doc/v610/rf510__wsnamedsets_8C.html

#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooChebychev.h"
#include "RooAddPdf.h"
#include "RooWorkspace.h"
#include "RooPlot.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "TFile.h"
#include "TH1.h"
using namespace RooFit;
void fillWorkspace(RooWorkspace& w) ;
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 fillWorkspace(RooWorkspace& w)
{
   // C r e a t e   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",5,0,10) ;
   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) ;
   // Import model into p.d.f.
   w.import(model) ;
   // E n c o d e   d e f i n i t i o n   o f   p a r a m e t e r s   i n   w o r k s p a c e
   // ---------------------------------------------------------------------------------------
   // Define named sets "parameters" and "observables", which list which variables should be considered
   // parameters and observables by the users convention
   //
   // Variables appearing in sets _must_ live in the workspace already, or the autoImport flag
   // of defineSet must be set to import them on the fly. Named sets contain only references
   // to the original variables, therefore the value of observables in named sets already
   // reflect their 'current' value
   RooArgSet* params = (RooArgSet*) model.getParameters(x) ;
   w.defineSet("parameters",*params) ;
   w.defineSet("observables",x) ;
   // E n c o d e   r e f e r e n c e   v a l u e   f o r   p a r a m e t e r s   i n   w o r k s p a c e
   // ---------------------------------------------------------------------------------------------------
   // Define a parameter 'snapshot' in the p.d.f.
   // Unlike a named set, a parameter snapshot stores an independent set of values for
   // a given set of variables in the workspace. The values can be stored and reloaded
   // into the workspace variable objects using the loadSnapshot() and saveSnapshot()
   // methods. A snapshot saves the value of each variable, any errors that are stored
   // with it as well as the 'Constant' flag that is used in fits to determine if a
   // parameter is kept fixed or not.
   // Do a dummy fit to a (supposedly) reference dataset here and store the results
   // of that fit into a snapshot
   RooDataSet* refData = model.generate(x,10000) ;
   model.fitTo(*refData,PrintLevel(-1)) ;
   // The kTRUE flag imports the values of the objects in (*params) into the workspace
   // If not set, the present values of the workspace parameters objects are stored
   w.saveSnapshot("reference_fit",*params,kTRUE) ;
   // Make another fit with the signal component forced to zero
   // and save those parameters too
   bkgfrac.setVal(1) ;
   bkgfrac.setConstant(kTRUE) ;
   bkgfrac.removeError() ;
   model.fitTo(*refData,PrintLevel(-1)) ;
   w.saveSnapshot("reference_fit_bkgonly",*params,kTRUE) ;
}