#include "TChain.h"
#include "TObjString.h"
#include "TLinearFitter.h"
+#include "TGraph2D.h"
+#include "TGraph.h"
//
// includes neccessary for test functions
Int_t * sindexS = new Int_t[n]; // temp array for sorting
Int_t * sindexF = new Int_t[2*n];
- for (Int_t i=0;i<n;i++) sindexF[i]=0;
+ for (Int_t i=0;i<n;i++) sindexS[i]=0;
+ for (Int_t i=0;i<2*n;i++) sindexF[i]=0;
//
TMath::Sort(n,inlist, sindexS, down);
Int_t last = inlist[sindexS[0]];
}
//___TStatToolkit__________________________________________________________________________
-void TStatToolkit::TruncatedMean(TH1F * his, TVectorD *param, Float_t down, Float_t up, Bool_t verbose){
+void TStatToolkit::TruncatedMean(const TH1 * his, TVectorD *param, Float_t down, Float_t up, Bool_t verbose){
//
//
//
}
}
-Double_t TStatToolkit::FitGaus(TH1F* his, TVectorD *param, TMatrixD *matrix, Float_t xmin, Float_t xmax, Bool_t verbose){
+Double_t TStatToolkit::FitGaus(TH1* his, TVectorD *param, TMatrixD */*matrix*/, Float_t xmin, Float_t xmax, Bool_t verbose){
//
// Fit histogram with gaussian function
//
Double_t chi2 = fitter.GetChisquare()/Float_t(npoints);
//fitter.GetParameters();
if (!param) param = new TVectorD(3);
- if (!matrix) matrix = new TMatrixD(3,3);
+ // if (!matrix) matrix = new TMatrixD(3,3); // Covariance matrix to be implemented
(*param)[1] = par[1]/(-2.*par[2]);
(*param)[2] = 1./TMath::Sqrt(TMath::Abs(-2.*par[2]));
(*param)[0] = TMath::Exp(par[0]+ par[1]* (*param)[1] + par[2]*(*param)[1]*(*param)[1]);
return chi2;
}
-Double_t TStatToolkit::FitGaus(Float_t *arr, Int_t nBins, Float_t xMin, Float_t xMax, TVectorD *param, TMatrixD *matrix, Bool_t verbose){
+Double_t TStatToolkit::FitGaus(Float_t *arr, Int_t nBins, Float_t xMin, Float_t xMax, TVectorD *param, TMatrixD */*matrix*/, Bool_t verbose){
//
// Fit histogram with gaussian function
//
fitter.ClearPoints();
TVectorD par(3);
TVectorD sigma(3);
- TMatrixD A(3,3);
+ TMatrixD matA(3,3);
TMatrixD b(3,1);
Float_t rms = TMath::RMS(nBins,arr);
Float_t max = TMath::MaxElement(nBins,arr);
Float_t val = TMath::Log(Float_t(entriesI));
fitter.AddPoint(&xcenter,val,error);
if (npoints<3){
- A(npoints,0)=1;
- A(npoints,1)=xcenter;
- A(npoints,2)=xcenter*xcenter;
+ matA(npoints,0)=1;
+ matA(npoints,1)=xcenter;
+ matA(npoints,2)=xcenter*xcenter;
b(npoints,0)=val;
meanCOG+=xcenter*entriesI;
rms2COG +=xcenter*entriesI*xcenter;
if (npoints>=3){
if ( npoints == 3 ){
//analytic calculation of the parameters for three points
- A.Invert();
+ matA.Invert();
TMatrixD res(1,3);
- res.Mult(A,b);
+ res.Mult(matA,b);
par[0]=res(0,0);
par[1]=res(0,1);
par[2]=res(0,2);
if (TMath::Abs(par[2])<kTol) return -4;
if (!param) param = new TVectorD(3);
- if (!matrix) matrix = new TMatrixD(3,3); // !!!!might be a memory leek. use dummy matrix pointer to call this function!
