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1 | |
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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 | |
17 | //----------------------------------------------------------------------------------- |
18 | // Jet finder based on Deterministic Annealing |
19 | // For further informations about the DA working features see: |
20 | // Phys.Lett. B601 (2004) 56-63 (http://arxiv.org/abs/hep-ph/0407214) |
21 | // Author: Davide Perrino (davide.perrino@ba.infn.it, davide.perrino@cern.ch) |
22 | //----------------------------------------------------------------------------------- |
23 | |
24 | #include <TMath.h> |
25 | #include <TRandom.h> |
26 | #include <TClonesArray.h> |
36b5cc43 |
27 | #include "AliJetReaderHeader.h" |
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28 | #include "AliJetReader.h" |
29 | #include "AliDAJetHeader.h" |
30 | #include "AliDAJetFinder.h" |
31 | |
7c679be0 |
32 | ClassImp(AliDAJetFinder) |
33 | |
34 | |
35 | //----------------------------------------------------------------------------------- |
36 | AliDAJetFinder::AliDAJetFinder(): |
36b5cc43 |
37 | AliJetFinder(), |
7c679be0 |
38 | fAlpha(1.01), |
39 | fDelta(1e-8), |
40 | fAvDist(1e-6), |
41 | fEps(1e-4), |
42 | fEpsMax(1e-2), |
43 | fNloopMax(100), |
44 | fBeta(0.1), |
45 | fNclustMax(0), |
9e4cc50d |
46 | fNin(0) |
7c679be0 |
47 | { |
48 | // Constructor |
49 | } |
50 | |
51 | //----------------------------------------------------------------------------------- |
52 | AliDAJetFinder::~AliDAJetFinder() |
53 | { |
54 | // Destructor |
7c679be0 |
55 | } |
56 | |
57 | //----------------------------------------------------------------------------------- |
58 | void AliDAJetFinder::FindJets() |
59 | { |
60 | // Find the jets in current event |
61 | // |
62 | Float_t betaStop=100.; |
63 | |
64 | Double_t dEtSum=0; |
36b5cc43 |
65 | Double_t *xData[2]; |
66 | TVectorD *vPx = new TVectorD(); |
67 | TVectorD *vPy = new TVectorD(); |
68 | TMatrixD *mPyx= new TMatrixD(); |
69 | TMatrixD *mY = new TMatrixD(); |
70 | InitDetAnn(dEtSum,xData,vPx,vPy,mPyx,mY); |
a9f9d6d5 |
71 | if (fNin < fNclustMax) return; |
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72 | |
36b5cc43 |
73 | Int_t nc=1, nk; |
74 | DoubleClusters(nc,nk,vPy,mY); |
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75 | do{ //loop over beta |
76 | fBeta*=fAlpha; |
36b5cc43 |
77 | Annealing(nk,xData,vPx,vPy,mPyx,mY); |
78 | NumCl(nc,nk,vPy,mPyx,mY); |
7c679be0 |
79 | }while((fBeta<betaStop || nc<4) && nc<fNclustMax); |
80 | |
81 | Int_t *xx=new Int_t[fNin]; |
36b5cc43 |
82 | EndDetAnn(nk,xData,xx,dEtSum,vPx,vPy,mPyx,mY); |
83 | StoreJets(nk,xData,xx,mY); |
7c679be0 |
84 | delete [] xx; |
85 | |
36b5cc43 |
86 | delete [] xData[0], delete [] xData[1]; |
87 | delete mPyx; |
88 | delete mY; |
89 | delete vPx; |
90 | delete vPy; |
91 | |
7c679be0 |
92 | } |
93 | |
94 | //----------------------------------------------------------------------------------- |
