code: 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.DataFrame({"phi":phi_vec},index=self.index)
          return pd.Series(data=phi_vec,index=self.index)
docs: 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