+ //if (!matrix) matrix = new TMatrixD(3,3); // !!!!might be a memory leek. use dummy matrix pointer to call this function! // Covariance matrix to be implemented
(*param)[1] = par[1]/(-2.*par[2]);
(*param)[2] = 1./TMath::Sqrt(TMath::Abs(-2.*par[2]));
}
-Float_t TStatToolkit::GetCOG(Short_t *arr, Int_t nBins, Float_t xMin, Float_t xMax, Float_t *rms, Float_t *sum)
+Float_t TStatToolkit::GetCOG(const Short_t *arr, Int_t nBins, Float_t xMin, Float_t xMax, Float_t *rms, Float_t *sum)
{
//
// calculate center of gravity rms and sum for array 'arr' with nBins an a x range xMin to xMax
for (Int_t iy=0; iy<nbiny;iy++){
Float_t xcenter = xaxis->GetBinCenter(ix);
Float_t ycenter = yaxis->GetBinCenter(iy);
- sprintf(name,"%s_%d_%d",his->GetName(), ix,iy);
+ snprintf(name,1000,"%s_%d_%d",his->GetName(), ix,iy);
TH1 *projection = his->ProjectionZ(name,ix-delta0,ix+delta0,iy-delta1,iy+delta1);
Float_t stat= 0;
if (type==0) stat = projection->GetMean();
for (Int_t ix=0; ix<nbinx;ix++){
Float_t xcenter = xaxis->GetBinCenter(ix);
// Float_t ycenter = yaxis->GetBinCenter(iy);
- sprintf(name,"%s_%d",his->GetName(), ix);
+ snprintf(name,1000,"%s_%d",his->GetName(), ix);
TH1 *projection = his->ProjectionZ(name,ix-delta1,ix+delta1,0,nbiny);
Float_t stat= 0;
if (type==0) stat = projection->GetMean();
-TString* TStatToolkit::FitPlane(TTree *tree, const char* drawCommand, const char* formula, const char* cuts, Double_t & chi2, Int_t &npoints, TVectorD &fitParam, TMatrixD &covMatrix, Float_t frac, Int_t start, Int_t stop){
+TString* TStatToolkit::FitPlane(TTree *tree, const char* drawCommand, const char* formula, const char* cuts, Double_t & chi2, Int_t &npoints, TVectorD &fitParam, TMatrixD &covMatrix, Float_t frac, Int_t start, Int_t stop,Bool_t fix0){
//
// fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts
// returns chi2, fitParam and covMatrix
Double_t **values = new Double_t*[dim+1] ;
//
entries = tree->Draw(ferr.Data(), cutStr.Data(), "goff", stop-start, start);
- if (entries == -1) return new TString("An ERROR has occured during fitting!");
+ if (entries == -1) {
+ delete []values;
+ return new TString("An ERROR has occured during fitting!");
+ }
Double_t *errors = new Double_t[entries];
memcpy(errors, tree->GetV1(), entries*sizeof(Double_t));
if (i < dim) centries = tree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff", stop-start,start);
else centries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start,start);
- if (entries != centries) return new TString("An ERROR has occured during fitting!");
+ if (entries != centries) {
+ delete []errors;
+ delete []values;
+ return new TString("An ERROR has occured during fitting!");
+ }
values[i] = new Double_t[entries];
memcpy(values[i], tree->GetV1(), entries*sizeof(Double_t));
}
fitter->Eval();
if (frac>0.5 && frac<1){
fitter->EvalRobust(frac);
+ }else{
+ if (fix0) {
+ fitter->FixParameter(0,0);
+ fitter->Eval();
+ }
}
fitter->GetParameters(fitParam);
fitter->GetCovarianceMatrix(covMatrix);
chi2 = fitter->GetChisquare();
- chi2 = chi2;
- npoints = entries;
-// TString *preturnFormula = new TString(Form("%f*(",fitParam[0])), &returnFormula = *preturnFormula;
+ npoints = entries;
+ TString *preturnFormula = new TString(Form("( %f+",fitParam[0])), &returnFormula = *preturnFormula;
