doc/v630/rf401__importttreethx_8C.html

#include "RooRealVar.h"

#include "RooDataSet.h"

#include "RooDataHist.h"

#include "RooCategory.h"

#include "RooGaussian.h"

#include "TCanvas.h"

#include "TAxis.h"

#include "RooPlot.h"

#include "TH1.h"

#include "TTree.h"

#include "TRandom.h"

#include <map>


using namespace RooFit;


TH1 *makeTH1(const char *name, double mean, double sigma);

TTree *makeTTree();


void rf401_importttreethx()

{

   // I m p o r t  m u l t i p l e   T H 1   i n t o   a   R o o D a t a H i s t

   // --------------------------------------------------------------------------


   // Create thee ROOT TH1 histograms

   TH1 *hh_1 = makeTH1("hh1", 0, 3);

   TH1 *hh_2 = makeTH1("hh2", -3, 1);

   TH1 *hh_3 = makeTH1("hh3", +3, 4);


   // Declare observable x

   RooRealVar x("x", "x", -10, 10);


   // Create category observable c that serves as index for the ROOT histograms

   RooCategory c("c", "c", {{"SampleA",0}, {"SampleB",1}, {"SampleC",2}});


   // Create a binned dataset that imports contents of all TH1 mapped by index category c

   RooDataHist *dh = new RooDataHist("dh", "dh", x, Index(c), Import("SampleA", *hh_1), Import("SampleB", *hh_2),

                                     Import("SampleC", *hh_3));

   dh->Print();


   // Alternative constructor form for importing multiple histograms

   std::map<std::string, TH1 *> hmap;

   hmap["SampleA"] = hh_1;

   hmap["SampleB"] = hh_2;

   hmap["SampleC"] = hh_3;

   RooDataHist *dh2 = new RooDataHist("dh", "dh", x, c, hmap);

   dh2->Print();


   // I m p o r t i n g   a   T T r e e   i n t o   a   R o o D a t a S e t   w i t h   c u t s

   // -----------------------------------------------------------------------------------------


   TTree *tree = makeTTree();


   // Define observables y,z

   RooRealVar y("y", "y", -10, 10);

   RooRealVar z("z", "z", -10, 10);


   // Import only observables (y,z)

   RooDataSet ds("ds", "ds", RooArgSet(x, y), Import(*tree));

   ds.Print();


   // Import observables (x,y,z) but only event for which (y+z<0) is true

   RooDataSet ds2("ds2", "ds2", RooArgSet(x, y, z), Import(*tree), Cut("y+z<0"));

   ds2.Print();


   // I m p o r t i n g   i n t e g e r   T T r e e   b r a n c h e s

   // ---------------------------------------------------------------


   // Import integer tree branch as RooRealVar

   RooRealVar i("i", "i", 0, 5);

   RooDataSet ds3("ds3", "ds3", RooArgSet(i, x), Import(*tree));

   ds3.Print();


   // Define category i

   RooCategory icat("i", "i");

   icat.defineType("State0", 0);

   icat.defineType("State1", 1);


   // Import integer tree branch as RooCategory (only events with i==0 and i==1

   // will be imported as those are the only defined states)

   RooDataSet ds4("ds4", "ds4", RooArgSet(icat, x), Import(*tree));

   ds4.Print();


   // I m p o r t  m u l t i p l e   R o o D a t a S e t s   i n t o   a   R o o D a t a S e t

   // ----------------------------------------------------------------------------------------


   // Create three RooDataSets in (y,z)

   RooDataSet *dsA = (RooDataSet *)ds2.reduce(RooArgSet(x, y), "z<-5");

   RooDataSet *dsB = (RooDataSet *)ds2.reduce(RooArgSet(x, y), "abs(z)<5");

   RooDataSet *dsC = (RooDataSet *)ds2.reduce(RooArgSet(x, y), "z>5");


   // Create a dataset that imports contents of all the above datasets mapped by index category c

   RooDataSet *dsABC = new RooDataSet("dsABC", "dsABC", RooArgSet(x, y), Index(c), Import("SampleA", *dsA),

