ClassImp(AliBaseCalibViewer)
AliBaseCalibViewer::AliBaseCalibViewer()
- :TObject(),
- fTree(0),
- fFile(0),
- fListOfObjectsToBeDeleted(0),
- fTreeMustBeDeleted(0),
- fAbbreviation(0),
- fAppendString(0)
+:TObject(),
+ fTree(0),
+ fFile(0),
+ fListOfObjectsToBeDeleted(0),
+ fTreeMustBeDeleted(0),
+ fAbbreviation(0),
+ fAppendString(0)
{
//
// Default constructor
//_____________________________________________________________________________
AliBaseCalibViewer::AliBaseCalibViewer(const AliBaseCalibViewer &c)
- :TObject(c),
- fTree(0),
- fFile(0),
- fListOfObjectsToBeDeleted(0),
- fTreeMustBeDeleted(0),
- fAbbreviation(0),
- fAppendString(0)
+:TObject(c),
+ fTree(0),
+ fFile(0),
+ fListOfObjectsToBeDeleted(0),
+ fTreeMustBeDeleted(0),
+ fAbbreviation(0),
+ fAppendString(0)
{
//
// dummy AliBaseCalibViewer copy constructor
//_____________________________________________________________________________
AliBaseCalibViewer::AliBaseCalibViewer(TTree* tree)
- :TObject(),
- fTree(0),
- fFile(0),
- fListOfObjectsToBeDeleted(0),
- fTreeMustBeDeleted(0),
- fAbbreviation(0),
- fAppendString(0)
+:TObject(),
+ fTree(0),
+ fFile(0),
+ fListOfObjectsToBeDeleted(0),
+ fTreeMustBeDeleted(0),
+ fAbbreviation(0),
+ fAppendString(0)
{
//
// Constructor that initializes the calibration viewer
//_____________________________________________________________________________
AliBaseCalibViewer::AliBaseCalibViewer(const Char_t* fileName, const Char_t* treeName)
- :TObject(),
- fTree(0),
- fFile(0),
- fListOfObjectsToBeDeleted(0),
- fTreeMustBeDeleted(0),
- fAbbreviation(0),
- fAppendString(0)
-
+:TObject(),
+ fTree(0),
+ fFile(0),
+ fListOfObjectsToBeDeleted(0),
+ fTreeMustBeDeleted(0),
+ fAbbreviation(0),
+ fAppendString(0)
+
{
- //
- // Constructor to initialize the calibration viewer
- // the file 'fileName' contains the tree 'treeName'
- //
- fFile = new TFile(fileName, "read");
- fTree = (TTree*) fFile->Get(treeName);
- fTreeMustBeDeleted = kTRUE;
- fListOfObjectsToBeDeleted = new TObjArray();
- fAbbreviation = "~";
- fAppendString = ".fElements";
+ //
+ // Constructor to initialize the calibration viewer
+ // the file 'fileName' contains the tree 'treeName'
+ //
+ fFile = new TFile(fileName, "read");
+ fTree = (TTree*) fFile->Get(treeName);
+ fTreeMustBeDeleted = kTRUE;
+ fListOfObjectsToBeDeleted = new TObjArray();
+ fAbbreviation = "~";
+ fAppendString = ".fElements";
}
//____________________________________________________________________________
AliBaseCalibViewer & AliBaseCalibViewer::operator =(const AliBaseCalibViewer & param)
{
- //
- // assignment operator - dummy
- // not yet working!!!
- //
+ //
+ // assignment operator - dummy
+ // not yet working!!!
+ //
if(¶m == this) return *this;
TObject::operator=(param);
- fTree = param.fTree;
- fTreeMustBeDeleted = param.fTreeMustBeDeleted;
- fListOfObjectsToBeDeleted = param.fListOfObjectsToBeDeleted;
- fAbbreviation = param.fAbbreviation;
- fAppendString = param.fAppendString;
- return (*this);
+ fTree = param.fTree;
+ fTreeMustBeDeleted = param.fTreeMustBeDeleted;
+ fListOfObjectsToBeDeleted = param.fListOfObjectsToBeDeleted;
+ fAbbreviation = param.fAbbreviation;
+ fAppendString = param.fAppendString;
+ return (*this);
}
//_____________________________________________________________________________
AliBaseCalibViewer::~AliBaseCalibViewer()
{
- //
- // AliBaseCalibViewer destructor
- // all objects will be deleted, the file will be closed, the pictures will disappear
- //
- if (fTree && fTreeMustBeDeleted) {
- fTree->SetCacheSize(0);
- fTree->Delete();
- }
- if (fFile) {
- fFile->Close();
- fFile = 0;
- }
-
- for (Int_t i = fListOfObjectsToBeDeleted->GetEntriesFast()-1; i >= 0; i--) {
- delete fListOfObjectsToBeDeleted->At(i);
- }
- delete fListOfObjectsToBeDeleted;
+ //
+ // AliBaseCalibViewer destructor
+ // all objects will be deleted, the file will be closed, the pictures will disappear
+ //
+ if (fTree && fTreeMustBeDeleted) {
+ fTree->SetCacheSize(0);
+ fTree->Delete();
+ }
+ if (fFile) {
+ fFile->Close();
+ fFile = 0;
+ }
+
+ for (Int_t i = fListOfObjectsToBeDeleted->GetEntriesFast()-1; i >= 0; i--) {
+ delete fListOfObjectsToBeDeleted->At(i);
+ }
+ delete fListOfObjectsToBeDeleted;
}
//_____________________________________________________________________________
void AliBaseCalibViewer::Delete(Option_t* /*option*/) {
- //
- // Should be called from AliBaseCalibViewerGUI class only.
- // If you use Delete() do not call the destructor.
- // All objects (except those contained in fListOfObjectsToBeDeleted) will be deleted, the file will be closed.
- //
-
- if (fTree && fTreeMustBeDeleted) {
- fTree->SetCacheSize(0);
- fTree->Delete();
- }
- delete fFile;
- delete fListOfObjectsToBeDeleted;
+ //
+ // Should be called from AliBaseCalibViewerGUI class only.
+ // If you use Delete() do not call the destructor.
+ // All objects (except those contained in fListOfObjectsToBeDeleted) will be deleted, the file will be closed.
+ //
+
+ if (fTree && fTreeMustBeDeleted) {
+ fTree->SetCacheSize(0);
+ fTree->Delete();
+ }
+ delete fFile;
+ delete fListOfObjectsToBeDeleted;
}
//_____________________________________________________________________________
void AliBaseCalibViewer::FormatHistoLabels(TH1 *histo) const {
- //
- // formats title and axis labels of histo
- // removes '.fElements'
- //
- if (!histo) return;
- TString replaceString(fAppendString.Data());
- TString *str = new TString(histo->GetTitle());
- str->ReplaceAll(replaceString, "");
- histo->SetTitle(str->Data());
- delete str;
- if (histo->GetXaxis()) {
- str = new TString(histo->GetXaxis()->GetTitle());
- str->ReplaceAll(replaceString, "");
- histo->GetXaxis()->SetTitle(str->Data());
- delete str;
- }
- if (histo->GetYaxis()) {
- str = new TString(histo->GetYaxis()->GetTitle());
- str->ReplaceAll(replaceString, "");
- histo->GetYaxis()->SetTitle(str->Data());
- delete str;
- }
- if (histo->GetZaxis()) {
- str = new TString(histo->GetZaxis()->GetTitle());
- str->ReplaceAll(replaceString, "");
- histo->GetZaxis()->SetTitle(str->Data());
- delete str;
- }
+ //
+ // formats title and axis labels of histo
+ // removes '.fElements'
+ //
+ if (!histo) return;
+ TString replaceString(fAppendString.Data());
+ TString *str = new TString(histo->GetTitle());
+ str->ReplaceAll(replaceString, "");
+ histo->SetTitle(str->Data());
+ delete str;
+ if (histo->GetXaxis()) {
+ str = new TString(histo->GetXaxis()->GetTitle());
+ str->ReplaceAll(replaceString, "");
+ histo->GetXaxis()->SetTitle(str->Data());
+ delete str;
+ }
+ if (histo->GetYaxis()) {
+ str = new TString(histo->GetYaxis()->GetTitle());
+ str->ReplaceAll(replaceString, "");
+ histo->GetYaxis()->SetTitle(str->Data());
+ delete str;
+ }
+ if (histo->GetZaxis()) {
+ str = new TString(histo->GetZaxis()->GetTitle());
+ str->ReplaceAll(replaceString, "");
+ histo->GetZaxis()->SetTitle(str->Data());
+ delete str;
+ }
}
//_____________________________________________________________________________
// add a reference tree to the current tree
// by default the treename is 'tree' and the reference treename is 'R'
//
- TFile *file = new TFile(filename);
- fListOfObjectsToBeDeleted->Add(file);
- TTree * tree = (TTree*)file->Get(treename);
- return AddFriend(tree, refname);
+ TFile *file = new TFile(filename);
+ fListOfObjectsToBeDeleted->Add(file);
+ TTree * tree = (TTree*)file->Get(treename);
+ return AddFriend(tree, refname);
}
//_____________________________________________________________________________
TString* AliBaseCalibViewer::Fit(const Char_t* drawCommand, const Char_t* formula, const Char_t* cuts,
- Double_t & chi2, TVectorD &fitParam, TMatrixD &covMatrix){
- //
- // 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);
-
- // abbreviations:
- drawStr.ReplaceAll(fAbbreviation, fAppendString);
- cutStr.ReplaceAll(fAbbreviation, fAppendString);
- formulaStr.ReplaceAll(fAbbreviation, fAppendString);
-
- formulaStr.ReplaceAll("++", fAbbreviation);
- TObjArray* formulaTokens = formulaStr.Tokenize(fAbbreviation.Data());
- 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 = Draw(drawStr.Data(), cutStr.Data(), "goff");
- if (entries == -1) {
- delete fitter;
- delete formulaTokens;
- 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 = fTree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff");
