macro to extract efficiency: small fix
[u/mrichter/AliRoot.git] / PWGPP / pid / doeffKa.C
CommitLineData
7484404e 1#include"TF1.h"
2#include"TH1D.h"
3#include"TH2F.h"
4#include"TMath.h"
5#include"TSystem.h"
6#include"TCanvas.h"
7#include"TFile.h"
8#include"TGraphErrors.h"
9#include"AliPIDperfContainer.h"
10
11int LoadLib();
12void doeffKa(Int_t pos=1,Float_t prob=0.1,Float_t etaminkp=-0.8,Float_t etamaxkp=0.8);
13TH2F *GetHistoKap(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);
14TH2F *GetHistoKan(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);
15void fit(TH1D *h,Float_t *a=NULL,char *opt="",char *opt2="",Float_t pt=1.5);
16void AddHisto(TH2F *h1,TH2F *h2,Float_t w);
17
a8ad4709 18TObject* fContPid1;
19TObject* fContPid2;
20const Int_t nBinPid = 14; // pt,eta, ptPip, ptPin, PPip, PPin, TOF3sigmaPip, TOF3sigmaPin, isPhiTrue, nsigmaPip, nsigmaPin
21// 0.985 < mass < 1.045 (60) and 0 < centrality < 100 (10)
22Int_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*/};
23Float_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};
24Float_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};
25
26TF1 *fsign;
27TF1 *fall;
28TF1 *fback;
29
30Int_t ifunc=0;
31
32Float_t fitmin = 0.99;
33Float_t fitmax = 1.045;
34
08b5b548 35Int_t cmin = 1;// min 1
6490e37e 36Int_t cmax = 8;// max 10
a8ad4709 37
6490e37e 38Float_t weightS = -1.;
a8ad4709 39
bf2e05d5 40Int_t rebinsize = 1;
a8ad4709 41
42Int_t parplotted = 2;
43
44Bool_t isMC = kFALSE; // don't change this (is set automatically)
45Bool_t selectTrue = kTRUE; // put it to true to remove background (only for MC)
46Bool_t keepTrue = kFALSE; // put it to false to fit only background (only for MC)
47
48Bool_t kGoodMatch = kFALSE; // to check good matching
49
50Bool_t kSigma2vs3 = kFALSE; // to check good matching
51
52Bool_t require5sigma = kFALSE; // don't touch this flag
53
54Bool_t bayesVsigma = kFALSE; // only to do checks
55
56Bool_t kTOFmatch = kFALSE; // for combined PID requires TOF matching
57
08b5b548 58Bool_t kOverAll = kFALSE;
e34b28fe 59Bool_t kOverAllTOFmatch = kFALSE;
08b5b548 60Bool_t kOverAll2Sigma = kFALSE;
61
62TH2F *hmatched;
63TH2F *htracked;
a8ad4709 64
65Bool_t kLoaded=kFALSE;
7484404e 66int LoadLib(){
6490e37e 67 weightS = -1.;
a8ad4709 68
bf2e05d5 69 require5sigma = kFALSE;
70
a8ad4709 71 if(! kLoaded){
72 gSystem->Load("libVMC.so");
73 gSystem->Load("libPhysics.so");
74 gSystem->Load("libTree.so");
75 gSystem->Load("libMinuit.so");
76 gSystem->Load("libSTEERBase.so");
77 gSystem->Load("libANALYSIS.so");
78 gSystem->Load("libAOD.so");
79 gSystem->Load("libESD.so");
80 gSystem->Load("libANALYSIS.so");
81 gSystem->Load("libANALYSISalice.so");
82 gSystem->Load("libCORRFW.so");
83 gSystem->Load("libNetx.so");
84 gSystem->Load("libPWGPPpid.so");
85
86 TFile *f = new TFile("AnalysisResults.root");
