sigpdf2" << j+1;
sigpdfs2[j] = new RooBDecay(out.str().c_str(),out.str().c_str(),dt,tau,*dgamma,*f0,*f1,*a2[0][j],*b2[0][j],dm,rf,RooBDecay::DoubleSided);
out.str("");
out << "sigpdf2b" << j+1;
sigpdfs2b[j] = new RooBDecay(out.str().c_str(),out.str().c_str(),dt,tau,*dgamma,*f0,*f1,*a2[1][j],*b2[1][j],dm,rf,RooBDecay::DoubleSided);
out.str("");
out << "PDF1" << j+1;
PDFs1[j] = new RooAddPdf(out.str().c_str(),out.str().c_str(),RooArgList(*sigpdfs1[j],rfpdf),RooArgList(fsig));
out.str("");
out << "PDF1b" << j+1;
PDFs1b[j] = new RooAddPdf(out.str().c_str(),out.str().c_str(),RooArgList(*sigpdfs1b[j],rfpdf),RooArgList(fsig));
out.str("");
out << "PDF2" << j+1;
PDFs2[j] = new RooAddPdf(out.str().c_str(),out.str().c_str(),RooArgList(*sigpdfs2[j],rfpdf),RooArgList(fsig));
out.str("");
out << "PDF2b" << j+1;
PDFs2b[j] = new RooAddPdf(out.str().c_str(),out.str().c_str(),RooArgList(*sigpdfs2b[j],rfpdf),RooArgList(fsig));
//Adding mistaging
out.str("");
out << "pdf1" << j+1;
pdfs1[j] = new RooAddPdf(out.str().c_str(),out.str().c_str(),RooArgList(*PDFs2[j],*PDFs1[j]),RooArgList(avgMisgat));
out.str("");
out << "pdf1b" << j+1;
pdfs1b[j] = new RooAddPdf(out.str().c_str(),out.str().c_str(),RooArgList(*PDFs2b[j],*PDFs1b[j]),RooArgList(avgMisgat));
out.str("");
out << "pdf2" << j+1;
pdfs2[j] = new RooAddPdf(out.str().c_str(),out.str().c_str(),RooArgList(*PDFs1[j],*PDFs2[j]),RooArgList(avgMisgat));
out.str("");
out << "pdf2b" << j+1;
pdfs2b[j] = new RooAddPdf(out.str().c_str(),out.str().c_str(),RooArgList(*PDFs1b[j],*PDFs2b[j]),RooArgList(avgMisgat));
out.str("");
out << "{" << j+1 << ";B0}";
pdf.addPdf(*pdfs1[j],out.str().c_str());
out.str("");
out << "{" << -(j+1) << ";B0}";
pdf.addPdf(*pdfs1b[j],out.str().c_str());
out.str("");
out << "{" << j+1 << ";anti-B0}";
pdf.addPdf(*pdfs2[j],out.str().c_str());
out.str("");
out << "{" << -(j+1) << ";anti-B0}";
pdf.addPdf(*pdfs2b[j],out.str().c_str());
}
cout << "Fitting..." << endl;
pdf.fitTo(d,Verbose(),Timer());
dt.setBins(50);
cout << "Drawing plots." << endl;
for(int i=0; i<8; i++){
RooPlot* dtFrame = dt.frame();
out.str("");
out << "tag == 1 && bin == " << i+1;
RooDataSet* ds = d.reduce(out.str().c_str());
ds->plotOn(dtFrame,DataError(RooAbsData::SumW2),MarkerSize(1),Cut(out.str().c_str()),MarkerColor(kBlue));
out.str("");
out << "{" << j+1 << ";B0}";
bintag = out.str().c_str();
pdf.plotOn(dtFrame,ProjWData(*ds),Slice(bintag),LineColor(kBlue));
double chi2 = dtFrame->chiSquare();
out.str("");
out << "tag == -1 && bin == " << i+1;
RooDataSet* dsb = d.reduce(out.str().c_str());
dsb->plotOn(dtFrame,DataError(RooAbsData::SumW2),MarkerSize(1),Cut(out.str().c_str()),MarkerColor(kRed));
out.str("");
out << "{" << j+1 << ";anti-B0}";
bintag = out.str().c_str();
pdf.plotOn(dtFrame,ProjWData(*dsb),Slice(bintag),LineColor(kRed));
double chi2b = dtFrame->chiSquare();
out.str("");
out << "#Delta t, toy MC, bin == " << i+1;
TCanvas* cm = new TCanvas(out.str().c_str(),out.str().c_str(),600,400);
cm->cd();
dtFrame->GetXaxis()->SetTitleSize(0.05);
dtFrame->GetXaxis()->SetTitleOffset(0.85);
