////////////////////////////////////////////////////////////////////////// // // 'BASIC FUNCTIONALITY' RooFit tutorial macro #103 // // Interpreted functions and p.d.f.s // // // // 07/2008 - Wouter Verkerke // ///////////////////////////////////////////////////////////////////////// #ifndef __CINT__ #include "RooGlobalFunc.h" #endif #include "RooRealVar.h" #include "RooDataSet.h" #include "RooGaussian.h" #include "TCanvas.h" #include "TAxis.h" #include "RooPlot.h" #include "RooFitResult.h" #include "RooGenericPdf.h" #include "RooConstVar.h" using namespace RooFit ; 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() ; }