                                      Import("SampleB", *dsB), Import("SampleC", *dsC));


   dsABC->Print();

}


TH1 *makeTH1(const char *name, double mean, double sigma)

{

   // Create ROOT TH1 filled with a Gaussian distribution


   TH1D *hh = new TH1D(name, name, 100, -10, 10);

   for (int i = 0; i < 1000; i++) {

      hh->Fill(gRandom->Gaus(mean, sigma));

   }

   return hh;

}


TTree *makeTTree()

{

   // Create ROOT TTree filled with a Gaussian distribution in x and a uniform distribution in y


   TTree *tree = new TTree("tree", "tree");

   double *px = new double;

   double *py = new double;

   double *pz = new double;

   Int_t *pi = new Int_t;

   tree->Branch("x", px, "x/D");

   tree->Branch("y", py, "y/D");

   tree->Branch("z", pz, "z/D");

   tree->Branch("i", pi, "i/I");

   for (int i = 0; i < 100; i++) {

      *px = gRandom->Gaus(0, 3);

      *py = gRandom->Uniform() * 30 - 15;

      *pz = gRandom->Gaus(0, 5);

      *pi = i % 3;

      tree->Fill();

   }

   return tree;

}

c
#define c(i)
Definition RSha256.hxx:101

RooCategory.h

RooDataHist.h

RooDataSet.h

RooGaussian.h

RooPlot.h

RooRealVar.h

Int_t
int Int_t
Definition RtypesCore.h:45

TAxis.h

TCanvas.h

name
char name[80]
Definition TGX11.cxx:110

TH1.h

TRandom.h

gRandom
R__EXTERN TRandom * gRandom
Definition TRandom.h:62

TTree.h

RooAbsData::reduce
RooFit::OwningPtr< RooAbsData > reduce(const RooCmdArg &arg1, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={})
Create a reduced copy of this dataset.
Definition RooAbsData.cxx:400

RooAbsData::Print
void Print(Option_t *options=nullptr) const override
This method must be overridden when a class wants to print itself.
Definition RooAbsData.h:225

RooArgSet
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition RooArgSet.h:55

RooCategory
Object to represent discrete states.
Definition RooCategory.h:28

RooDataHist
The RooDataHist is a container class to hold N-dimensional binned data.
Definition RooDataHist.h:39

RooDataSet
RooDataSet is a container class to hold unbinned data.
Definition RooDataSet.h:57

RooRealVar
RooRealVar represents a variable that can be changed from the outside.
Definition RooRealVar.h:37

TH1D
1-D histogram with a double per channel (see TH1 documentation)}
Definition TH1.h:620

TH1
TH1 is the base class of all histogram classes in ROOT.
Definition TH1.h:58

TH1::Fill
virtual Int_t Fill(Double_t x)
Increment bin with abscissa X by 1.
Definition TH1.cxx:3345

TRandom::Gaus
virtual Double_t Gaus(Double_t mean=0, Double_t sigma=1)
Samples a random number from the standard Normal (Gaussian) Distribution with the given mean and sigm...
Definition TRandom.cxx:274

TRandom::Uniform
virtual Double_t Uniform(Double_t x1=1)
Returns a uniform deviate on the interval (0, x1).
Definition TRandom.cxx:672

TTree
A TTree represents a columnar dataset.
Definition TTree.h:79

double

RooFit::Index
RooCmdArg Index(RooCategory &icat)
Definition RooGlobalFunc.cxx:303

RooFit::Import
RooCmdArg Import(const char *state, TH1 &histo)
Definition RooGlobalFunc.cxx:307

RooFit::Cut
RooCmdArg Cut(const char *cutSpec)
Definition RooGlobalFunc.cxx:245

sigma
const Double_t sigma
Definition h1analysisProxy.h:11

y
Double_t y[n]
Definition legend1.C:17

x
Double_t x[n]
Definition legend1.C:17

RooFit
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Definition JSONIO.h:26

rf401_importttreethx
Definition rf401_importttreethx.py:1

tree
Definition tree.py:1