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Commit | Line | Data |
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a8ad4709 | 1 | TObject* fContPid1; |
2 | TObject* fContPid2; | |
3 | const Int_t nBinPid = 14; // pt,eta, ptPip, ptPin, PPip, PPin, TOF3sigmaPip, TOF3sigmaPin, isPhiTrue, nsigmaPip, nsigmaPin | |
4 | // 0.985 < mass < 1.045 (60) and 0 < centrality < 100 (10) | |
5 | Int_t binPid[nBinPid] = {1/*ptPhi*/,8/*EtaPi*/,20/*pt+*/,20/*pt-*/,5/*P+*/,1/*P-*/,2/*TOFmatch+*/,2/*TOFmatch-*/,2/*istrue*/,4/*Nsigma+*/,4/*Nsigma-*/,1/*DeltaPhi+*/,1/*DeltaPhi-*/,1/*Psi*/}; | |
6 | Float_t xmin[nBinPid] = {1,-0.8,0.3,0.3,0,0,-0.5,-0.5,-0.5,0,0,-TMath::Pi(),-TMath::Pi(),-TMath::Pi()/2}; | |
7 | Float_t xmax[nBinPid] = {5,0.8,4.3,4.3,1,1,1.5,1.5,1.5,7.5,7.5,TMath::Pi(),TMath::Pi(),TMath::Pi()/2}; | |
8 | ||
9 | TF1 *fsign; | |
10 | TF1 *fall; | |
11 | TF1 *fback; | |
12 | ||
13 | Int_t ifunc=0; | |
14 | ||
15 | Float_t fitmin = 0.3; | |
16 | Float_t fitmax = 0.7; | |
17 | ||
08b5b548 | 18 | Int_t cmin = 1; // min 1 |
19 | Int_t cmax = 10;//max 10 | |
a8ad4709 | 20 | |
21 | Float_t weightS = -1.; | |
22 | ||
23 | Int_t rebinsize = 4; | |
24 | ||
25 | Int_t parplotted = 2; | |
26 | ||
27 | Bool_t isMC = kFALSE; // don't change this (is set automatically) | |
28 | Bool_t selectTrue = kTRUE; // put it to true to remove background (only for MC) | |
29 | Bool_t keepTrue = kFALSE; // put it to false to fit only background (only for MC) | |
30 | ||
31 | Bool_t kGoodMatch = kFALSE; // to check good matching | |
32 | ||
33 | Bool_t kSigma2vs3 = kFALSE; // to check good matching | |
34 | ||
35 | Bool_t require5sigma = kFALSE; // don't touch this flag | |
36 | ||
37 | Bool_t bayesVsigma = kFALSE; // only to do checks | |
38 | ||
39 | Bool_t kTOFmatch = kFALSE; // for combined PID requires TOF matching | |
40 | ||
08b5b548 | 41 | Bool_t kOverAll = kFALSE; |
e34b28fe | 42 | Bool_t kOverAllTOFmatch = kFALSE; |
08b5b548 | 43 | Bool_t kOverAll2Sigma = kFALSE; |
e34b28fe | 44 | |
08b5b548 | 45 | TH2F *hmatched; |
46 | TH2F *htracked; | |
a8ad4709 | 47 | |
48 | Bool_t kLoaded=kFALSE; | |
49 | LoadLib(){ | |
50 | weightS = -1.; | |
51 | ||
52 | if(! kLoaded){ | |
53 | gSystem->Load("libVMC.so"); | |
54 | gSystem->Load("libPhysics.so"); | |
55 | gSystem->Load("libTree.so"); | |
56 | gSystem->Load("libMinuit.so"); | |
57 | gSystem->Load("libSTEERBase.so"); | |
58 | gSystem->Load("libANALYSIS.so"); | |
59 | gSystem->Load("libAOD.so"); | |
60 | gSystem->Load("libESD.so"); | |
61 | gSystem->Load("libANALYSIS.so"); | |
62 | gSystem->Load("libANALYSISalice.so"); | |
63 | gSystem->Load("libCORRFW.so"); | |
