adding nsigma study
[u/mrichter/AliRoot.git] / PWG0 / esdTrackCuts / AliESDtrackCuts.cxx
CommitLineData
0b75bef2 1#include "AliESDtrackCuts.h"
2
aa93c798 3
4#include <AliESDtrack.h>
5#include <AliESD.h>
6#include <AliLog.h>
7
0b75bef2 8//____________________________________________________________________
f58f1a93 9ClassImp(AliESDtrackCuts)
0b75bef2 10
ebd60d8e 11// Cut names
ab2c1f0d 12const Char_t* AliESDtrackCuts::fgkCutNames[kNCuts] = {
ebd60d8e 13 "require TPC refit",
14 "require ITS refit",
15 "n clusters TPC",
16 "n clusters ITS",
17 "#Chi^{2}/clusters TPC",
18 "#Chi^{2}/clusters ITS",
19 "cov 11",
20 "cov 22",
21 "cov 33",
22 "cov 44",
23 "cov 55",
24 "trk-to-vtx",
25 "trk-to-vtx failed",
26 "kink daughters",
27
28 "p",
29 "p_{T}",
30 "p_{x}",
31 "p_{y}",
32 "p_{z}",
33 "y",
34 "eta"
35};
36
0b75bef2 37//____________________________________________________________________
ebd60d8e 38AliESDtrackCuts::AliESDtrackCuts()
39{
40 //
41 // constructor
42 //
43
44 Init();
0b75bef2 45
46 //##############################################################################
47 // setting default cuts
0b75bef2 48 SetMinNClustersTPC();
ebd60d8e 49 SetMinNClustersITS();
0b75bef2 50 SetMaxChi2PerClusterTPC();
51 SetMaxChi2PerClusterITS();
52 SetMaxCovDiagonalElements();
53 SetRequireTPCRefit();
54 SetRequireITSRefit();
55 SetAcceptKingDaughters();
56 SetMinNsigmaToVertex();
57 SetRequireSigmaToVertex();
58 SetPRange();
59 SetPtRange();
60 SetPxRange();
61 SetPyRange();
62 SetPzRange();
63 SetEtaRange();
64 SetRapRange();
65
66 SetHistogramsOn();
ebd60d8e 67}
68
69//_____________________________________________________________________________
70AliESDtrackCuts::AliESDtrackCuts(const AliESDtrackCuts &c) : TObject(c)
71{
72 //
73 // copy constructor
74 //
75
76 ((AliESDtrackCuts &) c).Copy(*this);
77}
78
79AliESDtrackCuts::~AliESDtrackCuts()
80{
81 //
82 // destructor
83 //
84
85 // ## TODO to be implemented
86}
87
88void AliESDtrackCuts::Init()
89{
90 //
91 // sets everything to zero
92 //
93
ab2c1f0d 94 fCutMinNClusterTPC = 0;
95 fCutMinNClusterITS = 0;
ebd60d8e 96
ab2c1f0d 97 fCutMaxChi2PerClusterTPC = 0;
98 fCutMaxChi2PerClusterITS = 0;
ebd60d8e 99
ab2c1f0d 100 fCutMaxC11 = 0;
101 fCutMaxC22 = 0;
102 fCutMaxC33 = 0;
103 fCutMaxC44 = 0;
104 fCutMaxC55 = 0;
ebd60d8e 105
ab2c1f0d 106 fCutAcceptKinkDaughters = 0;
107 fCutRequireTPCRefit = 0;
108 fCutRequireITSRefit = 0;
ebd60d8e 109
ab2c1f0d 110 fCutNsigmaToVertex = 0;
111 fCutSigmaToVertexRequired = 0;
ebd60d8e 112
113 fPMin = 0;
114 fPMax = 0;
115 fPtMin = 0;
116 fPtMax = 0;
117 fPxMin = 0;
118 fPxMax = 0;
119 fPyMin = 0;
120 fPyMax = 0;
121 fPzMin = 0;
122 fPzMax = 0;
123 fEtaMin = 0;
124 fEtaMax = 0;
125 fRapMin = 0;
126 fRapMax = 0;
