def overlay_cp2k_settings(self,
cp2k_settings: MutableMapping,
psf: Optional[PSFContainer] = None) -> None:
r"""Overlay **df** with all :math:`q`, :math:`\sigma` and :math:`\varepsilon` values from **cp2k_settings**.""" # noqa
charge = cp2k_settings['input']['force_eval']['mm']['forcefield'][
'charge']
charge_dict = {
block['atom']: float(block['charge'])
for block in charge
}
if psf is not None:
psf_charge_dict = dict(zip(psf.atom_type, psf.charge))
for k, v in psf_charge_dict.items():
if k not in charge_dict:
charge_dict[k] = v
epsilon_s = Settings() # type: ignore[var-annotated]
sigma_s = Settings() # type: ignore[var-annotated]
# Check if the settings are qmflows-style generic settings
lj = cp2k_settings.get('lennard-jones') or cp2k_settings.get(
'lennard_jones')
if lj is not None:
self._overlay_s_qmflows(cp2k_settings, sigma_s, epsilon_s)
else:
lj = cp2k_settings['input']['force_eval']['mm']['forcefield'][
'nonbonded']['lennard-jones'] # noqa
self._overlay_s_plams(lj, sigma_s, epsilon_s)
self.set_charge(charge_dict)
for unit, dct in epsilon_s.items():
self.set_epsilon_pairs(dct, unit=unit)
for unit, dct in sigma_s.items():
self.set_sigma_pairs(dct, unit=unit)
def get_template(template_name: str, from_cat_data: bool = True) -> Settings:
"""Grab a yaml template and return it as Settings object."""
if from_cat_data:
path = join('data/templates', template_name)
xs = pkg.resource_string('CAT', path)
return Settings(yaml.load(xs.decode(), Loader=yaml.FullLoader))
with open(template_name, 'r') as file:
return Settings(yaml.load(file, Loader=yaml.FullLoader))
def qd_opt(mol: Molecule,
jobs: Tuple[Optional[Type[Job]], ...],
settings: Tuple[Optional[Settings], ...],
use_ff: bool = False) -> None:
"""Perform an optimization of the quantum dot.
Performs an inplace update of **mol**.
Parameters
----------
mol : |plams.Molecule|_
The to-be optimized molecule.
job_recipe : |plams.Settings|_
A Settings instance containing all jon settings.
Expects 4 keys: ``"job1"``, ``"job2"``, ``"s1"``, ``"s2"``.
forcefield : bool
If ``True``, perform the job with CP2K with a user-specified forcefield.
"""
# Prepare the job settings
if use_ff:
qd_opt_ff(mol, jobs, settings)
return None
# Expand arguments
job1, job2 = jobs
s1, s2 = settings
# Extra options for AMSJob
if job1 is AMSJob:
s1 = Settings(s1)
s1.input.ams.constraints.atom = mol.properties.indices
if job2 is AMSJob:
s2 = Settings(s2)
s2.input.ams.constraints.atom = mol.properties.indices
# Run the first job and fix broken angles
mol.job_geometry_opt(job1, s1, name='QD_opt_part1')
fix_carboxyl(mol)
fix_h(mol)
mol.round_coords()
# Run the second job
if job2 is not None:
mol.job_geometry_opt(job2, s2, name='QD_opt_part2')
mol.round_coords()
return None
def frequencies():
s = Settings()
s.input.ams.properties.NormalModes = 'Yes'
s.input.ams.Properties.PESPointCharacter = 'No'
s.input.ams.NormalModes.ReScanFreqRange = '-1000 0'
s.input.ams.PESPointCharacter.NegativeEigenvalueTolerance = -0.001
return s
def set_header(s: Settings, *values: str) -> None:
"""Assign *value* to the ``["_h"]`` key in *s.input.compound*."""
s.input.compound = []
for item in values:
s.input.compound.append(Settings({'_h': item}))
s.input.compound[
0].frac1 = 1.0 # The first item in *values should be the solvent
def get_surface_charge(mol: Molecule, job: Type[Job],
s: Settings) -> Optional[str]:
"""Construct the COSMO surface of the **mol**.
