* provided "as is" without express or implied warranty. *
**************************************************************************/
-//--------------------------------------------------------------------//
-// //
-// AliCFUnfolding Class //
-// Class to handle general unfolding procedure //
-// For the moment only bayesian unfolding is supported //
-// The next steps are to add chi2 minimisation and weighting methods //
-// //
-// Author : renaud.vernet@cern.ch //
-//--------------------------------------------------------------------//
+//---------------------------------------------------------------------//
+// //
+// AliCFUnfolding Class //
+// Class to handle general unfolding procedure //
+// For the moment only bayesian unfolding is supported //
+// The next steps are to add chi2 minimisation and weighting methods //
+// //
+// //
+// //
+// Use : //
+// ------- //
+// The Bayesian unfolding consists of several iterations. //
+// At each iteration, an inverse response matrix is calculated, given //
+// the measured spectrum, the a priori (guessed) spectrum, //
+// the efficiency spectrum and the response matrix. //
+// //
+// Then at each iteration, the unfolded spectrum is calculated using //
+// the inverse response : the goal is to get an unfolded spectrum //
+// similar (according to some criterion) to the a priori one. //
+// If the difference is too big, another iteration is performed : //
+// the a priori spectrum is updated to the unfolded one from the //
+// previous iteration, and so on so forth, until the maximum number //
+// of iterations or the similarity criterion is reached. //
+// //
+// Currently the similarity criterion is the Chi2 between the a priori //
+// and the unfolded spectrum (OBSOLETE). //
+// //
+// Chi2 calculation became absolute with the correlated error //
+// calculation. //
+// Errors on the unfolded distribution are not known until the end //
+// Use the convergence criterion instead //
+// //
+// Currently the user has to define the max. number of iterations //
+// (::SetMaxNumberOfIterations) //
+// and //
+// - the chi2 below which the procedure will stop //
+// (::SetMaxChi2 or ::SetMaxChi2PerDOF) (OBSOLETE) //
+// - the convergence criterion below which the procedure will stop //
+// SetMaxConvergencePerDOF(Double_t val); //
+// //
+// Correlated error calculation can be activated by using: //
+// SetUseCorrelatedErrors(Bool_t b) in combination with convergence //
+// criterion //
+// Documentation about correlated error calculation method can be //
+// found in AliCFUnfolding::CalculateCorrelatedErrors() //
+// Author: marta.verweij@cern.ch //
+// //
+// An optional possibility is to smooth the unfolded spectrum at the //
+// end of each iteration, either using a fit function //
+// (only if #dimensions <=3) //
+// or a simple averaging using the neighbouring bins values. //
+// This is possible calling the function ::UseSmoothing //
+// If no argument is passed to this function, then the second option //
+// is used. //
+// //
+// IMPORTANT: //
+//----------- //
+// With this approach, the efficiency map must be calculated //
+// with *simulated* values only, otherwise the method won't work. //
+// //
+// ex: efficiency(bin_pt) = number_rec(bin_pt) / number_sim(bin_pt) //
+// //
+// the pt bin "bin_pt" must always be the same in both the efficiency //
+// numerator and denominator. //
+// This is why the efficiency map has to be created by a method //
+// from which both reconstructed and simulated values are accessible //
+// simultaneously. //
+// //
+// //
+//---------------------------------------------------------------------//
+// Author : renaud.vernet@cern.ch //
+//---------------------------------------------------------------------//
#include "AliCFUnfolding.h"
#include "TMath.h"
#include "TAxis.h"
#include "AliLog.h"
+#include "TF1.h"
+#include "TH1D.h"
+#include "TH2D.h"
+#include "TH3D.h"
+#include "TProfile.h"
+#include "TRandom3.h"
ClassImp(AliCFUnfolding)
TNamed(),
fResponse(0x0),
fPrior(0x0),
- fOriginalPrior(0x0),
fEfficiency(0x0),
fMeasured(0x0),
- fMaxNumIterations(0),
+ fMeasuredOrig(0x0),
+ fMaxNumIterations(20),
fNVariables(0),
- fMaxChi2(0),
fUseSmoothing(kFALSE),
+ fSmoothFunction(0x0),
+ fSmoothOption(""),
+ fMaxConvergence(0),
+ fUseCorrelatedErrors(kTRUE),
+ fNRandomIterations(20),
