#include <AliRhoParameter.h>
#include <AliLocalRhoParameter.h>
#include <AliAnalysisTaskRhoVnModulation.h>
+#include <AliClusterContainer.h>
class AliAnalysisTaskRhoVnModulation;
using namespace std;
ClassImp(AliAnalysisTaskRhoVnModulation)
AliAnalysisTaskRhoVnModulation::AliAnalysisTaskRhoVnModulation() : AliAnalysisTaskEmcalJet("AliAnalysisTaskRhoVnModulation", kTRUE),
- fDebug(0), fLocalInit(0), fAttachToEvent(kTRUE), fSemiCentralInclusive(kFALSE), fFillHistograms(kTRUE), fFillQAHistograms(kTRUE), fReduceBinsXByFactor(-1.), fReduceBinsYByFactor(-1.), fNoEventWeightsForQC(kTRUE), fCentralityClasses(0), fPtBinsHybrids(0), fPtBinsJets(0), fUserSuppliedV2(0), fUserSuppliedV3(0), fUserSuppliedR2(0), fUserSuppliedR3(0), fTracksCont(0), fJetsCont(0), fUseScaledRho(0), fNAcceptedTracks(0), fNAcceptedTracksQCn(0), fFitModulationType(kNoFit), fQCRecovery(kTryFit), fUsePtWeight(kTRUE), fDetectorType(kTPC), fFitModulationOptions("QWLI"), fRunModeType(kGrid), fDataType(kESD), fCollisionType(kPbPb), fRandom(0), fMappedRunNumber(0), fInCentralitySelection(-1), fFitModulation(0), fMinPvalue(0.01), fMaxPvalue(1), fNameJetClones(0), fNamePicoTrackClones(0), fNameRho(0), fLocalJetMinEta(-10), fLocalJetMaxEta(-10), fLocalJetMinPhi(-10), fLocalJetMaxPhi(-10), fSoftTrackMinPt(0.15), fSoftTrackMaxPt(5.), fAbsVertexZ(10), fHistCentrality(0), fHistVertexz(0), fHistRunnumbersPhi(0), fHistRunnumbersEta(0), fHistPvaluePDF(0), fHistPvalueCDF(0), fMinDisanceRCtoLJ(0), fRandomConeRadius(-1.), fMaxCones(-1), fAbsVnHarmonics(kTRUE), fExcludeLeadingJetsFromFit(1.), fRebinSwapHistoOnTheFly(kTRUE), fPercentageOfFits(10.), fUseV0EventPlaneFromHeader(kTRUE), fExplicitOutlierCut(-1), fMinLeadingHadronPt(0), fSubtractJetPt(kFALSE), fOutputList(0), fOutputListGood(0), fOutputListBad(0), fHistAnalysisSummary(0), fHistSwap(0), fProfV2(0), fProfV2Cumulant(0), fProfV3(0), fProfV3Cumulant(0), fHistPsiControl(0), fHistPsiSpread(0), fHistPsiVZEROA(0), fHistPsiVZEROC(0), fHistPsiVZERO(0), fHistPsiTPC(0), fHistPsiVZEROAV0M(0), fHistPsiVZEROCV0M(0), fHistPsiVZEROVV0M(0), fHistPsiTPCiV0M(0), fHistPsiVZEROATRK(0), fHistPsiVZEROCTRK(0), fHistPsiVZEROTRK(0), fHistPsiTPCTRK(0), fHistRhoVsMult(0), fHistRhoVsCent(0), fHistRhoAVsMult(0), fHistRhoAVsCent(0) {
+ fDebug(0), fRunToyMC(kFALSE), fLocalInit(0), fAttachToEvent(kTRUE), fSemiCentralInclusive(kFALSE), fFillHistograms(kTRUE), fFillQAHistograms(kTRUE), fReduceBinsXByFactor(-1.), fReduceBinsYByFactor(-1.), fNoEventWeightsForQC(kTRUE), fCentralityClasses(0), fPtBinsHybrids(0), fPtBinsJets(0), fExpectedRuns(0), fExpectedSemiGoodRuns(0), fUserSuppliedV2(0), fUserSuppliedV3(0), fUserSuppliedR2(0), fUserSuppliedR3(0), fTracksCont(0), fClusterCont(0), fJetsCont(0), fLeadingJet(0), fUseScaledRho(0), fNAcceptedTracks(0), fNAcceptedTracksQCn(0), fFitModulationType(kNoFit), fFitGoodnessTest(kChi2Poisson), fQCRecovery(kTryFit), fUsePtWeight(kTRUE), fUsePtWeightErrorPropagation(kTRUE), fDetectorType(kTPC), fAnalysisType( kCharged), fFitModulationOptions("QWLI"), fRunModeType(kGrid), fDataType(kESD), fCollisionType(kPbPb), fRandom(0), fRunNumber(-1), fMappedRunNumber(0), fInCentralitySelection(-1), fFitModulation(0), fFitControl(0), fMinPvalue(0.01), fMaxPvalue(1), fNameJetClones(0), fNamePicoTrackClones(0), fNameRho(0), fNameSmallRho(""), fCachedRho(0), fLocalJetMinEta(-10), fLocalJetMaxEta(-10), fLocalJetMinPhi(-10), fLocalJetMaxPhi(-10), fSoftTrackMinPt(0.15), fSoftTrackMaxPt(5.), fSemiGoodJetMinPhi(0.), fSemiGoodJetMaxPhi(4.), fSemiGoodTrackMinPhi(0.), fSemiGoodTrackMaxPhi(4.), fAbsVertexZ(10), fHistCentrality(0), fHistVertexz(0), fHistRunnumbersPhi(0), fHistRunnumbersEta(0), fHistPvalueCDFROOT(0), fHistPvalueCDFROOTCent(0), fHistChi2ROOTCent(0), fHistPChi2Root(0), fHistPvalueCDF(0), fHistPvalueCDFCent(0), fHistChi2Cent(0), fHistPChi2(0), fHistKolmogorovTest(0), fHistKolmogorovTestCent(0), fHistPKolmogorov(0), fHistRhoStatusCent(0), fHistUndeterminedRunQA(0), fMinDisanceRCtoLJ(0), fRandomConeRadius(-1.), fMaxCones(-1), fAbsVnHarmonics(kTRUE), fExcludeLeadingJetsFromFit(1.), fRebinSwapHistoOnTheFly(kTRUE), fPercentageOfFits(10.), fUseV0EventPlaneFromHeader(kTRUE), fExplicitOutlierCut(-1), fMinLeadingHadronPt(0), fSubtractJetPt(kFALSE), fOutputList(0), fOutputListGood(0), fOutputListBad(0), fHistAnalysisSummary(0), fHistSwap(0), fProfV2(0), fProfV2Cumulant(0), fProfV3(0), fProfV3Cumulant(0), fHistPsiControl(0), fHistPsiSpread(0), fHistPsiVZEROA(0), fHistPsiVZEROC(0), fHistPsiVZERO(0), fHistPsiTPC(0), fHistPsiVZEROAV0M(0), fHistPsiVZEROCV0M(0), fHistPsiVZEROVV0M(0), fHistPsiTPCiV0M(0), fHistPsiVZEROATRK(0), fHistPsiVZEROCTRK(0), fHistPsiVZEROTRK(0), fHistPsiTPCTRK(0), fHistRhoVsMult(0), fHistRhoVsCent(0), fHistRhoAVsMult(0), fHistRhoAVsCent(0) {
for(Int_t i(0); i < 10; i++) {
fProfV2Resolution[i] = 0;
fProfV3Resolution[i] = 0;
fHistPicoCat1[i] = 0;
fHistPicoCat2[i] = 0;
fHistPicoCat3[i] = 0;
- /* fHistClusterPt[i] = 0; */
- /* fHistClusterPhi[i] = 0; */
- /* fHistClusterEta[i] = 0; */
- /* fHistClusterCorrPt[i] = 0; */
- /* fHistClusterCorrPhi[i] = 0; */
- /* fHistClusterCorrEta[i] = 0; */
+ fHistClusterPt[i] = 0;
+ fHistClusterEtaPhi[i] = 0;
+ fHistClusterEtaPhiWeighted[i] = 0;
fHistRhoPackage[i] = 0;
fHistRho[i] = 0;
fHistRCPhiEta[i] = 0;
fHistRCPtExLJ[i] = 0;
fHistDeltaPtDeltaPhi2ExLJ[i] = 0;
fHistDeltaPtDeltaPhi3ExLJ[i] = 0;
- /* fHistRCPhiEtaRand[i] = 0; */
- /* fHistRhoVsRCPtRand[i] = 0; */
- /* fHistRCPtRand[i] = 0; */
- /* fHistDeltaPtDeltaPhi2Rand[i] = 0; */
- /* fHistDeltaPtDeltaPhi3Rand[i] = 0; */
fHistJetPtRaw[i] = 0;
fHistJetPt[i] = 0;
fHistJetEtaPhi[i] = 0;
fHistJetPtArea[i] = 0;
+ fHistJetPtEta[i] = 0;
fHistJetPtConstituents[i] = 0;
fHistJetEtaRho[i] = 0;
fHistJetPsi2Pt[i] = 0;
}
