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1 | /************************************************************************** |
| 2 | * Copyright(c) 1998-1999, ALICE Experiment at CERN, All rights reserved. * |
| 3 | * * |
| 4 | * Author: The ALICE Off-line Project. * |
| 5 | * Contributors are mentioned in the code where appropriate. * |
| 6 | * * |
| 7 | * Permission to use, copy, modify and distribute this software and its * |
| 8 | * documentation strictly for non-commercial purposes is hereby granted * |
| 9 | * without fee, provided that the above copyright notice appears in all * |
| 10 | * copies and that both the copyright notice and this permission notice * |
| 11 | * appear in the supporting documentation. The authors make no claims * |
| 12 | * about the suitability of this software for any purpose. It is * |
| 13 | * provided "as is" without express or implied warranty. * |
| 14 | **************************************************************************/ |
| 15 | // |
| 16 | // This class is used to store correlation maps, generated and reconstructed data of the jet spectrum |
| 17 | // It also contains functions to correct the spectrum using the bayesian unfolding |
| 18 | // |
| 19 | |
| 20 | #include "AliJetSpectrumUnfolding.h" |
| 21 | |
| 22 | #include <TFile.h> |
| 23 | #include <TH1F.h> |
| 24 | #include <TH2F.h> |
| 25 | #include <TH3F.h> |
| 26 | #include <TDirectory.h> |
| 27 | #include <TVirtualFitter.h> |
| 28 | #include <TCanvas.h> |
| 29 | #include <TString.h> |
| 30 | #include <TF1.h> |
| 31 | #include <TF2.h> |
| 32 | #include <TMath.h> |
| 33 | #include <TCollection.h> |
| 34 | #include <TLegend.h> |
| 35 | #include <TLine.h> |
| 36 | #include <TRandom.h> |
| 37 | #include <TProfile.h> |
| 38 | #include <TProfile2D.h> |
| 39 | #include <TStyle.h> |
| 40 | #include <TColor.h> |
| 41 | |
| 42 | #include <THnSparse.h> |
| 43 | |
| 44 | ClassImp(AliJetSpectrumUnfolding) |
| 45 | |
| 46 | const Int_t NBINSE = 50; |
| 47 | const Int_t NBINSZ = 50; |
| 48 | const Int_t NEVENTS = 500000; |
| 49 | const Double_t axisLowerLimitE = 0.; |
| 50 | const Double_t axisLowerLimitZ = 0.; |
| 51 | const Double_t axisUpperLimitE = 250.; |
| 52 | const Double_t axisUpperLimitZ = 1.; |
| 53 | |
| 54 | Float_t AliJetSpectrumUnfolding::fgBayesianSmoothing = 1; // smoothing parameter (0 = no smoothing) |
| 55 | Int_t AliJetSpectrumUnfolding::fgBayesianIterations = 100; // number of iterations in Bayesian method |
| 56 | |
| 57 | //____________________________________________________________________ |
| 58 | |
| 59 | AliJetSpectrumUnfolding::AliJetSpectrumUnfolding() : |
| 60 | TNamed(), fCurrentRec(0), fCurrentCorrelation(0), fGenSpectrum(0), |
| 61 | fRecSpectrum(0), fUnfSpectrum(0), fCorrelation(0), fLastChi2MC(0), fLastChi2MCLimit(0), fLastChi2Residuals(0), fRatioAverage(0) |
| 62 | { |
| 63 | // |
| 64 | // default constructor |
| 65 | // |
| 66 | |
| 67 | fGenSpectrum = 0; |
| 68 | fRecSpectrum = 0; |
| 69 | fUnfSpectrum = 0; |
| 70 | fCorrelation = 0; |
| 71 | } |
| 72 | |
| 73 | //____________________________________________________________________ |
| 74 | AliJetSpectrumUnfolding::AliJetSpectrumUnfolding(const Char_t* name, const Char_t* title) : |
| 75 | TNamed(name, title), fCurrentRec(0), fCurrentCorrelation(0), fGenSpectrum(0), |
| 76 | fRecSpectrum(0), fUnfSpectrum(0), fCorrelation(0), fLastChi2MC(0), fLastChi2MCLimit(0), fLastChi2Residuals(0), fRatioAverage(0) |
| 77 | { |
| 78 | // |
| 79 | // named constructor |
| 80 | // |
| 81 | |
| 82 | // do not add this hists to the directory |
| 83 | Bool_t oldStatus = TH1::AddDirectoryStatus(); |
| 84 | TH1::AddDirectory(kFALSE); |
| 85 | |
| 86 | #define ZBINNING NBINSZ, axisLowerLimitZ, axisUpperLimitZ // fragmentation of leading particle |
| 87 | #define EBINNING NBINSE, axisLowerLimitE, axisUpperLimitE // energy of the jet |
| 88 | |
| 89 | fRecSpectrum = new TH2F("fRecSpectrum", "Reconstructed Spectrum;E^{jet}_{rec} [GeV];z^{lp}_{rec}", EBINNING, ZBINNING); |
| 90 | fGenSpectrum = new TH2F("fGenSpectrum", "Generated Spectrum;E^{jet}_{gen} [GeV];z^{lp}_{gen}", EBINNING, ZBINNING); |
| 91 | fUnfSpectrum = new TH2F("fUnfSpectrum", "Unfolded Spectrum;E^{jet} [GeV];z^{lp}", EBINNING, ZBINNING); |
| 92 | |
| 93 | const Int_t nbin[4]={NBINSE, NBINSE, NBINSZ, NBINSZ}; |
| 94 | //arrays for bin limits |
| 95 | Double_t LowEdge[4] = {axisLowerLimitE, axisLowerLimitE, axisLowerLimitZ, axisLowerLimitZ}; |
| 96 | Double_t UpEdge[4] = {axisUpperLimitE, axisUpperLimitE, axisUpperLimitZ, axisUpperLimitZ}; |
| 97 | |
| 98 | fCorrelation = new THnSparseF("fCorrelation", "Correlation Function", 4, nbin, LowEdge, UpEdge); |
| 99 | |
| 100 | TH1::AddDirectory(oldStatus); |
| 101 | } |
| 102 | |
| 103 | //____________________________________________________________________ |
| 104 | AliJetSpectrumUnfolding::~AliJetSpectrumUnfolding() |
| 105 | { |
| 106 | // |
| 107 | // Destructor |
| 108 | // |
| 109 | |
| 110 | if (fGenSpectrum) |
| 111 | delete fGenSpectrum; |
| 112 | fGenSpectrum = 0; |
| 113 | |
| 114 | if (fRecSpectrum) |
| 115 | delete fRecSpectrum; |
| 116 | fRecSpectrum = 0; |
| 117 | |
| 118 | if (fUnfSpectrum) |
| 119 | delete fUnfSpectrum; |
| 120 | fUnfSpectrum = 0; |
| 121 | |
| 122 | if (fCorrelation) |
| 123 | delete fCorrelation; |
| 124 | fCorrelation = 0; |
| 125 | |
| 126 | } |
| 127 | |
| 128 | //____________________________________________________________________ |
| 129 | Long64_t AliJetSpectrumUnfolding::Merge(TCollection* list) |
| 130 | { |
| 131 | // Merge a list of AliJetSpectrumUnfolding objects with this (needed for |
| 132 | // PROOF). |
| 133 | // Returns the number of merged objects (including this). |
| 134 | |
| 135 | if (!list) |
| 136 | return 0; |
| 137 | |
| 138 | if (list->IsEmpty()) |
| 139 | return 1; |
| 140 | |
| 141 | TIterator* iter = list->MakeIterator(); |
| 142 | TObject* obj; |
| 143 | |
| 144 | // collections of all histograms |
| 145 | TList collections[4]; |
| 146 | |
| 147 | Int_t count = 0; |
| 148 | while ((obj = iter->Next())) { |
| 149 | |
| 150 | AliJetSpectrumUnfolding* entry = dynamic_cast<AliJetSpectrumUnfolding*> (obj); |
| 151 | if (entry == 0) |
| 152 | continue; |