- else centries = fTree->Draw(drawStr.Data(), cutStr.Data(), "goff");
-
- if (entries != centries) {
- delete fitter;
- delete [] values;
- delete formulaTokens;
- return new TString("An ERROR has occured during fitting!");
- }
- values[i] = new Double_t[entries];
- memcpy(values[i], fTree->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();
-
- TString *preturnFormula = new TString(Form("( %e+",fitParam[0])), &returnFormula = *preturnFormula;
-
- for (Int_t iparam = 0; iparam < dim; iparam++) {
- returnFormula.Append(Form("%s*(%e)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1]));
- if (iparam < dim-1) returnFormula.Append("+");
- }
- returnFormula.Append(" )");
- delete formulaTokens;
- delete fitter;
- for (Int_t i = 0; i < dim + 1; i++) delete [] values[i];
- delete[] values;
- return preturnFormula;
+ Double_t & chi2, TVectorD &fitParam, TMatrixD &covMatrix){
+ //
+ // 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);
+
+ // abbreviations:
+ drawStr.ReplaceAll(fAbbreviation, fAppendString);
+ cutStr.ReplaceAll(fAbbreviation, fAppendString);
+ formulaStr.ReplaceAll(fAbbreviation, fAppendString);
+
+ formulaStr.ReplaceAll("++", fAbbreviation);
+ TObjArray* formulaTokens = formulaStr.Tokenize(fAbbreviation.Data());
+ 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 = Draw(drawStr.Data(), cutStr.Data(), "goff");
+ if (entries == -1) {
+ delete fitter;
+ delete formulaTokens;
+ 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 = fTree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff");
+ else centries = fTree->Draw(drawStr.Data(), cutStr.Data(), "goff");
+
+ if (entries != centries) {
+ delete fitter;
+ delete [] values;
+ delete formulaTokens;
+ return new TString("An ERROR has occured during fitting!");
+ }
+ values[i] = new Double_t[entries];
+ memcpy(values[i], fTree->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();
+
+ TString *preturnFormula = new TString(Form("( %e+",fitParam[0])), &returnFormula = *preturnFormula;
+
+ for (Int_t iparam = 0; iparam < dim; iparam++) {
+ returnFormula.Append(Form("%s*(%e)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1]));
+ if (iparam < dim-1) returnFormula.Append("+");
+ }
+ returnFormula.Append(" )");
+ delete formulaTokens;
+ delete fitter;
+ for (Int_t i = 0; i < dim + 1; i++) delete [] values[i];
+ delete[] values;
+ return preturnFormula;
}
//_____________________________________________________________________________
Double_t AliBaseCalibViewer::GetLTM(Int_t n, Double_t *array, Double_t *sigma, Double_t fraction){
- //
- // returns the LTM and sigma
- //
- Double_t *ddata = new Double_t[n];
- Double_t mean = 0, lsigma = 0;
- UInt_t nPoints = 0;
- for (UInt_t i = 0; i < (UInt_t)n; i++) {
- ddata[nPoints]= array[nPoints];
- nPoints++;
- }
- Int_t hh = TMath::Min(TMath::Nint(fraction * nPoints), Int_t(n));
- AliMathBase::EvaluateUni(nPoints, ddata, mean, lsigma, hh);
- if (sigma) *sigma = lsigma;
- delete [] ddata;
- return mean;
+ //
+ // returns the LTM and sigma
+ //
+ Double_t *ddata = new Double_t[n];
+ Double_t mean = 0, lsigma = 0;
+ UInt_t nPoints = 0;
+ for (UInt_t i = 0; i < (UInt_t)n; i++) {
+ ddata[nPoints]= array[nPoints];
+ nPoints++;
+ }
+ Int_t hh = TMath::Min(TMath::Nint(fraction * nPoints), Int_t(n));
+ AliMathBase::EvaluateUni(nPoints, ddata, mean, lsigma, hh);
+ if (sigma) *sigma = lsigma;
+ delete [] ddata;
+ return mean;
}
//_____________________________________________________________________________
Int_t AliBaseCalibViewer::GetBin(Float_t value, Int_t nbins, Double_t binLow, Double_t binUp){
- // Returns the 'bin' for 'value'
- // The interval between 'binLow' and 'binUp' is divided into 'nbins' equidistant bins
- // avoid index out of bounds error: 'if (bin < binLow) bin = binLow' and vice versa
- /* Begin_Latex
- GetBin(value) = #frac{nbins - 1}{binUp - binLow} #upoint (value - binLow) +1
- End_Latex
- */
-
- Int_t bin = TMath::Nint( (Float_t)(value - binLow) / (Float_t)(binUp - binLow) * (nbins-1) ) + 1;
- // avoid index out of bounds:
- if (value < binLow) bin = 0;
- if (value > binUp) bin = nbins + 1;
- return bin;
-
+ // Returns the 'bin' for 'value'
+ // The interval between 'binLow' and 'binUp' is divided into 'nbins' equidistant bins
+ // avoid index out of bounds error: 'if (bin < binLow) bin = binLow' and vice versa
+ /* Begin_Latex
+ GetBin(value) = #frac{nbins - 1}{binUp - binLow} #upoint (value - binLow) +1
+ End_Latex
+ */
+
+ Int_t bin = TMath::Nint( (Float_t)(value - binLow) / (Float_t)(binUp - binLow) * (nbins-1) ) + 1;
+ // avoid index out of bounds:
+ if (value < binLow) bin = 0;
+ if (value > binUp) bin = nbins + 1;
+ return bin;
+
}
//_____________________________________________________________________________
TH1F* AliBaseCalibViewer::SigmaCut(TH1F *histogram, Float_t mean, Float_t sigma, Float_t sigmaMax,
- Float_t sigmaStep, Bool_t pm) {
- //
- // Creates a cumulative histogram Begin_Latex S(t, #mu, #sigma) End_Latex, where you can see, how much of the data are inside sigma-intervals around the mean value
- // The data of the distribution Begin_Latex f(x, #mu, #sigma) End_Latex are given in 'histogram'
- // 'mean' and 'sigma' are Begin_Latex #mu End_Latex and Begin_Latex #sigma End_Latex of the distribution in 'histogram', to be specified by the user
- // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma, Begin_Latex t #sigma End_Latex)
- // sigmaStep: the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
- // pm: Decide weather Begin_Latex t > 0 End_Latex (first case) or Begin_Latex t End_Latex arbitrary (secound case)
- // The actual work is done on the array.
- /* Begin_Latex
- f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = (#int_{#mu}^{#mu + t #sigma} f(x, #mu, #sigma) dx + #int_{#mu}^{#mu - t #sigma} f(x, #mu, #sigma) dx) / (#int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx), for t > 0
- or
- f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #int_{#mu}^{#mu + t #sigma} f(x, #mu, #sigma) dx / #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx
- End_Latex
- Begin_Macro(source)
- {
- Float_t mean = 0;
- Float_t sigma = 1.5;
- Float_t sigmaMax = 4;
- gROOT->SetStyle("Plain");
- TH1F *distribution = new TH1F("Distribution1", "Distribution f(x, #mu, #sigma)", 1000,-5,5);
- TRandom rand(23);
- for (Int_t i = 0; i <50000;i++) distribution->Fill(rand.Gaus(mean, sigma));
- Float_t *ar = distribution->GetArray();
-
- TCanvas* macro_example_canvas = new TCanvas("cAliBaseCalibViewer", "", 350, 350);
- macro_example_canvas->Divide(0,3);
- TVirtualPad *pad1 = macro_example_canvas->cd(1);
- pad1->SetGridy();
- pad1->SetGridx();
- distribution->Draw();
- TVirtualPad *pad2 = macro_example_canvas->cd(2);
- pad2->SetGridy();
- pad2->SetGridx();
-
- TH1F *shist = AliTPCCalibViewer::SigmaCut(distribution, mean, sigma, sigmaMax);
- shist->SetNameTitle("Cumulative","Cumulative S(t, #mu, #sigma)");
- shist->Draw();
- TVirtualPad *pad3 = macro_example_canvas->cd(3);
- pad3->SetGridy();
- pad3->SetGridx();
- TH1F *shistPM = AliTPCCalibViewer::SigmaCut(distribution, mean, sigma, sigmaMax, -1, kTRUE);
- shistPM->Draw();
- return macro_example_canvas;
- }
- End_Macro
- */
-
- Float_t *array = histogram->GetArray();
- Int_t nbins = histogram->GetXaxis()->GetNbins();
- Float_t binLow = histogram->GetXaxis()->GetXmin();
- Float_t binUp = histogram->GetXaxis()->GetXmax();
- return AliBaseCalibViewer::SigmaCut(nbins, array, mean, sigma, nbins, binLow, binUp, sigmaMax, sigmaStep, pm);
+ Float_t sigmaStep, Bool_t pm) {
+ //
+ // Creates a cumulative histogram Begin_Latex S(t, #mu, #sigma) End_Latex, where you can see, how much of the data are inside sigma-intervals around the mean value
+ // The data of the distribution Begin_Latex f(x, #mu, #sigma) End_Latex are given in 'histogram'
+ // 'mean' and 'sigma' are Begin_Latex #mu End_Latex and Begin_Latex #sigma End_Latex of the distribution in 'histogram', to be specified by the user
+ // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma, Begin_Latex t #sigma End_Latex)
+ // sigmaStep: the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
+ // pm: Decide weather Begin_Latex t > 0 End_Latex (first case) or Begin_Latex t End_Latex arbitrary (secound case)
+ // The actual work is done on the array.