a8ad4709 87 TList *l = (TList *) f->Get("contPhiBayes1");
08b5b548 88 TList *l2 = (TList *) f->Get("contPhiBayes2");
7484404e 89
90 if(!(l && l2)) return 0;
91
08b5b548 92 fContPid1 = (AliPIDperfContainer *) l->FindObject("contPID");
93 fContPid2 = (AliPIDperfContainer *) l->FindObject("contPID2");
94 hmatched = (TH2F *) l2->FindObject("hMatchKa");
95 htracked = (TH2F *) l2->FindObject("hTrackingKa");
a8ad4709 96 }
97 kLoaded = kTRUE;
98
99 // check if MC
100 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]};
101 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]};
102
7484404e 103 AliPIDperfContainer *tmp = (AliPIDperfContainer *) fContPid1;
a8ad4709 104 TH1D *h = tmp->GetQA(0, x, x2)->ProjectionX("checkMC");
105
106 if(h->GetEntries()) isMC = kTRUE;
107 else isMC=kFALSE;
108
109 if(!isMC){
110 selectTrue = kFALSE;
111 keepTrue = kTRUE;
112 }
113 else{
114 printf("MC truth found!!!!!!\nIt is MC!!!!!!");
115 }
116
117 fsign = new TF1("fsign","[0]*TMath::Voigt(x-[1],[3],[2])*(x>0.987)*(x > 1.005 && x < 1.035 || [4])",fitmin,fitmax);
118 fback = new TF1("fback","([0]*sqrt(x-0.987) + [1]*(x-0.987) + [2]*sqrt(x-0.987)*(x-0.987) +[3]*(x-0.987)*(x-0.987)+[4]*(x-0.987)*(x-0.987)*sqrt(x-0.987))*(x>0.987)",fitmin,fitmax);
119 fall = new TF1("fall","([0]*TMath::Voigt(x-[1],[3],[2])*(x > 1.005 && x < 1.035 || [9]) + [4]*sqrt(x-0.987) + [5]*(x-0.987) + [6]*sqrt(x-0.987)*(x-0.987) +[7]*(x-0.987)*(x-0.987)+[8]*(x-0.987)*(x-0.987)*sqrt(x-0.987))*(x>0.987)",0.987,1.05);
120
121 if(isMC){
122 fsign->SetParameter(4,0);
123 fall->FixParameter(9,0);
124 }
125 else{
126 fsign->SetParameter(4,1);
127 fall->FixParameter(9,1);
128 }
129
130 fsign->SetLineColor(2);
131 fback->SetLineColor(4);
132
133 if(kSigma2vs3){
134 kGoodMatch=kFALSE;
135 kOverAll = 0;
136 }
137
138 if(bayesVsigma){
139 kOverAll = 0;
140 kGoodMatch=kFALSE;
141 kSigma2vs3=kFALSE;
142 kTOFmatch=kTRUE;
6490e37e 143 weightS = -0.7;
a8ad4709 144 }
e34b28fe 145 if(kOverAll){
6490e37e 146 weightS = -0.7;
e34b28fe 147 }
7484404e 148
149 return 1;
a8ad4709 150}
151
7484404e 152void doeffKa(Int_t pos,Float_t prob,Float_t etaminkp,Float_t etamaxkp){
a8ad4709 153 LoadLib();
08b5b548 154 TH1D *hm = hmatched->ProjectionX("matchingKaEff",cmin,cmax);
155 TH1D *ht = htracked->ProjectionX("tracking",cmin,cmax);
156
157 hm->GetYaxis()->SetTitle("TOF matching eff.");
158 hm->SetTitle("Using probability as weights");
159
160 hm->Sumw2();
161 ht->Sumw2();
162
163 hm->Divide(hm,ht,1,1,"B");
a8ad4709 164
08b5b548 165
a8ad4709 166 Int_t nptbin = binPid[2];
167 Float_t minptbin = xmin[2];
168 Float_t maxptbin = xmax[2];
169
170 if(pos == 0){
171 nptbin = binPid[3];
172 minptbin = xmin[3];
173 maxptbin = xmax[3];
174 }
175
176 if(prob > 0.1999){
177 kGoodMatch = kFALSE;
178 kSigma2vs3 = kFALSE;
