docs: def fit_mle(self, data, fix_mean=False):
          """%(super)s
  
          Additional Parameters
          ----------------------
          fix_mean : bool
              Default False.  If True, fixes mean before optimizing sigma
  
          """
  
          if not fix_mean:
              sigma, _, scale = stats.lognorm.fit(data, floc=0)
              return np.log(scale), sigma
  
          else:
              mean = np.mean(data)
  
              # MLE fxn to be optmimized
              mle = lambda sigma, x, mean: -1 *\
                                      np.sum(self._pdf_w_mean(x, mean, sigma))
  
              sigma = optim.fmin(mle, np.array([np.std(np.log(data), ddof=1)]),
                                              args=(data, mean), disp=0)[0]
  
              return self.translate_args(mean, sigma)