+ /* Begin_Latex
+ f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = (#int_{#mu}^{#mu + t #sigma} f(x, #mu, #sigma) dx + #int_{#mu}^{#mu - t #sigma} f(x, #mu, #sigma) dx) / (#int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx), for t > 0
+ or
+ f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #int_{#mu}^{#mu + t #sigma} f(x, #mu, #sigma) dx / #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx
+ End_Latex
+ Begin_Macro(source)
+ {
+ Float_t mean = 0;
+ Float_t sigma = 1.5;
+ Float_t sigmaMax = 4;
+ gROOT->SetStyle("Plain");
+ TH1F *distribution = new TH1F("Distribution1", "Distribution f(x, #mu, #sigma)", 1000,-5,5);
+ TRandom rand(23);
+ for (Int_t i = 0; i <50000;i++) distribution->Fill(rand.Gaus(mean, sigma));
+ Float_t *ar = distribution->GetArray();
+
+ TCanvas* macro_example_canvas = new TCanvas("cAliBaseCalibViewer", "", 350, 350);
+ macro_example_canvas->Divide(0,3);
+ TVirtualPad *pad1 = macro_example_canvas->cd(1);
+ pad1->SetGridy();
+ pad1->SetGridx();
+ distribution->Draw();
+ TVirtualPad *pad2 = macro_example_canvas->cd(2);
+ pad2->SetGridy();
+ pad2->SetGridx();
+
+ TH1F *shist = AliTPCCalibViewer::SigmaCut(distribution, mean, sigma, sigmaMax);
+ shist->SetNameTitle("Cumulative","Cumulative S(t, #mu, #sigma)");
+ shist->Draw();
+ TVirtualPad *pad3 = macro_example_canvas->cd(3);
+ pad3->SetGridy();
+ pad3->SetGridx();
+ TH1F *shistPM = AliTPCCalibViewer::SigmaCut(distribution, mean, sigma, sigmaMax, -1, kTRUE);
+ shistPM->Draw();
+ return macro_example_canvas;
+ }
+ End_Macro
+ */
+
+ Float_t *array = histogram->GetArray();
+ Int_t nbins = histogram->GetXaxis()->GetNbins();
+ Float_t binLow = histogram->GetXaxis()->GetXmin();
+ Float_t binUp = histogram->GetXaxis()->GetXmax();
+ return AliBaseCalibViewer::SigmaCut(nbins, array, mean, sigma, nbins, binLow, binUp, sigmaMax, sigmaStep, pm);
}
-
+
//_____________________________________________________________________________
TH1F* AliBaseCalibViewer::SigmaCut(Int_t n, Float_t *array, Float_t mean, Float_t sigma, Int_t nbins, Float_t binLow, Float_t binUp, Float_t sigmaMax, Float_t sigmaStep, Bool_t pm){
- //
- // Creates a histogram Begin_Latex S(t, #mu, #sigma) End_Latex, where you can see, how much of the data are inside sigma-intervals around the mean value
- // The data of the distribution Begin_Latex f(x, #mu, #sigma) End_Latex are given in 'array', 'n' specifies the length of the array
- // 'mean' and 'sigma' are Begin_Latex #mu End_Latex and Begin_Latex #sigma End_Latex of the distribution in 'array', to be specified by the user
- // 'nbins': number of bins, 'binLow': first bin, 'binUp': last bin
- // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma, Begin_Latex t #sigma End_Latex)
- // sigmaStep: the binsize of the generated histogram
- // Here the actual work is done.
-
- if (TMath::Abs(sigma) < 1.e-10) return 0;
- Float_t binWidth = (binUp-binLow)/(nbins - 1);
- if (sigmaStep <= 0) sigmaStep = binWidth;
- Int_t kbins = (Int_t)(sigmaMax * sigma / sigmaStep) + 1; // + 1 due to overflow bin in histograms
- if (pm) kbins = 2 * (Int_t)(sigmaMax * sigma / sigmaStep) + 1;
- Float_t kbinLow = !pm ? 0 : -sigmaMax;
- Float_t kbinUp = sigmaMax;
- TH1F *hist = new TH1F("sigmaCutHisto","Cumulative; Multiples of #sigma; Fraction of included data", kbins, kbinLow, kbinUp);
- hist->SetDirectory(0);
- hist->Reset();
-
- // calculate normalization
- Double_t normalization = 0;
- for (Int_t i = 0; i <= n; i++) {
- normalization += array[i];
- }
-
- // given units: units from given histogram
- // sigma units: in units of sigma
- // iDelta: integrate in interval (mean +- iDelta), given units
- // x: ofset from mean for integration, given units
- // hist: needs
-
- // fill histogram
- for (Float_t iDelta = 0; iDelta <= sigmaMax * sigma; iDelta += sigmaStep) {
- // integrate array
- Double_t valueP = array[GetBin(mean, nbins, binLow, binUp)];
- Double_t valueM = array[GetBin(mean-binWidth, nbins, binLow, binUp)];
- // add bin of mean value only once to the histogram
- for (Float_t x = binWidth; x <= iDelta; x += binWidth) {
- valueP += (mean + x <= binUp) ? array[GetBin(mean + x, nbins, binLow, binUp)] : 0;
- valueM += (mean-binWidth - x >= binLow) ? array[GetBin(mean-binWidth - x, nbins, binLow, binUp)] : 0;
- }
-
- if (valueP / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", valueP, normalization);
- if (valueP / normalization > 100) return hist;
- if (valueM / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", valueM, normalization);
- if (valueM / normalization > 100) return hist;
- valueP = (valueP / normalization);
- valueM = (valueM / normalization);
- if (pm) {
- Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp);
- hist->SetBinContent(bin, valueP);
- bin = GetBin(-iDelta/sigma, kbins, kbinLow, kbinUp);
- hist->SetBinContent(bin, valueM);
- }
- else { // if (!pm)
- Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp);
- hist->SetBinContent(bin, valueP + valueM);
- }
- }
- if (!pm) hist->SetMaximum(1.2);
- return hist;
+ //
+ // Creates a histogram Begin_Latex S(t, #mu, #sigma) End_Latex, where you can see, how much of the data are inside sigma-intervals around the mean value
+ // The data of the distribution Begin_Latex f(x, #mu, #sigma) End_Latex are given in 'array', 'n' specifies the length of the array
+ // 'mean' and 'sigma' are Begin_Latex #mu End_Latex and Begin_Latex #sigma End_Latex of the distribution in 'array', to be specified by the user
+ // 'nbins': number of bins, 'binLow': first bin, 'binUp': last bin
+ // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma, Begin_Latex t #sigma End_Latex)
+ // sigmaStep: the binsize of the generated histogram
+ // Here the actual work is done.
+
+ if (TMath::Abs(sigma) < 1.e-10) return 0;
+ Float_t binWidth = (binUp-binLow)/(nbins - 1);
+ if (sigmaStep <= 0) sigmaStep = binWidth;
+ Int_t kbins = (Int_t)(sigmaMax * sigma / sigmaStep) + 1; // + 1 due to overflow bin in histograms
+ if (pm) kbins = 2 * (Int_t)(sigmaMax * sigma / sigmaStep) + 1;
+ Float_t kbinLow = !pm ? 0 : -sigmaMax;
+ Float_t kbinUp = sigmaMax;
+ TH1F *hist = new TH1F("sigmaCutHisto","Cumulative; Multiples of #sigma; Fraction of included data", kbins, kbinLow, kbinUp);
+ hist->SetDirectory(0);
+ hist->Reset();
+
+ // calculate normalization
+ Double_t normalization = 0;
+ for (Int_t i = 0; i <= n; i++) {
+ normalization += array[i];
+ }
+
+ // given units: units from given histogram
+ // sigma units: in units of sigma
+ // iDelta: integrate in interval (mean +- iDelta), given units
+ // x: ofset from mean for integration, given units
+ // hist: needs
+
+ // fill histogram
+ for (Float_t iDelta = 0; iDelta <= sigmaMax * sigma; iDelta += sigmaStep) {
+ // integrate array
+ Double_t valueP = array[GetBin(mean, nbins, binLow, binUp)];
+ Double_t valueM = array[GetBin(mean-binWidth, nbins, binLow, binUp)];
+ // add bin of mean value only once to the histogram
+ for (Float_t x = binWidth; x <= iDelta; x += binWidth) {
+ valueP += (mean + x <= binUp) ? array[GetBin(mean + x, nbins, binLow, binUp)] : 0;
+ valueM += (mean-binWidth - x >= binLow) ? array[GetBin(mean-binWidth - x, nbins, binLow, binUp)] : 0;
+ }
+
+ if (valueP / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", valueP, normalization);
+ if (valueP / normalization > 100) return hist;
+ if (valueM / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", valueM, normalization);
+ if (valueM / normalization > 100) return hist;
+ valueP = (valueP / normalization);
+ valueM = (valueM / normalization);
+ if (pm) {
+ Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp);
+ hist->SetBinContent(bin, valueP);
+ bin = GetBin(-iDelta/sigma, kbins, kbinLow, kbinUp);
+ hist->SetBinContent(bin, valueM);
+ }
+ else { // if (!pm)
+ Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp);
+ hist->SetBinContent(bin, valueP + valueM);
+ }
+ }
+ if (!pm) hist->SetMaximum(1.2);
+ return hist;
}
//_____________________________________________________________________________
TH1F* AliBaseCalibViewer::SigmaCut(Int_t /*n*/, Double_t */*array*/, Double_t /*mean*/, Double_t /*sigma*/,
- Int_t /*nbins*/, Double_t */*xbins*/, Double_t /*sigmaMax*/){
- //
- // SigmaCut for variable binsize
- // NOT YET IMPLEMENTED !!!