//_____________________________________________________________________________
AliAnalysisTaskRhoVnModulation::AliAnalysisTaskRhoVnModulation(const char* name, runModeType type) : AliAnalysisTaskEmcalJet(name, kTRUE),
- fDebug(0), fLocalInit(0), fAttachToEvent(kTRUE), fSemiCentralInclusive(kFALSE), fFillHistograms(kTRUE), fFillQAHistograms(kTRUE), fReduceBinsXByFactor(-1.), fReduceBinsYByFactor(-1.), fNoEventWeightsForQC(kTRUE), fCentralityClasses(0), fPtBinsHybrids(0), fPtBinsJets(0), fUserSuppliedV2(0), fUserSuppliedV3(0), fUserSuppliedR2(0), fUserSuppliedR3(0), fTracksCont(0), fJetsCont(0), fUseScaledRho(0), fNAcceptedTracks(0), fNAcceptedTracksQCn(0), fFitModulationType(kNoFit), fQCRecovery(kTryFit), fUsePtWeight(kTRUE), fDetectorType(kTPC), fFitModulationOptions("QWLI"), fRunModeType(type), fDataType(kESD), fCollisionType(kPbPb), fRandom(0), fMappedRunNumber(0), fInCentralitySelection(-1), fFitModulation(0), fMinPvalue(0.01), fMaxPvalue(1), fNameJetClones(0), fNamePicoTrackClones(0), fNameRho(0), fLocalJetMinEta(-10), fLocalJetMaxEta(-10), fLocalJetMinPhi(-10), fLocalJetMaxPhi(-10), fSoftTrackMinPt(0.15), fSoftTrackMaxPt(5.), fAbsVertexZ(10), fHistCentrality(0), fHistVertexz(0), fHistRunnumbersPhi(0), fHistRunnumbersEta(0), fHistPvaluePDF(0), fHistPvalueCDF(0), fMinDisanceRCtoLJ(0), fRandomConeRadius(-1.), fMaxCones(-1), fAbsVnHarmonics(kTRUE), fExcludeLeadingJetsFromFit(1.), fRebinSwapHistoOnTheFly(kTRUE), fPercentageOfFits(10.), fUseV0EventPlaneFromHeader(kTRUE), fExplicitOutlierCut(-1), fMinLeadingHadronPt(0), fSubtractJetPt(kFALSE), fOutputList(0), fOutputListGood(0), fOutputListBad(0), fHistAnalysisSummary(0), fHistSwap(0), fProfV2(0), fProfV2Cumulant(0), fProfV3(0), fProfV3Cumulant(0), fHistPsiControl(0), fHistPsiSpread(0), fHistPsiVZEROA(0), fHistPsiVZEROC(0), fHistPsiVZERO(0), fHistPsiTPC(0), fHistPsiVZEROAV0M(0), fHistPsiVZEROCV0M(0), fHistPsiVZEROVV0M(0), fHistPsiTPCiV0M(0), fHistPsiVZEROATRK(0), fHistPsiVZEROCTRK(0), fHistPsiVZEROTRK(0), fHistPsiTPCTRK(0), fHistRhoVsMult(0), fHistRhoVsCent(0), fHistRhoAVsMult(0), fHistRhoAVsCent(0) {
+ fDebug(0), fRunToyMC(kFALSE), fLocalInit(0), fAttachToEvent(kTRUE), fSemiCentralInclusive(kFALSE), fFillHistograms(kTRUE), fFillQAHistograms(kTRUE), fReduceBinsXByFactor(-1.), fReduceBinsYByFactor(-1.), fNoEventWeightsForQC(kTRUE), fCentralityClasses(0), fPtBinsHybrids(0), fPtBinsJets(0), fExpectedRuns(0), fExpectedSemiGoodRuns(0), fUserSuppliedV2(0), fUserSuppliedV3(0), fUserSuppliedR2(0), fUserSuppliedR3(0), fTracksCont(0), fClusterCont(0), fJetsCont(0), fLeadingJet(0), fUseScaledRho(0), fNAcceptedTracks(0), fNAcceptedTracksQCn(0), fFitModulationType(kNoFit), fFitGoodnessTest(kChi2Poisson), fQCRecovery(kTryFit), fUsePtWeight(kTRUE), fUsePtWeightErrorPropagation(kTRUE), fDetectorType(kTPC), fAnalysisType(kCharged), fFitModulationOptions("QWLI"), fRunModeType(type), fDataType(kESD), fCollisionType(kPbPb), fRandom(0), fRunNumber(-1), fMappedRunNumber(0), fInCentralitySelection(-1), fFitModulation(0), fFitControl(0), fMinPvalue(0.01), fMaxPvalue(1), fNameJetClones(0), fNamePicoTrackClones(0), fNameRho(0), fNameSmallRho(""), fCachedRho(0), fLocalJetMinEta(-10), fLocalJetMaxEta(-10), fLocalJetMinPhi(-10), fLocalJetMaxPhi(-10), fSoftTrackMinPt(0.15), fSoftTrackMaxPt(5.), fSemiGoodJetMinPhi(0.), fSemiGoodJetMaxPhi(4.), fSemiGoodTrackMinPhi(0.), fSemiGoodTrackMaxPhi(4.), fAbsVertexZ(10), fHistCentrality(0), fHistVertexz(0), fHistRunnumbersPhi(0), fHistRunnumbersEta(0), fHistPvalueCDFROOT(0), fHistPvalueCDFROOTCent(0), fHistChi2ROOTCent(0), fHistPChi2Root(0), fHistPvalueCDF(0), fHistPvalueCDFCent(0), fHistChi2Cent(0), fHistPChi2(0), fHistKolmogorovTest(0), fHistKolmogorovTestCent(0), fHistPKolmogorov(0), fHistRhoStatusCent(0), fHistUndeterminedRunQA(0), fMinDisanceRCtoLJ(0), fRandomConeRadius(-1.), fMaxCones(-1), fAbsVnHarmonics(kTRUE), fExcludeLeadingJetsFromFit(1.), fRebinSwapHistoOnTheFly(kTRUE), fPercentageOfFits(10.), fUseV0EventPlaneFromHeader(kTRUE), fExplicitOutlierCut(-1), fMinLeadingHadronPt(0), fSubtractJetPt(kFALSE), fOutputList(0), fOutputListGood(0), fOutputListBad(0), fHistAnalysisSummary(0), fHistSwap(0), fProfV2(0), fProfV2Cumulant(0), fProfV3(0), fProfV3Cumulant(0), fHistPsiControl(0), fHistPsiSpread(0), fHistPsiVZEROA(0), fHistPsiVZEROC(0), fHistPsiVZERO(0), fHistPsiTPC(0), fHistPsiVZEROAV0M(0), fHistPsiVZEROCV0M(0), fHistPsiVZEROVV0M(0), fHistPsiTPCiV0M(0), fHistPsiVZEROATRK(0), fHistPsiVZEROCTRK(0), fHistPsiVZEROTRK(0), fHistPsiTPCTRK(0), fHistRhoVsMult(0), fHistRhoVsCent(0), fHistRhoAVsMult(0), fHistRhoAVsCent(0) {
for(Int_t i(0); i < 10; i++) {
fProfV2Resolution[i] = 0;
fProfV3Resolution[i] = 0;
fHistPicoCat1[i] = 0;
fHistPicoCat2[i] = 0;
fHistPicoCat3[i] = 0;
- /* fHistClusterPt[i] = 0; */
- /* fHistClusterPhi[i] = 0; */
- /* fHistClusterEta[i] = 0; */
- /* fHistClusterCorrPt[i] = 0; */
- /* fHistClusterCorrPhi[i] = 0; */
- /* fHistClusterCorrEta[i] = 0; */
+ fHistClusterPt[i] = 0;
+ fHistClusterEtaPhi[i] = 0;
+ fHistClusterEtaPhiWeighted[i] = 0;
fHistRhoPackage[i] = 0;
fHistRho[i] = 0;
fHistRCPhiEta[i] = 0;
fHistRCPtExLJ[i] = 0;
fHistDeltaPtDeltaPhi2ExLJ[i] = 0;
fHistDeltaPtDeltaPhi3ExLJ[i] = 0;
- /* fHistRCPhiEtaRand[i] = 0; */
- /* fHistRhoVsRCPtRand[i] = 0; */
- /* fHistRCPtRand[i] = 0; */
- /* fHistDeltaPtDeltaPhi2Rand[i] = 0; */
- /* fHistDeltaPtDeltaPhi3Rand[i] = 0; */
fHistJetPtRaw[i] = 0;
fHistJetPt[i] = 0;
fHistJetEtaPhi[i] = 0;
fHistJetPtArea[i] = 0;
+ fHistJetPtEta[i] = 0;
fHistJetPtConstituents[i] = 0;
fHistJetEtaRho[i] = 0;
fHistJetPsi2Pt[i] = 0;
if(fFitModulation) delete fFitModulation;
if(fHistSwap) delete fHistSwap;
if(fCentralityClasses) delete fCentralityClasses;
+ if(fExpectedRuns) delete fExpectedRuns;
+ if(fExpectedSemiGoodRuns) delete fExpectedSemiGoodRuns;
