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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 "Riostream.h"
34
36#include "RooAbsData.h"
37#include "RooAbsPdf.h"
38#include "RooCmdConfig.h"
39#include "RooMsgService.h"
40#include "RooAbsDataStore.h"
41
42
43
44using namespace std;
45
47;
48
49
50////////////////////////////////////////////////////////////////////////////////
51/// Constructor of data weighted average of given p.d.f over given data. If nCPU>1 the calculation is parallelized
52/// over multuple processes. If showProgress is true a progress indicator printing a single dot for each evaluation
53/// is shown. If interleave is true, the dataset split over multiple processes is done with an interleave pattern
54/// rather than a bulk-split pattern.
55
56RooDataWeightedAverage::RooDataWeightedAverage(const char *name, const char *title, RooAbsReal& pdf, RooAbsData& indata,
57 const RooArgSet& projdeps, RooAbsTestStatistic::Configuration const& cfg,
58 bool showProgress) :
59 RooAbsOptTestStatistic(name,title,pdf,indata,projdeps,cfg),
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
const Bool_t kFALSE
Definition: RtypesCore.h:101
#define ClassImp(name)
Definition: Rtypes.h:375
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t result
char name[80]
Definition: TGX11.cxx:110
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:61
virtual const RooArgSet * get() const
Definition: RooAbsData.h:105
RooAbsDataStore * store()
Definition: RooAbsData.h:81
virtual Double_t sumEntries() const =0
Return effective number of entries in dataset, i.e., sum all weights.
virtual Double_t weight() const =0
virtual Int_t numEntries() const
Return number of entries in dataset, i.e., count unweighted entries.
Definition: RooAbsData.cxx:381
RooAbsOptTestStatistic is the abstract base class for test statistics objects that evaluate a functio...
RooAbsReal * _funcClone
Pointer to internal clone of input function.
RooArgSet * _normSet
Pointer to set with observables used for normalization.
RooAbsData * _dataClone
Pointer to internal clone if input data.
RooArgSet * _projDeps
Set of projected observable.
RooAbsReal is the common abstract base class for objects that represent a real value and implements f...
Definition: RooAbsReal.h:63
Double_t getVal(const RooArgSet *normalisationSet=nullptr) const
Evaluate object.
Definition: RooAbsReal.h:93
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgSet.h:57
Class RooDataWeightedAverage calculate a weighted average of a function or p.d.f given a dataset with...
Bool_t _showProgress
Show progress indication during evaluation if true.
Double_t _sumWeight
Global sum of weights needed for normalization.
Double_t evaluatePartition(std::size_t firstEvent, std::size_t lastEvent, std::size_t stepSize) const override
Calculate the data weighted average for events [firstEVent,lastEvent] with step size stepSize.
~RooDataWeightedAverage() override
Destructor.
Double_t globalNormalization() const override
Return global normalization term by which raw (combined) test statistic should be defined to obtain f...
const char * GetName() const override
Returns name of object.
Definition: TNamed.h:47