/// \file /// \ingroup tutorial_roofit /// 'SPECIAL PDFS' RooFit tutorial macro #707 /// /// Using non-parametric (multi-dimensional) kernel estimation p.d.f.s /// /// /// /// \macro_code /// \author 07/2008 - Wouter Verkerke #ifndef __CINT__ #include "RooGlobalFunc.h" #endif #include "RooRealVar.h" #include "RooDataSet.h" #include "RooGaussian.h" #include "RooConstVar.h" #include "RooPolynomial.h" #include "RooKeysPdf.h" #include "RooNDKeysPdf.h" #include "RooProdPdf.h" #include "TCanvas.h" #include "TAxis.h" #include "TH1.h" #include "RooPlot.h" using namespace RooFit ; void rf707_kernelestimation() { // C r e a t e l o w s t a t s 1 - D d a t a s e t // ------------------------------------------------------- // Create a toy pdf for sampling RooWorkspace *w; w = new RooWorkspace("w","workspace"); RooRealVar x("x","x",0,20) ; RooPolynomial p("p","p",x,RooArgList(RooConst(0.01),RooConst(-0.01),RooConst(0.0004))) ; // Sample 500 events from p RooDataSet* data1 = p.generate(x,200) ; // C r e a t e 1 - D k e r n e l e s t i m a t i o n p d f // --------------------------------------------------------------- // Create adaptive kernel estimation pdf. In this configuration the input data // is mirrored over the boundaries to minimize edge effects in distribution // that do not fall to zero towards the edges RooKeysPdf kest1("kest1","kest1",x,*data1,RooKeysPdf::MirrorBoth) ; // An adaptive kernel estimation pdf on the same data without mirroring option // for comparison RooKeysPdf kest2("kest2","kest2",x,*data1,RooKeysPdf::NoMirror) ; // Adaptive kernel estimation pdf with increased bandwidth scale factor // (promotes smoothness over detail preservation) RooKeysPdf kest3("kest1","kest1",x,*data1,RooKeysPdf::MirrorBoth,2) ; // Plot kernel estimation pdfs with and without mirroring over data RooPlot* frame = x.frame(Title("Adaptive kernel estimation pdf with and w/o mirroring"),Bins(20)) ; data1->plotOn(frame) ; kest1.plotOn(frame) ; kest2.plotOn(frame,LineStyle(kDashed),LineColor(kRed)) ; // Plot kernel estimation pdfs with regular and increased bandwidth RooPlot* frame2 = x.frame(Title("Adaptive kernel estimation pdf with regular, increased bandwidth")) ; kest1.plotOn(frame2) ; kest3.plotOn(frame2,LineColor(kMagenta)) ; // C r e a t e l o w s t a t s 2 - D d a t a s e t // ------------------------------------------------------- // Construct a 2D toy pdf for sampleing RooRealVar y("y","y",0,20) ; RooPolynomial py("py","py",y,RooArgList(RooConst(0.01),RooConst(0.01),RooConst(-0.0004))) ; RooProdPdf pxy("pxy","pxy",RooArgSet(p,py)) ; RooDataSet* data2 = pxy.generate(RooArgSet(x,y),1000) ; // C r e a t e 2 - D k e r n e l e s t i m a t i o n p d f // --------------------------------------------------------------- // Create 2D adaptive kernel estimation pdf with mirroring RooNDKeysPdf kest4("kest4","kest4",RooArgSet(x,y),*data2,"am") ; // Create 2D adaptive kernel estimation pdf with mirroring and double bandwidth RooNDKeysPdf kest5("kest5","kest5",RooArgSet(x,y),*data2,"am",2) ; w->import(kest4); // Create a histogram of the data TH1* hh_data = data2->createHistogram("hh_data",x,Binning(10),YVar(y,Binning(10))) ; // Create histogram of the 2d kernel estimation pdfs TH1* hh_pdf = kest4.createHistogram("hh_pdf",x,Binning(25),YVar(y,Binning(25))) ; TH1* hh_pdf2 = kest5.createHistogram("hh_pdf2",x,Binning(25),YVar(y,Binning(25))) ; hh_pdf->SetLineColor(kBlue) ; hh_pdf2->SetLineColor(kMagenta) ; TCanvas* c = new TCanvas("rf707_kernelestimation","rf707_kernelestimation",800,800) ; c->Divide(2,2) ; c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ; c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.8) ; frame2->Draw() ; c->cd(3) ; gPad->SetLeftMargin(0.15) ; hh_data->GetZaxis()->SetTitleOffset(1.4) ; hh_data->Draw("lego") ; c->cd(4) ; gPad->SetLeftMargin(0.20) ; hh_pdf->GetZaxis()->SetTitleOffset(2.4) ; hh_pdf->Draw("surf") ; hh_pdf2->Draw("surfsame") ; // w->writeToFile("TestingNKeysPerst.root") ; }