+ fOriginalPrior(0x0),
fInverseResponse(0x0),
fMeasuredEstimate(0x0),
fConditional(0x0),
fUnfolded(0x0),
fCoordinates2N(0x0),
fCoordinatesN_M(0x0),
- fCoordinatesN_T(0x0)
+ fCoordinatesN_T(0x0),
+ fRandomizedDist(0x0),
+ fRandom3(0x0),
+ fRandomUnfolded(0x0),
+ fDeltaUnfoldedP(0x0),
+ fNCalcCorrErrors(0),
+ fRandomSeed(0)
{
//
// default constructor
TNamed(name,title),
fResponse((THnSparse*)response->Clone()),
fPrior(0x0),
- fOriginalPrior(0x0),
fEfficiency((THnSparse*)efficiency->Clone()),
fMeasured((THnSparse*)measured->Clone()),
+ fMeasuredOrig((THnSparse*)measured->Clone()),
fMaxNumIterations(0),
fNVariables(nVar),
- fMaxChi2(0),
fUseSmoothing(kFALSE),
+ fSmoothFunction(0x0),
+ fSmoothOption(""),
+ fMaxConvergence(0),
+ fUseCorrelatedErrors(kTRUE),
+ fNRandomIterations(20),
+ fOriginalPrior(0x0),
fInverseResponse(0x0),
fMeasuredEstimate(0x0),
fConditional(0x0),
fUnfolded(0x0),
fCoordinates2N(0x0),
fCoordinatesN_M(0x0),
- fCoordinatesN_T(0x0)
+ fCoordinatesN_T(0x0),
+ fRandomizedDist(0x0),
+ fRandom3(0x0),
+ fRandomUnfolded(0x0),
+ fDeltaUnfoldedP(0x0),
+ fNCalcCorrErrors(0),
+ fRandomSeed(0)
{
//
// named constructor
else {
fPrior = (THnSparse*) prior->Clone();
fOriginalPrior = (THnSparse*)fPrior->Clone();
+ if (fPrior->GetNdimensions() != fNVariables)
+ AliFatal(Form("The prior matrix should have %d dimensions, and it has actually %d",fNVariables,fPrior->GetNdimensions()));
}
+
+ if (fEfficiency->GetNdimensions() != fNVariables)
+ AliFatal(Form("The efficiency matrix should have %d dimensions, and it has actually %d",fNVariables,fEfficiency->GetNdimensions()));
+ if (fMeasured->GetNdimensions() != fNVariables)
+ AliFatal(Form("The measured matrix should have %d dimensions, and it has actually %d",fNVariables,fMeasured->GetNdimensions()));
+ if (fResponse->GetNdimensions() != 2*fNVariables)
+ AliFatal(Form("The response matrix should have %d dimensions, and it has actually %d",2*fNVariables,fResponse->GetNdimensions()));
+
for (Int_t iVar=0; iVar<fNVariables; iVar++) {
AliInfo(Form("prior matrix has %d bins in dimension %d",fPrior ->GetAxis(iVar)->GetNbins(),iVar));
AliInfo(Form("efficiency matrix has %d bins in dimension %d",fEfficiency->GetAxis(iVar)->GetNbins(),iVar));
AliInfo(Form("measured matrix has %d bins in dimension %d",fMeasured ->GetAxis(iVar)->GetNbins(),iVar));
}
+
Init();
}
TNamed(c),
fResponse((THnSparse*)c.fResponse->Clone()),
fPrior((THnSparse*)c.fPrior->Clone()),
- fOriginalPrior((THnSparse*)c.fOriginalPrior->Clone()),
fEfficiency((THnSparse*)c.fEfficiency->Clone()),
fMeasured((THnSparse*)c.fMeasured->Clone()),
+ fMeasuredOrig((THnSparse*)c.fMeasuredOrig->Clone()),
fMaxNumIterations(c.fMaxNumIterations),
fNVariables(c.fNVariables),
- fMaxChi2(c.fMaxChi2),
fUseSmoothing(c.fUseSmoothing),
+ fSmoothFunction((TF1*)c.fSmoothFunction->Clone()),
+ fSmoothOption(c.fSmoothOption),
+ fMaxConvergence(c.fMaxConvergence),
+ fUseCorrelatedErrors(c.fUseCorrelatedErrors),
+ fNRandomIterations(c.fNRandomIterations),
+ fOriginalPrior((THnSparse*)c.fOriginalPrior->Clone()),
fInverseResponse((THnSparse*)c.fInverseResponse->Clone()),
fMeasuredEstimate((THnSparse*)fMeasuredEstimate->Clone()),
fConditional((THnSparse*)c.fConditional->Clone()),
fUnfolded((THnSparse*)c.fUnfolded->Clone()),
fCoordinates2N(new Int_t(*c.fCoordinates2N)),
fCoordinatesN_M(new Int_t(*c.fCoordinatesN_M)),
- fCoordinatesN_T(new Int_t(*c.fCoordinatesN_T))
+ fCoordinatesN_T(new Int_t(*c.fCoordinatesN_T)),
+ fRandomizedDist((THnSparse*)c.fRandomizedDist->Clone()),
+ fRandom3((TRandom3*)c.fRandom3->Clone()),
+ fRandomUnfolded((THnSparse*)c.fRandomUnfolded->Clone()),
+ fDeltaUnfoldedP((TProfile*)c.fDeltaUnfoldedP),
+ fNCalcCorrErrors(c.fNCalcCorrErrors),
+ fRandomSeed(c.fRandomSeed)
{
//
// copy constructor
TNamed::operator=(c);
fResponse = (THnSparse*)c.fResponse->Clone() ;
fPrior = (THnSparse*)c.fPrior->Clone() ;
- fOriginalPrior = (THnSparse*)c.fOriginalPrior->Clone() ;
fEfficiency = (THnSparse*)c.fEfficiency->Clone() ;
fMeasured = (THnSparse*)c.fMeasured->Clone() ;
+ fMeasuredOrig = ((THnSparse*)c.fMeasuredOrig->Clone()),
fMaxNumIterations = c.fMaxNumIterations ;
fNVariables = c.fNVariables ;
- fMaxChi2 = c.fMaxChi2 ;
+ fMaxConvergence = c.fMaxConvergence ;
fUseSmoothing = c.fUseSmoothing ;
+ fSmoothFunction = (TF1*)c.fSmoothFunction->Clone();
+ fSmoothOption = c.fSmoothOption ;
