code: def _transform(self,inplace=True):
          
          if self.istransformed:
              #raise Exception("ParameterEnsemble already transformed")
              return
  
          istransformed = self.pst.parameter_data.loc[:,"partrans"] == "log"
          if inplace:
              #self.loc[:,istransformed] = np.log10(self.loc[:,istransformed])
              self.loc[:,:] = (self.loc[:,:] * self.pst.parameter_data.scale) +\
                               self.pst.parameter_data.offset
              self.loc[:,istransformed] = self.loc[:,istransformed].applymap(lambda x: math.log10(x))
  
              self.__istransformed = True
          else:
              vals = self.pst.parameter_data.parval1.copy()
              new_en = ParameterEnsemble(pst=self.pst.get(),data=self.loc[:,:].copy(),
                                columns=self.columns,
                                mean_values=vals,istransformed=True)
              new_en.loc[:,:] = (new_en.loc[:,:] * self.pst.parameter_data.scale) +\
                               new_en.pst.parameter_data.offset
              new_en.loc[:,istransformed] = self.loc[:,istransformed].applymap(lambda x: math.log10(x))
              return new_en
docs: Private method to perform log10 transformation for ensemble
  
          Parameters
          ----------
          inplace: bool
              transform self in place
  
          Returns
          -------
          ParameterEnsemble : ParameterEnsemble
              if inplace is False
  
          Note
          ----
          Don't call this method unless you know what you are doing