+
+ for (Int_t iparam = 0; iparam < dim; iparam++) {
+ returnFormula.Append(Form("%s*(%f)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1]));
+ if (iparam < dim-1) returnFormula.Append("+");
+ }
+ returnFormula.Append(" )");
+
+
+ for (Int_t j=0; j<dim+1;j++) delete [] values[j];
+
+
+ delete formulaTokens;
+ delete fitter;
+ delete[] values;
+ delete[] errors;
+ return preturnFormula;
+}
+
+TString* TStatToolkit::FitPlaneConstrain(TTree *tree, const char* drawCommand, const char* formula, const char* cuts, Double_t & chi2, Int_t &npoints, TVectorD &fitParam, TMatrixD &covMatrix, Float_t frac, Int_t start, Int_t stop,Double_t constrain){
+ //
+ // fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts
+ // returns chi2, fitParam and covMatrix
+ // returns TString with fitted formula
+ //
+
+ TString formulaStr(formula);
+ TString drawStr(drawCommand);
+ TString cutStr(cuts);
+ TString ferr("1");
+
+ TString strVal(drawCommand);
+ if (strVal.Contains(":")){
+ TObjArray* valTokens = strVal.Tokenize(":");
+ drawStr = valTokens->At(0)->GetName();
+ ferr = valTokens->At(1)->GetName();
+ }
+
+
+ formulaStr.ReplaceAll("++", "~");
+ TObjArray* formulaTokens = formulaStr.Tokenize("~");
+ Int_t dim = formulaTokens->GetEntriesFast();
+
+ fitParam.ResizeTo(dim);
+ covMatrix.ResizeTo(dim,dim);
+
+ TLinearFitter* fitter = new TLinearFitter(dim+1, Form("hyp%d",dim));
+ fitter->StoreData(kTRUE);
+ fitter->ClearPoints();
+
+ Int_t entries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start, start);
+ if (entries == -1) return new TString("An ERROR has occured during fitting!");
+ Double_t **values = new Double_t*[dim+1] ;
+ //
+ entries = tree->Draw(ferr.Data(), cutStr.Data(), "goff", stop-start, start);
+ if (entries == -1) {
+ delete [] values;
+ return new TString("An ERROR has occured during fitting!");
+ }
+ Double_t *errors = new Double_t[entries];
+ memcpy(errors, tree->GetV1(), entries*sizeof(Double_t));
-// for (Int_t iparam = 0; iparam < dim; iparam++) {
-// returnFormula.Append(Form("%s*(%f)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1]/fitParam[0]));
-// if (iparam < dim-1) returnFormula.Append("+");
-// }
-// returnFormula.Append(" )");
+ for (Int_t i = 0; i < dim + 1; i++){
+ Int_t centries = 0;
+ if (i < dim) centries = tree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff", stop-start,start);
+ else centries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start,start);
+
+ if (entries != centries) {
+ delete []errors;
+ delete []values;
+ return new TString("An ERROR has occured during fitting!");
+ }
+ values[i] = new Double_t[entries];
+ memcpy(values[i], tree->GetV1(), entries*sizeof(Double_t));
+ }
+
+ // add points to the fitter
+ for (Int_t i = 0; i < entries; i++){
+ Double_t x[1000];
+ for (Int_t j=0; j<dim;j++) x[j]=values[j][i];
+ fitter->AddPoint(x, values[dim][i], errors[i]);
+ }
+ if (constrain>0){
+ for (Int_t i = 0; i < dim; i++){
+ Double_t x[1000];
+ for (Int_t j=0; j<dim;j++) if (i!=j) x[j]=0;
+ x[i]=1.;
+ fitter->AddPoint(x, 0, constrain);
+ }
+ }
+
+
+ fitter->Eval();
+ if (frac>0.5 && frac<1){
+ fitter->EvalRobust(frac);
+ }
+ fitter->GetParameters(fitParam);
+ fitter->GetCovarianceMatrix(covMatrix);