+ if(fFitControl) delete fFitControl;
}
//_____________________________________________________________________________
void AliAnalysisTaskRhoVnModulation::ExecOnce()
TString title(name);
if(c!=-1) { // format centrality dependent histograms accordingly
name = Form("%s_%i", name, c);
- title += Form("_%i-%i", fCentralityClasses->At(c), fCentralityClasses->At(1+c));
+ title += Form("_%i-%i", (int)(fCentralityClasses->At(c)), (int)(fCentralityClasses->At((1+c))));
}
title += Form(";%s;[counts]", x);
TH1F* histogram = new TH1F(name, title.Data(), bins, min, max);
TString title(name);
if(c!=-1) { // format centrality dependent histograms accordingly
name = Form("%s_%i", name, c);
- title += Form("_%i-%i", fCentralityClasses->At(c), fCentralityClasses->At(1+c));
+ title += Form("_%i-%i", (int)fCentralityClasses->At(c), (int)(fCentralityClasses->At((1+c))));
}
title += Form(";%s;%s", x, y);
TH2F* histogram = new TH2F(name, title.Data(), binsx, minx, maxx, binsy, miny, maxy);
fOutputList = new TList();
fOutputList->SetOwner(kTRUE);
if(!fCentralityClasses) { // classes must be defined at this point
- Int_t c[] = {0, 20, 40, 60, 80, 100};
- fCentralityClasses = new TArrayI(sizeof(c)/sizeof(c[0]), c);
+ Double_t c[] = {0., 20., 40., 60., 80., 100.};
+ fCentralityClasses = new TArrayD(sizeof(c)/sizeof(c[0]), c);
+ }
+ if(!fExpectedRuns) { // expected runs must be defined at this point
+ Int_t r[] = {167813, 167988, 168066, 168068, 168069, 168076, 168104, 168212, 168311, 168322, 168325, 168341, 168361, 168362, 168458, 168460, 168461, 168992, 169091, 169094, 169138, 169143, 169167, 169417, 169835, 169837, 169838, 169846, 169855, 169858, 169859, 169923, 169956, 170027, 170036, 170081, /* up till here original good TPC list */169975, 169981, 170038, 170040, 170083, 170084, 170085, 170088, 170089, 170091, 170152, 170155, 170159, 170163, 170193, 170195, 170203, 170204, 170205, 170228, 170230, 170264, 170268, 170269, 170270, 170306, 170308, 170309, /* original semi-good tpc list */169415, 169411, 169035, 168988, 168984, 168826, 168777, 168512, 168511, 168467, 168464, 168342, 168310, 168115, 168108, 168107, 167987, 167915, 167903, /*new runs, good according to RCT */ 169238, 169160, 169156, 169148, 169145, 169144 /* run swith missing OROC 8 but seem ok in QA */};
+ fExpectedRuns = new TArrayI(sizeof(r)/sizeof(r[0]), r);
+ }
+ if(!fExpectedSemiGoodRuns) {
+ Int_t r[] = {169975, 169981, 170038, 170040, 170083, 170084, 170085, 170088, 170089, 170091, 170152, 170155, 170159, 170163, 170193, 170195, 170203, 170204, 170205, 170228, 170230, 170264, 170268, 170269, 170270, 170306, 170308, 170309};
+ fExpectedSemiGoodRuns = new TArrayI(sizeof(r)/sizeof(r[0]), r);
}
// global QA
fHistCentrality = BookTH1F("fHistCentrality", "centrality", 102, -2, 100);
// pico track kinematics
for(Int_t i(0); i < fCentralityClasses->GetSize()-1; i++) {
- fHistPicoTrackPt[i] = BookTH1F("fHistPicoTrackPt", "p_{t} [GeV/c]", 100, 0, 50, i);
+ fHistPicoTrackPt[i] = BookTH1F("fHistPicoTrackPt", "p_{t} [GeV/c]", 100, 0, 100, i);
fHistPicoTrackMult[i] = BookTH1F("fHistPicoTrackMult", "multiplicity", 100, 0, 5000, i);
if(fFillQAHistograms) {
fHistPicoCat1[i] = BookTH2F("fHistPicoCat1", "#eta", "#phi", 50, -1, 1, 50, 0, TMath::TwoPi(), i);
fHistPicoCat3[i] = BookTH2F("fHistPicoCat3", "#eta", "#phi", 50, -1, 1, 50, 0, TMath::TwoPi(), i);
}
// emcal kinematics
- /* fHistClusterPt[i] = BookTH1F("fHistClusterPt", "p_{t} [GeV/c]", 100, 0, 100, i); */
- /* fHistClusterPhi[i] = BookTH1F("fHistClusterPhi", "#phi", 100, 0, TMath::TwoPi(), i); */
- /* fHistClusterEta[i] = BookTH1F("fHistClusterEta", "#eta", 100, -5, 5); */
+ fHistClusterPt[i] = BookTH1F("fHistClusterPt", "p_{t} [GeV/c]", 100, 0, 100, i);
+ fHistClusterEtaPhi[i] = BookTH2F("fHistClusterEtaPhi", "#eta", "#phi", 100, -1., 1., 100, 0, TMath::TwoPi(), i);
+ fHistClusterEtaPhiWeighted[i] = BookTH2F("fHistClusterEtaPhiWeighted", "#eta", "#phi", 100, -1., 1., 100, 0, TMath::TwoPi(), i);
- // emcal kinematics after hadronic correction
- /* fHistClusterCorrPt[i] = BookTH1F("fHistClusterCorrPt", "p_{t} [GeV/c]", 100, 0, 100, i); */
- /* fHistClusterCorrPhi[i] = BookTH1F("fHistClusterCorrPhi", "#phi", 100, 0, TMath::TwoPi(), i); */
- /* fHistClusterCorrEta[i] = BookTH1F("fHistClusterCorrEta", "#eta", 100, -5, 5, i); */
}
if(fFillQAHistograms) {
/* fHistDeltaPtDeltaPhi2Rand[i] = BookTH2F("fHistDeltaPtDeltaPhi2Rand", "#phi - #Psi_{TPC}", "#delta p_{t} [GeV/c]", 50, 0, TMath::Pi(), 100, -50, 100, i); */
/* fHistDeltaPtDeltaPhi3Rand[i] = BookTH2F("fHistDeltaPtDeltaPhi3Rand", "#phi - #Psi_{TPC}", "#delta p_{t} [GeV/c]", 50, 0, TMath::TwoPi()/3., 100, -50, 100, i); */
// jet histograms (after kinematic cuts)
- fHistJetPtRaw[i] = BookTH1F("fHistJetPtRaw", "p_{t} RAW [GeV/c]", 200, -50, 150, i);
- fHistJetPt[i] = BookTH1F("fHistJetPt", "p_{t} [GeV/c]", 350, -100, 250, i);
+ fHistJetPtRaw[i] = BookTH1F("fHistJetPtRaw", "p_{t, jet} RAW [GeV/c]", 200, -50, 150, i);
+ fHistJetPt[i] = BookTH1F("fHistJetPt", "p_{t, jet} [GeV/c]", 350, -100, 250, i);
if(fFillQAHistograms) fHistJetEtaPhi[i] = BookTH2F("fHistJetEtaPhi", "#eta", "#phi", 100, -1, 1, 100, 0, TMath::TwoPi(), i);
- fHistJetPtArea[i] = BookTH2F("fHistJetPtArea", "p_{t} [GeV/c]", "Area", 175, -100, 250, 30, 0, 0.9, i);
- fHistJetPtConstituents[i] = BookTH2F("fHistJetPtConstituents", "p_{t} [GeV/c]", "Area", 350, -100, 250, 60, 0, 150, i);
+ fHistJetPtArea[i] = BookTH2F("fHistJetPtArea", "p_{t, jet} [GeV/c]", "Area", 175, -100, 250, 30, 0, 0.9, i);
+ fHistJetPtEta[i] = BookTH2F("fHistJetPtEta", "p_{t, jet} [GeV/c]", "Eta", 175, -100, 250, 30, -0.9, 0.9, i);
+ fHistJetPtConstituents[i] = BookTH2F("fHistJetPtConstituents", "p_{t, jet} [GeV/c]", "Area", 350, -100, 250, 60, 0, 150, i);