36b5cc43 |
95 | void AliDAJetFinder::InitDetAnn(Double_t &dEtSum,Double_t **xData,TVectorD *vPx,TVectorD *vPy,TMatrixD *mPyx,TMatrixD *mY) |
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96 | { |
97 | //Initialise the variables used by the algorithm |
98 | fBeta=0.1; |
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99 | fNclustMax= ((AliDAJetHeader*)fHeader)->GetFixedCl() ? |
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100 | ((AliDAJetHeader*)fHeader)->GetNclustMax() : |
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101 | TMath::Max((Int_t)TMath::Sqrt(fNin),5); |
e53baffe |
102 | TClonesArray *lvArray = fReader->GetMomentumArray(); |
103 | Int_t nEntr = lvArray->GetEntries(); |
104 | fNin=0; |
105 | for (Int_t iEn=0; iEn<nEntr; iEn++) if (fReader->GetCutFlag(iEn)==1) fNin++; |
36b5cc43 |
106 | Double_t *xEta = new Double_t[fNin]; |
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107 | Double_t *xPhi = new Double_t[fNin]; |
36b5cc43 |
108 | xData[0]=xEta; xData[1]=xPhi; |
109 | vPx->ResizeTo(fNin); |
e53baffe |
110 | Int_t iIn=0; |
111 | for (Int_t iEn=0; iEn<nEntr; iEn++){ |
112 | if (fReader->GetCutFlag(iEn)==0) continue; |
113 | TLorentzVector *lv=(TLorentzVector*)lvArray->At(iEn); |
36b5cc43 |
114 | xEta[iIn] = lv->Eta(); |
115 | xPhi[iIn] = lv->Phi()<0 ? lv->Phi() + 2*TMath::Pi() : lv->Phi(); |
116 | (*vPx)(iIn)=lv->Pt(); |
117 | dEtSum+=(*vPx)(iIn); |
e53baffe |
118 | iIn++; |
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119 | } |
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120 | for (iIn=0; iIn<fNin; iIn++) (*vPx)(iIn)=(*vPx)(iIn)/dEtSum; |
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121 | |
122 | Int_t njdim=2*fNclustMax+1; |
36b5cc43 |
123 | mPyx->ResizeTo(fNin,njdim); |
124 | mY->ResizeTo(4,njdim); |
125 | vPy->ResizeTo(njdim); |
126 | mY->Zero();mPyx->Zero();vPy->Zero(); |
127 | (*vPy)(0)=1; |
128 | TMatrixDColumn(*mPyx,0)=1; |
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129 | Double_t ypos=0,xpos=0; |
19b36dbe |
130 | for (iIn=0; iIn<fNin; iIn++){ |
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131 | (*mY)(0,0)+=(*vPx)(iIn)*xEta[iIn]; |
132 | ypos+=(*vPx)(iIn)*TMath::Sin(xPhi[iIn]); |
133 | xpos+=(*vPx)(iIn)*TMath::Cos(xPhi[iIn]); |
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134 | } |
36b5cc43 |
135 | (*mY)(1,0)=(atan2(ypos,xpos)>0) ? atan2(ypos,xpos) : atan2(ypos,xpos)+2*TMath::Pi(); |
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136 | } |
137 | |
138 | //----------------------------------------------------------------------------------- |
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139 | void AliDAJetFinder::DoubleClusters(Int_t nc,Int_t &nk, TVectorD *vPy, TMatrixD *mY) const |
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140 | { |
141 | for(Int_t iClust=0; iClust<nc; iClust++){ |
36b5cc43 |
142 | (*vPy)(iClust)=(*vPy)(iClust)/2; |
143 | (*vPy)(nc+iClust)=(*vPy)(iClust); |
