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Using detector quality flag (taken from ALICE logbook) to decide whether to rpodcue...
[u/mrichter/AliRoot.git] / JETAN / AliDAJetFinder.cxx
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9e4cc50d 1
7c679be0 2// **************************************************************************
3// * Copyright(c) 1998-1999, ALICE Experiment at CERN, All rights reserved. *
4// * *
5// * Author: The ALICE Off-line Project. *
6// * Contributors are mentioned in the code where appropriate. *
7// * *
8// * Permission to use, copy, modify and distribute this software and its *
9// * documentation strictly for non-commercial purposes is hereby granted *
10// * without fee, provided that the above copyright notice appears in all *
11// * copies and that both the copyright notice and this permission notice *
12// * appear in the supporting documentation. The authors make no claims *
13// * about the suitability of this software for any purpose. It is *
14// * provided "as is" without express or implied warranty. *
15// **************************************************************************
16
139cbd96 17/* $Id$ */
18
7c679be0 19//-----------------------------------------------------------------------------------
20// Jet finder based on Deterministic Annealing
21// For further informations about the DA working features see:
22// Phys.Lett. B601 (2004) 56-63 (http://arxiv.org/abs/hep-ph/0407214)
23// Author: Davide Perrino (davide.perrino@ba.infn.it, davide.perrino@cern.ch)
139cbd96 24// alexandre.shabetai@cern.ch (Modification of the input object (reader/finder splitting))
25// ** 2011
26// Modified accordingly to reader/finder splitting and new handling of neutral information
7c679be0 27//-----------------------------------------------------------------------------------
28
29#include <TMath.h>
386b2e2f 30#include <TRandom2.h>
139cbd96 31
32#include "AliAODJet.h"
7c679be0 33#include "AliDAJetHeader.h"
34#include "AliDAJetFinder.h"
35
7c679be0 36ClassImp(AliDAJetFinder)
37
139cbd96 38///////////////////////////////////////////////////////////////////////
7c679be0 39
7c679be0 40AliDAJetFinder::AliDAJetFinder():
139cbd96 41 AliJetFinder(),
42 fAlpha(1.01),
43 fDelta(1e-8),
44 fAvDist(1e-6),
45 fEps(1e-4),
46 fEpsMax(1e-2),
47 fNloopMax(100),
48 fBeta(0.1),
49 fNclustMax(0),
50 fNin(0),
51 fNeff(0)
7c679be0 52{
139cbd96 53 // Constructor
7c679be0 54}
55
56//-----------------------------------------------------------------------------------
57AliDAJetFinder::~AliDAJetFinder()
58{
139cbd96 59 // Destructor
7c679be0 60}
61
62//-----------------------------------------------------------------------------------
63void AliDAJetFinder::FindJets()
64{
139cbd96 65 // Find the jets in current event
66 //
67 Float_t betaStop=100.;
68 fDebug = fHeader->GetDebug();
69
70 Double_t dEtSum=0;
71 Double_t *xData[2];
72 TVectorD *vPx = new TVectorD();
73 TVectorD *vPy = new TVectorD();
74 TMatrixD *mPyx= new TMatrixD();
75 TMatrixD *mY = new TMatrixD();
76 InitDetAnn(dEtSum,xData,vPx,vPy,mPyx,mY);
77 if (fNin < fNclustMax){
78 delete [] xData[0], delete [] xData[1];
79 delete vPx;
80 delete vPy;
81 delete mPyx;
82 delete mY;
83 return;
84 }
85 Int_t nc=1, nk;
86 DoubleClusters(nc,nk,vPy,mY);
87 do{ //loop over beta
88 fBeta*=fAlpha;
89 Annealing(nk,xData,vPx,vPy,mPyx,mY);
90 NumCl(nc,nk,vPy,mPyx,mY);
91 }while((fBeta<betaStop || nc<4) && nc<fNclustMax);
7c679be0 92
139cbd96 93 Int_t *xx=new Int_t[fNeff];
94 for (Int_t i = 0; i < fNeff; i++) xx[i] = 0;
95
96 EndDetAnn(nk,xData,xx,dEtSum,vPx,vPy,mPyx,mY);
97 StoreJets(nk,xData,xx,mY);
98 delete [] xx;
99
100 delete [] xData[0], delete [] xData[1];
101 delete mPyx;
102 delete mY;
103 delete vPx;
104 delete vPy;
36b5cc43 105
7c679be0 106}
107
108//-----------------------------------------------------------------------------------
36b5cc43 109void AliDAJetFinder::InitDetAnn(Double_t &dEtSum,Double_t **xData,TVectorD *vPx,TVectorD *vPy,TMatrixD *mPyx,TMatrixD *mY)
7c679be0 110{
139cbd96 111 //Initialise the variables used by the algorithm
112 fBeta=0.1;
113 fNclustMax = ((AliDAJetHeader*)fHeader)->GetFixedCl() ?
