def __init__(self, mf, frozen=None):
self.mol = mf.mol
self._scf = mf
self.verbose = self.mol.verbose
self.stdout = self.mol.stdout
self.max_memory = mf.max_memory
self.frozen = frozen
#TODO: implement frozen orbs
if not (self.frozen is None or self.frozen == 0):
raise NotImplementedError
# DF-GW must use density fitting integrals
if getattr(mf, 'with_df', None):
self.with_df = mf.with_df
else:
self.with_df = df.DF(mf.mol)
self.with_df.auxbasis = df.make_auxbasis(mf.mol, mp2fit=True)
self._keys.update(['with_df'])
##################################################
# don't modify the following attributes, they are not input options
self._nocc = None
self._nmo = None
# self.mo_energy: GW quasiparticle energy, not scf mo_energy
self.mo_energy = None
self.mo_coeff = mf.mo_coeff
self.mo_occ = mf.mo_occ
self.sigma = None
keys = set(('eta', 'linearized'))
self._keys = set(self.__dict__.keys()).union(keys)
def __init__(self, mf, frozen=None, auxbasis=None):
self.mol = mf.mol
self._scf = mf
self.verbose = self.mol.verbose
self.stdout = self.mol.stdout
self.max_memory = mf.max_memory
self.frozen = frozen
# DF-RPA must use density fitting integrals
if getattr(mf, 'with_df', None):
self.with_df = mf.with_df
else:
self.with_df = df.DF(mf.mol)
if auxbasis:
self.with_df.auxbasis = auxbasis
else:
self.with_df.auxbasis = df.make_auxbasis(mf.mol, mp2fit=True)
self._keys.update(['with_df'])
##################################################
# don't modify the following attributes, they are not input options
self._nocc = None
self._nmo = None
self.mo_energy = mf.mo_energy
self.mo_coeff = mf.mo_coeff
self.mo_occ = mf.mo_occ
self.e_corr = None
self.e_hf = None
self.e_tot = None
def __init__(self, mf, frozen=None, mo_coeff=None, mo_occ=None):
mp2.MP2.__init__(self, mf, frozen, mo_coeff, mo_occ)
if getattr(mf, 'with_df', None):
self.with_df = mf.with_df
else:
self.with_df = df.DF(mf.mol)
self.with_df.auxbasis = df.make_auxbasis(mf.mol, mp2fit=True)
self._keys.update(['with_df'])
def __init__(self, mf, frozen=0, mo_coeff=None, mo_occ=None):
mp2.MP2.__init__(self, mf, frozen, mo_coeff, mo_occ)
if getattr(mf, 'with_df', None):
self.with_df = mf.with_df
else:
self.with_df = df.DF(mf.mol)
self.with_df.auxbasis = df.make_auxbasis(mf.mol, mp2fit=True)
self._keys.update(['with_df'])
def __init__(self, mf, frozen=0, mo_coeff=None, mo_occ=None):
ccsd.CCSD.__init__(self, mf, frozen, mo_coeff, mo_occ)
if hasattr(mf, 'with_df') and mf.with_df:
self.with_df = mf.with_df
else:
self.with_df = df.DF(mf.mol)
self.with_df.auxbasis = df.make_auxbasis(mf.mol, mp2fit=True)
self._keys.update(['with_df'])
def __init__(self, mf, frozen=None, auxbasis=None):
'''
Args:
mf : RHF instance
frozen : number of frozen orbitals or list of frozen orbitals
auxbasis : name of auxiliary basis set, otherwise determined automatically
'''
if not isinstance(mf, scf.rhf.RHF):
raise TypeError('Class initialization with non-RHF object')
self.mo_coeff = mf.mo_coeff
self.mo_energy = mf.mo_energy
self.nocc = np.count_nonzero(mf.mo_occ)
self.nmo = self.mo_coeff.shape[1]
