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tgraphmultierrors.cc
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tgraphmultierrors.cc
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// @(#)root/hist:$Id$
// Author: Simon Spies 18/02/19
/*************************************************************************
* Copyright (C) 2018-2019, Simon Spies. *
* All rights reserved. *
* *
* Based on the ROOT class TGraphAsymmErrors *
*************************************************************************/
#include "TROOT.h"
#include "TClass.h"
#include "TStyle.h"
#include "TVirtualPad.h"
#include "TEfficiency.h"
#include "Riostream.h"
#include "TObject.h"
#include "TNamed.h"
#include "TAttLine.h"
#include "TAttFill.h"
#include "TAttMarker.h"
#include "TVector.h"
#include "TH1.h"
#include "TF1.h"
#include "TMath.h"
#include "TGaxis.h"
#include "TVirtualGraphPainter.h"
#include "TGraphPainter.h"
#include "TBox.h"
#include "TArrow.h"
#include "TRegexp.h"
#include "Math/QuantFuncMathCore.h"
#include "tgraphmultierrors.h"
ClassImp(TGraphMultiErrors)
/** \class TGraphMultiErrors
TGraph with asymmetric error bars and multiple y error dimensions.
The TGraphAsymmErrors painting is currently performed by its functions TGraphMultiErrors::Paint,
TGraphMultiErrors::PaintReverse and TGraphMultiErrors::PaintGraphMultiErrors. These are supposed to be included in
the TGraphPainter class if this class should become part of ROOT.
All drawing options of TGraphAsymmErrors are available.
For more information see the function TGraphMultiErrors::PaintGraphMultiErrors.
*/
///////////////////////////////////////////////////////////////////////////////
/// TGraphMultiErrors default constructor.
TGraphMultiErrors::TGraphMultiErrors() : TGraph(), fNErrorDimensions(0), fExL(NULL), fExH(NULL), fEyL(NULL), fEyH(NULL), fEyLSum(NULL), fEyHSum(NULL), fSumErrorsMode(TGraphMultiErrors::kOnlyFirst), fAttFill(NULL), fAttLine(NULL) {}
////////////////////////////////////////////////////////////////////////////////
/// TGraphMultiErrors normal constructor.
///
/// the arrays are preset to zero
TGraphMultiErrors::TGraphMultiErrors(Int_t n, Int_t dim) : TGraph(n), fNErrorDimensions(dim), fSumErrorsMode(TGraphMultiErrors::kOnlyFirst) {
if (!CtorAllocate())
return;
FillZero(0, fNpoints);
fAttFill = new TAttFill[fNErrorDimensions];
fAttLine = new TAttLine[fNErrorDimensions];
}
////////////////////////////////////////////////////////////////////////////////
/// TGraphMultiErrors normal constructor with a single y error dimension.
///
/// if exL,H or eyL,H are null, the corresponding arrays are preset to zero
TGraphMultiErrors::TGraphMultiErrors(Int_t n, const Float_t* x, const Float_t* y, const Float_t* exL, const Float_t* exH, const Float_t* eyL, const Float_t* eyH, Int_t m) : TGraph(n, x, y), fNErrorDimensions(1), fEyLSum(NULL), fEyHSum(NULL), fSumErrorsMode(m) {
if(!CtorAllocate())
return;
for (Int_t i = 0; i < n; i++) {
if (exL) fExL[i] = exL[i];
else fExL[i] = 0.;
if (exH) fExH[i] = exH[i];
else fExH[i] = 0.;
if (eyL) fEyL[0][i] = eyL[i];
else fEyL[0][i] = 0.;
if (eyH) fEyH[0][i] = eyH[i];
else fEyH[0][i] = 0.;
}
CalcYErrorSum();
fAttFill = new TAttFill[fNErrorDimensions];
fAttLine = new TAttLine[fNErrorDimensions];
}
////////////////////////////////////////////////////////////////////////////////
/// TGraphMultiErrors normal constructor with a single y error dimension.
///
/// if exL,H or eyL,H are null, the corresponding arrays are preset to zero
TGraphMultiErrors::TGraphMultiErrors(Int_t n, const Double_t* x, const Double_t* y, const Double_t* exL, const Double_t* exH, const Double_t* eyL, const Double_t* eyH, Int_t m) : TGraph(n, x, y), fNErrorDimensions(1), fEyLSum(NULL), fEyHSum(NULL), fSumErrorsMode(m) {
if (!CtorAllocate())
return;
n = fNpoints * sizeof(Double_t);
if (exL) memcpy(fExL, exL, n);
else memset(fExL, 0, n);
if (exH) memcpy(fExH, exH, n);
else memset(fExH, 0, n);
if (eyL) memcpy(fEyL[0], eyL, n);
else memset(fEyL[0], 0, n);
if (eyH) memcpy(fEyH[0], eyH, n);
else memset(fEyH[0], 0, n);
CalcYErrorSum();
fAttFill = new TAttFill[fNErrorDimensions];
fAttLine = new TAttLine[fNErrorDimensions];
}
////////////////////////////////////////////////////////////////////////////////
/// TGraphMultiErrors normal constructor with a multiple y error dimensions.