64 | gSystem->Load("libNetx.so"); | |
65 | gSystem->Load("libPWGPPpid.so"); | |
66 | ||
67 | TFile *f = new TFile("AnalysisResults.root"); | |
68 | f->ls(); | |
69 | TList *l = (TList *) f->Get("contK0sBayes1"); | |
08b5b548 | 70 | TList *l2 = (TList *) f->Get("contK0sBayes2"); |
a8ad4709 | 71 | l->ls(); |
08b5b548 | 72 | fContPid1 = (AliPIDperfContainer *) l->FindObject("contPID"); |
73 | fContPid2 = (AliPIDperfContainer *) l->FindObject("contPID2"); | |
74 | hmatched = (TH2F *) l2->FindObject("hMatchPi"); | |
75 | htracked = (TH2F *) l2->FindObject("hTrackingPi"); | |
a8ad4709 | 76 | } |
77 | kLoaded = kTRUE; | |
78 | ||
79 | // check if MC | |
80 | Float_t x[] = {xmin[0]+0.001,xmin[1]+0.001,xmin[2]+0.001,xmin[3]+0.001,xmin[4]+0.001,xmin[5]+0.001,xmin[6]+0.001,xmin[7]+0.001,1/*trueMC*/,xmin[9],xmin[10]}; | |
81 | Float_t x2[] = {xmax[0],xmax[1],xmax[2],xmax[3],xmax[4],xmax[5],xmax[6],xmax[7],xmax[8],xmax[9],xmax[10]}; | |
82 | ||
83 | AliPIDperfContainer *tmp = fContPid1; | |
84 | TH1D *h = tmp->GetQA(0, x, x2)->ProjectionX("checkMC"); | |
85 | ||
86 | if(h->GetEntries()) isMC = kTRUE; | |
87 | else isMC=kFALSE; | |
88 | ||
89 | if(!isMC){ | |
90 | selectTrue = kFALSE; | |
91 | keepTrue = kTRUE; | |
92 | } | |
93 | else{ | |
94 | printf("MC truth found!!!!!!\nIt is MC!!!!!!"); | |
95 | } | |
96 | ||
97 | fsign = new TF1("fsign","[0]*TMath::Voigt(x-[1],[3],[2])",fitmin,fitmax); | |
98 | fback = new TF1("fback","pol1",fitmin,fitmax); | |
99 | fall = new TF1("fall","[0]*TMath::Voigt(x-[1],[3],[2]) + pol1(4)",fitmin,fitmax); | |
100 | ||
101 | fsign->SetLineColor(2); | |
102 | fback->SetLineColor(4); | |
103 | ||
104 | if(kSigma2vs3){ | |
105 | kGoodMatch=kFALSE; | |
106 | kOverAll = 0; | |
107 | } | |
108 | ||
109 | if(bayesVsigma){ | |
110 | kOverAll = 0; | |
111 | kGoodMatch=kFALSE; | |
112 | kSigma2vs3=kFALSE; | |
113 | kTOFmatch=kTRUE; | |
114 | weightS = -0.7; | |
115 | } | |
116 | } | |
117 | ||
118 | doeffPi(Int_t pos=1,Float_t prob=0.1,Float_t etaminkp=-0.8,Float_t etamaxkp=0.8){ | |
119 | LoadLib(); | |
08b5b548 | 120 | TH1D *hm = hmatched->ProjectionX("matchingPiEff",cmin,cmax); |
121 | TH1D *ht = htracked->ProjectionX("tracking",cmin,cmax); | |
122 | ||
123 | hm->GetYaxis()->SetTitle("TOF matching eff."); | |
124 | hm->SetTitle("Using probability as weights"); | |
125 | ||
126 | hm->Sumw2(); | |
127 | ht->Sumw2(); | |
128 | ||
129 | hm->Divide(hm,ht,1,1,"B"); | |
a8ad4709 | 130 | |
131 | Int_t nptbin = binPid[2]; | |
132 | Float_t minptbin = xmin[2]; | |
133 | Float_t maxptbin = xmax[2]; | |
134 | ||
135 | if(pos == 0){ | |
136 | nptbin = binPid[3]; | |
137 | minptbin = xmin[3]; | |
138 | maxptbin = xmax[3]; | |