114 ((AliDAJetHeader*)fHeader)->GetNclustMax() :
115 TMath::Max((Int_t)TMath::Sqrt(fNin),5);
116 Float_t etaEff = ((AliDAJetHeader*)fHeader)->GetEtaEff();
117
118 fNin=0;
119 for (Int_t iTr=0; iTr<GetCalTrkEvent()->GetNCalTrkTracks(); iTr++) if (GetCalTrkEvent()->GetCalTrkTrack(iTr)->GetCutFlag()==1) fNin++;
120
121 fNeff = ((AliDAJetHeader*)fHeader)->GetNeff();
122 fNeff = TMath::Max(fNeff,fNin);
123 Double_t *xEta = new Double_t[fNeff];
124 Double_t *xPhi = new Double_t[fNeff];
125 xData[0]=xEta; xData[1]=xPhi;
126 vPx->ResizeTo(fNeff);
127 Int_t iIn=0;
128
129 for (Int_t iTr=0; iTr<GetCalTrkEvent()->GetNCalTrkTracks(); iTr++){
130 AliJetCalTrkTrack* ctT = GetCalTrkEvent()->GetCalTrkTrack(iTr);
131 if (ctT->GetCutFlag()==0) continue;
132 xEta[iIn] = ctT->GetEta();
133 xPhi[iIn] = ctT->GetPhi()<0 ? ctT->GetPhi() + 2*TMath::Pi() : ctT->GetPhi();
134 (*vPx)(iIn)=ctT->GetPt();
135 dEtSum+=(*vPx)(iIn);
136 iIn++;
137 }
138
139 TRandom2 r;
140 r.SetSeed(0);
141 for (iIn=fNin; iIn<fNeff; iIn++){
142 xEta[iIn]=r.Uniform(-1*etaEff,etaEff);
143 xPhi[iIn]=r.Uniform(0.,2*TMath::Pi());
144 (*vPx)(iIn)=r.Uniform(0.01,0.02);
145 dEtSum+=(*vPx)(iIn);
146 }
147 for (iIn=0; iIn<fNeff; iIn++) (*vPx)(iIn)=(*vPx)(iIn)/dEtSum;
148
149 Int_t njdim=2*fNclustMax+1;
150 mPyx->ResizeTo(fNeff,njdim);
151 mY->ResizeTo(4,njdim);
152 vPy->ResizeTo(njdim);
153 mY->Zero();mPyx->Zero();vPy->Zero();
154 (*vPy)(0)=1;
155 TMatrixDColumn(*mPyx,0)=1;
156 Double_t ypos=0,xpos=0;
157 for (iIn=0; iIn<fNeff; iIn++){
158 (*mY)(0,0)+=(*vPx)(iIn)*xEta[iIn];
159 ypos+=(*vPx)(iIn)*TMath::Sin(xPhi[iIn]);
160 xpos+=(*vPx)(iIn)*TMath::Cos(xPhi[iIn]);
161 }
162 (*mY)(1,0)=(atan2(ypos,xpos)>0) ? atan2(ypos,xpos) : atan2(ypos,xpos)+2*TMath::Pi();
163
7c679be0 164}
165
166//-----------------------------------------------------------------------------------
139cbd96 167void AliDAJetFinder::DoubleClusters(Int_t nc,Int_t &nk, TVectorD *vPy, TMatrixD *mY) const
7c679be0 168{
139cbd96 169 // Return double clusters
170 for(Int_t iClust=0; iClust<nc; iClust++){
171 (*vPy)(iClust)=(*vPy)(iClust)/2;
172 (*vPy)(nc+iClust)=(*vPy)(iClust);
173 for(Int_t iComp=0; iComp<3; iComp++) (*mY)(iComp,nc+iClust)=(*mY)(iComp,iClust);
174 }
175 nk=2*nc;
176
7c679be0 177}
178
179//-----------------------------------------------------------------------------------
139cbd96 180void AliDAJetFinder::Annealing(Int_t nk,Double_t **xData, const TVectorD *vPx, TVectorD *vPy, TMatrixD *mPyx, TMatrixD *mY)
7c679be0 181{
139cbd96 182 // Main part of the algorithm
183 const Double_t pi=TMath::Pi();