| 153 | |
| 154 | collections[0].Add(entry->fGenSpectrum); |
| 155 | collections[1].Add(entry->fRecSpectrum); |
| 156 | collections[2].Add(entry->fUnfSpectrum); |
| 157 | collections[3].Add(entry->fCorrelation); |
| 158 | |
| 159 | count++; |
| 160 | } |
| 161 | |
| 162 | fGenSpectrum->Merge(&collections[0]); |
| 163 | fRecSpectrum->Merge(&collections[1]); |
| 164 | fUnfSpectrum->Merge(&collections[2]); |
| 165 | fCorrelation->Merge(&collections[3]); |
| 166 | |
| 167 | delete iter; |
| 168 | |
| 169 | return count+1; |
| 170 | } |
| 171 | |
| 172 | //____________________________________________________________________ |
| 173 | Bool_t AliJetSpectrumUnfolding::LoadHistograms(const Char_t* dir) |
| 174 | { |
| 175 | // |
| 176 | // loads the histograms from a file |
| 177 | // if dir is empty a directory with the name of this object is taken (like in SaveHistogram) |
| 178 | // |
| 179 | |
| 180 | if (!dir) |
| 181 | dir = GetName(); |
| 182 | |
| 183 | if (!gDirectory->cd(dir)) |
| 184 | return kFALSE; |
| 185 | |
| 186 | Bool_t success = kTRUE; |
| 187 | |
| 188 | // store old histograms to delete them later |
| 189 | TList oldHistograms; |
| 190 | oldHistograms.SetOwner(1); |
| 191 | |
| 192 | if (fGenSpectrum) oldHistograms.Add(fGenSpectrum); |
| 193 | if (fRecSpectrum) oldHistograms.Add(fRecSpectrum); |
| 194 | if (fUnfSpectrum) oldHistograms.Add(fUnfSpectrum); |
| 195 | if (fCorrelation) oldHistograms.Add(fCorrelation); |
| 196 | |
| 197 | // load new histograms |
| 198 | fGenSpectrum = dynamic_cast<TH2F*> (gDirectory->Get(fGenSpectrum->GetName())); |
| 199 | if (!fGenSpectrum) |
| 200 | success = kFALSE; |
| 201 | |
| 202 | fRecSpectrum = dynamic_cast<TH2F*> (gDirectory->Get(fRecSpectrum->GetName())); |
| 203 | if (!fRecSpectrum) |
| 204 | success = kFALSE; |
| 205 | |
| 206 | fUnfSpectrum = dynamic_cast<TH2F*> (gDirectory->Get(fUnfSpectrum->GetName())); |
| 207 | if (!fUnfSpectrum) |
| 208 | success = kFALSE; |
| 209 | |
| 210 | fCorrelation = dynamic_cast<THnSparseF*> (gDirectory->Get(fCorrelation->GetName())); |
| 211 | if (!fCorrelation) |
| 212 | success = kFALSE; |
| 213 | |
| 214 | gDirectory->cd(".."); |
| 215 | |
| 216 | // delete old histograms |
| 217 | oldHistograms.Delete(); |
| 218 | |
| 219 | return success; |
| 220 | } |
| 221 | |
| 222 | //____________________________________________________________________ |
| 223 | void AliJetSpectrumUnfolding::SaveHistograms() |
| 224 | { |
| 225 | // |
| 226 | // saves the histograms |
| 227 | // |
| 228 | |
| 229 | gDirectory->mkdir(GetName()); |
| 230 | gDirectory->cd(GetName()); |
| 231 | |
| 232 | if (fGenSpectrum) |
| 233 | fGenSpectrum->Write(); |
| 234 | |
| 235 | if (fRecSpectrum) |
| 236 | fRecSpectrum->Write(); |
| 237 | |
| 238 | if (fUnfSpectrum) |
| 239 | fUnfSpectrum->Write(); |
| 240 | |
| 241 | if (fCorrelation) |
| 242 | fCorrelation->Write(); |
| 243 | |
| 244 | gDirectory->cd(".."); |
| 245 | } |
| 246 | |
| 247 | //____________________________________________________________________ |
| 248 | void AliJetSpectrumUnfolding::SetupCurrentHists(Bool_t createBigBin) |
| 249 | { |
| 250 | // |
| 251 | // resets fUnfSpectrum |
| 252 | // |
| 253 | |
| 254 | fUnfSpectrum->Reset(); |
| 255 | fUnfSpectrum->Sumw2(); |
| 256 | |
| 257 | fCurrentRec = (TH2F*)fRecSpectrum->Clone("fCurrentRec"); |
| 258 | fCurrentRec->Sumw2(); |
| 259 | |
| 260 | fCurrentCorrelation = (THnSparseF*)fCorrelation->Clone("fCurrentCorrelation"); |
| 261 | fCurrentCorrelation->Sumw2(); |
| 262 | |
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263 | Printf("Correlation Matrix has %.0E filled bins", fCurrentCorrelation->GetNbins()); |
| 264 | |
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265 | if (createBigBin) |
| 266 | { |
| 267 | Int_t maxBinE = 0, maxBinZ = 0; |
| 268 | Float_t maxE = 0, maxZ = 0; |
| 269 | for (Int_t me=1; me<=fCurrentRec->GetNbinsX(); me++) |
| 270 | for (Int_t mz=1; mz<=fCurrentRec->GetNbinsY(); mz++) |
| 271 | { |
| 272 | if (fCurrentRec->GetBinContent(me,mz) <= 5 && me>NBINSE/2 && mz>NBINSZ/2) |
| 273 | { |
| 274 | maxBinE = me; |
| 275 | maxBinZ = mz; |
| 276 | maxE = fCurrentRec->GetXaxis()->GetBinCenter(me); |
| 277 | maxZ = fCurrentRec->GetYaxis()->GetBinCenter(mz); |
| 278 | break; |
| 279 | } |
| 280 | } |
| 281 | |
| 282 | if (maxBinE > 0 || maxBinZ > 0) |
| 283 | { |
| 284 | printf("Bin limit in measured spectrum is e = %d and z = %d.\n", maxBinE, maxBinZ); |
| 285 | fCurrentRec->SetBinContent(maxBinE, maxBinZ, fCurrentRec->Integral(maxBinE, fCurrentRec->GetNbinsX(), maxBinZ, fCurrentRec->GetNbinsY())); |
| 286 | for (Int_t me=maxBinE+1; me<=fCurrentRec->GetNbinsX(); me++) |
| 287 | for (Int_t mz=maxBinZ+1; mz<=fCurrentRec->GetNbinsY(); mz++) |
| 288 | { |
| 289 | fCurrentRec->SetBinContent(me, mz, 0); |
| 290 | fCurrentRec->SetBinError(me, mz, 0); |
| 291 | } |
| 292 | // the error is set to sqrt(N), better solution possible?, sum of relative errors of all contributions??? |
| 293 | fCurrentRec->SetBinError(maxBinE, maxBinZ, TMath::Sqrt(fCurrentRec->GetBinContent(maxBinE, maxBinZ))); |
| 294 | |
| 295 | printf("This bin has now %f +- %f entries\n", fCurrentRec->GetBinContent(maxBinE, maxBinZ), fCurrentRec->GetBinError(maxBinE, maxBinZ)); |
| 296 | |
| 297 | /* for (Int_t te=1; te<=NBINSE; te++) |
| 298 | { |
| 299 | for (Int_t tz=1; tz<=NBINSZ; tz++) |
| 300 | { |
| 301 | Int_t binMin[4] = {te, maxBinE, tz, maxBinZ}; |
| 302 | Int_t binMax[4] = {NBINSE, NBINSE, NBINSZ, NBINSZ}; |
| 303 | Float_t sum=0; |
| 304 | for (Int_t ite=te; ite<=NBINSE; ite++) |
| 305 | for (Int_t itz=tz; itz<=NBINSZ; itz++) |
| 306 | for (Int_t ime=maxBinE; ime<=NBINSE; ime++) |
| 307 | for (Int_t imz=maxBinZ; imz<=NBINSZ; imz++) |
| 308 | { |
| 309 | Int_t bin[4] = {ite, ime, itz, imz}; |
| 310 | sum += fCurrentCorrelation->GetBinContent(bin); |
| 311 | } |
| 312 | fCurrentCorrelation->SetBinContent(binMin, sum); |
| 313 | fCurrentCorrelation->SetBinError(binMin, TMath::Sqrt(fCurrentCorrelation->GetBinContent(binMin))); |
| 314 | printf("create big bin1, nbins = %d, te = %d, tz = %d\n", NBINSE, te, tz); |
| 315 | for (Int_t me=maxBinE; me<=NBINSE; me++) |
| 316 | { |
| 317 | for (Int_t mz=maxBinZ; mz<=NBINSZ; mz++) |
