1#ifndef ROOT_TEfficiency_cxx
2#define ROOT_TEfficiency_cxx
683 Info(
"TEfficiency",
"given histograms are filled with weights");
688 Error(
"TEfficiency(const TH1&,const TH1&)",
"histograms are not consistent -> results are useless");
689 Warning(
"TEfficiency(const TH1&,const TH1&)",
"using two empty TH1D('h1','h1',10,0,10)");
909 fTotalHistogram =
new TH3D(
"total",
"total",nbinsx,xlow,xup,nbinsy,ylow,yup,nbinsz,zlow,zup);
910 fPassedHistogram =
new TH3D(
"passed",
"passed",nbinsx,xlow,xup,nbinsy,ylow,yup,nbinsz,zlow,zup);
985fBeta_alpha(rEff.fBeta_alpha),
986fBeta_beta(rEff.fBeta_beta),
987fBeta_bin_params(rEff.fBeta_bin_params),
988fConfLevel(rEff.fConfLevel),
1016 rEff.TAttLine::Copy(*
this);
1017 rEff.TAttFill::Copy(*
this);
1018 rEff.TAttMarker::Copy(*
this);
1079 Double_t mode = (passed + 0.5 * kappa * kappa) / (
total + kappa * kappa);
1083 return ((mode + delta) > 1) ? 1.0 : (mode + delta);
1085 return ((mode - delta) < 0) ? 0.0 : (mode - delta);
1102 ::Error(
"FeldmanCousins",
"Error running FC method - return 0 or 1");
1104 return (bUpper) ? upper : lower;
1133 double alpha = 1.-level;
1157 const double alpha = 1. - level;
1158 const bool equal_tailed =
true;
1159 const double alpha_min = equal_tailed ? alpha/2 : alpha;
1160 const double tol = 1
e-9;
1170 if ( passed > 0 && passed < 1) {
1173 p = (p1 - p0) * passed + p0;
1177 while (std::abs(pmax - pmin) > tol) {
1178 p = (pmin + pmax)/2;
1186 double vmin = (bUpper) ? alpha_min : 1.- alpha_min;
1258 return (bUpper) ? upper : lower;
1276 if((
a > 0) && (
b > 0))
1279 gROOT->Error(
"TEfficiency::BayesianCentral",
"Invalid input parameters - return 1");
1284 if((
a > 0) && (
b > 0))
1287 gROOT->Error(
"TEfficiency::BayesianCentral",
"Invalid input parameters - return 0");
1293struct Beta_interval_length {
1295 fCL(level), fAlpha(alpha), fBeta(
beta)
1331 if (
a <= 0 ||
b <= 0) {
1332 lower = 0; upper = 1;
1333 gROOT->Error(
"TEfficiency::BayesianShortest",
"Invalid input parameters - return [0,1]");
1352 if (
a==
b &&
a<=1.0) {
1360 Beta_interval_length intervalLength(level,
a,
b);
1364 minim.
SetFunction(func, 0, intervalLength.LowerMax() );
1366 bool ret = minim.
Minimize(100, 1.E-10,1.E-10);
1368 gROOT->Error(
"TEfficiency::BayesianShortes",
"Error finding the shortest interval");
1385 if (
a <= 0 ||
b <= 0 ) {
1386 gROOT->Error(
"TEfficiency::BayesianMean",
"Invalid input parameters - return 0");
1408 if (
a <= 0 ||
b <= 0 ) {
1409 gROOT->Error(
"TEfficiency::BayesianMode",
"Invalid input parameters - return 0");
1412 if (
a <= 1 ||
b <= 1) {
1413 if (
a <
b)
return 0;
1414 if (
a >
b)
return 1;
1415 if (
a ==
b)
return 0.5;
1455 const TAxis* ax1 = 0;
1456 const TAxis* ax2 = 0;
1463 ax2 =
total.GetXaxis();
1467 ax2 =
total.GetYaxis();
1471 ax2 =
total.GetZaxis();
1476 gROOT->Info(
"TEfficiency::CheckBinning",
"Histograms are not consistent: they have different number of bins");
1482 gROOT->Info(
"TEfficiency::CheckBinning",
"Histograms are not consistent: they have different bin edges");
1504 gROOT->Error(
"TEfficiency::CheckConsistency",
"passed TEfficiency objects have different dimensions");
1509 gROOT->Error(
"TEfficiency::CheckConsistency",
"passed TEfficiency objects have different binning");
1514 gROOT->Error(
"TEfficiency::CheckConsistency",
"passed TEfficiency objects do not have consistent bin contents");
1537 Int_t nbinsx, nbinsy, nbinsz, nbins;
1544 case 1: nbins = nbinsx + 2;
break;
1545 case 2: nbins = (nbinsx + 2) * (nbinsy + 2);
break;
1546 case 3: nbins = (nbinsx + 2) * (nbinsy + 2) * (nbinsz + 2);
break;
1550 for(
Int_t i = 0; i < nbins; ++i) {
1552 gROOT->Info(
"TEfficiency::CheckEntries",
"Histograms are not consistent: passed bin content > total bin content");
1565 if (pass.