127
128 fHistogramsOn = kFALSE;
129
130 for (Int_t i=0; i<2; ++i)
131 {
132 fhNClustersITS[i] = 0;
133 fhNClustersTPC[i] = 0;
134
135 fhChi2PerClusterITS[i] = 0;
136 fhChi2PerClusterTPC[i] = 0;
137
138 fhC11[i] = 0;
139 fhC22[i] = 0;
140 fhC33[i] = 0;
141 fhC44[i] = 0;
142 fhC55[i] = 0;
143
144 fhDXY[i] = 0;
145 fhDZ[i] = 0;
146 fhDXYvsDZ[i] = 0;
147
148 fhDXYNormalized[i] = 0;
149 fhDZNormalized[i] = 0;
150 fhDXYvsDZNormalized[i] = 0;
151 }
152
153 fhCutStatistics = 0;
154 fhCutCorrelation = 0;
155}
156
157//_____________________________________________________________________________
158AliESDtrackCuts &AliESDtrackCuts::operator=(const AliESDtrackCuts &c)
159{
160 //
161 // Assignment operator
162 //
0b75bef2 163
ebd60d8e 164 if (this != &c) ((AliESDtrackCuts &) c).Copy(*this);
165 return *this;
166}
167
168//_____________________________________________________________________________
169void AliESDtrackCuts::Copy(TObject &c) const
170{
171 //
172 // Copy function
173 //
174
175 AliESDtrackCuts& target = (AliESDtrackCuts &) c;
176
177 target.Init();
178
ab2c1f0d 179 target.fCutMinNClusterTPC = fCutMinNClusterTPC;
180 target.fCutMinNClusterITS = fCutMinNClusterITS;
ebd60d8e 181
ab2c1f0d 182 target.fCutMaxChi2PerClusterTPC = fCutMaxChi2PerClusterTPC;
183 target.fCutMaxChi2PerClusterITS = fCutMaxChi2PerClusterITS;
ebd60d8e 184
ab2c1f0d 185 target.fCutMaxC11 = fCutMaxC11;
186 target.fCutMaxC22 = fCutMaxC22;
187 target.fCutMaxC33 = fCutMaxC33;
188 target.fCutMaxC44 = fCutMaxC44;
189 target.fCutMaxC55 = fCutMaxC55;
ebd60d8e 190
ab2c1f0d 191 target.fCutAcceptKinkDaughters = fCutAcceptKinkDaughters;
192 target.fCutRequireTPCRefit = fCutRequireTPCRefit;
193 target.fCutRequireITSRefit = fCutRequireITSRefit;
ebd60d8e 194
ab2c1f0d 195 target.fCutNsigmaToVertex = fCutNsigmaToVertex;
196 target.fCutSigmaToVertexRequired = fCutSigmaToVertexRequired;
ebd60d8e 197
198 target.fPMin = fPMin;
199 target.fPMax = fPMax;
200 target.fPtMin = fPtMin;
201 target.fPtMax = fPtMax;
202 target.fPxMin = fPxMin;
203 target.fPxMax = fPxMax;
204 target.fPyMin = fPyMin;
205 target.fPyMax = fPyMax;
206 target.fPzMin = fPzMin;
207 target.fPzMax = fPzMax;
208 target.fEtaMin = fEtaMin;
209 target.fEtaMax = fEtaMax;
210 target.fRapMin = fRapMin;
211 target.fRapMax = fRapMax;
212
213 target.fHistogramsOn = fHistogramsOn;
214
215 for (Int_t i=0; i<2; ++i)
216 {
217 if (fhNClustersITS[i]) target.fhNClustersITS[i] = (TH1F*) fhNClustersITS[i]->Clone();
218 if (fhNClustersTPC[i]) target.fhNClustersTPC[i] = (TH1F*) fhNClustersTPC[i]->Clone();
219
220 if (fhChi2PerClusterITS[i]) target.fhChi2PerClusterITS[i] = (TH1F*) fhChi2PerClusterITS[i]->Clone();
221 if (fhChi2PerClusterTPC[i]) target.fhChi2PerClusterTPC[i] = (TH1F*) fhChi2PerClusterTPC[i]->Clone();