| 318 | { |
| 319 | Int_t bin[4] = {te, me, tz, mz}; |
| 320 | fCurrentCorrelation->SetBinContent(bin, 0.); |
| 321 | fCurrentCorrelation->SetBinError(bin, 0.); |
| 322 | printf("create big bin2\n"); |
| 323 | } |
| 324 | } |
| 325 | } |
| 326 | }*/ |
| 327 | |
| 328 | for(Int_t idx = 0; idx<=fCurrentCorrelation->GetNbins(); idx++) |
| 329 | { |
| 330 | Int_t bin[4]; |
| 331 | Float_t binContent = fCurrentCorrelation->GetBinContent(idx,bin); |
| 332 | Float_t binError = fCurrentCorrelation->GetBinError(idx); |
| 333 | Int_t binMin[4] = {bin[0], maxBinE, bin[2], maxBinZ}; |
| 334 | if ( (bin[1]>maxBinE && bin[1]<=NBINSE) && (bin[3]>maxBinZ && bin[3]<=NBINSZ) ) |
| 335 | { |
| 336 | fCurrentCorrelation->SetBinContent(binMin, binContent + fCurrentCorrelation->GetBinContent(binMin)); |
| 337 | fCurrentCorrelation->SetBinError(binMin, binError + TMath::Sqrt(fCurrentCorrelation->GetBinContent(binMin))); |
| 338 | fCurrentCorrelation->SetBinContent(bin, 0.); |
| 339 | fCurrentCorrelation->SetBinError(bin, 0.); |
| 340 | } |
| 341 | printf("create big bin1, nbins = %d, te = %d, tz = %d\n", NBINSE, bin[0], bin[1]); |
| 342 | } |
| 343 | |
| 344 | printf("Adjusted correlation matrix!\n"); |
| 345 | } |
| 346 | } // end Create Big Bin |
| 347 | |
| 348 | } |
| 349 | |
| 350 | //____________________________________________________________________ |
| 351 | void AliJetSpectrumUnfolding::SetBayesianParameters(Float_t smoothing, Int_t nIterations) |
| 352 | { |
| 353 | // |
| 354 | // sets the parameters for Bayesian unfolding |
| 355 | // |
| 356 | |
| 357 | fgBayesianSmoothing = smoothing; |
| 358 | fgBayesianIterations = nIterations; |
| 359 | |
| 360 | printf("AliJetSpectrumUnfolding::SetBayesianParameters --> Paramaters set to %d iterations with smoothing %f\n", fgBayesianIterations, fgBayesianSmoothing); |
| 361 | } |
| 362 | |
| 363 | //____________________________________________________________________ |
| 364 | void AliJetSpectrumUnfolding::NormalizeToBinWidth(TH2* hist) |
| 365 | { |
| 366 | // |
| 367 | // normalizes a 2-d histogram to its bin width (x width * y width) |
| 368 | // |
| 369 | |
| 370 | for (Int_t i=1; i<=hist->GetNbinsX(); i++) |
| 371 | for (Int_t j=1; j<=hist->GetNbinsY(); j++) |
| 372 | { |
| 373 | Double_t factor = hist->GetXaxis()->GetBinWidth(i) * hist->GetYaxis()->GetBinWidth(j); |
| 374 | hist->SetBinContent(i, j, hist->GetBinContent(i, j) / factor); |
| 375 | hist->SetBinError(i, j, hist->GetBinError(i, j) / factor); |
| 376 | } |
| 377 | } |
| 378 | |
| 379 | //____________________________________________________________________ |
| 380 | void AliJetSpectrumUnfolding::DrawHistograms() |
| 381 | { |
| 382 | // |
| 383 | // draws the histograms of this class |
| 384 | // |
| 385 | |
| 386 | gStyle->SetPalette(1); |
| 387 | |
| 388 | TCanvas* canvas1 = new TCanvas("fRecSpectrum", "fRecSpectrum", 900, 600); |
| 389 | gPad->SetLogz(); |
| 390 | fRecSpectrum->DrawCopy("COLZ"); |
| 391 | |
| 392 | TCanvas* canvas2 = new TCanvas("fGenSpectrum", "fGenSpectrum", 900, 600); |
| 393 | gPad->SetLogz(); |
| 394 | fGenSpectrum->DrawCopy("COLZ"); |
| 395 | |
| 396 | TCanvas* canvas3 = new TCanvas("fUnfSpectrum", "fUnfSpectrum", 900, 600); |
| 397 | gPad->SetLogz(); |
| 398 | fUnfSpectrum->DrawCopy("COLZ"); |
| 399 | |
| 400 | TCanvas* canvas4 = new TCanvas("fCorrelation", "fCorrelation", 500, 500); |
| 401 | canvas1->Divide(2); |
| 402 | |
| 403 | canvas4->cd(1); |
| 404 | gPad->SetLogz(); |
| 405 | TH2D* h0 = fCorrelation->Projection(1,0); |
| 406 | h0->SetXTitle("E^{jet}_{gen} [GeV]"); |
| 407 | h0->SetYTitle("E^{jet}_{rec} [GeV]"); |
| 408 | h0->SetTitle("Projection: Jet Energy"); |
| 409 | h0->DrawCopy("colz"); |
| 410 | |
| 411 | canvas1->cd(2); |
| 412 | gPad->SetLogz(); |
| 413 | TH2D* h1 = fCorrelation->Projection(3,2); |
| 414 | h1->SetXTitle("z^{lp}_{gen}"); |
| 415 | h1->SetYTitle("z^{lp}_{rec}"); |
| 416 | h1->SetTitle("Projection: Leading Particle Fragmentation"); |
| 417 | h1->DrawCopy("colz"); |
| 418 | |
| 419 | } |
| 420 | |
| 421 | //____________________________________________________________________ |
| 422 | void AliJetSpectrumUnfolding::DrawComparison(const char* name, TH2* genHist) |
| 423 | { |
| 424 | |
| 425 | if (fUnfSpectrum->Integral() == 0) |
| 426 | { |
| 427 | printf("ERROR. Unfolded histogram is empty\n"); |
| 428 | return; |
| 429 | } |
| 430 | |
| 431 | //regain measured distribution used for unfolding, because the bins were modified in SetupCurrentHists |
| 432 | //in create big bin |
| 433 | fCurrentRec = (TH2F*)fRecSpectrum->Clone(); |
| 434 | fCurrentRec->Sumw2(); |
| 435 | fCurrentRec->Scale(1.0 / fCurrentRec->Integral()); |
| 436 | |
| 437 | // normalize unfolded result to 1 |
| 438 | fUnfSpectrum->Scale(1.0 / fUnfSpectrum->Integral()); |
| 439 | |
| 440 | // find bin with <= 100 entries. this is used as limit for the chi2 calculation |
| 441 | Int_t mcBinLimitE = 0, mcBinLimitZ = 0; |
| 442 | for (Int_t i=0; i<genHist->GetNbinsX(); ++i) |
| 443 | for (Int_t j=0; j<genHist->GetNbinsY(); ++j) |
| 444 | { |
| 445 | if (genHist->GetBinContent(i,j) > 100) |
| 446 | { |
| 447 | mcBinLimitE = i; |
| 448 | mcBinLimitZ = j; |
| 449 | } |
| 450 | else |
| 451 | break; |
| 452 | } |
| 453 | Printf("AliJetSpectrumUnfolding::DrawComparison: Gen bin limit is (x,y) = (%d,%d)", mcBinLimitE,mcBinLimitZ); |
| 454 | |
| 455 | // scale to 1 true spectrum |
| 456 | genHist->Sumw2(); |
| 457 | genHist->Scale(1.0 / genHist->Integral()); |
| 458 | |
| 459 | // calculate residual |
| 460 | // for that we convolute the response matrix with the gathered result |
| 461 | TH2* tmpRecRecorrected = (TH2*) fUnfSpectrum->Clone("tmpRecRecorrected"); |
| 462 | TH2* convoluted = CalculateRecSpectrum(tmpRecRecorrected); |
| 463 | if (convoluted->Integral() > 0) |
| 464 | convoluted->Scale(1.0 / convoluted->Integral()); |
| 465 | else |
| 466 | printf("ERROR: convoluted is empty. Something went wrong calculating the convoluted histogram.\n"); |
| 467 | |
| 468 | TH2* residual = (TH2*) convoluted->Clone("residual"); |
| 469 | residual->SetTitle("(R#otimesUnfolded - Reconstructed)/Reconstructed;E^{jet} [GeV]; z^{lp}"); |
| 470 | |