GetSumw2N() == 0 &&
total.GetSumw2N() == 0)
return false;
1572 total.GetStats(stattotal);
1595 Error(
"CreatePaintingGraph",
"Call this function only for dimension == 1");
1602 graph->SetName(
"eff_graph");
1618 Bool_t plot0Bins =
false;
1619 if (option.
Contains(
"e0") ) plot0Bins =
true;
1628 double * px =
graph->GetX();
1629 double * py =
graph->GetY();
1630 double * exl =
graph->GetEXlow();
1631 double * exh =
graph->GetEXhigh();
1632 double * eyl =
graph->GetEYlow();
1633 double * eyh =
graph->GetEYhigh();
1635 for (
Int_t i = 0; i < npoints; ++i) {
1644 if (j >=
graph->GetN() ) {
1646 graph->SetPointError(j,xlow,xup,ylow,yup);
1664 if (oldTitle != newTitle ) {
1665 graph->SetTitle(newTitle);
1671 if (xlabel)
graph->GetXaxis()->SetTitle(xlabel);
1672 if (ylabel)
graph->GetYaxis()->SetTitle(ylabel);
1681 graph->GetHistogram();
1692 Error(
"CreatePaintingistogram",
"Call this function only for dimension == 2");
1743 for(
Int_t i = 0; i < nbinsx + 2; ++i) {
1744 for(
Int_t j = 0; j < nbinsy + 2; ++j) {
1805 Double_t alpha = (1.0 - level) / 2;
1916 for (
int i = 0; i <
n ; ++i) {
1917 if(pass[i] >
total[i]) {
1918 ::Error(
"TEfficiency::Combine",
"total events = %i < passed events %i",
total[i],pass[i]);
1919 ::Info(
"TEfficiency::Combine",
"stop combining");
1923 ntot += w[i] *
total[i];
1924 ktot += w[i] * pass[i];
1929 double norm = sumw/sumw2;
1933 ::Error(
"TEfficiency::Combine",
"total = %f < passed %f",ntot,ktot);
1934 ::Info(
"TEfficiency::Combine",
"stop combining");
1938 double a = ktot + alpha;
1939 double b = ntot - ktot +
beta;
1941 double mean =
a/(
a+
b);
1947 if (shortestInterval)
1954 if (option.
Contains(
"mode"))
return mode;
2005 std::vector<TH1*> vTotal; vTotal.reserve(
n);
2006 std::vector<TH1*> vPassed; vPassed.reserve(
n);
2007 std::vector<Double_t> vWeights; vWeights.reserve(
n);
2023 level = atof( opt(pos,opt.
Length() ).