///
/// if exL,H or eyL,H are null, the corresponding arrays are preset to zero
TGraphMultiErrors::TGraphMultiErrors(Int_t n, Int_t dim, const Float_t* x, const Float_t* y, const Float_t* exL, const Float_t* exH, Float_t** eyL, Float_t** eyH, Int_t m) : TGraph(n, x, y), fNErrorDimensions(dim), fEyLSum(NULL), fEyHSum(NULL), fSumErrorsMode(m) {
if(!CtorAllocate())
return;
for (Int_t i = 0; i < n; i++) {
if (exL) fExL[i] = exL[i];
else fExL[i] = 0.;
if (exH) fExH[i] = exH[i];
else fExH[i] = 0.;
for (Int_t j = 0; j < dim; j++) {
if (eyL) fEyL[j][i] = eyL[j][i];
else fEyL[j][i] = 0.;
if (eyH) fEyH[j][i] = eyH[j][i];
else fEyH[j][i] = 0.;
}
}
CalcYErrorSum();
fAttFill = new TAttFill[fNErrorDimensions];
fAttLine = new TAttLine[fNErrorDimensions];
}
////////////////////////////////////////////////////////////////////////////////
/// TGraphMultiErrors normal constructor with a multiple y error dimensions.
///
/// if exL,H or eyL,H are null, the corresponding arrays are preset to zero
TGraphMultiErrors::TGraphMultiErrors(Int_t n, Int_t dim, const Double_t* x, const Double_t* y, const Double_t* exL, const Double_t* exH, Double_t** eyL, Double_t** eyH, Int_t m) : TGraph(n, x, y), fNErrorDimensions(dim), fEyLSum(NULL), fEyHSum(NULL), fSumErrorsMode(m) {
if (!CtorAllocate())
return;
n = fNpoints * sizeof(Double_t);
if (exL) memcpy(fExL, exL, n);
else memset(fExL, 0, n);
if (exH) memcpy(fExH, exH, n);
else memset(fExH, 0, n);
for (Int_t j = 0; j < fNErrorDimensions; j++) {
if (eyL) memcpy(fEyL[j], eyL[j], n);
else memset(fEyL[j], 0, n);
if (eyH) memcpy(fEyH[j], eyH[j], n);
else memset(fEyH[j], 0, n);
}
CalcYErrorSum();
fAttFill = new TAttFill[fNErrorDimensions];
fAttLine = new TAttLine[fNErrorDimensions];
}
////////////////////////////////////////////////////////////////////////////////
/// Constructor with six vectors of floats in input and a single y error dimension
/// A grapherrors is built with the X coordinates taken from tvX and Y coord from tvY
/// and the errors from vectors tvExL/H and tvEyL/H.
/// The number of points in the graph is the minimum of number of points
/// in tvX and tvY.
TGraphMultiErrors::TGraphMultiErrors(const TVectorF& tvX, const TVectorF& tvY, const TVectorF& tvExL, const TVectorF& tvExH, const TVectorF& tvEyL, const TVectorF& tvEyH, Int_t m) : TGraph(), fNErrorDimensions(1), fEyLSum(NULL), fEyHSum(NULL), fSumErrorsMode(m) {
fNpoints = TMath::Min(tvX.GetNrows(), tvY.GetNrows());
if (!TGraph::CtorAllocate())
return;
if (!CtorAllocate())
return;
Int_t itvXL = tvX.GetLwb();
Int_t itvYL = tvY.GetLwb();
Int_t itvExLL = tvExL.GetLwb();
Int_t itvExHL = tvExH.GetLwb();
Int_t itvEyLL = tvEyL.GetLwb();
Int_t itvEyHL = tvEyH.GetLwb();
for (Int_t i = 0; i < fNpoints; i++) {
fX[i] = tvX(i + itvXL);
fY[i] = tvY(i + itvYL);
fExL[i] = tvExL(i + itvExLL);
fExH[i] = tvExH(i + itvExHL);
fEyL[0][i] = tvEyL(i + itvEyLL);
fEyH[0][i] = tvEyH(i + itvEyHL);
}
CalcYErrorSum();
fAttFill = new TAttFill[fNErrorDimensions];
fAttLine = new TAttLine[fNErrorDimensions];
}
////////////////////////////////////////////////////////////////////////////////
/// Constructor with six vectors of doubles in input and a single y error dimension
/// A grapherrors is built with the X coordinates taken from tvX and Y coord from tvY
/// and the errors from vectors tvExL/H and tvEyL/H.