184 TVectorD *py = new TVectorD(nk);
185 TVectorD *p = new TVectorD(nk);
186 TMatrixD *y = new TMatrixD(4,nk);
187 TMatrixD *y1 = new TMatrixD(4,nk);
188 TMatrixD *ry = new TMatrixD(2,nk);
189 Double_t *xEta = xData[0];
190 Double_t *xPhi = xData[1];
191 Double_t Dist(TVectorD,TVectorD);
192
193 Double_t df[2]={((AliDAJetHeader*)fHeader)->GetFiducialEtaMax(),pi};
194 TVectorD vPart(2);
195 Double_t *m = new Double_t[nk];
196 Double_t chi,chi1;
197 do{
198 Int_t nloop=0;
199 for (Int_t iClust=0; iClust<nk; iClust++){
200 for (Int_t i=0; i<3; i++)(*y1)(i,iClust)=(*mY)(i,iClust);
201 (*py)(iClust)=(*vPy)(iClust);
202 }
203 //perturbation of codevectors
204 Double_t seed=1000000*gRandom->Rndm(24);
205 ry->Randomize(-0.5,0.5,seed);
206 for (Int_t i=0; i<2; i++){
207 for (Int_t iClust=0; iClust<nk/2; iClust++)
208 (*y1)(i,iClust)+=((*ry)(i,iClust)+TMath::Sign(0.5,(*ry)(i,iClust)))*fDelta*df[i];
209 for (Int_t iClust=nk/2; iClust<nk; iClust++)
210 (*y1)(i,iClust)-=((*ry)(i,iClust-nk/2)+TMath::Sign(0.5,(*ry)(i,iClust-nk/2)))*fDelta*df[i];
211 }
212 do{
213 //recalculate conditional probabilities
214 nloop++;
215 for (Int_t iIn=0; iIn<fNeff; iIn++){
216 vPart(0)=xEta[iIn]; vPart(1)=xPhi[iIn];
217 for(Int_t iClust=0; iClust<nk; iClust++){
218 (*mPyx)(iIn,iClust)=-log((*py)(iClust))+fBeta*Dist(vPart,TMatrixDColumn(*y1,iClust));
219 m[iClust]=(*mPyx)(iIn,iClust);
7c679be0 220 }
139cbd96 221 Double_t pyxNorm=0;
222 Double_t minPyx=TMath::MinElement(nk,m);
223 for (Int_t iClust=0; iClust<nk; iClust++){
224 (*mPyx)(iIn,iClust)-=minPyx;
225 (*mPyx)(iIn,iClust)=exp(-(*mPyx)(iIn,iClust));
226 pyxNorm+=(*mPyx)(iIn,iClust);
227 }
228 for (Int_t iClust=0; iClust<nk; iClust++) (*mPyx)(iIn,iClust)/=pyxNorm;
229 }
230 p->Zero();
231 y->Zero();
232 //recalculate codevectors
233 for (Int_t iClust=0; iClust<nk; iClust++){
234 Double_t xpos=0,ypos=0,pxy;
235 for (Int_t iIn=0; iIn<fNeff; iIn++) (*p)(iClust)+=(*vPx)(iIn)*(*mPyx)(iIn,iClust);
236 for (Int_t iIn=0; iIn<fNeff; iIn++){
237 pxy=(*vPx)(iIn)*(*mPyx)(iIn,iClust)/(*p)(iClust);
238 ypos+=pxy*TMath::Sin(xPhi[iIn]);
239 xpos+=pxy*TMath::Cos(xPhi[iIn]);
240 (*y)(0,iClust)+=pxy*xEta[iIn];
241 }
242 (*y)(1,iClust)=(atan2(ypos,xpos)>0) ? atan2(ypos,xpos) : atan2(ypos,xpos)+2*pi;
243 }
244 //verify codevectors' stability
245 chi=0;
246 for (Int_t iClust=0; iClust<nk; iClust++){
247 chi1=TMath::CosH((*y1)(0,iClust)-(*y)(0,iClust))-TMath::Cos((*y1)(1,iClust)-(*y)(1,iClust));
248 chi1/=(2*TMath::CosH((*y1)(0,iClust))*TMath::CosH((*y)(0,iClust)));
249 chi1*=chi1;
250 if (chi1>chi) chi=chi1;