222
223 if (fhC11[i]) target.fhC11[i] = (TH1F*) fhC11[i]->Clone();
224 if (fhC22[i]) target.fhC22[i] = (TH1F*) fhC22[i]->Clone();
225 if (fhC33[i]) target.fhC33[i] = (TH1F*) fhC33[i]->Clone();
226 if (fhC44[i]) target.fhC44[i] = (TH1F*) fhC44[i]->Clone();
227 if (fhC55[i]) target.fhC55[i] = (TH1F*) fhC55[i]->Clone();
228
229 if (fhDXY[i]) target.fhDXY[i] = (TH1F*) fhDXY[i]->Clone();
230 if (fhDZ[i]) target.fhDZ[i] = (TH1F*) fhDZ[i]->Clone();
231 if (fhDXYvsDZ[i]) target.fhDXYvsDZ[i] = (TH2F*) fhDXYvsDZ[i]->Clone();
232
233 if (fhDXYNormalized[i]) target.fhDXYNormalized[i] = (TH1F*) fhDXYNormalized[i]->Clone();
234 if (fhDZNormalized[i]) target.fhDZNormalized[i] = (TH1F*) fhDZNormalized[i]->Clone();
235 if (fhDXYvsDZNormalized[i]) target.fhDXYvsDZNormalized[i] = (TH2F*) fhDXYvsDZNormalized[i]->Clone();
236 }
237
238 if (fhCutStatistics) target.fhCutStatistics = (TH1F*) fhCutStatistics->Clone();
239 if (fhCutCorrelation) target.fhCutCorrelation = (TH2F*) fhCutCorrelation->Clone();
0b75bef2 240
ebd60d8e 241 TObject::Copy(c);
0b75bef2 242}
243
0b75bef2 244//____________________________________________________________________
245Bool_t
246AliESDtrackCuts::AcceptTrack(AliESDtrack* esdTrack) {
247 //
248 // figure out if the tracks survives all the track cuts defined
249 //
ab2c1f0d 250 // the different quality parameter and kinematic values are first
251 // retrieved from the track. then it is found out what cuts the
252 // track did not survive and finally the cuts are imposed.
0b75bef2 253
1ae6c515 254
255
0b75bef2 256 UInt_t status = esdTrack->GetStatus();
ebd60d8e 257
258 // dummy array
259 Int_t fIdxInt[200];
260
0b75bef2 261 // getting quality parameters from the ESD track
262 Int_t nClustersITS = esdTrack->GetITSclusters(fIdxInt);
263 Int_t nClustersTPC = esdTrack->GetTPCclusters(fIdxInt);
264
1ae6c515 265
266
0b75bef2 267 Float_t chi2PerClusterITS = -1;
268 Float_t chi2PerClusterTPC = -1;
269 if (nClustersITS!=0)
270 chi2PerClusterITS = esdTrack->GetITSchi2()/Float_t(nClustersITS);
271 if (nClustersTPC!=0)
272 chi2PerClusterTPC = esdTrack->GetTPCchi2()/Float_t(nClustersTPC);
273
274 Double_t extCov[15];
ebd60d8e 275 esdTrack->GetExternalCovariance(extCov);
0b75bef2 276
277 // getting the track to vertex parameters
278 Float_t b[2];
279 Float_t bRes[2];
280 Float_t bCov[3];
281 esdTrack->GetImpactParameters(b,bCov);
282 if (bCov[0]<=0 || bCov[2]<=0) {
1ae6c515 283 AliDebug(1, "Estimated b resolution lower or equal zero!");
284 bCov[0]=0; bCov[2]=0;
285 }
0b75bef2 286 bRes[0] = TMath::Sqrt(bCov[0]);
287 bRes[1] = TMath::Sqrt(bCov[2]);
288
aa93c798 289 // -----------------------------------
290 // How to get to a n-sigma cut?