/// The number of points in the graph is the minimum of number of points
/// in tvX and tvY.
TGraphMultiErrors::TGraphMultiErrors(const TVectorD& tvX, const TVectorD& tvY, const TVectorD& tvExL, const TVectorD& tvExH, const TVectorD& tvEyL, const TVectorD& tvEyH, Int_t m) : TGraph(), fNErrorDimensions(1), fEyLSum(NULL), fEyHSum(NULL), fSumErrorsMode(m) {
fNpoints = TMath::Min(tvX.GetNrows(), tvY.GetNrows());
if (!TGraph::CtorAllocate())
return;
if (!CtorAllocate())
return;
Int_t itvXL = tvX.GetLwb();
Int_t itvYL = tvY.GetLwb();
Int_t itvExLL = tvExL.GetLwb();
Int_t itvExHL = tvExH.GetLwb();
Int_t itvEyLL = tvEyL.GetLwb();
Int_t itvEyHL = tvEyH.GetLwb();
for (Int_t i = 0; i < fNpoints; i++) {
fX[i] = tvX(i + itvXL);
fY[i] = tvY(i + itvYL);
fExL[i] = tvExL(i + itvExLL);
fExH[i] = tvExH(i + itvExHL);
fEyL[0][i] = tvEyL(i + itvEyLL);
fEyH[0][i] = tvEyH(i + itvEyHL);
}
CalcYErrorSum();
fAttFill = new TAttFill[fNErrorDimensions];
fAttLine = new TAttLine[fNErrorDimensions];
}
////////////////////////////////////////////////////////////////////////////////
/// Constructor with multiple vectors of floats in input and multiple y error dimensions
/// A grapherrors is built with the X coordinates taken from tvX and Y coord from tvY
/// and the errors from vectors tvExL/H and tvEyL/H[yErrorDimension].
/// The number of points in the graph is the minimum of number of points
/// in tvX and tvY.
TGraphMultiErrors::TGraphMultiErrors(Int_t dim, const TVectorF& tvX, const TVectorF& tvY, const TVectorF& tvExL, const TVectorF& tvExH, const TVectorF* tvEyL, const TVectorF* tvEyH, Int_t m) : TGraph(), fNErrorDimensions(dim), fEyLSum(NULL), fEyHSum(NULL), fSumErrorsMode(m) {
fNpoints = TMath::Min(tvX.GetNrows(), tvY.GetNrows());
if (!TGraph::CtorAllocate())
return;
if (!CtorAllocate())
return;
Int_t itvXL = tvX.GetLwb();
Int_t itvYL = tvY.GetLwb();
Int_t itvExLL = tvExL.GetLwb();
Int_t itvExHL = tvExH.GetLwb();
for (Int_t i = 0; i < fNpoints; i++) {
fX[i] = tvX(i + itvXL);
fY[i] = tvY(i + itvYL);
fExL[i] = tvExL(i + itvExLL);
fExH[i] = tvExH(i + itvExHL);
for (Int_t j = 0; j < dim; j++) {
fEyL[j][i] = tvEyL[j](i + tvEyL[j].GetLwb());
fEyH[j][i] = tvEyH[j](i + tvEyH[j].GetLwb());
}
}
CalcYErrorSum();
fAttFill = new TAttFill[fNErrorDimensions];
fAttLine = new TAttLine[fNErrorDimensions];
}
////////////////////////////////////////////////////////////////////////////////
/// Constructor with multiple vectors of doubles in input and multiple y error dimensions
/// A grapherrors is built with the X coordinates taken from tvX and Y coord from tvY
/// and the errors from vectors tvExL/H and tvEyL/H[yErrorDimension].
/// The number of points in the graph is the minimum of number of points
/// in tvX and tvY.