- //
- printf("SigmaCut with variable binsize, Not yet implemented\n");
-
- return 0;
+ Int_t /*nbins*/, Double_t */*xbins*/, Double_t /*sigmaMax*/){
+ //
+ // SigmaCut for variable binsize
+ // NOT YET IMPLEMENTED !!!
+ //
+ printf("SigmaCut with variable binsize, Not yet implemented\n");
+
+ return 0;
}
//_____________________________________________________________________________
Int_t AliBaseCalibViewer::DrawHisto1D(const Char_t* drawCommand, const Char_t* sector, const Char_t* cuts,
- const Char_t *sigmas, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM) const
+ const Char_t *sigmas, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM) const
{
- //
- // Easy drawing of data, in principle the same as EasyDraw1D
- // Difference: A line for the mean / median / LTM is drawn
- // in 'sigmas' you can specify in which distance to the mean/median/LTM you want to see a line in sigma-units, separated by ';'
- // example: sigmas = "2; 4; 6;" at Begin_Latex 2 #sigma End_Latex, Begin_Latex 4 #sigma End_Latex and Begin_Latex 6 #sigma End_Latex a line is drawn.
- // "plotMean", "plotMedian" and "plotLTM": what kind of lines do you want to see?
- //
+ //
+ // Easy drawing of data, in principle the same as EasyDraw1D
+ // Difference: A line for the mean / median / LTM is drawn
+ // in 'sigmas' you can specify in which distance to the mean/median/LTM you want to see a line in sigma-units, separated by ';'
+ // example: sigmas = "2; 4; 6;" at Begin_Latex 2 #sigma End_Latex, Begin_Latex 4 #sigma End_Latex and Begin_Latex 6 #sigma End_Latex a line is drawn.
+ // "plotMean", "plotMedian" and "plotLTM": what kind of lines do you want to see?
+ //
Int_t oldOptStat = gStyle->GetOptStat();
gStyle->SetOptStat(0000000);
Double_t ltmFraction = 0.8;
-
+
TObjArray *sigmasTokens = TString(sigmas).Tokenize(";");
TVectorF nsigma(sigmasTokens->GetEntriesFast());
for (Int_t i = 0; i < sigmasTokens->GetEntriesFast(); i++) {
drawStr += " >> tempHist";
Int_t entries = EasyDraw1D(drawStr.Data(), sector, cuts);
TH1F *htemp = (TH1F*)gDirectory->Get("tempHist");
- // FIXME is this histogram deleted automatically?
+ // FIXME is this histogram deleted automatically?
Double_t *values = fTree->GetV1(); // value is the array containing 'entries' numbers
-
+
Double_t mean = TMath::Mean(entries, values);
Double_t median = TMath::Median(entries, values);
Double_t sigma = TMath::RMS(entries, values);
Double_t maxY = htemp->GetMaximum();
-
+
TLegend * legend = new TLegend(.7,.7, .99, .99, "Statistical information");
- //fListOfObjectsToBeDeleted->Add(legend);
-
+ //fListOfObjectsToBeDeleted->Add(legend);
+
if (plotMean) {
- // draw Mean
+ // draw Mean
TLine* line = new TLine(mean, 0, mean, maxY);
- //fListOfObjectsToBeDeleted->Add(line);
+ //fListOfObjectsToBeDeleted->Add(line);
line->SetLineColor(kRed);
line->SetLineWidth(2);
line->SetLineStyle(1);
line->Draw();
legend->AddEntry(line, Form("Mean: %f", mean), "l");
- // draw sigma lines
+ // draw sigma lines
for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
TLine* linePlusSigma = new TLine(mean + nsigma[i] * sigma, 0, mean + nsigma[i] * sigma, maxY);
- //fListOfObjectsToBeDeleted->Add(linePlusSigma);
+ //fListOfObjectsToBeDeleted->Add(linePlusSigma);
linePlusSigma->SetLineColor(kRed);
linePlusSigma->SetLineStyle(2 + i);
linePlusSigma->Draw();
TLine* lineMinusSigma = new TLine(mean - nsigma[i] * sigma, 0, mean - nsigma[i] * sigma, maxY);
- //fListOfObjectsToBeDeleted->Add(lineMinusSigma);
+ //fListOfObjectsToBeDeleted->Add(lineMinusSigma);
lineMinusSigma->SetLineColor(kRed);
lineMinusSigma->SetLineStyle(2 + i);
lineMinusSigma->Draw();
}
}
if (plotMedian) {
- // draw median
+ // draw median
TLine* line = new TLine(median, 0, median, maxY);
- //fListOfObjectsToBeDeleted->Add(line);
+ //fListOfObjectsToBeDeleted->Add(line);
line->SetLineColor(kBlue);
line->SetLineWidth(2);
line->SetLineStyle(1);
line->Draw();
legend->AddEntry(line, Form("Median: %f", median), "l");
- // draw sigma lines
+ // draw sigma lines
for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
TLine* linePlusSigma = new TLine(median + nsigma[i] * sigma, 0, median + nsigma[i]*sigma, maxY);
- //fListOfObjectsToBeDeleted->Add(linePlusSigma);
+ //fListOfObjectsToBeDeleted->Add(linePlusSigma);
linePlusSigma->SetLineColor(kBlue);
linePlusSigma->SetLineStyle(2 + i);
linePlusSigma->Draw();
TLine* lineMinusSigma = new TLine(median - nsigma[i] * sigma, 0, median - nsigma[i]*sigma, maxY);
- //fListOfObjectsToBeDeleted->Add(lineMinusSigma);
+ //fListOfObjectsToBeDeleted->Add(lineMinusSigma);
lineMinusSigma->SetLineColor(kBlue);
lineMinusSigma->SetLineStyle(2 + i);
lineMinusSigma->Draw();
}
}
if (plotLTM) {
- // draw LTM
+ // draw LTM
Double_t ltmRms = 0;
Double_t ltm = GetLTM(entries, values, <mRms, ltmFraction);
TLine* line = new TLine(ltm, 0, ltm, maxY);
- //fListOfObjectsToBeDeleted->Add(line);
+ //fListOfObjectsToBeDeleted->Add(line);
line->SetLineColor(kGreen+2);
line->SetLineWidth(2);
line->SetLineStyle(1);
line->Draw();
legend->AddEntry(line, Form("LTM: %f", ltm), "l");
- // draw sigma lines
+ // draw sigma lines
for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
TLine* linePlusSigma = new TLine(ltm + nsigma[i] * ltmRms, 0, ltm + nsigma[i] * ltmRms, maxY);
- //fListOfObjectsToBeDeleted->Add(linePlusSigma);
+ //fListOfObjectsToBeDeleted->Add(linePlusSigma);
linePlusSigma->SetLineColor(kGreen+2);
linePlusSigma->SetLineStyle(2+i);
linePlusSigma->Draw();
-
+
TLine* lineMinusSigma = new TLine(ltm - nsigma[i] * ltmRms, 0, ltm - nsigma[i] * ltmRms, maxY);
- //fListOfObjectsToBeDeleted->Add(lineMinusSigma);
+ //fListOfObjectsToBeDeleted->Add(lineMinusSigma);
lineMinusSigma->SetLineColor(kGreen+2);
lineMinusSigma->SetLineStyle(2+i);
lineMinusSigma->Draw();
//_____________________________________________________________________________
Int_t AliBaseCalibViewer::SigmaCut(const Char_t* drawCommand, const Char_t* sector, const Char_t* cuts,
- Float_t sigmaMax, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM, Bool_t pm,
- const Char_t *sigmas, Float_t sigmaStep) const {
- //
- // Creates a histogram, where you can see, how much of the data are inside sigma-intervals
- // around the mean/median/LTM
- // with drawCommand, sector and cuts you specify your input data, see EasyDraw
- // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma)
- // sigmaStep: the binsize of the generated histogram
- // plotMean/plotMedian/plotLTM: specifies where to put the center
- //
-
- Double_t ltmFraction = 0.8;
-
- TString drawStr(drawCommand);
- Bool_t dangerousToDraw = drawStr.Contains(":") || drawStr.Contains(">>");
- if (dangerousToDraw) {
- Warning("SigmaCut", "The draw string must not contain ':' or '>>'.");
- return -1;
- }
- drawStr += " >> tempHist";
-
- Int_t entries = EasyDraw1D(drawStr.Data(), sector, cuts, "goff");
- TH1F *htemp = (TH1F*)gDirectory->Get("tempHist");
- // FIXME is this histogram deleted automatically?