dtFrame->GetXaxis()->SetLabelSize(0.05);
dtFrame->GetYaxis()->SetTitleOffset(1.6);
TPaveText *ptB = new TPaveText(0.7,0.65,0.98,0.99,"brNDC");
ptB->SetFillColor(0);
ptB->SetTextAlign(12);
out.str("");
out << "bin = " << (i+1);
ptB->AddText(out.str().c_str());
out.str("");
out << "#chi^{2}(B^{0}) = " << chi2;
ptB->AddText(out.str().c_str());
out.str("");
out << "#chi^{2}(#barB^{0}) = " << chi2b;
ptB->AddText(out.str().c_str());
out.str("");
out << "#sigma/#tau = " << _sigma_over_tau;
ptB->AddText(out.str().c_str());
out.str("");
out << "purity = " << _purity;
ptB->AddText(out.str().c_str());
out.str("");
out << "mistag = " << mistag_rate;
ptB->AddText(out.str().c_str());
dtFrame->Draw();
ptB->Draw();
out.str("");
out << "../Reports/cpfitfigs/dt_bin" << (i+1) << ".root";
cm->Print(out.str().c_str());
out.str("");
out << "../Reports/cpfitfigs/dt_bin" << (i+1) << ".png";
cm->Print(out.str().c_str());
}
for(int i=0; i<8; i++){
RooPlot* dtFrame = dt.frame();
out.str("");
out << "tag == 1 && bin == " << -(i+1);
RooDataSet* ds2 = (RooDataSet*)d.reduce(out.str().c_str());
ds2->plotOn(dtFrame,DataError(RooAbsData::SumW2),MarkerSize(1),Cut(out.str().c_str()),MarkerColor(kBlue));
out.str("");
out << "{" << -(j+1) << ";B0}";
bintag = out.str().c_str();
pdf.plotOn(dtFrame,ProjWData(*ds2),Slice(bintag),LineColor(kBlue));
double chi2 = dtFrame->chiSquare();
out.str("");
out << "tag == -1 && bin == " << -(i+1);
RooDataSet* ds2b = (RooDataSet*)d.reduce(out.str().c_str());
ds2b->plotOn(dtFrame,DataError(RooAbsData::SumW2),MarkerSize(1),Cut(out.str().c_str()),MarkerColor(kRed));
out.str("");
out << "{" << -(j+1) << ";anti-B0}";
bin = out.str().c_str();
pdf.plotOn(dtFrame,ProjWData(*ds2b),Slice(bintag),LineColor(kRed));
double chi2b = dtFrame->chiSquare();
out.str("");
out << "#Delta t, toy MC, bin == " << -(i+1);
TCanvas* cm = new TCanvas(out.str().c_str(),out.str().c_str(),600,400);
cm->cd();
dtFrame->GetXaxis()->SetTitleSize(0.05);
dtFrame->GetXaxis()->SetTitleOffset(0.85);
dtFrame->GetXaxis()->SetLabelSize(0.05);
dtFrame->GetYaxis()->SetTitleOffset(1.6);
TPaveText *ptB = new TPaveText(0.7,0.65,0.98,0.99,"brNDC");
ptB->SetFillColor(0);
ptB->SetTextAlign(12);
out.str("");
out << "bin = " << -(i+1);
ptB->AddText(out.str().c_str());
out.str("");
out << "#chi^{2}(B^{0}) = " << chi2;
ptB->AddText(out.str().c_str());
out.str("");
out << "#chi^{2}(#barB^{0}) = " << chi2b;
ptB->AddText(out.str().c_str());
out.str("");
out << "#sigma/#tau = " << _sigma_over_tau;
ptB->AddText(out.str().c_str());
out.str("");
out << "purity = " << _purity;
ptB->AddText(out.str().c_str());
out.str("");
out << "mistag = " << mistag_rate;
ptB->AddText(out.str().c_str());
dtFrame->Draw();
ptB->Draw();
out.str("");
out << "../Reports/cpfitfigs/dt_bin" << -(i+1) << ".root";
cm->Print(out.str().c_str());
out.str("");
out << "../Reports/cpfitfigs/dt_bin" << -(i+1) << ".png";
cm->Print(out.str().c_str());
}
return;
}
void MakePlots(RooWorkspace* ws){
// Here we make plots of the discriminating variable (invMass) after the fit
// and of the control variable (isolation) after unfolding with sPlot.