TGraphMultiErrors::TGraphMultiErrors(Int_t dim, const TVectorD& tvX, const TVectorD& tvY, const TVectorD& tvExL, const TVectorD& tvExH, const TVectorD* tvEyL, const TVectorD* tvEyH, Int_t m) : TGraph(), fNErrorDimensions(dim), fEyLSum(NULL), fEyHSum(NULL), fSumErrorsMode(m) {
fNpoints = TMath::Min(tvX.GetNrows(), tvY.GetNrows());
if (!TGraph::CtorAllocate())
return;
if (!CtorAllocate())
return;
Int_t itvXL = tvX.GetLwb();
Int_t itvYL = tvY.GetLwb();
Int_t itvExLL = tvExL.GetLwb();
Int_t itvExHL = tvExH.GetLwb();
for (Int_t i = 0; i < fNpoints; i++) {
fX[i] = tvX(i + itvXL);
fY[i] = tvY(i + itvYL);
fExL[i] = tvExL(i + itvExLL);
fExH[i] = tvExH(i + itvExHL);
for (Int_t j = 0; j < dim; j++) {
fEyL[j][i] = tvEyL[j](i + tvEyL[j].GetLwb());
fEyH[j][i] = tvEyH[j](i + tvEyH[j].GetLwb());
}
}
CalcYErrorSum();
fAttFill = new TAttFill[fNErrorDimensions];
fAttLine = new TAttLine[fNErrorDimensions];
}
////////////////////////////////////////////////////////////////////////////////
/// TGraphMultiErrors copy constructor
TGraphMultiErrors::TGraphMultiErrors(const TGraphMultiErrors& tgme) : TGraph(tgme), fEyLSum(NULL), fEyHSum(NULL) {
fNErrorDimensions = tgme.fNErrorDimensions;
fSumErrorsMode = tgme.fSumErrorsMode;
if (!CtorAllocate())
return;
Int_t n = fNpoints * sizeof(Double_t);
memcpy(fExL, tgme.fExL, n);
memcpy(fExH, tgme.fExH, n);
for (Int_t j = 0; j < fNErrorDimensions; j++) {
memcpy(fEyL[j], tgme.fEyL[j], n);
memcpy(fEyH[j], tgme.fEyH[j], n);
}
CalcYErrorSum();
fAttFill = new TAttFill[fNErrorDimensions];
fAttLine = new TAttLine[fNErrorDimensions];
for (Int_t j = 0; j < fNErrorDimensions; j++) {
tgme.GetAttFill(j)->Copy(fAttFill[j]);
tgme.GetAttLine(j)->Copy(fAttLine[j]);
}
}
////////////////////////////////////////////////////////////////////////////////
/// TGraphMultiErrors assignment operator
TGraphMultiErrors& TGraphMultiErrors::operator = (const TGraphMultiErrors& tgme) {
if (this != &tgme) {
TGraph::operator = (tgme);
// delete arrays
if (fExL) delete [] fExL;
if (fExH) delete [] fExH;
if (fEyL) {
for (Int_t j = 0; j < fNErrorDimensions; j++)
delete [] fEyL[j];
delete [] fEyL;
}
if (fEyH) {
for (Int_t j = 0; j < fNErrorDimensions; j++)
delete [] fEyH[j];
delete [] fEyH;
}
if (fAttFill) delete [] fAttFill;
if (fAttLine) delete [] fAttLine;
fNErrorDimensions = tgme.fNErrorDimensions;
fSumErrorsMode = tgme.fSumErrorsMode;
if (!CtorAllocate())
return *this;
Int_t n = fNpoints * sizeof(Double_t);
memcpy(fExL, tgme.fExL, n);
memcpy(fExH, tgme.fExH, n);
for (Int_t j = 0; j < fNErrorDimensions; j++) {
memcpy(fEyL[j], tgme.fEyL[j], n);
memcpy(fEyH[j], tgme.fEyH[j], n);
}
CalcYErrorSum();
fAttFill = new TAttFill[fNErrorDimensions];
fAttLine = new TAttLine[fNErrorDimensions];
for (Int_t j = 0; j < fNErrorDimensions; j++) {
tgme.GetAttFill(j)->Copy(fAttFill[j]);
tgme.GetAttLine(j)->Copy(fAttLine[j]);
}
}
return *this;
}
////////////////////////////////////////////////////////////////////////////////
/// TGraphMultiErrors constructor importing its parameters from the TH1 object passed as argument
/// the low and high errors are set to the bin error of the histogram.
