PsiMarginal.py 文件源码

def __genprior(self, x, distr='uniform', mu=0, sig=1):
        """Generate prior probability distribution for variable.

        Arguments
        ---------
            x   :  1D numpy array (float64)
                    points to evaluate the density at.

            distr :  string
                    Distribution to use a prior :
                        'uniform'   (default) discrete uniform distribution

                        'normal'   normal distribution

                        'gamma'    gamma distribution

                        'beta'     beta distribution

            mu :  scalar float
                first parameter of distr distribution (check scipy for parameterization)

            sig : scalar float
                second parameter of distr distribution

        Returns
        -------
        1D numpy array of prior probabilities (unnormalized)
        """
        if distr == 'uniform':
            nx = len(x)
            p = np.ones(nx) / nx
        elif distr == 'normal':
            p = norm.pdf(x, mu, sig)
        elif distr == 'beta':
            p = beta.pdf(x, mu, sig)
        elif distr == 'gamma':
            p = gamma.pdf(x, mu, scale=sig)
        else:
            nx = len(x)
            p = np.ones(nx) / nx
        return p