std::cout << "make plots" << std::endl;
// make our canvas
TCanvas* cdata = new TCanvas("sPlot","sPlot demo", 400, 600);
cdata->Divide(1,3);
// get what we need out of the workspace
RooAbsPdf* model = ws->pdf("model");
RooAbsPdf* zModel = ws->pdf("zModel");
RooAbsPdf* qcdModel = ws->pdf("qcdModel");
RooRealVar* isolation = ws->var("isolation");
RooRealVar* invMass = ws->var("invMass");
// note, we get the dataset with sWeights
RooDataSet* data = (RooDataSet*) ws->data("dataWithSWeights");
// this shouldn't be necessary, need to fix something with workspace
// do this to set parameters back to their fitted values.
model->fitTo(*data, Extended() );
//plot invMass for data with full model and individual componenets overlayed
// TCanvas* cdata = new TCanvas();
cdata->cd(1);
RooPlot* frame = invMass->frame() ;
data->plotOn(frame ) ;
model->plotOn(frame) ;
model->plotOn(frame,Components(*zModel),LineStyle(kDashed), LineColor(kRed)) ;
model->plotOn(frame,Components(*qcdModel),LineStyle(kDashed),LineColor(kGreen)) ;
frame->SetTitle("Fit of model to discriminating variable");
frame->Draw() ;
// Now use the sWeights to show isolation distribution for Z and QCD.
// The SPlot class can make this easier, but here we demonstrait in more
// detail how the sWeights are used. The SPlot class should make this
// very easy and needs some more development.
// Plot isolation for Z component.
// Do this by plotting all events weighted by the sWeight for the Z component.
// The SPlot class adds a new variable that has the name of the corresponding
// yield + "_sw".
cdata->cd(2);
// create weightfed data set
RooDataSet * dataw_z = new RooDataSet(data->GetName(),data->GetTitle(),data,*data->get(),0,"zYield_sw") ;
RooPlot* frame2 = isolation->frame() ;
dataw_z->plotOn(frame2, DataError(RooAbsData::SumW2) ) ;
frame2->SetTitle("isolation distribution for Z");
frame2->Draw() ;
// Plot isolation for QCD component.
// Eg. plot all events weighted by the sWeight for the QCD component.
// The SPlot class adds a new variable that has the name of the corresponding
// yield + "_sw".
cdata->cd(3);
RooDataSet * dataw_qcd = new RooDataSet(data->GetName(),data->GetTitle(),data,*data->get(),0,"qcdYield_sw") ;
RooPlot* frame3 = isolation->frame() ;
dataw_qcd->plotOn(frame3,DataError(RooAbsData::SumW2) ) ;
frame3->SetTitle("isolation distribution for QCD");
frame3->Draw() ;
// cdata->SaveAs("SPlot.gif");
}
RooGaussian fitZToMuMuGammaMassUnbinned(const char *filename = "ZToMuMuGammaMass.txt",
const char* plotOpt = "NEU",
const int nbins = 25)
{
gROOT->ProcessLine(".L tdrstyle.C");
setTDRStyle();
gStyle->SetPadRightMargin(0.05);
double minMass = 60;
double maxMass = 120;
RooRealVar mass("mass","M(#mu#mu#gamma})", minMass, maxMass,"GeV/c^{2}");
// Read data set
RooDataSet *data = RooDataSet::read(filename,RooArgSet(mass));
// RooDataSet *dataB = RooDataSet::read(filenameB,RooArgSet(mass));
// Build p.d.f.