291 //
292 // The accumulated statistics from 0 to d is
293 //
294 // -> Erf(d/Sqrt(2)) for a 1-dim gauss (d = n_sigma)
295 // -> 1 - Exp(-d**2) for a 2-dim gauss (d*d = dx*dx + dy*dy != n_sigma)
296 //
297 // It means that for a 2-dim gauss: n_sigma(d) = Sqrt(2)*ErfInv(1 - Exp((-x**2)/2)
298 // Can this be expressed in a different way?
0b75bef2 299 //
e712d815 300 //
301 // FIX: I don't think this is correct!!! Keeping d as n_sigma for now...
302
0b75bef2 303 Float_t nSigmaToVertex = -1;
aa93c798 304 if (bRes[0]!=0 && bRes[1]!=0) {
305 Float_t d = TMath::Sqrt(TMath::Power(b[0]/bRes[0],2) + TMath::Power(b[1]/bRes[1],2));
10ebe68d 306 nSigmaToVertex = d;
307
308 // JF solution:
309 //nSigmaToVertex = TMath::ErfInverse(TMath::Sqrt(2.0/TMath::Pi()) * TMath::Erf(d / TMath::Sqrt(2))) * TMath::Sqrt(2);
310 // Claus solution:
311 //nSigmaToVertex = TMath::Sqrt(2)*(TMath::ErfInverse(1 - TMath::Exp(0.5*(-d*d))));
aa93c798 312 }
0b75bef2 313
314 // getting the kinematic variables of the track
315 // (assuming the mass is known)
316 Double_t p[3];
317 esdTrack->GetPxPyPz(p);
318 Float_t momentum = TMath::Sqrt(TMath::Power(p[0],2) + TMath::Power(p[1],2) + TMath::Power(p[2],2));
319 Float_t pt = TMath::Sqrt(TMath::Power(p[0],2) + TMath::Power(p[1],2));
320 Float_t energy = TMath::Sqrt(TMath::Power(esdTrack->GetMass(),2) + TMath::Power(momentum,2));
321
1ae6c515 322
0b75bef2 323 //y-eta related calculations
324 Float_t eta = -100.;
325 Float_t y = -100.;
326 if((momentum != TMath::Abs(p[2]))&&(momentum != 0))
327 eta = 0.5*TMath::Log((momentum + p[2])/(momentum - p[2]));
328 if((energy != TMath::Abs(p[2]))&&(momentum != 0))
329 y = 0.5*TMath::Log((energy + p[2])/(energy - p[2]));
330
331
332 //########################################################################
333 // cut the track?
334
ebd60d8e 335 Bool_t cuts[kNCuts];
336 for (Int_t i=0; i<kNCuts; i++) cuts[i]=kFALSE;
0b75bef2 337
338 // track quality cuts
ab2c1f0d 339 if (fCutRequireTPCRefit && (status&AliESDtrack::kTPCrefit)==0)
0b75bef2 340 cuts[0]=kTRUE;
ab2c1f0d 341 if (fCutRequireITSRefit && (status&AliESDtrack::kITSrefit)==0)
0b75bef2 342 cuts[1]=kTRUE;
ab2c1f0d 343 if (nClustersTPC<fCutMinNClusterTPC)
0b75bef2 344 cuts[2]=kTRUE;
ab2c1f0d 345 if (nClustersITS<fCutMinNClusterITS)
0b75bef2 346 cuts[3]=kTRUE;
ab2c1f0d 347 if (chi2PerClusterTPC>fCutMaxChi2PerClusterTPC)
0b75bef2 348 cuts[4]=kTRUE;
ab2c1f0d 349 if (chi2PerClusterITS>fCutMaxChi2PerClusterITS)
0b75bef2 350 cuts[5]=kTRUE;
ab2c1f0d 351 if (extCov[0] > fCutMaxC11)
0b75bef2 352 cuts[6]=kTRUE;
ab2c1f0d 353 if (extCov[2] > fCutMaxC22)
0b75bef2 354 cuts[7]=kTRUE;
ab2c1f0d 355 if (extCov[5] > fCutMaxC33)
0b75bef2 356 cuts[8]=kTRUE;
ab2c1f0d 357 if (extCov[9] > fCutMaxC44)
0b75bef2 358 cuts[9]=kTRUE;
ab2c1f0d 359 if (extCov[14] > fCutMaxC55)
