class RooStats::AsymptoticCalculator: public RooStats::HypoTestCalculatorGeneric

 set print level (static function)
 0 minimal, 1 normal,  2 debug

 constructor for asymptotic calculator from Data set  and ModelConfig

 Initialize the calculator
 The initialization will perform a global fit of the model to the data
 and build an Asimov data set.
 It will then also fit the model to the Asimov data set to find the likelihood value
 of the Asimov data set
 nominalAsimov is an option for using Asimov data set obtained using nominal nuisance parameter values
 By default the nuisance parameters are fitted to the data
 NOTE: If a fit has been done before, one for speeding up could set all the initial prameters
 to the fit value and in addition set the null snapshot to the best fit

 It performs an hypothesis tests using the likelihood function
 and computes the p values for the null and the alternate using the asymptotic
 formulae for the profile likelihood ratio.
 See G. Cowan, K. Cranmer, E. Gross and O. Vitells.
 Asymptotic formulae for likelihood- based tests of new physics. Eur. Phys. J., C71:1–19, 2011.
 The formulae are valid only for one POI. If more than one POI exists consider as POI only the
 first one

 function given the null and the alt p value - return the expected one given the N - sigma value

 fill bins by looping recursivly on observables

 iterate a Prod pdf to find all the Poisson or Gaussian part to set the observed value to expected one

 set observed value to the expected one
 works for Gaussian, Poisson or LogNormal
 assumes mean parameter value is the argument not constant and not depoending on observables
 (if more than two arguments are not constant will use first one but printr a warning !)
 need to iterate on the components of the POisson to get n and nu (nu can be a RooAbsReal)
 (code from G. Petrucciani and extended by L.M.)

 generate counting Asimov data for the case when the pdf cannot be extended
 assume pdf is a RooPoisson or can be decomposed in a product of RooPoisson,
 otherwise we cannot know how to make the Asimov data sets in the other cases

 compute the asimov data set for an observable of a pdf
 use the number of bins sets in the observables
 to do :  (possibility to change number of bins)
 implement integration over bin content

 generate the asimov data for the observables (not the global ones)
 need to deal with the case of a sim pdf

 static function to the an Asimov data set
 given an observed dat set, a model and a snapshot of poi.
 Return the asimov data set + global observables set to values satisfying the constraints

 static function to the an Asimov data set
 given the model and the values of all parameters including the nuisance
 Return the asimov data set + global observables set to values satisfying the constraints

    HypoTestCalculatorGeneric(data, altModel, nullModel, 0)
 {
 }

 set test statistic for one sided (upper limits)

 set the test statistics for two sided (in case of upper limits
 for discovery does not make really sense)

 set the test statistics for one-sided discovery

 re-implement setters since they needs to re-initialize the calculator

 set using of qtilde, by default is controlled if RoORealVar is limited or not

 return snapshot of the best fit parameter

 return best fit parameter (firs of poi)

 return best fit value for all parameters