--- /dev/null
+/**************************************************************************
+ * Copyright(c) 1998-1999, ALICE Experiment at CERN, All rights reserved. *
+ * *
+ * Author: The ALICE Off-line Project. *
+ * Contributors are mentioned in the code where appropriate. *
+ * *
+ * Permission to use, copy, modify and distribute this software and its *
+ * documentation strictly for non-commercial purposes is hereby granted *
+ * without fee, provided that the above copyright notice appears in all *
+ * copies and that both the copyright notice and this permission notice *
+ * appear in the supporting documentation. The authors make no claims *
+ * about the suitability of this software for any purpose. It is *
+ * provided "as is" without express or implied warranty. *
+ **************************************************************************/
+
+
+///////////////////////////////////////////////////////////////////////////
+// Class TStatToolkit
+//
+// Subset of matheamtical functions not included in the TMath
+//
+
+///////////////////////////////////////////////////////////////////////////
+#include "TMath.h"
+#include "Riostream.h"
+#include "TH1F.h"
+#include "TH3.h"
+#include "TF1.h"
+#include "TTree.h"
+#include "TChain.h"
+#include "TObjString.h"
+#include "TLinearFitter.h"
+
+//
+// includes neccessary for test functions
+//
+#include "TSystem.h"
+#include "TRandom.h"
+#include "TStopwatch.h"
+#include "TTreeStream.h"
+
+#include "TStatToolkit.h"
+
+
+ClassImp(TStatToolkit) // Class implementation to enable ROOT I/O
+
+TStatToolkit::TStatToolkit() : TObject()
+{
+ //
+ // Default constructor
+ //
+}
+///////////////////////////////////////////////////////////////////////////
+TStatToolkit::~TStatToolkit()
+{
+ //
+ // Destructor
+ //
+}
+
+
+//_____________________________________________________________________________
+void TStatToolkit::EvaluateUni(Int_t nvectors, Double_t *data, Double_t &mean
+ , Double_t &sigma, Int_t hh)
+{
+ //
+ // Robust estimator in 1D case MI version - (faster than ROOT version)
+ //
+ // For the univariate case
+ // estimates of location and scatter are returned in mean and sigma parameters
+ // the algorithm works on the same principle as in multivariate case -
+ // it finds a subset of size hh with smallest sigma, and then returns mean and
+ // sigma of this subset
+ //
+
+ if (hh==0)
+ hh=(nvectors+2)/2;
+ Double_t faclts[]={2.6477,2.5092,2.3826,2.2662,2.1587,2.0589,1.9660,1.879,1.7973,1.7203,1.6473};
+ Int_t *index=new Int_t[nvectors];
+ TMath::Sort(nvectors, data, index, kFALSE);
+
+ Int_t nquant = TMath::Min(Int_t(Double_t(((hh*1./nvectors)-0.5)*40))+1, 11);
+ Double_t factor = faclts[TMath::Max(0,nquant-1)];
+
+ Double_t sumx =0;
+ Double_t sumx2 =0;
+ Int_t bestindex = -1;
+ Double_t bestmean = 0;
+ Double_t bestsigma = (data[index[nvectors-1]]-data[index[0]]+1.); // maximal possible sigma