251 }
252 chi=TMath::Sqrt(chi);
253 for (Int_t iClust=0; iClust<nk; iClust++){
254 for (Int_t i=0; i<2; i++) (*y1)(i,iClust)=(*y)(i,iClust);
255 (*py)(iClust)=(*p)(iClust);
256 }
257 if (nloop>fNloopMax){
258 if (chi<fEpsMax || nloop>500) break;
259 }
260 }while (chi>fEps);
261 }while (chi>fEpsMax);
262 for (Int_t iClust=0; iClust<nk; iClust++){ //set codevectors and probability equal to those calculated
263 for (Int_t i=0; i<2; i++) (*mY)(i,iClust)=(*y)(i,iClust);
264 (*vPy)(iClust)=(*p)(iClust);
265 }
266 delete py;
267 delete p;
268 delete y;
269 delete y1;
270 delete ry;
271 delete [] m;
272
7c679be0 273}
274
275//-----------------------------------------------------------------------------------
139cbd96 276void AliDAJetFinder::NumCl(Int_t &nc,Int_t &nk,TVectorD *vPy, TMatrixD *mPyx,TMatrixD *mY)
7c679be0 277{
139cbd96 278 // Number of clusters
279 static Bool_t growcl=true;
7c679be0 280
139cbd96 281 if (nk==2) growcl=true;
282 if (growcl){
283 //verify if two codevectors are equal within fAvDist
284 Int_t *nSame = new Int_t[nk];
285 Int_t **iSame = new Int_t*[nk];
286 Int_t **cont = new Int_t*[nk];
287 for (Int_t iClust=0; iClust<nk; iClust++) {
288 cont[iClust] =new Int_t[nk];
289 iSame[iClust]=new Int_t[nk];
290 }
291
292 for (Int_t iClust=0; iClust<nk; iClust++){
293 iSame[iClust][iClust]=1;
294 for (Int_t iClust1=iClust+1; iClust1<nk; iClust1++){
295 Double_t eta = (*mY)(0,iClust) ; Double_t phi = (*mY)(1,iClust);
296 Double_t eta1 = (*mY)(0,iClust1); Double_t phi1 = (*mY)(1,iClust1);
297 Double_t distCl=(TMath::CosH(eta-eta1)-TMath::Cos(phi-phi1))/(2*TMath::CosH(eta)*TMath::CosH(eta1));
298 if (distCl < fAvDist) iSame[iClust][iClust1]=iSame[iClust1][iClust]=1;
299 else iSame[iClust][iClust1]=iSame[iClust1][iClust]=0;
300 }
301 }
302 ReduceClusters(iSame,nk,nc,cont,nSame);
303 if (nc >= fNclustMax) growcl=false;
304 //recalculate the nc distinct codevectors
305 TMatrixD *pyx = new TMatrixD(fNeff,2*nc);
306 TVectorD *py = new TVectorD(nk);
307 TMatrixD *y1 = new TMatrixD(3,nk);
308 for (Int_t iClust=0; iClust<nc; iClust++){
309 for(Int_t j=0; j<nSame[iClust]; j++){
310 Int_t iClust1 = cont[iClust][j];
311 for (Int_t iIn=0; iIn<fNeff; iIn++) (*pyx)(iIn,iClust)+=(*mPyx)(iIn,iClust1);
312 (*py)(iClust)+=(*vPy)(iClust1);
313 for (Int_t i=0; i<2; i++) (*y1)(i,iClust)+=(*mY)(i,iClust1);
314 }
315 for (Int_t i=0; i<2; i++) (*y1)(i,iClust)/=nSame[iClust];
316 }
317 for (Int_t iClust=0; iClust<nk; iClust++) delete [] cont[iClust], delete [] iSame[iClust];
318 delete [] iSame;
319 delete [] cont;