144 | for(Int_t iComp=0; iComp<3; iComp++) (*mY)(iComp,nc+iClust)=(*mY)(iComp,iClust); |
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145 | } |
146 | nk=2*nc; |
147 | } |
148 | |
149 | //----------------------------------------------------------------------------------- |
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150 | void AliDAJetFinder::Annealing(Int_t nk,Double_t **xData, TVectorD *vPx, TVectorD *vPy, TMatrixD *mPyx, TMatrixD *mY) |
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151 | { |
152 | // Main part of the algorithm |
153 | const Double_t pi=TMath::Pi(); |
154 | TVectorD *py = new TVectorD(nk); |
155 | TVectorD *p = new TVectorD(nk); |
156 | TMatrixD *y = new TMatrixD(4,nk); |
157 | TMatrixD *y1 = new TMatrixD(4,nk); |
158 | TMatrixD *ry = new TMatrixD(2,nk); |
36b5cc43 |
159 | Double_t *xEta = xData[0]; |
160 | Double_t *xPhi = xData[1]; |
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161 | Double_t Dist(TVectorD,TVectorD); |
162 | |
36b5cc43 |
163 | Double_t df[2]={fReader->GetReaderHeader()->GetFiducialEtaMax(),pi}; |
7c679be0 |
164 | TVectorD vPart(2); |
165 | Double_t *m = new Double_t[nk]; |
166 | Double_t chi,chi1; |
167 | do{ |
168 | Int_t nloop=0; |
169 | for (Int_t iClust=0; iClust<nk; iClust++){ |
36b5cc43 |
170 | for (Int_t i=0; i<3; i++)(*y1)(i,iClust)=(*mY)(i,iClust); |
171 | (*py)(iClust)=(*vPy)(iClust); |
7c679be0 |
172 | } |
173 | //perturbation of codevectors |
174 | Double_t seed=1000000*gRandom->Rndm(24); |
175 | ry->Randomize(-0.5,0.5,seed); |
176 | for (Int_t i=0; i<2; i++){ |
177 | for (Int_t iClust=0; iClust<nk/2; iClust++) |
178 | (*y1)(i,iClust)+=((*ry)(i,iClust)+TMath::Sign(0.5,(*ry)(i,iClust)))*fDelta*df[i]; |
179 | for (Int_t iClust=nk/2; iClust<nk; iClust++) |
180 | (*y1)(i,iClust)-=((*ry)(i,iClust-nk/2)+TMath::Sign(0.5,(*ry)(i,iClust-nk/2)))*fDelta*df[i]; |
181 | } |
182 | do{ |
183 | //recalculate conditional probabilities |
184 | nloop++; |
185 | for (Int_t iIn=0; iIn<fNin; iIn++){ |
36b5cc43 |
186 | vPart(0)=xEta[iIn]; vPart(1)=xPhi[iIn]; |
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187 | for(Int_t iClust=0; iClust<nk; iClust++){ |
36b5cc43 |
188 | (*mPyx)(iIn,iClust)=-log((*py)(iClust))+fBeta*Dist(vPart,TMatrixDColumn(*y1,iClust)); |
189 | m[iClust]=(*mPyx)(iIn,iClust); |
7c679be0 |
190 | } |
191 | Double_t pyxNorm=0; |
192 | Double_t minPyx=TMath::MinElement(nk,m); |
193 | for (Int_t iClust=0; iClust<nk; iClust++){ |
36b5cc43 |
194 | (*mPyx)(iIn,iClust)-=minPyx; |
195 | (*mPyx)(iIn,iClust)=exp(-(*mPyx)(iIn,iClust)); |
196 | pyxNorm+=(*mPyx)(iIn,iClust); |
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197 | } |
36b5cc43 |
198 | for (Int_t iClust=0; iClust<nk; iClust++) (*mPyx)(iIn,iClust)/=pyxNorm; |
7c679be0 |
199 | } |
200 | p->Zero(); |
201 | y->Zero(); |
202 | //recalculate codevectors |
203 | for (Int_t iClust=0; iClust<nk; iClust++){ |
204 | Double_t xpos=0,ypos=0,pxy; |
36b5cc43 |
205 | for (Int_t iIn=0; iIn<fNin; iIn++) (*p)(iClust)+=(*vPx)(iIn)*(*mPyx)(iIn,iClust); |
