code: def get_errvar_dataframe(self, singular_values=None):
          
          if singular_values is None:
              singular_values = \
                  np.arange(0, min(self.pst.nnz_obs, self.pst.npar_adj) + 1)
          if not isinstance(singular_values, list) and \
                  not isinstance(singular_values, np.ndarray):
              singular_values = [singular_values]
          results = {}
          for singular_value in singular_values:
              sv_results = self.variance_at(singular_value)
              for key, val in sv_results.items():
                  if key not in results.keys():
                      results[key] = []
                  results[key].append(val)
          return pd.DataFrame(results, index=singular_values)
docs: get a pandas dataframe of error variance results indexed
              on singular value and (prediction name,<errvar term>)
  
          Parameters
          ----------
          singular_values : list
              singular values to test.  defaults to
              range(0,min(nnz_obs,nadj_par) + 1)
  
          Returns
          -------
          pandas.DataFrame : pandas.DataFrame
              multi-indexed pandas dataframe