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Reference Guide
RooDataWeightedAverage.cxx
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1/*****************************************************************************
2 * Project: RooFit *
3 * Package: RooFitCore *
4 * @(#)root/roofitcore:$Id$
5 * Authors: *
6 * WV, Wouter Verkerke, UC Santa Barbara, verkerke@slac.stanford.edu *
7 * DK, David Kirkby, UC Irvine, dkirkby@uci.edu *
8 * *
9 * Copyright (c) 2000-2005, Regents of the University of California *
10 * and Stanford University. All rights reserved. *
11 * *
12 * Redistribution and use in source and binary forms, *
13 * with or without modification, are permitted according to the terms *
14 * listed in LICENSE (http://roofit.sourceforge.net/license.txt) *
15 *****************************************************************************/
16
17/**
18\file RooDataWeightedAverage.cxx
19\class RooDataWeightedAverage
20\ingroup Roofitcore
21
22Class RooDataWeightedAverage calculate a weighted
23average of a function or p.d.f given a dataset with observable
24values, i.e. DWA(f(x),D(x)) = sum_i f(x_i) where x_i is draw from
25D(i). This class is an implementation of RooAbsOptTestStatistics
26can make use of the optimization and parallization infrastructure
27of that base class. The main use of RooDataWeightedAverage is
28to calculate curves in RooPlots that are added with ProjWData()
29plot option.
30
31**/
32
33#include "RooFit.h"
34#include "Riostream.h"
35
37#include "RooAbsData.h"
38#include "RooAbsPdf.h"
39#include "RooCmdConfig.h"
40#include "RooMsgService.h"
41#include "RooAbsDataStore.h"
42
43
44
45using namespace std;
46
48;
49
50
51////////////////////////////////////////////////////////////////////////////////
52/// Constructor of data weighted average of given p.d.f over given data. If nCPU>1 the calculation is parallelized
53/// over multuple processes. If showProgress is true a progress indicator printing a single dot for each evaluation
54/// is shown. If interleave is true, the dataset split over multiple processes is done with an interleave pattern
55/// rather than a bulk-split pattern.
56
57RooDataWeightedAverage::RooDataWeightedAverage(const char *name, const char *title, RooAbsReal& pdf, RooAbsData& indata,
58 const RooArgSet& projdeps, Int_t nCPU, RooFit::MPSplit interleave, Bool_t showProgress, Bool_t verbose) :
59 RooAbsOptTestStatistic(name,title,pdf,indata,projdeps,0,0,nCPU,interleave,verbose,kFALSE),
60 _showProgress(showProgress)
61{
62 if (_showProgress) {
63 coutI(Plotting) << "RooDataWeightedAverage::ctor(" << GetName() << ") constructing data weighted average of function " << pdf.GetName()
64 << " over " << indata.numEntries() << " data points of " << *(indata.get()) << " with a total weight of " << indata.sumEntries() << endl ;
65 }
66 _sumWeight = indata.sumEntries() ;
67}
68
69
70////////////////////////////////////////////////////////////////////////////////
71/// Copy constructor
72
75 _sumWeight(other._sumWeight),
76 _showProgress(other._showProgress)
77{
78}
79
80
81
82////////////////////////////////////////////////////////////////////////////////
83/// Destructor
84
86{
87}
88
89
90
91////////////////////////////////////////////////////////////////////////////////
92/// Return global normalization term by which raw (combined) test statistic should
93/// be defined to obtain final test statistic. For a data weighted avarage this
94/// the the sum of all weights
95
97{
98 return _sumWeight ;
99}
100
101
102
103////////////////////////////////////////////////////////////////////////////////
104/// Calculate the data weighted average for events [firstEVent,lastEvent] with step size stepSize
105
106Double_t RooDataWeightedAverage::evaluatePartition(std::size_t firstEvent, std::size_t lastEvent, std::size_t stepSize) const
107{
108 Double_t result(0) ;
109
110 _dataClone->store()->recalculateCache( _projDeps, firstEvent, lastEvent, stepSize,kFALSE) ;
111
112 if (setNum()==0 && _showProgress) {
113 ccoutP(Plotting) << "." ;
114 cout.flush() ;
115 }
116
117 for (auto i=firstEvent ; i<lastEvent ; i+=stepSize) {
118
119 // get the data values for this event
120 _dataClone->get(i);
121 if (_dataClone->weight()==0) continue ;
122
124 result += term;
125 }
126
127 return result ;
128}
129
130
131
#define coutI(a)
Definition: RooMsgService.h:30
#define ccoutP(a)
Definition: RooMsgService.h:39
int Int_t
Definition: RtypesCore.h:43
const Bool_t kFALSE
Definition: RtypesCore.h:90
bool Bool_t
Definition: RtypesCore.h:61
double Double_t
Definition: RtypesCore.h:57
#define ClassImp(name)
Definition: Rtypes.h:361
char name[80]
Definition: TGX11.cxx:109
virtual void recalculateCache(const RooArgSet *, Int_t, Int_t, Int_t, Bool_t)
RooAbsData is the common abstract base class for binned and unbinned datasets.
Definition: RooAbsData.h:44
virtual const RooArgSet * get() const
Definition: RooAbsData.h:87
RooAbsDataStore * store()
Definition: RooAbsData.h:63
virtual Double_t sumEntries() const =0
virtual Double_t weight() const =0
virtual Int_t numEntries() const
Definition: RooAbsData.cxx:306
RooAbsOptTestStatistic is the abstract base class for test statistics objects that evaluate a functio...
RooAbsReal is the common abstract base class for objects that represent a real value and implements f...
Definition: RooAbsReal.h:60
Double_t getVal(const RooArgSet *normalisationSet=nullptr) const
Evaluate object.
Definition: RooAbsReal.h:90
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgSet.h:28
Class RooDataWeightedAverage calculate a weighted average of a function or p.d.f given a dataset with...
virtual ~RooDataWeightedAverage()
Destructor.
virtual Double_t evaluatePartition(std::size_t firstEvent, std::size_t lastEvent, std::size_t stepSize) const
Calculate the data weighted average for events [firstEVent,lastEvent] with step size stepSize.
virtual Double_t globalNormalization() const
Return global normalization term by which raw (combined) test statistic should be defined to obtain f...
virtual const char * GetName() const
Returns name of object.
Definition: TNamed.h:47