+ bestsigma *=bestsigma;
+
+ for (Int_t i=0; i<hh; i++){
+ sumx += data[index[i]];
+ sumx2 += data[index[i]]*data[index[i]];
+ }
+
+ Double_t norm = 1./Double_t(hh);
+ Double_t norm2 = 1./Double_t(hh-1);
+ for (Int_t i=hh; i<nvectors; i++){
+ Double_t cmean = sumx*norm;
+ Double_t csigma = (sumx2 - hh*cmean*cmean)*norm2;
+ if (csigma<bestsigma){
+ bestmean = cmean;
+ bestsigma = csigma;
+ bestindex = i-hh;
+ }
+
+ sumx += data[index[i]]-data[index[i-hh]];
+ sumx2 += data[index[i]]*data[index[i]]-data[index[i-hh]]*data[index[i-hh]];
+ }
+
+ Double_t bstd=factor*TMath::Sqrt(TMath::Abs(bestsigma));
+ mean = bestmean;
+ sigma = bstd;
+ delete [] index;
+
+}
+
+
+
+void TStatToolkit::EvaluateUniExternal(Int_t nvectors, Double_t *data, Double_t &mean, Double_t &sigma, Int_t hh, Float_t externalfactor)
+{
+ // Modified version of ROOT robust EvaluateUni
+ // robust estimator in 1D case MI version
+ // added external factor to include precision of external measurement
+ //
+
+ if (hh==0)
+ hh=(nvectors+2)/2;
+ Double_t faclts[]={2.6477,2.5092,2.3826,2.2662,2.1587,2.0589,1.9660,1.879,1.7973,1.7203,1.6473};
+ Int_t *index=new Int_t[nvectors];
+ TMath::Sort(nvectors, data, index, kFALSE);
+ //
+ Int_t nquant = TMath::Min(Int_t(Double_t(((hh*1./nvectors)-0.5)*40))+1, 11);
+ Double_t factor = faclts[0];
+ if (nquant>0){
+ // fix proper normalization - Anja
+ factor = faclts[nquant-1];
+ }
+
+ //
+ //
+ Double_t sumx =0;
+ Double_t sumx2 =0;
+ Int_t bestindex = -1;
+ Double_t bestmean = 0;
+ Double_t bestsigma = -1;
+ for (Int_t i=0; i<hh; i++){
+ sumx += data[index[i]];
+ sumx2 += data[index[i]]*data[index[i]];
+ }
+ //
+ Double_t kfactor = 2.*externalfactor - externalfactor*externalfactor;
+ Double_t norm = 1./Double_t(hh);
+ for (Int_t i=hh; i<nvectors; i++){
+ Double_t cmean = sumx*norm;
+ Double_t csigma = (sumx2*norm - cmean*cmean*kfactor);
+ if (csigma<bestsigma || bestsigma<0){
+ bestmean = cmean;
+ bestsigma = csigma;
+ bestindex = i-hh;
+ }
+ //
+ //
+ sumx += data[index[i]]-data[index[i-hh]];
+ sumx2 += data[index[i]]*data[index[i]]-data[index[i-hh]]*data[index[i-hh]];
+ }
+
+ Double_t bstd=factor*TMath::Sqrt(TMath::Abs(bestsigma));
+ mean = bestmean;
+ sigma = bstd;
+ delete [] index;
+}
+
+
+//_____________________________________________________________________________
+Int_t TStatToolkit::Freq(Int_t n, const Int_t *inlist
+ , Int_t *outlist, Bool_t down)
+{
+ //
+ // Sort eleements according occurancy
+ // The size of output array has is 2*n
+ //
+
+ 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;
+ //
+ TMath::Sort(n,inlist, sindexS, down);
+ Int_t last = inlist[sindexS[0]];
+ Int_t val = last;
+ sindexF[0] = 1;
+ sindexF[0+n] = last;
+ Int_t countPos = 0;
+ //
+ // find frequency
+ for(Int_t i=1;i<n; i++){
+ val = inlist[sindexS[i]];
+ if (last == val) sindexF[countPos]++;
+ else{
+ countPos++;
+ sindexF[countPos+n] = val;