139 | } | |
140 | ||
141 | if(prob > 0.1999){ | |
142 | kGoodMatch = kFALSE; | |
143 | kSigma2vs3 = kFALSE; | |
144 | if(! kOverAll) require5sigma = kTRUE; | |
145 | if(!isMC) weightS = -0.95; | |
146 | } | |
147 | ||
148 | TCanvas *c = new TCanvas(); | |
149 | c->Divide((nptbin+1)/2,2); | |
150 | TH2F *hh.*hh2; | |
151 | TH1D *h,*h2; | |
152 | char name[100]; | |
153 | Float_t b[50][3]; | |
154 | ||
155 | Double_t xx[50],yy[50]; | |
156 | Double_t exx[50],eyy[50]; | |
157 | ||
158 | for(Int_t i=0;i < nptbin;i++){ | |
08b5b548 | 159 | c->cd(i+1);//->SetLogy(); |
a8ad4709 | 160 | Float_t ptmin = minptbin+(maxptbin-minptbin)/nptbin*(i); |
161 | Float_t ptmax = minptbin+(maxptbin-minptbin)/nptbin*(i+1); | |
162 | ||
163 | xx[i] = (ptmin+ptmax)/2; | |
164 | exx[i] = (-ptmin+ptmax)/2; | |
165 | ||
166 | Float_t pp=0.1; | |
167 | if(prob < 0.2) pp = 0.; | |
168 | if(pos) hh=GetHistoPip(ptmin,ptmax,pp,0.0,etaminkp,etamaxkp); | |
169 | else hh=GetHistoPin(ptmin,ptmax,pp,0.0); | |
170 | sprintf(name,"TOF matched: %f < p_{T} < %f GeV/#it{c}",ptmin,ptmax); | |
171 | hh->SetTitle(name); | |
172 | sprintf(name,"hNoPid%i",i); | |
173 | ||
174 | pp=prob; | |
175 | if(prob < 0.2) pp = 0.1; | |
176 | if(pos) hh2=GetHistoPip(ptmin,ptmax,pp,0.0,etaminkp,etamaxkp); | |
177 | else hh2=GetHistoPin(ptmin,ptmax,pp,0.0); | |
178 | AddHisto(hh,hh2,weightS); | |
179 | ||
180 | h = hh->ProjectionX(name,cmin,cmax); | |
181 | h->RebinX(rebinsize); | |
182 | h->Draw("ERR"); | |
183 | h->SetMarkerStyle(24); | |
184 | b[i][0]=-1; | |
185 | Int_t ntrial = 0; | |
186 | Float_t chi2 = 10000; | |
08b5b548 | 187 | while(ntrial < 3 && (chi2 > 20 + 1000*selectTrue)){ |
a8ad4709 | 188 | fit(h,b[i],"WW","",xx[i]); |
189 | c1->Update(); | |
190 | // getchar(); | |
191 | fit(h,b[i],"","",xx[i]); | |
192 | ntrial++; | |
193 | chi2 = b[i][2]; | |
194 | printf("chi2 = %f\n",chi2); | |
195 | c1->Update(); | |
196 | // getchar(); | |
197 | ||
198 | } | |
199 | ||
200 | yy[i] = fall->GetParameter(parplotted); | |
201 | eyy[i] = fall->GetParError(parplotted); | |
202 | } | |
203 | ||
204 | TGraphErrors *gpar = new TGraphErrors(nptbin,xx,yy,exx,eyy); | |
205 | c->cd(8); | |
08b5b548 | 206 | // gpar->Draw("AP"); |
a8ad4709 | 207 | gpar->SetMarkerStyle(20); |
208 | ||
209 | TCanvas *c2 = new TCanvas(); | |
210 | c2->Divide((nptbin+1)/2,2); | |
211 | Float_t b2[50][3]; | |
212 | ||
213 | for(Int_t i=0;i < nptbin;i++){ | |
214 | c2->cd(i+1); | |
215 | Float_t ptmin = minptbin+(maxptbin-minptbin)/nptbin*(i); | |
216 | Float_t ptmax = minptbin+(maxptbin-minptbin)/nptbin*(i+1); | |
217 | ||
218 | Float_t pp=prob; | |
219 | if(prob < 0.2) pp = 0.1; | |