- Double_t *values = fTree->GetV1(); // value is the array containing 'entries' numbers
-
- Double_t mean = TMath::Mean(entries, values);
- Double_t median = TMath::Median(entries, values);
- Double_t sigma = TMath::RMS(entries, values);
-
- TLegend * legend = new TLegend(.7,.7, .99, .99, "Cumulative");
- //fListOfObjectsToBeDeleted->Add(legend);
- TH1F *cutHistoMean = 0;
- TH1F *cutHistoMedian = 0;
- TH1F *cutHistoLTM = 0;
-
- TObjArray *sigmasTokens = TString(sigmas).Tokenize(";");
- TVectorF nsigma(sigmasTokens->GetEntriesFast());
- for (Int_t i = 0; i < sigmasTokens->GetEntriesFast(); i++) {
- TString str(((TObjString*)sigmasTokens->At(i))->GetString());
- Double_t sig = (str.IsFloat()) ? str.Atof() : 0;
- nsigma[i] = sig;
- }
- delete sigmasTokens;
- //
- if (plotMean) {
- cutHistoMean = SigmaCut(htemp, mean, sigma, sigmaMax, sigmaStep, pm);
- if (cutHistoMean) {
- //fListOfObjectsToBeDeleted->Add(cutHistoMean);
- cutHistoMean->SetLineColor(kRed);
- legend->AddEntry(cutHistoMean, "Mean", "l");
- cutHistoMean->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
- cutHistoMean->Draw();
- DrawLines(cutHistoMean, nsigma, legend, kRed, pm);
- } // if (cutHistoMean)
-
- }
- if (plotMedian) {
- cutHistoMedian = SigmaCut(htemp, median, sigma, sigmaMax, sigmaStep, pm);
- if (cutHistoMedian) {
- //fListOfObjectsToBeDeleted->Add(cutHistoMedian);
- cutHistoMedian->SetLineColor(kBlue);
- legend->AddEntry(cutHistoMedian, "Median", "l");
- cutHistoMedian->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
- if (plotMean && cutHistoMean) cutHistoMedian->Draw("same");
- else cutHistoMedian->Draw();
- DrawLines(cutHistoMedian, nsigma, legend, kBlue, pm);
- } // if (cutHistoMedian)
- }
- if (plotLTM) {
- Double_t ltmRms = 0;
- Double_t ltm = GetLTM(entries, values, <mRms, ltmFraction);
- cutHistoLTM = SigmaCut(htemp, ltm, ltmRms, sigmaMax, sigmaStep, pm);
- if (cutHistoLTM) {
- //fListOfObjectsToBeDeleted->Add(cutHistoLTM);
- cutHistoLTM->SetLineColor(kGreen+2);
- legend->AddEntry(cutHistoLTM, "LTM", "l");
- cutHistoLTM->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
- if ((plotMean && cutHistoMean) || (plotMedian && cutHistoMedian)) cutHistoLTM->Draw("same");
- else cutHistoLTM->Draw();
- DrawLines(cutHistoLTM, nsigma, legend, kGreen+2, pm);
- }
- }
- if (!plotMean && !plotMedian && !plotLTM) return -1;
- legend->Draw();
- return 1;
+ Float_t sigmaMax, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM, Bool_t pm,
+ const Char_t *sigmas, Float_t sigmaStep) const {
+ //
+ // Creates a histogram, where you can see, how much of the data are inside sigma-intervals
+ // around the mean/median/LTM
+ // with drawCommand, sector and cuts you specify your input data, see EasyDraw
+ // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma)
+ // sigmaStep: the binsize of the generated histogram
+ // plotMean/plotMedian/plotLTM: specifies where to put the center
+ //
+
+ Double_t ltmFraction = 0.8;
+
+ TString drawStr(drawCommand);
+ Bool_t dangerousToDraw = drawStr.Contains(":") || drawStr.Contains(">>");
+ if (dangerousToDraw) {
+ Warning("SigmaCut", "The draw string must not contain ':' or '>>'.");
+ return -1;
+ }
+ drawStr += " >> tempHist";
+
+ Int_t entries = EasyDraw1D(drawStr.Data(), sector, cuts, "goff");
+ TH1F *htemp = (TH1F*)gDirectory->Get("tempHist");
+ // FIXME is this histogram deleted automatically?
+ Double_t *values = fTree->GetV1(); // value is the array containing 'entries' numbers
+
+ Double_t mean = TMath::Mean(entries, values);
+ Double_t median = TMath::Median(entries, values);
+ Double_t sigma = TMath::RMS(entries, values);
+
+ TLegend * legend = new TLegend(.7,.7, .99, .99, "Cumulative");
+ //fListOfObjectsToBeDeleted->Add(legend);
+ TH1F *cutHistoMean = 0;
+ TH1F *cutHistoMedian = 0;
+ TH1F *cutHistoLTM = 0;
+
+ TObjArray *sigmasTokens = TString(sigmas).Tokenize(";");
+ TVectorF nsigma(sigmasTokens->GetEntriesFast());
+ for (Int_t i = 0; i < sigmasTokens->GetEntriesFast(); i++) {
+ TString str(((TObjString*)sigmasTokens->At(i))->GetString());
+ Double_t sig = (str.IsFloat()) ? str.Atof() : 0;
+ nsigma[i] = sig;
+ }
+ delete sigmasTokens;
+ //
+ if (plotMean) {
+ cutHistoMean = SigmaCut(htemp, mean, sigma, sigmaMax, sigmaStep, pm);
+ if (cutHistoMean) {
+ //fListOfObjectsToBeDeleted->Add(cutHistoMean);
+ cutHistoMean->SetLineColor(kRed);
+ legend->AddEntry(cutHistoMean, "Mean", "l");
+ cutHistoMean->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
+ cutHistoMean->Draw();
+ DrawLines(cutHistoMean, nsigma, legend, kRed, pm);
+ } // if (cutHistoMean)
+
+ }
+ if (plotMedian) {
+ cutHistoMedian = SigmaCut(htemp, median, sigma, sigmaMax, sigmaStep, pm);
+ if (cutHistoMedian) {
+ //fListOfObjectsToBeDeleted->Add(cutHistoMedian);
+ cutHistoMedian->SetLineColor(kBlue);
+ legend->AddEntry(cutHistoMedian, "Median", "l");
+ cutHistoMedian->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
+ if (plotMean && cutHistoMean) cutHistoMedian->Draw("same");
+ else cutHistoMedian->Draw();
+ DrawLines(cutHistoMedian, nsigma, legend, kBlue, pm);
+ } // if (cutHistoMedian)
+ }
+ if (plotLTM) {
+ Double_t ltmRms = 0;
+ Double_t ltm = GetLTM(entries, values, <mRms, ltmFraction);
+ cutHistoLTM = SigmaCut(htemp, ltm, ltmRms, sigmaMax, sigmaStep, pm);
+ if (cutHistoLTM) {
+ //fListOfObjectsToBeDeleted->Add(cutHistoLTM);
+ cutHistoLTM->SetLineColor(kGreen+2);
+ legend->AddEntry(cutHistoLTM, "LTM", "l");
+ cutHistoLTM->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
+ if ((plotMean && cutHistoMean) || (plotMedian && cutHistoMedian)) cutHistoLTM->Draw("same");
+ else cutHistoLTM->Draw();
+ DrawLines(cutHistoLTM, nsigma, legend, kGreen+2, pm);
+ }
+ }
+ if (!plotMean && !plotMedian && !plotLTM) return -1;
+ legend->Draw();
+ return 1;
}
//_____________________________________________________________________________
Int_t AliBaseCalibViewer::Integrate(const Char_t* drawCommand, const Char_t* sector, const Char_t* cuts,
- Float_t /*sigmaMax*/, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM,
- const Char_t *sigmas, Float_t /*sigmaStep*/) const {
- //
- // Creates an integrated histogram Begin_Latex S(t, #mu, #sigma) End_Latex, out of the input distribution distribution Begin_Latex f(x, #mu, #sigma) End_Latex, given in "histogram"
- // "mean" and "sigma" are Begin_Latex #mu End_Latex and Begin_Latex #sigma End_Latex of the distribution in "histogram", to be specified by the user
- // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate
- // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated
- // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
- // The actual work is done on the array.
