ensemble_hic/forward_models.py
def _evaluate_jacobi_matrix(self, structures, smooth_steepness, norm):
        """
        In theory, this evaluates the Jacobian matrix of the forward model,
        but I usually hardcode the multiplication of this with the error
        model gradient in Cython (see :module:`.likelihoods_c`)
        """        
        raise NotImplementedError("Use fast likelihood gradients in " +
                                  "likelihoods_c.pyx instead!")

        In theory, this evaluates the Jacobian matrix of the forward model,
        but I usually hardcode the multiplication of this with the error
        model gradient in Cython (see :module:`.likelihoods_c`)
        
In theory, this evaluates the Jacobian matrix of the forward model,
but I usually hardcode the multiplication of this with the error
model gradient in Cython
In theory this evaluates the Jacobian matrix of the forward model but I usually hardcode the multiplication of this with the error model gradient in Cython
def _evaluate_jacobi_matrix(self, structures, smooth_steepness, norm):
        raise NotImplementedError("Use fast likelihood gradients in " +
                                  "likelihoods_c.pyx instead!")
def _evaluate_jacobi_matrix ( self , structures , smooth_steepness , norm ) : raise NotImplementedError ( "Use fast likelihood gradients in " + "likelihoods_c.pyx instead!" )
In theory, this evaluates the Jacobian matrix of the forward model,
but I usually hardcode the multiplication of this with the error
model gradient in Cython (see :module:`.likelihoods_c`)
In theory this evaluates the Jacobian matrix of the forward model but I usually hardcode the multiplication of this with the error model gradient in Cython ( see : module : ` . likelihoods_c ` )
"""
        In theory, this evaluates the Jacobian matrix of the forward model,
        but I usually hardcode the multiplication of this with the error
        model gradient in Cython (see :module:`.likelihoods_c`)
        """
self
structures
smooth_steepness
norm