80d8d480 179// if(! kOverAll) require5sigma = kTRUE;
6490e37e 180 if(!isMC && !kOverAll) weightS = -0.95;
a8ad4709 181 }
182
7484404e 183 TCanvas *c1 = new TCanvas();
184 c1->Divide((nptbin+1)/2,2);
185 TH2F *hh,*hh2;
186 TH1D *h;
a8ad4709 187 char name[100];
188 Float_t b[50][3];
189
190 Double_t xx[50],yy[50];
191 Double_t exx[50],eyy[50];
192
193 for(Int_t i=0;i < nptbin;i++){
7484404e 194 c1->cd(i+1);//->SetLogy();
a8ad4709 195 Float_t ptmin = minptbin+(maxptbin-minptbin)/nptbin*(i);
196 Float_t ptmax = minptbin+(maxptbin-minptbin)/nptbin*(i+1);
197
198 xx[i] = (ptmin+ptmax)/2;
199 exx[i] = (-ptmin+ptmax)/2;
200
201 Float_t pp=0.1;
202 if(prob < 0.2) pp = 0.;
203 if(pos) hh=GetHistoKap(ptmin,ptmax,pp,0.0,etaminkp,etamaxkp);
204 else hh=GetHistoKan(ptmin,ptmax,pp,0.0);
205 sprintf(name,"TOF matched: %f < p_{T} < %f GeV/#it{c}",ptmin,ptmax);
206 hh->SetTitle(name);
207 sprintf(name,"hNoPid%i",i);
208
209 pp=prob;
210 if(prob < 0.2) pp = 0.1;
211 if(pos) hh2=GetHistoKap(ptmin,ptmax,pp,0.0,etaminkp,etamaxkp);
212 else hh2=GetHistoKan(ptmin,ptmax,pp,0.0);
213 AddHisto(hh,hh2,weightS);
214
215 h = hh->ProjectionX(name,cmin,cmax);
216 h->RebinX(rebinsize);
217 h->Draw("ERR");
218 h->SetMarkerStyle(24);
219 b[i][0]=-1;
220 Int_t ntrial = 0;
221 Float_t chi2 = 10000;
08b5b548 222 while(ntrial < 3 && (chi2 > 20 + 1000*selectTrue)){
a8ad4709 223 fit(h,b[i],"WW","",xx[i]);
224 c1->Update();
225// getchar();
226 fit(h,b[i],"","",xx[i]);
227 ntrial++;
228 chi2 = b[i][2];
229 printf("chi2 = %f\n",chi2);
230 c1->Update();
231// getchar();
232
233 }
234
235 yy[i] = fall->GetParameter(parplotted);
236 eyy[i] = fall->GetParError(parplotted);
237 }
238
239 TGraphErrors *gpar = new TGraphErrors(nptbin,xx,yy,exx,eyy);
7484404e 240 c1->cd(8);
08b5b548 241// gpar->Draw("AP");
a8ad4709 242 gpar->SetMarkerStyle(20);
243
244 TCanvas *c2 = new TCanvas();
245 c2->Divide((nptbin+1)/2,2);
246 Float_t b2[50][3];
247
248 for(Int_t i=0;i < nptbin;i++){
249 c2->cd(i+1);
250 Float_t ptmin = minptbin+(maxptbin-minptbin)/nptbin*(i);
251 Float_t ptmax = minptbin+(maxptbin-minptbin)/nptbin*(i+1);
252
253 Float_t pp=prob;
254 if(prob < 0.2) pp = 0.1;
255 if(pos) hh=GetHistoKap(ptmin,ptmax,pp,0.0,etaminkp,etamaxkp);
256 else hh=GetHistoKan(ptmin,ptmax,pp,0.0);
257 sprintf(name,"P_{TOF} > 0.8: %f < p_{T} < %f GeV/#it{c}",ptmin,ptmax);
258 hh->SetTitle(name);
259 sprintf(name,"hPid60_%i",i);
260 h = hh->ProjectionX(name,cmin,cmax);
261 h->RebinX(rebinsize);
262 h->Draw("ERR");
263 h->SetMarkerStyle(24);
264 b2[i][0]=-1;
265 Int_t ntrial = 0;
266 Float_t chi2 = 10000;
08b5b548 267 while(ntrial < 3 && (chi2 > 20 + 1000*selectTrue)){
a8ad4709 268 fit(h,b2[i],"WW","");
269 fit(h,b2[i],"","");
270 ntrial++;
271 chi2 = b2[i][2];
272 printf("chi2 = %f\n",chi2);
273 }
274 yy[i] = fall->GetParameter(parplotted);
