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TEfficiency.h
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1#ifndef ROOT_TEfficiency
2#define ROOT_TEfficiency
3
4//standard header
5#include <vector>
6#include <utility>
7
8//ROOT header
9#include "TNamed.h"
10
11#include "TAttLine.h"
12
13#include "TAttFill.h"
14
15#include "TAttMarker.h"
16
17#include "TFitResultPtr.h"
18
19
20class TCollection;
21class TF1;
23class TH1;
24class TH2;
25class TList;
26
27class TEfficiency: public TNamed, public TAttLine, public TAttFill, public TAttMarker
28{
29public:
30 /// Enumeration type for different statistic options for calculating confidence intervals
31 /// kF* ... frequentist methods; kB* ... bayesian methods
33 kFCP = 0, ///< Clopper-Pearson interval (recommended by PDG)
34 kFNormal, ///< Normal approximation
35 kFWilson, ///< Wilson interval
36 kFAC, ///< Agresti-Coull interval
37 kFFC, ///< Feldman-Cousins interval
38 kBJeffrey, ///< Jeffrey interval (Prior ~ Beta(0.5,0.5)
39 kBUniform, ///< Prior ~ Uniform = Beta(1,1)
40 kBBayesian, ///< User specified Prior ~ Beta(fBeta_alpha,fBeta_beta)
41 kMidP ///< Mid-P Lancaster interval
42 };
43
44protected:
45
46 Double_t fBeta_alpha; ///< Global parameter for prior beta distribution (default = 1)
47 Double_t fBeta_beta; ///< Global parameter for prior beta distribution (default = 1)
48 std::vector<std::pair<Double_t, Double_t> > fBeta_bin_params; ///< Parameter for prior beta distribution different bin by bin
49 ///< (default vector is empty)
50 Double_t (*fBoundary)(Double_t,Double_t,Double_t,Bool_t); ///<! Pointer to a method calculating the boundaries of confidence intervals
51 Double_t fConfLevel; ///< Confidence level (default = 0.683, 1 sigma)
52 TDirectory* fDirectory; ///<! Pointer to directory holding this TEfficiency object
53 TList* fFunctions; ///<->Pointer to list of functions
54 TGraphAsymmErrors* fPaintGraph; ///<! Temporary graph for painting
55 TH2* fPaintHisto; ///<! Temporary histogram for painting
56 TH1* fPassedHistogram; ///< Histogram for events which passed certain criteria
57 EStatOption fStatisticOption; ///< Defines how the confidence intervals are determined
58 TH1* fTotalHistogram; ///< Histogram for total number of events
59 Double_t fWeight; ///< Weight for all events (default = 1)
60
62 kIsBayesian = BIT(14), ///< Bayesian statistics are used
63 kPosteriorMode = BIT(15), ///< Use posterior mean for best estimate (Bayesian statistics)
64 kShortestInterval = BIT(16), ///< Use shortest interval
65 kUseBinPrior = BIT(17), ///< Use a different prior for each bin
66 kUseWeights = BIT(18) ///< Use weights
67 };
68
69 void Build(const char* name,const char* title);
70 void FillGraph(TGraphAsymmErrors * graph, Option_t * opt) const;
71 void FillHistogram(TH2 * h2) const;
72
73public:
75 TEfficiency(const TH1& passed,const TH1& total);
76 TEfficiency(const char* name,const char* title,Int_t nbins,
77 const Double_t* xbins);
78 TEfficiency(const char* name,const char* title,Int_t nbins,Double_t xlow,
79 Double_t xup);
80 TEfficiency(const char* name,const char* title,Int_t nbinsx,
81 Double_t xlow,Double_t xup,Int_t nbinsy,Double_t ylow,
82 Double_t yup);
83 TEfficiency(const char* name,const char* title,Int_t nbinsx,
84 const Double_t* xbins,Int_t nbinsy,const Double_t* ybins);
85 TEfficiency(const char* name,const char* title,Int_t nbinsx,