+ chi2 = fitter->GetChisquare();
+ npoints = entries;
TString *preturnFormula = new TString(Form("( %f+",fitParam[0])), &returnFormula = *preturnFormula;
}
returnFormula.Append(" )");
+ for (Int_t j=0; j<dim+1;j++) delete [] values[j];
delete formulaTokens;
delete fitter;
delete[] values;
+ delete[] errors;
+ return preturnFormula;
+}
+
+
+
+TString* TStatToolkit::FitPlaneFixed(TTree *tree, const char* drawCommand, const char* formula, const char* cuts, Double_t & chi2, Int_t &npoints, TVectorD &fitParam, TMatrixD &covMatrix, Float_t frac, Int_t start, Int_t stop){
+ //
+ // fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts
+ // returns chi2, fitParam and covMatrix
+ // returns TString with fitted formula
+ //
+
+ TString formulaStr(formula);
+ TString drawStr(drawCommand);
+ TString cutStr(cuts);
+ TString ferr("1");
+
+ TString strVal(drawCommand);
+ if (strVal.Contains(":")){
+ TObjArray* valTokens = strVal.Tokenize(":");
+ drawStr = valTokens->At(0)->GetName();
+ ferr = valTokens->At(1)->GetName();
+ }
+
+
+ formulaStr.ReplaceAll("++", "~");
+ TObjArray* formulaTokens = formulaStr.Tokenize("~");
+ Int_t dim = formulaTokens->GetEntriesFast();
+
+ fitParam.ResizeTo(dim);
+ covMatrix.ResizeTo(dim,dim);
+ TString fitString="x0";
+ for (Int_t i=1; i<dim; i++) fitString+=Form("++x%d",i);
+ TLinearFitter* fitter = new TLinearFitter(dim, fitString.Data());
+ fitter->StoreData(kTRUE);
+ fitter->ClearPoints();
+
+ Int_t entries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start, start);
+ if (entries == -1) return new TString("An ERROR has occured during fitting!");
+ Double_t **values = new Double_t*[dim+1] ;
+ //
+ entries = tree->Draw(ferr.Data(), cutStr.Data(), "goff", stop-start, start);
+ if (entries == -1) {
+ delete []values;
+ return new TString("An ERROR has occured during fitting!");
+ }
+ Double_t *errors = new Double_t[entries];
+ memcpy(errors, tree->GetV1(), entries*sizeof(Double_t));
+
+ for (Int_t i = 0; i < dim + 1; i++){
+ Int_t centries = 0;
+ if (i < dim) centries = tree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff", stop-start,start);
+ else centries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff", stop-start,start);
+
+ if (entries != centries) {
+ delete []errors;
+ delete []values;
+ return new TString("An ERROR has occured during fitting!");
+ }
+ values[i] = new Double_t[entries];
+ memcpy(values[i], tree->GetV1(), entries*sizeof(Double_t));
+ }
+
+ // add points to the fitter
+ for (Int_t i = 0; i < entries; i++){
+ Double_t x[1000];
+ for (Int_t j=0; j<dim;j++) x[j]=values[j][i];
+ fitter->AddPoint(x, values[dim][i], errors[i]);
+ }
+
+ fitter->Eval();
+ if (frac>0.5 && frac<1){
+ fitter->EvalRobust(frac);
+ }
+ fitter->GetParameters(fitParam);
+ fitter->GetCovarianceMatrix(covMatrix);
+ chi2 = fitter->GetChisquare();
+ npoints = entries;
+
+ TString *preturnFormula = new TString("("), &returnFormula = *preturnFormula;
+
+ for (Int_t iparam = 0; iparam < dim; iparam++) {
+ returnFormula.Append(Form("%s*(%f)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam]));
+ if (iparam < dim-1) returnFormula.Append("+");
+ }
+ returnFormula.Append(" )");
+
+
+ for (Int_t j=0; j<dim+1;j++) delete [] values[j];
+
+ delete formulaTokens;
+ delete fitter;
