@inherit_docstring_from(rv_continuous_meco)<EOL><INDENT>def fit_mle(self, data, fix_mean=False):<DEDENT>
if not fix_mean:<EOL><INDENT>sigma, _, scale = stats.lognorm.fit(data, floc=<NUM_LIT:0>)<EOL>return np.log(scale), sigma<EOL><DEDENT>else:<EOL><INDENT>mean = np.mean(data)<EOL>mle = lambda sigma, x, mean: -<NUM_LIT:1> *np.sum(self._pdf_w_mean(x, mean, sigma))<EOL>sigma = optim.fmin(mle, np.array([np.std(np.log(data), ddof=<NUM_LIT:1>)]),<EOL>args=(data, mean), disp=<NUM_LIT:0>)[<NUM_LIT:0>]<EOL>return self.translate_args(mean, sigma)<EOL><DEDENT>
%(super)s

        Additional Parameters
        ----------------------
        fix_mean : bool
            Default False.  If True, fixes mean before optimizing sigma