def fit_mle(self, data, b=None):
        """%(super)s
        In addition to data, requires ``b``, the upper limit of the
        distribution.
        """
        # Take mean of data as MLE of distribution mean, then calculate p
        mu = np.mean(data)
        if not b:
            b = np.sum(data)
        p = _geom_solve_p_from_mu_vect(mu, b)

        # Just return float, not len 1 array
        if len(np.atleast_1d(p)) == 1:
            return float(p), b
        else:
            return p, b
def fit_mle ( self , data , b = None ) : mu = np . mean ( data ) if not b : b = np . sum ( data ) p = _geom_solve_p_from_mu_vect ( mu , b ) if len ( np . atleast_1d ( p ) ) == 1 : return float ( p ) , b else : return p , b
%(super)s
        In addition to data, requires ``b``, the upper limit of the
        distribution.