#include "TChain.h"
#include "TObjString.h"
#include "TLinearFitter.h"
+#include "TGraph2D.h"
+#include "TGraph.h"
//
// includes neccessary for test functions
}
//___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
//
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);
}
-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
-Int_t TStatToolkit::GetFitIndex(TString fString, TString subString){
+Int_t TStatToolkit::GetFitIndex(const TString fString, const TString subString){
//
// fitString - ++ separated list of fits
// substring - ++ separated list of the requiered substrings
}
-TString TStatToolkit::FilterFit(TString &input, TString filter, TVectorD ¶m, TMatrixD & covar){
+TString TStatToolkit::FilterFit(const TString &input, const TString filter, TVectorD ¶m, TMatrixD & covar){
//
// Filter fit expression make sub-fit
//
-void TStatToolkit::Constrain1D(TString &input, TString filter, TVectorD ¶m, TMatrixD & covar, Double_t mean, Double_t sigma){
+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
// 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);}
- 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);//
+ 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++){
}
}
-TString TStatToolkit::MakeFitString(TString &input, TVectorD ¶m, TMatrixD & covar){
+TString TStatToolkit::MakeFitString(const TString &input, const TVectorD ¶m, const TMatrixD & covar, Bool_t verbose){
//
//
//
TObjArray *array0= input.Tokenize("++");
- TString result="(0.0";
+ 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]);
- printf("%f\t%f\t%s\n", param[i+1], TMath::Sqrt(covar(i+1,i+1)),str.Data());
+ 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