fHistJetEtaRho[i] = BookTH2F("fHistJetEtaRho", "#eta", "#rho", 100, -1, 1, 100, 0, 300, i);
// in plane and out of plane spectra
- fHistJetPsi2Pt[i] = BookTH2F("fHistJetPsi2Pt", Form("#phi_{jet} - #Psi_{2, %s}", detector.Data()), "p_{t} [GeV/c]", 40, 0., TMath::Pi(), 350, -100, 250, i);
- fHistJetPsi3Pt[i] = BookTH2F("fHistJetPsi3Pt", Form("#phi_{jet} - #Psi_{3, %s}", detector.Data()), "p_{t} [GeV/c]", 40, 0., TMath::TwoPi()/3., 350, -100, 250, i);
+ fHistJetPsi2Pt[i] = BookTH2F("fHistJetPsi2Pt", Form("#phi_{jet} - #Psi_{2, %s}", detector.Data()), "p_{t, jet} [GeV/c]", 40, 0., TMath::Pi(), 350, -100, 250, i);
+ fHistJetPsi3Pt[i] = BookTH2F("fHistJetPsi3Pt", Form("#phi_{jet} - #Psi_{3, %s}", detector.Data()), "p_{t, jet} [GeV/c]", 40, 0., TMath::TwoPi()/3., 350, -100, 250, i);
// profiles for all correlator permutations which are necessary to calculate each second and third order event plane resolution
fProfV2Resolution[i] = new TProfile(Form("fProfV2Resolution_%i", i), Form("fProfV2Resolution_%i", i), 11, -0.5, 10.5);
fProfV2Resolution[i]->GetXaxis()->SetBinLabel(3, "<cos(2(#Psi_{VZEROA} - #Psi_{VZEROC}))>");
fProfV3Resolution[i]->GetXaxis()->SetBinLabel(11, "<cos(3(#Psi_{TPC_A} - #Psi_{TPC_B}))>");
fOutputList->Add(fProfV3Resolution[i]);
}
- // cdf and pdf of chisquare distribution
- fHistPvaluePDF = BookTH1F("fHistPvaluePDF", "PDF #chi^{2}", 500, 0, 1);
- fHistPvalueCDF = BookTH1F("fHistPvalueCDF", "CDF #chi^{2}", 500, 0, 1);
- // vn profile
+ // vn profile
Float_t temp[fCentralityClasses->GetSize()];
for(Int_t i(0); i < fCentralityClasses->GetSize(); i++) temp[i] = fCentralityClasses->At(i);
fProfV2 = new TProfile("fProfV2", "fProfV2", fCentralityClasses->GetSize()-1, temp);
fReduceBinsXByFactor = 1;
fReduceBinsYByFactor = 1;
if(fFillQAHistograms) {
- fHistRunnumbersEta = new TH2F("fHistRunnumbersEta", "fHistRunnumbersEta", 100, -.5, 99.5, 100, -1.1, 1.1);
+ fHistRunnumbersEta = new TH2F("fHistRunnumbersEta", "fHistRunnumbersEta", fExpectedRuns->GetSize()+1, -.5, fExpectedRuns->GetSize()+.5, 100, -1.1, 1.1);
fHistRunnumbersEta->Sumw2();
fOutputList->Add(fHistRunnumbersEta);
- fHistRunnumbersPhi = new TH2F("fHistRunnumbersPhi", "fHistRunnumbersPhi", 100, -.5, 99.5, 100, -0.2, TMath::TwoPi()+0.2);
+ fHistRunnumbersPhi = new TH2F("fHistRunnumbersPhi", "fHistRunnumbersPhi", fExpectedRuns->GetSize()+1, -.5, fExpectedRuns->GetSize()+.5, 100, -0.2, TMath::TwoPi()+0.2);
fHistRunnumbersPhi->Sumw2();
fOutputList->Add(fHistRunnumbersPhi);
+ for(Int_t i(0); i < fExpectedRuns->GetSize(); i++) {
+ fHistRunnumbersPhi->GetXaxis()->SetBinLabel(i+1, Form("%i", fExpectedRuns->At(i)));
+ fHistRunnumbersEta->GetXaxis()->SetBinLabel(i+1, Form("%i", fExpectedRuns->At(i)));
+ }
+ fHistRunnumbersPhi->GetXaxis()->SetBinLabel(fExpectedRuns->GetSize()+1, "undetermined");
+ fHistRunnumbersEta->GetXaxis()->SetBinLabel(fExpectedRuns->GetSize()+1, "undetermined");
}
- fHistAnalysisSummary = BookTH1F("fHistAnalysisSummary", "flag", 52, -0.5, 52.5);
+ fHistAnalysisSummary = BookTH1F("fHistAnalysisSummary", "flag", 54, -0.5, 54.5);
fHistSwap = new TH1F("fHistSwap", "fHistSwap", 20, 0, TMath::TwoPi());
if(fUsePtWeight) fHistSwap->Sumw2();
if(fUserSuppliedR3) fOutputList->Add(fUserSuppliedR3);
// increase readability of output list
fOutputList->Sort();
+ // cdf and pdf of chisquare distribution
+ fHistPvalueCDF = BookTH1F("fHistPvalueCDF", "CDF #chi^{2}", 50, 0, 1);
+ fHistPvalueCDFCent = BookTH2F("fHistPvalueCDFCent", "centrality", "p-value", 40, 0, 100, 40, 0, 1);
+ fHistChi2Cent = BookTH2F("fHistChi2Cent", "centrality", "#tilde{#chi^{2}}", 100, 0, 100, 100, 0, 5);
+ fHistPChi2 = BookTH2F("fHistPChi2", "p-value", "#tilde{#chi^{2}}", 1000, 0, 1, 100, 0, 5);
+ fHistKolmogorovTest = BookTH1F("fHistKolmogorovTest", "KolmogorovTest", 50, 0, 1);
+ fHistKolmogorovTestCent = BookTH2F("fHistKolmogorovTestCent", "centrality", "Kolmogorov p", 40, 0, 100, 45, 0, 1);
+ fHistPvalueCDFROOT = BookTH1F("fHistPvalueCDFROOT", "CDF #chi^{2} ROOT", 50, 0, 1);
+ fHistPvalueCDFROOTCent = BookTH2F("fHistPvalueCDFROOTCent", "centrality", "p-value ROOT", 40, 0, 100, 45, 0, 1);
+ fHistChi2ROOTCent = BookTH2F("fHistChi2ROOTCent", "centrality", "#tilde{#chi^{2}}", 40, 0, 100, 45, 0, 5);
+ fHistPChi2Root = BookTH2F("fHistPChi2Root", "p-value", "#tilde{#chi^{2}} ROOT", 1000, 0, 1, 100, 0, 5);
+ fHistPKolmogorov = BookTH2F("fHistPKolmogorov", "p-value", "kolmogorov p",40, 0, 1, 40, 0, 1);
+ fHistRhoStatusCent = BookTH2F("fHistRhoStatusCent", "centrality", "status [-1=lin was better, 0=ok, 1 = failed]", 101, -1, 100, 3, -1.5, 1.5);
+ fHistUndeterminedRunQA = BookTH1F("fHistUndeterminedRunQA", "runnumber", 10, 0, 10);
+
PostData(1, fOutputList);
switch (fRunModeType) {
// get the containers
fTracksCont = GetParticleContainer("Tracks");
+ fClusterCont = GetClusterContainer(0); // get the default cluster container
fJetsCont = GetJetContainer("Jets");
}
//_____________________________________________________________________________
if(!fLocalInit) fLocalInit = InitializeAnalysis();
// reject the event if expected data is missing
if(!PassesCuts(InputEvent())) return kFALSE;
- if(!fCaloClusters && fDebug > 0) printf(" > Warning: couldn't retreive calo clusters! < \n");
+ fLeadingJet = GetLeadingJet(); // store the leading jet
// set the rho value
fLocalRho->SetVal(fRho->GetVal());
// [0][0] psi2a [1,0] psi2c
}
// if all went well, update the local rho parameter
fLocalRho->SetLocalRho(fFitModulation);
- // fill a number of histograms
- if(fFillHistograms) FillHistogramsAfterSubtraction(psi2, psi3, vzero, vzeroComb, tpc);
+ // fill a number of histograms. event qa needs to be filled first as it also determines the runnumber for the track qa
if(fFillQAHistograms) FillQAHistograms(InputEvent());