86 Double_t xlow,Double_t xup,Int_t nbinsy,Double_t ylow,
87 Double_t yup,Int_t nbinsz,Double_t zlow,Double_t zup);
88 TEfficiency(const char* name,const char* title,Int_t nbinsx,
89 const Double_t* xbins,Int_t nbinsy,const Double_t* ybins,
90 Int_t nbinsz,const Double_t* zbins);
91 TEfficiency(const TEfficiency& heff);
93
94 void Add(const TEfficiency& rEff) {*this += rEff;}
95 void Browse(TBrowser*){Draw();}
96 TGraphAsymmErrors* CreateGraph(Option_t * opt = "") const;
97 TH2* CreateHistogram(Option_t * opt = "") const;
98 virtual Int_t DistancetoPrimitive(Int_t px, Int_t py);
99 void Draw(Option_t* opt = "");
100 virtual void ExecuteEvent(Int_t event, Int_t px, Int_t py);
101 void Fill(Bool_t bPassed,Double_t x,Double_t y=0,Double_t z=0);
102 void FillWeighted(Bool_t bPassed,Double_t weight,Double_t x,Double_t y=0,Double_t z=0);
104 TFitResultPtr Fit(TF1* f1,Option_t* opt="");
105 // use trick of -1 to return global parameters
106 Double_t GetBetaAlpha(Int_t bin = -1) const {return (fBeta_bin_params.size() > (UInt_t)bin) ? fBeta_bin_params[bin].first : fBeta_alpha;}
107 Double_t GetBetaBeta(Int_t bin = -1) const {return (fBeta_bin_params.size() > (UInt_t)bin) ? fBeta_bin_params[bin].second : fBeta_beta;}
109 TH1* GetCopyPassedHisto() const;
110 TH1* GetCopyTotalHisto() const;
111 Int_t GetDimension() const;
113 Double_t GetEfficiency(Int_t bin) const;
116 Int_t GetGlobalBin(Int_t binx,Int_t biny=0,Int_t binz=0) const;
120 const TH1* GetPassedHistogram() const {return fPassedHistogram;}
122 const TH1* GetTotalHistogram() const {return fTotalHistogram;}
123 Double_t GetWeight() const {return fWeight;}
126 TEfficiency& operator=(const TEfficiency& rhs);
127 void Paint(Option_t* opt);
128 void SavePrimitive(std::ostream& out,Option_t* opt="");
129 void SetBetaAlpha(Double_t alpha);
132 void SetConfidenceLevel(Double_t level);
133 void SetDirectory(TDirectory* dir);
134 void SetName(const char* name);
135 Bool_t SetPassedEvents(Int_t bin,Int_t events);
136 Bool_t SetPassedHistogram(const TH1& rPassed,Option_t* opt);
141 void SetStatisticOption(EStatOption option);
143 Bool_t SetBins(Int_t nx, const Double_t *xBins);
145 Bool_t SetBins(Int_t nx, const Double_t *xBins, Int_t ny, const Double_t *yBins);
147 Int_t nz, Double_t zmin, Double_t zmax);
148 Bool_t SetBins(Int_t nx, const Double_t *xBins, Int_t ny, const Double_t * yBins, Int_t nz,
149 const Double_t *zBins);
150
151 void SetTitle(const char* title);
152 Bool_t SetTotalEvents(Int_t bin,Int_t events);
153 Bool_t SetTotalHistogram(const TH1& rTotal,Option_t* opt);
155 void SetWeight(Double_t weight);
162
163 static Bool_t CheckBinning(const TH1& pass,const TH1& total);
164 static Bool_t CheckConsistency(const TH1& pass,const TH1& total,Option_t* opt="");
165 static Bool_t CheckEntries(const TH1& pass,const TH1& total,Option_t* opt="");
166 static Bool_t CheckWeights(const TH1& pass,const TH1& total);
167 static Double_t Combine(Double_t& up,Double_t& low,Int_t n,const Int_t* pass,const Int_t* total,
168 Double_t alpha,Double_t beta,Double_t level=0.683,
169 const Double_t* w=0,Option_t* opt="");
170 static TGraphAsymmErrors* Combine(TCollection* pList,Option_t* opt="",Int_t n=0,const Double_t* w=0);
171
172 //calculating boundaries of confidence intervals
173 static Double_t AgrestiCoull(Double_t total,Double_t passed,Double_t level,Bool_t bUpper);
174 static Double_t ClopperPearson(Double_t total,Double_t passed,Double_t level,Bool_t bUpper);