Data() );
2024 if((level <= 0) || (level >= 1))
2032 for(
Int_t k = 0; k <
n; ++k) {
2034 vWeights.push_back(w[k]);
2036 gROOT->Error(
"TEfficiency::Combine",
"invalid custom weight found w = %.2lf",w[k]);
2037 gROOT->Info(
"TEfficiency::Combine",
"stop combining");
2046 while((obj = next())) {
2072 vWeights.push_back(pEff->
fWeight);
2087 if(vTotal.empty()) {
2088 gROOT->Error(
"TEfficiency::Combine",
"no TEfficiency objects in given list");
2089 gROOT->Info(
"TEfficiency::Combine",
"stop combining");
2094 if(bWeights && (
n != (
Int_t)vTotal.size())) {
2095 gROOT->Error(
"TEfficiency::Combine",
"number of weights n=%i differs from number of TEfficiency objects k=%i which should be combined",
n,(
Int_t)vTotal.size());
2096 gROOT->Info(
"TEfficiency::Combine",
"stop combining");
2100 Int_t nbins_max = vTotal.at(0)->GetNbinsX();
2102 for(
UInt_t i=0; i<vTotal.size(); ++i) {
2104 gROOT->Warning(
"TEfficiency::Combine",
"histograms have not the same binning -> results may be useless");
2105 if(vTotal.at(i)->GetNbinsX() < nbins_max) nbins_max = vTotal.at(i)->GetNbinsX();
2110 gROOT->Info(
"TEfficiency::Combine",
"combining %i TEfficiency objects",(
Int_t)vTotal.size());
2112 gROOT->Info(
"TEfficiency::Combine",
"using custom weights");
2114 gROOT->Info(
"TEfficiency::Combine",
"using the following prior probability for the efficiency: P(e) ~ Beta(e,%.3lf,%.3lf)",alpha,
beta);
2117 gROOT->Info(
"TEfficiency::Combine",
"using individual priors of each TEfficiency object");
2118 gROOT->Info(
"TEfficiency::Combine",
"confidence level = %.2lf",level);
2122 std::vector<Double_t>
x(nbins_max);
2123 std::vector<Double_t> xlow(nbins_max);
2124 std::vector<Double_t> xhigh(nbins_max);
2125 std::vector<Double_t> eff(nbins_max);
2126 std::vector<Double_t> efflow(nbins_max);
2127 std::vector<Double_t> effhigh(nbins_max);
2131 Int_t num = vTotal.size();
2132 std::vector<Int_t> pass(num);
2133 std::vector<Int_t>
total(num);
2138 for(
Int_t i=1; i <= nbins_max; ++i) {
2140 x[i-1] = vTotal.at(0)->GetBinCenter(i);
2141 xlow[i-1] =
x[i-1] - vTotal.at(0)->GetBinLowEdge(i);
2142 xhigh[i-1] = vTotal.at(0)->GetBinWidth(i) - xlow[i-1];
2144 for(
Int_t j = 0; j < num; ++j) {
2145 pass[j] = (
Int_t)(vPassed.at(j)->GetBinContent(i) + 0.5);
2146 total[j] = (
Int_t)(vTotal.at(j)->GetBinContent(i) + 0.5);
2152 if(eff[i-1] == -1) {
2153 gROOT->Error(
"TEfficiency::Combine",
"error occurred during combining");
2154 gROOT->Info(
"TEfficiency::Combine",
"stop combining");
2157 efflow[i-1]= eff[i-1] - low;
2158 effhigh[i-1]= up - eff[i-1];
2201 if (option.
IsNull() ) option =
"ap";
2207 if (!option.
Contains(
"a") ) option +=
"a";
2211 if (!option.
