def phi_vector(self):
        """property decorated method to get a vector of L2 norm (phi)
        for the realizations.  The ObservationEnsemble.pst.weights can be
        updated prior to calling this method to evaluate new weighting strategies

        Return
        ------
        pandas.DataFrame : pandas.DataFrame

        """
        weights = self.pst.observation_data.loc[self.names,"weight"]
        obsval = self.pst.observation_data.loc[self.names,"obsval"]
        phi_vec = []
        for idx in self.index.values:
            simval = self.loc[idx,self.names]
            phi = (((simval - obsval) * weights)**2).sum()
            phi_vec.append(phi)
        #return pd.DataFrame({"phi":phi_vec},index=self.index)
        return pd.Series(data=phi_vec,index=self.index)
def phi_vector ( self ) : weights = self . pst . observation_data . loc [ self . names , "weight" ] obsval = self . pst . observation_data . loc [ self . names , "obsval" ] phi_vec = [ ] for idx in self . index . values : simval = self . loc [ idx , self . names ] phi = ( ( ( simval - obsval ) * weights ) ** 2 ) . sum ( ) phi_vec . append ( phi ) return pd . Series ( data = phi_vec , index = self . index )
property decorated method to get a vector of L2 norm (phi)
        for the realizations.  The ObservationEnsemble.pst.weights can be
        updated prior to calling this method to evaluate new weighting strategies

        Return
        ------
        pandas.DataFrame : pandas.DataFrame