+ fUseCorrelatedErrors = c.fUseCorrelatedErrors;
+ fNRandomIterations = c.fNRandomIterations;
+ fOriginalPrior = (THnSparse*)c.fOriginalPrior->Clone() ;
fInverseResponse = (THnSparse*)c.fInverseResponse->Clone() ;
fMeasuredEstimate = (THnSparse*)fMeasuredEstimate->Clone() ;
fConditional = (THnSparse*)c.fConditional->Clone() ;
fCoordinates2N = new Int_t(*c.fCoordinates2N) ;
fCoordinatesN_M = new Int_t(*c.fCoordinatesN_M) ;
fCoordinatesN_T = new Int_t(*c.fCoordinatesN_T) ;
+ fRandomizedDist = (THnSparse*)c.fRandomizedDist->Clone();
+ fRandom3 = (TRandom3*)c.fRandom3->Clone();
+ fRandomUnfolded = (THnSparse*)c.fRandomUnfolded->Clone();
+ fDeltaUnfoldedP = (TProfile*)c.fDeltaUnfoldedP;
+ fNCalcCorrErrors = c.fNCalcCorrErrors;
+ fRandomSeed = c.fRandomSeed ;
}
return *this;
}
//
if (fResponse) delete fResponse;
if (fPrior) delete fPrior;
- if (fOriginalPrior) delete fOriginalPrior;
if (fEfficiency) delete fEfficiency;
if (fMeasured) delete fMeasured;
+ if (fMeasuredOrig) delete fMeasuredOrig;
+ if (fSmoothFunction) delete fSmoothFunction;
+ if (fOriginalPrior) delete fOriginalPrior;
if (fInverseResponse) delete fInverseResponse;
if (fMeasuredEstimate) delete fMeasuredEstimate;
if (fConditional) delete fConditional;
if (fCoordinates2N) delete [] fCoordinates2N;
if (fCoordinatesN_M) delete [] fCoordinatesN_M;
if (fCoordinatesN_T) delete [] fCoordinatesN_T;
+ if (fRandomizedDist) delete fRandomizedDist;
+ if (fRandom3) delete fRandom3;
+ if (fRandomUnfolded) delete fRandomUnfolded;
+ if (fDeltaUnfoldedP) delete fDeltaUnfoldedP;
}
//______________________________________________________________
// initialisation function : creates internal settings
//
+ fRandom3 = new TRandom3(fRandomSeed);
+
fCoordinates2N = new Int_t[2*fNVariables];
fCoordinatesN_M = new Int_t[fNVariables];
fCoordinatesN_T = new Int_t[fNVariables];
fInverseResponse = (THnSparse*) fResponse->Clone();
// create the frame of the unfolded spectrum
fUnfolded = (THnSparse*) fPrior->Clone();
+ // create the frame of the random unfolded spectrum
+ fRandomUnfolded = (THnSparse*) fPrior->Clone();
// create the frame of the measurement estimate spectrum
fMeasuredEstimate = (THnSparse*) fMeasured->Clone();
+ // create the frame of the original measurement spectrum
+ fMeasuredOrig = (THnSparse*) fMeasured->Clone();
+
+ InitDeltaUnfoldedProfile();
+
}
//______________________________________________________________
void AliCFUnfolding::CreateEstMeasured() {
//
// This function creates a estimate (M) of the reconstructed spectrum
- // given the a priori distribution (T) and the conditional matrix (COND)
+ // given the a priori distribution (T), the efficiency (E) and the conditional matrix (COND)
//
// --> P(M) = SUM { P(M|T) * P(T) }
- // --> M(i) = SUM_k { COND(i,k) * T(k) }
+ // --> M(i) = SUM_k { COND(i,k) * T(k) * E (k)}
//
// This is needed to calculate the inverse response matrix
//
// clean the measured estimate spectrum
- for (Long64_t i=0; i<fMeasuredEstimate->GetNbins(); i++) {
- fMeasuredEstimate->GetBinContent(i,fCoordinatesN_M);
- fMeasuredEstimate->SetBinContent(fCoordinatesN_M,0.);
- }
-
+ fMeasuredEstimate->Reset();
+
+ THnSparse* priorTimesEff = (THnSparse*) fPrior->Clone();
+ priorTimesEff->Multiply(fEfficiency);
+
// fill it
- for (Int_t iBin=0; iBin<fConditional->GetNbins(); iBin++) {
+ for (Long_t iBin=0; iBin<fConditional->GetNbins(); iBin++) {
Double_t conditionalValue = fConditional->GetBinContent(iBin,fCoordinates2N);
GetCoordinates();
- Double_t priorValue = fPrior->GetBinContent(fCoordinatesN_T);
- Double_t fill = fMeasuredEstimate->GetBinContent(fCoordinatesN_M) + conditionalValue * priorValue * fEfficiency->GetBinContent(fCoordinatesN_T);
- if (fill>0.) fMeasuredEstimate->SetBinContent(fCoordinatesN_M,fill);
+ Double_t priorTimesEffValue = priorTimesEff->GetBinContent(fCoordinatesN_T);
+ Double_t fill = conditionalValue * priorTimesEffValue ;
+
+ if (fill>0.) {
+ fMeasuredEstimate->AddBinContent(fCoordinatesN_M,fill);
+ fMeasuredEstimate->SetBinError(fCoordinatesN_M,0.);
+ }
}
+ delete priorTimesEff ;
}
//______________________________________________________________
// --> INV(i,j) = COND(i,j) * T(j) * E(j) / SUM_k { COND(i,k) * T(k) }
//
- for (Int_t iBin=0; iBin<fConditional->GetNbins(); iBin++) {
+ THnSparse* priorTimesEff = (THnSparse*) fPrior->Clone();
