def gradient_write(wfn: core.Wavefunction):
if core.get_option('FINDIF', 'GRADIENT_WRITE'):
filename = core.get_writer_file_prefix(wfn.molecule().name()) + ".grad"
qcdb.gradparse.to_string(np.asarray(wfn.gradient()),
filename,
dtype='GRD',
mol=wfn.molecule(),
energy=wfn.energy())
def _core_wavefunction_to_file(wfn: core.Wavefunction,
filename: str = None) -> Dict:
"""Converts a Wavefunction object to a base class
Parameters
----------
wfn
A Wavefunction or inherited class
filename
An optional filename to write the data to
Returns
-------
dict
A dictionary and NumPy representation of the Wavefunction.
"""
# collect the wavefunction's variables in a dictionary indexed by varaible type
# some of the data types have to be made numpy-friendly first
if wfn.basisset().name().startswith("anonymous"):
raise ValidationError(
"Cannot serialize wavefunction with custom basissets.")
wfn_data = {
'molecule': wfn.molecule().to_dict(),
'matrix': {
'Ca': wfn.Ca().to_array() if wfn.Ca() else None,
'Cb': wfn.Cb().to_array() if wfn.Cb() else None,
'Da': wfn.Da().to_array() if wfn.Da() else None,
'Db': wfn.Db().to_array() if wfn.Db() else None,
'Fa': wfn.Fa().to_array() if wfn.Fa() else None,
'Fb': wfn.Fb().to_array() if wfn.Fb() else None,
'H': wfn.H().to_array() if wfn.H() else None,
'S': wfn.S().to_array() if wfn.S() else None,
'X': wfn.lagrangian().to_array() if wfn.lagrangian() else None,
'aotoso': wfn.aotoso().to_array() if wfn.aotoso() else None,
'gradient': wfn.gradient().to_array() if wfn.gradient() else None,
'hessian': wfn.hessian().to_array() if wfn.hessian() else None
},
'vector': {
'epsilon_a': wfn.epsilon_a().to_array() if wfn.epsilon_a() else None,
'epsilon_b': wfn.epsilon_b().to_array() if wfn.epsilon_b() else None,
'frequencies': wfn.frequencies().to_array() if wfn.frequencies() else None
},
'dimension': {
'doccpi': wfn.doccpi().to_tuple(),
'frzcpi': wfn.frzcpi().to_tuple(),
'frzvpi': wfn.frzvpi().to_tuple(),
'nalphapi': wfn.nalphapi().to_tuple(),
'nbetapi': wfn.nbetapi().to_tuple(),
'nmopi': wfn.nmopi().to_tuple(),
'nsopi': wfn.nsopi().to_tuple(),
'soccpi': wfn.soccpi().to_tuple()
},
'int': {
'nalpha': wfn.nalpha(),
'nbeta': wfn.nbeta(),
'nfrzc': wfn.nfrzc(),
'nirrep': wfn.nirrep(),
'nmo': wfn.nmo(),
'nso': wfn.nso(),
'print': wfn.get_print(),
},
'string': {
'name': wfn.name(),
'module': wfn.module(),
'basisname': wfn.basisset().name()
},
'boolean': {
'PCM_enabled': wfn.PCM_enabled(),
'same_a_b_dens': wfn.same_a_b_dens(),
'same_a_b_orbs': wfn.same_a_b_orbs(),
'density_fitted': wfn.density_fitted(),
'basispuream': wfn.basisset().has_puream()
},
'float': {
'energy': wfn.energy(),
'efzc': wfn.efzc(),
'dipole_field_x': wfn.get_dipole_field_strength()[0],
'dipole_field_y': wfn.get_dipole_field_strength()[1],
'dipole_field_z': wfn.get_dipole_field_strength()[2]
},
'floatvar': wfn.scalar_variables(),
'matrixarr': {k: v.to_array() for k, v in wfn.array_variables().items()}
} # yapf: disable
if filename is not None:
if not filename.endswith('.npy'): filename += '.npy'
np.save(filename, wfn_data, allow_pickle=True)
return wfn_data