0b75bef2 360 cuts[10]=kTRUE;
ab2c1f0d 361 if (nSigmaToVertex > fCutNsigmaToVertex)
0b75bef2 362 cuts[11] = kTRUE;
363 // if n sigma could not be calculated
10ebe68d 364 if (nSigmaToVertex<0 && fCutSigmaToVertexRequired)
0b75bef2 365 cuts[12]=kTRUE;
ab2c1f0d 366 if (!fCutAcceptKinkDaughters && esdTrack->GetKinkIndex(0)>0)
0b75bef2 367 cuts[13]=kTRUE;
368 // track kinematics cut
369 if((momentum < fPMin) || (momentum > fPMax))
370 cuts[14]=kTRUE;
371 if((pt < fPtMin) || (pt > fPtMax))
372 cuts[15] = kTRUE;
373 if((p[0] < fPxMin) || (p[0] > fPxMax))
374 cuts[16] = kTRUE;
375 if((p[1] < fPyMin) || (p[1] > fPyMax))
376 cuts[17] = kTRUE;
377 if((p[2] < fPzMin) || (p[2] > fPzMax))
378 cuts[18] = kTRUE;
379 if((eta < fEtaMin) || (eta > fEtaMax))
380 cuts[19] = kTRUE;
381 if((y < fRapMin) || (y > fRapMax))
382 cuts[20] = kTRUE;
383
384 Bool_t cut=kFALSE;
ebd60d8e 385 for (Int_t i=0; i<kNCuts; i++)
0b75bef2 386 if (cuts[i]) cut = kTRUE;
387
388 //########################################################################
389 // filling histograms
390 if (fHistogramsOn) {
f58f1a93 391 fhCutStatistics->Fill(fhCutStatistics->GetBinCenter(fhCutStatistics->GetXaxis()->FindBin("n tracks")));
0b75bef2 392
393 if (cut)
f58f1a93 394 fhCutStatistics->Fill(fhCutStatistics->GetBinCenter(fhCutStatistics->GetXaxis()->FindBin("n cut tracks")));
0b75bef2 395
ebd60d8e 396 for (Int_t i=0; i<kNCuts; i++) {
0b75bef2 397 if (cuts[i])
ab2c1f0d 398 fhCutStatistics->Fill(fhCutStatistics->GetBinCenter(fhCutStatistics->GetXaxis()->FindBin(fgkCutNames[i])));
0b75bef2 399
ebd60d8e 400 for (Int_t j=i; j<kNCuts; j++) {
0b75bef2 401 if (cuts[i] && cuts[j]) {
ab2c1f0d 402 Float_t x = fhCutCorrelation->GetXaxis()->GetBinCenter(fhCutCorrelation->GetXaxis()->FindBin(fgkCutNames[i]));
403 Float_t y = fhCutCorrelation->GetYaxis()->GetBinCenter(fhCutCorrelation->GetYaxis()->FindBin(fgkCutNames[j]));
f58f1a93 404 fhCutCorrelation->Fill(x,y);
0b75bef2 405 }
406 }
407 }
408
409
f58f1a93 410 fhNClustersITS[0]->Fill(nClustersITS);
411 fhNClustersTPC[0]->Fill(nClustersTPC);
412 fhChi2PerClusterITS[0]->Fill(chi2PerClusterITS);
413 fhChi2PerClusterTPC[0]->Fill(chi2PerClusterTPC);
0b75bef2 414
f58f1a93 415 fhC11[0]->Fill(extCov[0]);
416 fhC22[0]->Fill(extCov[2]);
417 fhC33[0]->Fill(extCov[5]);
418 fhC44[0]->Fill(extCov[9]);
419 fhC55[0]->Fill(extCov[14]);
0b75bef2 420
f58f1a93 421 fhDZ[0]->Fill(b[1]);
422 fhDXY[0]->Fill(b[0]);
423 fhDXYvsDZ[0]->Fill(b[1],b[0]);
0b75bef2 424
425 if (bRes[0]!=0 && bRes[1]!=0) {
10ebe68d 426 fhDZNormalized[0]->Fill(b[1]/bRes[1]);
f58f1a93 427 fhDXYNormalized[0]->Fill(b[0]/bRes[0]);
428 fhDXYvsDZNormalized[0]->Fill(b[1]/bRes[1], b[0]/bRes[0]);
0b75bef2 429 }
430 }
431
432 //########################################################################
433 // cut the track!
