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FinaliseSystematicErrorsCalo_Gammas_pPb.C
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FinaliseSystematicErrorsCalo_Gammas_pPb.C
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// ------------------------------------------------
// This code finalizes the systematic errors for --
// the direct photon analysis. Gamma spectrum as --
// well as double ratio systematics are produced --
// ------------------------------------------------
// Code is maintained by: --
// - Friederike Bock --
// for the Photon Conversion Group --
// ------------------------------------------------
#include <Riostream.h>
#include <fstream>
#include "TMath.h"
#include <stdlib.h>
#include <fstream>
#include <math.h>
#include <TROOT.h>
#include <TApplication.h>
#include <TPaveLabel.h>
#include <TSystem.h>
#include <TFrame.h>
#include <TStyle.h>
#include <TString.h>
#include "TGaxis.h"
#include "TFile.h"
#include "TH1F.h"
#include "TH1D.h"
#include "TH2F.h"
#include "TF1.h"
#include "TVirtualFitter.h"
#include "TObject.h"
#include "TCanvas.h"
#include "TMultiGraph.h"
#include "TLegend.h"
#include "TDatabasePDG.h"
#include "TMinuit.h"
#include "TBenchmark.h"
#include "TRandom.h"
#include "TLatex.h"
#include "TASImage.h"
#include "TPostScript.h"
#include "TGraphErrors.h"
#include "TArrow.h"
#include "TGraphAsymmErrors.h"
#include "TGaxis.h"
#include "TMarker.h"
#include "CommonHeaders/PlottingGammaConversionHistos.h"
#include "CommonHeaders/PlottingGammaConversionAdditional.h"
#include "CommonHeaders/FittingGammaConversion.h"
#include "CommonHeaders/ConversionFunctionsBasicsAndLabeling.h"
#include "CommonHeaders/ConversionFunctions.h"
//*************************************************************************************************************
//*********** Main function to calculate gamma errors for conversion measurements in pPb collisions ************
//*************************************************************************************************************
void FinaliseSystematicErrorsCalo_Gammas_pPb( TString nameDataFileErrors = "",
TString energy = "",
TString cent = "0-100%",
TString spectrumName = "",
Int_t numberCutStudies = 1,
Double_t minPt = 0,
Double_t maxPt = 10,
TString suffix = "pdf",
Int_t mode = 0
){
//**************************************************************
// Set plotting style and labels
StyleSettingsThesis();
SetPlotStyle();
Double_t textSizeSpectra = 0.04;
TString date = ReturnDateString();
TString collisionSystem = ReturnFullCollisionsSystem(energy);
TString detectionProcess = ReturnFullTextReconstructionProcess(4);
TString dateForOutput = ReturnDateStringForOutput();
TString energyForOutput = ReturnCollisionEnergyOutputString(energy);
TString centForOutput = cent;
centForOutput.ReplaceAll("%","");
if (centForOutput.CompareTo("0-100") == 0)
centForOutput = "";
TLatex *labelGamma = new TLatex(0.95,0.88,detectionProcess);
SetStyleTLatex( labelGamma, 0.038,4);
labelGamma->SetTextAlign(31);
Double_t posYLabel = 0.84;
TLatex *labelEnergy = new TLatex(0.95,0.92,Form("%s %s",cent.Data(), collisionSystem.Data()));
SetStyleTLatex( labelEnergy, 0.038,4);
labelEnergy->SetTextAlign(31);
TLatex *labelSpectrum;
if(!spectrumName.CompareTo("IncRatio"))
labelSpectrum = new TLatex(0.95,posYLabel,"#gamma_{inc}/#pi^{0}");
if(!spectrumName.CompareTo("DoubleRatio"))
labelSpectrum = new TLatex(0.95,posYLabel,"R_{#gamma}");
if(!spectrumName.CompareTo("Gamma"))
labelSpectrum = new TLatex(0.95,posYLabel,"#gamma_{inc}");
SetStyleTLatex( labelSpectrum, 0.038,4);
labelSpectrum->SetTextAlign(31);
//**************************************************************
// setup to read different error sources from root file
Int_t numberOfEntriesPos = 0;
Int_t numberOfEntriesNeg = 0;
// read root file with different cutvariations
TFile* fileErrorInput = new TFile(nameDataFileErrors.Data());
const Int_t nPtBins = 60;
Int_t nPtBinsActive = 60;
const Int_t nCuts = numberCutStudies;
Int_t nCutsActive = nCuts;
Double_t* ptBins = 0;
Double_t* ptBinsErr = 0;
Double_t yRangesSysPlotting[2] = {-0.5,24.9};
if (energy.CompareTo("pPb_5.023TeV") == 0 ){
if (spectrumName.Contains("Gamma"))