+ sindexF[countPos]++;
+ last =val;
+ }
+ }
+ if (last==val) countPos++;
+ // sort according frequency
+ TMath::Sort(countPos, sindexF, sindexS, kTRUE);
+ for (Int_t i=0;i<countPos;i++){
+ outlist[2*i ] = sindexF[sindexS[i]+n];
+ outlist[2*i+1] = sindexF[sindexS[i]];
+ }
+ delete [] sindexS;
+ delete [] sindexF;
+
+ return countPos;
+
+}
+
+//___TStatToolkit__________________________________________________________________________
+void TStatToolkit::TruncatedMean(TH1F * his, TVectorD *param, Float_t down, Float_t up, Bool_t verbose){
+ //
+ //
+ //
+ Int_t nbins = his->GetNbinsX();
+ Float_t nentries = his->GetEntries();
+ Float_t sum =0;
+ Float_t mean = 0;
+ Float_t sigma2 = 0;
+ Float_t ncumul=0;
+ for (Int_t ibin=1;ibin<nbins; ibin++){
+ ncumul+= his->GetBinContent(ibin);
+ Float_t fraction = Float_t(ncumul)/Float_t(nentries);
+ if (fraction>down && fraction<up){
+ sum+=his->GetBinContent(ibin);
+ mean+=his->GetBinCenter(ibin)*his->GetBinContent(ibin);
+ sigma2+=his->GetBinCenter(ibin)*his->GetBinCenter(ibin)*his->GetBinContent(ibin);
+ }
+ }
+ mean/=sum;
+ sigma2= TMath::Sqrt(TMath::Abs(sigma2/sum-mean*mean));
+ if (param){
+ (*param)[0] = his->GetMaximum();
+ (*param)[1] = mean;
+ (*param)[2] = sigma2;
+
+ }
+ if (verbose) printf("Mean\t%f\t Sigma2\t%f\n", mean,sigma2);
+}
+
+void TStatToolkit::LTM(TH1F * his, TVectorD *param , Float_t fraction, Bool_t verbose){
+ //
+ // LTM
+ //
+ Int_t nbins = his->GetNbinsX();
+ Int_t nentries = (Int_t)his->GetEntries();
+ Double_t *data = new Double_t[nentries];
+ Int_t npoints=0;
+ for (Int_t ibin=1;ibin<nbins; ibin++){
+ Float_t entriesI = his->GetBinContent(ibin);
+ Float_t xcenter= his->GetBinCenter(ibin);
+ for (Int_t ic=0; ic<entriesI; ic++){
+ if (npoints<nentries){
+ data[npoints]= xcenter;
+ npoints++;
+ }
+ }
+ }
+ Double_t mean, sigma;
+ Int_t npoints2=TMath::Min(Int_t(fraction*Float_t(npoints)),npoints-1);
+ npoints2=TMath::Max(Int_t(0.5*Float_t(npoints)),npoints2);
+ TStatToolkit::EvaluateUni(npoints, data, mean,sigma,npoints2);
+ delete [] data;
+ if (verbose) printf("Mean\t%f\t Sigma2\t%f\n", mean,sigma);if (param){
+ (*param)[0] = his->GetMaximum();
+ (*param)[1] = mean;
+ (*param)[2] = sigma;
+ }
+}
+
+Double_t TStatToolkit::FitGaus(TH1F* his, TVectorD *param, TMatrixD *matrix, Float_t xmin, Float_t xmax, Bool_t verbose){
+ //
+ // Fit histogram with gaussian function
+ //
+ // Prameters:
+ // return value- chi2 - if negative ( not enough points)
+ // his - input histogram
+ // param - vector with parameters
+ // xmin, xmax - range to fit - if xmin=xmax=0 - the full histogram range used
+ // Fitting:
+ // 1. Step - make logarithm
+ // 2. Linear fit (parabola) - more robust - always converge
+ // 3. In case of small statistic bins are averaged
+ //
+ static TLinearFitter fitter(3,"pol2");
+ TVectorD par(3);
+ TVectorD sigma(3);
+ TMatrixD mat(3,3);
+ if (his->GetMaximum()<4) return -1;