////////////////////////////////////////////////
// Parameters //
////////////////////////////////////////////////
// Signal p.d.f. parameters
// Parameters for a Gaussian and a Crystal Ball Lineshape
RooRealVar m0 ("m_{0}", "Bias", 91.19, minMass, maxMass,"GeV/c^{2}");
RooRealVar sigma("#sigma","Width", 3.0,1.0,10.0,"GeV/c^{2}");
RooRealVar cut ("#alpha","Cut", 0.6,0.6,2.0);
RooRealVar power("power","Power", 10.0, 0.5, 20.0);
// Background p.d.f. parameters
// Parameters for a polynomial lineshape
RooRealVar c0("c_{0}", "c0", 0., -10, 10);
RooRealVar c1("c_{1}", "c1", 0., -100, 0);
RooRealVar c2("c_{2}", "c2", 0., -100, 100);
// c0.setConstant();
// fraction of signal
// RooRealVar frac("frac", "Signal Fraction", 0.1,0.,0.3.);
RooRealVar nsig("N_{S}", "#signal events", 9000, 0.,10000.);
RooRealVar nbkg("N_{B}", "#background events", 1000,2,10000.);
////////////////////////////////////////////////
// P.D.F.s //
////////////////////////////////////////////////
// Di-photon mass signal p.d.f.
RooGaussian signal("signal", "A Gaussian Lineshape", mass, m0, sigma);
// RooCBShape signal("signal", "A Crystal Ball Lineshape", mass, m0,sigma, cut, power);
// Di-photon mass background p.d.f.
RooPolynomial bg("bg", "Backgroung Distribution", mass, RooArgList(c0,c1));
// Di-photon mass model p.d.f.
// RooAddPdf model("model", "Di-photon mass model", signal, bg, frac);
RooAddPdf model("model", "Di-photon mass model", RooArgList(signal, bg), RooArgList(nsig, nbkg));
TStopwatch t ;
t.Start() ;
// model->fitTo(*data,FitOptions("mh"),Optimize(0),Timer(1));
signal->fitTo(*data,FitOptions("mh"),Optimize(0),Timer(1));
t.Print() ;
c = new TCanvas("c","Unbinned Invariant Mass Fit", 0,0,800,600);
// Plot the fit results
RooPlot* plot = mass.frame(Range(minMass,maxMass),Bins(nbins));
// Plot 1
// dataB->plotOn(plot, MarkerColor(kRed), LineColor(kRed));
data->plotOn(plot);
// model.plotOn(plot);
model.plotOn(plot);
//model.paramOn(plot, Format(plotOpt, AutoPrecision(1)), Parameters(RooArgSet(nsig, nbkg, m0, sigma)));
model.paramOn(plot, Format(plotOpt, AutoPrecision(1)), Parameters(RooArgSet(m0, sigma)));
/// model.plotOn(plot, Components("signal"), LineStyle(kDashed), LineColor(kRed));
// model.plotOn(plot, Components("bg"), LineStyle(kDashed), LineColor(kRed));
plot->Draw();
TLatex * tex = new TLatex(0.2,0.8,"CMS preliminary");
tex->SetNDC();
tex->SetTextFont(42);
tex->SetLineWidth(2);
tex->Draw();
tex->DrawLatex(0.2, 0.725, "7 TeV Data, L = 258 pb^{-1}");
float fsig_peak = NormalizedIntegral(signal,
mass,
m0.getVal() - 2.5*sigma.getVal(),
m0.getVal() + 2.5*sigma.getVal()
);
// float fbkg_peak = NormalizedIntegral(bg,
// mass,
// m0.getVal() - 2.5*sigma.getVal(),
// m0.getVal() + 2.5*sigma.getVal()
// );
double nsigVal = fsig_peak * nsig.getVal();
double nsigErr = fsig_peak * nsig.getError();
double nsigErrRel = nsigErr / nsigVal;
// double nbkgVal = fbkg_peak * nbkg.getVal();
// double nbkgErr = fbkg_peak * nbkg.getError();