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206 | for (Int_t iIn=0; iIn<fNin; iIn++){ |
36b5cc43 |
207 | pxy=(*vPx)(iIn)*(*mPyx)(iIn,iClust)/(*p)(iClust); |
208 | ypos+=pxy*TMath::Sin(xPhi[iIn]); |
209 | xpos+=pxy*TMath::Cos(xPhi[iIn]); |
210 | (*y)(0,iClust)+=pxy*xEta[iIn]; |
7c679be0 |
211 | } |
212 | (*y)(1,iClust)=(atan2(ypos,xpos)>0) ? atan2(ypos,xpos) : atan2(ypos,xpos)+2*pi; |
213 | } |
214 | //verify codevectors' stability |
215 | chi=0; |
216 | for (Int_t iClust=0; iClust<nk; iClust++){ |
217 | chi1=TMath::CosH((*y1)(0,iClust)-(*y)(0,iClust))-TMath::Cos((*y1)(1,iClust)-(*y)(1,iClust)); |
218 | chi1/=(2*TMath::CosH((*y1)(0,iClust))*TMath::CosH((*y)(0,iClust))); |
219 | chi1*=chi1; |
220 | if (chi1>chi) chi=chi1; |
221 | } |
222 | chi=TMath::Sqrt(chi); |
223 | for (Int_t iClust=0; iClust<nk; iClust++){ |
224 | for (Int_t i=0; i<2; i++) (*y1)(i,iClust)=(*y)(i,iClust); |
225 | (*py)(iClust)=(*p)(iClust); |
226 | } |
227 | if (nloop>fNloopMax){ |
228 | if (chi<fEpsMax || nloop>500) break; |
229 | } |
230 | }while (chi>fEps); |
231 | }while (chi>fEpsMax); |
232 | for (Int_t iClust=0; iClust<nk; iClust++){ //set codevectors and probability equal to those calculated |
36b5cc43 |
233 | for (Int_t i=0; i<2; i++) (*mY)(i,iClust)=(*y)(i,iClust); |
234 | (*vPy)(iClust)=(*p)(iClust); |
7c679be0 |
235 | } |
236 | delete py; |
237 | delete p; |
238 | delete y; |
239 | delete y1; |
240 | delete ry; |
c0a5117c |
241 | delete [] m; |
7c679be0 |
242 | } |
243 | |
244 | //----------------------------------------------------------------------------------- |
c0a5117c |
245 | void AliDAJetFinder::NumCl(Int_t &nc,Int_t &nk,TVectorD *vPy, TMatrixD *mPyx,TMatrixD *mY) |
7c679be0 |
246 | { |
247 | static Bool_t growcl=true; |
248 | |
249 | if (nk==2) growcl=true; |
250 | if (growcl){ |
251 | //verify if two codevectors are equal within fAvDist |
252 | Int_t *nSame = new Int_t[nk]; |
253 | Int_t **iSame = new Int_t*[nk]; |
254 | Int_t **cont = new Int_t*[nk]; |
255 | for (Int_t iClust=0; iClust<nk; iClust++) cont[iClust]=new Int_t[nk],iSame[iClust]=new Int_t[nk]; |
256 | for (Int_t iClust=0; iClust<nk; iClust++){ |
257 | iSame[iClust][iClust]=1; |
258 | for (Int_t iClust1=iClust+1; iClust1<nk; iClust1++){ |
36b5cc43 |
259 | Double_t eta = (*mY)(0,iClust) ; Double_t phi = (*mY)(1,iClust); |
260 | Double_t eta1 = (*mY)(0,iClust1); Double_t phi1 = (*mY)(1,iClust1); |
7c679be0 |
261 | Double_t distCl=(TMath::CosH(eta-eta1)-TMath::Cos(phi-phi1))/(2*TMath::CosH(eta)*TMath::CosH(eta1)); |
262 | if (distCl < fAvDist) iSame[iClust][iClust1]=iSame[iClust1][iClust]=1; |
852db00e |
263 | else iSame[iClust][iClust1]=iSame[iClust1][iClust]=0; |
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264 | } |
265 | } |
266 | ReduceClusters(iSame,nk,nc,cont,nSame); |
267 | if (nc >= fNclustMax) growcl=false; |
268 | //recalculate the nc distinct codevectors |
269 | TMatrixD *pyx = new TMatrixD(fNin,2*nc); |
270 | TVectorD *py = new TVectorD(nk); |