+ if (his->GetEntries()<12) return -1;
+ if (his->GetRMS()<mat.GetTol()) return -1;
+ Float_t maxEstimate = his->GetEntries()*his->GetBinWidth(1)/TMath::Sqrt((TMath::TwoPi()*his->GetRMS()));
+ Int_t dsmooth = TMath::Nint(6./TMath::Sqrt(maxEstimate));
+
+ if (maxEstimate<1) return -1;
+ Int_t nbins = his->GetNbinsX();
+ Int_t npoints=0;
+ //
+
+
+ if (xmin>=xmax){
+ xmin = his->GetXaxis()->GetXmin();
+ xmax = his->GetXaxis()->GetXmax();
+ }
+ for (Int_t iter=0; iter<2; iter++){
+ fitter.ClearPoints();
+ npoints=0;
+ for (Int_t ibin=1;ibin<nbins+1; ibin++){
+ Int_t countB=1;
+ Float_t entriesI = his->GetBinContent(ibin);
+ for (Int_t delta = -dsmooth; delta<=dsmooth; delta++){
+ if (ibin+delta>1 &&ibin+delta<nbins-1){
+ entriesI += his->GetBinContent(ibin+delta);
+ countB++;
+ }
+ }
+ entriesI/=countB;
+ Double_t xcenter= his->GetBinCenter(ibin);
+ if (xcenter<xmin || xcenter>xmax) continue;
+ Double_t error=1./TMath::Sqrt(countB);
+ Float_t cont=2;
+ if (iter>0){
+ if (par[0]+par[1]*xcenter+par[2]*xcenter*xcenter>20) return 0;
+ cont = TMath::Exp(par[0]+par[1]*xcenter+par[2]*xcenter*xcenter);
+ if (cont>1.) error = 1./TMath::Sqrt(cont*Float_t(countB));
+ }
+ if (entriesI>1&&cont>1){
+ fitter.AddPoint(&xcenter,TMath::Log(Float_t(entriesI)),error);
+ npoints++;
+ }
+ }
+ if (npoints>3){
+ fitter.Eval();
+ fitter.GetParameters(par);
+ }else{
+ break;
+ }
+ }
+ if (npoints<=3){
+ return -1;
+ }
+ fitter.GetParameters(par);
+ fitter.GetCovarianceMatrix(mat);
+ if (TMath::Abs(par[1])<mat.GetTol()) return -1;
+ if (TMath::Abs(par[2])<mat.GetTol()) return -1;
+ Double_t chi2 = fitter.GetChisquare()/Float_t(npoints);
+ //fitter.GetParameters();
+ if (!param) param = new TVectorD(3);
+ if (!matrix) matrix = new TMatrixD(3,3);
+ (*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]);
+ if (verbose){
+ par.Print();
+ mat.Print();
+ param->Print();
+ printf("Chi2=%f\n",chi2);
+ TF1 * f1= new TF1("f1","[0]*exp(-(x-[1])^2/(2*[2]*[2]))",his->GetXaxis()->GetXmin(),his->GetXaxis()->GetXmax());
+ f1->SetParameter(0, (*param)[0]);
+ f1->SetParameter(1, (*param)[1]);
+ f1->SetParameter(2, (*param)[2]);
+ f1->Draw("same");
+ }
+ 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){
+ //
+ // Fit histogram with gaussian function
+ //
+ // Prameters:
+ // nbins: size of the array and number of histogram bins
+ // xMin, xMax: histogram range
+ // param: paramters of the fit (0-Constant, 1-Mean, 2-Sigma)
+ // matrix: covariance matrix -- not implemented yet, pass dummy matrix!!!
+ //
+ // Return values:
+ // >0: the chi2 returned by TLinearFitter
+ // -3: only three points have been used for the calculation - no fitter was used
+ // -2: only two points have been used for the calculation - center of gravity was uesed for calculation
+ // -1: only one point has been used for the calculation - center of gravity was uesed for calculation
+ // -4: invalid result!!