- /* Begin_Latex
- f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #int_{-#infty}^{#mu + t #sigma} f(x, #mu, #sigma) dx / #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx
- End_Latex
- */
-
- Double_t ltmFraction = 0.8;
-
- TString drawStr(drawCommand);
- Bool_t dangerousToDraw = drawStr.Contains(":") || drawStr.Contains(">>");
- if (dangerousToDraw) {
- Warning("Integrate", "The draw string must not contain ':' or '>>'.");
- return -1;
- }
- drawStr += " >> tempHist";
-
- Int_t entries = EasyDraw1D(drawStr.Data(), sector, cuts, "goff");
- TH1F *htemp = (TH1F*)gDirectory->Get("tempHist");
- TGraph *integralGraphMean = 0;
- TGraph *integralGraphMedian = 0;
- TGraph *integralGraphLTM = 0;
- Double_t *values = fTree->GetV1(); // value is the array containing 'entries' numbers
- Int_t *index = new Int_t[entries];
- Float_t *xarray = new Float_t[entries];
- Float_t *yarray = new Float_t[entries];
- TMath::Sort(entries, values, index, kFALSE);
-
- Double_t mean = TMath::Mean(entries, values);
- Double_t median = TMath::Median(entries, values);
- Double_t sigma = TMath::RMS(entries, values);
-
- // parse sigmas string
- TObjArray *sigmasTokens = TString(sigmas).Tokenize(";");
- TVectorF nsigma(sigmasTokens->GetEntriesFast());
- for (Int_t i = 0; i < sigmasTokens->GetEntriesFast(); i++) {
- TString str(((TObjString*)sigmasTokens->At(i))->GetString());
- Double_t sig = (str.IsFloat()) ? str.Atof() : 0;
- nsigma[i] = sig;
- }
- delete sigmasTokens;
- TLegend * legend = new TLegend(.7,.7, .99, .99, "Integrated histogram");
- //fListOfObjectsToBeDeleted->Add(legend);
-
- if (plotMean) {
- for (Int_t i = 0; i < entries; i++) {
- xarray[i] = (values[index[i]] - mean) / sigma;
- yarray[i] = float(i) / float(entries);
- }
- integralGraphMean = new TGraph(entries, xarray, yarray);
- if (integralGraphMean) {
- //fListOfObjectsToBeDeleted->Add(integralGraphMean);
- integralGraphMean->SetLineColor(kRed);
- legend->AddEntry(integralGraphMean, "Mean", "l");
- integralGraphMean->SetTitle(Form("%s, integrated; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
- integralGraphMean->Draw("alu");
- DrawLines(integralGraphMean, nsigma, legend, kRed, kTRUE);
- }
- }
- if (plotMedian) {
- for (Int_t i = 0; i < entries; i++) {
- xarray[i] = (values[index[i]] - median) / sigma;
- yarray[i] = float(i) / float(entries);
- }
- integralGraphMedian = new TGraph(entries, xarray, yarray);
- if (integralGraphMedian) {
- //fListOfObjectsToBeDeleted->Add(integralGraphMedian);
- integralGraphMedian->SetLineColor(kBlue);
- legend->AddEntry(integralGraphMedian, "Median", "l");
- integralGraphMedian->SetTitle(Form("%s, integrated; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
- if (plotMean && integralGraphMean) integralGraphMedian->Draw("samelu");
- else integralGraphMedian->Draw("alu");
- DrawLines(integralGraphMedian, nsigma, legend, kBlue, kTRUE);
- }
- }
- if (plotLTM) {
- Double_t ltmRms = 0;
- Double_t ltm = GetLTM(entries, values, <mRms, ltmFraction);
- for (Int_t i = 0; i < entries; i++) {
- xarray[i] = (values[index[i]] - ltm) / ltmRms;
- yarray[i] = float(i) / float(entries);
- }
- integralGraphLTM = new TGraph(entries, xarray, yarray);
- if (integralGraphLTM) {
- //fListOfObjectsToBeDeleted->Add(integralGraphLTM);
- integralGraphLTM->SetLineColor(kGreen+2);
- legend->AddEntry(integralGraphLTM, "LTM", "l");
- integralGraphLTM->SetTitle(Form("%s, integrated; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
- if ((plotMean && integralGraphMean) || (plotMedian && integralGraphMedian)) integralGraphLTM->Draw("samelu");
- else integralGraphLTM->Draw("alu");
- DrawLines(integralGraphLTM, nsigma, legend, kGreen+2, kTRUE);
- }
- }
- delete [] index;
- delete [] xarray;
- delete [] yarray;
- if (!plotMean && !plotMedian && !plotLTM) return -1;
- legend->Draw();
- return entries;
+ Float_t /*sigmaMax*/, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM,
+ const Char_t *sigmas, Float_t /*sigmaStep*/) const {
+ //
+ // Creates an integrated histogram Begin_Latex S(t, #mu, #sigma) End_Latex, out of the input distribution distribution Begin_Latex f(x, #mu, #sigma) End_Latex, given in "histogram"
+ // "mean" and "sigma" are Begin_Latex #mu End_Latex and Begin_Latex #sigma End_Latex of the distribution in "histogram", to be specified by the user
+ // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate
+ // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated
+ // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
+ // The actual work is done on the array.
+ /* Begin_Latex
+ f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #int_{-#infty}^{#mu + t #sigma} f(x, #mu, #sigma) dx / #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx
+ End_Latex
+ */
+
+ Double_t ltmFraction = 0.8;
+
+ TString drawStr(drawCommand);
+ Bool_t dangerousToDraw = drawStr.Contains(":") || drawStr.Contains(">>");
+ if (dangerousToDraw) {
+ Warning("Integrate", "The draw string must not contain ':' or '>>'.");
+ return -1;
+ }
+ drawStr += " >> tempHist";
+
+ Int_t entries = EasyDraw1D(drawStr.Data(), sector, cuts, "goff");
+ TH1F *htemp = (TH1F*)gDirectory->Get("tempHist");
+ TGraph *integralGraphMean = 0;
+ TGraph *integralGraphMedian = 0;
+ TGraph *integralGraphLTM = 0;
+ Double_t *values = fTree->GetV1(); // value is the array containing 'entries' numbers
+ Int_t *index = new Int_t[entries];
+ Float_t *xarray = new Float_t[entries];
+ Float_t *yarray = new Float_t[entries];
+ TMath::Sort(entries, values, index, kFALSE);
+
+ Double_t mean = TMath::Mean(entries, values);
+ Double_t median = TMath::Median(entries, values);
+ Double_t sigma = TMath::RMS(entries, values);
+
+ // parse sigmas string
+ TObjArray *sigmasTokens = TString(sigmas).Tokenize(";");
+ TVectorF nsigma(sigmasTokens->GetEntriesFast());
+ for (Int_t i = 0; i < sigmasTokens->GetEntriesFast(); i++) {
+ TString str(((TObjString*)sigmasTokens->At(i))->GetString());
+ Double_t sig = (str.IsFloat()) ? str.Atof() : 0;
+ nsigma[i] = sig;
+ }
+ delete sigmasTokens;
+ TLegend * legend = new TLegend(.7,.7, .99, .99, "Integrated histogram");
+ //fListOfObjectsToBeDeleted->Add(legend);
+
+ if (plotMean) {
+ for (Int_t i = 0; i < entries; i++) {
+ xarray[i] = (values[index[i]] - mean) / sigma;
+ yarray[i] = float(i) / float(entries);
+ }
+ integralGraphMean = new TGraph(entries, xarray, yarray);
+ if (integralGraphMean) {
+ //fListOfObjectsToBeDeleted->Add(integralGraphMean);
+ integralGraphMean->SetLineColor(kRed);
+ legend->AddEntry(integralGraphMean, "Mean", "l");
+ integralGraphMean->SetTitle(Form("%s, integrated; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
+ integralGraphMean->Draw("alu");
+ DrawLines(integralGraphMean, nsigma, legend, kRed, kTRUE);
+ }
+ }
+ if (plotMedian) {
+ for (Int_t i = 0; i < entries; i++) {
+ xarray[i] = (values[index[i]] - median) / sigma;
+ yarray[i] = float(i) / float(entries);
+ }
+ integralGraphMedian = new TGraph(entries, xarray, yarray);
+ if (integralGraphMedian) {
+ //fListOfObjectsToBeDeleted->Add(integralGraphMedian);
+ integralGraphMedian->SetLineColor(kBlue);
+ legend->AddEntry(integralGraphMedian, "Median", "l");
+ integralGraphMedian->SetTitle(Form("%s, integrated; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
+ if (plotMean && integralGraphMean) integralGraphMedian->Draw("samelu");
+ else integralGraphMedian->Draw("alu");
+ DrawLines(integralGraphMedian, nsigma, legend, kBlue, kTRUE);
+ }
+ }
+ if (plotLTM) {
+ Double_t ltmRms = 0;
+ Double_t ltm = GetLTM(entries, values, <mRms, ltmFraction);
+ for (Int_t i = 0; i < entries; i++) {
+ xarray[i] = (values[index[i]] - ltm) / ltmRms;
+ yarray[i] = float(i) / float(entries);
+ }
+ integralGraphLTM = new TGraph(entries, xarray, yarray);
+ if (integralGraphLTM) {
+ //fListOfObjectsToBeDeleted->Add(integralGraphLTM);
+ integralGraphLTM->SetLineColor(kGreen+2);
+ legend->AddEntry(integralGraphLTM, "LTM", "l");
+ integralGraphLTM->SetTitle(Form("%s, integrated; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
+ if ((plotMean && integralGraphMean) || (plotMedian && integralGraphMedian)) integralGraphLTM->Draw("samelu");
+ else integralGraphLTM->Draw("alu");
+ DrawLines(integralGraphLTM, nsigma, legend, kGreen+2, kTRUE);
+ }
+ }
+ delete [] index;
+ delete [] xarray;
+ delete [] yarray;
+ if (!plotMean && !plotMedian && !plotLTM) return -1;
+ legend->Draw();
+ return entries;
}
//_____________________________________________________________________________
TH1F* AliBaseCalibViewer::Integrate(TH1F *histogram, Float_t mean, Float_t sigma, Float_t sigmaMax, Float_t sigmaStep){
- //
- // Creates an integrated histogram Begin_Latex S(t, #mu, #sigma) End_Latex, out of the input distribution distribution Begin_Latex f(x, #mu, #sigma) End_Latex, given in "histogram"
- // "mean" and "sigma" are Begin_Latex #mu End_Latex and Begin_Latex #sigma End_Latex of the distribution in "histogram", to be specified by the user
- // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate
- // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated
- // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
- // The actual work is done on the array.