| 471 | fCurrentRec->Scale(1./fCurrentRec->Integral()); |
| 472 | residual->Add(fCurrentRec, -1); |
| 473 | //residual->Divide(residual, fCurrentRec, 1, 1, "B"); |
| 474 | |
| 475 | // draw canvas |
| 476 | TCanvas* canvas1 = new TCanvas(name, name, 1000, 1000); |
| 477 | canvas1->Divide(2, 3); |
| 478 | |
| 479 | Int_t style = 1; |
| 480 | const Int_t NRGBs = 5; |
| 481 | const Int_t NCont = 500; |
| 482 | |
| 483 | Double_t stops[NRGBs] = { 0.00, 0.34, 0.61, 0.84, 1.00 }; |
| 484 | Double_t red[NRGBs] = { 0.00, 0.00, 0.87, 1.00, 0.51 }; |
| 485 | Double_t green[NRGBs] = { 0.00, 0.81, 1.00, 0.20, 0.00 }; |
| 486 | Double_t blue[NRGBs] = { 0.51, 1.00, 0.12, 0.00, 0.00 }; |
| 487 | TColor::CreateGradientColorTable(NRGBs, stops, red, green, blue, NCont); |
| 488 | gStyle->SetNumberContours(NCont); |
| 489 | |
| 490 | canvas1->cd(1); |
| 491 | gStyle->SetPalette(style); |
| 492 | gPad->SetLogz(); |
| 493 | genHist->SetTitle("Generated Spectrum;E^{jet}_{gen} [GeV];z^{lp}"); |
| 494 | genHist->SetStats(0); |
| 495 | genHist->DrawCopy("colz"); |
| 496 | |
| 497 | canvas1->cd(2); |
| 498 | gStyle->SetPalette(style); |
| 499 | gPad->SetLogz(); |
| 500 | fUnfSpectrum->SetStats(0); |
| 501 | fUnfSpectrum->DrawCopy("colz"); |
| 502 | |
| 503 | canvas1->cd(3); |
| 504 | gStyle->SetPalette(style); |
| 505 | gPad->SetLogz(); |
| 506 | fCurrentRec->SetTitle(fRecSpectrum->GetTitle()); |
| 507 | fCurrentRec->SetStats(0); |
| 508 | fCurrentRec->DrawCopy("colz"); |
| 509 | |
| 510 | canvas1->cd(4); |
| 511 | gStyle->SetPalette(style); |
| 512 | gPad->SetLogy(); |
| 513 | TH1D* projGenX = genHist->ProjectionX(); |
| 514 | projGenX->SetTitle("Projection: Jet Energy; E^{jet} [GeV]"); |
| 515 | TH1D* projUnfX = fUnfSpectrum->ProjectionX(); |
| 516 | TH1D* projRecX = fCurrentRec->ProjectionX(); |
| 517 | projGenX->SetStats(0); |
| 518 | projRecX->SetStats(0); |
| 519 | projUnfX->SetStats(0); |
| 520 | projGenX->SetLineColor(8); |
| 521 | projRecX->SetLineColor(2); |
| 522 | projGenX->DrawCopy(); |
| 523 | projUnfX->DrawCopy("same"); |
| 524 | projRecX->DrawCopy("same"); |
| 525 | |
| 526 | TLegend* legend = new TLegend(0.6, 0.85, 0.98, 0.98); |
| 527 | legend->AddEntry(projGenX, "Generated Spectrum"); |
| 528 | legend->AddEntry(projUnfX, "Unfolded Spectrum"); |
| 529 | legend->AddEntry(projRecX, "Reconstructed Spectrum"); |
| 530 | //legend->SetFillColor(0); |
| 531 | legend->Draw("same"); |
| 532 | |
| 533 | canvas1->cd(5); |
| 534 | gPad->SetLogy(); |
| 535 | gStyle->SetPalette(style); |
| 536 | TH1D* projGenY = genHist->ProjectionY(); |
| 537 | projGenY->SetTitle("Projection: Leading Particle Fragmentation; z^{lp}"); |
| 538 | TH1D* projUnfY = fUnfSpectrum->ProjectionY(); |
| 539 | TH1D* projRecY = fCurrentRec->ProjectionY(); |
| 540 | projGenY->SetStats(0); |
| 541 | projRecY->SetStats(0); |
| 542 | projUnfY->SetStats(0); |
| 543 | projGenY->SetLineColor(8); |
| 544 | projRecY->SetLineColor(2); |
| 545 | projGenY->DrawCopy(); |
| 546 | projUnfY->DrawCopy("same"); |
| 547 | projRecY->DrawCopy("same"); |
| 548 | |
| 549 | TLegend* legend1 = new TLegend(0.6, 0.85, 0.98, 0.98); |
| 550 | legend1->AddEntry(projGenY, "Generated Spectrum"); |
| 551 | legend1->AddEntry(projUnfY, "Unfolded Spectrum"); |
| 552 | legend1->AddEntry(projRecY, "Recontructed Spectrum"); |
| 553 | //legend1->SetFillColor(0); |
| 554 | legend1->Draw("same"); |
| 555 | |
| 556 | // Draw residuals |
| 557 | canvas1->cd(6); |
| 558 | gStyle->SetPalette(style); |
| 559 | gPad->SetLogz(); |
| 560 | residual->SetStats(0); |
| 561 | residual->DrawCopy("colz"); |
| 562 | |
| 563 | canvas1->SaveAs(Form("%s.png", canvas1->GetName())); |
| 564 | } |
| 565 | |
| 566 | |
| 567 | //____________________________________________________________________ |
| 568 | void AliJetSpectrumUnfolding::GetComparisonResults(Float_t* gen, Int_t* genLimit, Float_t* residuals, Float_t* ratioAverage) const |
| 569 | { |
| 570 | // Returns the chi2 between the Generated and the unfolded Reconstructed spectrum as well as between the Reconstructed and the folded unfolded |
| 571 | // These values are computed during DrawComparison, thus this function picks up the |
| 572 | // last calculation |
| 573 | |
| 574 | if (gen) |
| 575 | *gen = fLastChi2MC; |
| 576 | if (genLimit) |
| 577 | *genLimit = fLastChi2MCLimit; |
| 578 | if (residuals) |
| 579 | *residuals = fLastChi2Residuals; |
| 580 | if (ratioAverage) |
| 581 | *ratioAverage = fRatioAverage; |
| 582 | } |
| 583 | |
| 584 | //____________________________________________________________________ |
| 585 | void AliJetSpectrumUnfolding::ApplyBayesianMethod(Float_t regPar, Int_t nIterations, TH2* initialConditions, Bool_t determineError) |
| 586 | { |
| 587 | // |
| 588 | // correct spectrum using bayesian unfolding |
| 589 | // |
| 590 | |
| 591 | // initialize seed with current time |
| 592 | gRandom->SetSeed(0); |
| 593 | |
| 594 | printf("seting up current arrays and histograms...\n"); |
| 595 | SetupCurrentHists(kFALSE); // kFALSE to not create big bin |
| 596 | |
| 597 | // normalize Correlation Map to convert number of events into probabilities |
| 598 | /*for (Int_t te=1; te<=NBINSE; te++) |
| 599 | for (Int_t tz=1; tz<=NBINSZ; tz++) |
| 600 | { |
| 601 | Int_t bin[4]; |
| 602 | Float_t sum=0.; |
| 603 | for (Int_t me = 1; me<=NBINSE; me++) |
| 604 | for (Int_t mz = 1; mz<=NBINSZ; mz++) |
| 605 | { |
| 606 | bin[0] = te; bin[1] = me; |
| 607 | bin[2] = tz; bin[3] = mz; |
| 608 | sum += fCurrentCorrelation->GetBinContent(bin); |
| 609 | } |
| 610 | if (sum > 0.) |
| 611 | for (Int_t me = 1; me<=NBINSE; me++) |
| 612 | for (Int_t mz = 1; mz<=NBINSZ; mz++) |
| 613 | { |
| 614 | bin[0] = te; bin[1] = me; |
| 615 | bin[2] = tz; bin[3] = mz; |
| 616 | fCurrentCorrelation->SetBinContent(bin, fCurrentCorrelation->GetBinContent(bin)/sum); |
| 617 | fCurrentCorrelation->SetBinError(bin, fCurrentCorrelation->GetBinError(bin)/sum); |
| 618 | } |
| 619 | }*/ |
| 620 | Float_t sum[NBINSE+2][NBINSZ+2]; |
| 621 | memset(sum,0,sizeof(Float_t)*(NBINSE+2)*(NBINSZ+2)); |
| 622 | |
| 623 | for (Int_t idx=0; idx<=fCurrentCorrelation->GetNbins(); idx++) |
| 624 | { |
| 625 | Int_t bin[4]; |
| 626 | Float_t binContent = fCurrentCorrelation->GetBinContent(idx, bin); |