yRangesSysPlotting[1] = 20.5;
else if (spectrumName.Contains("IncRatio"))
yRangesSysPlotting[1] = 20.5;
else
yRangesSysPlotting[1] = 20.5;
}
// Set names of cut variations for file input
TString nameCutVariationSC[15] = { "ClusterNonLinearity", "ClusterM02", "ClusterMinEnergy", "ClusterTrackMatchingCalo", "ClusterTiming",
"ClusterNCells", "ClusterMaterialTRD", "SPD", "BG","Alpha" ,
"Cocktail", "IntRange", "Efficiency", "Rapidity", "OpeningAngle"
};
// Set colors and markers
Color_t color[15];
Color_t markerStyle[15];
// Set names of cut variations for legends
TString nameCutVariation[15];
for (Int_t k =0; k<nCuts; k++ ){
cout << "variation: " << nameCutVariationSC[k].Data() << endl;
color[k] = GetColorSystematics( nameCutVariationSC[k] );
markerStyle[k] = GetMarkerStyleSystematics( nameCutVariationSC[k] );
nameCutVariation[k] = GetSystematicsName(nameCutVariationSC[k]);
cout << "name for writing: " << nameCutVariation[k].Data() << "\t"<< color[k] << "\t" << markerStyle[k] << endl;
}
// Create output folder
gSystem->Exec("mkdir -p GammaSystematicErrorsCalculated");
// ***************************************************************************************************
// ******************************** Booleans for enabling systematic errors **************************
// ***************************************************************************************************
Bool_t benable[15] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0 };
Bool_t benableIncGammapPb5TeV[15] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
0, 0, 1, 0, 0 };
Bool_t benableIncRatiopPb5TeV[15] = { 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
0, 1, 1, 1, 1 };
Bool_t benableDRpPb5TeV[15] = { 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
1, 1, 1, 1, 1 };
// ***************************************************************************************************
// ******************************** Booleans for smoothing *******************************************
// ***************************************************************************************************
Bool_t bsmooth[15] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0 };
Bool_t bsmoothIncGammapPb5TeV[15] = { 1, 1, 1, 1, 1, 0, 1, 1, 0, 0,
0, 0, 1, 0, 0};
Bool_t bsmoothIncRatiopPb5TeV[15] = { 1, 1, 1, 1, 1, 0, 1, 1, 0, 0,
0, 0, 1, 1, 1 };
Bool_t bsmoothDRpPb5TeV[15] = { 1, 1, 1, 1, 1, 0, 1, 1, 0, 0,
0, 0, 1, 1, 1};
for (Int_t i = 0; i < numberCutStudies; i++){
if (energy.CompareTo("pPb_5.023TeV") == 0){
if(!spectrumName.CompareTo("IncRatio")){
bsmooth[i] = bsmoothIncRatiopPb5TeV[i];
benable[i] = benableIncRatiopPb5TeV[i];
} else if(!spectrumName.CompareTo("DoubleRatio")){
bsmooth[i] = bsmoothDRpPb5TeV[i];
benable[i] = benableDRpPb5TeV[i];
} else if(!spectrumName.CompareTo("Gamma")){
bsmooth[i] = bsmoothIncGammapPb5TeV[i];
benable[i] = benableIncGammapPb5TeV[i];
}
}
if (!benable[i]) nCutsActive--;
}
// ***************************************************************************************************
// ****************************** Initialize error vectors & graphs **********************************
// ***************************************************************************************************
Double_t* errorsNeg[nCuts];
Double_t errorsNegCorr[nCuts][nPtBins];
Double_t errorsNegSummed[nPtBins];
Double_t errorsNegCorrSummed[nPtBins];
Double_t errorsNegCorrMatSummed[nPtBins];
Double_t* errorsNegErr[nCuts];
Double_t errorsNegErrCorr[nCuts][nPtBins];
Double_t errorsNegErrSummed[nPtBins];
Double_t errorsNegErrCorrSummed[nPtBins];
Double_t* errorsPos[nCuts];
Double_t errorsPosCorr[nCuts][nPtBins];
Double_t errorsPosSummed[nPtBins];
Double_t errorsPosCorrSummed[nPtBins];
Double_t errorsPosCorrMatSummed[nPtBins];
Double_t* errorsPosErr[nCuts];
Double_t errorsPosErrSummed[nPtBins];
Double_t errorsPosErrCorr[nCuts][nPtBins];
Double_t errorsPosErrCorrSummed[nPtBins];
Double_t errorsMean[nCuts][nPtBins];
Double_t errorsMeanCorr[nCuts][nPtBins];
Double_t errorsMeanSummed[nPtBins];
Double_t errorsMeanCorrSummed[nPtBins];
Double_t errorsMeanCorrMatSummed[nPtBins];
Double_t errorsMeanErr[nCuts][nPtBins];
Double_t errorsMeanErrCorr[nCuts][nPtBins];
Double_t errorsMeanErrSummed[nPtBins];