- /* Begin_Latex
- f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #int_{-#infty}^{#mu + t #sigma} f(x, #mu, #sigma) dx / #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx
- End_Latex
- Begin_Macro(source)
- {
- Float_t mean = 0;
- Float_t sigma = 1.5;
- Float_t sigmaMax = 4;
- gROOT->SetStyle("Plain");
- TH1F *distribution = new TH1F("Distribution2", "Distribution f(x, #mu, #sigma)", 1000,-5,5);
- TRandom rand(23);
- for (Int_t i = 0; i <50000;i++) distribution->Fill(rand.Gaus(mean, sigma));
- Float_t *ar = distribution->GetArray();
-
- TCanvas* macro_example_canvas = new TCanvas("macro_example_canvas_Integrate", "", 350, 350);
- macro_example_canvas->Divide(0,2);
- TVirtualPad *pad1 = macro_example_canvas->cd(1);
- pad1->SetGridy();
- pad1->SetGridx();
- distribution->Draw();
- TVirtualPad *pad2 = macro_example_canvas->cd(2);
- pad2->SetGridy();
- pad2->SetGridx();
- TH1F *shist = AliTPCCalibViewer::Integrate(distribution, mean, sigma, sigmaMax);
- shist->SetNameTitle("Cumulative","Cumulative S(t, #mu, #sigma)");
- shist->Draw();
-
- return macro_example_canvas_Integrate;
- }
- End_Macro
- */
-
-
- Float_t *array = histogram->GetArray();
- Int_t nbins = histogram->GetXaxis()->GetNbins();
- Float_t binLow = histogram->GetXaxis()->GetXmin();
- Float_t binUp = histogram->GetXaxis()->GetXmax();
- return Integrate(nbins, array, nbins, binLow, binUp, mean, sigma, sigmaMax, sigmaStep);
+ //
+ // Creates an integrated histogram Begin_Latex S(t, #mu, #sigma) End_Latex, out of the input distribution distribution Begin_Latex f(x, #mu, #sigma) End_Latex, given in "histogram"
+ // "mean" and "sigma" are Begin_Latex #mu End_Latex and Begin_Latex #sigma End_Latex of the distribution in "histogram", to be specified by the user
+ // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate
+ // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated
+ // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
+ // The actual work is done on the array.
+ /* Begin_Latex
+ f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #int_{-#infty}^{#mu + t #sigma} f(x, #mu, #sigma) dx / #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx
+ End_Latex
+ Begin_Macro(source)
+ {
+ Float_t mean = 0;
+ Float_t sigma = 1.5;
+ Float_t sigmaMax = 4;
+ gROOT->SetStyle("Plain");
+ TH1F *distribution = new TH1F("Distribution2", "Distribution f(x, #mu, #sigma)", 1000,-5,5);
+ TRandom rand(23);
+ for (Int_t i = 0; i <50000;i++) distribution->Fill(rand.Gaus(mean, sigma));
+ Float_t *ar = distribution->GetArray();
+
+ TCanvas* macro_example_canvas = new TCanvas("macro_example_canvas_Integrate", "", 350, 350);
+ macro_example_canvas->Divide(0,2);
+ TVirtualPad *pad1 = macro_example_canvas->cd(1);
+ pad1->SetGridy();
+ pad1->SetGridx();
+ distribution->Draw();
+ TVirtualPad *pad2 = macro_example_canvas->cd(2);
+ pad2->SetGridy();
+ pad2->SetGridx();
+ TH1F *shist = AliTPCCalibViewer::Integrate(distribution, mean, sigma, sigmaMax);
+ shist->SetNameTitle("Cumulative","Cumulative S(t, #mu, #sigma)");
+ shist->Draw();
+
+ return macro_example_canvas_Integrate;
+ }
+ End_Macro
+ */
+
+
+ Float_t *array = histogram->GetArray();
+ Int_t nbins = histogram->GetXaxis()->GetNbins();
+ Float_t binLow = histogram->GetXaxis()->GetXmin();
+ Float_t binUp = histogram->GetXaxis()->GetXmax();
+ return Integrate(nbins, array, nbins, binLow, binUp, mean, sigma, sigmaMax, sigmaStep);
}
//_____________________________________________________________________________
TH1F* AliBaseCalibViewer::Integrate(Int_t n, Float_t *array, Int_t nbins, Float_t binLow, Float_t binUp,
- Float_t mean, Float_t sigma, Float_t sigmaMax, Float_t sigmaStep){
- // Creates an integrated histogram Begin_Latex S(t, #mu, #sigma) End_Latex, out of the input distribution distribution Begin_Latex f(x, #mu, #sigma) End_Latex, given in "histogram"
- // "mean" and "sigma" are Begin_Latex #mu End_Latex and Begin_Latex #sigma End_Latex of the distribution in "histogram", to be specified by the user
- // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate
- // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated
- // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
- // Here the actual work is done.
-
- Bool_t givenUnits = kTRUE;
- if (TMath::Abs(sigma) < 1.e-10 && TMath::Abs(sigmaMax) < 1.e-10) givenUnits = kFALSE;
- if (givenUnits) {
- sigma = 1;
- sigmaMax = (binUp - binLow) / 2.;
- }
-
- Float_t binWidth = (binUp-binLow)/(nbins - 1);
- if (sigmaStep <= 0) sigmaStep = binWidth;
- Int_t kbins = (Int_t)(sigmaMax * sigma / sigmaStep) + 1; // + 1 due to overflow bin in histograms
- Float_t kbinLow = givenUnits ? binLow : -sigmaMax;
- Float_t kbinUp = givenUnits ? binUp : sigmaMax;
- TH1F *hist = 0;
- if (givenUnits) hist = new TH1F("integratedHisto","Integrated Histogram; Given x; Fraction of included data", kbins, kbinLow, kbinUp);
- if (!givenUnits) hist = new TH1F("integratedHisto","Integrated Histogram; Multiples of #sigma; Fraction of included data", kbins, kbinLow, kbinUp);
- hist->SetDirectory(0);
- hist->Reset();
-
- // calculate normalization
- // printf("calculating normalization, integrating from bin 1 to %i \n", n);
- Double_t normalization = 0;
- for (Int_t i = 1; i <= n; i++) {
- normalization += array[i];
- }
- // printf("normalization: %f \n", normalization);
-
- // given units: units from given histogram
- // sigma units: in units of sigma
- // iDelta: integrate in interval (mean +- iDelta), given units
- // x: ofset from mean for integration, given units
- // hist: needs
-
- // fill histogram
- for (Float_t iDelta = mean - sigmaMax * sigma; iDelta <= mean + sigmaMax * sigma; iDelta += sigmaStep) {
- // integrate array
- Double_t value = 0;
- for (Float_t x = mean - sigmaMax * sigma; x <= iDelta; x += binWidth) {
- value += (x <= binUp && x >= binLow) ? array[GetBin(x, nbins, binLow, binUp)] : 0;
- }
- if (value / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", value, normalization);
- if (value / normalization > 100) return hist;
- Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp);
+ Float_t mean, Float_t sigma, Float_t sigmaMax, Float_t sigmaStep){
+ // Creates an integrated histogram Begin_Latex S(t, #mu, #sigma) End_Latex, out of the input distribution distribution Begin_Latex f(x, #mu, #sigma) End_Latex, given in "histogram"
+ // "mean" and "sigma" are Begin_Latex #mu End_Latex and Begin_Latex #sigma End_Latex of the distribution in "histogram", to be specified by the user
+ // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate
+ // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated
+ // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
+ // Here the actual work is done.