220 | if(pos) hh=GetHistoPip(ptmin,ptmax,pp,0.0,etaminkp,etamaxkp); | |
221 | else hh=GetHistoPin(ptmin,ptmax,pp,0.0); | |
222 | sprintf(name,"P_{TOF} > 0.8: %f < p_{T} < %f GeV/#it{c}",ptmin,ptmax); | |
223 | hh->SetTitle(name); | |
224 | sprintf(name,"hPid60_%i",i); | |
225 | h = hh->ProjectionX(name,cmin,cmax); | |
226 | h->RebinX(rebinsize); | |
227 | h->Draw("ERR"); | |
228 | h->SetMarkerStyle(24); | |
229 | b2[i][0]=-1; | |
230 | Int_t ntrial = 0; | |
231 | Float_t chi2 = 10000; | |
08b5b548 | 232 | while(ntrial < 3 && (chi2 > 20 + 1000*selectTrue)){ |
a8ad4709 | 233 | fit(h,b2[i],"WW",""); |
234 | fit(h,b2[i],"",""); | |
235 | ntrial++; | |
236 | chi2 = b2[i][2]; | |
237 | printf("chi2 = %f\n",chi2); | |
238 | } | |
239 | yy[i] = fall->GetParameter(parplotted); | |
240 | eyy[i] = fall->GetParError(parplotted); | |
241 | ||
242 | } | |
243 | ||
244 | TGraphErrors *gpar2 = new TGraphErrors(nptbin,xx,yy,exx,eyy); | |
245 | c2->cd(8); | |
08b5b548 | 246 | // gpar2->Draw("AP"); |
a8ad4709 | 247 | gpar2->SetMarkerStyle(20); |
248 | ||
249 | Double_t xpt[50],expt[50],eff[50],efferr[50]; | |
250 | for(Int_t i=0;i<nptbin;i++){ | |
251 | printf("%f +/- %f - %f +/- %f\n",b[i][0],b[i][1],b2[i][0],b2[i][1]); | |
252 | ||
253 | Float_t ptmin = minptbin+(maxptbin-minptbin)/nptbin*(i); | |
254 | Float_t ptmax = minptbin+(maxptbin-minptbin)/nptbin*(i+1); | |
255 | ||
256 | xpt[i] = (ptmin+ptmax)/2; | |
257 | expt[i] = (-ptmin+ptmax)/2; | |
258 | eff[i] = b2[i][0]/(b[i][0]-b2[i][0]*weightS); | |
259 | ||
260 | b[i][0] = b[i][0]-b2[i][0]*weightS; | |
261 | ||
262 | efferr[i] = TMath::Sqrt(b[i][1]*b[i][1]/b[i][0]/b[i][0] + b2[i][1]*b2[i][1]/b2[i][0]/b2[i][0])*(b2[i][0]+b2[i][1])*(1+weightS*(b2[i][0]-b2[i][1])/b[i][0])/b[i][0];//*(1-eff[i]);//der2*der2*(b[i][1]*b[i][1] - b2[i][1]*b2[i][1])); | |
263 | ||
264 | if(TMath::Abs(efferr[i]) > 1)efferr[i]=1; | |
265 | } | |
266 | new TCanvas(); | |
267 | TGraphErrors *geff = new TGraphErrors(nptbin,xpt,eff,expt,efferr); | |
268 | geff->Draw("AP"); | |
269 | ||
270 | char flag[100]; | |
271 | sprintf(flag,""); | |
272 | ||
273 | if(isMC){ | |
274 | if(selectTrue) sprintf(flag,"true"); | |
275 | else if(!keepTrue) sprintf(flag,"back"); | |
276 | } | |
277 | ||
08b5b548 | 278 | Bool_t kWriteME = kFALSE; |
279 | ||
a8ad4709 | 280 | char flag2[100]; |
281 | sprintf(flag2,""); | |
282 | ||
283 | char etarange[100]; | |
284 | sprintf(etarange,"_%.1f-%.1f_",etaminkp,etamaxkp); | |
285 | ||
286 | if(kGoodMatch) | |
287 | sprintf(flag2,"GM"); | |
288 | ||
289 | if(bayesVsigma) | |
290 | sprintf(flag2,"BayesVsSigma"); | |
291 | ||
292 | if(kSigma2vs3) | |