Double_t errorsMeanErrCorrSummed[nPtBins];
Double_t errorsMeanErrCorrMatSummed[nPtBins];
TGraphErrors* negativeErrors[nCuts];
TGraphErrors* negativeErrorsSummed;
TGraphErrors* positiveErrors[nCuts];
TGraphErrors* positiveErrorsSummed;
TGraphErrors* negativeErrorsCorr[nCuts];
TGraphErrors* negativeErrorsCorrSummed;
TGraphErrors* positiveErrorsCorr[nCuts];
TGraphErrors* positiveErrorsCorrSummed;
TGraphErrors* meanErrors[nCuts];
TGraphErrors* meanErrorsSummed;
TGraphErrors* meanErrorsCorr[nCuts];
TGraphErrors* meanErrorsCorrSummed;
TGraphErrors* meanErrorsCorrSummedIncMat;
for (Int_t l = 0; l < nPtBins; l++){
errorsPosSummed[l] = 0.;
errorsNegSummed[l] = 0.;
errorsMeanSummed[l] = 0.;
errorsPosCorrSummed[l] = 0.;
errorsNegCorrSummed[l] = 0.;
errorsMeanCorrSummed[l] = 0.;
}
for (Int_t i = 0; i < nCuts; i++){
if (!benable[i]) {
cout << "*****************************************************************" << endl;
cout << "skipping: " << nameCutVariationSC[i].Data() << endl;
cout << "*****************************************************************" << endl;
for (Int_t l = 0; l < nPtBins; l++){
errorsMean[i][l] = 0;
errorsMeanErr[i][l] = 0.0;
errorsMeanCorr[i][l] = 0;
errorsMeanErrCorr[i][l] = 0.0;
}
continue;
}
TGraphAsymmErrors* graphPosErrors = NULL;
TGraphAsymmErrors* graphNegErrors = NULL;
TString currCent = cent;
if (! (i == 0 || i==11))
currCent = "0-100%";
// Set currently undetermined uncertainties
if ( nameCutVariationSC[i].CompareTo("SPD")==0 || nameCutVariationSC[i].CompareTo("Efficiency")==0 || nameCutVariationSC[i].CompareTo("ClusterTiming")==0){
TString nameGraphPos;
TString nameGraphNeg;
nameGraphPos = Form("%s_SystErrorRelPos_%s_%s",spectrumName.Data(),nameCutVariationSC[0].Data(), currCent.Data());
nameGraphNeg = Form("%s_SystErrorRelNeg_%s_%s",spectrumName.Data(),nameCutVariationSC[0].Data(), currCent.Data() );
cout << "Cutstudies " << i << "\t" <<nameGraphPos.Data() << "\t" << nameGraphNeg.Data()<< "\t fixing" << endl;
graphPosErrors = (TGraphAsymmErrors*)fileErrorInput->Get(nameGraphPos.Data());
graphNegErrors = (TGraphAsymmErrors*)fileErrorInput->Get(nameGraphNeg.Data());
} else {
// Load input graphs from systematics file
TString nameGraphPos = Form("%s_SystErrorRelPos_%s_%s",spectrumName.Data(),nameCutVariationSC[i].Data(), currCent.Data() );
TString nameGraphNeg = Form("%s_SystErrorRelNeg_%s_%s",spectrumName.Data(),nameCutVariationSC[i].Data(), currCent.Data() );
cout << "Cutstudies " << i<< "\t" <<nameGraphPos.Data() << "\t" << nameGraphNeg.Data()<< endl;
graphPosErrors = (TGraphAsymmErrors*)fileErrorInput->Get(nameGraphPos.Data());
graphNegErrors = (TGraphAsymmErrors*)fileErrorInput->Get(nameGraphNeg.Data());
}
// Remove first points depending on chosen offset
while (graphPosErrors->GetX()[0] < minPt ){
graphPosErrors->RemovePoint(0);
graphNegErrors->RemovePoint(0);
}
while (graphPosErrors->GetX()[graphPosErrors->GetN()-1] > maxPt){
graphPosErrors->RemovePoint(graphPosErrors->GetN()-1);
graphNegErrors->RemovePoint(graphNegErrors->GetN()-1);
}
if (i == 0) {
nPtBinsActive = graphNegErrors->GetN();
ptBins = graphNegErrors->GetX();
ptBinsErr = graphNegErrors->GetEXhigh();
}
errorsNeg[i] = graphNegErrors->GetY();
errorsNegErr[i] = graphNegErrors->GetEYhigh();
errorsPos[i] = graphPosErrors->GetY();
errorsPosErr[i] = graphPosErrors->GetEYhigh();
// Calculate systematic error from input spectrum
cout << nameCutVariationSC[i].Data() << endl;
CalculateMeanSysErr( errorsMean[i], errorsMeanErr[i], errorsPos[i], errorsNeg[i], nPtBinsActive);
CorrectSystematicErrorsWithMean(errorsPos[i], errorsPosErr[i], errorsPosCorr[i], errorsPosErrCorr[i], nPtBinsActive);
CorrectSystematicErrorsWithMean(errorsNeg[i], errorsNegErr[i], errorsNegCorr[i], errorsNegErrCorr[i], nPtBinsActive);
CorrectSystematicErrorsWithMean(errorsMean[i], errorsMeanErr[i], errorsMeanCorr[i], errorsMeanErrCorr[i], nPtBinsActive);
// ***************************************************************************************************
// ************************ Adjust errors if requested to fixed values *******************************
// ***************************************************************************************************
