rf402_datahandling.C File Reference

Detailed Description

'DATA AND CATEGORIES' RooFit tutorial macro #402

Tools for manipulation of (un)binned datasets

Processing /mnt/vdb/lsf/workspace/root-makedoc-v608/rootspi/rdoc/src/v6-08-00-patches/tutorials/roofit/rf402_datahandling.C...

RooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby 
                Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
                All rights reserved, please read http://roofit.sourceforge.net/license.txt

DataStore d (d)
  Contains 1000 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  y = 31.607  L(0 - 40)  "y"
    3)  c = Plus(idx = 1)
  "c"

  1) 0x3727e20 RooRealVar:: x = 9  L(-10 - 10)  "x"
  2) 0x374e440 RooRealVar:: y = 31.607  L(0 - 40)  "y"
  3) 0x2c10f50 RooCategory:: c = Plus(idx = 1)
  "c"

  1) 0x3727e20 RooRealVar:: x = 8  L(-10 - 10)  "x"
  2) 0x374e440 RooRealVar:: y = 30  L(0 - 40)  "y"
  3) 0x2c10f50 RooCategory:: c = Minus(idx = -1)
  "c"


>> d1 has only columns x,c
DataStore d (d)
  Contains 1000 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  c = Plus(idx = 1)
  "c"

>> d2 has only column y
DataStore d (d)
  Contains 1000 entries
  Observables: 
    1)  y = 31.607  L(0 - 40)  "y"

>> d3 has only the points with y>5.17
DataStore d (d)
  Contains 973 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  y = 31.607  L(0 - 40)  "y"
    3)  c = Plus(idx = 1)
  "c"

>> d4 has only columns x,c for data points with y>5.17
DataStore d (d)
  Contains 973 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  c = Plus(idx = 1)
  "c"

>> merge d2(y) with d1(x,c) to form d1(x,c,y)
DataStore d (d)
  Contains 1000 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  c = Plus(idx = 1)
  "c"
    3)  y = 31.607  L(0 - 40)  "y"

>> append data points of d3 to d1
DataStore d (d)
  Contains 1973 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  c = Plus(idx = 1)
  "c"
    3)  y = 31.607  L(0 - 40)  "y"
>> construct dh (binned) from d(unbinned) but only take the x and y dimensions,
>> the category 'c' will be projected in the filling process
DataStore dh (binned version of d)
  Contains 100 entries
  Observables: 
    1)  x = 9  L(-10 - 10) B(10)  "x"
    2)  y = 31.607  L(0 - 40) B(10)  "y"
Binned Dataset dh (binned version of d)
  Contains 100 bins with a total weight of 1000
  Observables:     1)  x = 9  L(-10 - 10) B(10)  "x"
    2)  y = 31.607  L(0 - 40) B(10)  "y"
>> number of bins in dh   : 100
>> sum of weights in dh   : 1000
>> integral over histogram: 8000
>> retrieving the properties of the bin enclosing coordinate (x,y) = (0.3,20.5) 
 bin center:
  1) 0x385e930 RooRealVar:: x = 1  L(-10 - 10) B(10)  "x"
  2) 0x333bf30 RooRealVar:: y = 22  L(0 - 40) B(10)  "y"
 weight = 76
>> Creating 1-dimensional projection on y of dh for bins with x>0
DataStore dh (binned version of d)
  Contains 10 entries
  Observables: 
    1)  y = 38  L(0 - 40) B(10)  "y"
Binned Dataset dh (binned version of d)
  Contains 10 bins with a total weight of 500
  Observables:     1)  y = 38  L(0 - 40) B(10)  "y"
[#1] INFO:Plotting -- RooPlot::updateFitRangeNorm: New event count of 500 will supercede previous event count of 1000 for normalization of PDF projections

>> Persisting d via ROOT I/O
TFile**     rf402_datahandling.root 
 TFile*     rf402_datahandling.root 
  KEY: RooDataSet d;1   d
  KEY: TProcessID ProcessID0;1   096ff4f4-957f-11e6-8cdc-e2c88e80beef

#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooCategory.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
#include "TFile.h"
using namespace RooFit ;

// WVE Add reduction by range


void rf402_datahandling()
{

   // Binned (RooDataHist) and unbinned datasets (RooDataSet) share
   // many properties and inherit from a common abstract base class
   // (RooAbsData), that provides an interface for all operations
   // that can be performed regardless of the data format

   RooRealVar  x("x","x",-10,10) ;
   RooRealVar  y("y","y", 0, 40) ;
   RooCategory c("c","c") ;
   c.defineType("Plus",+1) ;
   c.defineType("Minus",-1) ;



   // B a s i c   O p e r a t i o n s   o n   u n b i n n e d   d a t a s e t s 
   // --------------------------------------------------------------

   // RooDataSet is an unbinned dataset (a collection of points in N-dimensional space)
   RooDataSet d("d","d",RooArgSet(x,y,c)) ;

   // Unlike RooAbsArgs (RooAbsPdf,RooFormulaVar,....) datasets are not attached to 
   // the variables they are constructed from. Instead they are attached to an internal 
   // clone of the supplied set of arguments

   // Fill d with dummy values
   Int_t i ;
   for (i=0 ; i<1000 ; i++) {
      x = i/50 - 10 ;
      y = sqrt(1.0*i) ;
      c.setLabel((i%2)?"Plus":"Minus") ;