Contains(
"p") ) option +=
"p";
2344 Bool_t bDeleteOld =
true;
2360 while((obj = next())) {
2482 if (tw2 <= 0 )
return pw/tw;
2485 double norm = tw/tw2;
2486 aa = pw * norm + alpha;
2487 bb = (tw - pw) * norm +
beta;
2491 aa = passed + alpha;
2535 if (tw2 <= 0)
return 0;
2556 Warning(
"GetEfficiencyErrorLow",
"frequentist confidence intervals for weights are only supported by the normal approximation");
2557 Info(
"GetEfficiencyErrorLow",
"setting statistic option to kFNormal");
2561 Double_t variance = ( pw2 * (1. - 2 * eff) + tw2 * eff *eff ) / ( tw * tw) ;
2568 return (eff - delta < 0) ? eff : delta;
2615 if (tw2 <= 0)
return 0;
2636 Warning(
"GetEfficiencyErrorUp",
"frequentist confidence intervals for weights are only supported by the normal approximation");
2637 Info(
"GetEfficiencyErrorUp",
"setting statistic option to kFNormal");
2641 Double_t variance = ( pw2 * (1. - 2 * eff) + tw2 * eff *eff ) / ( tw * tw) ;
2647 return (eff + delta > 1) ? 1.-eff : delta;
2704 while((obj = next())) {
2738 if (
total == 0)
return (bUpper) ? 1 : 0;
2744 return ((average + delta) > 1) ? 1.0 : (average + delta);
2746 return ((average - delta) < 0) ? 0.0 : (average - delta);
2771 Fatal(
"operator+=",
"Adding to a non consistent TEfficiency object which has not a total or a passed histogram ");
2776 Warning(
"operator+=",
"no operation: adding an empty object");
2780 Fatal(
"operator+=",
"Adding a non consistent TEfficiency object which has not a total or a passed histogram ");
2836 rhs.TAttLine::Copy(*
this);
2837 rhs.TAttFill::Copy(*
this);
2838 rhs.TAttMarker::Copy(*
this);
2890 while((obj = next())) {
2893 ((
TF1*)obj)->Paint(
"sameC");
2913 Warning(
"Paint",
"Painting 3D efficiency is not implemented");
2927 static Int_t naxis = 0;
2928 TString sxaxis=
"xAxis",syaxis=
"yAxis",szaxis=
"zAxis";
2951 out <<
indent <<
"Double_t " << sxaxis <<
"["
2954 if (i != 0) out <<
", ";
2957 out <<
"}; " << std::endl;
2960 out <<
indent <<
"Double_t " << syaxis <<
"["
2963 if (i != 0) out <<
", ";
2966 out <<
"}; " << std::endl;
2970 out <<
indent <<
"Double_t " << szaxis <<
"["
2973 if (i != 0) out <<
", ";
2976 out <<
"}; " << std::endl;
2981 static Int_t eff_count = 0;
2984 eff_name += eff_count;
2986 const char*
name = eff_name.
Data();
2989 const char quote =
'"';
2990 out <<
indent << std::endl;
2992 <<
"(" << quote <<
GetName() << quote <<
"," << quote
3020 out <<
");" << std::endl;
3021 out <<
indent << std::endl;
3035 out <<
indent <<
name <<
"->SetUseWeightedEvents();" << std::endl;
3052 for(
Int_t i = 0; i < nbins; ++i) {
3053 out <<
indent <<
name <<
"->SetTotalEvents(" << i <<
"," <<
3055 out <<
indent <<
name <<
"->SetPassedEvents(" << i <<
"," <<
3062 while((obj = next())) {
3065 out <<
indent <<
name <<
"->GetListOfFunctions()->Add("
3066 << obj->
GetName() <<
");" << std::endl;
3079 out<<
indent <<
name<<
"->Draw(" << quote << opt << quote <<
");"
3098 Warning(
"SetBetaAlpha(Double_t)",
"invalid shape parameter %.2lf",alpha);
3116 Warning(
"SetBetaBeta(Double_t)",
"invalid shape parameter %.2lf",
beta);
3156 Error(
"SetBins",
"Using wrong SetBins function for a %d-d histogram",
GetDimension());
3160 Warning(
"SetBins",
"Histogram entries will be lost after SetBins");
3176 Error(
"SetBins",
"Using wrong SetBins function for a %d-d histogram",
GetDimension());
3180 Warning(
"SetBins",
"Histogram entries will be lost after SetBins");
3196 Error(
"SetBins",
"Using wrong SetBins function for a %d-d histogram",
GetDimension());
3200 Warning(
"SetBins",
"Histogram entries will be lost after SetBins");
3216 Error(
"SetBins",
"Using wrong SetBins function for a %d-d histogram",
GetDimension());
3220 Warning(
"SetBins",
"Histogram entries will be lost after SetBins");
3237 Error(
"SetBins",
"Using wrong SetBins function for a %d-d histogram",
GetDimension());
3241 Warning(
"SetBins",
"Histogram entries will be lost after SetBins");
3258 Error(
"SetBins",
"Using wrong SetBins function for a %d-d histogram",
GetDimension());
3262 Warning(
"SetBins",
"Histogram entries will be lost after SetBins");
3277 if((level > 0) && (level < 1))
3280 Warning(
"SetConfidenceLevel(Double_t)",
"invalid confidence level %.2lf",level);
3332 if(events <= fTotalHistogram->GetBinContent(bin)) {
3337 Error(
"SetPassedEvents(Int_t,Int_t)",
"total number of events (%.1lf) in bin %i is less than given number of passed events %i",
fTotalHistogram->
GetBinContent(bin),bin,events);
3497 title_passed.