if (bsmooth[i]){
cout << "smoothing on" << endl;
Double_t errorFixed = -1;
Bool_t adjustPtDependent = kFALSE;
// 0 "ClusterNonLinearity"
if (!nameCutVariationSC[i].CompareTo("ClusterNonLinearity")){
if (spectrumName.Contains("Ratio")){
adjustPtDependent = kTRUE;
for (Int_t k = 0; k < nPtBins; k++){
errorFixed = 1.2+(0.01)*ptBins[k]+(0.01)*ptBins[k]*ptBins[k]; // non lin only
errorFixed = errorFixed+0.2*7.2;
if (errorFixed != -1){
errorsMean[i][k] = errorFixed;
errorsMeanErr[i][k] = errorFixed*0.01;
errorsMeanCorr[i][k] = errorFixed;
errorsMeanErrCorr[i][k] = errorFixed*0.01;
}
}
} else {
adjustPtDependent = kTRUE;
for (Int_t k = 0; k < nPtBins; k++){
errorFixed = 1.05+(0.01)*ptBins[k]+(0.01)*ptBins[k]*ptBins[k]; // non lin only
errorFixed = errorFixed+0.2*7.2;
if (errorFixed != -1){
errorsMean[i][k] = errorFixed;
errorsMeanErr[i][k] = errorFixed*0.01;
errorsMeanCorr[i][k] = errorFixed;
errorsMeanErrCorr[i][k] = errorFixed*0.01;
}
}
}
}
// 1 "ClusterM02"
if (!nameCutVariationSC[i].CompareTo("ClusterM02")){
if (spectrumName.Contains("Ratio")){
adjustPtDependent = kTRUE;
for (Int_t k = 0; k < nPtBins; k++){
// if (ptBins[k] > 2.8)
// errorFixed = 1.45+(0.06)*ptBins[k]+(0.04)*ptBins[k]*ptBins[k];
// errorFixed = 3.45+(0.06)*ptBins[k]+(0.04)*ptBins[k]*ptBins[k];
errorFixed = 4.7;
if (errorFixed != -1){
errorsMean[i][k] = errorFixed;
errorsMeanErr[i][k] = errorFixed*0.01;
errorsMeanCorr[i][k] = errorFixed;
errorsMeanErrCorr[i][k] = errorFixed*0.01;
}
}
} else {
adjustPtDependent = kTRUE;
for (Int_t k = 0; k < nPtBins; k++){
// if (ptBins[k] > 5.5)
// errorFixed = 10.5;
errorFixed = 4.3+(0.3)*ptBins[k]+(0.02)*ptBins[k]*ptBins[k];
if (errorFixed != -1){
errorsMean[i][k] = errorFixed;
errorsMeanErr[i][k] = errorFixed*0.01;
errorsMeanCorr[i][k] = errorFixed;
errorsMeanErrCorr[i][k] = errorFixed*0.01;
}
}
}
}
// 2 "ClusterMinEnergy"
if (!nameCutVariationSC[i].CompareTo("ClusterMinEnergy")){
if (spectrumName.Contains("Ratio")){
adjustPtDependent = kTRUE;
for (Int_t k = 0; k < nPtBins; k++){
errorFixed = 0.4+55/pow(6.4,ptBins[k]); ;
if (errorFixed != -1){
errorsMean[i][k] = errorFixed;
errorsMeanErr[i][k] = errorFixed*0.01;
errorsMeanCorr[i][k] = errorFixed;
errorsMeanErrCorr[i][k] = errorFixed*0.01;
}
}
} else {
errorFixed = 0.5;
}
}
// 3 "ClusterTrackMatching"
if (!nameCutVariationSC[i].CompareTo("ClusterTrackMatchingCalo")){
if (spectrumName.Contains("Ratio")){
adjustPtDependent = kTRUE;
for (Int_t k = 0; k < nPtBins; k++){
if (ptBins[k] > 3.0)
errorFixed = 0.95+(0.003)*ptBins[k]+(0.014)*ptBins[k]*ptBins[k]; // parametrisation
if (errorFixed != -1){
errorsMean[i][k] = errorFixed;
errorsMeanErr[i][k] = errorFixed*0.01;
errorsMeanCorr[i][k] = errorFixed;
errorsMeanErrCorr[i][k] = errorFixed*0.01;
}
}
} else {
adjustPtDependent = kTRUE;
for (Int_t k = 0; k < nPtBins; k++){
if (ptBins[k] > 3.0)
errorFixed = 0.95;
if (errorFixed != -1){
errorsMean[i][k] = errorFixed;
errorsMeanErr[i][k] = errorFixed*0.01;
errorsMeanCorr[i][k] = errorFixed;
errorsMeanErrCorr[i][k] = errorFixed*0.01;
}
}
}
}
// fix ClusterTiming pileup sys #4
if (!nameCutVariationSC[i].CompareTo("ClusterTiming")){
errorFixed = 0.3;
}
// 5 "ClusterNCells"
if (!nameCutVariationSC[i].CompareTo("ClusterNCells")){
errorFixed = 0.23;
}
// fix ClusterMaterialTRD sys #6
if (!nameCutVariationSC[i].CompareTo("ClusterMaterialTRD")){
if (spectrumName.Contains("Ratio"))
errorFixed = 4.2;
else
errorFixed = 2.8;
}
// fix SPD pileup sys #7
if (!nameCutVariationSC[i].CompareTo("SPD")){
errorFixed = 0.25;
}
// fix BG sys #8
if (!nameCutVariationSC[i].CompareTo("BG")){
adjustPtDependent = kTRUE;
for (Int_t k = 0; k < nPtBinsActive; k++){
errorFixed = 0.3+pow(ptBins[k],2)*0.0035;
if (errorFixed != -1){
errorsMean[i][k] = errorFixed;
errorsMeanErr[i][k] = errorFixed*0.01;
errorsMeanCorr[i][k] = errorFixed;
errorsMeanErrCorr[i][k] = errorFixed*0.01;
}
}
}
// fix alpha uncertainties #9
if (!nameCutVariationSC[i].CompareTo("Alpha")){
errorFixed = 0.25;
}
// fix "Cocktail" #10
// fix IntRange sys #11
if (!nameCutVariationSC[i].CompareTo("IntRange")){
if (spectrumName.Contains("Ratio")){
adjustPtDependent = kTRUE;
for (Int_t k = 0; k < nPtBinsActive; k++){
if (!energy.CompareTo("pPb_5.023TeV")){