175 static Double_t Normal(Double_t total,Double_t passed,Double_t level,Bool_t bUpper);
176 static Double_t Wilson(Double_t total,Double_t passed,Double_t level,Bool_t bUpper);
177 static Double_t FeldmanCousins(Double_t total,Double_t passed,Double_t level,Bool_t bUpper);
178 static Bool_t FeldmanCousinsInterval(Double_t total,Double_t passed,Double_t level,Double_t & lower, Double_t & upper);
179 static Double_t MidPInterval(Double_t total,Double_t passed,Double_t level,Bool_t bUpper);
180 // Bayesian functions
181 static Double_t Bayesian(Double_t total,Double_t passed,Double_t level,Double_t alpha,Double_t beta,Bool_t bUpper, Bool_t bShortest = false);
182 // helper functions for Bayesian statistics
184 static Bool_t BetaShortestInterval(Double_t level,Double_t alpha,Double_t beta,Double_t & lower, Double_t & upper);
185 static Double_t BetaMean(Double_t alpha,Double_t beta);
186 static Double_t BetaMode(Double_t alpha,Double_t beta);
187
188 ClassDef(TEfficiency,2) //calculating efficiencies
189};
190
191const TEfficiency operator+(const TEfficiency& lhs,const TEfficiency& rhs);
192
193#endif
int Int_t
Definition: RtypesCore.h:45
unsigned int UInt_t
Definition: RtypesCore.h:46
bool Bool_t
Definition: RtypesCore.h:63
double Double_t
Definition: RtypesCore.h:59
long long Long64_t
Definition: RtypesCore.h:80
const Bool_t kTRUE
Definition: RtypesCore.h:100
const char Option_t
Definition: RtypesCore.h:66
#define ClassDef(name, id)
Definition: Rtypes.h:325
#define BIT(n)
Definition: Rtypes.h:85
const TEfficiency operator+(const TEfficiency &lhs, const TEfficiency &rhs)
Addition operator.
static unsigned int total
char name[80]
Definition: TGX11.cxx:110
float xmin
Definition: THbookFile.cxx:95
float ymin
Definition: THbookFile.cxx:95
float xmax
Definition: THbookFile.cxx:95
float ymax
Definition: THbookFile.cxx:95
Fill Area Attributes class.
Definition: TAttFill.h:19
Line Attributes class.
Definition: TAttLine.h:18
Marker Attributes class.
Definition: TAttMarker.h:19
Using a TBrowser one can browse all ROOT objects.
Definition: TBrowser.h:37
Collection abstract base class.
Definition: TCollection.h:65
Describe directory structure in memory.
Definition: TDirectory.h:45
Class to handle efficiency histograms.
Definition: TEfficiency.h:28
Bool_t UsesCentralInterval() const
Definition: TEfficiency.h:160
static Bool_t FeldmanCousinsInterval(Double_t total, Double_t passed, Double_t level, Double_t &lower, Double_t &upper)
Calculates the interval boundaries using the frequentist methods of Feldman-Cousins.
void SetPosteriorMode(Bool_t on=true)
Definition: TEfficiency.h:137
static Double_t BetaMode(Double_t alpha, Double_t beta)
Compute the mode of the beta distribution.
Bool_t SetPassedEvents(Int_t bin, Int_t events)
Sets the number of passed events in the given global bin.
TH2 * CreateHistogram(Option_t *opt="") const
Create the histogram used to be painted (for dim=2 TEfficiency) The return object is managed by the c...
static Bool_t BetaShortestInterval(Double_t level, Double_t alpha, Double_t beta, Double_t &lower, Double_t &upper)
Calculates the boundaries for a shortest confidence interval for a Beta distribution.
static Bool_t CheckWeights(const TH1 &pass, const TH1 &total)
Check if both histogram are weighted.
static Double_t BetaMean(Double_t alpha, Double_t beta)
Compute the mean (average) of the beta distribution.