+ //
+ // Fitting:
+ // 1. Step - make logarithm
+ // 2. Linear fit (parabola) - more robust - always converge
+ //
+ static TLinearFitter fitter(3,"pol2");
+ static TMatrixD mat(3,3);
+ static Double_t kTol = mat.GetTol();
+ fitter.StoreData(kFALSE);
+ fitter.ClearPoints();
+ TVectorD par(3);
+ TVectorD sigma(3);
+ TMatrixD A(3,3);
+ TMatrixD b(3,1);
+ Float_t rms = TMath::RMS(nBins,arr);
+ Float_t max = TMath::MaxElement(nBins,arr);
+ Float_t binWidth = (xMax-xMin)/(Float_t)nBins;
+
+ Float_t meanCOG = 0;
+ Float_t rms2COG = 0;
+ Float_t sumCOG = 0;
+
+ Float_t entries = 0;
+ Int_t nfilled=0;
+
+ for (Int_t i=0; i<nBins; i++){
+ entries+=arr[i];
+ if (arr[i]>0) nfilled++;
+ }
+
+ if (max<4) return -4;
+ if (entries<12) return -4;
+ if (rms<kTol) return -4;
+
+ Int_t npoints=0;
+ //
+
+ //
+ for (Int_t ibin=0;ibin<nBins; ibin++){
+ Float_t entriesI = arr[ibin];
+ if (entriesI>1){
+ Double_t xcenter = xMin+(ibin+0.5)*binWidth;
+
+ Float_t error = 1./TMath::Sqrt(entriesI);
+ 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;
+ b(npoints,0)=val;
+ meanCOG+=xcenter*entriesI;
+ rms2COG +=xcenter*entriesI*xcenter;
+ sumCOG +=entriesI;
+ }
+ npoints++;
+ }
+ }
+
+
+ Double_t chi2 = 0;
+ if (npoints>=3){
+ if ( npoints == 3 ){
+ //analytic calculation of the parameters for three points
+ A.Invert();
+ TMatrixD res(1,3);
+ res.Mult(A,b);
+ par[0]=res(0,0);
+ par[1]=res(0,1);
+ par[2]=res(0,2);
+ chi2 = -3.;
+ } else {
+ // use fitter for more than three points
+ fitter.Eval();
+ fitter.GetParameters(par);
+ fitter.GetCovarianceMatrix(mat);
+ chi2 = fitter.GetChisquare()/Float_t(npoints);
+ }
+ if (TMath::Abs(par[1])<kTol) return -4;
+ 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!
+
+ (*param)[1] = par[1]/(-2.*par[2]);
+ (*param)[2] = 1./TMath::Sqrt(TMath::Abs(-2.*par[2]));
+ Double_t lnparam0 = par[0]+ par[1]* (*param)[1] + par[2]*(*param)[1]*(*param)[1];
+ if ( lnparam0>307 ) return -4;
+ (*param)[0] = TMath::Exp(lnparam0);
+ if (verbose){
+ par.Print();
+ mat.Print();
+ param->Print();
+ printf("Chi2=%f\n",chi2);
+ TF1 * f1= new TF1("f1","[0]*exp(-(x-[1])^2/(2*[2]*[2]))",xMin,xMax);
+ f1->SetParameter(0, (*param)[0]);
+ f1->SetParameter(1, (*param)[1]);
+ f1->SetParameter(2, (*param)[2]);
+ f1->Draw("same");
+ }
+ return chi2;
+ }
+
+ if (npoints == 2){
+ //use center of gravity for 2 points
+ meanCOG/=sumCOG;
+ rms2COG /=sumCOG;
+ (*param)[0] = max;
+ (*param)[1] = meanCOG;
+ (*param)[2] = TMath::Sqrt(TMath::Abs(meanCOG*meanCOG-rms2COG));
+ chi2=-2.;
+ }
+ if ( npoints == 1 ){
+ meanCOG/=sumCOG;
+ (*param)[0] = max;
+ (*param)[1] = meanCOG;
+ (*param)[2] = binWidth/TMath::Sqrt(12);
+ chi2=-1.;
+ }
+ return chi2;
+
+}
+
+
+Float_t TStatToolkit::GetCOG(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
+ // return COG; in case of failure return xMin
+ //
+ Float_t meanCOG = 0;
+ Float_t rms2COG = 0;
+ Float_t sumCOG = 0;
