rf406_cattocatfuncs.C

Go to the documentation of this file.

/// \file
/// \ingroup tutorial_roofit
/// \notebook -nodraw
///  'DATA AND CATEGORIES' RooFit tutorial macro #406
///
///  Demonstration of discrete-->discrete (invertable) functions
///
/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke
 
 
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooPolynomial.h"
#include "RooCategory.h"
#include "RooMappedCategory.h"
#include "RooMultiCategory.h"
#include "RooSuperCategory.h"
#include "Roo1DTable.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
using namespace RooFit ;
 
 
void rf406_cattocatfuncs()
{
   // C o n s t r u c t  t w o   c a t e g o r i e s
   // ----------------------------------------------
 
   // Define a category with labels only
   RooCategory tagCat("tagCat","Tagging category") ;
   tagCat.defineType("Lepton") ;
   tagCat.defineType("Kaon") ;
   tagCat.defineType("NetTagger-1") ;
   tagCat.defineType("NetTagger-2") ;
   tagCat.Print() ;
 
   // Define a category with explicitly numbered states
   RooCategory b0flav("b0flav","B0 flavour eigenstate") ;
   b0flav.defineType("B0",-1) ;
   b0flav.defineType("B0bar",1) ;
   b0flav.Print() ;
 
   // Construct a dummy dataset with random values of tagCat and b0flav
   RooRealVar x("x","x",0,10) ;
   RooPolynomial p("p","p",x) ;
   RooDataSet* data = p.generate(RooArgSet(x,b0flav,tagCat),10000) ;
 
 
 
   // C r e a t e   a   c a t - > c a t   m  a p p i n g   c a t e g o r y
   // ---------------------------------------------------------------------
 
   // A RooMappedCategory is category->category mapping function based on string expression
   // The constructor takes an input category an a default state name to which unassigned
   // states are mapped
   RooMappedCategory tcatType("tcatType","tagCat type",tagCat,"Cut based") ;
 
   // Enter fully specified state mappings
   tcatType.map("Lepton","Cut based") ;
   tcatType.map("Kaon","Cut based") ;
 
   // Enter a wilcard expression mapping
   tcatType.map("NetTagger*","Neural Network") ;
 
   // Make a table of the mapped category state multiplicity in data
   Roo1DTable* mtable = data->table(tcatType) ;
   mtable->Print("v") ;
 
 
 
   // C r e a t e   a   c a t   X   c a t   p r o d u c t   c a t e g o r y
   // ----------------------------------------------------------------------
 
   // A SUPER-category is 'product' of _lvalue_ categories. The state names of a super
   // category is a composite of the state labels of the input categories
   RooSuperCategory b0Xtcat("b0Xtcat","b0flav X tagCat",RooArgSet(b0flav,tagCat)) ;
 
   // Make a table of the product category state multiplicity in data
   Roo1DTable* stable = data->table(b0Xtcat) ;
   stable->Print("v") ;
 
   // Since the super category is an lvalue, assignment is explicitly possible
   b0Xtcat.setLabel("{B0bar;Lepton}") ;
 
 
 
   // A MULTI-category is a 'product' of any category (function). The state names of a super
   // category is a composite of the state labels of the input categories
   RooMultiCategory b0Xttype("b0Xttype","b0flav X tagType",RooArgSet(b0flav,tcatType)) ;
 
   // Make a table of the product category state multiplicity in data
   Roo1DTable* xtable = data->table(b0Xttype) ;
   xtable->Print("v") ;
 
 
}