275 eyy[i] = fall->GetParError(parplotted);
276
277 }
278
279 TGraphErrors *gpar2 = new TGraphErrors(nptbin,xx,yy,exx,eyy);
280 c2->cd(8);
08b5b548 281// gpar2->Draw("AP");
a8ad4709 282 gpar2->SetMarkerStyle(20);
283
284 Double_t xpt[50],expt[50],eff[50],efferr[50];
285 for(Int_t i=0;i<nptbin;i++){
286 printf("%f +/- %f - %f +/- %f\n",b[i][0],b[i][1],b2[i][0],b2[i][1]);
287
288 Float_t ptmin = minptbin+(maxptbin-minptbin)/nptbin*(i);
289 Float_t ptmax = minptbin+(maxptbin-minptbin)/nptbin*(i+1);
290
291 xpt[i] = (ptmin+ptmax)/2;
292 expt[i] = (-ptmin+ptmax)/2;
293 eff[i] = b2[i][0]/(b[i][0]-b2[i][0]*weightS);
294
295 b[i][0] = b[i][0]-b2[i][0]*weightS;
296
297 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]));
298
299 if(TMath::Abs(efferr[i]) > 1)efferr[i]=1;
300 }
301 new TCanvas();
302 TGraphErrors *geff = new TGraphErrors(nptbin,xpt,eff,expt,efferr);
303 geff->Draw("AP");
304
305 char flag[100];
7484404e 306 flag[0] = '\0';
a8ad4709 307
308 if(isMC){
309 if(selectTrue) sprintf(flag,"true");
310 else if(!keepTrue) sprintf(flag,"back");
311 }
312
313 char flag2[100];
7484404e 314 flag2[0] = '\0';
a8ad4709 315
08b5b548 316 Bool_t kWriteME = kFALSE;
317
a8ad4709 318 char etarange[100];
319 sprintf(etarange,"_%.1f-%.1f_",etaminkp,etamaxkp);
320
321 if(kGoodMatch)
322 sprintf(flag2,"GM");
323
324 if(bayesVsigma)
325 sprintf(flag2,"BayesVsSigma");
326
327 if(kSigma2vs3)
328 sprintf(flag2,"Sigma2vs3");
329
330 if(kOverAll)
331 sprintf(flag2,"OverAll");
e34b28fe 332 if(kOverAllTOFmatch)
333 sprintf(flag2,"OverAllTOF");
334 if(kOverAll2Sigma)
335 sprintf(flag2,"OverAll2sigma");
a8ad4709 336
337 if(pos){
338 if(prob >=0.2) sprintf(name,"kaonPos%sP%iEff%i_%i%s%s.root",etarange,Int_t(prob*100),(cmin-1)*10,cmax*10,flag,flag2);
08b5b548 339 else{
340 sprintf(name,"kaonPos%sMatchEff%i_%i%s%s.root",etarange,(cmin-1)*10,cmax*10,flag,flag2);
6490e37e 341 if(!(kOverAll || bayesVsigma || kGoodMatch || kSigma2vs3)) kWriteME = kTRUE;
08b5b548 342 }
a8ad4709 343 }
344 else{
345 if(prob >=0.2) sprintf(name,"kaonNeg%sP%iEff%i_%i%s%s.root",etarange,Int_t(prob*100),(cmin-1)*10,cmax*10,flag,flag2);
346 else sprintf(name,"kaonNeg%sMatchEff%i_%i%s%s.root",etarange,(cmin-1)*10,cmax*10,flag,flag2);
347 }
348
08b5b548 349 geff->SetTitle("K efficiency (from #phi);p_{T} (GeV/#it{c};efficiency");
a8ad4709 350 TFile *fout = new TFile(name,"RECREATE");
351 geff->Write();
08b5b548 352 if(kWriteME) hm->Write();
a8ad4709 353 fout->Close();
354
08b5b548 355 if(kWriteME) hm->Draw("SAME");
a8ad4709 356}
357
7484404e 358TH2F *GetHistoKap(Float_t pt,Float_t ptM,Float_t pMinkp,Float_t pMinkn,Float_t etaminkp,Float_t etamaxkp){
a8ad4709 359
360 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]};
361 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]};
362
e34b28fe 363 if(kOverAll){
