class AliTRDCalibraFit: public TObject

 Singleton implementation

 Singleton implementation
 Deletes the instance of this class

 Default constructor

 Copy constructor

 Delete instance

 Delete DebugStreamer

 Fit the 1D histos, projections of the 2D ch on the Xaxis, for each
 calibration group normalized the resulted coefficients (to 1 normally)

 Reconstruct a 1D histo from the vectorCH for each calibration group,
 fit the histo, normalized the resulted coefficients (to 1 normally)

 Fit the 1D histos, projections of the 2D ch on the Xaxis, for each
 calibration group normalized the resulted coefficients (to 1 normally)

 Take the 1D profiles (average pulse height), projections of the 2D PH
 on the Xaxis, for each calibration group
 Reconstruct a drift velocity
 A first calibration of T0 is also made  using the same method

 Reconstruct the average pulse height from the vectorPH for each
 calibration group
 Reconstruct a drift velocity
 A first calibration of T0 is also made  using the same method (slope method)

 Take the 1D profiles (average pulse height), projections of the 2D PH
 on the Xaxis, for each calibration group
 Reconstruct a drift velocity
 A first calibration of T0 is also made  using the same method

 Take the 1D profiles (pad response function), projections of the 2D PRF
 on the Xaxis, for each calibration group
 Fit with a gaussian to reconstruct the sigma of the pad response function

 Take the 1D profiles (pad response function), projections of the 2D PRF
 on the Xaxis, for each calibration group
 Fit with a gaussian to reconstruct the sigma of the pad response function

 Reconstruct the 1D histo (pad response function) from the vectorPRD for
 each calibration group
 Fit with a gaussian to reconstruct the sigma of the pad response function

 Reconstruct the 1D histo (pad response function) from the vectorPRD for
 each calibration group
 Fit with a gaussian to reconstruct the sigma of the pad response function

 The linear method

 The linear method

 The linear method

 Get numberofgroupsprf

 Set fNz[i] and fNrphi[i] of the AliTRDCalibraFit::Instance()
 corresponding to the given name

 Set fNrphi[i] of the AliTRDCalibraFit::Instance()
 corresponding to the given TObject

 Set fNz[i] of the AliTRDCalibraFit::Instance()
 corresponding to the given TObject

 Remove the results too far from the mean value and rms
 type: 0 gain, 1 vdrift
 perdetector

 Remove the results too far from the mean and rms
 perdetector

 ofwhat is equaled to 0: mean value of all passing detectors
 ofwhat is equaled to 1: mean value of the detector, otherwise supermodule, otherwise all

 ofwhat is equaled to 0: mean value of all passing detectors
 ofwhat is equaled to 1: mean value of the detector, otherwise supermodule, otherwise all

 It creates the AliTRDCalDet object from the AliTRDFitInfo
 It takes the mean value of the coefficients per detector
 This object has to be written in the database

 It creates the AliTRDCalDet object from the AliTRDFitInfo
 It takes the mean value of the coefficients per detector
 This object has to be written in the database

 It creates the AliTRDCalDet object from the AliTRDFitInfo2
 It takes the min value of the coefficients per detector
 This object has to be written in the database

 It creates the AliTRDCalDet object from the AliTRDFitInfo2
 It takes the min value of the coefficients per detector
 This object has to be written in the database

 It creates the AliTRDCalDet object from the AliTRDFitInfo2
 It takes the min value of the coefficients per detector
 This object has to be written in the database

 It Creates the AliTRDCalPad object from AliTRDFitInfo
 You need first to create the object for the detectors,
 where the mean value is put.
 This object has to be written in the database

 It Creates the AliTRDCalPad object from AliTRDFitInfo
 You need first to create the object for the detectors,
 where the mean value is put.
 This object has to be written in the database

 It Creates the AliTRDCalPad object from AliTRDFitInfo2
 You need first to create the object for the detectors,
 where the mean value is put.
 This object has to be written in the database

 It Creates the AliTRDCalPad object from AliTRDFitInfo
 This object has to be written in the database

 It Creates the AliTRDCalDet object from AliTRDFitInfo
 0 successful fit 1 not successful fit
 mean is the mean value over the successful fit
 do not use it for t0: no meaning

 It Creates the AliTRDCalPad object from AliTRDFitInfo
 0 not successful fit 1 successful fit
 mean mean value over the successful fit

 Set FitPH if 1 then each detector will be fitted

 The fit of the deposited charge distribution begins at
 histo->Mean()/beginFitCharge
 You can here set beginFitCharge

 The t0 calculated with the maximum positif slope is shift from t0Shift0
 You can here set t0Shift0

 The t0 calculated with the maximum of the amplification region is shift from t0Shift1
 You can here set t0Shift1

 The fit of the PRF is from -rangeFitPRF to rangeFitPRF
 You can here set rangeFitPRF

 Minimum entries for fitting

 Rebin with rebin time less bins the Ch histo
 You can set here rebin that should divide the number of bins of CH histo

 For the Fit functions fill the vector Fit

 For the Fit functions fill the vector Fit

 Init the number of expected bins and fDect1[i] fDect2[i]

 Init the fVectorFitCH for normalisation
 Init the histo for debugging

 Init the arrays of results
 Init the histos for debugging

 Init the calibration mode (Nz, Nrphi), the histograms for
 debugging the fit methods if fDebug > 0,

 Init the fCalDet, fVectorFit fCurrentCoefDetector

 Init the fCalDet, fVectorFit fCurrentCoefDetector

 Init the current detector where we are fCountDet and the
 next fCount for the functions Fit...