if (ptBins[k] > 1.6)
errorFixed = 1.6+pow(ptBins[k],2)*0.018;
}
if (errorFixed != -1){
errorsMean[i][k] = errorFixed;
errorsMeanErr[i][k] = errorFixed*0.01;
errorsMeanCorr[i][k] = errorFixed;
errorsMeanErrCorr[i][k] = errorFixed*0.01;
}
}
}
}
// fix Efficiency sys #12
if (!nameCutVariationSC[i].CompareTo("Efficiency")){
if (!cent.CompareTo("0-100%") && spectrumName.Contains("Ratio"))
errorFixed = TMath::Sqrt(2*2+1.5*1.5);
else if (!cent.CompareTo("0-100%") )
errorFixed = 1.5;
else if (!spectrumName.Contains("Ratio"))
errorFixed = TMath::Sqrt(1*1+1.5*1.5);
else
errorFixed = TMath::Sqrt(2*2+1.5*1.5+1.5*1.5);
}
// 13 "Rapidity"
if (!nameCutVariationSC[i].CompareTo("Rapidity")){
if (spectrumName.Contains("Ratio")){
errorFixed = 2.05;
}
}
// 14 "OpeningAngle"
if (!nameCutVariationSC[i].CompareTo("OpeningAngle")){
if (spectrumName.Contains("Ratio")){
adjustPtDependent = kTRUE;
for (Int_t k = 0; k < nPtBinsActive; k++){
errorFixed = 0.1+pow(ptBins[k],2)*0.004;
if (errorFixed != -1){
errorsMean[i][k] = errorFixed;
errorsMeanErr[i][k] = errorFixed*0.01;
errorsMeanCorr[i][k] = errorFixed;
errorsMeanErrCorr[i][k] = errorFixed*0.01;
}
}
}
}
// put fixed values for pt independent errors, which were adjusted
if (!adjustPtDependent && errorFixed != -1){
for (Int_t k = 0; k < nPtBinsActive; k++){
errorsMean[i][k] = errorFixed;
errorsMeanErr[i][k] = errorFixed*0.01;
errorsMeanCorr[i][k] = errorFixed;
errorsMeanErrCorr[i][k] = errorFixed*0.01;
}
}
}
// Add systematic error contribution from current cutvariation to total summed error
for (Int_t l = 0; l < nPtBinsActive; l++){
errorsPosSummed[l] = errorsPosSummed[l]+pow(errorsPos[i][l],2);
errorsNegSummed[l] = errorsNegSummed[l]+ pow(errorsNeg[i][l],2);
errorsMeanSummed[l] = errorsMeanSummed[l]+ pow(errorsMean[i][l],2);
errorsPosCorrSummed[l] = errorsPosCorrSummed[l]+pow(errorsPosCorr[i][l],2);
errorsNegCorrSummed[l] = errorsNegCorrSummed[l] +pow(errorsNegCorr[i][l],2);
errorsMeanCorrSummed[l] = errorsMeanCorrSummed[l]+ pow(errorsMeanCorr[i][l],2);
}
negativeErrors[i] = new TGraphErrors(nPtBinsActive,ptBins ,errorsNeg[i] ,ptBinsErr ,errorsNegErr[i] );
meanErrors[i] = new TGraphErrors(nPtBinsActive,ptBins ,errorsMean[i] ,ptBinsErr ,errorsMeanErr[i] );
positiveErrors[i] = new TGraphErrors(nPtBinsActive,ptBins ,errorsPos[i] ,ptBinsErr ,errorsPosErr[i] );
negativeErrorsCorr[i] = new TGraphErrors(nPtBinsActive,ptBins ,errorsNegCorr[i] ,ptBinsErr ,errorsNegErrCorr[i] );
meanErrorsCorr[i] = new TGraphErrors(nPtBinsActive,ptBins ,errorsMeanCorr[i] ,ptBinsErr ,errorsMeanErrCorr[i] );
positiveErrorsCorr[i] = new TGraphErrors(nPtBinsActive,ptBins ,errorsPosCorr[i] ,ptBinsErr ,errorsPosErrCorr[i] );
}
for (Int_t l = 0; l < nPtBinsActive; l++){
errorsPosSummed[l] = pow(errorsPosSummed[l],0.5);
errorsMeanSummed[l] = pow(errorsMeanSummed[l],0.5);
errorsPosErrSummed[l] = errorsPosSummed[l]*0.001;
errorsMeanErrSummed[l] = errorsMeanSummed[l]*0.001;
errorsNegSummed[l] = -pow(errorsNegSummed[l],0.5);
errorsNegErrSummed[l] = errorsNegSummed[l]*0.001;
errorsPosCorrSummed[l] = pow(errorsPosCorrSummed[l],0.5);
errorsMeanCorrSummed[l] = pow(errorsMeanCorrSummed[l],0.5);
errorsPosErrCorrSummed[l] = errorsPosCorrSummed[l]*0.001;
errorsMeanErrCorrSummed[l] = errorsMeanCorrSummed[l]*0.001;
errorsNegCorrSummed[l] = -pow(errorsNegCorrSummed[l],0.5);
errorsNegErrCorrSummed[l] = errorsNegCorrSummed[l]*0.001;
}
negativeErrorsSummed = new TGraphErrors(nPtBinsActive,ptBins ,errorsNegSummed ,ptBinsErr ,errorsNegErrSummed );
negativeErrorsCorrSummed = new TGraphErrors(nPtBinsActive,ptBins ,errorsNegCorrSummed ,ptBinsErr ,errorsNegErrCorrSummed );
positiveErrorsSummed = new TGraphErrors(nPtBinsActive,ptBins ,errorsPosSummed ,ptBinsErr ,errorsPosErrSummed );
positiveErrorsCorrSummed = new TGraphErrors(nPtBinsActive,ptBins ,errorsPosCorrSummed ,ptBinsErr ,errorsPosErrCorrSummed );
meanErrorsSummed = new TGraphErrors(nPtBinsActive,ptBins ,errorsMeanSummed ,ptBinsErr ,errorsMeanErrSummed );
meanErrorsCorrSummed = new TGraphErrors(nPtBinsActive,ptBins ,errorsMeanCorrSummed ,ptBinsErr ,errorsMeanErrCorrSummed );
//++++++++++++++++++++++++++++++ PLOTTING OF SYSMEAN +++++++++++++++++++++++++++++++++++++++++