364 x[6] = 0.0001;
6490e37e 365 x2[9] = 5.9;
366 if(pMinkp > 0.19) x2[9] = 4.9;
e34b28fe 367 }
368
369 if(kOverAllTOFmatch && pMinkp > 0.19){
370 x[6] = 1.0001;
6490e37e 371 x2[9] = 4.9;
e34b28fe 372 }
373
374 if(kOverAll2Sigma && pMinkp > 0.09){
375 x2[9] = 2;
376 x[6] = 1.0001;
377 }
378
a8ad4709 379 if(kGoodMatch){
380 x[6] = 1.0001;
381 if(pMinkp > 0)
382 x2[9] = 4.9;
383
384 }
385
386 if(kTOFmatch){
387 x[6] = 1.0001;
388 }
389
390 if(kSigma2vs3){
391 x[6] = 1.0001;
392 x2[9] = 3;
393 if(pMinkp > 0)
394 x2[9] = 2;
395 }
396
397 if(bayesVsigma){
398 if(pMinkp > 0){
399 x[4] = 0.2001;
400 x2[9] = 5;
401 }
402 else{
403 x2[9] = 3;
404 }
405
406
407 }
408
409 if(require5sigma) x2[9] = 4.9;
410
7484404e 411 AliPIDperfContainer *tmp = (AliPIDperfContainer *) fContPid1;
a8ad4709 412
413 TH2F *h = tmp->GetQA(0, x, x2);
414
415 h->GetXaxis()->SetTitle("M_{#phi} (GeV/#it{c}^{2})");
416 h->GetYaxis()->SetTitle("centrality [%]");
417
418 return h;
419}
420
7484404e 421TH2F *GetHistoKan(Float_t pt,Float_t ptM,Float_t pMinkn,Float_t pMinkp,Float_t etaminkp,Float_t etamaxkp){
a8ad4709 422
423 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]};
424 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]};
425
e34b28fe 426 if(kOverAll){
427 x[7] = 0.0001;
6490e37e 428 x2[10] = 5.9;
429 if(pMinkn > 0.19) x2[10] = 4.9;
e34b28fe 430 }
431
432 if(kOverAllTOFmatch && pMinkn > 0.19){
433 x[7] = 1.0001;
6490e37e 434 x2[10] = 4.9;
e34b28fe 435 }
436
437 if(kOverAll2Sigma && pMinkn > 0.09){
438 x2[10] = 2;
439 x[7] = 1.0001;
440 }
441
a8ad4709 442 if(kGoodMatch){
443 x[7] = 1.0001;
444 if(pMinkn > 0)
445 x2[10] = 4.9;
446
447 }
448
449 if(kTOFmatch){
450 x[7] = 1.0001;
451 }
452
453 if(kSigma2vs3){
454 x[7] = 1.0001;
455 x2[10] = 3;
456 if(pMinkn > 0)
457 x2[10] = 2;
458 }
459
460 if(bayesVsigma){
461 if(pMinkn > 0){
462 x[5] = 0.2001;
463 x2[10] = 5;
464 }
465 else{
466 x2[10] = 3;
467 }
468 }
469
470 if(require5sigma) x2[10] = 4.9;
471
7484404e 472 AliPIDperfContainer *tmp = (AliPIDperfContainer *) fContPid2;
a8ad4709 473
474 TH2F *h = tmp->GetQA(0, x, x2);
475
476 h->GetXaxis()->SetTitle("M_{#phi} (GeV/#it{c}^{2})");
477 h->GetYaxis()->SetTitle("centrality [%]");
478
479 return h;
480}
481
482
7484404e 483void fit(TH1D *h,Float_t *a,char *opt,char *opt2,Float_t pt){
a8ad4709 484 if(h->GetEntries() < 1){
485 if(a){
486 a[0]=0.01;
487 a[1]=1;
488 }
489 return;
490 }
491
492
493 fall->SetParameter(0,100);
494 fall->SetParameter(0,1.01898 + 2.4e-04*pt);
495 fall->SetParameter(2,0.0044);
496 fall->SetParameter(3,0.0015);
497
498 fall->SetParLimits(0,-100,100000);
499 fall->SetParLimits(1,1.01898 + 2.4e-04*pt-1e-03,1.01898 + 2.4e-04*pt+1e-03);
500 fall->SetParLimits(2,0.0005,0.006);
501 fall->SetParLimits(3,0.001,0.0017);
502
503 fall->FixParameter(1,1.01884 + 2.9891e-04*pt);
