def sample_u(self, q):
        r"""Extract a sample from random variates uniform on :math:`[0, 1]`.
        
        For a univariate distribution, this is simply evaluating the inverse
        CDF. To facilitate efficient sampling, this function returns a *vector*
        of PPF values, one value for each variable. Basically, the idea is that,
        given a vector :math:`q` of `num_params` values each of which is
        distributed uniformly on :math:`[0, 1]`, this function will return
        corresponding samples for each variable.
        
        Parameters
        ----------
        q : array of float
            Values between 0 and 1 to evaluate inverse CDF at.
        """
        q = scipy.atleast_1d(q)
        if len(q) != len(self.sigma):
            raise ValueError("length of q must equal the number of parameters!")
        if q.ndim != 1:
            raise ValueError("q must be one-dimensional!")
        if (q < 0).any() or (q > 1).any():
            raise ValueError("q must be within [0, 1]!")
        return scipy.asarray([scipy.stats.lognorm.ppf(v, s, loc=0, scale=em) for v, s, em in zip(q, self.sigma, self.emu)])
def sample_u ( self , q ) : q = scipy . atleast_1d ( q ) if len ( q ) != len ( self . sigma ) : raise ValueError ( "length of q must equal the number of parameters!" ) if q . ndim != 1 : raise ValueError ( "q must be one-dimensional!" ) if ( q < 0 ) . any ( ) or ( q > 1 ) . any ( ) : raise ValueError ( "q must be within [0, 1]!" ) return scipy . asarray ( [ scipy . stats . lognorm . ppf ( v , s , loc = 0 , scale = em ) for v , s , em in zip ( q , self . sigma , self . emu ) ] )
r"""Extract a sample from random variates uniform on :math:`[0, 1]`.
        
        For a univariate distribution, this is simply evaluating the inverse
        CDF. To facilitate efficient sampling, this function returns a *vector*
        of PPF values, one value for each variable. Basically, the idea is that,
        given a vector :math:`q` of `num_params` values each of which is
        distributed uniformly on :math:`[0, 1]`, this function will return
        corresponding samples for each variable.
        
        Parameters
        ----------
        q : array of float
            Values between 0 and 1 to evaluate inverse CDF at.