TGraphMultiErrors::TGraphMultiErrors(const TH1* h, Int_t dim) : TGraph(h), fNErrorDimensions(dim), fEyLSum(NULL), fEyHSum(NULL), fSumErrorsMode(TGraphMultiErrors::kOnlyFirst) {
if (!CtorAllocate())
return;
for (Int_t i = 0; i < fNpoints; i++) {
fExL[i] = h->GetBinWidth(i + 1) * gStyle->GetErrorX();
fExH[i] = h->GetBinWidth(i + 1) * gStyle->GetErrorX();
fEyL[0][i] = h->GetBinError(i + 1);
fEyH[0][i] = h->GetBinError(i + 1);
for (Int_t j = 1; j < fNErrorDimensions; j++) {
fEyL[j][i] = 0.;
fEyH[j][i] = 0.;
}
}
CalcYErrorSum();
fAttFill = new TAttFill[fNErrorDimensions];
fAttLine = new TAttLine[fNErrorDimensions];
TAttFill::Copy(fAttFill[0]);
TAttLine::Copy(fAttLine[0]);
}
////////////////////////////////////////////////////////////////////////////////
/// Creates a TGraphMultiErrors by dividing two input TH1 histograms:
/// pass/total. (see TGraphMultiErrors::Divide)
TGraphMultiErrors::TGraphMultiErrors(const TH1* pass, const TH1* total, Int_t dim, Option_t *option) : TGraph(pass ? pass->GetNbinsX() : 0), fNErrorDimensions(dim), fEyLSum(NULL), fEyHSum(NULL), fSumErrorsMode(TGraphMultiErrors::kOnlyFirst) {
if (!pass || !total) {
Error("TGraphMultiErrors", "Invalid histogram pointers");
return;
}
if (!CtorAllocate())
return;
std::string sname = "divide_" + std::string(pass->GetName()) + "_by_" + std::string(total->GetName());
SetName(sname.c_str());
SetTitle(pass->GetTitle());
//copy style from pass
pass->TAttLine::Copy(*this);
pass->TAttFill::Copy(*this);
pass->TAttMarker::Copy(*this);
Divide(pass, total, option);
CalcYErrorSum();
fAttFill = new TAttFill[fNErrorDimensions];
fAttLine = new TAttLine[fNErrorDimensions];
TAttFill::Copy(fAttFill[0]);
TAttLine::Copy(fAttLine[0]);
}
////////////////////////////////////////////////////////////////////////////////
/// TGraphMultiErrors default destructor.
TGraphMultiErrors::~TGraphMultiErrors() {
if (fExL) delete [] fExL;
if (fExH) delete [] fExH;
if (fEyL) {
for (Int_t j = 0; j < fNErrorDimensions; j++)
delete [] fEyL[j];
delete [] fEyL;
}
if (fEyH) {
for (Int_t j = 0; j < fNErrorDimensions; j++)
delete [] fEyH[j];
delete [] fEyH;
}
if (fEyLSum) delete [] fEyLSum;
if (fEyHSum) delete [] fEyHSum;
if (fAttFill) delete [] fAttFill;
if (fAttLine) delete [] fAttLine;
}
////////////////////////////////////////////////////////////////////////////////
/// Apply a function to all data points `y = f(x,y)`
///
/// Errors are calculated as `eyh = f(x,y+eyh)-f(x,y)` and
/// `eyl = f(x,y)-f(x,y-eyl)`
/// Only the first error dimension is affected!!!
///
/// Special treatment has to be applied for the functions where the
/// role of "up" and "down" is reversed.
/// function suggested/implemented by Miroslav Helbich <[email protected]>
void TGraphMultiErrors::Apply(TF1* f) {
Double_t x, y, eyL, eyH, eyLNew, eyHNew, fxy;
if (fHistogram) {
delete fHistogram;
fHistogram = 0;
}
for (Int_t i = 0; i < fNpoints; i++) {
GetPoint(i, x, y);
eyL = GetErrorYlow(i, 0);
eyH = GetErrorYhigh(i, 0);
fxy = f->Eval(x, y);
SetPoint(i, x, fxy);
if (f->Eval(x, y - eyL) < f->Eval(x, y + eyH)) {
eyLNew = TMath::Abs(fxy - f->Eval(x, y - eyL));
eyHNew = TMath::Abs(f->Eval(x, y + eyH) - fxy);
} else {
eyHNew = TMath::Abs(fxy - f->Eval(x, y - eyL));
eyLNew = TMath::Abs(f->Eval(x, y + eyH) - fxy);
}
//systematic errors and error on x doesn't change
SetPointEY(i, 0, eyLNew, eyHNew);
}
if (gPad)
gPad->Modified();
}
////////////////////////////////////////////////////////////////////////////////
/// This function is only kept for backward compatibility.
/// You should rather use the Divide method.
/// It calls `Divide(pass,total,"cl=0.683 b(1,1) mode")` which is equivalent to the
/// former BayesDivide method.
void TGraphMultiErrors::BayesDivide(const TH1* pass, const TH1* total, Option_t*) {
Divide(pass, total, "cl=0.683 b(1,1) mode");
}
////////////////////////////////////////////////////////////////////////////////
/// This function was adapted from the TGraphAsymmErrors class.