271 | TMatrixD *y1 = new TMatrixD(3,nk); |
272 | for (Int_t iClust=0; iClust<nc; iClust++){ |
273 | for(Int_t j=0; j<nSame[iClust]; j++){ |
274 | Int_t iClust1 = cont[iClust][j]; |
36b5cc43 |
275 | for (Int_t iIn=0; iIn<fNin; iIn++) (*pyx)(iIn,iClust)+=(*mPyx)(iIn,iClust1); |
276 | (*py)(iClust)+=(*vPy)(iClust1); |
277 | for (Int_t i=0; i<2; i++) (*y1)(i,iClust)+=(*mY)(i,iClust1); |
7c679be0 |
278 | } |
279 | for (Int_t i=0; i<2; i++) (*y1)(i,iClust)/=nSame[iClust]; |
280 | } |
852db00e |
281 | for (Int_t iClust=0; iClust<nk; iClust++) delete [] cont[iClust], delete [] iSame[iClust]; |
282 | delete [] iSame; |
283 | delete [] cont; |
284 | delete [] nSame; |
7c679be0 |
285 | if (nc > nk/2){ |
286 | for (Int_t iClust=0; iClust<nc; iClust++){ |
36b5cc43 |
287 | for (Int_t iIn=0; iIn<fNin; iIn++) (*mPyx)(iIn,iClust)=(*pyx)(iIn,iClust); |
288 | for (Int_t iComp=0; iComp<2; iComp++) (*mY)(iComp,iClust)=(*y1)(iComp,iClust); |
289 | (*vPy)(iClust)=(*py)(iClust); |
7c679be0 |
290 | } |
291 | nk=nc; |
36b5cc43 |
292 | if (growcl) DoubleClusters(nc,nk,vPy,mY); |
7c679be0 |
293 | } |
7c679be0 |
294 | delete pyx; |
295 | delete py; |
296 | delete y1; |
297 | } |
298 | |
299 | } |
300 | |
301 | //----------------------------------------------------------------------------------- |
c0a5117c |
302 | void AliDAJetFinder::ReduceClusters(Int_t **iSame,Int_t nc,Int_t &ncout,Int_t **cont,Int_t *nSameOut) const |
7c679be0 |
303 | { |
304 | Int_t *nSame = new Int_t[nc]; |
305 | Int_t *iperm = new Int_t[nc]; |
306 | Int_t *go = new Int_t[nc]; |
307 | for (Int_t iCl=0; iCl<nc; iCl++){ |
308 | nSame[iCl]=0; |
852db00e |
309 | for (Int_t jCl=0; jCl<nc; jCl++) nSame[iCl]+=iSame[iCl][jCl], cont[iCl][jCl]=0; |
7c679be0 |
310 | iperm[iCl]=iCl; |
311 | go[iCl]=1; |
312 | } |
313 | TMath::Sort(nc,nSame,iperm,true); |
314 | Int_t l=0; |
315 | for (Int_t iCl=0; iCl<nc; iCl++){ |
316 | Int_t k=iperm[iCl]; |
317 | if (go[k] == 1){ |
318 | Int_t m=0; |
319 | for (Int_t jCl=0; jCl<nc; jCl++){ |
320 | if (iSame[k][jCl] == 1){ |
321 | cont[l][m]=jCl; |
322 | go[jCl]=0; |
323 | m++; |
324 | } |
325 | } |
326 | nSameOut[l]=m; |
327 | l++; |
328 | } |
329 | } |
330 | ncout=l; |
331 | delete [] nSame; |
332 | delete [] iperm; |
333 | delete [] go; |
334 | } |
335 | |
336 | //----------------------------------------------------------------------------------- |
36b5cc43 |
337 | void AliDAJetFinder::EndDetAnn(Int_t &nk,Double_t **xData,Int_t *xx,Double_t etx, |
338 | TVectorD *vPx,TVectorD *vPy,TMatrixD *mPyx,TMatrixD *mY) |
7c679be0 |
339 | { |
340 | //now assign each particle to only one cluster |
341 | Double_t *clusters=new Double_t[nk]; |
342 | for (Int_t iIn=0; iIn<fNin; iIn++){ |
36b5cc43 |
343 | for (Int_t iClust=0; iClust<nk; iClust++) clusters[iClust]=(*mPyx)(iIn,iClust); |
7c679be0 |
344 | xx[iIn]=TMath::LocMax(nk,clusters); |
345 | } |
346 | delete [] clusters; |
347 | |
348 | //recalculate codevectors, having all p(y|x)=0 or 1 |
36b5cc43 |
349 | Double_t *xEta = xData[0]; |