TH2 * GetPaintedHistogram() const
Definition: TEfficiency.h:118
TEfficiency()
Default constructor.
Double_t GetBetaAlpha(Int_t bin=-1) const
Definition: TEfficiency.h:106
void SetPosteriorAverage(Bool_t on=true)
Definition: TEfficiency.h:138
void FillWeighted(Bool_t bPassed, Double_t weight, Double_t x, Double_t y=0, Double_t z=0)
This function is used for filling the two histograms with a weight.
~TEfficiency()
default destructor
TList * GetListOfFunctions()
static Double_t Bayesian(Double_t total, Double_t passed, Double_t level, Double_t alpha, Double_t beta, Bool_t bUpper, Bool_t bShortest=false)
Calculates the boundaries for a Bayesian confidence interval (shortest or central interval depending ...
static Double_t AgrestiCoull(Double_t total, Double_t passed, Double_t level, Bool_t bUpper)
Calculates the boundaries for the frequentist Agresti-Coull interval.
Long64_t Merge(TCollection *list)
Merges the TEfficiency objects in the given list to the given TEfficiency object using the operator+=...
std::vector< std::pair< Double_t, Double_t > > fBeta_bin_params
Parameter for prior beta distribution different bin by bin (default vector is empty)
Definition: TEfficiency.h:48
static Double_t FeldmanCousins(Double_t total, Double_t passed, Double_t level, Bool_t bUpper)
Calculates the boundaries for the frequentist Feldman-Cousins interval.
EStatOption fStatisticOption
Defines how the confidence intervals are determined.
Definition: TEfficiency.h:57
void SetStatisticOption(EStatOption option)
Sets the statistic option which affects the calculation of the confidence interval.
void SetWeight(Double_t weight)
Sets the global weight for this TEfficiency object.
TH1 * fTotalHistogram
Histogram for total number of events.
Definition: TEfficiency.h:58
Int_t GetDimension() const
returns the dimension of the current TEfficiency object
TEfficiency & operator+=(const TEfficiency &rhs)
Adds the histograms of another TEfficiency object to current histograms.
Bool_t SetBins(Int_t nx, Double_t xmin, Double_t xmax)
Set the bins for the underlined passed and total histograms If the class have been already filled the...
void Build(const char *name, const char *title)
Building standard data structure of a TEfficiency object.
TH1 * GetCopyPassedHisto() const
Returns a cloned version of fPassedHistogram.
Double_t GetEfficiencyErrorUp(Int_t bin) const
Returns the upper error on the efficiency in the given global bin.
void Draw(Option_t *opt="")
Draws the current TEfficiency object.
virtual Int_t DistancetoPrimitive(Int_t px, Int_t py)
Compute distance from point px,py to a graph.
Double_t fBeta_alpha
Global parameter for prior beta distribution (default = 1)
Definition: TEfficiency.h:46
Bool_t UsesBayesianStat() const
Definition: TEfficiency.h:156
const TH1 * GetPassedHistogram() const
Definition: TEfficiency.h:120
void SetBetaBeta(Double_t beta)
Sets the shape parameter β.
Double_t GetConfidenceLevel() const
Definition: TEfficiency.h:108
static Bool_t CheckBinning(const TH1 &pass, const TH1 &total)
Checks binning for each axis.
static Double_t BetaCentralInterval(Double_t level, Double_t alpha, Double_t beta, Bool_t bUpper)
Calculates the boundaries for a central confidence interval for a Beta distribution.
Int_t GetGlobalBin(Int_t binx, Int_t biny=0, Int_t binz=0) const
Returns the global bin number which can be used as argument for the following functions:
TH1 * fPassedHistogram
Histogram for events which passed certain criteria.
Definition: TEfficiency.h:56
static Double_t MidPInterval(Double_t total, Double_t passed, Double_t level, Bool_t bUpper)
Calculates the boundaries using the mid-P binomial interval (Lancaster method) from B.
void SetBetaAlpha(Double_t alpha)
Sets the shape parameter α.
@ kIsBayesian
Bayesian statistics are used.
