def __call__(self, theta, hyper_deriv=None):
        """Evaluate the prior log-PDF at the given values of the hyperparameters, theta.
        
        The log-PDFs of the two priors are summed.
        
        Parameters
        ----------
        theta : array-like, (`num_params`,)
            The hyperparameters to evaluate the log-PDF at.
        """
        p1_num_params = len(self.p1.bounds)
        if hyper_deriv is not None:
            if hyper_deriv < p1_num_params:
                return self.p1(theta[:p1_num_params], hyper_deriv=hyper_deriv)
            else:
                return self.p2(theta[p1_num_params:], hyper_deriv=hyper_deriv - p1_num_params)
        return self.p1(theta[:p1_num_params]) + self.p2(theta[p1_num_params:])
def __call__ ( self , theta , hyper_deriv = None ) : p1_num_params = len ( self . p1 . bounds ) if hyper_deriv is not None : if hyper_deriv < p1_num_params : return self . p1 ( theta [ : p1_num_params ] , hyper_deriv = hyper_deriv ) else : return self . p2 ( theta [ p1_num_params : ] , hyper_deriv = hyper_deriv - p1_num_params ) return self . p1 ( theta [ : p1_num_params ] ) + self . p2 ( theta [ p1_num_params : ] )
Evaluate the prior log-PDF at the given values of the hyperparameters, theta.
        
        The log-PDFs of the two priors are summed.
        
        Parameters
        ----------
        theta : array-like, (`num_params`,)
            The hyperparameters to evaluate the log-PDF at.