293 | sprintf(flag2,"Sigma2vs3"); | |
294 | ||
295 | if(kOverAll) | |
296 | sprintf(flag2,"OverAll"); | |
e34b28fe | 297 | if(kOverAllTOFmatch) |
298 | sprintf(flag2,"OverAllTOF"); | |
299 | if(kOverAll2Sigma) | |
300 | sprintf(flag2,"OverAll2sigma"); | |
a8ad4709 | 301 | |
302 | if(pos){ | |
303 | if(prob >=0.2) sprintf(name,"pionPos%sP%iEff%i_%i%s%s.root",etarange,Int_t(prob*100),(cmin-1)*10,cmax*10,flag,flag2); | |
08b5b548 | 304 | else{ |
305 | sprintf(name,"pionPos%sMatchEff%i_%i%s%s.root",etarange,(cmin-1)*10,cmax*10,flag,flag2); | |
306 | if(!(kOverAll || bayesVsigma || kGoodMatch || kSigma2vs3)) kWriteME = kTRUE; | |
307 | } | |
a8ad4709 | 308 | } |
309 | else{ | |
310 | if(prob >=0.2) sprintf(name,"pionNeg%sP%iEff%i_%i%s%s.root",etarange,Int_t(prob*100),(cmin-1)*10,cmax*10,flag,flag2); | |
311 | else sprintf(name,"pionNeg%sMatchEff%i_%i%s%s.root",etarange,(cmin-1)*10,cmax*10,flag,flag2); | |
312 | } | |
313 | ||
08b5b548 | 314 | geff->SetTitle("#pi efficiency (from K^{0}_{s});p_{T} (GeV/#it{c};efficiency"); |
a8ad4709 | 315 | TFile *fout = new TFile(name,"RECREATE"); |
316 | geff->Write(); | |
08b5b548 | 317 | if(kWriteME) hm->Write(); |
a8ad4709 | 318 | fout->Close(); |
319 | ||
08b5b548 | 320 | if(kWriteME) hm->Draw("SAME"); |
a8ad4709 | 321 | } |
322 | ||
323 | TH2F *GetHistoPip(Float_t pt=1,Float_t ptM=1.1,Float_t pMinkp=0,Float_t pMinkn=0.,Float_t etaminkp=-0.8,Float_t etamaxkp=0.8){ | |
324 | ||
325 | Float_t x[] = {xmin[0]+0.001,etaminkp+0.001,pt+0.001,xmin[3]+0.001,pMinkp+0.001,pMinkn+0.001,(pMinkp>0.09)+0.001,kTOFmatch+0.001,selectTrue,xmin[9],xmin[10],xmin[11],xmin[12],xmin[13]}; | |
326 | Float_t x2[] = {xmax[0],etamaxkp-0.001,ptM-0.001,xmax[3],xmax[4],xmax[5],xmax[6],xmax[7],keepTrue,xmax[9],xmax[10],xmax[11],xmax[12],xmax[13]}; | |
327 | ||
e34b28fe | 328 | if(kOverAll){ |
329 | x[6] = 0.0001; | |
330 | x2[9] = 5.9; | |
331 | if(pMinkp > 0.19) x2[9] = 4.9; | |
332 | } | |
333 | ||
334 | if(kOverAllTOFmatch && pMinkp > 0.19){ | |
335 | x[6] = 1.0001; | |
336 | x2[9] = 4.9; | |
337 | } | |
338 | ||
339 | if(kOverAll2Sigma && pMinkp > 0.09){ | |
08b5b548 | 340 | x2[9] = 2.; |
e34b28fe | 341 | x[6] = 1.0001; |
342 | } | |
343 | ||
a8ad4709 | 344 | if(kGoodMatch){ |
345 | x[6] = 1.0001; | |
346 | if(pMinkp > 0) | |
347 | x2[9] = 4.9; | |
348 | ||
349 | } | |
350 | ||
351 | if(kTOFmatch){ | |
352 | x[6] = 1.0001; | |
353 | } | |
354 | ||
355 | if(kSigma2vs3){ | |
356 | x[6] = 1.0001; | |
357 | x2[9] = 3; | |
358 | if(pMinkp > 0) | |
359 | x2[9] = 2; | |
360 | } | |
361 | ||
362 | if(bayesVsigma){ | |
363 | if(pMinkp > 0){ | |
364 | x[4] = 0.2001; | |
365 | x2[9] = 5; | |
366 | } | |
