code: def map_forecast_estimate(self):
          
          assert self.forecasts is not None
          islog = self.pst.parameter_data.partrans == "log"
          par_map = self.map_parameter_estimate
          par_map.loc[islog,:] = np.log10(par_map.loc[islog,:])
          par_map = Matrix.from_dataframe(par_map.loc[:,["post_expt"]])
          posts,priors = [],[]
          post_expt = (self.predictions.T * par_map).to_dataframe()
          for fname in self.forecast_names:
              #fname = forecast.col_names[0]
              pr = self.pst.res.loc[fname,"modelled"]
              priors.append(pr)
              posts.append(pr + post_expt.loc[fname,"post_expt"])
          return pd.DataFrame(data=np.array([priors,posts]).transpose(),
                              columns=["prior_expt","post_expt"],
                              index=self.forecast_names)
docs: get the prior and posterior forecast (prediction) expectations.
  
          Returns
          -------
          pandas.DataFrame : pandas.DataFrame
              dataframe with prior and posterior forecast expected values