+ priorTimesEff->Multiply(fEfficiency);
+
+ for (Long_t iBin=0; iBin<fConditional->GetNbins(); iBin++) {
Double_t conditionalValue = fConditional->GetBinContent(iBin,fCoordinates2N);
GetCoordinates();
- Double_t priorValue = fPrior->GetBinContent(fCoordinatesN_T);
- Double_t estimatedMeasured = fMeasuredEstimate->GetBinContent(fCoordinatesN_M);
- Double_t fill = (estimatedMeasured>0. ? conditionalValue * priorValue * fEfficiency->GetBinContent(fCoordinatesN_T) / estimatedMeasured : 0. ) ;
- if (fill>0. || fInverseResponse->GetBinContent(fCoordinates2N)>0.) fInverseResponse->SetBinContent(fCoordinates2N,fill);
- }
+ Double_t estMeasuredValue = fMeasuredEstimate->GetBinContent(fCoordinatesN_M);
+ Double_t priorTimesEffValue = priorTimesEff ->GetBinContent(fCoordinatesN_T);
+ Double_t fill = (estMeasuredValue>0. ? conditionalValue * priorTimesEffValue / estMeasuredValue : 0. ) ;
+ if (fill>0. || fInverseResponse->GetBinContent(fCoordinates2N)>0.) {
+ fInverseResponse->SetBinContent(fCoordinates2N,fill);
+ fInverseResponse->SetBinError (fCoordinates2N,0.);
+ }
+ }
+ delete priorTimesEff ;
}
//______________________________________________________________
void AliCFUnfolding::Unfold() {
//
// Main routine called by the user :
- // it calculates the unfolded spectrum from the response matrix and the measured spectrum
- // several iterations are performed until a reasonable chi2 is reached
+ // it calculates the unfolded spectrum from the response matrix, measured spectrum and efficiency
+ // several iterations are performed until a reasonable chi2 or convergence criterion is reached
//
- Int_t iIterBayes=0 ;
- Double_t chi2=0 ;
+ Int_t iIterBayes = 0 ;
+ Double_t convergence = 0.;
for (iIterBayes=0; iIterBayes<fMaxNumIterations; iIterBayes++) { // bayes iterations
CreateEstMeasured();
CreateInvResponse();
CreateUnfolded();
- chi2 = GetChi2();
- //printf("chi2 = %e\n",chi2);
- if (fMaxChi2>0. && chi2<fMaxChi2) {
+ if (fUseCorrelatedErrors) {
+ convergence = GetConvergence();
+ AliDebug(0,Form("convergence at iteration %d is %e",iIterBayes,convergence));
+ }
+ else AliWarning("No errors will be calculated. Activate SetUseCorrelatedErrors(kTRUE)\n");
+
+ if (fMaxConvergence>0. && convergence<fMaxConvergence && fNCalcCorrErrors<1) {
+ fNRandomIterations=iIterBayes;
break;
}
+
// update the prior distribution
- if (fUseSmoothing) Smooth();
- fPrior = (THnSparse*)fUnfolded->Clone() ; // this should be changed (memory)
+ if (fUseSmoothing) {
+ if (Smooth()) {
+ AliError("Couldn't smooth the unfolded spectrum!!");
+ if (fUseCorrelatedErrors) {
+ if (fNCalcCorrErrors>0) {
+ AliInfo(Form("=======================\nUnfolding of randomized distribution finished at iteration %d with convergence %e \n",iIterBayes,convergence));
+ }
+ else {
+ AliInfo(Form("\n\n=======================\nFinished at iteration %d : convergence is %e and you required it to be < %e\n=======================\n\n",iIterBayes,convergence,fMaxConvergence));
+ }
+ }
+ return;
+ }
+ }
+ if(fNCalcCorrErrors>0) {
+ if (fPrior) delete fPrior ;
+ fPrior = (THnSparse*)fRandomUnfolded->Clone() ;
+ }
+ else {
+ if (fPrior) delete fPrior ;
+ fPrior = (THnSparse*)fUnfolded->Clone() ;
+ }
+ }
+
+ if (fUseCorrelatedErrors && fNCalcCorrErrors==0) {
+ fNCalcCorrErrors=1;
+ CalculateCorrelatedErrors();
+ }
+
+ if (fUseCorrelatedErrors) {
+ if (fNCalcCorrErrors>1) {
+ AliInfo(Form("\n\n=======================\nFinished at iteration %d : convergence is %e and you required it to be < %e\n=======================\n\n",iIterBayes,convergence,fMaxConvergence));
+ }
+ else if(fNCalcCorrErrors>0) {
+ AliInfo(Form("=======================\nUnfolding of randomized distribution finished at iteration %d with convergence %e \n",iIterBayes,convergence));
+ }
}
- AliInfo(Form("Finished at iteration %d : Chi2 is %e and you required it to be < %e",iIterBayes,chi2,fMaxChi2));
}
//______________________________________________________________
// clear the unfolded spectrum
- for (Long64_t i=0; i<fUnfolded->GetNbins(); i++) {
- fUnfolded->GetBinContent(i,fCoordinatesN_T);
- fUnfolded->SetBinContent(fCoordinatesN_T,0.);
+ if(fNCalcCorrErrors>0) {
+ //unfold randomized dist
+ fRandomUnfolded->Reset();
+ }
+ else {
+ //unfold measured dist
+ fUnfolded->Reset();
}
- for (Int_t iBin=0; iBin<fInverseResponse->GetNbins(); iBin++) {
