def test_direct_bindm1(self):
numpy.random.seed(1)
nao = mol.nao_nr(cart=True)
dm = numpy.random.random((nao,nao))
vhfopt = _vhf.VHFOpt(mol, 'int2e_cart', 'CVHFnrs8_prescreen',
'CVHFsetnr_direct_scf',
'CVHFsetnr_direct_scf_dm')
vj0, vk0 = _vhf.direct(dm, mol._atm, mol._bas, mol._env,
vhfopt=vhfopt, hermi=0, cart=True)
vj = _vhf.direct_bindm('int2e_cart', 's1', 'kl->s1ij', dm, 1,
mol._atm, mol._bas, mol._env, vhfopt)
self.assertTrue(numpy.allclose(vj0,vj))
def get_jk(self,
dm,
hermi=1,
vhfopt=None,
with_j=True,
with_k=True,
direct_scf_tol=getattr(__config__, 'scf_hf_SCF_direct_scf_tol',
1e-13),
omega=None):
if omega is not None:
# A temporary treatment for RSH integrals
key = '%.6f' % omega
if key in self._rsh_df:
rsh_df = self._rsh_df[key]
else:
rsh_df = copy.copy(self)
rsh_df._rsh_df = None # to avoid circular reference
# Not all attributes need to be reset. Resetting _vjopt
# because it is used by get_j method of regular DF object.
rsh_df._vjopt = None
self._rsh_df[key] = rsh_df
logger.info(self, 'Create RSH-SGX object %s for omega=%s',
rsh_df, omega)
with rsh_df.mol.with_range_coulomb(omega):
return rsh_df.get_jk(dm, hermi, with_j, with_k, direct_scf_tol)
if with_j and self.dfj:
vj = df_jk.get_j(self, dm, hermi, direct_scf_tol)
if with_k:
vk = sgx_jk.get_jk(self, dm, hermi, False, with_k,
direct_scf_tol)[1]
else:
vk = None
elif with_j and self.direct_j:
vj, _ = _vhf.direct(dm, self.mol._atm, self.mol._bas,
self.mol._env, vhfopt, hermi, self.mol.cart,
True, False)
if with_k:
vk = sgx_jk.get_jk(self, dm, hermi, False, with_k,
direct_scf_tol)[1]
else:
vk = None
else:
vj, vk = sgx_jk.get_jk(self, dm, hermi, with_j, with_k,
direct_scf_tol)
return vj, vk
def get_jk(mol, dm, hermi=1, vhfopt=None):
'''Compute J, K matrices for the given density matrix
Args:
mol : an instance of :class:`Mole`
dm : ndarray or list of ndarrays
A density matrix or a list of density matrices
Kwargs:
hermi : int
Whether J, K matrix is hermitian
| 0 : no hermitian or symmetric
| 1 : hermitian
| 2 : anti-hermitian
vhfopt :
A class which holds precomputed quantities to optimize the
computation of J, K matrices
Returns:
Depending on the given dm, the function returns one J and one K matrix,
or a list of J matrices and a list of K matrices, corresponding to the
input density matrices.
Examples:
>>> from pyscf import gto, scf
>>> from pyscf.scf import _vhf
>>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1')
>>> dms = numpy.random.random((3,mol.nao_nr(),mol.nao_nr()))
>>> j, k = scf.hf.get_jk(mol, dms, hermi=0)
>>> print(j.shape)
(3, 2, 2)
'''
dm = numpy.asarray(dm, order='C')
nao = dm.shape[-1]
vj, vk = _vhf.direct(dm.reshape(-1, nao, nao),
mol._atm,
mol._bas,
mol._env,
vhfopt=vhfopt,
hermi=hermi,
cart=mol.cart)
return vj.reshape(dm.shape), vk.reshape(dm.shape)
def get_jk(mol, dm, hermi=1, vhfopt=None):
'''Compute J, K matrices for the given density matrix
Args:
mol : an instance of :class:`Mole`
dm : ndarray or list of ndarrays
A density matrix or a list of density matrices
Kwargs:
hermi : int
Whether J, K matrix is hermitian
| 0 : no hermitian or symmetric
| 1 : hermitian
| 2 : anti-hermitian
vhfopt :
A class which holds precomputed quantities to optimize the
computation of J, K matrices
Returns:
Depending on the given dm, the function returns one J and one K matrix,
or a list of J matrices and a list of K matrices, corresponding to the
input density matrices.
Examples:
>>> from pyscf import gto, scf
>>> from pyscf.scf import _vhf
>>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1')
>>> dms = numpy.random.random((3,mol.nao_nr(),mol.nao_nr()))
>>> j, k = scf.hf.get_jk(mol, dms, hermi=0)
>>> print(j.shape)
(3, 2, 2)
'''
dm = numpy.asarray(dm, order='C')
nao = dm.shape[-1]
vj, vk = _vhf.direct(dm.reshape(-1,nao,nao), mol._atm, mol._bas, mol._env,
vhfopt=vhfopt, hermi=hermi)
return vj.reshape(dm.shape), vk.reshape(dm.shape)