367 | else{ | |
368 | x2[9] = 3; | |
369 | } | |
370 | ||
371 | ||
372 | } | |
373 | ||
374 | if(require5sigma) x2[9] = 4.9; | |
375 | ||
376 | AliPIDperfContainer *tmp = fContPid1; | |
377 | ||
378 | TH2F *h = tmp->GetQA(0, x, x2); | |
379 | ||
380 | h->GetXaxis()->SetTitle("M_{K^{0}_{s}} (GeV/#it{c}^{2})"); | |
381 | h->GetYaxis()->SetTitle("centrality [%]"); | |
382 | ||
383 | return h; | |
384 | } | |
385 | ||
386 | TH2F *GetHistoPin(Float_t pt=1,Float_t ptM=1.1,Float_t pMinkn=0,Float_t pMinkp=0.,Float_t etaminkp=-0.8,Float_t etamaxkp=0.8){ | |
387 | ||
388 | Float_t x[] = {xmin[0]+0.001,etaminkp+0.001,xmin[2]+0.001,pt+0.001,pMinkp+0.001,pMinkn+0.001,kTOFmatch+0.001,(pMinkn>0.09)+0.001,selectTrue,xmin[9],xmin[10],xmin[11],xmin[12],xmin[13]}; | |
389 | Float_t x2[] = {xmax[0],etamaxkp-0.001,xmax[2],ptM-0.001,xmax[4],xmax[5],xmax[6],xmax[7],keepTrue,xmax[9],xmax[10],xmax[11],xmax[12],xmax[13]}; | |
390 | ||
e34b28fe | 391 | if(kOverAll){ |
392 | x[7] = 0.0001; | |
393 | x2[10] = 5.9; | |
394 | if(pMinkn > 0.19) x2[10] = 4.9; | |
395 | } | |
396 | ||
397 | if(kOverAllTOFmatch && pMinkn > 0.19){ | |
398 | x[7] = 1.0001; | |
399 | x2[10] = 4.9; | |
400 | } | |
401 | ||
402 | if(kOverAll2Sigma && pMinkn > 0.09){ | |
403 | x2[10] = 2; | |
404 | x[7] = 1.0001; | |
405 | } | |
406 | ||
a8ad4709 | 407 | if(kGoodMatch){ |
408 | x[7] = 1.0001; | |
409 | if(pMinkn > 0) | |
410 | x2[10] = 4.9; | |
411 | ||
412 | } | |
413 | ||
414 | if(kTOFmatch){ | |
415 | x[7] = 1.0001; | |
416 | } | |
417 | ||
418 | if(kSigma2vs3){ | |
419 | x[7] = 1.0001; | |
420 | x2[10] = 3; | |
421 | if(pMinkn > 0) | |
422 | x2[10] = 2; | |
423 | } | |
424 | ||
425 | if(bayesVsigma){ | |
426 | if(pMinkn > 0){ | |
427 | x[5] = 0.2001; | |
428 | x2[10] = 5; | |
429 | } | |
430 | else{ | |
431 | x2[10] = 3; | |
432 | } | |
433 | } | |
434 | ||
435 | if(require5sigma) x2[10] = 4.9; | |
436 | ||
437 | AliPIDperfContainer *tmp = fContPid2; | |
438 | ||
439 | TH2F *h = tmp->GetQA(0, x, x2); | |
440 | ||
441 | h->GetXaxis()->SetTitle("M_{K^{0}_{s}} (GeV/#it{c}^{2})"); | |
442 | h->GetYaxis()->SetTitle("centrality [%]"); | |
443 | ||
444 | return h; | |
445 | } | |
446 | ||
447 | ||
448 | fit(TH1D *h,Float_t *a=NULL,char *opt="",char *opt2="",Float_t pt=1.5){ | |
449 | if(h->Integral(1,h->GetNbinsX()) < 1){ | |
450 | if(a){ | |
451 | a[0]=0.001; | |
452 | a[1]=1; | |
453 | } | |
454 | return; | |
455 | } | |
456 | ||
457 | ||
458 | fall->SetParameter(0,100); | |
459 | fall->SetParameter(1,0.4971); | |
460 | fall->SetParameter(2,0.00006); | |
461 | fall->SetParameter(3,0.0035); | |
462 | ||
463 | fall->SetParLimits(0,0.00001,10000); | |