+ Int_t npoints = 0;
+
+ Float_t binWidth = (xMax-xMin)/(Float_t)nBins;
+
+ for (Int_t ibin=0; ibin<nBins; ibin++){
+ Float_t entriesI = (Float_t)arr[ibin];
+ Double_t xcenter = xMin+(ibin+0.5)*binWidth;
+ if ( entriesI>0 ){
+ meanCOG += xcenter*entriesI;
+ rms2COG += xcenter*entriesI*xcenter;
+ sumCOG += entriesI;
+ npoints++;
+ }
+ }
+ if ( sumCOG == 0 ) return xMin;
+ meanCOG/=sumCOG;
+
+ if ( rms ){
+ rms2COG /=sumCOG;
+ (*rms) = TMath::Sqrt(TMath::Abs(meanCOG*meanCOG-rms2COG));
+ if ( npoints == 1 ) (*rms) = binWidth/TMath::Sqrt(12);
+ }
+
+ if ( sum )
+ (*sum) = sumCOG;
+
+ return meanCOG;
+}
+
+
+
+///////////////////////////////////////////////////////////////
+////////////// TEST functions /////////////////////////
+///////////////////////////////////////////////////////////////
+
+
+
+
+
+void TStatToolkit::TestGausFit(Int_t nhistos){
+ //
+ // Test performance of the parabolic - gaussian fit - compare it with
+ // ROOT gauss fit
+ // nhistos - number of histograms to be used for test
+ //
+ TTreeSRedirector *pcstream = new TTreeSRedirector("fitdebug.root");
+
+ Float_t *xTrue = new Float_t[nhistos];
+ Float_t *sTrue = new Float_t[nhistos];
+ TVectorD **par1 = new TVectorD*[nhistos];
+ TVectorD **par2 = new TVectorD*[nhistos];
+ TMatrixD dummy(3,3);
+
+
+ TH1F **h1f = new TH1F*[nhistos];
+ TF1 *myg = new TF1("myg","gaus");
+ TF1 *fit = new TF1("fit","gaus");
+ gRandom->SetSeed(0);
+
+ //init
+ for (Int_t i=0;i<nhistos; i++){
+ par1[i] = new TVectorD(3);
+ par2[i] = new TVectorD(3);
+ h1f[i] = new TH1F(Form("h1f%d",i),Form("h1f%d",i),20,-10,10);
+ xTrue[i]= gRandom->Rndm();
+ gSystem->Sleep(2);
+ sTrue[i]= .75+gRandom->Rndm()*.5;
+ myg->SetParameters(1,xTrue[i],sTrue[i]);
+ h1f[i]->FillRandom("myg");
+ }
+
+ TStopwatch s;
+ s.Start();
+ //standard gaus fit
+ for (Int_t i=0; i<nhistos; i++){
+ h1f[i]->Fit(fit,"0q");
+ (*par1[i])(0) = fit->GetParameter(0);
+ (*par1[i])(1) = fit->GetParameter(1);
+ (*par1[i])(2) = fit->GetParameter(2);
+ }
+ s.Stop();
+ printf("Gaussian fit\t");
+ s.Print();
+
+ s.Start();
+ //TStatToolkit gaus fit
+ for (Int_t i=0; i<nhistos; i++){
+ TStatToolkit::FitGaus(h1f[i]->GetArray()+1,h1f[i]->GetNbinsX(),h1f[i]->GetXaxis()->GetXmin(),h1f[i]->GetXaxis()->GetXmax(),par2[i],&dummy);
+ }
+
+ s.Stop();
+ printf("Parabolic fit\t");
+ s.Print();
+ //write stream
+ for (Int_t i=0;i<nhistos; i++){
+ Float_t xt = xTrue[i];
+ Float_t st = sTrue[i];
+ (*pcstream)<<"data"
+ <<"xTrue="<<xt
+ <<"sTrue="<<st
+ <<"pg.="<<(par1[i])
+ <<"pa.="<<(par2[i])
+ <<"\n";
+ }
+ //delete pointers
+ for (Int_t i=0;i<nhistos; i++){
+ delete par1[i];
+ delete par2[i];
+ delete h1f[i];
+ }
+ delete pcstream;
+ delete []h1f;
+ delete []xTrue;
+ delete []sTrue;
+ //
+ delete []par1;
+ delete []par2;
+
+}
+
+
+
+TGraph2D * TStatToolkit::MakeStat2D(TH3 * his, Int_t delta0, Int_t delta1, Int_t type){
+ //
+ //
+ //