+ delete[] values;
+ delete[] errors;
return preturnFormula;
}
+
+
+
+
+
+Int_t TStatToolkit::GetFitIndex(const TString fString, const TString subString){
+ //
+ // fitString - ++ separated list of fits
+ // substring - ++ separated list of the requiered substrings
+ //
+ // return the last occurance of substring in fit string
+ //
+ TObjArray *arrFit = fString.Tokenize("++");
+ TObjArray *arrSub = subString.Tokenize("++");
+ Int_t index=-1;
+ for (Int_t i=0; i<arrFit->GetEntries(); i++){
+ Bool_t isOK=kTRUE;
+ TString str =arrFit->At(i)->GetName();
+ for (Int_t isub=0; isub<arrSub->GetEntries(); isub++){
+ if (str.Contains(arrSub->At(isub)->GetName())==0) isOK=kFALSE;
+ }
+ if (isOK) index=i;
+ }
+ return index;
+}
+
+
+TString TStatToolkit::FilterFit(const TString &input, const TString filter, TVectorD ¶m, TMatrixD & covar){
+ //
+ // Filter fit expression make sub-fit
+ //
+ TObjArray *array0= input.Tokenize("++");
+ TObjArray *array1= filter.Tokenize("++");
+ //TString *presult=new TString("(0");
+ TString result="(0.0";
+ for (Int_t i=0; i<array0->GetEntries(); i++){
+ Bool_t isOK=kTRUE;
+ TString str(array0->At(i)->GetName());
+ for (Int_t j=0; j<array1->GetEntries(); j++){
+ if (str.Contains(array1->At(j)->GetName())==0) isOK=kFALSE;
+ }
+ if (isOK) {
+ result+="+"+str;
+ result+=Form("*(%f)",param[i+1]);
+ printf("%f\t%f\t%s\n",param[i+1], TMath::Sqrt(covar(i+1,i+1)),str.Data());
+ }
+ }
+ result+="-0.)";
+ return result;
+}
+
+void TStatToolkit::Update1D(Double_t delta, Double_t sigma, Int_t s1, TMatrixD &vecXk, TMatrixD &covXk){
+ //
+ // Update parameters and covariance - with one measurement
+ // Input:
+ // vecXk - input vector - Updated in function
+ // covXk - covariance matrix - Updated in function
+ // delta, sigma, s1 - new measurement, rms of new measurement and the index of measurement
+ const Int_t knMeas=1;
+ Int_t knElem=vecXk.GetNrows();
+
+ TMatrixD mat1(knElem,knElem); // update covariance matrix
+ TMatrixD matHk(1,knElem); // vector to mesurement
+ TMatrixD vecYk(knMeas,1); // Innovation or measurement residual
+ TMatrixD matHkT(knElem,knMeas); // helper matrix Hk transpose
+ TMatrixD matSk(knMeas,knMeas); // Innovation (or residual) covariance
+ TMatrixD matKk(knElem,knMeas); // Optimal Kalman gain
+ TMatrixD covXk2(knElem,knElem); // helper matrix
+ TMatrixD covXk3(knElem,knElem); // helper matrix
+ TMatrixD vecZk(1,1);
+ TMatrixD measR(1,1);
+ vecZk(0,0)=delta;
+ measR(0,0)=sigma*sigma;
+ //
+ // reset matHk
+ for (Int_t iel=0;iel<knElem;iel++)
+ for (Int_t ip=0;ip<knMeas;ip++) matHk(ip,iel)=0;
+ //mat1
+ for (Int_t iel=0;iel<knElem;iel++) {
+ for (Int_t jel=0;jel<knElem;jel++) mat1(iel,jel)=0;
+ mat1(iel,iel)=1;
+ }
+ //
+ matHk(0, s1)=1;
+ vecYk = vecZk-matHk*vecXk; // Innovation or measurement residual
+ matHkT=matHk.T(); matHk.T();
+ matSk = (matHk*(covXk*matHkT))+measR; // Innovation (or residual) covariance
+ matSk.Invert();
+ matKk = (covXk*matHkT)*matSk; // Optimal Kalman gain
+ vecXk += matKk*vecYk; // updated vector
+ covXk2= (mat1-(matKk*matHk));
+ covXk3 = covXk2*covXk;
+ covXk = covXk3;
+ Int_t nrows=covXk3.GetNrows();
+
+ for (Int_t irow=0; irow<nrows; irow++)
+ for (Int_t icol=0; icol<nrows; icol++){
+ // rounding problems - make matrix again symteric