//++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
TCanvas* canvasNewSysErrMean = new TCanvas("canvasNewSysErrMean", "", 200, 10, 1200, 1100); // gives the page size
DrawGammaCanvasSettings( canvasNewSysErrMean, 0.075, 0.01, 0.015, 0.095);
canvasNewSysErrMean->SetLogx(1);
Double_t minXLegend = 0.11;
Double_t maxYLegend = 0.95;
Double_t widthLegend = 0.23;
Double_t heightLegend = 1.12* 0.035*0.85 * (nCutsActive+1);
Int_t nColumnsLegend = 1;
if ( nCutsActive+1 > 7){
widthLegend = 0.5;
heightLegend = 1.05* 0.035*0.85 * (nCutsActive/1.5+1);
nColumnsLegend = 2;
}
Double_t textSizeLabelsPixelmean = 55;
Double_t textSizeLabelsRelmean = 55./1200;
TH2F * histo2DNewSysErrMean;
histo2DNewSysErrMean = new TH2F("histo2DNewSysErrMean", "histo2DNewSysErrMean",1000, ptBins[0]-(ptBins[1]-ptBins[0])/2-0.07,1.2*(ptBins[nPtBinsActive-1]+ptBinsErr[nPtBinsActive-1]), 1000.,
yRangesSysPlotting[0], yRangesSysPlotting[1]);
SetStyleHistoTH2ForGraphs( histo2DNewSysErrMean, "#it{p}_{T} (GeV/#it{c})", "mean smoothed systematic Err %",
0.85*textSizeLabelsRelmean, textSizeLabelsRelmean, 0.85*textSizeLabelsRelmean, textSizeLabelsRelmean, 0.9, 0.75);//(#times #epsilon_{pur})
histo2DNewSysErrMean->GetYaxis()->SetLabelOffset(0.001);
histo2DNewSysErrMean->GetXaxis()->SetLabelOffset(-0.01);
histo2DNewSysErrMean->GetXaxis()->SetMoreLogLabels(kTRUE);
histo2DNewSysErrMean->DrawCopy();
// create legend
TLegend* legendMeanNew = GetAndSetLegend2(minXLegend, maxYLegend-heightLegend, minXLegend+widthLegend, maxYLegend, 40*0.85, nColumnsLegend, "", 43, 0.1);
for(Int_t i = 0;i< nCuts ;i++){
if (benable[i]){
DrawGammaSetMarkerTGraphErr(meanErrorsCorr[i], markerStyle[i], 1.,color[i],color[i]);
meanErrorsCorr[i]->Draw("pX0,csame");
legendMeanNew->AddEntry(meanErrorsCorr[i],nameCutVariation[i].Data(),"p");
}
}
DrawGammaSetMarkerTGraphErr(meanErrorsCorrSummed, 20, 1.,kBlack,kBlack);
meanErrorsCorrSummed->Draw("p,csame");
legendMeanNew->AddEntry(meanErrorsCorrSummed,"qd. sum","p");
legendMeanNew->Draw();
// labeling
labelGamma->Draw();
labelEnergy->Draw();
labelSpectrum->Draw();
canvasNewSysErrMean->Update();
canvasNewSysErrMean->SaveAs(Form("GammaSystematicErrorsCalculated/SysMeanNewWithMeanEMC_%s_%s%s_%s.%s",spectrumName.Data(), energyForOutput.Data(), centForOutput.Data(), dateForOutput.Data(),suffix.Data()));
//+++++++++++++++++++++++++ SAVING SYSTEMATICS TO DAT FILE +++++++++++++++++++++++++++++++++++
//++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
const char *SysErrDatname = Form("GammaSystematicErrorsCalculated/SystematicErrorEMC_%s_%s%s_%s.dat",spectrumName.Data(), energyForOutput.Data(), centForOutput.Data(), dateForOutput.Data());
fstream SysErrDat;
cout << SysErrDatname << endl;
SysErrDat.open(SysErrDatname, ios::out);
for (Int_t l=0; l< nPtBinsActive; l++){
SysErrDat << ptBins[l] << "\t" <<errorsNegCorrSummed[l] << "\t" <<errorsPosCorrSummed[l] << "\t" <<errorsNegCorrSummed[l] << "\t" <<errorsPosCorrSummed[l] << endl;
}
SysErrDat.close();
//+++++++++++++++++++++++++ SAVING AVERAGE SYSTEMATICS TO DAT ++++++++++++++++++++++++++++++++
//++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
const char *SysErrDatnameMean = Form("GammaSystematicErrorsCalculated/SystematicErrorAveragedEMC_%s_%s%s_%s.dat", spectrumName.Data(), energyForOutput.Data(), centForOutput.Data(), dateForOutput.Data());
fstream SysErrDatAver;
cout << SysErrDatnameMean << endl;
SysErrDatAver.open(SysErrDatnameMean, ios::out);
for (Int_t l=0; l< nPtBinsActive; l++){
SysErrDatAver << ptBins[l] << "\t" << "-"<< errorsMeanCorrSummed[l] << "\t" <<errorsMeanCorrSummed[l] << "\t" << "-"<< errorsMeanCorrSummed[l] << "\t" <<errorsMeanCorrSummed[l] << endl;
}
SysErrDatAver.close();
//+++++++++++++++++++++++++ SAVING DETAILED SYSTEMATICS ++++++++++++++++++++++++++++++++++++++
//++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
const char *SysErrDatnameMeanSingleErr = Form("GammaSystematicErrorsCalculated/SystematicErrorAveragedSingleEMC_%s_%s%s_%s.dat", spectrumName.Data(),energyForOutput.Data(), centForOutput.Data(), dateForOutput.Data());
fstream SysErrDatAverSingle;
cout << SysErrDatnameMeanSingleErr << endl;
SysErrDatAverSingle.open(SysErrDatnameMeanSingleErr, ios::out);