350 | Double_t *xPhi = xData[1]; |
351 | mY->Zero(); |
352 | mPyx->Zero(); |
353 | vPy->Zero(); |
7c679be0 |
354 | for (Int_t iIn=0; iIn<fNin; iIn++){ |
355 | Int_t iClust=xx[iIn]; |
36b5cc43 |
356 | (*mPyx)(iIn,iClust)=1; |
357 | (*vPy)(iClust)+=(*vPx)(iIn); |
358 | (*mY)(0,iClust)+=(*vPx)(iIn)*xEta[iIn]; |
359 | (*mY)(3,iClust)+=(*vPx)(iIn)*etx; |
7c679be0 |
360 | } |
361 | Int_t k=0; |
362 | for (Int_t iClust=0; iClust<nk; iClust++){ |
36b5cc43 |
363 | if ((*vPy)(iClust)>0){ |
7c679be0 |
364 | Double_t xpos=0,ypos=0,pxy; |
365 | for (Int_t iIn=0; iIn<fNin; iIn++){ |
36b5cc43 |
366 | pxy=(*vPx)(iIn)*(*mPyx)(iIn,iClust)/(*vPy)(iClust); |
367 | ypos+=pxy*TMath::Sin(xPhi[iIn]); |
368 | xpos+=pxy*TMath::Cos(xPhi[iIn]); |
7c679be0 |
369 | if (xx[iIn]==iClust) xx[iIn]=k; |
370 | } |
36b5cc43 |
371 | (*mY)(0,k)=(*mY)(0,iClust)/(*vPy)(iClust); |
372 | (*mY)(1,k)=(atan2(ypos,xpos)>0) ? atan2(ypos,xpos) : atan2(ypos,xpos)+2*TMath::Pi(); |
373 | (*mY)(3,k)=(*mY)(3,iClust); |
7c679be0 |
374 | k++; |
375 | } |
376 | } |
377 | nk=k; |
378 | } |
379 | |
380 | //----------------------------------------------------------------------------------- |
c0a5117c |
381 | void AliDAJetFinder::StoreJets(Int_t nk,Double_t **xData, Int_t *xx, TMatrixD *mY) |
7c679be0 |
382 | { |
383 | //evaluate significant clusters properties |
384 | const Double_t pi=TMath::Pi(); |
36b5cc43 |
385 | AliJetReaderHeader *rHeader=fReader->GetReaderHeader(); |
386 | Float_t dFiducialEta=rHeader->GetFiducialEtaMax()-rHeader->GetFiducialEtaMin(); |
387 | Double_t dMeanDist=TMath::Sqrt(2*dFiducialEta*pi/fNin); |
388 | Double_t *xEta = xData[0]; |
389 | Double_t *xPhi = xData[1]; |
7c679be0 |
390 | Bool_t *isJet = new Bool_t[nk]; |
391 | Double_t *etNoBg= new Double_t[nk]; |
392 | Double_t *dDeltaEta=new Double_t[nk]; |
393 | Double_t *dDeltaPhi=new Double_t[nk]; |
394 | Double_t *surf = new Double_t[nk]; |
395 | Double_t *etDens= new Double_t[nk]; |
396 | for (Int_t iClust=0; iClust<nk; iClust++){ //clusters loop |
397 | isJet[iClust]=false; |
398 | Double_t dEtaMin=10.,dEtaMax=-10.,dPhiMin=10.,dPhiMax=0.; |
399 | for (Int_t iIn=0; iIn<fNin; iIn++){ |
400 | if (xx[iIn]!=iClust) continue; |
36b5cc43 |
401 | if (xEta[iIn] < dEtaMin) dEtaMin=xEta[iIn]; |
402 | if (xEta[iIn] > dEtaMax) dEtaMax=xEta[iIn]; |
403 | Double_t dPhi=xPhi[iIn]-(*mY)(1,iClust); |
7c679be0 |
404 | if (dPhi > pi ) dPhi-=2*pi; |
405 | else if (dPhi < (-1)*pi) dPhi+=2*pi; |
406 | if (dPhi < dPhiMin) dPhiMin=dPhi; |
407 | else if (dPhi > dPhiMax) dPhiMax=dPhi; |
408 | } |
409 | dDeltaEta[iClust]=dEtaMax-dEtaMin+dMeanDist; |
410 | dDeltaPhi[iClust]=dPhiMax-dPhiMin+dMeanDist; |
411 | surf[iClust]=0.25*pi*dDeltaEta[iClust]*dDeltaPhi[iClust]; |
36b5cc43 |
412 | etDens[iClust]=(*mY)(3,iClust)/surf[iClust]; |
7c679be0 |
413 | } |
414 | |
9e4cc50d |
415 | if (((AliDAJetHeader*)fHeader)->GetSelJets()){ |
7c679be0 |
416 | for (Int_t iClust=0; iClust<nk; iClust++){ |
417 | if (!isJet[iClust]){ |
418 | Double_t etDensMed=0.; |