/// See TGraphAsymmErrors::Divide for the documentation
///
/// Only the first error dimension is affected!!!
void TGraphMultiErrors::Divide(const TH1* pass, const TH1* total, Option_t *opt) {
//check pointers
if(!pass || !total) {
Error("Divide", "one of the passed pointers is zero");
return;
}
//check dimension of histograms; only 1-dimensional ones are accepted
if((pass->GetDimension() > 1) || (total->GetDimension() > 1)) {
Error("Divide","passed histograms are not one-dimensional");
return;
}
//check whether histograms are filled with weights -> use number of effective
//entries
Bool_t bEffective = false;
//compare sum of weights with sum of squares of weights
// re-compute here to be sure to get the right values
Double_t psumw = 0;
Double_t psumw2 = 0;
if (pass->GetSumw2()->fN > 0) {
for (int i = 0; i < pass->GetNbinsX(); ++i) {
psumw += pass->GetBinContent(i);
psumw2 += pass->GetSumw2()->At(i);
}
} else {
psumw = pass->GetSumOfWeights();
psumw2 = psumw;
}
if (TMath::Abs(psumw - psumw2) > 1e-6)
bEffective = true;
Double_t tsumw = 0;
Double_t tsumw2 = 0;
if (total->GetSumw2()->fN > 0) {
for (int i = 0; i < total->GetNbinsX(); ++i) {
tsumw += total->GetBinContent(i);
tsumw2 += total->GetSumw2()->At(i);
}
} else {
tsumw = total->GetSumOfWeights();
tsumw2 = tsumw;
}
if (TMath::Abs(tsumw - tsumw2) > 1e-6)
bEffective = true;
// we do not want to ignore the weights
// if (bEffective && (pass->GetSumw2()->fN == 0 || total->GetSumw2()->fN == 0) ) {
// Warning("Divide","histogram have been computed with weights but the sum of weight squares are not stored in the histogram. Error calculation is performed ignoring the weights");
// bEffective = false;
// }
//parse option
TString option = opt;
option.ToLower();
Bool_t bVerbose = false;
//pointer to function returning the boundaries of the confidence interval
//(is only used in the frequentist cases.)
//Double_t (*pBound)(Int_t,Int_t,Double_t,Bool_t) = &TEfficiency::ClopperPearson; // default method
Double_t (*pBound)(Double_t,Double_t,Double_t,Bool_t) = &TEfficiency::ClopperPearson; // default method
//confidence level
Double_t conf = 0.682689492137;
//values for bayesian statistics
Bool_t bIsBayesian = false;
Double_t alpha = 1;
Double_t beta = 1;
//verbose mode
if(option.Contains("v")) {
option.ReplaceAll("v", "");
bVerbose = true;
if (bEffective)
Info("Divide", "weight will be considered in the Histogram Ratio");
}
//confidence level
if(option.Contains("cl=")) {
Double_t level = -1;
// coverity [secure_coding : FALSE]
sscanf(strstr(option.Data(),"cl="),"cl=%lf",&level);
if((level > 0) && (level < 1))
conf = level;
else
Warning("Divide","given confidence level %.3lf is invalid",level);
option.ReplaceAll("cl=","");
}
//normal approximation
if(option.Contains("n")) {
option.ReplaceAll("n","");
pBound = &TEfficiency::Normal;
}
//clopper pearson interval
if(option.Contains("cp")) {
option.ReplaceAll("cp","");
pBound = &TEfficiency::ClopperPearson;
}
//wilson interval
if(option.Contains("w")) {
option.ReplaceAll("w","");
pBound = &TEfficiency::Wilson;
}
//agresti coull interval
if(option.Contains("ac")) {
option.ReplaceAll("ac","");
pBound = &TEfficiency::AgrestiCoull;
}
// Feldman-Cousins interval
if(option.Contains("fc")) {
option.ReplaceAll("fc","");
pBound = &TEfficiency::FeldmanCousins;
}
// mid-P Lancaster interval (In a later ROOT Version!)