| 627 | if ( (bin[1]>0 && bin[1]<=NBINSE) && (bin[3]>0 && bin[3]<=NBINSZ) ) |
| 628 | sum[bin[0]][bin[2]] += binContent; |
| 629 | } |
| 630 | |
| 631 | for (Int_t idx=0; idx<=fCurrentCorrelation->GetNbins(); idx++) |
| 632 | { |
| 633 | Int_t bin[4]; |
| 634 | Float_t binContent = fCurrentCorrelation->GetBinContent(idx, bin); |
| 635 | Float_t binError = fCurrentCorrelation->GetBinError(bin); |
| 636 | if (sum[bin[0]][bin[2]]>0 && (bin[1]>0 && bin[1]<=NBINSE) && |
| 637 | (bin[3]>0 && bin[3]<=NBINSZ) && (bin[0]>0 && bin[0]<=NBINSE) && (bin[2]>0 && bin[2]<=NBINSZ) ) |
| 638 | { |
| 639 | fCurrentCorrelation->SetBinContent(bin, binContent/sum[bin[0]][bin[2]]); |
| 640 | fCurrentCorrelation->SetBinError(bin, binError/sum[bin[0]][bin[2]]); |
| 641 | } |
| 642 | } |
| 643 | |
| 644 | printf("calling UnfoldWithBayesian\n"); |
| 645 | Int_t success = UnfoldWithBayesian(fCurrentCorrelation, fCurrentRec, initialConditions, fUnfSpectrum, regPar, nIterations, kFALSE); |
| 646 | |
| 647 | if ( success != 0) |
| 648 | return; |
| 649 | |
| 650 | if (!determineError) |
| 651 | { |
| 652 | Printf("AliJetSpectrumUnfolding::ApplyBayesianMethod: WARNING: No errors calculated."); |
| 653 | return; |
| 654 | } |
| 655 | |
| 656 | // evaluate errors, this is done by randomizing the measured spectrum following Poission statistics |
| 657 | // this (new) measured spectrum is then unfolded and the different to the result from the "real" measured |
| 658 | // spectrum calculated. This is performed N times and the maximum difference is taken as the systematic |
| 659 | // error of the unfolding method itself. |
| 660 | |
| 661 | TH2* maxError = (TH2*) fUnfSpectrum->Clone("maxError"); |
| 662 | maxError->Reset(); |
| 663 | |
| 664 | TH2* normalizedResult = (TH2*) fUnfSpectrum->Clone("normalizedResult"); |
| 665 | normalizedResult->Scale(1.0 / normalizedResult->Integral()); |
| 666 | |
| 667 | const Int_t kErrorIterations = 20; |
| 668 | |
| 669 | printf("Spectrum unfolded. Determining error (%d iterations)...\n", kErrorIterations); |
| 670 | |
| 671 | TH2* randomized = (TH2*) fCurrentRec->Clone("randomized"); |
| 672 | TH2* result2 = (TH2*) fUnfSpectrum->Clone("result2"); |
| 673 | for (Int_t n=0; n<kErrorIterations; ++n) |
| 674 | { |
| 675 | // randomize the content of clone following a poisson with the mean = the value of that bin |
| 676 | for (Int_t x=1; x<=randomized->GetNbinsX(); x++) |
| 677 | for (Int_t y=1; y<=randomized->GetNbinsY(); y++) |
| 678 | { |
| 679 | Float_t randomValue = fCurrentRec->GetBinContent(x,y); |
| 680 | TF1* poisson = new TF1("poisson", "TMath::Poisson(x,[0])",randomValue*0.25, randomValue*1.25); |
| 681 | poisson->SetParameters(randomValue,0.); |
| 682 | randomValue = poisson->GetRandom(); |
| 683 | //printf("%e --> %e\n", fCurrentRec->GetBinContent(x,y), (Double_t)randomValue); |
| 684 | randomized->SetBinContent(x, y, randomValue); |
| 685 | delete poisson; |
| 686 | } |
| 687 | |
| 688 | result2->Reset(); |
| 689 | if (UnfoldWithBayesian(fCurrentCorrelation, randomized, initialConditions, result2, regPar, nIterations) != 0) |
| 690 | return; |
| 691 | |
| 692 | result2->Scale(1.0 / result2->Integral()); |
| 693 | |
| 694 | // calculate ratio |
| 695 | result2->Divide(normalizedResult); |
| 696 | |
| 697 | //new TCanvas; result2->DrawCopy("HIST"); |
| 698 | |
| 699 | // find max. deviation |
| 700 | for (Int_t i=1; i<=result2->GetNbinsX(); i++) |
| 701 | for (Int_t j=1; j<=result2->GetNbinsY(); j++) |
| 702 | maxError->SetBinContent(i, j, TMath::Max(maxError->GetBinContent(i,j), TMath::Abs(1 - result2->GetBinContent(i,j)))); |
| 703 | } |
| 704 | delete randomized; |
| 705 | delete result2; |
| 706 | |
| 707 | for (Int_t i=1; i<=fUnfSpectrum->GetNbinsX(); i++) |
| 708 | for (Int_t j=1; j<=fUnfSpectrum->GetNbinsY(); j++) |
| 709 | fUnfSpectrum->SetBinError(i, j, fUnfSpectrum->GetBinError(i,j) + maxError->GetBinContent(i,j)*fUnfSpectrum->GetBinContent(i,j)); |
| 710 | |
| 711 | delete maxError; |
| 712 | delete normalizedResult; |
| 713 | } |
| 714 | |
| 715 | //____________________________________________________________________ |
| 716 | Int_t AliJetSpectrumUnfolding::UnfoldWithBayesian(THnSparseF* correlation, TH2* measured, TH2* initialConditions, TH2* aResult, Float_t regPar, Int_t nIterations, Bool_t calculateErrors) |
| 717 | { |
| 718 | // |
| 719 | // unfolds a spectrum |
| 720 | // |
| 721 | |
| 722 | if (measured->Integral() <= 0) |
| 723 | { |
| 724 | Printf("AliJetSpectrumUnfolding::UnfoldWithBayesian: ERROR: The measured spectrum is empty"); |
| 725 | return 1; |
| 726 | } |
| 727 | const Int_t NFilledBins = correlation->GetNbins(); |
| 728 | const Int_t kStartBin = 1; |
| 729 | |
| 730 | const Int_t kMaxTZ = NBINSZ; // max true axis fragmentation function |
| 731 | const Int_t kMaxMZ = NBINSZ; // max measured axis fragmentation function |
| 732 | const Int_t kMaxTE = NBINSE; // max true axis energy |
| 733 | const Int_t kMaxME = NBINSE; // max measured axis energy |
| 734 | |
| 735 | printf("NbinsE=%d - NbinsZ=%d\n", NBINSE, NBINSZ); |
| 736 | |
| 737 | // store information in arrays, to increase processing speed |
| 738 | Double_t measuredCopy[kMaxME+1][kMaxMZ+1]; |
| 739 | Double_t prior[kMaxTE+1][kMaxTZ+1]; |
| 740 | Double_t errors[kMaxTE+1][kMaxTZ+1]; |
| 741 | Double_t result[kMaxTE+1][kMaxTZ+1]; |
| 742 | |
| 743 | THnSparseF *inverseCorrelation; |
| 744 | inverseCorrelation = (THnSparseF*)correlation->Clone("inverseCorrelation"); |
| 745 | inverseCorrelation->Reset(); |
| 746 | |
| 747 | Float_t inputDistIntegral = 1; |
| 748 | if (initialConditions) |
| 749 | { |
| 750 | printf("Using different starting conditions...\n"); |
| 751 | inputDistIntegral = initialConditions->Integral(); |
| 752 | } |
| 753 | Float_t measuredIntegral = measured->Integral(); |
| 754 | for (Int_t me=1; me<=kMaxME; me++) |
| 755 | for (Int_t mz=1; mz<=kMaxMZ; mz++) |
| 756 | { |
| 757 | // normalization of the measured spectrum |
| 758 | measuredCopy[me][mz] = measured->GetBinContent(me,mz) / measuredIntegral; |
| 759 | errors[me][mz] = measured->GetBinError(me, mz) / measuredIntegral; |
| 760 | // pick prior distribution and normalize it |
| 761 | if (initialConditions) |