+ if(fFillHistograms) FillHistogramsAfterSubtraction(psi2, psi3, vzero, vzeroComb, tpc);
// send the output to the connected output container
PostData(1, fOutputList);
switch (fRunModeType) {
if(fTracks) {
Float_t excludeInEta = -999;
if(fExcludeLeadingJetsFromFit > 0 ) { // remove the leading jet from ep estimate
- AliEmcalJet* leadingJet(GetJetContainer()->GetLeadingJet());
- if(leadingJet) excludeInEta = leadingJet->Eta();
+ if(fLeadingJet) excludeInEta = fLeadingJet->Eta();
}
Int_t iTracks(fTracks->GetEntriesFast());
for(Int_t iTPC(0); iTPC < iTracks; iTPC++) {
etaJet = jet->Eta();
phiJet = jet->Phi();
}
- // force the random cones to at least be within detector acceptance
+ // the random cone acceptance has to equal the jet acceptance
+ // this also insures safety when runnnig on the semi-good tpc runs for 11h data,
+ // where jet acceptance is adjusted to reduced acceptance - hence random cone acceptance as well
Float_t minPhi(GetJetContainer()->GetJetPhiMin()), maxPhi(GetJetContainer()->GetJetPhiMax());
if(maxPhi > TMath::TwoPi()) maxPhi = TMath::TwoPi();
if(minPhi < 0 ) minPhi = 0;
// in all cases, a cut can be made on the p-value of the chi-squared value of the fit
// and a check can be performed to see if rho has no negative local minimum
if(fDebug > 0) printf("__FILE__ = %s \n __LINE __ %i , __FUNC__ %s \n ", __FILE__, __LINE__, __func__);
+ Int_t freeParams(2); // free parameters of the fit (for NDF)
switch (fFitModulationType) { // for approaches where no fitting is required
case kQC2 : {
fFitModulation->FixParameter(4, psi2);
Double_t excludeInPt = -999;
if(iTracks <= 0 || fLocalRho->GetVal() <= 0 ) return kFALSE; // no use fitting an empty event ...
if(fExcludeLeadingJetsFromFit > 0 ) {
- AliEmcalJet* leadingJet(GetJetContainer()->GetLeadingJet());
- if(leadingJet) {
- excludeInEta = leadingJet->Eta();
- excludeInPhi = leadingJet->Phi();
- excludeInPt = leadingJet->Pt();
+ if(fLeadingJet) {
+ excludeInEta = fLeadingJet->Eta();
+ excludeInPhi = fLeadingJet->Phi();
+ excludeInPt = fLeadingJet->Pt();
}
}
+ // check the acceptance of the track selection that will be used
+ // if one uses e.g. semi-good tpc tracks, accepance in phi is reduced to 0 < phi < 4
+ // the defaults (-10 < phi < 10) which accept all, are then overwritten
+ Double_t lowBound(0.), upBound(TMath::TwoPi()); // bounds for fit
+ if(GetParticleContainer()->GetParticlePhiMin() > lowBound) lowBound = GetParticleContainer()->GetParticlePhiMin();
+ if(GetParticleContainer()->GetParticlePhiMax() < upBound) upBound = GetParticleContainer()->GetParticlePhiMax();
+
fHistSwap->Reset(); // clear the histogram
- TH1F _tempSwap;
+ TH1F _tempSwap; // on stack for quick access
+ TH1F _tempSwapN; // on stack for quick access, bookkeeping histogram
if(fRebinSwapHistoOnTheFly) {
if(fNAcceptedTracks < 49) fNAcceptedTracks = 49; // avoid aliasing effects
- _tempSwap = TH1F("_tempSwap", "_tempSwap", TMath::CeilNint(TMath::Sqrt(fNAcceptedTracks)), 0, TMath::TwoPi());
+ _tempSwap = TH1F("_tempSwap", "_tempSwap", TMath::CeilNint(TMath::Sqrt(fNAcceptedTracks)), lowBound, upBound);
+ if(fUsePtWeightErrorPropagation) _tempSwapN = TH1F("_tempSwapN", "_tempSwapN", TMath::CeilNint(TMath::Sqrt(fNAcceptedTracks)), lowBound, upBound);
if(fUsePtWeight) _tempSwap.Sumw2();
}
else _tempSwap = *fHistSwap; // now _tempSwap holds the desired histo
+ // non poissonian error when using pt weights
+ Double_t totalpts(0.), totalptsquares(0.), totalns(0.);
for(Int_t i(0); i < iTracks; i++) {
AliVTrack* track = static_cast<AliVTrack*>(fTracks->At(i));
if(fExcludeLeadingJetsFromFit > 0 &&( (TMath::Abs(track->Eta() - excludeInEta) < GetJetContainer()->GetJetRadius()*fExcludeLeadingJetsFromFit ) || (TMath::Abs(track->Eta()) - GetJetContainer()->GetJetRadius() - GetJetContainer()->GetJetEtaMax() ) > 0 )) continue;
if(!PassesCuts(track) || track->Pt() > fSoftTrackMaxPt || track->Pt() < fSoftTrackMinPt) continue;
- if(fUsePtWeight) _tempSwap.Fill(track->Phi(), track->Pt());
+ if(fUsePtWeight) {
+ _tempSwap.Fill(track->Phi(), track->Pt());
+ if(fUsePtWeightErrorPropagation) {
+ totalpts += track->Pt();
+ totalptsquares += track->Pt()*track->Pt();
+ totalns += 1;
+ _tempSwapN.Fill(track->Phi());
+ }
+ }
else _tempSwap.Fill(track->Phi());
}
+ if(fUsePtWeight && fUsePtWeightErrorPropagation) {
+ // in the case of pt weights overwrite the poissonian error estimate which is assigned by root by a more sophisticated appraoch
+ // the assumption here is that the bin error will be dominated by the uncertainty in the mean pt in a bin and in the uncertainty
+ // of the number of tracks in a bin, the first of which will be estimated from the sample standard deviation of all tracks in the
+ // event, for the latter use a poissonian estimate. the two contrubitions are assumed to be uncorrelated
+ if(totalns < 1) return kFALSE; // not one track passes the cuts
+ for(Int_t l = 0; l < _tempSwap.GetNbinsX(); l++) {
+ if(_tempSwapN.GetBinContent(l+1) == 0) {
+ _tempSwap.SetBinContent(l+1,0);
+ _tempSwap.SetBinError(l+1,0);
+ }
+ else {
+ Double_t vartimesnsq = totalptsquares*totalns - totalpts*totalpts;
+ Double_t variance = vartimesnsq/(totalns*(totalns-1.));
+ Double_t SDOMSq = variance / _tempSwapN.GetBinContent(l+1);
+ Double_t SDOMSqOverMeanSq = SDOMSq * _tempSwapN.GetBinContent(l+1) * _tempSwapN.GetBinContent(l+1) / (_tempSwapN.GetBinContent(l+1) * _tempSwapN.GetBinContent(l+1));
+ Double_t poissonfrac = 1./_tempSwapN.GetBinContent(l+1);
+ Double_t vartotalfrac = SDOMSqOverMeanSq + poissonfrac;
+ Double_t vartotal = vartotalfrac * _tempSwap.GetBinContent(l+1) * _tempSwap.GetBinContent(l+1);
+ if(vartotal > 0.0001) _tempSwap.SetBinError(l+1,TMath::Sqrt(vartotal));
+ else {
+ _tempSwap.SetBinContent(l+1,0);