if(option.Contains("midp")) {
option.ReplaceAll("midp","");
//pBound = &TEfficiency::MidPInterval;
}
//bayesian with prior
if(option.Contains("b(")) {
Double_t a = 0;
Double_t b = 0;
sscanf(strstr(option.Data(),"b("),"b(%lf,%lf)",&a,&b);
if(a > 0)
alpha = a;
else
Warning("Divide", "given shape parameter for alpha %.2lf is invalid",a);
if(b > 0)
beta = b;
else
Warning("Divide","given shape parameter for beta %.2lf is invalid",b);
option.ReplaceAll("b(","");
bIsBayesian = true;
}
// use posterior mode
Bool_t usePosteriorMode = false;
if(bIsBayesian && option.Contains("mode") ) {
usePosteriorMode = true;
option.ReplaceAll("mode","");
}
Bool_t plot0Bins = false;
if(option.Contains("e0") ) {
plot0Bins = true;
option.ReplaceAll("e0","");
}
Bool_t useShortestInterval = false;
if (bIsBayesian && ( option.Contains("sh") || (usePosteriorMode && !option.Contains("cen") ) ) ) {
useShortestInterval = true;
}
// interpret as Poisson ratio
Bool_t bPoissonRatio = false;
if(option.Contains("pois") ) {
bPoissonRatio = true;
option.ReplaceAll("pois","");
}
// weights works only in case of Normal approximation or Bayesian for binomial interval
// in case of Poisson ratio we can use weights by rescaling the obtained results using the effective entries
if ( ( bEffective && !bPoissonRatio) && !bIsBayesian && pBound != &TEfficiency::Normal ) {
Warning("Divide","Histograms have weights: only Normal or Bayesian error calculation is supported");
Info("Divide","Using now the Normal approximation for weighted histograms");
}
if(bPoissonRatio) {
if(pass->GetDimension() != total->GetDimension()) {
Error("Divide","passed histograms are not of the same dimension");
return;
}
if(!TEfficiency::CheckBinning(*pass,*total)) {
Error("Divide","passed histograms are not consistent");
return;
}
} else {
//check consistency of histograms, allowing weights
if(!TEfficiency::CheckConsistency(*pass,*total,"w")) {
Error("Divide","passed histograms are not consistent");
return;
}
}
//Set the graph to have a number of points equal to the number of histogram
//bins
Int_t nbins = pass->GetNbinsX();
Set(nbins);
// Ok, now set the points for each bin
// (Note: the TH1 bin content is shifted to the right by one:
// bin=0 is underflow, bin=nbins+1 is overflow.)
//efficiency with lower and upper boundary of confidence interval
double eff, low, upper;
//this keeps track of the number of points added to the graph
int npoint=0;
//number of total and passed events
Double_t t = 0 , p = 0;
Double_t tw = 0, tw2 = 0, pw = 0, pw2 = 0, wratio = 1; // for the case of weights
//loop over all bins and fill the graph
for (Int_t b=1; b<=nbins; ++b) {
// default value when total =0;
eff = 0;
low = 0;
upper = 0;
// special case in case of weights we have to consider the sum of weights and the sum of weight squares
if(bEffective) {
tw = total->GetBinContent(b);
tw2 = (total->GetSumw2()->fN > 0) ? total->GetSumw2()->At(b) : tw;
pw = pass->GetBinContent(b);
pw2 = (pass->GetSumw2()->fN > 0) ? pass->GetSumw2()->At(b) : pw;
if(bPoissonRatio) {
// tw += pw;
// tw2 += pw2;
// compute ratio on the effective entries ( p and t)
// special case is when (pw=0, pw2=0) in this case we cannot get the bin weight.
// we use then the overall weight of the full histogram
if (pw == 0 && pw2 == 0)
p = 0;
else
p = (pw*pw)/pw2;
if (tw == 0 && tw2 == 0)
t = 0;
else
t = (tw*tw)/tw2;
if (pw > 0 && tw > 0)
// this is the ratio of the two bin weights ( pw/p / t/tw )
wratio = (pw*t)/(p * tw);
else if (pw == 0 && tw > 0)
// case p histogram has zero compute the weights from all the histogram
// weight of histogram - sumw2/sumw
wratio = (psumw2 * t) /(psumw * tw );
else if (tw == 0 && pw > 0)
// case t histogram has zero compute the weights from all the histogram
// weight of histogram - sumw2/sumw
wratio = (pw * tsumw) /(p * tsumw2 );
else if (p > 0)
wratio = pw/p; //not sure if needed
else {
// case both pw and tw are zero - we skip these bins
if (!plot0Bins) continue; // skip bins with total <= 0
}
t += p;
//std::cout << p << " " << t << " " << wratio << std::endl;
} else if (tw <= 0 && !plot0Bins) continue; // skip bins with total <= 0