434 if (cut) return kFALSE;
435
436 //########################################################################
437 // filling histograms after cut
438 if (fHistogramsOn) {
f58f1a93 439 fhNClustersITS[1]->Fill(nClustersITS);
440 fhNClustersTPC[1]->Fill(nClustersTPC);
441 fhChi2PerClusterITS[1]->Fill(chi2PerClusterITS);
442 fhChi2PerClusterTPC[1]->Fill(chi2PerClusterTPC);
0b75bef2 443
f58f1a93 444 fhC11[1]->Fill(extCov[0]);
445 fhC22[1]->Fill(extCov[2]);
10ebe68d 446 fhC33[1]->Fill(extCov[5]);
f58f1a93 447 fhC44[1]->Fill(extCov[9]);
448 fhC55[1]->Fill(extCov[14]);
0b75bef2 449
f58f1a93 450 fhDZ[1]->Fill(b[1]);
451 fhDXY[1]->Fill(b[0]);
452 fhDXYvsDZ[1]->Fill(b[1],b[0]);
0b75bef2 453
10ebe68d 454 if (bRes[0]!=0 && bRes[1]!=0)
455 {
456 fhDZNormalized[1]->Fill(b[1]/bRes[1]);
457 fhDXYNormalized[1]->Fill(b[0]/bRes[0]);
458 fhDXYvsDZNormalized[1]->Fill(b[1]/bRes[1], b[0]/bRes[0]);
459 }
0b75bef2 460 }
461
462 return kTRUE;
463}
464
465//____________________________________________________________________
466TObjArray*
ebd60d8e 467AliESDtrackCuts::GetAcceptedTracks(AliESD* esd)
468{
469 //
0b75bef2 470 // returns an array of all tracks that pass the cuts
ebd60d8e 471 //
472
473 TObjArray* acceptedTracks = new TObjArray();
c5858666 474
0b75bef2 475 // loop over esd tracks
476 for (Int_t iTrack = 0; iTrack < esd->GetNumberOfTracks(); iTrack++) {
477 AliESDtrack* track = esd->GetTrack(iTrack);
c5858666 478
ebd60d8e 479 if (AcceptTrack(track))
480 acceptedTracks->Add(track);
0b75bef2 481 }
482
ebd60d8e 483 return acceptedTracks;
0b75bef2 484}
485
c5858666 486//____________________________________________________________________
487Int_t
488AliESDtrackCuts::CountAcceptedTracks(AliESD* esd)
489{
490 //
491 // returns an the number of tracks that pass the cuts
492 //
493
494 Int_t count = 0;
495
496 // loop over esd tracks
497 for (Int_t iTrack = 0; iTrack < esd->GetNumberOfTracks(); iTrack++) {
498 AliESDtrack* track = esd->GetTrack(iTrack);
499
500 if (AcceptTrack(track))
501 count++;
502 }
503
504 return count;
505}
506
0b75bef2 507//____________________________________________________________________
f58f1a93 508 void AliESDtrackCuts::DefineHistograms(Int_t color) {
ab2c1f0d 509 //
510 // diagnostics histograms are defined
511 //
0b75bef2 512
f58f1a93 513 fHistogramsOn=kTRUE;
0b75bef2 514
ab2c1f0d 515 //###################################################################################
f58f1a93 516 // defining histograms
0b75bef2 517
ebd60d8e 518 fhCutStatistics = new TH1F("cut_statistics","cut statistics",kNCuts+4,-0.5,kNCuts+3.5);
0b75bef2 519
f58f1a93 520 fhCutStatistics->GetXaxis()->SetBinLabel(1,"n tracks");
521 fhCutStatistics->GetXaxis()->SetBinLabel(2,"n cut tracks");
0b75bef2 522