419 | Double_t etDensSqr=0.; |
420 | Int_t norm=0; |
421 | for (Int_t iClust1=0; iClust1<nk; iClust1++){ |
422 | if(iClust1!=iClust){ |
423 | etDensMed+=etDens[iClust1]; |
424 | etDensSqr+=TMath::Power(etDens[iClust1],2); |
425 | norm++; |
426 | } |
427 | } |
428 | etDensMed/=TMath::Max(norm,1); |
429 | etDensSqr/=TMath::Max(norm,1); |
430 | Double_t deltaEtDens=TMath::Sqrt(etDensSqr-TMath::Power(etDensMed,2)); |
36b5cc43 |
431 | if ((*mY)(3,iClust) > (etDensMed+deltaEtDens)*surf[iClust]) isJet[iClust]=kTRUE; |
432 | etNoBg[iClust]=(*mY)(3,iClust)-etDensMed*surf[iClust]; |
7c679be0 |
433 | } |
434 | } |
435 | for (Int_t iClust=0; iClust<nk; iClust++){ |
436 | if (isJet[iClust]){ |
437 | Double_t etDensMed=0; |
36b5cc43 |
438 | Double_t extSurf=2*dFiducialEta*pi; |
7c679be0 |
439 | for (Int_t iClust1=0; iClust1<nk; iClust1++){ |
36b5cc43 |
440 | if (!isJet[iClust1]) etDensMed+=(*mY)(3,iClust1); |
7c679be0 |
441 | else extSurf-=surf[iClust1]; |
442 | } |
443 | etDensMed/=extSurf; |
36b5cc43 |
444 | etNoBg[iClust]=(*mY)(3,iClust)-etDensMed*surf[iClust]; |
10bd125d |
445 | if (etNoBg[iClust]<((AliDAJetHeader*)fHeader)->GetEtMin()){ |
446 | isJet[iClust]=kFALSE; |
447 | iClust=-1; |
448 | } |
7c679be0 |
449 | } |
450 | } |
451 | } else { |
452 | for (Int_t iClust=0; iClust<nk; iClust++) isJet[iClust]=true; |
453 | } |
454 | delete [] etDens; |
455 | delete [] surf; |
456 | |
457 | //now add selected jets to the list |
458 | Int_t *inJet = new Int_t[fNin]; |
459 | TRefArray *refs = 0; |
460 | Bool_t fromAod = !strcmp(fReader->ClassName(),"AliJetAODReader"); |
461 | if (fromAod) refs = fReader->GetReferences(); |
462 | for (Int_t iClust=0; iClust<nk; iClust++){ //clusters loop |
463 | if (isJet[iClust]){ //choose cluster |
464 | Float_t px,py,pz,en; |
36b5cc43 |
465 | px = (*mY)(3,iClust)*TMath::Cos((*mY)(1,iClust)); |
466 | py = (*mY)(3,iClust)*TMath::Sin((*mY)(1,iClust)); |
467 | pz = (*mY)(3,iClust)/TMath::Tan(2.0 * TMath::ATan(TMath::Exp(-(*mY)(0,iClust)))); |
7c679be0 |
468 | en = TMath::Sqrt(px * px + py * py + pz * pz); |
469 | AliAODJet jet(px, py, pz, en); |
e53baffe |
470 | if (fromAod){ |
471 | Int_t iIn=0; |
472 | Int_t nEntr = fReader->GetMomentumArray()->GetEntries(); |
473 | for (Int_t iEn=0; iEn<nEntr; iEn++){ |
474 | if (fReader->GetCutFlag(iEn)==0) continue; |
475 | if (xx[iIn]==iClust) jet.AddTrack(refs->At(iEn)); |
476 | iIn++; |
477 | } |
478 | } |
7c679be0 |
479 | AddJet(jet); |
7c679be0 |
480 | } |
481 | } |
482 | delete [] dDeltaEta; delete [] dDeltaPhi; |
483 | delete [] etNoBg; |
484 | delete [] isJet; |
485 | delete [] inJet; |
486 | } |
487 | |
488 | //----------------------------------------------------------------------------------- |
489 | Double_t Dist(TVectorD x,TVectorD y) |
490 | { |
491 | // Squared distance |
492 | const Double_t pi=TMath::Pi(); |
493 | Double_t dphi=TMath::Abs(x(1)-y(1)); |
494 | if (dphi > pi) dphi=2*pi-dphi; |
495 | Double_t dist=pow(x(0)-y(0),2)+pow(dphi,2); |
496 | return dist; |
497 | } |