// in the case of weights have the formula only for
// the normal and bayesian statistics (see below)
}
//use bin contents
else {
t = int( total->GetBinContent(b) + 0.5);
p = int(pass->GetBinContent(b) + 0.5);
if(bPoissonRatio) t += p;
if (!t && !plot0Bins) continue; // skip bins with total = 0
}
//using bayesian statistics
if(bIsBayesian) {
double aa,bb;
if ((bEffective && !bPoissonRatio) && tw2 <= 0) {
// case of bins with zero errors
eff = pw/tw;
low = eff; upper = eff;
} else {
if (bEffective && !bPoissonRatio) {
// tw/tw2 re-normalize the weights
double norm = tw/tw2; // case of tw2 = 0 is treated above
aa = pw * norm + alpha;
bb = (tw - pw) * norm + beta;
} else {
aa = double(p) + alpha;
bb = double(t-p) + beta;
}
if (usePosteriorMode)
eff = TEfficiency::BetaMode(aa,bb);
else
eff = TEfficiency::BetaMean(aa,bb);
if (useShortestInterval) {
TEfficiency::BetaShortestInterval(conf,aa,bb,low,upper);
}
else {
low = TEfficiency::BetaCentralInterval(conf,aa,bb,false);
upper = TEfficiency::BetaCentralInterval(conf,aa,bb,true);
}
}
}
// case of non-bayesian statistics
else {
if (bEffective && !bPoissonRatio) {
if (tw > 0) {
eff = pw/tw;
// use normal error calculation using variance of MLE with weights (F.James 8.5.2)
// this is the same formula used in ROOT for TH1::Divide("B")
double variance = ( pw2 * (1. - 2 * eff) + tw2 * eff *eff ) / ( tw * tw) ;
double sigma = sqrt(variance);
double prob = 0.5 * (1.-conf);
double delta = ROOT::Math::normal_quantile_c(prob, sigma);
low = eff - delta;
upper = eff + delta;
if (low < 0) low = 0;
if (upper > 1) upper = 1.;
}
} else {
// when not using weights (all cases) or in case of Poisson ratio with weights
if(t)
eff = ((Double_t)p)/t;
low = pBound(t,p,conf,false);
upper = pBound(t,p,conf,true);
}
}
// treat as Poisson ratio
if(bPoissonRatio)
{
Double_t ratio = eff/(1 - eff);
// take the intervals in eff as intervals in the Poisson ratio
low = low/(1. - low);
upper = upper/(1.-upper);
eff = ratio;
if (bEffective) {
//scale result by the ratio of the weight
eff *= wratio;
low *= wratio;
upper *= wratio;
}
}
//Set the point center and its errors
if (TMath::Finite(eff)) {
SetPoint(npoint,pass->GetBinCenter(b),eff);
SetPointEX(npoint, pass->GetBinCenter(b)-pass->GetBinLowEdge(b), pass->GetBinLowEdge(b)-pass->GetBinCenter(b)+pass->GetBinWidth(b));
SetPointEY(npoint, 0, eff - low, upper - eff);
npoint++;//we have added a point to the graph
}
}
Set(npoint);//tell the graph how many points we've really added
if (npoint < nbins)
Warning("Divide","Number of graph points is different than histogram bins - %d points have been skipped",nbins-npoint);
if (bVerbose) {
Info("Divide","made a graph with %d points from %d bins",npoint,nbins);
Info("Divide","used confidence level: %.2lf\n",conf);
if(bIsBayesian)
Info("Divide","used prior probability ~ beta(%.2lf,%.2lf)",alpha,beta);
Print();
}
}
////////////////////////////////////////////////////////////////////////////////
/// Compute Range
void TGraphMultiErrors::ComputeRange(Double_t& xmin, Double_t& ymin, Double_t& xmax, Double_t& ymax) const {
TGraph::ComputeRange(xmin, ymin, xmax, ymax);
for (Int_t i = 0; i < fNpoints; i++) {
if (fX[i] - fExL[i] < xmin) {
if (gPad && gPad->GetLogx()) {
if (fExL[i] < fX[i])
xmin = fX[i] - fExL[i];
else
xmin = TMath::Min(xmin, fX[i] / 3.);
} else
xmin = fX[i] - fExL[i];
}
if (fX[i] + fExH[i] > xmax)
xmax = fX[i] + fExH[i];
Double_t eyLMax = 0., eyHMax = 0.;
for (Int_t j = 0; j < fNErrorDimensions; j++) {
eyLMax = TMath::Max(eyLMax, fEyL[j][i]);
eyHMax = TMath::Max(eyHMax, fEyH[j][i]);
}
if (fY[i] - eyLMax < ymin) {
if (gPad && gPad->GetLogy()) {
if (eyLMax < fY[i])
ymin = fY[i] - eyLMax;
else
ymin = TMath::Min(ymin, fY[i] / 3.);
} else
ymin = fY[i] - eyLMax;
}
if (fY[i] + eyHMax > ymax)
ymax = fY[i] + eyHMax;
}
}
////////////////////////////////////////////////////////////////////////////////
/// Copy and release.
void TGraphMultiErrors::CopyAndRelease(Double_t** newarrays, Int_t ibegin, Int_t iend, Int_t obegin) {
CopyPoints(newarrays, ibegin, iend, obegin);
if (newarrays) {
delete[] fX;
fX = newarrays[0];
delete[] fY;
fY = newarrays[1];
delete[] fExL;