+ _tempSwap.SetBinError(l+1,0);
+ }
+ }
+ }
+ }
+
fFitModulation->SetParameter(0, fLocalRho->GetVal());
switch (fFitModulationType) {
- case kNoFit : { fFitModulation->FixParameter(0, fLocalRho->GetVal() );
+ case kNoFit : {
+ fFitModulation->FixParameter(0, fLocalRho->GetVal() );
+ freeParams = 0;
} break;
case kV2 : {
fFitModulation->FixParameter(4, psi2);
+ freeParams = 1;
} break;
case kV3 : {
fFitModulation->FixParameter(4, psi3);
+ freeParams = 1;
} break;
case kCombined : {
fFitModulation->FixParameter(4, psi2);
fFitModulation->FixParameter(6, psi3);
+ freeParams = 2;
} break;
case kFourierSeries : {
// in this approach, an explicit calculation will be made of vn = sqrt(xn^2+yn^2)
} break;
default : break;
}
- _tempSwap.Fit(fFitModulation, fFitModulationOptions.Data(), "", 0, TMath::TwoPi());
+ if(fRunToyMC) {
+ // toy mc, just here to check procedure, azimuthal profile is filled from hypothesis so p-value distribution should be flat
+ Int_t _bins = _tempSwap.GetXaxis()->GetNbins();
+ TF1* _tempFit = new TF1("temp_fit_kCombined", "[0]*([1]+[2]*([3]*TMath::Cos([2]*(x-[4]))+[7]*TMath::Cos([5]*(x-[6]))))", 0, TMath::TwoPi());
+ _tempFit->SetParameter(0, fFitModulation->GetParameter(0)); // normalization
+ _tempFit->SetParameter(3, 0.1); // v2
+ _tempFit->FixParameter(1, 1.); // constant
+ _tempFit->FixParameter(2, 2.); // constant
+ _tempFit->FixParameter(5, 3.); // constant
+ _tempFit->FixParameter(4, fFitModulation->GetParameter(4));
+ _tempFit->FixParameter(6, fFitModulation->GetParameter(6));
+ _tempFit->SetParameter(7, 0.1); // v3
+ _tempSwap.Reset(); // rese bin content
+ for(int _binsI = 0; _binsI < _bins*_bins; _binsI++) _tempSwap.Fill(_tempFit->GetRandom());
+ }
+ _tempSwap.Fit(fFitModulation, fFitModulationOptions.Data(), "", lowBound, upBound);
// the quality of the fit is evaluated from 1 - the cdf of the chi square distribution
- Double_t CDF(1.-ChiSquareCDF(fFitModulation->GetNDF(), fFitModulation->GetChisquare()));
+ // three methods are available, all with their drawbacks. all are stored, one is selected to do the cut
+ Int_t NDF(_tempSwap.GetXaxis()->GetNbins()-freeParams);
+ if(NDF == 0) return kFALSE;
+ Double_t CDF(1.-ChiSquareCDF(NDF, ChiSquare(_tempSwap, fFitModulation)));
+ Double_t CDFROOT(1.-ChiSquareCDF(NDF, fFitModulation->GetChisquare()));
+ Double_t CDFKolmogorov(KolmogorovTest(_tempSwap, fFitModulation));
+ // fill the values and centrality correlation (redundant but easy on the eyes)
fHistPvalueCDF->Fill(CDF);
- if(CDF > fMinPvalue && CDF < fMaxPvalue && ( fAbsVnHarmonics && fFitModulation->GetMinimum(0, TMath::TwoPi()) > 0)) { // fit quality
+ fHistPvalueCDFCent->Fill(fCent, CDF);
+ fHistPvalueCDFROOT->Fill(CDFROOT);
+ fHistPvalueCDFROOTCent->Fill(fCent, CDFROOT);
+ fHistKolmogorovTest->Fill(CDFKolmogorov);
+ fHistChi2ROOTCent->Fill(fCent, fFitModulation->GetChisquare()/((float)NDF));
+ fHistChi2Cent->Fill(fCent, ChiSquare(_tempSwap, fFitModulation)/((float)NDF));
+ fHistKolmogorovTestCent->Fill(fCent, CDFKolmogorov);
+ fHistPChi2Root->Fill(CDFROOT, fFitModulation->GetChisquare()/((float)NDF));
+ fHistPChi2->Fill(CDF, ChiSquare(_tempSwap, fFitModulation)/((float)NDF));
+ fHistPKolmogorov->Fill(CDF, CDFKolmogorov);
+
+ // variable CDF is used for making cuts, so we fill it with the selected p-value
+ switch (fFitGoodnessTest) {
+ case kChi2ROOT : {
+ CDF = CDFROOT;
+ } break;
+ case kChi2Poisson : break; // CDF is already CDF
+ case kKolmogorov : {
+ CDF = CDFKolmogorov;
+ } break;
+ default: break;
+ }
+
+ if(fFitControl) {
+ // as an additional quality check, see if fitting a control fit has a higher significance
+ _tempSwap.Fit(fFitControl, fFitModulationOptions.Data(), "", lowBound, upBound);
+ Double_t CDFControl(-1.);
+ switch (fFitGoodnessTest) {
+ case kChi2ROOT : {
+ CDFControl = 1.-ChiSquareCDF(fFitControl->GetNDF(), fFitModulation->GetChisquare());
+ } break;
+ case kChi2Poisson : {
+ CDFControl = 1.-ChiSquareCDF(fFitControl->GetNDF(), ChiSquare(_tempSwap, fFitModulation));
+ } break;
+ case kKolmogorov : {
+ CDFControl = KolmogorovTest(_tempSwap, fFitControl);
+ } break;
+ default: break;
+ }
+ if(CDFControl > CDF) {
+ CDF = -1.; // control fit is more significant, so throw out the 'old' fit
+ fHistRhoStatusCent->Fill(fCent, -1);
+ }
+ }
+ if(CDF >= fMinPvalue && CDF <= fMaxPvalue && ( fAbsVnHarmonics && fFitModulation->GetMinimum(0, TMath::TwoPi()) > 0)) { // fit quality. not that although with limited acceptance the fit is performed on just
+ // part of phase space, the requirement that energy desntiy is larger than zero is applied
+ // to the FULL spectrum
+ fHistRhoStatusCent->Fill(fCent, 0.);
// for LOCAL didactic purposes, save the best and the worst fits
// this routine can produce a lot of output histograms (it's not memory 'safe') and will not work on GRID
// since the output will become unmergeable (i.e. different nodes may produce conflicting output)
if(fRandom->Uniform(0, 100) > fPercentageOfFits) break;
static Int_t didacticCounterBest(0);
TProfile* didacticProfile = (TProfile*)_tempSwap.Clone(Form("Fit_%i_1-CDF_%.3f_cen_%i_%s", didacticCounterBest, CDF, fInCentralitySelection, detector.Data()));
- TF1* didactifFit = (TF1*)fFitModulation->Clone(Form("fit_%i_CDF_%.3f_cen_%i_%s", didacticCounterBest, CDF, fInCentralitySelection, detector.Data()));
- didacticProfile->GetListOfFunctions()->Add(didactifFit);
+ TF1* didacticFit = (TF1*)fFitModulation->Clone(Form("fit_%i_CDF_%.3f_cen_%i_%s", didacticCounterBest, CDF, fInCentralitySelection, detector.Data()));
+ switch(fFitModulationType) {
+ case kCombined : {
+ // to make a nice picture also plot the separate components (v2 and v3) of the fit
+ // only done for cobined fit where there are actually components to split ...