+ for (Long_t iBin=0; iBin<fInverseResponse->GetNbins(); iBin++) {
Double_t invResponseValue = fInverseResponse->GetBinContent(iBin,fCoordinates2N);
GetCoordinates();
- Double_t effValue = fEfficiency->GetBinContent(fCoordinatesN_T);
- Double_t fill = fUnfolded->GetBinContent(fCoordinatesN_T) + (effValue>0. ? invResponseValue*fMeasured->GetBinContent(fCoordinatesN_M)/effValue : 0.) ;
- if (fill>0.) fUnfolded->SetBinContent(fCoordinatesN_T,fill);
+ Double_t effValue = fEfficiency->GetBinContent(fCoordinatesN_T);
+ Double_t measuredValue = fMeasured ->GetBinContent(fCoordinatesN_M);
+ Double_t fill = (effValue>0. ? invResponseValue * measuredValue / effValue : 0.) ;
+
+ if (fill>0.) {
+
+ Double_t err = 0.;
+
+ if(fNCalcCorrErrors>0) {
+ fRandomUnfolded->SetBinError(fCoordinatesN_T,err);
+ fRandomUnfolded->AddBinContent(fCoordinatesN_T,fill);
+ }
+ else {
+ fUnfolded->SetBinError(fCoordinatesN_T,err);
+ fUnfolded->AddBinContent(fCoordinatesN_T,fill);
+ }
+ }
}
}
-
+
+//______________________________________________________________
+
+void AliCFUnfolding::CalculateCorrelatedErrors() {
+
+ fRandomizedDist = (THnSparse*) fMeasuredOrig->Clone();
+ fPrior = (THnSparse*) fOriginalPrior->Clone();
+
+ // Step 1: Create randomized distribution (fRandomizedDist) of each bin of the measured spectrum to calculate correlated errors. Poisson statistics: mean = measured value of bin
+ // Step 2: Unfold randomized distribution
+ // Step 3: Store difference of unfolded spectrum from measured distribution and unfolded distribution from randomized distribution -> fDeltaUnfoldedP (TProfile with option "S")
+ // Step 4: Repeat Step 1-3 several times (fNRandomIterations)
+ // Step 5: The spread of fDeltaUnfoldedP for each bin is the error on the unfolded spectrum of that specific bin
+
+ //Do fNRandomIterations = bayes iterations performed
+ for(int i=0; i<fNRandomIterations; i++) {
+ if (fPrior) delete fPrior ;
+ if (fRandomizedDist) delete fRandomizedDist ;
+ fPrior = (THnSparse*) fOriginalPrior->Clone();
+ fRandomizedDist = (THnSparse*) fMeasuredOrig->Clone();
+ CreateRandomizedDist();
+ if (fMeasured) delete fMeasured ;
+ fMeasured = (THnSparse*) fRandomizedDist->Clone();
+ //Unfold fRandomizedDist
+ Unfold();
+ FillDeltaUnfoldedProfile();
+ }
+
+ // Get statistical errors for final unfolded spectrum
+ // ie. spread of each pt bin in fDeltaUnfoldedP
+ Double_t sigma = 0.;
+ Double_t dummy = 0.;
+ for (Long_t iBin=0; iBin<fRandomUnfolded->GetNbins(); iBin++) {
+ dummy = fUnfolded->GetBinContent(iBin,fCoordinatesN_M);
+ sigma = fDeltaUnfoldedP->GetBinError(fCoordinatesN_M[0]);
+ fUnfolded->SetBinError(fCoordinatesN_M,sigma);
+ fNCalcCorrErrors = 2;
+ }
+
+}
+
+//______________________________________________________________
+void AliCFUnfolding::InitDeltaUnfoldedProfile() {
+ //
+ //Initialize the fDeltaUnfoldedP profile
+ //Errors will be filled with spread between unfolded measured and unfolded randomized spectra
+ //
+
+ Int_t nbinsx = fResponse->GetAxis(0)->GetNbins();
+ Double_t xbins[nbinsx];
+ for(int ix=0; ix<nbinsx; ix++) {
+ xbins[ix] = fResponse->GetAxis(0)->GetBinLowEdge(ix+1);
+ }
+ xbins[nbinsx] = fResponse->GetAxis(0)->GetBinUpEdge(nbinsx);
+ fDeltaUnfoldedP = new TProfile("fDeltaUnfoldedP","Profile of pTUnfolded with spread in error",nbinsx,xbins,"S");
+}
+//______________________________________________________________
+void AliCFUnfolding::CreateRandomizedDist() {
+ //
+ // Create randomized dist from measured distribution
+ //
+
+ Double_t random = 0.;
+ Double_t measuredValue = 0.;
+ Double_t measuredError = 0.;
+ for (Long_t iBin=0; iBin<fRandomizedDist->GetNbins(); iBin++) {
+ measuredValue = fMeasuredOrig->GetBinContent(iBin,fCoordinatesN_M); //used as mean
+ measuredError = fMeasuredOrig->GetBinError(fCoordinatesN_M); //used as sigma
+ // random = fRandom3->PoissonD(measuredValue); //doesn't work for normalized spectra, use Gaus (assuming raw counts in bin is large >10)
+ random = fRandom3->Gaus(measuredValue,measuredError);
+ fRandomizedDist->SetBinContent(iBin,random);
+ }
+}
+
+//______________________________________________________________
+void AliCFUnfolding::FillDeltaUnfoldedProfile() {
+ //
+ // Store difference of unfolded spectrum from measured distribution and unfolded distribution from randomized distribution
+ //
+
+ for (Long_t iBin2=0; iBin2<fRandomUnfolded->GetNbins(); iBin2++) {
