return chi2;
}
-
-Double_t AliMathBase::FitGaus(Int_t size, Float_t *arr, Float_t firstBinX, Float_t binWidth, TVectorD *param, TMatrixD *matrix, Float_t xmin, Float_t xmax, Bool_t verbose){
+Double_t AliMathBase::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:
- // 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
+ // 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
- // 3. In case of small statistic bins are averaged
//
static TLinearFitter fitter(3,"pol2");
static TMatrixD mat(3,3);
TVectorD sigma(3);
TMatrixD A(3,3);
TMatrixD b(3,1);
- Float_t rms = TMath::RMS(size,arr);
- Float_t max = TMath::MaxElement(size,arr);
+ 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 entries = 0;
Int_t nfilled=0;
- for (Int_t i=0; i<size; i++){
+ for (Int_t i=0; i<nBins; i++){
entries+=arr[i];
if (arr[i]>0) nfilled++;
}
- if (max<4) return -1;
- if (entries<12) return -1;
- if (rms<kTol) return -1;
+ if (max<4) return -4;
+ if (entries<12) return -4;
+ if (rms<kTol) return -4;
Int_t npoints=0;
//
-
- if (xmin>=xmax){
- xmin = firstBinX;
- xmax = firstBinX+(size-1)*binWidth;
- }
//
- for (Int_t ibin=0;ibin<size; ibin++){
- Float_t entriesI = arr[ibin];
+ for (Int_t ibin=0;ibin<nBins; ibin++){
+ Float_t entriesI = arr[ibin];
if (entriesI>1){
- Double_t xcenter = firstBinX+ibin*binWidth;
- if (xcenter<xmin || xcenter>xmax) continue;
+ 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++;
}
}
-
- if (npoints<=3){
- for (Int_t ibin=0;ibin<size; ibin++){
- Float_t entriesI = arr[ibin];
- if (entriesI>1){
- Double_t xcenter = firstBinX+ibin*binWidth;
- if (xcenter<xmin || xcenter>xmax) continue;
- Float_t val = TMath::Log(Float_t(entriesI));
- // if less than 3 point the fitter will crash!
- // for three points calculate the parameters analytically
- // for one and two points use center of gravity
- A(npoints,0)=1;
- A(npoints,1)=xcenter;
- A(npoints,2)=xcenter*xcenter;
- b(npoints,0)=val;
- meanCOG+=xcenter*val;
- rms2COG +=xcenter*val*xcenter*val;
- sumCOG +=val;
- npoints++;
- }
- }
- }
-
Double_t chi2 = 0;
fitter.GetCovarianceMatrix(mat);
chi2 = fitter.GetChisquare()/Float_t(npoints);
}
- if (TMath::Abs(par[1])<kTol) return -1;
- if (TMath::Abs(par[2])<kTol) return -1;
+ 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]));
- (*param)[0] = TMath::Exp(par[0]+ par[1]* (*param)[1] + par[2]*(*param)[1]*(*param)[1]);
+ 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);
+ 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]);
chi2=-2.;
}
if ( npoints == 1 ){
+ meanCOG/=sumCOG;
(*param)[0] = max;
(*param)[1] = meanCOG;
(*param)[2] = binWidth/TMath::Sqrt(12);
}
+Float_t AliMathBase::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 /////////////////////////
s.Start();
//AliMathBase gaus fit
for (Int_t i=0; i<nhistos; i++){
- AliMathBase::FitGaus(20,h1f[i]->GetArray()+1,-9.5,1,par2[i],&dummy);
+ AliMathBase::FitGaus(h1f[i]->GetArray()+1,h1f[i]->GetNbinsX(),h1f[i]->GetXaxis()->GetXmin(),h1f[i]->GetXaxis()->GetXmax(),par2[i],&dummy);
}
s.Stop();
git://git.uio.no / u / mrichter / AliRoot.git / commitdiff