Commit 42a0c86a authored by Michele Maris's avatar Michele Maris
Browse files

u

parent ecf91fb6

src/yapsut/montecarlo_fitting.py

+28 −3
Original line number Diff line number Diff line
@@ -17,6 +17,10 @@ class EnsembleFitting_Base : 
        self._initial_values=this

    #

    @property

    def npar(self) :

        return self._npar

    #

    @property

    def param_names(self) :

        return self._param_names

    #
@@ -27,6 +31,7 @@ class EnsembleFitting_Base : 
    @Model.setter

    def Model(self,Model,param_names) :

        self._param_names=param_names

        self._npar=len(param_names)

        self._Model=this

    #

    @property
@@ -73,6 +78,7 @@ class EnsembleFitting_Base : 
        self._success=False

        self._fit=None

        self._fitting_kargs={}

        self._npar=0

    #

    @property

    def fitting_kargs(self) :
@@ -145,13 +151,32 @@ class EnsembleFitting_Base : 
        return out

    #

    @property

    def montecarlo_ensemble_averages(self) :

        """returns the ensemble averaged values from the montecarlo simulation"""

    def montecarlo_ensemble_averages(self,useMedian=True) :

        """returns the ensemble averaged values from the montecarlo simulation

        :keywords: 

           useMedian == True (default) uses np.median() to extract the column average, otherwise uses np.mean()

           

        """

        try :

            return np.array([np.median(k) for k in self._mc.T])

           if useMedian == True :

              return np.array([np.median(k) for k in self._mc.T])

           else :

              return np.array([np.mean(k) for k in self._mc.T])

        except :

            return np.ones(len(self._param_names))+np.nan

    #

    @property

    def montecarlo_ensemble_std(self,useMedian=True) :

        """returns the ensemble covariance matrix from the montecarlo simulation"""

        out = self._mc.std(axis=0)

        return out

    #

    @property

    def montecarlo_ensemble_covariance(self) :

        """returns the ensemble covariance matrix from the montecarlo simulation"""

        out = np.cov(self._mc.T)

        return out 

    #

    def montecarlo_cdf(self) :

        """returns a dictionary with the cdf for the montecarlo simulations.

        CDF are instatiations of .stats/CumulativeOfData so they are interpolating functions