+ Double_t delta = fUnfolded->GetBinContent(iBin2,fCoordinatesN_M)-fRandomUnfolded->GetBinContent(iBin2,fCoordinatesN_M);
+ fDeltaUnfoldedP->Fill(fDeltaUnfoldedP->GetBinCenter(fCoordinatesN_M[0]),delta);
+ }
+}
+
+//______________________________________________________________
+
void AliCFUnfolding::GetCoordinates() {
//
// assign coordinates in Measured and True spaces (dim=N) from coordinates in global space (dim=2N)
fConditional = (THnSparse*) fResponse->Clone(); // output of this function
fProjResponseInT = (THnSparse*) fPrior->Clone(); // output denominator :
// projection of the response matrix on the TRUE axis
-
- // set in fProjResponseInT zero everywhere
- for (Int_t iBin=0; iBin<fProjResponseInT->GetNbins(); iBin++) {
- fProjResponseInT->GetBinContent(iBin,fCoordinatesN_T);
- fProjResponseInT->SetBinContent(fCoordinatesN_T,0.);
- }
+ Int_t* dim = new Int_t [fNVariables];
+ for (Int_t iDim=0; iDim<fNVariables; iDim++) dim[iDim] = fNVariables+iDim ; //dimensions corresponding to TRUE values (i.e. from N to 2N-1)
+ fProjResponseInT = fConditional->Projection(fNVariables,dim,"E"); //project
+ delete [] dim;
- // calculate the response projection on T axis
- for (Int_t iBin=0; iBin<fResponse->GetNbins(); iBin++) {
- Double_t responseValue = fResponse->GetBinContent(iBin,fCoordinates2N);
- GetCoordinates();
- Double_t fill = fProjResponseInT->GetBinContent(fCoordinatesN_T) + responseValue ;
- if (fill>0.) fProjResponseInT->SetBinContent(fCoordinatesN_T,fill);
- }
-
// fill the conditional probability matrix
- for (Int_t iBin=0; iBin<fResponse->GetNbins(); iBin++) {
+ for (Long_t iBin=0; iBin<fResponse->GetNbins(); iBin++) {
Double_t responseValue = fResponse->GetBinContent(iBin,fCoordinates2N);
GetCoordinates();
- Double_t fill = responseValue / fProjResponseInT->GetBinContent(fCoordinatesN_T) ;
- if (fill>0. || fConditional->GetBinContent(fCoordinates2N)) fConditional->SetBinContent(fCoordinates2N,fill);
+ Double_t projValue = fProjResponseInT->GetBinContent(fCoordinatesN_T);
+
+ Double_t fill = responseValue / projValue ;
+ if (fill>0. || fConditional->GetBinContent(fCoordinates2N)>0.) {
+ fConditional->SetBinContent(fCoordinates2N,fill);
+ Double_t err = 0.;
+ fConditional->SetBinError (fCoordinates2N,err);
+ }
}
}
+//______________________________________________________________
+
+Int_t AliCFUnfolding::GetDOF() {
+ //
+ // number of dof = number of bins
+ //
+
+ Int_t nDOF = 1 ;
+ for (Int_t iDim=0; iDim<fNVariables; iDim++) {
+ nDOF *= fPrior->GetAxis(iDim)->GetNbins();
+ }
+ AliDebug(0,Form("Number of degrees of freedom = %d",nDOF));
+ return nDOF;
+}
//______________________________________________________________
Double_t AliCFUnfolding::GetChi2() {
//
// Returns the chi2 between unfolded and a priori spectrum
+ // This function became absolute with the correlated error calculation.
+ // Errors on the unfolded distribution are not known until the end
+ // Use the convergence criterion instead
//
- Double_t chi2 = 0. ;
- for (Int_t iBin=0; iBin<fPrior->GetNbins(); iBin++) {
- Double_t priorValue = fPrior->GetBinContent(iBin);
- chi2 += (priorValue>0. ? TMath::Power(fUnfolded->GetBinContent(iBin) - priorValue,2) / priorValue : 0.) ;
+ Double_t chi2 = 0. ;
+ Double_t error_unf = 0.;
+ for (Long_t iBin=0; iBin<fPrior->GetNbins(); iBin++) {
+ Double_t priorValue = fPrior->GetBinContent(iBin,fCoordinatesN_T);
+ error_unf = fUnfolded->GetBinError(fCoordinatesN_T);
+ chi2 += (error_unf > 0. ? TMath::Power((fUnfolded->GetBinContent(fCoordinatesN_T) - priorValue)/error_unf,2) / priorValue : 0.) ;
}
return chi2;
}
//______________________________________________________________
-void AliCFUnfolding::SetMaxChi2PerDOF(Double_t val) {
+Double_t AliCFUnfolding::GetConvergence() {
+ //
+ // Returns convergence criterion = \sum_t ((U_t^{n-1}-U_t^n)/U_t^{n-1})^2
+ // U is unfolded spectrum, t is the bin, n = current, n-1 = previous
+ //
+ Double_t convergence = 0.;
+ Double_t priorValue = 0.;
+ Double_t currentValue = 0.;
+ for (Long_t iBin=0; iBin < fPrior->GetNbins(); iBin++) {
+ priorValue = fPrior->GetBinContent(iBin,fCoordinatesN_T);
+ if (fNCalcCorrErrors > 0)
+ currentValue = fRandomUnfolded->GetBinContent(fCoordinatesN_T);
+ else
+ currentValue = fUnfolded->GetBinContent(fCoordinatesN_T);
+
+ if (priorValue > 0.)