      // We must explicitly refer to x,y,c here to pass the values because
      // d is not linked to them (as explained above)
      d.add(RooArgSet(x,y,c)) ;
   }
   d.Print("v") ;
   cout << endl ;

   // The get() function returns a pointer to the internal copy of the RooArgSet(x,y,c)
   // supplied in the constructor
   const RooArgSet* row = d.get() ;
   row->Print("v") ;
   cout << endl ;

   // Get with an argument loads a specific data point in row and returns
   // a pointer to row argset. get() always returns the same pointer, unless
   // an invalid row number is specified. In that case a null ptr is returned
   d.get(900)->Print("v") ;
   cout << endl ;



   // R e d u c i n g ,   A p p e n d i n g   a n d   M e r g i n g
   // -------------------------------------------------------------

   // The reduce() function returns a new dataset which is a subset of the original
   cout << endl << ">> d1 has only columns x,c" << endl ;
   RooDataSet* d1 = (RooDataSet*) d.reduce(RooArgSet(x,c)) ; 
   d1->Print("v") ;

   cout << endl << ">> d2 has only column y" << endl ;
   RooDataSet* d2 = (RooDataSet*) d.reduce(RooArgSet(y)) ;   
   d2->Print("v") ;

   cout << endl << ">> d3 has only the points with y>5.17" << endl ;
   RooDataSet* d3 = (RooDataSet*) d.reduce("y>5.17") ; 
   d3->Print("v") ;

   cout << endl << ">> d4 has only columns x,c for data points with y>5.17" << endl ;
   RooDataSet* d4 = (RooDataSet*) d.reduce(RooArgSet(x,c),"y>5.17") ; 
   d4->Print("v") ;

   // The merge() function adds two data set column-wise
   cout << endl << ">> merge d2(y) with d1(x,c) to form d1(x,c,y)" << endl ;
   d1->merge(d2) ; 
   d1->Print("v") ;

   // The append() function addes two datasets row-wise
   cout << endl << ">> append data points of d3 to d1" << endl ;
   d1->append(*d3) ;
   d1->Print("v") ;  



   // O p e r a t i o n s   o n   b i n n e d   d a t a s e t s 
   // ---------------------------------------------------------

   // A binned dataset can be constructed empty, from an unbinned dataset, or
   // from a ROOT native histogram (TH1,2,3)

   cout << ">> construct dh (binned) from d(unbinned) but only take the x and y dimensions," << endl 
         << ">> the category 'c' will be projected in the filling process" << endl ;
      
   // The binning of real variables (like x,y) is done using their fit range
   // 'get/setRange()' and number of specified fit bins 'get/setBins()'.
   // Category dimensions of binned datasets get one bin per defined category state
   x.setBins(10) ;
   y.setBins(10) ;
   RooDataHist dh("dh","binned version of d",RooArgSet(x,y),d) ;
   dh.Print("v") ;

   RooPlot* yframe = y.frame(Bins(10),Title("Operations on binned datasets")) ;
   dh.plotOn(yframe) ; // plot projection of 2D binned data on y

   // Examine the statistics of a binned dataset
   cout << ">> number of bins in dh   : " << dh.numEntries() << endl ;
   cout << ">> sum of weights in dh   : " << dh.sum(kFALSE)  << endl ;
   cout << ">> integral over histogram: " << dh.sum(kTRUE)   << endl ; // accounts for bin volume

   // Locate a bin from a set of coordinates and retrieve its properties
   x = 0.3 ;  y = 20.5 ;
   cout << ">> retrieving the properties of the bin enclosing coordinate (x,y) = (0.3,20.5) " << endl ;
   cout << " bin center:" << endl ;
   dh.get(RooArgSet(x,y))->Print("v") ; // load bin center coordinates in internal buffer
   cout << " weight = " << dh.weight() << endl ; // return weight of last loaded coordinates

   // Reduce the 2-dimensional binned dataset to a 1-dimensional binned dataset
   //
   // All reduce() methods are interfaced in RooAbsData. All reduction techniques
   // demonstrated on unbinned datasets can be applied to binned datasets as well.
   cout << ">> Creating 1-dimensional projection on y of dh for bins with x>0" << endl ;
   RooDataHist* dh2 = (RooDataHist*) dh.reduce(y,"x>0") ;
   dh2->Print("v") ;

   // Add dh2 to yframe and redraw
   dh2->plotOn(yframe,LineColor(kRed),MarkerColor(kRed)) ;



   // S a v i n g   a n d   l o a d i n g   f r o m   f i l e 
   // -------------------------------------------------------

   // Datasets can be persisted with ROOT I/O
   cout << endl << ">> Persisting d via ROOT I/O" << endl ;
   TFile f("rf402_datahandling.root","RECREATE") ;
   d.Write() ;
   f.ls() ;

   // To read back in future session:
   // > TFile f("rf402_datahandling.root") ;
   // > RooDataSet* d = (RooDataSet*) f.FindObject("d") ;



   new TCanvas("rf402_datahandling","rf402_datahandling",600,600) ;
   gPad->SetLeftMargin(0.15) ; yframe->GetYaxis()->SetTitleOffset(1.4) ; yframe->Draw() ;

}

Author

07/2008 - Wouter Verkerke

Definition in file rf402_datahandling.C.