class AliBaseCalibViewer: public TObject

 AliBaseCalibViewer destructor
 all objects will be deleted, the file will be closed, the pictures will disappear

 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.

 formats title and axis labels of histo
 removes '.fElements'

 add a reference tree to the current tree
 by default the treename is 'tree' and the reference treename is 'R'

 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

  returns the LTM and sigma

 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

 Creates a cumulative histogram , where you can see, how much of the data are inside sigma-intervals around the mean value
 The data of the distribution  are given in 'histogram'
 'mean' and 'sigma' are  and   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, )
 sigmaStep: the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
 pm: Decide weather  (first case) or  arbitrary (secound case)
 The actual work is done on the array.

     {
     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;
     }  
     

 Creates a histogram , where you can see, how much of the data are inside sigma-intervals around the mean value
 The data of the distribution  are given in 'array', 'n' specifies the length of the array
 'mean' and 'sigma' are  and   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, )
 sigmaStep: the binsize of the generated histogram
 Here the actual work is done.

 SigmaCut for variable binsize
 NOT YET IMPLEMENTED !!!

 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 ,  and   a line is drawn.
 "plotMean", "plotMedian" and "plotLTM": what kind of lines do you want to see?

 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

 Creates an integrated histogram , out of the input distribution distribution , given in "histogram"
 "mean" and "sigma" are  and   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.

 Creates an integrated histogram , out of the input distribution distribution , given in "histogram"
 "mean" and "sigma" are  and   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.

     {
     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;
     }  
     

 Creates an integrated histogram , out of the input distribution distribution , given in "histogram"
 "mean" and "sigma" are  and   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.

 Private function for SigmaCut(...) and Integrate(...)
 Draws lines into the given histogram, specified by "nsigma", the lines are addeed to the legend

 Private function for SigmaCut(...) and Integrate(...)
 Draws lines into the given histogram, specified by "nsigma", the lines are addeed to the legend

virtual void GetTimeInfoOCDB(const Char_t* runList, const Char_t* outFile,
Int_t firstRun, Int_t lastRun, UInt_t infoFlags,
const Char_t* ocdbStorage) = 0;

 easy drawing of data, use '~' for abbreviation of '.fElements'

 easy drawing of data, use '~' for abbreviation of '.fElements'
 easy drawing of data, use '~' for abbreviation of '.fElements'

 easy drawing of data, use '~' for abbreviation of '.fElements'
 formats title and axis labels of histo, removes '.fElements'