 Calculate the number of bins expected (calibration groups)

 Calculate the range of fits

 Check if fFitVoir is in the range

 See if we are in a new detector and update the
 variables fNfragZ and fNfragRphi if yes
 Will never happen for only one detector (3 and 4)
 Doesn't matter for 2

 Reconstruct the min pad row, max pad row, min pad col and
 max pad col of the calibration group for the Fit functions
 idect is the calibration group inside the detector

 For the case where there are not enough entries in the histograms
 of the calibration group, the value present in the choosen database
 will be put. A negativ sign enables to know that a fit was not possible.

 For the case where there are not enough entries in the histograms
 of the calibration group, the value present in the choosen database
 will be put. A negativ sign enables to know that a fit was not possible.

 For the case where there are not enough entries in the histograms
 of the calibration group, the value present in the choosen database
 will be put. A negativ sign enables to know that a fit was not possible.

 For the case where there are not enough entries in the histograms
 of the calibration group, the value present in the choosen database
 will be put. A negativ sign enables to know that a fit was not possible.

 For the case where there are not enough entries in the histograms
 of the calibration group, the value present in the choosen database
 will be put. A negativ sign enables to know that a fit was not possible.

 Fill the coefficients found with the fits or other
 methods from the Fit functions

 Fill the coefficients found with the fits or other
 methods from the Fit functions

 Fill the coefficients found with the fits or other
 methods from the Fit functions

 Fill the coefficients found with the fits or other
 methods from the Fit functions

 Fill the coefficients found with the fits or other
 methods from the Fit functions

 DebugStream and fVectorFit

 DebugStream and fVectorFit and fVectorFit2

 DebugStream and fVectorFit

 DebugStream and fVectorFit

 DebugStream and fVectorFit

 For the detector Dect calcul the mean time 0
 for the calibration group idect from the choosen database

 For the detector Dect calcul the mean gain factor
 for the calibration group idect from the choosen database

 For the detector Dect calcul the mean sigma of pad response
 function for the calibration group idect from the choosen database

 For the detector dect calcul the mean drift velocity for the
 calibration group idect from the choosen database

 For the detector fCountDet, mean drift velocity and tan lorentzangle

 Default width of the PRF if there is no database as reference

 Set the calib object for fCountDet

 Slope methode for the drift velocity

 Slope methode but with polynomes de Lagrange

 Fit methode for the drift velocity

 Fit methode for the sigma of the pad response function

 Fit methode for the sigma of the pad response function

 Fit methode for the sigma of the pad response function

 Fit methode for the sigma of the pad response function

 Fit methode for the sigma of the pad response function with 2*nbg tan bins

 Only mean methode for the gain factor

 mean w methode for the gain factor

 mean w methode for the gain factor

 Fit methode for the gain factor

 Fit methode for the gain factor

 Fit methode for the gain factor more time consuming

 Fit methode for the gain factor more time consuming

 Calcul the coefficients of the polynome passant par ces trois points de degre 2

 Calcul the coefficients of the polynome passant par ces quatre points de degre 3

 Calcul the coefficients of the polynome passant par ces cinqs points de degre 4

 Normalisation of the gain factor resulting for the fits

 Rebin of the 1D histo for the gain calibration if needed.
 you have to choose fRebin, divider of fNumberBinCharge

 Rebin of the 1D histo for the gain calibration if needed
 you have to choose fRebin divider of fNumberBinCharge

 Reconstruct the plane number from the detector number

 Reconstruct the stack number from the detector number

 Reconstruct the sector number from the detector number

 Reset the VectorFits

 Function for the fit

 Function for the fit

 Sum Landau + Gaus with identical mean

 Function for the fit

 Fit parameters:
 par[0]=Width (scale) parameter of Landau density
 par[1]=Most Probable (MP, location) parameter of Landau density
 par[2]=Total area (integral -inf to inf, normalization constant)
 par[3]=Width (sigma) of convoluted Gaussian function

 In the Landau distribution (represented by the CERNLIB approximation),
 the maximum is located at x=-0.22278298 with the location parameter=0.
 This shift is corrected within this function, so that the actual
 maximum is identical to the MP parameter.

 Function for the fit

 Fit parameters:
 par[0]=Width (scale) parameter of Landau density
 par[1]=Most Probable (MP, location) parameter of Landau density
 par[2]=Total area (integral -inf to inf, normalization constant)
 par[3]=Width (sigma) of convoluted Gaussian function
 par[4]=Exponential Slope Parameter

 In the Landau distribution (represented by the CERNLIB approximation),
 the maximum is located at x=-0.22278298 with the location parameter=0.
 This shift is corrected within this function, so that the actual
 maximum is identical to the MP parameter.

 Function for the fit

 Function for the fit

 ExB calibration

 Set or Get the variables

 Fit

 Statistics

 Debug

 Magnetic field

 Get the scale factor

 Vector Fit getter

 AliTRDCalibraMode