// double nbkgErrRel = nbkgErr / nbkgVal;
cout << "nsig " << nsigVal << " +/- " << nsigErr << endl;
// cout << "S/B_{#pm2.5#sigma} " << nsigVal/nbkgVal << " +/- "
// << (nsigVal/nbkgVal)*sqrt(nsigErrRel*nsigErrRel + nbkgErrRel*nbkgErrRel)
// << endl;
// tex->DrawLatex(0.2, 0.6, Form("N_{S} = %.0f#pm%.0f", nsigVal, nsigErr) );
// tex->DrawLatex(0.2, 0.525, Form("S/B_{#pm2.5#sigma} = %.1f", nsigVal/nbkgVal) );
// tex->DrawLatex(0.2, 0.45, Form("#frac{S}{#sqrt{B}}_{#pm2.5#sigma} = %.1f", nsigVal/sqrt(nbkgVal)));
leg = new TLegend(0.65,0.6,0.9,0.75);
leg->SetFillColor(kWhite);
leg->SetLineColor(kWhite);
leg->SetShadowColor(kWhite);
leg->SetTextFont(42);
// TLegendEntry * ldata = leg->AddEntry(data, "Opposite Sign");
// TLegendEntry * ldataB = leg->AddEntry(dataB, "Same Sign");
// ldata->SetMarkerStyle(20);
// ldataB->SetMarkerStyle(20);
// ldataB->SetMarkerColor(kRed);
leg->Draw();
return signal;
}
void runUPWW() {
int higgsMass=125;
double intLumi=5.1;
int nToys = 10;
bool draw=true;
using namespace RooFit;
gROOT->ProcessLine(".L ~/tdrstyle.C");
setTDRStyle();
gStyle->SetPadLeftMargin(0.16);
gROOT->ForceStyle();
gROOT->ProcessLine(".L statsFactory.cc+");
//
// set up test kind
//
double sigRate;
double bkgRate;
if(higgsMass==125){
sigRate = 7.;
bkgRate = 66.;
}else{
cout << "HMMMM.... I don't know that mass point...BYE!" << endl;
return;
}
RooRealVar* mll = new RooRealVar("mll","dilepton mass [GeV]", 12, 80.);
mll->setBins(17);
RooArgSet* obs = new RooArgSet(*mll) ;
// read signal hypothesis 1
TChain *tsigHyp1 = new TChain("angles");
tsigHyp1->Add(Form("datafiles/bdtpresel/%i/SMHiggsWW_%i_JHU.root",higgsMass, higgsMass));
RooDataSet *sigHyp1Data = new RooDataSet("sigHyp1Data","sigHyp1Data",tsigHyp1,*obs);
RooDataHist *sigHyp1Hist = sigHyp1Data->binnedClone(0);
RooHistPdf* sigHyp1Pdf = new RooHistPdf("sigHyp1Pdf", "sigHyp1Pdf", *obs, *sigHyp1Hist);
// read background
TChain *bkgTree = new TChain("angles");
bkgTree->Add(Form("datafiles/bdtpresel/%i/WW_madgraph_8TeV.root",higgsMass));
RooDataSet *bkgData = new RooDataSet("bkgData","bkgData",bkgTree,*obs);
RooDataHist *bkgHist = bkgData->binnedClone(0);
RooHistPdf* bkgPdf = new RooHistPdf("bkgPdf", "bkgPdf", *obs, *bkgHist);
char statResults[25];
statsFactory *hwwuls;
sprintf(statResults,"uls_hww125_%.0ffb.root", intLumi);
hwwuls = new statsFactory(obs, sigHyp1Pdf, sigHyp1Pdf, statResults);
hwwuls->runUpperLimitWithBackground(sigRate*intLumi, bkgRate*intLumi, bkgPdf, nToys);
delete hwwuls;
// draw plots
if(draw) {
RooPlot* plot1 = mll->frame();
TString plot1Name = "mll";
TCanvas* c1 = new TCanvas("c1","c1",400,400);
bkgData->plotOn(plot1,MarkerColor(kBlack));
bkgPdf->plotOn(plot1, LineColor(kBlack), LineStyle(kDashed));
sigHyp1Data->plotOn(plot1,MarkerColor(kRed));
sigHyp1Pdf->plotOn(plot1,LineColor(kRed), LineStyle(kDashed));
// draw...
plot1->Draw();
c1->SaveAs(Form("plots/ul/epsfiles/%s.eps", plot1Name.Data()));
c1->SaveAs(Form("plots/ul/pngfiles/%s.png", plot1Name.Data()));
delete c1;
}
}