| 762 | prior[me][mz] = initialConditions->GetBinContent(me,mz) / inputDistIntegral; |
| 763 | else |
| 764 | prior[me][mz] = measured->GetBinContent(me,mz) / measuredIntegral; |
| 765 | } |
| 766 | |
| 767 | // unfold... |
| 768 | for (Int_t i=0; i<nIterations; i++) |
| 769 | { |
| 770 | // calculate Inverse Correlation Map from Bayes theorem: |
| 771 | // IR_ji = R_ij * prior_i / sum_k(R_kj * prior_k) |
| 772 | /*Float_t norm = 0; |
| 773 | for (Int_t me=1; me<=kMaxME; me++) |
| 774 | for (Int_t mz=1; mz<=kMaxMZ; mz++) |
| 775 | { |
| 776 | norm = 0; |
| 777 | for (Int_t te=kStartBin; te<=kMaxTE; te++) |
| 778 | for (Int_t tz=kStartBin; tz<=kMaxTZ; tz++) |
| 779 | { |
| 780 | Int_t bin[4] = {te, me, tz, mz}; |
| 781 | norm += correlation->GetBinContent(bin)*prior[te][tz]; |
| 782 | } |
| 783 | if (norm > 0) |
| 784 | for (Int_t te = kStartBin; te <= kMaxTE; te++) |
| 785 | for (Int_t tz = kStartBin; tz <= kMaxTZ; tz++) |
| 786 | { |
| 787 | Int_t bin[4] = {te, me, tz, mz}; |
| 788 | inverseCorrelation->SetBinContent(bin, correlation->GetBinContent(bin)*prior[te][tz]/norm ); |
| 789 | } |
| 790 | //else |
| 791 | // inverse response set to '0' wich has been already done in line 2069 |
| 792 | }*/ |
| 793 | inverseCorrelation->Reset(); |
| 794 | Float_t norm[kMaxTE+2][kMaxTZ+2]; |
| 795 | for (Int_t te=0; te<(kMaxTE+2); te++) |
| 796 | for (Int_t tz=0; tz<(kMaxTZ+2); tz++) |
| 797 | norm[te][tz]=0; |
| 798 | for (Int_t idx=0; idx<=correlation->GetNbins(); idx++) |
| 799 | { |
| 800 | Int_t bin[4]; |
| 801 | Float_t binContent = correlation->GetBinContent(idx, bin); |
| 802 | if (bin[1]>0 && bin[1]<=NBINSE && bin[3]>0 && bin[3]<=NBINSZ && |
| 803 | bin[0]>0 && bin[0]<=NBINSE && bin[2]>0 && bin[2]<=NBINSZ) |
| 804 | norm[bin[1]][bin[3]] += binContent*prior[bin[0]][bin[2]]; |
| 805 | } |
| 806 | Float_t chi2Measured=0, diff; |
| 807 | for (Int_t idx=0; idx<=correlation->GetNbins(); idx++) |
| 808 | { |
| 809 | Int_t bin[4]; |
| 810 | Float_t binContent = correlation->GetBinContent(idx, bin); |
| 811 | if (norm[bin[1]][bin[3]]>0 && bin[1]>0 && bin[1]<=NBINSE && |
| 812 | bin[3]>0 && bin[3]<=NBINSZ && bin[0]>0 && bin[2]>0 && bin[0]<=NBINSE && bin[2]<=NBINSZ) |
| 813 | { |
| 814 | inverseCorrelation->SetBinContent(bin, binContent*prior[bin[0]][bin[2]]/norm[bin[1]][bin[3]]); |
| 815 | if (errors[bin[1]][bin[3]]>0) |
| 816 | { |
| 817 | diff = ((measuredCopy[bin[1]][bin[3]]-norm[bin[1]][bin[3]])/(errors[bin[1]][bin[3]])); |
| 818 | chi2Measured += diff*diff; |
| 819 | } |
| 820 | } |
| 821 | } |
| 822 | |
| 823 | // calculate "generated" spectrum |
| 824 | for (Int_t te = kStartBin; te<=kMaxTE; te++) |
| 825 | for (Int_t tz = kStartBin; tz<=kMaxTZ; tz++) |
| 826 | { |
| 827 | Float_t value = 0; |
| 828 | for (Int_t me=1; me<=kMaxME; me++) |
| 829 | for (Int_t mz=1; mz<=kMaxMZ; mz++) |
| 830 | { |
| 831 | Int_t bin[4] = {te, me, tz, mz}; |
| 832 | value += inverseCorrelation->GetBinContent(bin)*measuredCopy[me][mz]; |
| 833 | } |
| 834 | result[te][tz] = value; |
| 835 | //printf("%e\n", result[te][tz]); |
| 836 | } |
| 837 | |
| 838 | // regularization (simple smoothing) |
| 839 | Float_t chi2LastIter = 0; |
| 840 | for (Int_t te=kStartBin; te<=kMaxTE; te++) |
| 841 | for (Int_t tz=kStartBin; tz<=kMaxTZ; tz++) |
| 842 | { |
| 843 | Float_t newValue = 0; |
| 844 | // 0 bin excluded from smoothing |
| 845 | if (( te >(kStartBin+1) && te<(kMaxTE-1) ) && ( tz > (kStartBin+1) && tz<(kMaxTZ-1) )) |
| 846 | { |
| 847 | Float_t average = ((result[te-1][tz-1] + result[te-1][tz] + result[te-1][tz+1])+(result[te][tz-1] + result[te][tz] + result[te][tz+1])+(result[te+1][tz-1] + result[te+1][tz] + result[te+1][tz+1]))/9.; |
| 848 | |
| 849 | // weight the average with the regularization parameter |
| 850 | newValue = (1 - regPar) * result[te][tz] + regPar * average; |
| 851 | } |
| 852 | else |
| 853 | newValue = result[te][tz]; |
| 854 | if (prior[te][tz]>1.e-5) |
| 855 | { |
| 856 | diff = ((prior[te][tz]-newValue)/prior[te][tz]); |
| 857 | chi2LastIter = diff*diff; |
| 858 | } |
| 859 | prior[te][tz] = newValue; |
| 860 | } |
| 861 | //printf(" iteration %d - chi2LastIter = %e - chi2Measured = %e \n", i, chi2LastIter/((Float_t)kMaxTE*(Float_t)kMaxTZ), chi2Measured/((Float_t)kMaxTE*(Float_t)kMaxTZ)); |
| 862 | if (chi2LastIter/((Float_t)kMaxTE*(Float_t)kMaxTZ)<5.e-6 && chi2Measured/((Float_t)kMaxTE*(Float_t)kMaxTZ)<5.e-3) |
| 863 | break; |
| 864 | } // end of iterations |
| 865 | |
| 866 | // propagate errors of the reconstructed distribution through the unfolding |
| 867 | for (Int_t te = kStartBin; te<=kMaxTE; te++) |
| 868 | for (Int_t tz = kStartBin; tz<=kMaxTZ; tz++) |
| 869 | { |
| 870 | Float_t valueError = 0; |
| 871 | Float_t binError = 0; |
| 872 | for (Int_t me=1; me<=kMaxME; me++) |
| 873 | for (Int_t mz=1; mz<=kMaxMZ; mz++) |
| 874 | { |
| 875 | Int_t bin[4] = {te, me, tz, mz}; |
| 876 | valueError += inverseCorrelation->GetBinContent(bin)*inverseCorrelation->GetBinContent(bin)*errors[me][mz]*errors[me][mz]; |
| 877 | } |
| 878 | //if (errors[te][tz]!=0)printf("errors[%d][%d]=%e\n", te, tz, valueError); |
| 879 | aResult->SetBinContent(te, tz, prior[te][tz]); |
| 880 | aResult->SetBinError(te, tz, TMath::Sqrt(valueError)); |
| 881 | } |
| 882 | |
| 883 | // *********************************************************************************************************** |
| 884 | // Calculate the covariance matrix, all arguments are taken from G. D'Agostini (p.6-8) |
| 885 | if (calculateErrors) |
| 886 | { |
| 887 | printf("Covariance matrix will be calculated... this will take a lot of time (>1 day) ;)\n"); |
| 888 | |
| 889 | //Variables and Matrices that will be use along the calculation |
| 890 | const Int_t binsV[4] = {NBINSE,NBINSE, NBINSZ, NBINSZ}; |
| 891 | const Double_t LowEdgeV[4] = {axisLowerLimitE, axisLowerLimitE, axisLowerLimitZ, axisLowerLimitZ}; |
| 892 | const Double_t UpEdgeV[4] = {axisUpperLimitE, axisUpperLimitE, axisUpperLimitZ, axisUpperLimitZ}; |
| 893 | |
| 894 | const Double_t Ntrue = (Double_t)measured->Integral(); |
| 895 | |
| 896 | THnSparseF *V = new THnSparseF("V","",4, binsV, LowEdgeV, UpEdgeV); |
| 897 | V->Reset(); |
| 898 | Double_t invCorrContent1, Nt; |