Insert(pos,
" (passed)");
3498 title_total.
Insert(pos,
" (total)");
3501 title_passed.
Append(
" (passed)");
3502 title_total.
Append(
" (total)");
3530 Error(
"SetTotalEvents(Int_t,Int_t)",
"passed number of events (%.1lf) in bin %i is bigger than given number of total events %i",
fPassedHistogram->
GetBinContent(bin),bin,events);
3590 gROOT->Info(
"TEfficiency::SetUseWeightedEvents",
"Handle weighted events for computing efficiency");
3610 Warning(
"SetWeight",
"invalid weight %.2lf",weight);
3638 if (
total == 0)
return (bUpper) ? 1 : 0;
3642 Double_t mode = (passed + 0.5 * kappa * kappa) / (
total + kappa * kappa);
3644 * (1 - average) + kappa * kappa / 4);
3646 return ((mode + delta) > 1) ? 1.0 : (mode + delta);
3648 return ((mode - delta) < 0) ? 0.0 : (mode - delta);
static void indent(ostringstream &buf, int indent_level)
const TEfficiency operator+(const TEfficiency &lhs, const TEfficiency &rhs)
Addition operator.
const Double_t kDefBetaAlpha
const Double_t kDefWeight
const Double_t kDefBetaBeta
const TEfficiency::EStatOption kDefStatOpt
const Double_t kDefConfLevel
static unsigned int total
TRObject operator()(const T1 &t1) const
static struct mg_connection * fc(struct mg_context *ctx)
User class for performing function minimization.
virtual bool Minimize(int maxIter, double absTol=1.E-8, double relTol=1.E-10)
Find minimum position iterating until convergence specified by the absolute and relative tolerance or...
virtual double XMinimum() const
Return current estimate of the position of the minimum.
void SetFunction(const ROOT::Math::IGenFunction &f, double xlow, double xup)
Sets function to be minimized.
void SetNpx(int npx)
Set the number of point used to bracket root using a grid.
virtual double FValMinimum() const
Return function value at current estimate of the minimum.
Template class to wrap any C++ callable object which takes one argument i.e.
Double_t At(Int_t i) const
const Double_t * GetArray() const
Fill Area Attributes class.
void Copy(TAttFill &attfill) const
Copy this fill attributes to a new TAttFill.
virtual void SaveFillAttributes(std::ostream &out, const char *name, Int_t coldef=1, Int_t stydef=1001)
Save fill attributes as C++ statement(s) on output stream out.
void Copy(TAttLine &attline) const
Copy this line attributes to a new TAttLine.
virtual void SaveLineAttributes(std::ostream &out, const char *name, Int_t coldef=1, Int_t stydef=1, Int_t widdef=1)
Save line attributes as C++ statement(s) on output stream out.
virtual void SaveMarkerAttributes(std::ostream &out, const char *name, Int_t coldef=1, Int_t stydef=1, Int_t sizdef=1)
Save line attributes as C++ statement(s) on output stream out.
void Copy(TAttMarker &attmarker) const
Copy this marker attributes to a new TAttMarker.
Class to manage histogram axis.
Bool_t IsVariableBinSize() const
const TArrayD * GetXbins() const
virtual Double_t GetBinLowEdge(Int_t bin) const
Return low edge of bin.
virtual Int_t FindFixBin(Double_t x) const
Find bin number corresponding to abscissa x.
const char * GetTitle() const
Returns title of object.
Binomial fitter for the division of two histograms.
TFitResultPtr Fit(TF1 *f1, Option_t *option="")
Carry out the fit of the given function to the given histograms.