+ // delta - number of bins to integrate
+ // type - 0 - mean value
+
+ TAxis * xaxis = his->GetXaxis();
+ TAxis * yaxis = his->GetYaxis();
+ // TAxis * zaxis = his->GetZaxis();
+ Int_t nbinx = xaxis->GetNbins();
+ Int_t nbiny = yaxis->GetNbins();
+ char name[1000];
+ Int_t icount=0;
+ TGraph2D *graph = new TGraph2D(nbinx*nbiny);
+ TF1 f1("f1","gaus");
+ for (Int_t ix=0; ix<nbinx;ix++)
+ 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);
+ TH1 *projection = his->ProjectionZ(name,ix-delta0,ix+delta0,iy-delta1,iy+delta1);
+ Float_t stat= 0;
+ if (type==0) stat = projection->GetMean();
+ if (type==1) stat = projection->GetRMS();
+ if (type==2 || type==3){
+ TVectorD vec(3);
+ TStatToolkit::LTM((TH1F*)projection,&vec,0.7);
+ if (type==2) stat= vec[1];
+ if (type==3) stat= vec[0];
+ }
+ if (type==4|| type==5){
+ projection->Fit(&f1);
+ if (type==4) stat= f1.GetParameter(1);
+ if (type==5) stat= f1.GetParameter(2);
+ }
+ //printf("%d\t%f\t%f\t%f\n", icount,xcenter, ycenter, stat);
+ graph->SetPoint(icount,xcenter, ycenter, stat);
+ icount++;
+ }
+ return graph;
+}
+
+TGraph * TStatToolkit::MakeStat1D(TH3 * his, Int_t delta1, Int_t type){
+ //
+ //
+ //
+ // delta - number of bins to integrate
+ // type - 0 - mean value
+
+ TAxis * xaxis = his->GetXaxis();
+ TAxis * yaxis = his->GetYaxis();
+ // TAxis * zaxis = his->GetZaxis();
+ Int_t nbinx = xaxis->GetNbins();
+ Int_t nbiny = yaxis->GetNbins();
+ char name[1000];
+ Int_t icount=0;
+ TGraph *graph = new TGraph(nbinx);
+ TF1 f1("f1","gaus");
+ 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);
+ TH1 *projection = his->ProjectionZ(name,ix-delta1,ix+delta1,0,nbiny);
+ Float_t stat= 0;
+ if (type==0) stat = projection->GetMean();
+ if (type==1) stat = projection->GetRMS();
+ if (type==2 || type==3){
+ TVectorD vec(3);
+ TStatToolkit::LTM((TH1F*)projection,&vec,0.7);
+ if (type==2) stat= vec[1];
+ if (type==3) stat= vec[0];
+ }
+ if (type==4|| type==5){
+ projection->Fit(&f1);
+ if (type==4) stat= f1.GetParameter(1);
+ if (type==5) stat= f1.GetParameter(2);
+ }
+ //printf("%d\t%f\t%f\t%f\n", icount,xcenter, ycenter, stat);
+ graph->SetPoint(icount,xcenter, stat);
+ icount++;
+ }
+ return graph;
+}
+
+
+
+
+
+TString* TStatToolkit::FitPlane(TTree *tree, const char* drawCommand, const char* formula, const char* cuts, Double_t & chi2, Int_t &npoints, TVectorD &fitParam, TMatrixD &covMatrix, 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);
+
+ 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] ;
+
+ 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) 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], 1);
+ }
+
+ 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;
+
+ 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(" )");
+ delete formulaTokens;
+ delete fitter;
+ delete[] values;
+ return preturnFormula;
+}
git://git.uio.no / u / mrichter / AliRoot.git / commitdiff