| 899 | Double_t invCorrContent2, v11, v12, v2; |
| 900 | // calculate V1 and V2 |
| 901 | for (Int_t idx1=0; idx1<=NFilledBins; idx1++) |
| 902 | { |
| 903 | printf("Covariance Matrix calculation: iteration idx1=%d of %d\n", idx1, NFilledBins); |
| 904 | for (Int_t idx2=0; idx2<=NFilledBins; idx2++) |
| 905 | { |
| 906 | Int_t bin1[4]; |
| 907 | Int_t bin2[4]; |
| 908 | invCorrContent1 = inverseCorrelation->GetBinContent(idx1, bin1); |
| 909 | invCorrContent2 = inverseCorrelation->GetBinContent(idx2, bin2); |
| 910 | v11=0; v12=0; v2=0; |
| 911 | if(bin1[0]>0 && bin1[0]<=NBINSE && bin1[1]>0 && bin1[1]<=NBINSE && |
| 912 | bin1[2]>0 && bin1[2]<=NBINSZ && bin1[3]>0 && bin1[3]<=NBINSZ && |
| 913 | bin2[0]>0 && bin2[0]<=NBINSE && bin2[1]>0 && bin2[1]<=NBINSE && |
| 914 | bin2[2]>0 && bin2[2]<=NBINSZ && bin2[3]>0 && bin2[3]<=NBINSZ) |
| 915 | { |
| 916 | if (bin1[1]==bin2[1] && bin1[3]==bin2[3]) |
| 917 | v11 = invCorrContent1*invCorrContent2*measuredCopy[bin1[1]][bin1[3]] |
| 918 | *(1. - measuredCopy[bin2[1]][bin2[3]]/Ntrue); |
| 919 | else |
| 920 | v12 = invCorrContent1*invCorrContent2*measuredCopy[bin1[1]][bin1[3]]* |
| 921 | measuredCopy[bin2[1]][bin2[3]]/Ntrue; |
| 922 | Nt = (Double_t)prior[bin2[0]][bin2[2]]; |
| 923 | v2 = measuredCopy[bin1[1]][bin1[3]]*measuredCopy[bin2[1]][bin2[3]]* |
| 924 | invCorrContent1*invCorrContent2* |
| 925 | BayesUncertaintyTerms(inverseCorrelation, correlation, bin1, bin2, Nt); |
| 926 | Int_t binV[4] = {bin1[0],bin2[0],bin1[2],bin2[2]}; |
| 927 | V->SetBinContent(binV,v11-v12 + v2); |
| 928 | } |
| 929 | } |
| 930 | } |
| 931 | |
| 932 | for(Int_t te = 1; te<=NBINSE; te++) |
| 933 | for(Int_t tz = 1; tz<=NBINSZ; tz++) |
| 934 | { |
| 935 | Int_t binV[4] = {te,te,tz,tz}; |
| 936 | aResult->SetBinError( te, tz, V->GetBinContent(binV) ); |
| 937 | } |
| 938 | |
| 939 | TFile* f = new TFile("Covariance_UnfSpectrum.root"); |
| 940 | f->Open("RECREATE"); |
| 941 | V->Write(); |
| 942 | f->Close(); |
| 943 | } |
| 944 | |
| 945 | return 0; |
| 946 | |
| 947 | } |
| 948 | |
| 949 | //____________________________________________________________________ |
| 950 | Double_t AliJetSpectrumUnfolding::BayesUncertaintyTerms(THnSparseF *M, THnSparseF *C, Int_t* binTM, Int_t* binTM1, Double_t Nt) |
| 951 | { |
| 952 | // |
| 953 | // helper function for the covariance matrix of the bayesian method |
| 954 | // |
| 955 | |
| 956 | Double_t result = 0; |
| 957 | Float_t term[9]; |
| 958 | Int_t tmpBin[4], tmpBin1[4]; |
| 959 | const Int_t nFilledBins = C->GetNbins(); |
| 960 | if (Nt==0) |
| 961 | return 0; |
| 962 | |
| 963 | Float_t CorrContent; |
| 964 | Float_t InvCorrContent; |
| 965 | |
| 966 | tmpBin[0] =binTM[0]; tmpBin[1] =binTM[1]; tmpBin[2] =binTM[2]; tmpBin[3] =binTM[3]; |
| 967 | tmpBin1[0]=binTM[0]; tmpBin1[1]=binTM1[1]; tmpBin1[2]=binTM[2]; tmpBin1[3]=binTM1[3]; |
| 968 | if (C->GetBinContent(tmpBin)!=0 && C->GetBinContent(tmpBin1)!=0) |
| 969 | { |
| 970 | if (binTM[0]==binTM1[0] && binTM[2]==binTM1[2]) |
| 971 | term[0] = BayesCov(M, C, tmpBin, tmpBin1)/ |
| 972 | (C->GetBinContent(tmpBin)*C->GetBinContent(tmpBin1)); |
| 973 | term[2] = term[0]*M->GetBinContent(tmpBin1); |
| 974 | } |
| 975 | else |
| 976 | { |
| 977 | term[0] = 0; |
| 978 | term[2] = 0; |
| 979 | } |
| 980 | |
| 981 | tmpBin[0]=binTM1[0]; tmpBin[1]=binTM[1]; tmpBin[2]=binTM1[2]; tmpBin[3]=binTM[3]; |
| 982 | tmpBin1[0]=binTM1[0]; tmpBin1[1]=binTM1[1]; tmpBin1[2]=binTM1[2]; tmpBin1[3]=binTM1[3]; |
| 983 | if (C->GetBinContent(tmpBin)!=0 && C->GetBinContent(tmpBin1)!=0) |
| 984 | term[6] = BayesCov(M, C, tmpBin, tmpBin1)* |
| 985 | M->GetBinContent(tmpBin)/ |
| 986 | (C->GetBinContent(tmpBin)*C->GetBinContent(tmpBin1)); |
| 987 | else |
| 988 | term[6] = 0; |
| 989 | |
| 990 | for(Int_t idx1=0; idx1<=nFilledBins; idx1++) |
| 991 | { |
| 992 | Int_t bin1[4]; |
| 993 | CorrContent = C->GetBinContent(idx1, bin1); |
| 994 | InvCorrContent = M->GetBinContent(idx1, bin1); |
| 995 | if(bin1[0]>0 && bin1[0]<=NBINSE && bin1[1]>0 && bin1[1]<=NBINSE && |
| 996 | bin1[2]>0 && bin1[2]<=NBINSZ && bin1[3]>0 && bin1[3]<=NBINSZ) |
| 997 | { |
| 998 | tmpBin[0] =binTM[0]; tmpBin[1] =binTM[1]; tmpBin[2] =binTM[2]; tmpBin[3] =binTM[3]; |
| 999 | tmpBin1[0]=binTM[0]; tmpBin1[1]=bin1[1]; tmpBin1[2]=binTM[2]; tmpBin1[3]=bin1[3]; |
| 1000 | if (C->GetBinContent(tmpBin)!=0 && |
| 1001 | binTM[0]==binTM1[0] && binTM[2]==binTM1[2]) |
| 1002 | term[1] = BayesCov(M, C, tmpBin, tmpBin1)/C->GetBinContent(tmpBin); |
| 1003 | else |
| 1004 | term[1] = 0; |
| 1005 | |
| 1006 | tmpBin[0] =binTM[0]; tmpBin[1] =bin1[1]; tmpBin[2] =binTM[2]; tmpBin[3] =bin1[3]; |
| 1007 | tmpBin1[0]=binTM[0]; tmpBin1[1]=binTM1[1]; tmpBin1[2]=binTM[2]; tmpBin1[3]=binTM1[3]; |
| 1008 | if (C->GetBinContent(tmpBin1)!=0) |
| 1009 | { |
| 1010 | if (binTM[0]==binTM1[0] && binTM[2]==binTM1[2]) |
| 1011 | term[3] = BayesCov(M, C, tmpBin, tmpBin1)/ |
| 1012 | C->GetBinContent(tmpBin1); |
| 1013 | term[5] = BayesCov(M, C, tmpBin, tmpBin1)*M->GetBinContent(tmpBin1)/ |
| 1014 | C->GetBinContent(tmpBin1); |
| 1015 | } |
| 1016 | else |
| 1017 | { |
| 1018 | term[3] = 0; |
| 1019 | term[5] = 0; |
| 1020 | } |
| 1021 | |
| 1022 | tmpBin[0] =binTM1[0]; tmpBin[1] =binTM[1]; tmpBin[2] =binTM1[2]; tmpBin[3] =binTM[3]; |
| 1023 | tmpBin1[0]=binTM1[0]; tmpBin1[1]=bin1[1]; tmpBin1[2]=binTM1[2]; tmpBin1[3]=bin1[3]; |
| 1024 | if (C->GetBinContent(tmpBin)!=0) |
| 1025 | term[7] = BayesCov(M, C, tmpBin, tmpBin1)*M->GetBinContent(tmpBin)/ |
| 1026 | C->GetBinContent(tmpBin); |
| 1027 | else |
| 1028 | term[7] = 0; |
| 1029 | |
| 1030 | tmpBin[0] =bin1[0]; tmpBin[1] =binTM[1]; tmpBin[2] =bin1[2]; tmpBin[3] =binTM[3]; |
| 1031 | tmpBin1[0]=bin1[0]; tmpBin1[1]=binTM1[1]; tmpBin1[2]=bin1[2]; tmpBin1[3]=binTM1[3]; |
| 1032 | if (C->GetBinContent(tmpBin)!=0 && C->GetBinContent(tmpBin1)!=0) |
| 1033 | term[8] = BayesCov(M, C, tmpBin, tmpBin1)* |
| 1034 | M->GetBinContent(tmpBin)*M->GetBinContent(tmpBin)/ |
| 1035 | (C->GetBinContent(tmpBin)*C->GetBinContent(tmpBin1)); |
| 1036 | else |
| 1037 | term[8] = 0; |
| 1038 | |
| 1039 | for (Int_t i=0; i<9; i++) |
| 1040 | result += term[i]/Nt; |
| 1041 | } |
| 1042 | } |
| 1043 | |
| 1044 | return result; |