Definition: TEfficiency.h:62
@ kUseWeights
Use weights.
Definition: TEfficiency.h:66
@ kPosteriorMode
Use posterior mean for best estimate (Bayesian statistics)
Definition: TEfficiency.h:63
@ kUseBinPrior
Use a different prior for each bin.
Definition: TEfficiency.h:65
@ kShortestInterval
Use shortest interval.
Definition: TEfficiency.h:64
static Bool_t CheckEntries(const TH1 &pass, const TH1 &total, Option_t *opt="")
Checks whether bin contents are compatible with binomial statistics.
static Double_t Normal(Double_t total, Double_t passed, Double_t level, Bool_t bUpper)
Returns the confidence limits for the efficiency supposing that the efficiency follows a normal distr...
Double_t fWeight
Weight for all events (default = 1)
Definition: TEfficiency.h:59
Bool_t SetPassedHistogram(const TH1 &rPassed, Option_t *opt)
Sets the histogram containing the passed events.
Bool_t SetTotalEvents(Int_t bin, Int_t events)
Sets the number of total events in the given global bin.
Double_t GetBetaBeta(Int_t bin=-1) const
Definition: TEfficiency.h:107
Double_t(* fBoundary)(Double_t, Double_t, Double_t, Bool_t)
! Pointer to a method calculating the boundaries of confidence intervals
Definition: TEfficiency.h:50
void FillGraph(TGraphAsymmErrors *graph, Option_t *opt) const
Fill the graph to be painted with information from TEfficiency Internal method called by TEfficiency:...
Bool_t UsesPosteriorMode() const
Definition: TEfficiency.h:157
void SetShortestInterval(Bool_t on=true)
Definition: TEfficiency.h:139
const TH1 * GetTotalHistogram() const
Definition: TEfficiency.h:122
Bool_t UsesWeights() const
Definition: TEfficiency.h:161
void SetName(const char *name)
Sets the name.
void FillHistogram(TH2 *h2) const
Fill the 2d histogram to be painted with information from TEfficiency 2D Internal method called by TE...
Bool_t UsesPosteriorAverage() const
Definition: TEfficiency.h:159
Int_t FindFixBin(Double_t x, Double_t y=0, Double_t z=0) const
Returns the global bin number containing the given values.
TDirectory * GetDirectory() const
Definition: TEfficiency.h:112
TDirectory * fDirectory
! Pointer to directory holding this TEfficiency object
Definition: TEfficiency.h:52
static Double_t Combine(Double_t &up, Double_t &low, Int_t n, const Int_t *pass, const Int_t *total, Double_t alpha, Double_t beta, Double_t level=0.683, const Double_t *w=0, Option_t *opt="")
void SetUseWeightedEvents(Bool_t on=kTRUE)
static Double_t Wilson(Double_t total, Double_t passed, Double_t level, Bool_t bUpper)
Calculates the boundaries for the frequentist Wilson interval.
TEfficiency & operator=(const TEfficiency &rhs)
Assignment operator.
Double_t fConfLevel
Confidence level (default = 0.683, 1 sigma)
Definition: TEfficiency.h:51
void SetCentralInterval(Bool_t on=true)
Definition: TEfficiency.h:140
Double_t fBeta_beta
Global parameter for prior beta distribution (default = 1)
Definition: TEfficiency.h:47
void SavePrimitive(std::ostream &out, Option_t *opt="")
Have histograms fixed bins along each axis?
Double_t GetEfficiency(Int_t bin) const
Returns the efficiency in the given global bin.
Bool_t SetTotalHistogram(const TH1 &rTotal, Option_t *opt)
Sets the histogram containing all events.
void Fill(Bool_t bPassed, Double_t x, Double_t y=0, Double_t z=0)
This function is used for filling the two histograms.
void SetDirectory(TDirectory *dir)
Sets the directory holding this TEfficiency object.
void Browse(TBrowser *)
Browse object. May be overridden for another default action.