+ covXk(irow,icol)=(covXk3(irow,icol)+covXk3(icol,irow))*0.5;
+ }
+}
+
+
+
+void TStatToolkit::Constrain1D(const TString &input, const TString filter, TVectorD ¶m, TMatrixD & covar, Double_t mean, Double_t sigma){
+ //
+ // constrain linear fit
+ // input - string description of fit function
+ // filter - string filter to select sub fits
+ // param,covar - parameters and covariance matrix of the fit
+ // mean,sigma - new measurement uning which the fit is updated
+ //
+
+ TObjArray *array0= input.Tokenize("++");
+ TObjArray *array1= filter.Tokenize("++");
+ TMatrixD paramM(param.GetNrows(),1);
+ for (Int_t i=0; i<=array0->GetEntries(); i++){paramM(i,0)=param(i);}
+
+ if (filter.Length()==0){
+ TStatToolkit::Update1D(mean, sigma, 0, paramM, covar);//
+ }else{
+ for (Int_t i=0; i<array0->GetEntries(); i++){
+ Bool_t isOK=kTRUE;
+ TString str(array0->At(i)->GetName());
+ for (Int_t j=0; j<array1->GetEntries(); j++){
+ if (str.Contains(array1->At(j)->GetName())==0) isOK=kFALSE;
+ }
+ if (isOK) {
+ TStatToolkit::Update1D(mean, sigma, i+1, paramM, covar);//
+ }
+ }
+ }
+ for (Int_t i=0; i<=array0->GetEntries(); i++){
+ param(i)=paramM(i,0);
+ }
+}
+
+TString TStatToolkit::MakeFitString(const TString &input, const TVectorD ¶m, const TMatrixD & covar, Bool_t verbose){
+ //
+ //
+ //
+ TObjArray *array0= input.Tokenize("++");
+ TString result=Form("(%f",param[0]);
+ printf("%f\t%f\t\n", param[0], TMath::Sqrt(covar(0,0)));
+ for (Int_t i=0; i<array0->GetEntries(); i++){
+ TString str(array0->At(i)->GetName());
+ result+="+"+str;
+ result+=Form("*(%f)",param[i+1]);
+ if (verbose) printf("%f\t%f\t%s\n", param[i+1], TMath::Sqrt(covar(i+1,i+1)),str.Data());
+ }
+ result+="-0.)";
+ return result;
+}
+
+
+TGraph * TStatToolkit::MakeGraphSparse(TTree * tree, const char * expr, const char * cut){
+ //
+ // Make a sparse draw of the variables
+ //
+ const Int_t entries = tree->Draw(expr,cut,"goff");
+ // TGraph * graph = (TGraph*)gPad->GetPrimitive("Graph"); // 2D
+ TGraph * graph = new TGraph (entries, tree->GetV2(),tree->GetV1());
+ //
+ Int_t *index = new Int_t[entries];
+ TMath::Sort(entries,graph->GetX(),index,kFALSE);
+
+ Double_t *tempArray = new Double_t[entries];
+
+ Double_t count = 0.5;
+ Double_t *vrun = new Double_t[entries];
+ Int_t icount=0;
+ //
+ tempArray[index[0]] = count;
+ vrun[0] = graph->GetX()[index[0]];
+ for(Int_t i=1;i<entries;i++){
+ if(graph->GetX()[index[i]]==graph->GetX()[index[i-1]])
+ tempArray[index[i]] = count;
+ else if(graph->GetX()[index[i]]!=graph->GetX()[index[i-1]]){
+ count++;
+ icount++;
+ tempArray[index[i]] = count;
+ vrun[icount]=graph->GetX()[index[i]];
+ }
+ }
+
+ const Int_t newNbins = int(count+0.5);
+ Double_t *newBins = new Double_t[newNbins+1];
+ for(Int_t i=0; i<=count+1;i++){
+ newBins[i] = i;
+ }
+
+ TGraph *graphNew = new TGraph(entries,tempArray,graph->GetY());
+ graphNew->GetXaxis()->Set(newNbins,newBins);
+
+ Char_t xName[50];
+ for(Int_t i=0;i<count;i++){
+ snprintf(xName,50,"%d",Int_t(vrun[i]));
+ graphNew->GetXaxis()->SetBinLabel(i+1,xName);
+ }
+ graphNew->GetHistogram()->SetTitle("");
+
+ delete [] tempArray;
+ delete [] index;
+ delete [] newBins;
+ delete [] vrun;
+ return graphNew;
+}
+
git://git.uio.no / u / mrichter / AliRoot.git / blobdiff