+ convergence += ((priorValue-currentValue)/priorValue)*((priorValue-currentValue)/priorValue);
+ else {
+ AliWarning(Form("priorValue = %f. Adding 0 to convergence criterion.",priorValue));
+ convergence += 0.;
+ }
+ }
+ return convergence;
+}
+
+//______________________________________________________________
+
+void AliCFUnfolding::SetMaxConvergencePerDOF(Double_t val) {
//
- // Max. chi2 per degree of freedom : user setting
+ // Max. convergence criterion per degree of freedom : user setting
+ // convergence criterion = DOF*val; DOF = number of bins
+ // In Jan-Fiete's multiplicity note: Convergence criterion = DOF*0.001^2
//
- Int_t nDOF = 1 ;
- for (Int_t iDim=0; iDim<fNVariables; iDim++) {
- nDOF *= fPrior->GetAxis(iDim)->GetNbins();
- }
- AliInfo(Form("Number of degrees of freedom = %d",nDOF));
- fMaxChi2 = val * nDOF ;
+ fUseCorrelatedErrors = kTRUE;
+ Int_t nDOF = GetDOF() ;
+ fMaxConvergence = val * nDOF ;
+ AliInfo(Form("MaxConvergence = %e. Number of degrees of freedom = %d",fMaxConvergence,nDOF));
}
//______________________________________________________________
-void AliCFUnfolding::Smooth() {
+Short_t AliCFUnfolding::Smooth() {
//
// Smoothes the unfolded spectrum
- // Each cell content is replaced by the average with the neighbouring bins (but not diagonally-neighbouring bins)
+ //
+ // By default each cell content is replaced by the average with the neighbouring bins (but not diagonally-neighbouring bins)
+ // However, if a specific function fcn has been defined in UseSmoothing(fcn), the unfolded will be fit and updated using fcn
//
- Int_t* numBins = new Int_t[fNVariables];
- for (Int_t iVar=0; iVar<fNVariables; iVar++) numBins[iVar]=fUnfolded->GetAxis(iVar)->GetNbins();
+ if (fSmoothFunction) {
+ AliDebug(2,Form("Smoothing spectrum with fit function %p",fSmoothFunction));
+ return SmoothUsingFunction();
+ }
+ else return SmoothUsingNeighbours(fUnfolded);
+}
+
+//______________________________________________________________
+
+Short_t AliCFUnfolding::SmoothUsingNeighbours(THnSparse* hist) {
+ //
+ // Smoothes the unfolded spectrum using neighouring bins
+ //
+
+ Int_t const nDimensions = hist->GetNdimensions() ;
+ Int_t* coordinates = new Int_t[nDimensions];
+
+ Int_t* numBins = new Int_t[nDimensions];
+ for (Int_t iVar=0; iVar<nDimensions; iVar++) numBins[iVar] = hist->GetAxis(iVar)->GetNbins();
- //need a copy because fUnfolded will be updated during the loop, and this creates problems
- THnSparse* copy = (THnSparse*)fUnfolded->Clone();
+ //need a copy because hist will be updated during the loop, and this creates problems
+ THnSparse* copy = (THnSparse*)hist->Clone();
- for (Int_t iBin=0; iBin<copy->GetNbins(); iBin++) { //loop on non-empty bins
- Double_t content = copy->GetBinContent(iBin,fCoordinatesN_T);
+ for (Long_t iBin=0; iBin<copy->GetNbins(); iBin++) { //loop on non-empty bins
+ Double_t content = copy->GetBinContent(iBin,coordinates);
+ Double_t error2 = TMath::Power(copy->GetBinError(iBin),2);
// skip the under/overflow bins...
