def _core_jk_build(orbital_basis: core.BasisSet, aux: core.BasisSet = None, jk_type: str = None, do_wK: bool = None, memory: int = None) -> core.JK:
"""
Constructs a Psi4 JK object from an input basis.
Parameters
----------
orbital_basis
Orbital basis to use in the JK object.
aux
Optional auxiliary basis set for density-fitted tensors. Defaults
to the DF_BASIS_SCF if set, otherwise the correspond JKFIT basis
to the passed in `orbital_basis`.
jk_type
Type of JK object to build (DF, Direct, PK, etc). Defaults to the
current global SCF_TYPE option.
Returns
-------
JK
Uninitialized JK object.
Example
-------
jk = psi4.core.JK.build(bas)
jk.set_memory(int(5e8)) # 4GB of memory
jk.initialize()
...
jk.C_left_add(matirx)
jk.compute()
jk.C_clear()
...
"""
optstash = optproc.OptionsState(["SCF_TYPE"])
if jk_type is not None:
core.set_global_option("SCF_TYPE", jk_type)
if aux is None:
if core.get_global_option("SCF_TYPE") == "DF":
aux = core.BasisSet.build(orbital_basis.molecule(), "DF_BASIS_SCF", core.get_option("SCF", "DF_BASIS_SCF"),
"JKFIT", orbital_basis.name(), orbital_basis.has_puream())
else:
aux = core.BasisSet.zero_ao_basis_set()
if (do_wK is None) or (memory is None):
jk = core.JK.build_JK(orbital_basis, aux)
else:
jk = core.JK.build_JK(orbital_basis, aux, bool(do_wK), int(memory))
optstash.restore()
return jk
def __init__(self, scf, df_basis_name=''):
self.df_basis_name = df_basis_name
self.nocc, self.ntot, self.e, self.gao, self.E_scf, self.molecule = scf.nocc, scf.ntot, scf.e, scf.g, scf.E_scf, scf.molecule
if self.df_basis_name:
df_basis = BasisSet.build(self.molecule, 'DF_BASIS_MP2', df_basis_name, puream=0)
self.gmo = transform_integrals_df(scf.C, scf.basis, df_basis, bool(scf.spin))
else:
if scf.spin != 0 and len(scf.H) == len(self.gao):
self.gao = block_tei(self.gao)
self.gmo = transform_integrals(scf.C, self.gao)
def modeVectorsQCDB(mol, hessian, modesToReturn=None, print_lvl=1, pr=print):
Natom = mol.natom()
geom = mol.geometry().to_array()
masses = np.asarray([mol.mass(at) for at in range(Natom)])
atom_symbols = [mol.symbol(at) for at in range(Natom)]
pr("\n\tCalculating normal modes; Input coordinates (bohr)\n")
for i in range(Natom):
pr("\t{:5s}{:10.5f}{:15.10f}{:15.10f}{:15.10f}\n".format(
atom_symbols[i],masses[i],geom[i,0],geom[i,1],geom[i,2]))
basis = BasisSet.build(mol) # dummy bs to lookup symmetry info
irrep_lbls = mol.irrep_labels() # labels of IRREPs if molecule has symmetry
dipder = None # unneeded dipole-moment derivatives unless we add intensities
vibinfo, vibtext = qcdb.vib.harmonic_analysis(hessian, geom, masses, basis,
irrep_lbls, dipder, project_trans=True, project_rot=True)
# pr(vibtext)
freqs = vibinfo['omega'].data.real
# modes from vibinfo are unmass-weighted, not normalized, returned as cols
# in amu^-1/2 ; convert here to au^-1/2
modes = sqrt(pc_au2amu) * vibinfo['w'].data.T
# Resort from high to low freqs
modes[:] = np.flip(modes, axis=0)
freqs[:] = np.flip(freqs)
pr("\n\t----------------------\n")
pr("\t Mode # Harmonic \n")
pr("\t Freq. (cm-1) \n")
pr("\t------------------------\n")
for i in range(0,3*Natom):
if freqs[i] < 0.0:
pr("\t %3d %10.2fi\n" % (i, abs(freqs[i])))
else:
pr("\t %3d %10.2f \n" % (i, freqs[i]))
selected_modes = np.zeros( (len(modesToReturn),3*Natom))
selected_freqs = np.zeros( len(modesToReturn) )
for i, mode in enumerate(modesToReturn):
selected_modes[i,:] = modes[mode,:]
selected_freqs[i] = freqs[mode]
pr("\nFrequencies (with modes) returned from modeVectorsPsi4.\n")
pr(str(selected_freqs)+'\n')
if print_lvl > 1:
pr("\nVectors returned from modeVectorsPsi4 are rows.\n")
pr(str(selected_modes)+'\n')
return (selected_modes, selected_freqs)
def eri(bs1, bs2, bs3=0, bs4=0):
"""
Electron repulsion integral helper function
Automatically forms any needed zero basis and squezes the result
"""
zero = BasisSet.zero_ao_basis_set()
mints = MintsHelper(bs1)
if not bs3:
res = mints.ao_eri(bs1, zero, bs2, zero)
elif not bs4:
res = mints.ao_eri(bs1, bs2, bs3, zero)
return np.squeeze(res)
def __init__(self, scf, df_basis_name=''):
self.df_basis_name = df_basis_name
self.nocc, self.ntot, self.e, self.gao, self.E_scf, self.molecule = scf.nocc, scf.ntot, scf.e, scf.g, scf.E_scf, scf.molecule
if self.df_basis_name:
df_basis = BasisSet.build(self.molecule,
'DF_BASIS_MP2',
df_basis_name,
puream=0)
self.gmo = transform_integrals_df(scf.C, scf.basis, df_basis,
bool(scf.spin))
else:
if scf.spin != 0 and len(scf.H) == len(self.gao):
self.gao = block_tei(self.gao)
self.gmo = transform_integrals(scf.C, self.gao)