+ TF1* v0(new TF1("dfit_kV2", "[0]", 0, TMath::TwoPi()));
+ v0->SetParameter(0, didacticFit->GetParameter(0)); // normalization
+ v0->SetLineColor(kMagenta);
+ v0->SetLineStyle(7);
+ didacticProfile->GetListOfFunctions()->Add(v0);
+ TF1* v2(new TF1("dfit_kV2", "[0]*([1]+[2]*[3]*TMath::Cos([2]*(x-[4])))", 0, TMath::TwoPi()));
+ v2->SetParameter(0, didacticFit->GetParameter(0)); // normalization
+ v2->SetParameter(3, didacticFit->GetParameter(3)); // v2
+ v2->FixParameter(1, 1.); // constant
+ v2->FixParameter(2, 2.); // constant
+ v2->FixParameter(4, didacticFit->GetParameter(4)); // psi2
+ v2->SetLineColor(kGreen);
+ didacticProfile->GetListOfFunctions()->Add(v2);
+ TF1* v3(new TF1("dfit_kV3", "[0]*([1]+[2]*[3]*TMath::Cos([5]*(x-[4])))", 0, TMath::TwoPi()));
+ v3->SetParameter(0, didacticFit->GetParameter(0)); // normalization
+ v3->SetParameter(3, didacticFit->GetParameter(7)); // v3
+ v3->FixParameter(1, 1.); // constant
+ v3->FixParameter(2, 2.); // constant
+ v3->FixParameter(4, didacticFit->GetParameter(6)); // psi3
+ v3->FixParameter(5, 3.); // constant
+ v3->SetLineColor(kCyan);
+ didacticProfile->GetListOfFunctions()->Add(v3);
+ }
+ default : break;
+ }
+ didacticProfile->GetListOfFunctions()->Add(didacticFit);
+ didacticProfile->GetYaxis()->SetTitle("#frac{d #sum #it{p}_{T}}{d #varphi} [GeV/#it{c}]");
+ didacticProfile->GetXaxis()->SetTitle("#varphi");
fOutputListGood->Add(didacticProfile);
didacticCounterBest++;
TH2F* didacticSurface = BookTH2F(Form("surface_%s", didacticProfile->GetName()), "#phi", "#eta", 50, 0, TMath::TwoPi(), 50, -1, 1, -1, kFALSE);
static Int_t didacticCounterWorst(0);
if(fRandom->Uniform(0, 100) > fPercentageOfFits) break;
TProfile* didacticProfile = (TProfile*)_tempSwap.Clone(Form("Fit_%i_1-CDF_%.3f_cen_%i_%s", didacticCounterWorst, CDF, fInCentralitySelection, detector.Data() ));
- TF1* didactifFit = (TF1*)fFitModulation->Clone(Form("fit_%i_p_%.3f_cen_%i_%s", didacticCounterWorst, CDF, fInCentralitySelection, detector.Data()));
- didacticProfile->GetListOfFunctions()->Add(didactifFit);
+ TF1* didacticFit = (TF1*)fFitModulation->Clone(Form("fit_%i_p_%.3f_cen_%i_%s", didacticCounterWorst, CDF, fInCentralitySelection, detector.Data()));
+ didacticProfile->GetListOfFunctions()->Add(didacticFit);
fOutputListBad->Add(didacticProfile);
didacticCounterWorst++;
} break;
fFitModulation->SetParameter(0, fLocalRho->GetVal());
} break;
}
+ if(CDF > -.5) fHistRhoStatusCent->Fill(fCent, 1.);
return kFALSE; // return false if the fit is rejected
}
return kTRUE;
{
// event cuts
if(fDebug > 0) printf("__FILE__ = %s \n __LINE __ %i , __FUNC__ %s \n ", __FILE__, __LINE__, __func__);
+ // determine the run number to see if the track and jet cuts should be refreshed for semi-good TPC runs
+ // only done if the runnumber changes, could be moved to a call to AliAnalysisTaskSE::Notify()
+ if(fRunNumber != InputEvent()->GetRunNumber()) {
+ fRunNumber = InputEvent()->GetRunNumber(); // set the current run number
+ if(fDebug > 0) printf("__FUNC__ %s > NEW RUNNUMBER DETECTED \n ", __func__);
+ // reset the cuts. should be a pointless operation except for the case where the run number changes
+ // from semi-good back to good on one node, which is not a likely scenario
+ AliAnalysisTaskEmcal::SetTrackPhiLimits(-10., 10.);
+ AliAnalysisTaskEmcalJet::SetJetPhiLimits(-10., 10.);
+ if(fCachedRho) { // if there's a cached rho, it's the default, so switch back
+ if(fDebug > 0) printf("__FUNC__ %s > replacing rho with cached rho \n ", __func__);
+ fRho = fCachedRho; // reset rho back to cached value. again, should be pointless
+ }
+ Bool_t flaggedAsSemiGood(kFALSE); // not flagged as anything
+ for(Int_t i(0); i < fExpectedSemiGoodRuns->GetSize(); i++) {
+ if(fExpectedSemiGoodRuns->At(i) == fRunNumber) { // run is semi-good
+ if(fDebug > 0) printf("__FUNC__ %s > semi-good tpc run detected, adjusting acceptance \n ", __func__);
+ flaggedAsSemiGood = kTRUE;
+ AliAnalysisTaskEmcalJet::SetJetPhiLimits(fSemiGoodJetMinPhi, fSemiGoodJetMaxPhi); // just an acceptance cut, jets are obtained from full azimuth, so no edge effects
+ AliAnalysisTaskEmcal::SetTrackPhiLimits(fSemiGoodTrackMinPhi, fSemiGoodTrackMaxPhi); // only affects vn extraction, NOT jet finding
+ // for semi-good runs, also try to get the 'small rho' estimate, if it is available
+ AliRhoParameter* tempRho(dynamic_cast<AliRhoParameter*>(InputEvent()->FindListObject(fNameSmallRho.Data())));
+ if(tempRho) {
+ if(fDebug > 0) printf("__FUNC__ %s > switching to small rho, caching normal rho \n ", __func__);
+ fHistAnalysisSummary->SetBinContent(54, 1.); // bookkeep the fact that small rho is used
+ fCachedRho = fRho; // cache the original rho ...
+ fRho = tempRho; // ... and use the small rho
+ }
+ }
+ }
+ if(!flaggedAsSemiGood) {
+ // in case the run is not a semi-good run, check if it is recognized as another run
+ // only done to catch unexpected runs
+ for(Int_t i(0); i < fExpectedRuns->GetSize(); i++) {
+ if(fExpectedRuns->At(i) == fRunNumber) break; // run is known, break the loop else store the number in a random bin
+ fHistUndeterminedRunQA->SetBinContent(TMath::Nint(10.*gRandom->Uniform(0.,.9))+1, fRunNumber);
+ }
+ fHistAnalysisSummary->SetBinContent(53, 1.); // bookkeep which rho estimate is used
+ }
+ }
+ // continue with event selection
if(!event || !AliAnalysisTaskEmcal::IsEventSelected()) return kFALSE;
if(fSemiCentralInclusive && ! (event->GetTriggerMask() & (ULong64_t(1)<<7))) return kFALSE;
if(TMath::Abs(InputEvent()->GetPrimaryVertex()->GetZ()) > 10.) return kFALSE;
if(fExplicitOutlierCut == 2010 || fExplicitOutlierCut == 2011) {
if(!PassesCuts(fExplicitOutlierCut)) return kFALSE;
}
- if(fRho->GetVal() <= 0 ) return kFALSE;
+ // see if input containers are filled
if(fTracks->GetEntries() < 1) return kFALSE;
+ if(fRho->GetVal() <= 0 ) return kFALSE;
+ if(fAnalysisType == AliAnalysisTaskRhoVnModulation::kCharged && !fClusterCont) return kFALSE;
return kTRUE;
}
//_____________________________________________________________________________
if (!track->TestFilterBit(16) || track->Chi2perNDF() < 0.1) continue;
Double_t b[2] = {-99., -99.};
Double_t bCov[3] = {-99., -99., -99.};
- if (track->PropagateToDCA(event->GetPrimaryVertex(), event->GetMagneticField(), 100., b, bCov) && TMath::Abs(b[0]) < 0.3 && TMath::Abs(b[1]) < 0.3) multGlob++;
+ AliAODTrack copy(*track);
+ if (copy.PropagateToDCA(event->GetPrimaryVertex(), event->GetMagneticField(), 100., b, bCov) && TMath::Abs(b[0]) < 0.3 && TMath::Abs(b[1]) < 0.3) multGlob++;
}
if(year == 2010 && multTPC > (-40.3+1.22*multGlob) && multTPC < (32.1+1.59*multGlob)) return kTRUE;
if(year == 2011 && multTPC > (-36.73 + 1.48*multGlob) && multTPC < (62.87 + 1.78*multGlob)) return kTRUE;
return kFALSE;
}
//_____________________________________________________________________________
-Bool_t AliAnalysisTaskRhoVnModulation::PassesCuts(const AliVCluster* cluster) const
-{
- // cluster cuts
- if(fDebug > 1) printf("__FILE__ = %s \n __LINE __ %i , __FUNC__ %s \n ", __FILE__, __LINE__, __func__);
- if(!cluster) return kFALSE;
- return kTRUE;
-}
-//_____________________________________________________________________________
