////////////////////////////////////////////////////////////////////////// // // 'SPECIAL PDFS' RooFit tutorial macro #702 // // Unbinned maximum likelihood fit of an efficiency eff(x) function to // a dataset D(x,cut), where cut is a category encoding a selection whose // efficiency as function of x should be described by eff(x) // // ///////////////////////////////////////////////////////////////////////// #ifndef __CINT__ #include "RooGlobalFunc.h" #endif #include "RooRealVar.h" #include "RooDataSet.h" #include "RooGaussian.h" #include "RooConstVar.h" #include "RooCategory.h" #include "RooEfficiency.h" #include "RooPolynomial.h" #include "RooProdPdf.h" #include "RooFormulaVar.h" #include "TCanvas.h" #include "TAxis.h" #include "TH1.h" #include "RooPlot.h" using namespace RooFit ; void rf702_efficiencyfit_2D(Bool_t flat=kFALSE) { // C o n s t r u c t e f f i c i e n c y f u n c t i o n e ( x , y ) // ----------------------------------------------------------------------- // Declare variables x,mean,sigma with associated name, title, initial value and allowed range RooRealVar x("x","x",-10,10) ; RooRealVar y("y","y",-10,10) ; // Efficiency function eff(x;a,b) RooRealVar ax("ax","ay",0.6,0,1) ; RooRealVar bx("bx","by",5) ; RooRealVar cx("cx","cy",-1,-10,10) ; RooRealVar ay("ay","ay",0.2,0,1) ; RooRealVar by("by","by",5) ; RooRealVar cy("cy","cy",-1,-10,10) ; RooFormulaVar effFunc("effFunc","((1-ax)+ax*cos((x-cx)/bx))*((1-ay)+ay*cos((y-cy)/by))",RooArgList(ax,bx,cx,x,ay,by,cy,y)) ; // Acceptance state cut (1 or 0) RooCategory cut("cut","cutr") ; cut.defineType("accept",1) ; cut.defineType("reject",0) ; // C o n s t r u c t c o n d i t i o n a l e f f i c i e n c y p d f E ( c u t | x , y ) // --------------------------------------------------------------------------------------------- // Construct efficiency p.d.f eff(cut|x) RooEfficiency effPdf("effPdf","effPdf",effFunc,cut,"accept") ; // G e n e r a t e d a t a ( x , y , c u t ) f r o m a t o y m o d e l // ------------------------------------------------------------------------------- // Construct global shape p.d.f shape(x) and product model(x,cut) = eff(cut|x)*shape(x) // (These are _only_ needed to generate some toy MC here to be used later) RooPolynomial shapePdfX("shapePdfX","shapePdfX",x,RooConst(flat?0:-0.095)) ; RooPolynomial shapePdfY("shapePdfY","shapePdfY",y,RooConst(flat?0:+0.095)) ; RooProdPdf shapePdf("shapePdf","shapePdf",RooArgSet(shapePdfX,shapePdfY)) ; RooProdPdf model("model","model",shapePdf,Conditional(effPdf,cut)) ; // Generate some toy data from model RooDataSet* data = model.generate(RooArgSet(x,y,cut),10000) ; // F i t c o n d i t i o n a l e f f i c i e n c y p d f t o d a t a // -------------------------------------------------------------------------- // Fit conditional efficiency p.d.f to data effPdf.fitTo(*data,ConditionalObservables(RooArgSet(x,y))) ; // P l o t f i t t e d , d a t a e f f i c i e n c y // -------------------------------------------------------- // Make 2D histograms of all data, selected data and efficiency function TH1* hh_data_all = data->createHistogram("hh_data_all",x,Binning(8),YVar(y,Binning(8))) ; TH1* hh_data_sel = data->createHistogram("hh_data_sel",x,Binning(8),YVar(y,Binning(8)),Cut("cut==cut::accept")) ; TH1* hh_eff = effFunc.createHistogram("hh_eff",x,Binning(50),YVar(y,Binning(50))) ; // Some adjustsment for good visualization hh_data_all->SetMinimum(0) ; hh_data_sel->SetMinimum(0) ; hh_eff->SetMinimum(0) ; hh_eff->SetLineColor(kBlue) ; // Draw all frames on a canvas TCanvas* ca = new TCanvas("rf702_efficiency_2D","rf702_efficiency_2D",1200,400) ; ca->Divide(3) ; ca->cd(1) ; gPad->SetLeftMargin(0.15) ; hh_data_all->GetZaxis()->SetTitleOffset(1.8) ; hh_data_all->Draw("lego") ; ca->cd(2) ; gPad->SetLeftMargin(0.15) ; hh_data_sel->GetZaxis()->SetTitleOffset(1.8) ; hh_data_sel->Draw("lego") ; ca->cd(3) ; gPad->SetLeftMargin(0.15) ; hh_eff->GetZaxis()->SetTitleOffset(1.8) ; hh_eff->Draw("surf") ; return ; }