Definition: TEfficiency.h:95
TGraphAsymmErrors * fPaintGraph
! Temporary graph for painting
Definition: TEfficiency.h:54
TGraphAsymmErrors * CreateGraph(Option_t *opt="") const
Create the graph used be painted (for dim=1 TEfficiency) The return object is managed by the caller.
EStatOption GetStatisticOption() const
Definition: TEfficiency.h:121
TGraphAsymmErrors * GetPaintedGraph() const
Definition: TEfficiency.h:117
TList * fFunctions
->Pointer to list of functions
Definition: TEfficiency.h:53
void Paint(Option_t *opt)
Paints this TEfficiency object.
void SetBetaBinParameters(Int_t bin, Double_t alpha, Double_t beta)
Sets different shape parameter α and β for the prior distribution for each bin.
static Bool_t CheckConsistency(const TH1 &pass, const TH1 &total, Option_t *opt="")
Checks the consistence of the given histograms.
Double_t GetWeight() const
Definition: TEfficiency.h:123
void Add(const TEfficiency &rEff)
Definition: TEfficiency.h:94
TH1 * GetCopyTotalHisto() const
Returns a cloned version of fTotalHistogram.
virtual void ExecuteEvent(Int_t event, Int_t px, Int_t py)
Execute action corresponding to one event.
static Double_t ClopperPearson(Double_t total, Double_t passed, Double_t level, Bool_t bUpper)
Calculates the boundaries for the frequentist Clopper-Pearson interval.
void SetConfidenceLevel(Double_t level)
Sets the confidence level (0 < level < 1) The default value is 1-sigma :~ 0.683.
Double_t GetEfficiencyErrorLow(Int_t bin) const
Returns the lower error on the efficiency in the given global bin.
Bool_t UsesShortestInterval() const
Definition: TEfficiency.h:158
EStatOption
Enumeration type for different statistic options for calculating confidence intervals kF* ....
Definition: TEfficiency.h:32
@ kBJeffrey
Jeffrey interval (Prior ~ Beta(0.5,0.5)
Definition: TEfficiency.h:38
@ kFWilson
Wilson interval.
Definition: TEfficiency.h:35
@ kFAC
Agresti-Coull interval.
Definition: TEfficiency.h:36
@ kMidP
Mid-P Lancaster interval.
Definition: TEfficiency.h:41
@ kBUniform
Prior ~ Uniform = Beta(1,1)
Definition: TEfficiency.h:39
@ kFFC
Feldman-Cousins interval.
Definition: TEfficiency.h:37
@ kBBayesian
User specified Prior ~ Beta(fBeta_alpha,fBeta_beta)
Definition: TEfficiency.h:40
@ kFNormal
Normal approximation.
Definition: TEfficiency.h:34
@ kFCP
Clopper-Pearson interval (recommended by PDG)
Definition: TEfficiency.h:33
void SetTitle(const char *title)
Sets the title.
TFitResultPtr Fit(TF1 *f1, Option_t *opt="")
Fits the efficiency using the TBinomialEfficiencyFitter class.
TH2 * fPaintHisto
! Temporary histogram for painting
Definition: TEfficiency.h:55
1-Dim function class
Definition: TF1.h:213
Provides an indirection to the TFitResult class and with a semantics identical to a TFitResult pointe...
Definition: TFitResultPtr.h:32
TGraph with asymmetric error bars.
TH1 is the base class of all histogram classes in ROOT.
Definition: TH1.h:58
Service class for 2-D histogram classes.
Definition: TH2.h:30
A doubly linked list.
Definition: TList.h:44
The TNamed class is the base class for all named ROOT classes.
Definition: TNamed.h:29
R__ALWAYS_INLINE Bool_t TestBit(UInt_t f) const
Definition: TObject.h:187
void SetBit(UInt_t f, Bool_t set)
Set or unset the user status bits as specified in f.
Definition: TObject.cxx:696
EStatusBits
Definition: TObject.h:57
double beta(double x, double y)
Calculates the beta function.
Double_t y[n]
Definition: legend1.C:17
Double_t x[n]
Definition: legend1.C:17
const Int_t n
Definition: legend1.C:16
TF1 * f1
Definition: legend1.C:11
const double xbins[xbins_n]
Definition: graph.py:1