Collection abstract base class.
virtual Bool_t IsEmpty() const
Describe directory structure in memory.
virtual void Append(TObject *obj, Bool_t replace=kFALSE)
Append object to this directory.
virtual TObject * Remove(TObject *)
Remove an object from the in-memory list.
Class to handle efficiency histograms.
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.
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.
TEfficiency()
default constructor
Double_t GetBetaAlpha(Int_t bin=-1) const
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
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
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.
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.
Bool_t UsesBayesianStat() const
void SetBetaBeta(Double_t beta)
Sets the shape parameter β.
Double_t GetConfidenceLevel() const
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
temporary histogram for painting
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.
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...
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
Double_t(* fBoundary)(Double_t, Double_t, Double_t, Bool_t)
void FillGraph(TGraphAsymmErrors *graph, Option_t *opt) const
Fill the graph to be painted with information from TEfficiency Internal method called by TEfficiency:...
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...
Int_t FindFixBin(Double_t x, Double_t y=0, Double_t z=0) const
Returns the global bin number containing the given values.
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
pointer to a method calculating the boundaries of confidence intervals
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.
TGraphAsymmErrors * fPaintGraph
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
TList * fFunctions
pointer to directory holding this TEfficiency object
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
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.
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 graph for painting
Provides an indirection to the TFitResult class and with a semantics identical to a TFitResult pointe...
TGraph with asymmetric error bars.
virtual void Paint(Option_t *chopt="")
Draw this graph with its current attributes.
virtual void PaintStats(TF1 *fit)
Draw the stats.
virtual Int_t DistancetoPrimitive(Int_t px, Int_t py)
Compute distance from point px,py to a graph.
virtual void ExecuteEvent(Int_t event, Int_t px, Int_t py)
Execute action corresponding to one event.
1-D histogram with a double per channel (see TH1 documentation)}
1-D histogram with a float per channel (see TH1 documentation)}
virtual void SetDirectory(TDirectory *dir)
By default when an histogram is created, it is added to the list of histogram objects in the current ...
virtual void SetTitle(const char *title)
See GetStatOverflows for more information.
virtual void SetNormFactor(Double_t factor=1)
virtual Double_t GetBinCenter(Int_t bin) const
Return bin center for 1D histogram.
virtual void GetStats(Double_t *stats) const
fill the array stats from the contents of this histogram The array stats must be correctly dimensione...
virtual Int_t GetNbinsY() const
virtual Int_t GetNbinsZ() const
virtual Int_t GetDimension() const
static void AddDirectory(Bool_t add=kTRUE)
Sets the flag controlling the automatic add of histograms in memory.
@ kIsAverage
Bin contents are average (used by Add)
virtual void Reset(Option_t *option="")
Reset this histogram: contents, errors, etc.
TAxis * GetXaxis()
Get the behaviour adopted by the object about the statoverflows. See EStatOverflows for more informat...
virtual Int_t GetNcells() const
TObject * Clone(const char *newname=0) const
Make a complete copy of the underlying object.
virtual Int_t GetBin(Int_t binx, Int_t biny=0, Int_t binz=0) const
Return Global bin number corresponding to binx,y,z.
virtual Int_t GetNbinsX() const
virtual Bool_t Add(TF1 *h1, Double_t c1=1, Option_t *option="")
Performs the operation: this = this + c1*f1 if errors are defined (see TH1::Sumw2),...
virtual Int_t Fill(Double_t x)
Increment bin with abscissa X by 1.
virtual void SetBinContent(Int_t bin, Double_t content)
Set bin content see convention for numbering bins in TH1::GetBin In case the bin number is greater th...
virtual Double_t GetBinLowEdge(Int_t bin) const
Return bin lower edge for 1D histogram.
virtual Double_t GetEntries() const
Return the current number of entries.
virtual void SetName(const char *name)
Change the name of this histogram.
virtual Double_t GetBinContent(Int_t bin) const
Return content of bin number bin.
virtual TArrayD * GetSumw2()
virtual void ExecuteEvent(Int_t event, Int_t px, Int_t py)
Execute action corresponding to one event.
virtual Double_t GetBinWidth(Int_t bin) const
Return bin width for 1D histogram.
virtual void Paint(Option_t *option="")
Control routine to paint any kind of histograms.
virtual Int_t GetSumw2N() const
virtual void SetBins(Int_t nx, Double_t xmin, Double_t xmax)
Redefine x axis parameters.
virtual void Sumw2(Bool_t flag=kTRUE)
Create structure to store sum of squares of weights.
static Bool_t AddDirectoryStatus()
Static function: cannot be inlined on Windows/NT.
virtual Int_t DistancetoPrimitive(Int_t px, Int_t py)
Compute distance from point px,py to a line.
virtual void SetStats(Bool_t stats=kTRUE)
Set statistics option on/off.
