class AliFMDDensityCalculator: public TNamed

 Constructor

 Constructor

 Parameters:
    name Name of object

 Copy constructor

 Parameters:
    o Object to copy from

 Destructor

 Assignement operator

 Parameters:
    o Object to assign from

 Return:
    Reference to this object

 Intialize this sub-algorithm

 Parameters:
   etaAxis   Eta axis

 Get the ring histogram container

 Parameters:
    d Detector
    r Ring

 Return:
    Ring histogram container

 Get the multiplicity cut.  If the user has set fMultCut (via
 SetMultCut) then that value is used.  If not, then the lower
 value of the fit range for the enery loss fits is returned.

 Return:
    Lower cut on multiplicity

 Get the multiplicity cut.  If the user has set fMultCut (via
 SetMultCut) then that value is used.  If not, then the lower
 value of the fit range for the enery loss fits is returned.

 Return:
    Lower cut on multiplicity

 Do the calculations

 Parameters:
    fmd      AliESDFMD object (possibly) corrected for sharing
    hists    Histogram cache
    vtxBin   Vertex bin
    lowFlux  Low flux flag.

 Return:
    true on successs

 Find the max weight to use for FMD<i>dr</i> in eta bin @a iEta

 Parameters:
    cor   Correction
    d     Detector
    r     Ring
    iEta  Eta bin

 Find the max weight to use for FMD<i>dr</i> in eta bin @a iEta

 Parameters:
    cor   Correction
    d     Detector
    r     Ring
    eta   Eta

 Find the max weights and cache them

 Find the (cached) maximum weight for FMD<i>dr</i> in
 @f$\eta@f$ bin @a iEta

 Parameters:
    d     Detector
    r     Ring
    iEta  Eta bin

 Return:
    max weight or <= 0 in case of problems

 Find the (cached) maximum weight for FMD<i>dr</i> iat
 @f$\eta@f$

 Parameters:
    d     Detector
    r     Ring
    eta   Eta bin

 Return:
    max weight or <= 0 in case of problems

 Get the number of particles corresponding to the signal mult

 Parameters:
    mult     Signal
    d        Detector
    r        Ring
    s        Sector
    t        Strip (not used)
    v        Vertex bin
    eta      Pseudo-rapidity
    lowFlux  Low-flux flag

 Return:
    The number of particles

 if (mult <= GetMultCut()) return 0;

 Get the inverse correction factor.  This consist of

 - acceptance correction (corners of sensors)
 - double hit correction (for low-flux events)
 - dead strip correction

 Parameters:
    d        Detector
    r        Ring
    s        Sector
    t        Strip (not used)
    v        Vertex bin
    eta      Pseudo-rapidity
    lowFlux  Low-flux flag

 Return:

 Generate the acceptance corrections

 Parameters:
    r Ring to generate for

 Return:
    Newly allocated histogram of acceptance corrections

 Get the acceptance correction for strip @a t in an ring of type @a r

 Parameters:
    r  Ring type ('I' or 'O')
    t  Strip number

 Return:
    Inverse acceptance correction

 Scale the histograms to the total number of events

 Parameters:
    dir     where to put the output
    nEvents Number of events

 Output diagnostic histograms to directory

 Parameters:
    dir List to write in

 Print information

 Parameters:
    option Not used

   * Set the debug level.  The higher the value the more output
   *
   * @param dbg Debug level

   * Maximum particle weight to use
   *
   * @param m

   * Set whether to use poisson statistics to estimate the
   * number of particles that has hit within a region.  If this is true,
   * then the average charge particle density is given by
   * @f[
   *  \lambda = -\log\left(\frac{N_e}{N_t}\right)
   * @f]
   * where $N_e$ is the number of strips within the region that has no
   * hits over threshold, and $N_t$ is the total number of strips in the
   * region/
   *
   * @param u Whether to use poisson statistics to estimate the
   * number of particles that has hit within a region.

   * In case of a displaced vertices recalculate eta and angle correction
   *
   * @param use recalculate or not
   *

   * Set whether to use the phi acceptance correction.
   *
   * How the phi acceptance is used depends on the value passed.
   * - 0:  No phi acceptance
   * - 1:  Phi acceptance correction done to estimate of particles
   * - 2:  Phi acceptance correction done to energy deposited
   *
   * @param u If >0, use the phi acceptance (default is false)

   * Set the luming factors used in the Poisson method
   *
   * @param eta Must be 1 or larger
   * @param phi Must be 1 or larger

   * Set the minimum quality of the energy loss fits
   *
   * @param cut Cut value

   * Set the maximum ratio of outlier bins to the total number of bins
   * with data.
   *
   * @param ratio Maximum ratio (number between 0 and 1)

   * Set the maximum relative diviation between @f$N_{ch}^{Poisson}@f$
   * and @f$N_{ch}^{\Delta}@f$
   *
   * @param cut Relative cut (number between 0 and 1)

   * Set the minimum quality of the energy loss fits
   *
   * @return Cut value

   * Get the cuts used
   *
   * @return Reference to cuts object

   * Set the cuts to use
   *
   * @param c Cuts to use