SysErrDatAverSingle << "Pt bin\t" ;
for (Int_t i= 0; i< numberCutStudies; i++){
if (benable[i]) SysErrDatAverSingle << nameCutVariationSC[i] << "\t";
}
SysErrDatAverSingle << endl;
for (Int_t l=0;l< nPtBinsActive;l++){
SysErrDatAverSingle << ptBins[l] << "\t";
for (Int_t i= 0; i< numberCutStudies; i++){
if (benable[i]) SysErrDatAverSingle << errorsMeanCorr[i][l] << "\t";
}
SysErrDatAverSingle << errorsMeanCorrSummed[l] << endl;
}
SysErrDatAverSingle.close();
Double_t errorsMeanCorrSignalExtraction[nPtBinsActive];
Double_t errorsMeanCorrClusterProp[nPtBinsActive];
for (Int_t l=0; l< nPtBinsActive; l++){
// 0 "ClusterNonLinearity"
// 1 "ClusterM02"
// 2 "ClusterMinEnergy"
// 3 "ClusterTrackMatching"
// 4 "ClusterTiming"
// 5 "ClusterNCells"
// 6 "ClusterMaterialTRD"
// 7 "SPD"
// 8 "BG"
// 9 "Alpha"
// 10 "Cocktail"
// 11 "IntRange"
// 12 "Efficiency"
// 13 "Rapidity"
// 14 "OpeningAngle"
// Signal extraction: Pi0Yield extraction, Alpha ,BG, Openangle, Rapidity
errorsMeanCorrSignalExtraction[l] = TMath::Sqrt(pow(errorsMeanCorr[9][l],2)+ // Alpha
pow(errorsMeanCorr[8][l],2)+ // BG
pow(errorsMeanCorr[13][l],2)+ // Rapidity
pow(errorsMeanCorr[14][l],2)+ // OpeningAngle
pow(errorsMeanCorr[11][l],2)); // Pi0 Yield Extraction
errorsMeanCorrClusterProp[l] = TMath::Sqrt(pow(errorsMeanCorr[2][l],2)+ // ClusterMinEnergy
pow(errorsMeanCorr[5][l],2)+ // ClusterNCells
pow(errorsMeanCorr[1][l],2)+ // ClusterM02
pow(errorsMeanCorr[4][l],2)); // ClusterTiming
}
TGraphErrors* meanErrorsSignalExtraction = new TGraphErrors(nPtBinsActive,ptBins ,errorsMeanCorrSignalExtraction ,ptBinsErr ,errorsMeanErrCorrSummed );
TGraphErrors* meanErrorsClusterProp = new TGraphErrors(nPtBinsActive,ptBins ,errorsMeanCorrClusterProp ,ptBinsErr ,errorsMeanErrCorrSummed );
//++++++++++++++++++++++++ PLOTTING OF SYSERRORSUMMEDVISU ++++++++++++++++++++++++++++++++++++
//++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Double_t textSizeLabelsPixel = 55;
Double_t textSizeLabelsRel = 55./1200;
TCanvas* canvasSummedErrMean = new TCanvas("canvasSummedErrMean", "", 200, 10, 1200, 1100); // gives the page size
DrawGammaCanvasSettings( canvasSummedErrMean, 0.075, 0.01, 0.015, 0.095);
canvasSummedErrMean->SetLogx(1);
TH2F * histo2DSummedErrMean;
histo2DSummedErrMean = new TH2F("histo2DSummedErrMean", "histo2DSummedErrMean",1000, ptBins[0]-(ptBins[1]-ptBins[0])/2-0.07, 1.2*(ptBins[nPtBinsActive-1]+ptBinsErr[nPtBinsActive-1]),
1000.,yRangesSysPlotting[0],yRangesSysPlotting[1] );
SetStyleHistoTH2ForGraphs( histo2DSummedErrMean, "#it{p}_{T} (GeV/#it{c})", "mean smoothed systematic Err %",
0.85*textSizeLabelsRel, textSizeLabelsRel, 0.85*textSizeLabelsRel, textSizeLabelsRel, 0.9, 0.75);//(#times #epsilon_{pur})
histo2DSummedErrMean->GetYaxis()->SetLabelOffset(0.001);
histo2DSummedErrMean->GetXaxis()->SetLabelOffset(-0.01);
histo2DSummedErrMean->GetXaxis()->SetMoreLogLabels(kTRUE);
histo2DSummedErrMean->DrawCopy();
Double_t minXLegend2 = 0.11;
Double_t maxYLegend2 = 0.95;
Double_t widthLegend2 = 0.45;
Double_t heightLegend2 = 4*0.042;
// create legend
TLegend* legendSummedMeanNew = GetAndSetLegend2(minXLegend2, maxYLegend2-heightLegend2, minXLegend2+widthLegend2, maxYLegend2, 40, 2, "", 43, 0.1);
Size_t markersizeSummed = 1.3;
// 0 "ClusterNonLinearity"
// 1 "ClusterM02"
// 2 "ClusterMinEnergy"
// 3 "ClusterTrackMatching"
// 4 "ClusterTiming"
// 5 "ClusterNCells"
// 6 "ClusterMaterialTRD"
// 7 "SPD"
// 8 "BG"
// 9 "Alpha"
// 10 "Cocktail"
// 11 "IntRange"
// 12 "Efficiency"
// 13 "Rapidity"
// 14 "OpeningAngle"
if (benable[8] || benable[9] || benable[11] || benable[13] || benable[14]){
DrawGammaSetMarkerTGraphErr(meanErrorsSignalExtraction, GetMarkerStyleSystematics("IntRange"), markersizeSummed, GetColorSystematics("IntRange"),GetColorSystematics("IntRange"));
meanErrorsSignalExtraction->Draw("p,csame");
legendSummedMeanNew->AddEntry(meanErrorsSignalExtraction,"sig. ext. #pi^{0}","p");
}
if (benable[7] ){
DrawGammaSetMarkerTGraphErr(meanErrorsCorr[7], GetMarkerStyleSystematics("Pileup"), markersizeSummed, GetColorSystematics("Pileup"),GetColorSystematics("Pileup"));
meanErrorsCorr[7]->Draw("p,csame");
legendSummedMeanNew->AddEntry(meanErrorsCorr[7],"pile-up","p");
}