self.e_scf = mf.e_tot
self._scf = mf
# Process the frozen core option correctly as an integer or a list.
# self.frozen_mask sets a flag for each orbital if it is frozen (True) or not (False).
# Only occupied orbitals can be frozen.
self.frozen_mask = np.zeros(self.nmo, dtype=bool)
if frozen is None:
pass
elif lib.isinteger(frozen):
if frozen > self.nocc:
raise ValueError('only occupied orbitals can be frozen')
self.frozen_mask[:frozen] = True
elif lib.isintsequence(frozen):
if max(frozen) > self.nocc - 1:
raise ValueError('only occupied orbitals can be frozen')
self.frozen_mask[frozen] = True
else:
raise TypeError('frozen must be an integer or a list of integers')
# mask for occupied orbitals that are not frozen
self.occ_mask = np.zeros(self.nmo, dtype=bool)
self.occ_mask[:self.nocc] = True
self.occ_mask[self.frozen_mask] = False
self.mol = mf.mol
if not auxbasis:
auxbasis = df.make_auxbasis(self.mol, mp2fit=True)
self.auxmol = df.make_auxmol(self.mol, auxbasis)
self.verbose = self.mol.verbose
self.stdout = self.mol.stdout
self.max_memory = self.mol.max_memory
self._intsfile = None
self.e_corr = None
# Spin component scaling factors
self.ps = 1.0
self.pt = 1.0
self.cphf_max_cycle = 100
self.cphf_tol = mf.conv_tol
def __init__(self, mf, frozen=None, mo_energy=None, mo_coeff=None, mo_occ=None):
uagf2.UAGF2.__init__(self, mf, frozen=frozen, mo_energy=mo_energy,
mo_coeff=mo_coeff, mo_occ=mo_occ)
if getattr(mf, 'with_df', None) is not None:
self.with_df = mf.with_df
else:
self.with_df = df.DF(mf.mol)
self.with_df.auxbasis = df.make_auxbasis(mf.mol, mp2fit=True)
self.allow_lowmem_build = True
self._keys.update(['_with_df', 'allow_lowmem_build'])
def __init__(self, mf):
self.mol = mf.mol
self.verbose = self.mol.verbose
self.stdout = self.mol.stdout
self.max_memory = mf.max_memory
self._scf = mf
if hasattr(mf, 'with_df') and mf.with_df:
self.with_df = None
else:
self.with_df = df.DF(mol)
self.with_df.auxbasis = df.make_auxbasis(mol, mp2fit=True)
self.emp2 = None
self.t2 = None
mol.verbose = 0
mol.atom = [[8, (0., 0., 0.)], [1, (0., -0.757, 0.587)],
[1, (0., 0.757, 0.587)]]
mol.basis = 'cc-pvdz'
mol.build()
mf = scf.RHF(mol).run()
pt = DFMP2(mf)
emp2, t2 = pt.kernel()
print(emp2 - -0.204004830285)
pt.with_df = df.DF(mol)
pt.with_df.auxbasis = 'weigend'
emp2, t2 = pt.kernel()
print(emp2 - -0.204254500453)
mf = scf.density_fit(scf.RHF(mol), 'weigend')
mf.kernel()
pt = DFMP2(mf)
emp2, t2 = pt.kernel()
print(emp2 - -0.203986171133)
pt.with_df = df.DF(mol)
pt.with_df.auxbasis = df.make_auxbasis(mol, mp2fit=True)
emp2, t2 = pt.kernel()
print(emp2 - -0.203738031827)
pt.frozen = 2
emp2, t2 = pt.kernel()
print(emp2 - -0.14433975122418313)
[8 , (0. , 0. , 0.)],
[1 , (0. , -0.757 , 0.587)],
[1 , (0. , 0.757 , 0.587)]]
mol.basis = 'cc-pvdz'
mol.build()
mf = scf.RHF(mol).run()
pt = DFMP2(mf)
emp2, t2 = pt.kernel()
print(emp2 - -0.204004830285)
pt.with_df = df.DF(mol)
pt.with_df.auxbasis = 'weigend'
emp2, t2 = pt.kernel()
print(emp2 - -0.204254500453)
mf = scf.density_fit(scf.RHF(mol), 'weigend')
mf.kernel()
pt = DFMP2(mf)
emp2, t2 = pt.kernel()
print(emp2 - -0.203986171133)
pt.with_df = df.DF(mol)
pt.with_df.auxbasis = df.make_auxbasis(mol, mp2fit=True)
emp2, t2 = pt.kernel()
print(emp2 - -0.203738031827)
pt.frozen = 2
emp2, t2 = pt.kernel()
print(emp2 - -0.14433975122418313)
# future use, specify the filename in the attribute mf.with_df._cderi_to_save
#
mf = scf.RHF(mol).density_fit()
mf.with_df._cderi_to_save = 'saved_cderi.h5'
mf.kernel()
#
# To load the precomputed CD tensors in another calculation.
# See also example/df/40-precompute_df_integrals.py
#
# mf = scf.RHF(mol).density_fit()
# mf.with_df._cderi = '/path/to/saved/tensor/file'
# mf.kernel()
#
# _cderi can be generated in the DF object without the DF-SCF calculations
#
mydf = df.DF(mol)
mydf.auxbasis = df.make_auxbasis(mol)
mydf._cderi_to_save = 'saved_cderi.h5'
mydf.build()
#
# DF integral tensor can also be generated through the call to cholesky_eri
# function
#
cderi = df.incore.cholesky_eri(mol, auxbasis='weigend')
df.outcore.cholesky_eri(mol, 'saved_cderi.h5', dataname='j3c',
auxbasis=df.make_auxbasis(mol))
from pyscf import gto, scf, df
#
# If auxbasis is not specified, default optimal auxiliary basis (if possible)
# or even-tempered gaussian functions will be generated as auxbasis
#
mol = gto.M(atom='N1 0 0 0; N2 0 0 1.2', basis={'N1':'ccpvdz', 'N2':'tzp'})
mf = scf.RHF(mol).density_fit()
mf.kernel()
print('Default auxbasis', mf.with_df.auxmol.basis)
