class AliTPCCalibViewer: public TObject

 Default constructor

 dummy AliTPCCalibViewer copy constructor
 not yet working!!!

 Constructor that initializes the calibration viewer

 Constructor to initialize the calibration viewer
 the file 'fileName' contains the tree 'treeName'

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

 Should be called from AliTPCCalibViewerGUI 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.

 Replace all "<variable>" with "<variable><fAbbreviation>" (Adds forgotten "~")
 but take care on the statistical information, like "CEQmean_Mean"
 and also take care on correct given variables, like "CEQmean~"

 For each variable out of "listOfVariables":
 - 'Save' correct items:
   - form <replaceString>, take <variable>'s first char, add <removeString>, add rest of <variable>, e.g. "C!#EQmean" (<removeString> = "!#")
   - For each statistical information in "listOfNormalizationVariables":
     - ReplaceAll <variable><statistical_Information> with <replaceString><statistical_Information>
   - ReplaceAll <variable><abbreviation> with <replaceString><abbreviation>, e.g. "CEQmean~" -> "C!#EQmean~"
   - ReplaceAll <variable><appendStr> with <replaceString><appendStr>, e.g. "CEQmean.fElements" -> "C!#EQmean.fElements"

 - Do actual replacing:
   - ReplaceAll <variable> with <variable><fAbbreviation>, e.g. "CEQmean" -> "CEQmean~"

 - Undo saving:
   - For each statistical information in "listOfNormalizationVariables":
     - ReplaceAll <replaceString><statistical_Information> with <variable><statistical_Information>
   - ReplaceAll <replaceString><abbreviation> with <variable><abbreviation>, e.g. "C!#EQmean~" -> "CEQmean~"
   - ReplaceAll <replaceString><appendStr> with <variable><appendStr>, e.g. "C!#EQmean.fElements" -> "CEQmean.fElements"

 Now all the missing "~" should be added.

 easy drawing of data, use '~' for abbreviation of '.fElements'
 example: EasyDraw("CETmean~-CETmean_mean", "A", "(CETmean~-CETmean_mean)>0")
 sector: sector-number - only the specified sector will be drwawn
         'A'/'C' or 'a'/'c' - side A/C will be drawn
         'ALL' - whole TPC will be drawn, projected on one side
 cuts: specifies cuts
 drawOptions: draw options like 'same'
 writeDrawCommand: write the command, that is passed to TTree::Draw

 easy drawing of data, use '~' for abbreviation of '.fElements'
 example: EasyDraw("CETmean~-CETmean_mean", 34, "(CETmean~-CETmean_mean)>0")
 sector: sector-number - only the specified sector will be drwawn
 cuts: specifies cuts
 drawOptions: draw options like 'same'
 writeDrawCommand: write the command, that is passed to TTree::Draw

 easy drawing of data, use '~' for abbreviation of '.fElements'
 example: EasyDraw("CETmean~-CETmean_mean", "A", "(CETmean~-CETmean_mean)>0")
 sector: sector-number - the specified sector will be drwawn
         'A'/'C' or 'a'/'c' - side A/C will be drawn
         'ALL' - whole TPC will be drawn, projected on one side
 cuts: specifies cuts
 drawOptions: draw options like 'same'
 writeDrawCommand: write the command, that is passed to TTree::Draw

 easy drawing of data, use '~' for abbreviation of '.fElements'
 example: EasyDraw("CETmean~-CETmean_mean", 34, "(CETmean~-CETmean_mean)>0")
 sector: sector-number - the specified sector will be drwawn
 cuts: specifies cuts
 drawOptions: draw options like 'same'
 writeDrawCommand: write the command, that is passed to TTree::Draw

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

 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?

 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 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




 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 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 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.

 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.

 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

 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

  returns the LTM and sigma

 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("Distrib1", "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("cAliTPCCalibViewer1", "", 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 !!!

 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
 Here the actual work is done.

 creates a AliTPCCalPad out of the 'desiredData'
 the functionality of EasyDraw1D is used
 calPadName specifies the name of the created AliTPCCalPad
  - this takes a while -

 creates a AliTPCCalPad out of the 'desiredData'
 the functionality of EasyDraw1D is used
 calPadName specifies the name of the created AliTPCCalPad
  - this takes a while -

 creates a AliTPCCalROC out of the desiredData
 the functionality of EasyDraw1D is used
 sector specifies the sector of the created AliTPCCalROC

 scan the tree  - produces a list of available variables in the tree
 printList: print the list to the screen, after the scan is done

 produces a list of available variables for normalization in the tree
 printList: print the list to the screen, after the scan is done

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

 Returns a TObjArray with all AliTPCCalPads that are stored in the tree
  - this takes a while -

 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

 Write tree with all available information
 im mapFileName is speciefied, the Map information are also written to the tree
 AliTPCCalPad-Objects are written directly to the tree, so that they can be accessd later on
 (does not work!!!)

 Write a tree with all available information
 if mapFileName is speciefied, the Map information are also written to the tree
 pads specified in outlierPad are not used for calculating statistics
 The following statistical information on the basis of a ROC are calculated:
 "_Median", "_Mean", "_LTM", "_RMS_LTM"
 "_Median_OutlierCutted", "_Mean_OutlierCutted", "_RMS_OutlierCutted", "_LTM_OutlierCutted", "_RMS_LTM_OutlierCutted"
 The following position variables are available:
 "row", "pad", "lx", "ly", "gx", "gy", "rpad", "channel"

 The tree out of this function is the basis for the AliTPCCalibViewer and the AliTPCCalibViewerGUI.

 make standard aliases for standard calibration trees

 Function to create a calibration Tree with all available information.
 See also documentation to MakeTree
 the file "inputFileName" must be in the following format (see also CreateObjectList):
 (each colum separated by tabs, "dependingOnType" can have 2 or 3 colums)

 type	path	dependingOnType

 type == "CE":
 dependingOnType = CETmean	CEQmean	CETrms

 type == "Pulser":
 dependingOnType = PulserTmean	PulsterQmean	PulserTrms

 type == "Pedestals":
 dependingOnType = Pedestals	Noise

 type == "CalPad":
 dependingOnType = NameInFile	NameToWriteToFile

 Function to create a TObjArray out of a given file
 the file must be in the following format:
 (each colum separated by tabs, "dependingOnType" can have 2 or 3 colums)


 type	path	dependingOnType

 type == "CE":
 dependingOnType = CETmean	CEQmean	CETrms

 type == "Pulser":
 dependingOnType = PulserTmean	PulsterQmean	PulserTrms

 type == "Pedestals":
 dependingOnType = Pedestals	Noise

 type == "CalPad":
 dependingOnType = NameInFile	NameToWriteToFile