| 1045 | } |
| 1046 | |
| 1047 | //____________________________________________________________________ |
| 1048 | Double_t AliJetSpectrumUnfolding::BayesCov(THnSparseF *M, THnSparseF *correlation, Int_t* binTM, Int_t* bin1) |
| 1049 | { |
| 1050 | Double_t result, result1, result2, result3; |
| 1051 | |
| 1052 | if (binTM[0]==bin1[0] && binTM[2]==bin1[2]) |
| 1053 | { |
| 1054 | if (correlation->GetBinContent(bin1)!=0) |
| 1055 | result1 = 1./correlation->GetBinContent(bin1); |
| 1056 | else |
| 1057 | result1 = 0; |
| 1058 | result2 = 1.; |
| 1059 | } |
| 1060 | else |
| 1061 | { |
| 1062 | result1 = 0; |
| 1063 | result2 = 0; |
| 1064 | } |
| 1065 | |
| 1066 | if (binTM[1]==bin1[1] && binTM[3]==bin1[3]) |
| 1067 | { |
| 1068 | Int_t tmpbin[4] = {bin1[0], binTM[1], bin1[2], binTM[3]}; |
| 1069 | if(correlation->GetBinContent(tmpbin)!=0) |
| 1070 | result3 = M->GetBinContent(tmpbin)/correlation->GetBinContent(tmpbin); |
| 1071 | else |
| 1072 | result3 = 0; |
| 1073 | } |
| 1074 | else |
| 1075 | { |
| 1076 | result1 = 0; |
| 1077 | result3 = 0; |
| 1078 | } |
| 1079 | |
| 1080 | return result = result1 + result2 + result3; |
| 1081 | } |
| 1082 | |
| 1083 | //____________________________________________________________________ |
| 1084 | TH2F* AliJetSpectrumUnfolding::CalculateRecSpectrum(TH2* inputGen) |
| 1085 | { |
| 1086 | // runs the distribution given in inputGen through the correlation histogram identified by |
| 1087 | // fCorrelation and produces a reconstructed spectrum |
| 1088 | |
| 1089 | if (!inputGen) |
| 1090 | return 0; |
| 1091 | |
| 1092 | // normalize to convert number of events into probability |
| 1093 | /*for (Int_t te=1; te<=NBINSE; te++) |
| 1094 | for (Int_t tz=1; tz<=NBINSZ; tz++) |
| 1095 | { |
| 1096 | Int_t bin[4]; |
| 1097 | Float_t sum=0.; |
| 1098 | for (Int_t me = 1; me<=NBINSE; me++) |
| 1099 | for (Int_t mz = 1; mz<=NBINSZ; mz++) |
| 1100 | { |
| 1101 | bin[0] = te; bin[1] = me; |
| 1102 | bin[2] = tz; bin[3] = mz; |
| 1103 | sum += fCorrelation[correlationMap]->GetBinContent(bin); |
| 1104 | } |
| 1105 | if (sum > 0.) |
| 1106 | for (Int_t me = 1; me<=NBINSE; me++) |
| 1107 | for (Int_t mz = 1; mz<=NBINSZ; mz++) |
| 1108 | { |
| 1109 | bin[0] = te; bin[1] = me; |
| 1110 | bin[2] = tz; bin[3] = mz; |
| 1111 | fCorrelation[correlationMap]->SetBinContent(bin, fCorrelation[correlationMap]->GetBinContent(bin)/sum); |
| 1112 | fCorrelation[correlationMap]->SetBinError(bin, fCorrelation[correlationMap]->GetBinError(bin)/sum); |
| 1113 | } |
| 1114 | }*/ |
| 1115 | // normalize to convert number of events into probability (the following loop is much faster) |
| 1116 | Float_t sum[NBINSE+2][NBINSZ+2]; |
| 1117 | memset(sum,0,sizeof(Float_t)*(NBINSE+2)*(NBINSZ+2)); |
| 1118 | |
| 1119 | for (Int_t idx=0; idx<fCorrelation->GetNbins(); idx++) |
| 1120 | { |
| 1121 | Int_t bin[4]; |
| 1122 | Float_t binContent = fCorrelation->GetBinContent(idx, bin); |
| 1123 | if (bin[1]>0 && bin[1]<=NBINSE && bin[3]>0 && bin[3]<=NBINSZ){ |
| 1124 | sum[bin[0]][bin[2]] += binContent; |
| 1125 | } |
| 1126 | } |
| 1127 | |
| 1128 | for (Int_t idx=0; idx<fCorrelation->GetNbins(); idx++) |
| 1129 | { |
| 1130 | Int_t bin[4]; |
| 1131 | Float_t binContent = fCorrelation->GetBinContent(idx, bin); |
| 1132 | Float_t binError = fCorrelation->GetBinError(bin); |
| 1133 | if (sum[bin[0]][bin[2]]>0 && bin[1]>0 && bin[1]<=NBINSE && |
| 1134 | bin[3]>0 && bin[3]<=NBINSZ && bin[0]>0 && bin[2]>0 && bin[0]<=NBINSE && bin[2]<=NBINSZ) |
| 1135 | { |
| 1136 | fCorrelation->SetBinContent(bin, binContent/sum[bin[0]][bin[2]]); |
| 1137 | fCorrelation->SetBinError(bin, binError/sum[bin[0]][bin[2]]); |
| 1138 | } |
| 1139 | } |
| 1140 | |
| 1141 | TH2F* target = dynamic_cast<TH2F*> (fRecSpectrum->Clone(Form("reconstructed_%s", inputGen->GetName()))); |
| 1142 | target->Reset(); |
| 1143 | |
| 1144 | for (Int_t me=1; me<=NBINSE; ++me) |
| 1145 | for (Int_t mz=1; mz<=NBINSZ; ++mz) |
| 1146 | { |
| 1147 | Float_t measured = 0; |
| 1148 | Float_t error = 0; |
| 1149 | |
| 1150 | for (Int_t te=1; te<=NBINSE; ++te) |
| 1151 | for (Int_t tz=1; tz<=NBINSZ; ++tz) |
| 1152 | { |
| 1153 | Int_t bin[4] = {te, me, tz, mz}; |
| 1154 | measured += inputGen->GetBinContent(te,tz) * fCorrelation->GetBinContent(bin); |
| 1155 | error += inputGen->GetBinError(te,tz) * fCorrelation->GetBinContent(bin); |
| 1156 | } |
| 1157 | target->SetBinContent(me, mz, measured); |
| 1158 | target->SetBinError(me, mz, error); |
| 1159 | } |
| 1160 | |
| 1161 | return target; |
| 1162 | } |
| 1163 | |
| 1164 | //__________________________________________________________________________________________________ |
| 1165 | void AliJetSpectrumUnfolding::SetGenRecFromFunc(TF2* inputGen) |
| 1166 | { |
| 1167 | // uses the given function to fill the input Generated histogram and generates from that |
| 1168 | // the reconstructed histogram by applying the response histogram |
| 1169 | // this can be used to evaluate if the methods work indepedently of the input |
| 1170 | // distribution |
| 1171 | |
| 1172 | if (!inputGen) |
| 1173 | return; |
| 1174 | |
| 1175 | TH2F* histtmp = new TH2F("histtmp", "tmp", EBINNING, ZBINNING); |
| 1176 | TH2F* gen = fGenSpectrum; |
| 1177 | |
| 1178 | histtmp->Reset(); |
| 1179 | gen->Reset(); |
| 1180 | |
| 1181 | histtmp->FillRandom(inputGen->GetName(), NEVENTS); |
| 1182 | |
| 1183 | for (Int_t i=1; i<=gen->GetNbinsX(); ++i) |
| 1184 | for (Int_t j=1; j<=gen->GetNbinsY(); ++j) |
| 1185 | { |
| 1186 | gen->SetBinContent(i, j, histtmp->GetBinContent(i,j)); |
| 1187 | gen->SetBinError(i, j, histtmp->GetBinError(i,j)); |
| 1188 | } |
| 1189 | |
| 1190 | delete histtmp; |
| 1191 | |
| 1192 | //new TCanvas; |
| 1193 | //gStyle->SetPalette(1); |
| 1194 | //gPad->SetLogz(); |
| 1195 | //gen->Draw("COLZ"); |
| 1196 | |
| 1197 | |
| 1198 | TH2 *recsave = fRecSpectrum; |
| 1199 | |
| 1200 | fRecSpectrum = CalculateRecSpectrum(gen); |
| 1201 | fRecSpectrum->SetName(recsave->GetName()); |
| 1202 | delete recsave; |
| 1203 | |
| 1204 | return; |
| 1205 | } |
| 1206 | //________________________________________________________________________________________ |
git://git.uio.no / u / mrichter / AliRoot.git / blame