2-D histogram with a double per channel (see TH1 documentation)}
2-D histogram with a float per channel (see TH1 documentation)}
Service class for 2-Dim histogram classes.
virtual void SetBinContent(Int_t bin, Double_t content)
Set bin content.
3-D histogram with a double per channel (see TH1 documentation)}
The 3-D histogram classes derived from the 1-D histogram classes.
virtual void Add(TObject *obj)
virtual TObject * Remove(TObject *obj)
Remove object from the list.
virtual void Delete(Option_t *option="")
Remove all objects from the list AND delete all heap based objects.
virtual TObject * First() const
Return the first object in the list. Returns 0 when list is empty.
The TNamed class is the base class for all named ROOT classes.
virtual void SetTitle(const char *title="")
Set the title of the TNamed.
virtual void SetName(const char *name)
Set the name of the TNamed.
virtual const char * GetTitle() const
Returns title of object.
virtual const char * GetName() const
Returns name of object.
Mother of all ROOT objects.
virtual const char * GetName() const
Returns name of object.
R__ALWAYS_INLINE Bool_t TestBit(UInt_t f) const
@ kNotDeleted
object has not been deleted
virtual const char * ClassName() const
Returns name of class to which the object belongs.
virtual void Warning(const char *method, const char *msgfmt,...) const
Issue warning message.
virtual void AppendPad(Option_t *option="")
Append graphics object to current pad.
virtual void SavePrimitive(std::ostream &out, Option_t *option="")
Save a primitive as a C++ statement(s) on output stream "out".
void SetBit(UInt_t f, Bool_t set)
Set or unset the user status bits as specified in f.
virtual Bool_t InheritsFrom(const char *classname) const
Returns kTRUE if object inherits from class "classname".
virtual void Error(const char *method, const char *msgfmt,...) const
Issue error message.
virtual void Fatal(const char *method, const char *msgfmt,...) const
Issue fatal error message.
@ kInvalidObject
if object ctor succeeded but object should not be used
virtual void Info(const char *method, const char *msgfmt,...) const
Issue info message.
void ToLower()
Change string to lower-case.
TString & Insert(Ssiz_t pos, const char *s)
Ssiz_t First(char c) const
Find first occurrence of a character c.
const char * Data() const
TString & ReplaceAll(const TString &s1, const TString &s2)
TString & Append(const char *cs)
Bool_t Contains(const char *pat, ECaseCompare cmp=kExact) const
Ssiz_t Index(const char *pat, Ssiz_t i=0, ECaseCompare cmp=kExact) const
double beta_pdf(double x, double a, double b)
Probability density function of the beta distribution.
double beta_cdf(double x, double a, double b)
Cumulative distribution function of the beta distribution Upper tail of the integral of the beta_pdf.
double beta_cdf_c(double x, double a, double b)
Complement of the cumulative distribution function of the beta distribution.
double normal_quantile(double z, double sigma)
Inverse ( ) of the cumulative distribution function of the lower tail of the normal (Gaussian) distri...
double normal_quantile_c(double z, double sigma)
Inverse ( ) of the cumulative distribution function of the upper tail of the normal (Gaussian) distri...
double beta_quantile_c(double x, double a, double b)
Inverse ( ) of the cumulative distribution function of the lower tail of the beta distribution (beta_...
double beta_quantile(double x, double a, double b)
Inverse ( ) of the cumulative distribution function of the upper tail of the beta distribution (beta_...
double beta(double x, double y)
Calculates the beta function.
Bool_t AreEqualRel(Double_t af, Double_t bf, Double_t relPrec)