code: def get_evanno_table(self, kvalues, max_var_multiple=0, quiet=False):
          
          ## do not allow bad vals
          if max_var_multiple:
              if max_var_multiple < 1:
                  raise ValueError('max_variance_multiplier must be >1')
  
          table = _get_evanno_table(self, kvalues, max_var_multiple, quiet)
          return table
docs: Calculates the Evanno table from results files for tests with 
          K-values in the input list kvalues. The values lnPK, lnPPK,
          and deltaK are calculated. The max_var_multiplier arg can be used
          to exclude results files based on variance of the likelihood as a 
          proxy for convergence. 
  
          Parameters:
          -----------
          kvalues : list
              The list of K-values for which structure was run for this object.
              e.g., kvalues = [3, 4, 5]
  
          max_var_multiple: int
              A multiplier value to use as a filter for convergence of runs. 
              Default=0=no filtering. As an example, if 10 replicates 
              were run then the variance of the run with the minimum variance is
              used as a benchmark. If other runs have a variance that is N times 
              greater then that run will be excluded. Remember, if replicate runs 
              sampled different distributions of SNPs then it is not unexpected that 
              they will have very different variances. However, you may still want 
              to exclude runs with very high variance since they likely have 
              not converged. 
  
          quiet: bool
              Suppresses printed messages about convergence.
  
          Returns:
          --------
          table : pandas.DataFrame
              A data frame with LPK, LNPPK, and delta K. The latter is typically
              used to find the best fitting value of K. But be wary of over
              interpreting a single best K value.