504 fall->FixParameter(2,0.0044);
505 fall->FixParameter(3,7.57574e-04 + 3.85408e-04*pt);
506
507 fall->ReleaseParameter(4);
508 fall->ReleaseParameter(5);
509 fall->ReleaseParameter(6);
510 fall->ReleaseParameter(7);
511 fall->ReleaseParameter(8);
512
513
514 if(!kGoodMatch && !kSigma2vs3){
515 if(pt > 1.5){
516 fall->FixParameter(7,0);
517 fall->FixParameter(8,0);
518 }
519 if(pt > 1.7){
520 fall->FixParameter(6,0);
521 }
522 }
523
524 if(selectTrue){
525 fall->FixParameter(4,0);
526 fall->FixParameter(5,0);
527 fall->FixParameter(6,0);
528 fall->FixParameter(7,0);
529 fall->FixParameter(8,0);
530 }
531
532 char name[100];
533 TF1 *ftmp=fall;
534
535 TF1 *ftmp2=new TF1(*fsign);
536 sprintf(name,"fsign%i",ifunc);
537 ftmp2->SetName(name);
538
539 TF1 *ftmp3=new TF1(*fback);
540 sprintf(name,"ftmp3%i",ifunc);
541 ftmp3->SetName(name);
542
543 ifunc++;
544
545 h->Fit(ftmp,opt,opt2,fitmin,fitmax);
546 h->Draw("ERR");
547
548 ftmp2->SetParameter(0,ftmp->GetParameter(0));
549 ftmp2->SetParameter(1,ftmp->GetParameter(1));
550 ftmp2->SetParameter(2,ftmp->GetParameter(2));
551 ftmp2->SetParameter(3,ftmp->GetParameter(3));
552 ftmp2->Draw("SAME");
553 ftmp3->SetParameter(0,ftmp->GetParameter(4));
554 ftmp3->SetParameter(1,ftmp->GetParameter(5));
555 ftmp3->SetParameter(2,ftmp->GetParameter(6));
556 ftmp3->SetParameter(3,ftmp->GetParameter(7));
557 ftmp3->SetParameter(4,ftmp->GetParameter(8));
558 ftmp3->Draw("SAME");
559
560 Float_t mean = ftmp->GetParameter(1);
561 Float_t sigma = 0.0044;//TMath::Abs(ftmp->GetParameter(2));
562
563 Float_t signI = ftmp2->Integral(mean-10*sigma,mean+10*sigma)/h->GetBinWidth(1);
564 Float_t backI = ftmp3->Integral(mean-3*sigma,mean+3*sigma)/h->GetBinWidth(1);
565
566 Float_t errI = TMath::Sqrt(ftmp->GetParError(0)*ftmp->GetParError(0)/(0.001+ftmp->GetParameter(0))/(0.001+ftmp->GetParameter(0)));
567
568 printf("signal(5 sigma) = %f +/- %f(fit) +/- %f(stat)\n",signI,errI*signI,TMath::Sqrt(signI));
569 printf("backgr(3sigma) = %f\n",backI);
570 printf("significance(3 sigma) = %f\n",signI/sqrt(signI+backI));
571
572 if(a){
573 a[0]=signI;
574 a[1]=signI*errI*signI*errI + signI;
575 a[1] = TMath::Sqrt(a[1]);
576 if(ftmp->GetNDF()) a[2] = ftmp->GetChisquare()/ftmp->GetNDF();
577
578
579 if(selectTrue){
6490e37e 580 a[0] = h->GetEntries();
a8ad4709 581 a[1] = TMath::Sqrt(a[0]);
582 }
583 }
584}
585
7484404e 586void AddHisto(TH2F *h1,TH2F *h2,Float_t w){
a8ad4709 587 Int_t nbinx = h1->GetNbinsX();
588 Int_t nbiny = h1->GetNbinsY();
589
590 for(Int_t i=1;i<=nbinx;i++){
591 for(Int_t j=1;j<=nbiny;j++){
7484404e 592 Double_t val = h1->GetBinContent(i,j) + h2->GetBinContent(i,j)*w;
a8ad4709 593 Float_t err = TMath::Min(TMath::Sqrt(val),val);
594 h1->SetBinContent(i,j,val);
595 h1->SetBinError(i,j,err);
596 }
597 }
598}