class AliFMDEnergyFitter::RingHistos: public AliForwardUtil::RingHistos

 fEtaEDists(0),

 Default CTOR

 fEtaEDists(0),

 Constructor

 Parameters:
    d detector
    r ring

 Destructor

 Make an axis with increasing bins

 Parameters:
    n    Number of bins
    min  Minimum
    max  Maximum

 Return:
    An axis with quadratically increasing bin size

 Make E/E_mip histogram

 Parameters:
    deMax   Maximum energy loss
    nDeBins Number energy loss bins
    incr    Whether to make bins of increasing size

 Define outputs

 Parameters:
    dir

 Initialise object

 Parameters:
    eAxis      Eta axis
    maxDE      Max energy loss to consider
    nDEbins    Number of bins
    useIncrBin Whether to use an increasing bin size

 Fill histogram

 Parameters:
    empty  True if event is empty
    ieta   Eta bin (0-based)
    icent  Centrality bin (1-based)
    mult   Signal

 Make a parameter histogram

 Parameters:
    name   Name of histogram.
    title  Title of histogram.
    eta    Eta axis

 Return:

 Make a histogram that contains the results of the fit over the full ring

 Parameters:
    name  Name
    title Title
    eta   Eta axis
    low   Least bin
    high  Largest bin
    val   Value of parameter
    err   Error on parameter

 Return:
    The newly allocated histogram

 Fit each histogram to up to @a nParticles particle responses.

 Parameters:
    dir         Output list
    eta         Eta axis
    lowCut      Lower cut
    nParticles  Max number of convolved landaus to fit
    minEntries  Minimum number of entries
    minusBins   Number of bins from peak to subtract to
                    get the fit range
    relErrorCut Cut applied to relative error of parameter.
                    Note, for multi-particle weights, the cut
                    is loosend by a factor of 2
    chi2nuCut   Cut on @f$ \chi^2/\nu@f$ -
                    the reduced @f$\chi^2@f$

 Scale to the bin-width

 Fit a signal histogram.  First, the bin @f$ b_{min}@f$ with
 maximum bin content in the range @f$ [E_{min},\infty]@f$ is
 found.  Then the fit range is set to the bin range
 @f$ [b_{min}-\Delta b,b_{min}+2\Delta b]@f$, and a 1
 particle signal is fitted to that.  The parameters of that fit
 is then used as seeds for a fit of the @f$ N@f$ particle response
 to the data in the range
 @f$ [b_{min}-\Delta b,N(\Delta_1+\xi_1\log(N))+2N\xi@f$

 Parameters:
    dist        Histogram to fit
    lowCut      Lower cut @f$ E_{min}@f$ on signal
    nParticles  Max number @f$ N@f$ of convolved landaus to fit
    minusBins   Number of bins @f$ \Delta b@f$ from peak to
                    subtract to get the fit range
    relErrorCut Cut applied to relative error of parameter.
                    Note, for multi-particle weights, the cut
                    is loosend by a factor of 2
    chi2nuCut   Cut on @f$ \chi^2/\nu@f$ -
                    the reduced @f$\chi^2@f$

 Return:
    The best fit function

 Find the best fit

 Parameters:
    dist           Histogram
    relErrorCut    Cut applied to relative error of parameter.
                       Note, for multi-particle weights, the cut
                       is loosend by a factor of 2
    chi2nuCut      Cut on @f$ \chi^2/\nu@f$ -
                       the reduced @f$\chi^2@f$
    minWeightCut   Least valid @f$ a_i@f$

 Return:
    Best fit or null

 Find the best fits

 Parameters:
    d              Parent list
    obj            Object to add fits to
    eta            Eta axis
    relErrorCut    Cut applied to relative error of parameter.
                       Note, for multi-particle weights, the cut
                       is loosend by a factor of 2
    chi2nuCut      Cut on @f$ \chi^2/\nu@f$ -
                       the reduced @f$\chi^2@f$
    minWeightCut   Least valid @f$ a_i@f$

   * Assignment operator
   *
   * @param o Object to assign from
   *
   * @return Reference to this

     * Default CTOR