320 delete [] nSame;
321 if (nc > nk/2){
322 for (Int_t iClust=0; iClust<nc; iClust++){
323 for (Int_t iIn=0; iIn<fNeff; iIn++) (*mPyx)(iIn,iClust)=(*pyx)(iIn,iClust);
324 for (Int_t iComp=0; iComp<2; iComp++) (*mY)(iComp,iClust)=(*y1)(iComp,iClust);
325 (*vPy)(iClust)=(*py)(iClust);
326 }
327 nk=nc;
328 if (growcl) DoubleClusters(nc,nk,vPy,mY);
329 }
330 delete pyx;
331 delete py;
332 delete y1;
333 }
7c679be0 334
335}
336
337//-----------------------------------------------------------------------------------
c0a5117c 338void AliDAJetFinder::ReduceClusters(Int_t **iSame,Int_t nc,Int_t &ncout,Int_t **cont,Int_t *nSameOut) const
7c679be0 339{
139cbd96 340 // Reduction step
341 Int_t *nSame = new Int_t[nc];
342 Int_t *iperm = new Int_t[nc];
343 Int_t *go = new Int_t[nc];
344 for (Int_t iCl=0; iCl<nc; iCl++){
345 nSame[iCl]=0;
346 for (Int_t jCl=0; jCl<nc; jCl++) nSame[iCl]+=iSame[iCl][jCl], cont[iCl][jCl]=0;
347 iperm[iCl]=iCl;
348 go[iCl]=1;
349 }
350 TMath::Sort(nc,nSame,iperm,true);
351 Int_t l=0;
352 for (Int_t iCl=0; iCl<nc; iCl++){
353 Int_t k=iperm[iCl];
354 if (go[k] == 1){
355 Int_t m=0;
356 for (Int_t jCl=0; jCl<nc; jCl++){
357 if (iSame[k][jCl] == 1){
358 cont[l][m]=jCl;
359 go[jCl]=0;
360 m++;
7c679be0 361 }
139cbd96 362 }
363 nSameOut[l]=m;
364 l++;
365 }
366 }
367 ncout=l;
368 delete [] nSame;
369 delete [] iperm;
370 delete [] go;
371
7c679be0 372}
373
374//-----------------------------------------------------------------------------------
139cbd96 375void AliDAJetFinder::EndDetAnn(Int_t &nk,Double_t **xData,Int_t *xx,Double_t etx,const TVectorD *vPx,TVectorD *vPy,TMatrixD *mPyx,TMatrixD *mY)
7c679be0 376{
139cbd96 377 //now assign each particle to only one cluster
378 Double_t *clusters=new Double_t[nk];
379 for (Int_t iIn=0; iIn<fNeff; iIn++){
380 for (Int_t iClust=0; iClust<nk; iClust++) clusters[iClust]=(*mPyx)(iIn,iClust);
381 xx[iIn]=TMath::LocMax(nk,clusters);
382 }
383 delete [] clusters;
7c679be0 384
139cbd96 385 //recalculate codevectors, having all p(y|x)=0 or 1
386 Double_t *xEta = xData[0];
387 Double_t *xPhi = xData[1];
388 mY->Zero();
389 mPyx->Zero();
390 vPy->Zero();
391 for (Int_t iIn=0; iIn<fNin; iIn++){
392 Int_t iClust=xx[iIn];
393 (*mPyx)(iIn,iClust)=1;
394 (*vPy)(iClust)+=(*vPx)(iIn);
395 (*mY)(0,iClust)+=(*vPx)(iIn)*xEta[iIn];
396 (*mY)(3,iClust)+=(*vPx)(iIn)*etx;
397 }
398 Int_t k=0;
399 for (Int_t iClust=0; iClust<nk; iClust++){
400 if ((*vPy)(iClust)>0){
401 Double_t xpos=0,ypos=0,pxy;
402 for (Int_t iIn=0; iIn<fNin; iIn++){
403 pxy=(*vPx)(iIn)*(*mPyx)(iIn,iClust)/(*vPy)(iClust);
