code: def get_parameter_summary(self,include_map=False):
          
          prior_mat = self.parcov.get(self.posterior_parameter.col_names)
          if prior_mat.isdiagonal:
              prior = prior_mat.x.flatten()
          else:
              prior = np.diag(prior_mat.x)
          post = np.diag(self.posterior_parameter.x)
          if include_map:
              par_data = self.map_parameter_estimate
              prior = pd.DataFrame(data=prior,index=prior_mat.col_names)
              islog = self.pst.parameter_data.partrans == "log"
              par_data.loc[islog,:] = np.log10(par_data.loc[islog,:])
              par_data.loc[:,"prior_stdev"] = prior
              post = pd.DataFrame(data=post,index=prior.index)
              par_data.loc[:,"post_stdev"] = post
              par_data.loc[:,"is_log"] = islog
              return par_data
          else:
              ureduce = 100.0 * (1.0 - (post / prior))
  
              return pd.DataFrame({"prior_var":prior,"post_var":post,
                                       "percent_reduction":ureduce},
                                      index=self.posterior_parameter.col_names)
docs: get a summary of the parameter uncertainty
  
          Parameters
          ----------
          include_map : bool
              if True, add the prior and posterior expectations
              and report standard deviation instead of variance
  
          Returns
          -------
          pandas.DataFrame : pandas.DataFrame
              dataframe of prior,posterior variances and percent
              uncertainty reduction of each parameter
  
          Note
          ----
          this is the primary method for accessing parameter uncertainty
          estimates - use this!
  
          Example
          -------
          ``>>>import matplotlib.pyplot as plt``
  
          ``>>>import pyemu``
  
          ``>>>sc = pyemu.Schur(jco="pest.jcb")``
  
          ``>>>sc = pyemu.Schur(jco="pest.jcb",forecasts=["fore1","fore2"])``
  
          ``>>>par_sum = sc.get_parameter_summary()``
  
          ``>>>par_sum.plot(kind="bar")``
  
          ``>>>plt.show()``