ebd60d8e 523 fhCutCorrelation = new TH2F("cut_correlation","cut correlation",kNCuts,-0.5,kNCuts-0.5,kNCuts,-0.5,kNCuts-0.5);;
0b75bef2 524
ebd60d8e 525 for (Int_t i=0; i<kNCuts; i++) {
ab2c1f0d 526 fhCutStatistics->GetXaxis()->SetBinLabel(i+4,fgkCutNames[i]);
527 fhCutCorrelation->GetXaxis()->SetBinLabel(i+1,fgkCutNames[i]);
528 fhCutCorrelation->GetYaxis()->SetBinLabel(i+1,fgkCutNames[i]);
f58f1a93 529 }
530
531 fhCutStatistics ->SetLineColor(color);
532 fhCutCorrelation ->SetLineColor(color);
533 fhCutStatistics ->SetLineWidth(2);
534 fhCutCorrelation ->SetLineWidth(2);
535
0b75bef2 536 Char_t str[256];
537 for (Int_t i=0; i<2; i++) {
538 if (i==0) sprintf(str," ");
539 else sprintf(str,"_cut");
540
ebd60d8e 541 fhNClustersITS[i] = new TH1F(Form("nClustersITS%s",str),"",8,-0.5,7.5);
542 fhNClustersTPC[i] = new TH1F(Form("nClustersTPC%s",str),"",165,-0.5,164.5);
543 fhChi2PerClusterITS[i] = new TH1F(Form("chi2PerClusterITS%s",str),"",500,0,10);
544 fhChi2PerClusterTPC[i] = new TH1F(Form("chi2PerClusterTPC%s",str),"",500,0,10);
545
546 fhC11[i] = new TH1F(Form("covMatrixDiagonal11%s",str),"",1000,0,5);
547 fhC22[i] = new TH1F(Form("covMatrixDiagonal22%s",str),"",1000,0,5);
548 fhC33[i] = new TH1F(Form("covMatrixDiagonal33%s",str),"",1000,0,0.5);
549 fhC44[i] = new TH1F(Form("covMatrixDiagonal44%s",str),"",1000,0,5);
550 fhC55[i] = new TH1F(Form("covMatrixDiagonal55%s",str),"",1000,0,5);
551
552 fhDXY[i] = new TH1F(Form("dXY%s",str),"",500,-10,10);
553 fhDZ[i] = new TH1F(Form("dZ%s",str),"",500,-10,10);
554 fhDXYvsDZ[i] = new TH2F(Form("dXYvsDZ%s",str),"",200,-10,10,200,-10,10);
555
556 fhDXYNormalized[i] = new TH1F(Form("dXYNormalized%s",str),"",500,-10,10);
557 fhDZNormalized[i] = new TH1F(Form("dZNormalized%s",str),"",500,-10,10);
558 fhDXYvsDZNormalized[i] = new TH2F(Form("dXYvsDZNormalized%s",str),"",200,-10,10,200,-10,10);
559
560
561 fhNClustersITS[i]->SetXTitle("n ITS clusters");
562 fhNClustersTPC[i]->SetXTitle("n TPC clusters");
563 fhChi2PerClusterITS[i]->SetXTitle("#Chi^{2} per ITS cluster");
564 fhChi2PerClusterTPC[i]->SetXTitle("#Chi^{2} per TPC cluster");
565
566 fhC11[i]->SetXTitle("cov 11 : #sigma_{y}^{2} [cm^{2}]");
567 fhC22[i]->SetXTitle("cov 22 : #sigma_{z}^{2} [cm^{2}]");
568 fhC33[i]->SetXTitle("cov 33 : #sigma_{sin(#phi)}^{2}");
569 fhC44[i]->SetXTitle("cov 44 : #sigma_{tan(#theta_{dip})}^{2}");
570 fhC55[i]->SetXTitle("cov 55 : #sigma_{1/p_{T}}^{2} [(c/GeV)^2]");
571
572 fhDXY[i]->SetXTitle("transverse impact parameter");
573 fhDZ[i]->SetXTitle("longitudinal impact parameter");
574 fhDXYvsDZ[i]->SetXTitle("longitudinal impact parameter");
575 fhDXYvsDZ[i]->SetYTitle("transverse impact parameter");
576
577 fhDXYNormalized[i]->SetXTitle("normalized trans impact par");