Bool_t isOutside = kFALSE ;
- for (Int_t iVar=0; iVar<fNVariables; iVar++) {
- if (fCoordinatesN_T[iVar]<1 || fCoordinatesN_T[iVar]>numBins[iVar]) {
+ for (Int_t iVar=0; iVar<nDimensions; iVar++) {
+ if (coordinates[iVar]<1 || coordinates[iVar]>numBins[iVar]) {
isOutside=kTRUE;
break;
}
Int_t neighbours = 0; // number of neighbours to average with
- for (Int_t iVar=0; iVar<fNVariables; iVar++) {
- if (fCoordinatesN_T[iVar] > 1) { // must not be on low edge border
- fCoordinatesN_T[iVar]-- ; //get lower neighbouring bin
- Double_t contentNeighbour = copy->GetBinContent(fCoordinatesN_T);
- content += contentNeighbour;
+ for (Int_t iVar=0; iVar<nDimensions; iVar++) {
+ if (coordinates[iVar] > 1) { // must not be on low edge border
+ coordinates[iVar]-- ; //get lower neighbouring bin
+ content += copy->GetBinContent(coordinates);
+ error2 += TMath::Power(copy->GetBinError(coordinates),2);
neighbours++;
- fCoordinatesN_T[iVar]++ ; //back to initial coordinate
+ coordinates[iVar]++ ; //back to initial coordinate
}
- if (fCoordinatesN_T[iVar] < numBins[iVar]) { // must not be on up edge border
- fCoordinatesN_T[iVar]++ ; //get upper neighbouring bin
- Double_t contentNeighbour = copy->GetBinContent(fCoordinatesN_T);
- content += contentNeighbour ;
+ if (coordinates[iVar] < numBins[iVar]) { // must not be on up edge border
+ coordinates[iVar]++ ; //get upper neighbouring bin
+ content += copy->GetBinContent(coordinates);
+ error2 += TMath::Power(copy->GetBinError(coordinates),2);
neighbours++;
- fCoordinatesN_T[iVar]-- ; //back to initial coordinate
+ coordinates[iVar]-- ; //back to initial coordinate
}
}
- content /= (1+neighbours) ; // make an average
- fUnfolded->SetBinContent(fCoordinatesN_T,content);
+ // make an average
+ hist->SetBinContent(coordinates,content/(1.+neighbours));
+ hist->SetBinError (coordinates,TMath::Sqrt(error2)/(1.+neighbours));
}
delete [] numBins;
+ delete [] coordinates ;
delete copy;
+ return 0;
}
+//______________________________________________________________
+
+Short_t AliCFUnfolding::SmoothUsingFunction() {
+ //
+ // Fits the unfolded spectrum using the function fSmoothFunction
+ //
+
+ AliDebug(0,Form("Smooth function is a %s with option \"%s\" and has %d parameters : ",fSmoothFunction->ClassName(),fSmoothOption,fSmoothFunction->GetNpar()));
+
+ for (Int_t iPar=0; iPar<fSmoothFunction->GetNpar(); iPar++) AliDebug(0,Form("par[%d]=%e",iPar,fSmoothFunction->GetParameter(iPar)));
+
+ Int_t fitResult = 0;
+
+ switch (fNVariables) {
+ case 1 : fitResult = fUnfolded->Projection(0) ->Fit(fSmoothFunction,fSmoothOption); break;
+ case 2 : fitResult = fUnfolded->Projection(1,0) ->Fit(fSmoothFunction,fSmoothOption); break; // (1,0) instead of (0,1) -> TAxis issue
+ case 3 : fitResult = fUnfolded->Projection(0,1,2)->Fit(fSmoothFunction,fSmoothOption); break;
+ default: AliFatal(Form("Cannot handle such fit in %d dimensions",fNVariables)) ; return 1;
+ }
+
+ if (fitResult != 0) {
+ AliWarning(Form("Fit failed with status %d, stopping the loop",fitResult));
+ return 1;
+ }
+
+ Int_t nDim = fNVariables;
+ Int_t* bins = new Int_t[nDim]; // number of bins for each variable
+ Long_t nBins = 1; // used to calculate the total number of bins in the THnSparse
+
+ for (Int_t iVar=0; iVar<nDim; iVar++) {
+ bins[iVar] = fUnfolded->GetAxis(iVar)->GetNbins();
+ nBins *= bins[iVar];
+ }
+
+ Int_t *bin = new Int_t[nDim]; // bin to fill the THnSparse (holding the bin coordinates)
+ Double_t x[3] = {0,0,0} ; // value in bin center (max dimension is 3 (TF3))
+
+ // loop on the bins and update of fUnfolded
+ // THnSparse::Multiply(TF1*) doesn't exist, so let's do it bin by bin
+ for (Long_t iBin=0; iBin<nBins; iBin++) {
+ Long_t bin_tmp = iBin ;
+ for (Int_t iVar=0; iVar<nDim; iVar++) {
+ bin[iVar] = 1 + bin_tmp % bins[iVar] ;
+ bin_tmp /= bins[iVar] ;
+ x[iVar] = fUnfolded->GetAxis(iVar)->GetBinCenter(bin[iVar]);
+ }
+ Double_t functionValue = fSmoothFunction->Eval(x[0],x[1],x[2]) ;
+ fUnfolded->SetBinError (bin,fUnfolded->GetBinError(bin)*functionValue/fUnfolded->GetBinContent(bin));
+ fUnfolded->SetBinContent(bin,functionValue);
+ }
+ delete [] bins;
+ delete [] bin ;
+ return 0;
+}
//______________________________________________________________
// create the frame of the THnSparse given (for example) the one from the efficiency map
fPrior = (THnSparse*) fEfficiency->Clone();
+ if (fNVariables != fPrior->GetNdimensions())
+ AliFatal(Form("The prior matrix should have %d dimensions, and it has actually %d",fNVariables,fPrior->GetNdimensions()));
+
Int_t nDim = fNVariables;
Int_t* bins = new Int_t[nDim]; // number of bins for each variable
Long_t nBins = 1; // used to calculate the total number of bins in the THnSparse
bin_tmp /= bins[iVar] ;
}
fPrior->SetBinContent(bin,1.); // put 1 everywhere
+ fPrior->SetBinError (bin,0.); // put 0 everywhere
}
fOriginalPrior = (THnSparse*)fPrior->Clone();
git://git.uio.no / u / mrichter / AliRoot.git / blobdiff