if (benable[1] || benable[2] || benable[4] || benable[5] ){
DrawGammaSetMarkerTGraphErr(meanErrorsClusterProp, GetMarkerStyleSystematics("ClusterM02"), markersizeSummed, GetColorSystematics("ClusterM02"),GetColorSystematics("ClusterM02"));
meanErrorsClusterProp->Draw("p,csame");
legendSummedMeanNew->AddEntry(meanErrorsClusterProp,"cl. prop.","p");
}
if (benable[0]){
DrawGammaSetMarkerTGraphErr(meanErrorsCorr[0], markerStyle[0], markersizeSummed,color[0],color[0]);
meanErrorsCorr[0]->Draw("p,csame");
legendSummedMeanNew->AddEntry(meanErrorsCorr[0],"cl. #it{E}-scale","p");
}
if (benable[3]){
DrawGammaSetMarkerTGraphErr(meanErrorsCorr[3], markerStyle[3], markersizeSummed,color[3],color[3]);
meanErrorsCorr[3]->Draw("p,csame");
legendSummedMeanNew->AddEntry(meanErrorsCorr[3],"track match. cl.","p");
}
if (benable[12]){
DrawGammaSetMarkerTGraphErr(meanErrorsCorr[12], markerStyle[12], markersizeSummed,color[12],color[12]);
meanErrorsCorr[12]->Draw("p,csame");
legendSummedMeanNew->AddEntry(meanErrorsCorr[12],"#varepsilon_{rec}","p");
}
if (benable[10]){
DrawGammaSetMarkerTGraphErr(meanErrorsCorr[10], markerStyle[10], markersizeSummed,color[10],color[10]);
meanErrorsCorr[10]->Draw("p,csame");
legendSummedMeanNew->AddEntry(meanErrorsCorr[10],"cocktail","p");
}
if (benable[6]){
DrawGammaSetMarkerTGraphErr(meanErrorsCorr[6], markerStyle[6], markersizeSummed,color[6],color[6]);
meanErrorsCorr[6]->Draw("p,csame");
legendSummedMeanNew->AddEntry(meanErrorsCorr[6],"outer mat.","p");
}
DrawGammaSetMarkerTGraphErr(meanErrorsCorrSummed, 20, markersizeSummed,kBlack,kBlack);
meanErrorsCorrSummed->Draw("p,csame");
legendSummedMeanNew->AddEntry(meanErrorsCorrSummed,"qd. sum","p");
legendSummedMeanNew->Draw();
labelGamma->Draw();
labelEnergy->Draw();
labelSpectrum->Draw();
canvasSummedErrMean->Update();
canvasSummedErrMean->SaveAs(Form("GammaSystematicErrorsCalculated/SysErrorSummedVisuEMC_%s_%s%s_%s.%s",spectrumName.Data(), energyForOutput.Data(), centForOutput.Data(), dateForOutput.Data(),suffix.Data()));
delete canvasSummedErrMean;
//+++++++++++++++++++++++++ SAVING DETAILED SYSTEMATICS ++++++++++++++++++++++++++++++++++++++
//++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
const char *SysErrDatnameMeanSinglePaperErr = Form("GammaSystematicErrorsCalculated/SystematicErrorAveragedSinglePaperEMC_%s_%s%s_%s.dat", spectrumName.Data(),
energyForOutput.Data(), centForOutput.Data(), dateForOutput.Data());
fstream SysErrDatAverSinglePaper;
cout << SysErrDatnameMeanSinglePaperErr << endl;
SysErrDatAverSinglePaper.open(SysErrDatnameMeanSinglePaperErr, ios::out);
SysErrDatAverSinglePaper << "Pt bin\t" ;
if (benable[8] || benable[9] || benable[11] || benable[13] || benable[14]) SysErrDatAverSinglePaper << "SignalExtraction" << "\t";
if (benable[7]) SysErrDatAverSinglePaper << "Pileup" << "\t";
if (benable[1] || benable[2] || benable[4] || benable[5] ) SysErrDatAverSinglePaper << "ClusterProp" << "\t";
if (benable[0]) SysErrDatAverSinglePaper << "ClusterEnergyScale" << "\t";
if (benable[3]) SysErrDatAverSinglePaper << "ClusterTM" << "\t";
if (benable[12]) SysErrDatAverSinglePaper << "Efficiency" << "\t";
if (benable[10]) SysErrDatAverSinglePaper << "Cocktail" << "\t";
if (benable[6]) SysErrDatAverSinglePaper << "OuterMaterial" << "\t";
SysErrDatAverSinglePaper << endl;
for (Int_t l=0;l< nPtBinsActive;l++){
SysErrDatAverSinglePaper << ptBins[l] << "\t";
if (benable[8] || benable[9] || benable[11] || benable[13] || benable[14]) SysErrDatAverSinglePaper << errorsMeanCorrSignalExtraction[l] << "\t";
if (benable[7]) SysErrDatAverSinglePaper << errorsMeanCorr[7][l] << "\t";
if (benable[1] || benable[2] || benable[4] || benable[5] ) SysErrDatAverSinglePaper << errorsMeanCorrClusterProp[l] << "\t";
if (benable[0]) SysErrDatAverSinglePaper << errorsMeanCorr[0][l] << "\t";
if (benable[3]) SysErrDatAverSinglePaper << errorsMeanCorr[3][l] << "\t";
if (benable[12]) SysErrDatAverSinglePaper << errorsMeanCorr[12][l] << "\t";
if (benable[10]) SysErrDatAverSinglePaper << errorsMeanCorr[10][l] << "\t";
if (benable[6]) SysErrDatAverSinglePaper << errorsMeanCorr[6][l] << "\t";
SysErrDatAverSinglePaper << errorsMeanCorrSummed[l] << endl;
}
SysErrDatAverSinglePaper.close();
}