class AliFMDCorrELossFit::ELossFit: public TObject

 Assignment operator

 Parameters:
    o Object to assign from

 Return:
    Reference to this object

 Draw this object

 Parameters:
    option Options.  Possible values are
  - err Plot error bars

 Print this object.

 Parameters:
    option Options
   - R   Print recursive

 Browse this object

 Parameters:
    b

     * Default constructor
     *

     * Construct from a function
     *
     * @param quality Quality flag
     * @param f       Function

     * Constructor with full parameter set
     *
     * @param quality   Quality flag
     * @param n         @f$ N@f$ - Number of fitted peaks
     * @param chi2      @f$ \chi^2 @f$
     * @param nu        @f$ \nu @f$ - number degrees of freedom
     * @param c         @f$ C@f$ - scale constant
     * @param ec        @f$ \delta C@f$ - error on @f$ C@f$
     * @param delta     @f$ \Delta@f$ - Most probable value		
     * @param edelta    @f$ \delta\Delta@f$ - error on @f$\Delta@f$
     * @param xi        @f$ \xi@f$ - width
     * @param exi       @f$ \delta\xi@f$ - error on @f$\xi@f$
     * @param sigma     @f$ \sigma@f$ - Width of Gaussian		
     * @param esigma    @f$ \delta\sigma@f$ - error on @f$\sigma@f$
     * @param sigman    @f$ \sigma_n@f$ - Noise width		
     * @param esigman   @f$ \delta\sigma_n@f$ - error on @f$\sigma_n@f$
     * @param a         Array of @f$ N-1@f$ weights @f$ a_i@f$ for
     *                  @f$ i=2,\ldots@f$
     * @param ea        Array of @f$ N-1@f$ error on the weights @f$ a_i@f$ for
     *                  @f$ i=2,\ldots@f$

     * Destructor

     * @{
     * @name Access to parameters


     * @return Number of peaks fitted

     * @return Number degrees of freedom

     * @return Chi square from fit

     * @return Scaling constant

     * @return Most probable value

     * @return Width parameter of Landau

     * @return Sigma on folded gaussian

     * @return Sigma of detector noise

     * @return Weights

     * @return Error on C

     * @return Error on Delta

     * @return Error on Xi

     * @return Error on sigma

     * @return Error on sigma (noise)

     * @return Error on weights

     * @return Assigned quality

     * @return Detector

     * @return Ring

     * @return Eta bin

@}

     * @{
     * @name Evaluation


     * Evaluate
     * @f[
     *  f_N(x;\Delta,\xi,\sigma') =
     *     \sum_{i=1}^{n} a_i f(x;\Delta_i,\xi_i,\sigma_i')
     * @f]
     *
     * (see AliForwardUtil::NLandauGaus) for the maximum @f$ N @f$
     * that fulfills the requirements
     *
     * @param x           Where to evaluate
     * @param maxN 	  @f$ \max{N}@f$
     *
     * @return @f$ f_N(x;\Delta,\xi,\sigma')@f$

     * Evaluate
     * @f[
     *   f_W(x;\Delta,\xi,\sigma') =
     *   \frac{\sum_{i=1}^{n} i a_i f_i(x;\Delta,\xi,\sigma')}{
     *     f_N(x;\Delta,\xi,\sigma')} =
     *   \frac{\sum_{i=1}^{n} i a_i f(x;\Delta_i,\xi_i,\sigma_i')}{
     *     \sum_{i=1}^{n} a_i f(x;\Delta_i,\xi_i,\sigma_i')}
     * @f]
     * where @f$ n@f$ fulfills the requirements (see FindMaxWeight).
     *
     * If the denominator is zero, then 1 is returned.
     *
     * See also AliForwardUtil::ILandauGaus and AliForwardUtil::NLandauGaus
     * for more information on the evaluated functions.
     *
     * @param x           Where to evaluate
     * @param maxN 	  @f$ \max{N}@f$
     *
     * @return @f$ f_W(x;\Delta,\xi,\sigma')@f$.


     * Find the maximum weight to use.  The maximum weight is the
     * largest i for which
     *
     * - @f$ i \leq \max{N}@f$
     * - @f$ a_i > \min{a}@f$
     * - @f$ \delta a_i/a_i > \delta_{max}@f$
     *
     * @param maxRelError @f$ \min{a}@f$
     * @param leastWeight @f$ \delta_{max}@f$
     * @param maxN        @f$ \max{N}@f$
     *
     * @return The largest index @f$ i@f$ for which the above
     * conditions hold.  Will never return less than 1.

     * Get a function that expresses this fit.
     *
     * @f[
     *  f_N(x;\Delta,\xi,\sigma') =
     *     \sum_{i=1}^{n} a_i f(x;\Delta_i,\xi_i,\sigma_i')
     * @f]
     * (see AliForwardUtil::NLandauGaus) or, if @a i is 1 or larger
     * @f[
     *  f_i(x;\Delta,\xi,\sigma') = a_i f(x;\Delta_i,\xi_i,\sigma_i')
     * @f]
     * (see AliForwardUtil::ILandauGaus).
     *
     * @param i Component to get.  If @a i is 0 or less, then the full
     * function is returned, otherwise the specified component (if
     * valid).
     * @param max Upper bound on function
     *
     * @return Pointer to newly allocated function.  The caller owns
     * this object, and must clean it up.


     * Find the x value that corresponds to a (normalized) probability
     * of @a low or less.  That is, we can use this to say: "Give me
     * the x value under which it is unlikely that a particle gave a
     * signal".
     *
     * @param low Threshold (between 0 and 1)
     *
     * @return Cut value, or 1000 in case of problems

@}

     * @{
     * @name TObject Sortable interface


     * Declare this object as sortable
     *
     * @return Always true

     * Compare to another ELossFit object.
     *
     * - +1, if this quality is better (larger) than other objects quality
     * - -1, if this quality is worse (smaller) than other objects quality
     * - +1, if this @f$|\chi^2/\nu-1|@f$ is smaller than the same for other
     * - -1, if this @f$|\chi^2/\nu-1|@f$ is larger than the same for other
     * - 0 otherwise
     *
     * @param o Other object to compare to
     *
     * @return See above

     * Get the name of this object
     *
     *
     * @return

     * Calculate the quality
     *
     * @param maxChi2nu   Maximum reduced @f$\chi^2@f$
     * @param maxRelError Maximum relative error
     * @param leastWeight Least weight to use