464 | fall->SetParLimits(1,0.4965,0.4985); | |
465 | fall->SetParLimits(2,0.00005,0.001); | |
466 | fall->SetParLimits(3,0.002,0.01); | |
467 | ||
468 | fall->ReleaseParameter(4); | |
469 | fall->ReleaseParameter(5); | |
470 | ||
471 | if(selectTrue){ | |
472 | fall->FixParameter(4,0); | |
473 | fall->FixParameter(5,0); | |
474 | } | |
475 | ||
476 | char name[100]; | |
477 | TF1 *ftmp=fall; | |
478 | ||
479 | TF1 *ftmp2=new TF1(*fsign); | |
480 | sprintf(name,"fsign%i",ifunc); | |
481 | ftmp2->SetName(name); | |
482 | ||
483 | TF1 *ftmp3=new TF1(*fback); | |
484 | sprintf(name,"ftmp3%i",ifunc); | |
485 | ftmp3->SetName(name); | |
486 | ||
487 | ifunc++; | |
488 | ||
489 | h->Fit(ftmp,opt,opt2,fitmin,fitmax); | |
490 | h->Draw("ERR"); | |
491 | ||
492 | ftmp2->SetParameter(0,ftmp->GetParameter(0)); | |
493 | ftmp2->SetParameter(1,ftmp->GetParameter(1)); | |
494 | ftmp2->SetParameter(2,ftmp->GetParameter(2)); | |
495 | ftmp2->SetParameter(3,ftmp->GetParameter(3)); | |
496 | ftmp2->Draw("SAME"); | |
497 | ftmp3->SetParameter(0,ftmp->GetParameter(4)); | |
498 | ftmp3->SetParameter(1,ftmp->GetParameter(5)); | |
499 | ftmp3->Draw("SAME"); | |
500 | ||
501 | Float_t mean = ftmp->GetParameter(1); | |
502 | Float_t sigma = 0.0044; | |
503 | ||
504 | Float_t signI = ftmp2->Integral(mean-10*sigma,mean+10*sigma)/h->GetBinWidth(1); | |
505 | Float_t backI = ftmp3->Integral(mean-3*sigma,mean+3*sigma)/h->GetBinWidth(1); | |
506 | ||
507 | Float_t errI = TMath::Sqrt(ftmp->GetParError(0)*ftmp->GetParError(0)/(0.001+ftmp->GetParameter(0))/(0.001+ftmp->GetParameter(0))); | |
508 | ||
509 | printf("signal(5 sigma) = %f +/- %f(fit) +/- %f(stat)\n",signI,errI*signI,TMath::Sqrt(signI)); | |
510 | printf("backgr(3sigma) = %f\n",backI); | |
511 | printf("significance(3 sigma) = %f\n",signI/sqrt(signI+backI)); | |
512 | ||
513 | if(a){ | |
514 | a[0]=signI; | |
515 | a[1]=signI*errI*signI*errI + signI; | |
516 | a[1] = TMath::Sqrt(a[1]); | |
517 | if(ftmp->GetNDF()) a[2] = ftmp->GetChisquare()/ftmp->GetNDF(); | |
518 | ||
519 | ||
520 | if(selectTrue){ | |
521 | a[0] = h->Integral(1,h->GetNbinsX()); | |
522 | a[1] = TMath::Sqrt(a[0]); | |
523 | } | |
524 | } | |
525 | } | |
526 | ||
527 | AddHisto(TH2F *h1,TH2F *h2,Float_t w){ | |
528 | Int_t nbinx = h1->GetNbinsX(); | |
529 | Int_t nbiny = h1->GetNbinsY(); | |
530 | ||
531 | for(Int_t i=1;i<=nbinx;i++){ | |
532 | for(Int_t j=1;j<=nbiny;j++){ | |
533 | Float_t val = h1->GetBinContent(i,j) + h2->GetBinContent(i,j)*w; | |
534 | Float_t err = TMath::Min(TMath::Sqrt(val),val); | |
535 | h1->SetBinContent(i,j,val); | |
536 | h1->SetBinError(i,j,err); | |
537 | } | |
538 | } | |
539 | } |