void AliAnalysisTaskRhoVnModulation::FillHistogramsAfterSubtraction(Double_t psi2, Double_t psi3, Double_t vzero[2][2], Double_t* vzeroComb, Double_t* tpc)
{
// fill histograms
if(fDebug > 0) printf("__FILE__ = %s \n __LINE __ %i , __FUNC__ %s \n ", __FILE__, __LINE__, __func__);
FillTrackHistograms();
- /* FillClusterHistograms(); */
+ FillClusterHistograms();
FillJetHistograms(psi2, psi3);
- /* FillCorrectedClusterHistograms(); */
if(fFillQAHistograms) FillEventPlaneHistograms(vzero, vzeroComb, tpc);
FillRhoHistograms();
FillDeltaPtHistograms(psi2, psi3);
{
// fill cluster histograms
if(fDebug > 0) printf("__FILE__ = %s \n __LINE __ %i , __FUNC__ %s \n ", __FILE__, __LINE__, __func__);
- /* Int_t iClusters(fCaloClusters->GetEntriesFast());
+ if(!fClusterCont) return;
+ Int_t iClusters(fClusterCont->GetNClusters());
for(Int_t i(0); i < iClusters; i++) {
- AliVCluster* cluster = static_cast<AliVCluster*>(fCaloClusters->At(iClusters));
+ AliVCluster* cluster = fClusterCont->GetCluster(i);
if (!PassesCuts(cluster)) continue;
TLorentzVector clusterLorentzVector;
cluster->GetMomentum(clusterLorentzVector, const_cast<Double_t*>(fVertex));
fHistClusterPt[fInCentralitySelection]->Fill(clusterLorentzVector.Pt());
- fHistClusterEta[fInCentralitySelection]->Fill(clusterLorentzVector.Eta());
- fHistClusterPhi[fInCentralitySelection]->Fill(clusterLorentzVector.Phi());
+ fHistClusterEtaPhi[fInCentralitySelection]->Fill(clusterLorentzVector.Eta(), clusterLorentzVector.Phi());
+ fHistClusterEtaPhiWeighted[fInCentralitySelection]->Fill(clusterLorentzVector.Eta(), clusterLorentzVector.Phi(), clusterLorentzVector.Pt());
}
- return; */
-}
-//_____________________________________________________________________________
-void AliAnalysisTaskRhoVnModulation::FillCorrectedClusterHistograms() const
-{
- // fill clusters after hadronic correction FIXME implement
- if(fDebug > 0) printf("__FILE__ = %s \n __LINE __ %i , __FUNC__ %s \n ", __FILE__, __LINE__, __func__);
+ return;
}
//_____________________________________________________________________________
void AliAnalysisTaskRhoVnModulation::FillEventPlaneHistograms(Double_t vzero[2][2], Double_t* vzeroComb, Double_t* tpc) const
// fill delta pt histograms
if(fDebug > 0) printf("__FILE__ = %s \n __LINE __ %i , __FUNC__ %s \n ", __FILE__, __LINE__, __func__);
Int_t i(0);
- AliEmcalJet* leadingJet(GetJetContainer()->GetLeadingJet());
- if(!leadingJet && fDebug > 0) printf(" > failed to retrieve leading jet ! < \n");
const Float_t areaRC = fRandomConeRadius*fRandomConeRadius*TMath::Pi();
// we're retrieved the leading jet, now get a random cone
for(i = 0; i < fMaxCones; i++) {
fHistDeltaPtDeltaPhi3[fInCentralitySelection]->Fill(PhaseShift(phi-psi3, 3.), pt - areaRC*fLocalRho->GetLocalVal(phi, GetJetContainer()->GetJetRadius(), fLocalRho->GetVal()));
}
// get a random cone excluding leading jet area
- CalculateRandomCone(pt, eta, phi, leadingJet);
+ CalculateRandomCone(pt, eta, phi, fLeadingJet);
if(pt > 0) {
if(fFillQAHistograms) fHistRCPhiEtaExLJ[fInCentralitySelection]->Fill(phi, eta);
fHistRhoVsRCPtExLJ[fInCentralitySelection]->Fill(pt, fLocalRho->GetLocalVal(phi, GetJetContainer()->GetJetRadius(), fLocalRho->GetVal())*areaRC);
fHistJetPt[fInCentralitySelection]->Fill(pt-area*rho);
if(fFillQAHistograms) fHistJetEtaPhi[fInCentralitySelection]->Fill(eta, phi);
fHistJetPtArea[fInCentralitySelection]->Fill(pt-area*rho, area);
+ fHistJetPtEta[fInCentralitySelection]->Fill(pt-area*rho, eta);
fHistJetPsi2Pt[fInCentralitySelection]->Fill(PhaseShift(phi-psi2, 2.), pt-area*rho);
fHistJetPsi3Pt[fInCentralitySelection]->Fill(PhaseShift(phi-psi3, 3.), pt-area*rho);
fHistJetPtConstituents[fInCentralitySelection]->Fill(pt-area*rho, jet->Nch());
fHistVertexz->Fill(vevent->GetPrimaryVertex()->GetZ());
fHistCentrality->Fill(fCent);
Int_t runNumber(InputEvent()->GetRunNumber());
- Int_t runs[] = {167813, 167988, 168066, 168068, 168069, 168076, 168104, 168212, 168311, 168322, 168325, 168341, 168361, 168362, 168458, 168460, 168461, 168992, 169091, 169094, 169138, 169143, 169167, 169417, 169835, 169837, 169838, 169846, 169855, 169858, 169859, 169923, 169956, 170027, 170036, 170081, 169975, 169981, 170038, 170040, 170083, 170084, 170085, 170088, 170089, 170091, 170152, 170155, 170159, 170163, 170193, 170195, 170203, 170204, 170205, 170228, 170230, 170264, 170268, 170269, 170270, 170306, 170308, 170309};
- for(fMappedRunNumber = 0; fMappedRunNumber < 64; fMappedRunNumber++) {
- if(runs[fMappedRunNumber]==runNumber) break;
+ for(fMappedRunNumber = 0; fExpectedRuns->GetSize()+1; fMappedRunNumber++) {
+ if(fExpectedRuns->At(fMappedRunNumber) == runNumber) break;
}
}
//_____________________________________________________________________________
fHistAnalysisSummary->SetBinContent(51, fMaxCones);
fHistAnalysisSummary->GetXaxis()->SetBinLabel(52, "fUseScaledRho");
fHistAnalysisSummary->SetBinContent(52, fUseScaledRho);
+ fHistAnalysisSummary->GetXaxis()->SetBinLabel(53, "used rho");
+ fHistAnalysisSummary->GetXaxis()->SetBinLabel(54, "used small rho");
}
//_____________________________________________________________________________
void AliAnalysisTaskRhoVnModulation::Terminate(Option_t *)
switch (fRunModeType) {
case kLocal : {
printf("__FILE__ = %s \n __LINE __ %i , __FUNC__ %s \n ", __FILE__, __LINE__, __func__);
- if(fFillQAHistograms) {
- Int_t runs[] = {167813, 167988, 168066, 168068, 168069, 168076, 168104, 168212, 168311, 168322, 168325, 168341, 168361, 168362, 168458, 168460, 168461, 168992, 169091, 169094, 169138, 169143, 169167, 169417, 169835, 169837, 169838, 169846, 169855, 169858, 169859, 169923, 169956, 170027, 170036, 170081, 169975, 169981, 170038, 170040, 170083, 170084, 170085, 170088, 170089, 170091, 170152, 170155, 170159, 170163, 170193, 170195, 170203, 170204, 170205, 170228, 170230, 170264, 170268, 170269, 170270, 170306, 170308, 170309};
- for(Int_t i(0); i < 64; i++) {
- fHistRunnumbersPhi->GetXaxis()->SetBinLabel(i+1, Form("%i", runs[i]));
- fHistRunnumbersEta->GetXaxis()->SetBinLabel(i+1, Form("%i", runs[i]));
- }
- fHistRunnumbersPhi->GetXaxis()->SetBinLabel(65, "undetermined");
- fHistRunnumbersEta->GetXaxis()->SetBinLabel(65, "undetermined");
- }
AliAnalysisTaskRhoVnModulation::Dump();
for(Int_t i(0); i < fHistAnalysisSummary->GetXaxis()->GetNbins(); i++) printf( " > flag: %s \t content %.2f \n", fHistAnalysisSummary->GetXaxis()->GetBinLabel(1+i), fHistAnalysisSummary->GetBinContent(1+i));
} break;
//_____________________________________________________________________________
void AliAnalysisTaskRhoVnModulation::SetModulationFit(TF1* fit)
{
-// set modulation fit
+ // set modulation fit
if (fFitModulation) delete fFitModulation;
fFitModulation = fit;
}
//_____________________________________________________________________________
+void AliAnalysisTaskRhoVnModulation::SetUseControlFit(Bool_t c)
+{
+ // set control fit
+ if (fFitControl) delete fFitControl;
+ if (c) {
+ fFitControl = new TF1("controlFit", "pol0", 0, TMath::TwoPi());
+ } else fFitControl = 0x0;
+}
+//_____________________________________________________________________________
TH1F* AliAnalysisTaskRhoVnModulation::GetResolutionFromOuptutFile(detectorType det, Int_t h, TArrayD* cen)
{
// INTERFACE METHOD FOR OUTPUTFILE
git://git.uio.no / u / mrichter / AliRoot.git / blobdiff