404 ypos+=pxy*TMath::Sin(xPhi[iIn]);
405 xpos+=pxy*TMath::Cos(xPhi[iIn]);
406 if (xx[iIn]==iClust) xx[iIn]=k;
407 }
408 (*mY)(0,k)=(*mY)(0,iClust)/(*vPy)(iClust);
409 (*mY)(1,k)=(atan2(ypos,xpos)>0) ? atan2(ypos,xpos) : atan2(ypos,xpos)+2*TMath::Pi();
410 (*mY)(3,k)=(*mY)(3,iClust);
411 k++;
412 }
413 }
414 nk=k;
415
7c679be0 416}
417
418//-----------------------------------------------------------------------------------
139cbd96 419void AliDAJetFinder::StoreJets(Int_t nk, Double_t **xData, const Int_t *xx, const TMatrixD *mY)
7c679be0 420{
139cbd96 421 //evaluate significant clusters properties
422 const Double_t pi=TMath::Pi();
423 Float_t dFidEtaMax = ((AliDAJetHeader*)fHeader)->GetFiducialEtaMax();
424 Float_t dFidEtaMin = ((AliDAJetHeader*)fHeader)->GetFiducialEtaMin();
425 Float_t dFiducialEta= dFidEtaMax - dFidEtaMin;
426 Double_t *xEta = xData[0];
427 Double_t *xPhi = xData[1];
428 Int_t nEff = 0;
429 for (Int_t i=0; i<fNeff; i++) if (xEta[i]<dFidEtaMax && xEta[i]>dFidEtaMin) nEff++;
430 Double_t dMeanDist=0.;
431 if (nEff > 0)
432 dMeanDist=TMath::Sqrt(2*dFiducialEta*pi/nEff);
433 Bool_t *isJet = new Bool_t[nk];
434 Double_t *etNoBg= new Double_t[nk];
435 Double_t *dDeltaEta=new Double_t[nk];
436 Double_t *dDeltaPhi=new Double_t[nk];
437 Double_t *surf = new Double_t[nk];
438 Double_t *etDens= new Double_t[nk];
439 for (Int_t iClust=0; iClust<nk; iClust++){
440 isJet[iClust]=false;
441 Double_t dEtaMin=10.,dEtaMax=-10.,dPhiMin=10.,dPhiMax=0.;
442 for (Int_t iIn=0; iIn<fNeff; iIn++){
443 if (xx[iIn]!=iClust || xEta[iIn]>dFidEtaMax || xEta[iIn]<dFidEtaMin) continue;
444 if (xEta[iIn] < dEtaMin) dEtaMin=xEta[iIn];
445 if (xEta[iIn] > dEtaMax) dEtaMax=xEta[iIn];
446 Double_t dPhi=xPhi[iIn]-(*mY)(1,iClust);
447 if (dPhi > pi ) dPhi-=2*pi;
448 else if (dPhi < (-1)*pi) dPhi+=2*pi;
449 if (dPhi < dPhiMin) dPhiMin=dPhi;
450 else if (dPhi > dPhiMax) dPhiMax=dPhi;
451 }
452 dDeltaEta[iClust]=dEtaMax-dEtaMin+dMeanDist;
453 dDeltaPhi[iClust]=dPhiMax-dPhiMin+dMeanDist;
454 surf[iClust]=0.25*pi*dDeltaEta[iClust]*dDeltaPhi[iClust];
455 etDens[iClust]=(*mY)(3,iClust)/surf[iClust];
456 }
7c679be0 457
139cbd96 458 if (((AliDAJetHeader*)fHeader)->GetSelJets()){
459 for (Int_t iClust=0; iClust<nk; iClust++){
460 if (!isJet[iClust] && (*mY)(0,iClust)<dFidEtaMax && (*mY)(0,iClust)>dFidEtaMin){
461 Double_t etDensMed=0.;
462 Double_t etDensSqr=0.;
463 Int_t norm=0;
464 for (Int_t iClust1=0; iClust1<nk; iClust1++){
465 if(iClust1!=iClust && (*mY)(0,iClust)<dFidEtaMax && (*mY)(0,iClust)>dFidEtaMin){