+
+ Bool_t givenUnits = kTRUE;
+ if (TMath::Abs(sigma) < 1.e-10 && TMath::Abs(sigmaMax) < 1.e-10) givenUnits = kFALSE;
+ if (givenUnits) {
+ sigma = 1;
+ sigmaMax = (binUp - binLow) / 2.;
+ }
+
+ Float_t binWidth = (binUp-binLow)/(nbins - 1);
+ if (sigmaStep <= 0) sigmaStep = binWidth;
+ Int_t kbins = (Int_t)(sigmaMax * sigma / sigmaStep) + 1; // + 1 due to overflow bin in histograms
+ Float_t kbinLow = givenUnits ? binLow : -sigmaMax;
+ Float_t kbinUp = givenUnits ? binUp : sigmaMax;
+ TH1F *hist = 0;
+ if (givenUnits) hist = new TH1F("integratedHisto","Integrated Histogram; Given x; Fraction of included data", kbins, kbinLow, kbinUp);
+ if (!givenUnits) hist = new TH1F("integratedHisto","Integrated Histogram; Multiples of #sigma; Fraction of included data", kbins, kbinLow, kbinUp);
+ hist->SetDirectory(0);
+ hist->Reset();
+
+ // calculate normalization
+ // printf("calculating normalization, integrating from bin 1 to %i \n", n);
+ Double_t normalization = 0;
+ for (Int_t i = 1; i <= n; i++) {
+ normalization += array[i];
+ }
+ // printf("normalization: %f \n", normalization);
+
+ // given units: units from given histogram
+ // sigma units: in units of sigma
+ // iDelta: integrate in interval (mean +- iDelta), given units
+ // x: ofset from mean for integration, given units
+ // hist: needs
+
+ // fill histogram
+ for (Float_t iDelta = mean - sigmaMax * sigma; iDelta <= mean + sigmaMax * sigma; iDelta += sigmaStep) {
+ // integrate array
+ Double_t value = 0;
+ for (Float_t x = mean - sigmaMax * sigma; x <= iDelta; x += binWidth) {
+ value += (x <= binUp && x >= binLow) ? array[GetBin(x, nbins, binLow, binUp)] : 0;
+ }
+ if (value / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", value, normalization);
+ if (value / normalization > 100) return hist;
+ Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp);
// printf("first integration bin: %i, last integration bin: %i \n", GetBin(mean - sigmaMax * sigma, nbins, binLow, binUp), GetBin(iDelta, nbins, binLow, binUp));
// printf("value: %f, normalization: %f, normalized value: %f, iDelta: %f, Bin: %i \n", value, normalization, value/normalization, iDelta, bin);
- value = (value / normalization);
- hist->SetBinContent(bin, value);
- }
- return hist;
+ value = (value / normalization);
+ hist->SetBinContent(bin, value);
+ }
+ return hist;
}
//_____________________________________________________________________________
void AliBaseCalibViewer::DrawLines(TH1F *histogram, TVectorF nsigma, TLegend *legend, Int_t color, Bool_t pm) const {
- //
- // Private function for SigmaCut(...) and Integrate(...)
- // Draws lines into the given histogram, specified by "nsigma", the lines are addeed to the legend
- //
-
- // start to draw the lines, loop over requested sigmas
+ //
+ // Private function for SigmaCut(...) and Integrate(...)
+ // Draws lines into the given histogram, specified by "nsigma", the lines are addeed to the legend
+ //
+
+ // start to draw the lines, loop over requested sigmas
for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
if (!pm) {
Int_t bin = histogram->GetXaxis()->FindBin(nsigma[i]);
TLine* lineUp = new TLine(nsigma[i], 0, nsigma[i], histogram->GetBinContent(bin));
- //fListOfObjectsToBeDeleted->Add(lineUp);
+ //fListOfObjectsToBeDeleted->Add(lineUp);
lineUp->SetLineColor(color);
lineUp->SetLineStyle(2 + i);
lineUp->Draw();
TLine* lineLeft = new TLine(nsigma[i], histogram->GetBinContent(bin), 0, histogram->GetBinContent(bin));
- //fListOfObjectsToBeDeleted->Add(lineLeft);
+ //fListOfObjectsToBeDeleted->Add(lineLeft);
lineLeft->SetLineColor(color);
lineLeft->SetLineStyle(2 + i);
lineLeft->Draw();
else { // if (pm)
Int_t bin = histogram->GetXaxis()->FindBin(nsigma[i]);
TLine* lineUp1 = new TLine(nsigma[i], 0, nsigma[i], histogram->GetBinContent(bin));
- //fListOfObjectsToBeDeleted->Add(lineUp1);
+ //fListOfObjectsToBeDeleted->Add(lineUp1);
lineUp1->SetLineColor(color);
lineUp1->SetLineStyle(2 + i);
lineUp1->Draw();
TLine* lineLeft1 = new TLine(nsigma[i], histogram->GetBinContent(bin), histogram->GetBinLowEdge(0)+histogram->GetBinWidth(0), histogram->GetBinContent(bin));
- //fListOfObjectsToBeDeleted->Add(lineLeft1);
+ //fListOfObjectsToBeDeleted->Add(lineLeft1);
lineLeft1->SetLineColor(color);
lineLeft1->SetLineStyle(2 + i);
lineLeft1->Draw();
legend->AddEntry(lineLeft1, Form("Fraction(+%f #sigma) = %f",nsigma[i], histogram->GetBinContent(bin)), "l");
bin = histogram->GetXaxis()->FindBin(-nsigma[i]);
TLine* lineUp2 = new TLine(-nsigma[i], 0, -nsigma[i], histogram->GetBinContent(bin));
- //fListOfObjectsToBeDeleted->Add(lineUp2);
+ //fListOfObjectsToBeDeleted->Add(lineUp2);
lineUp2->SetLineColor(color);
lineUp2->SetLineStyle(2 + i);
lineUp2->Draw();
TLine* lineLeft2 = new TLine(-nsigma[i], histogram->GetBinContent(bin), histogram->GetBinLowEdge(0)+histogram->GetBinWidth(0), histogram->GetBinContent(bin));
- //fListOfObjectsToBeDeleted->Add(lineLeft2);
+ //fListOfObjectsToBeDeleted->Add(lineLeft2);
lineLeft2->SetLineColor(color);
lineLeft2->SetLineStyle(2 + i);
lineLeft2->Draw();
//_____________________________________________________________________________
void AliBaseCalibViewer::DrawLines(TGraph *graph, TVectorF nsigma, TLegend *legend, Int_t color, Bool_t pm) const {
- //
- // Private function for SigmaCut(...) and Integrate(...)
- // Draws lines into the given histogram, specified by "nsigma", the lines are addeed to the legend
- //
-
- // start to draw the lines, loop over requested sigmas
+ //
+ // Private function for SigmaCut(...) and Integrate(...)
+ // Draws lines into the given histogram, specified by "nsigma", the lines are addeed to the legend
+ //
+
+ // start to draw the lines, loop over requested sigmas
for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
if (!pm) {
TLine* lineUp = new TLine(nsigma[i], 0, nsigma[i], graph->Eval(nsigma[i]));
- //fListOfObjectsToBeDeleted->Add(lineUp);
+ //fListOfObjectsToBeDeleted->Add(lineUp);
lineUp->SetLineColor(color);
lineUp->SetLineStyle(2 + i);
lineUp->Draw();
TLine* lineLeft = new TLine(nsigma[i], graph->Eval(nsigma[i]), 0, graph->Eval(nsigma[i]));
- //fListOfObjectsToBeDeleted->Add(lineLeft);
+ //fListOfObjectsToBeDeleted->Add(lineLeft);
lineLeft->SetLineColor(color);
lineLeft->SetLineStyle(2 + i);
lineLeft->Draw();
}
else { // if (pm)
TLine* lineUp1 = new TLine(nsigma[i], 0, nsigma[i], graph->Eval(nsigma[i]));
- //fListOfObjectsToBeDeleted->Add(lineUp1);
+ //fListOfObjectsToBeDeleted->Add(lineUp1);
lineUp1->SetLineColor(color);
lineUp1->SetLineStyle(2 + i);
lineUp1->Draw();
TLine* lineLeft1 = new TLine(nsigma[i], graph->Eval(nsigma[i]), graph->GetHistogram()->GetXaxis()->GetBinLowEdge(0), graph->Eval(nsigma[i]));
- //fListOfObjectsToBeDeleted->Add(lineLeft1);
+ //fListOfObjectsToBeDeleted->Add(lineLeft1);
lineLeft1->SetLineColor(color);
lineLeft1->SetLineStyle(2 + i);
lineLeft1->Draw();
legend->AddEntry(lineLeft1, Form("Fraction(+%f #sigma) = %f",nsigma[i], graph->Eval(nsigma[i])), "l");
TLine* lineUp2 = new TLine(-nsigma[i], 0, -nsigma[i], graph->Eval(-nsigma[i]));
- //fListOfObjectsToBeDeleted->Add(lineUp2);
+ //fListOfObjectsToBeDeleted->Add(lineUp2);
lineUp2->SetLineColor(color);
lineUp2->SetLineStyle(2 + i);
lineUp2->Draw();
TLine* lineLeft2 = new TLine(-nsigma[i], graph->Eval(-nsigma[i]), graph->GetHistogram()->GetXaxis()->GetBinLowEdge(0), graph->Eval(-nsigma[i]));
- //fListOfObjectsToBeDeleted->Add(lineLeft2);
+ //fListOfObjectsToBeDeleted->Add(lineLeft2);
lineLeft2->SetLineColor(color);
lineLeft2->SetLineStyle(2 + i);
lineLeft2->Draw();
git://git.uio.no / u / mrichter / AliRoot.git / blobdiff