578 fhDZNormalized[i]->SetXTitle("normalized long impact par");
579 fhDXYvsDZNormalized[i]->SetXTitle("normalized long impact par");
580 fhDXYvsDZNormalized[i]->SetYTitle("normalized trans impact par");
581
582 fhNClustersITS[i]->SetLineColor(color); fhNClustersITS[i]->SetLineWidth(2);
583 fhNClustersTPC[i]->SetLineColor(color); fhNClustersTPC[i]->SetLineWidth(2);
584 fhChi2PerClusterITS[i]->SetLineColor(color); fhChi2PerClusterITS[i]->SetLineWidth(2);
585 fhChi2PerClusterTPC[i]->SetLineColor(color); fhChi2PerClusterTPC[i]->SetLineWidth(2);
586
587 fhC11[i]->SetLineColor(color); fhC11[i]->SetLineWidth(2);
588 fhC22[i]->SetLineColor(color); fhC22[i]->SetLineWidth(2);
589 fhC33[i]->SetLineColor(color); fhC33[i]->SetLineWidth(2);
590 fhC44[i]->SetLineColor(color); fhC44[i]->SetLineWidth(2);
591 fhC55[i]->SetLineColor(color); fhC55[i]->SetLineWidth(2);
592
593 fhDXY[i]->SetLineColor(color); fhDXY[i]->SetLineWidth(2);
594 fhDZ[i]->SetLineColor(color); fhDZ[i]->SetLineWidth(2);
595
596 fhDXYNormalized[i]->SetLineColor(color); fhDXYNormalized[i]->SetLineWidth(2);
597 fhDZNormalized[i]->SetLineColor(color); fhDZNormalized[i]->SetLineWidth(2);
0b75bef2 598 }
599}
600
601//____________________________________________________________________
602void
49dc84d9 603AliESDtrackCuts::Print(const Option_t*) const {
ab2c1f0d 604 //
605 // print method - still to be implemented
606 //
0b75bef2 607
608 AliInfo("AliESDtrackCuts...");
609}
610
611
612//____________________________________________________________________
f58f1a93 613void AliESDtrackCuts::SaveHistograms(Char_t* dir) {
ab2c1f0d 614 //
615 // saves the histograms in a directory (dir)
616 //
617
0b75bef2 618
619 if (!fHistogramsOn) {
620 AliDebug(0, "Histograms not on - cannot save histograms!!!");
621 return;
622 }
623
624 gDirectory->mkdir(dir);
625 gDirectory->cd(dir);
626
627 gDirectory->mkdir("before_cuts");
628 gDirectory->mkdir("after_cuts");
629
f58f1a93 630 fhCutStatistics->Write();
631 fhCutCorrelation->Write();
0b75bef2 632
633 for (Int_t i=0; i<2; i++) {
634 if (i==0)
635 gDirectory->cd("before_cuts");
636 else
637 gDirectory->cd("after_cuts");
638
f58f1a93 639 fhNClustersITS[i] ->Write();
640 fhNClustersTPC[i] ->Write();
641 fhChi2PerClusterITS[i] ->Write();
642 fhChi2PerClusterTPC[i] ->Write();
0b75bef2 643
f58f1a93 644 fhC11[i] ->Write();
645 fhC22[i] ->Write();
646 fhC33[i] ->Write();
647 fhC44[i] ->Write();
648 fhC55[i] ->Write();
649
650 fhDXY[i] ->Write();
651 fhDZ[i] ->Write();
652 fhDXYvsDZ[i] ->Write();
0b75bef2 653
f58f1a93 654 fhDXYNormalized[i] ->Write();
655 fhDZNormalized[i] ->Write();
656 fhDXYvsDZNormalized[i] ->Write();
0b75bef2 657
658 gDirectory->cd("../");
659 }
660
661 gDirectory->cd("../");
662}
663
664
665