#
# The default basis is generated in the function df.make_auxbasis. It returns
# a basis dict for the DF auxiliary basis. In the real calculations, you can
# first generate the default basis then make modification.
#
auxbasis = df.make_auxbasis(mol)
print(mf.with_df.auxmol.basis == auxbasis)
auxbasis['N2'] = 'ccpvdz jkfit'
mf = scf.RHF(mol).density_fit(auxbasis=auxbasis)
mf.kernel()
#
# Input with key argument auxbasis='xxx' in .density_fit function
# This auxbasis will be used for all elements in the system.
#
mol = gto.M(atom='N1 0 0 0; N2 0 0 1.2', basis='ccpvdz')
mf = scf.RHF(mol).density_fit(auxbasis='weigend')
mf.kernel()
#
# The DF basis can be assigned to with_df.auxbasis attribute.
from pyscf import gto, scf, df
#
# If auxbasis is not specified, default optimal auxiliary basis (if possible)
# or even-tempered gaussian functions will be generated as auxbasis
#
mol = gto.M(atom='N1 0 0 0; N2 0 0 1.2', basis={'N1': 'ccpvdz', 'N2': 'tzp'})
mf = scf.RHF(mol).density_fit()
mf.kernel()
print('Default auxbasis', mf.with_df.auxmol.basis)
#
# The default basis is generated in the function df.make_auxbasis. It returns
# a basis dict for the DF auxiliary basis. In the real calculations, you can
# first generate the default basis then make modification.
#
auxbasis = df.make_auxbasis(mol)
print(mf.with_df.auxmol.basis == auxbasis)
auxbasis['N2'] = 'ccpvdz jkfit'
mf = scf.RHF(mol).density_fit(auxbasis=auxbasis)
mf.kernel()
#
# Input with key argument auxbasis='xxx' in .density_fit function
# This auxbasis will be used for all elements in the system.
#
mol = gto.M(atom='N1 0 0 0; N2 0 0 1.2', basis='ccpvdz')
mf = scf.RHF(mol).density_fit(auxbasis='weigend')
mf.kernel()
#
# The DF basis can be assigned to with_df.auxbasis attribute.
def __init__(self, mf, frozen=None, auxbasis=None):
'''
Args:
mf : UHF instance
frozen : number of frozen orbitals or list of frozen orbitals
auxbasis : name of auxiliary basis set, otherwise determined automatically
'''
if not isinstance(mf, scf.uhf.UHF):
raise TypeError('Class initialization with non-UHF object')
# UHF quantities are stored as numpy arrays
self.mo_coeff = np.array(mf.mo_coeff)
self.mo_energy = np.array(mf.mo_energy)
self.nocc = np.array(
[np.count_nonzero(mf.mo_occ[0]),
np.count_nonzero(mf.mo_occ[1])])
# UHF MO coefficient matrix shape: (2, number of AOs, number of MOs)
self.nmo = self.mo_coeff.shape[2]
self.e_scf = mf.e_tot
self._scf = mf
# Process the frozen core option correctly as either an integer or two lists (alpha, beta).
# self.frozen_mask sets a flag for each orbital if it is frozen (True) or not (False).
# Only occupied orbitals can be frozen.
self.frozen_mask = np.zeros((2, self.nmo), dtype=bool)
if frozen is None:
pass
elif lib.isinteger(frozen):
if frozen > min(self.nocc):
raise ValueError('only occupied orbitals can be frozen')
self.frozen_mask[:, :frozen] = True
else:
try:
if len(frozen) != 2:
raise ValueError
for s in 0, 1:
if not lib.isintsequence(frozen[s]):
raise TypeError
except (TypeError, ValueError):
raise TypeError(
'frozen must be an integer or two integer lists')
if len(frozen[0]) != len(frozen[1]):
raise ValueError('frozen orbital lists not of equal length')
for s in 0, 1:
self.frozen_mask[s, frozen[s]] = True
# mask for occupied orbitals that are not frozen
self.occ_mask = np.zeros((2, self.nmo), dtype=bool)
for s in 0, 1:
self.occ_mask[s, :self.nocc[s]] = True
self.occ_mask[self.frozen_mask] = False
self.mol = mf.mol
if not auxbasis:
auxbasis = df.make_auxbasis(self.mol, mp2fit=True)
self.auxmol = df.make_auxmol(self.mol, auxbasis)
self.verbose = self.mol.verbose
self.stdout = self.mol.stdout
self.max_memory = self.mol.max_memory
# _intsfile will be a list with two elements for the alpha and beta integrals
self._intsfile = []
self.e_corr = None
# Spin component scaling factors
self.ps = 1.0
self.pt = 1.0
self.cphf_max_cycle = 100
self.cphf_tol = mf.conv_tol