466 etDensMed+=etDens[iClust1];
467 etDensSqr+=TMath::Power(etDens[iClust1],2);
468 norm++;
469 }
7c679be0 470 }
139cbd96 471 etDensMed/=TMath::Max(norm,1);
472 etDensSqr/=TMath::Max(norm,1);
473 Double_t deltaEtDens=TMath::Sqrt(etDensSqr-TMath::Power(etDensMed,2));
474 if ((*mY)(3,iClust) > (etDensMed+deltaEtDens)*surf[iClust]) isJet[iClust]=kTRUE;
475 etNoBg[iClust]=(*mY)(3,iClust)-etDensMed*surf[iClust];
476 }
477 }
478 for (Int_t iClust=0; iClust<nk; iClust++){
479 if (isJet[iClust]){
480 Double_t etDensMed=0;
481 Double_t extSurf=2*dFiducialEta*pi;
482 for (Int_t iClust1=0; iClust1<nk; iClust1++){
483 if (!isJet[iClust1]) etDensMed+=(*mY)(3,iClust1);
484 else extSurf-=surf[iClust1];
7c679be0 485 }
139cbd96 486 etDensMed/=extSurf;
487 etNoBg[iClust]=(*mY)(3,iClust)-etDensMed*surf[iClust];
488 if (etNoBg[iClust]<((AliDAJetHeader*)fHeader)->GetEtMin()){
489 isJet[iClust]=kFALSE;
490 iClust=-1;
491 }
492 }
493 }
494 } else {
495 for (Int_t iClust=0; iClust<nk; iClust++){
496 isJet[iClust]=true;
497 etNoBg[iClust]=(*mY)(3,iClust);
498 }
499 }
500 delete [] etDens;
501 delete [] surf;
502
503 //now add selected jets to the list
504 Int_t *iSort = new Int_t[nk];
505 TMath::Sort(nk,etNoBg,iSort,true);
506 Int_t iCl = 0;
507
508 for (Int_t iClust=0; iClust<nk; iClust++){ //clusters loop
509 iCl=iSort[iClust];
510 if (isJet[iCl]){ //choose cluster
511 Float_t px,py,pz,en;
512 px = (*mY)(3,iCl)*TMath::Cos((*mY)(1,iCl));
513 py = (*mY)(3,iCl)*TMath::Sin((*mY)(1,iCl));
514 pz = (*mY)(3,iCl)/TMath::Tan(2.0 * TMath::ATan(TMath::Exp(-(*mY)(0,iCl))));
515 en = TMath::Sqrt(px * px + py * py + pz * pz);
516 AliAODJet jet(px, py, pz, en);
517 Int_t iIn=0;
518 Int_t nTr = GetCalTrkEvent()->GetNCalTrkTracks();
519 for (Int_t iTr=0; iTr<nTr; iTr++){
520 AliJetCalTrkTrack* ctT = GetCalTrkEvent()->GetCalTrkTrack(iTr);
521 if (ctT->GetCutFlag()==0) continue;
522 if (xx[iIn]==iCl) jet.AddTrack(ctT->GetTrackObject());
523 iIn++;
524 }
525 AddJet(jet);
526 if (fDebug > 0) printf("jet %d, Eta: %f, Phi: %f, Et: %f\n",iCl,jet.Eta(),jet.Phi(),jet.Pt());
527 }
528 }
529 delete [] dDeltaEta; delete [] dDeltaPhi;
530 delete [] etNoBg;
531 delete [] isJet;
532 delete [] iSort;
533
7c679be0 534}
535
536//-----------------------------------------------------------------------------------
537Double_t Dist(TVectorD x,TVectorD y)
538{
139cbd96 539 // Squared distance
540 const Double_t pi=TMath::Pi();
541 Double_t dphi=TMath::Abs(x(1)-y(1));
542 if (dphi > pi) dphi=2*pi-dphi;
543 Double_t dist=pow(x(0)-y(0),2)+pow(dphi,2);
544 return dist;
545
7c679be0 546}