Parameters
----------
mol : |plams.Molecule|_
A PLAMS Molecule.
job : |Callable|_
A type Callable of a class derived from :class:`Job`, e.g. :class:`AMSJob`
or :class:`Cp2kJob`.
s : |plams.Settings|_
The settings for **job**.
Returns
-------
|plams.Settings|_
Optional: The path+filename of a file containing COSMO surface charges.
"""
s = Settings(s)
# Special procedure for ADF jobs
# Use the gas-phase electronic structure as a fragment for the COSMO single point
if job is ADFJob:
s = get_surface_charge_adf(mol, job, s)
s.runscript.post = '$ADFBIN/cosmo2kf "mopac.cos" "mopac.coskf"'
results = mol.job_single_point(job, s, ret_results=True)
return get_coskf(results)
def recreate_settings(self) -> Settings:
"""Construct a |Settings| instance from ``"$JN.run"``."""
runfile = self['$JN.run']
# Ignore the first 2 lines
with open(runfile, 'r') as f:
for i in f:
if 'MATCH.pl' in i:
args = next(f).split()
break
else:
raise FileError(
f"Failed to parse the content of ...{os.sep}{runfile!r}")
# Delete the executable and pop the .pdb filename
del args[0]
pdb_file = args.pop(-1)
s = Settings()
for k, v in zip(args[0::2], args[1::2]):
k = k[1:].lower()
s.input[k] = v
s.input.filename = pdb_file
return s
def set_cp2k_param(settings: Settings, param_dict: dict) -> None:
"""Placeholder."""
for block_name, block in param_dict.items():
# Create a to-be formatted string with user-specified units
unit = f'[{block.unit}]' + ' {}' if 'unit' in block else '{}'
# Get the to-be update list of settings
s = settings.get_nested(block['keys'])
if not isinstance(s, list):
_s = settings.get_nested(block['keys'][:-1])
s = _s[block['keys'][-1]] = []
for k, v in block.items():
if k in ('keys', 'unit'): # Skip
continue
value = unit.format(v)
atom = 'atoms' if len(k.split()) > 1 else 'atom'
atom_list = [i[atom] for i in s]
try: # Intersecting set
idx = atom_list.index(k)
s[idx].update({block_name: value})
except ValueError: # Disjoint set
new_block = Settings({atom: k, block_name: value})
s.append(new_block)
def DFTB():
s = Settings()
s.input.ams.task = 'GeometryOptimization'
s.input.DFTB
s.input.DFTB.Model = "GFN1-xTB"
s.input.ams.System.Charge = 0
return s
def update_adf_defaults(ams_settings: Settings) -> None:
"""Add the COSMO-RS compound defaults to *ams_settings*, returning a copy of the new settings.
The engine block (*ams_settings.input.adf*) is soft updated
with the following Settings:
.. code::
Engine ADF
Basis
Type TZP
Core Small
End
XC
GGA BP86
End
Relativity
Level Scalar
End
BeckeGrid
Quality Good
End
EndEngine
"""
# Find the solvation key
# solvation = ams_settings.input.adf.find_case('solvation')
# solvation_block = ams_settings.input.adf[solvation]
adf = ams_settings.input.find_case('adf')
adf_block = ams_settings.input[adf]
# Find all keys for within the adf block
keys = ('basis', 'xc', 'relativity', 'BeckeGrid')
basis, xc, relativity, BeckeGrid = [
adf_block.find_case(item) for item in keys
]
# Construct the default solvation block
adf_defaults_block = Settings({
basis: {
'Type': 'TZP',
'Core': 'Small'
},
xc: {
'GGA': 'BP86'
},
relativity: {
'Level': 'Scalar'
},
BeckeGrid: {
'Quality': 'Good'
}
})
# Copy ams_settings and perform a soft update
ret = ams_settings.copy()
ret.input.adf.soft_update(adf_defaults_block)
return ret
def test_settings_to_molecule():
s = Settings()
s.input.ams.system.Atoms._1 = ' H 0.0000000000 0.0000000000 0.0000000000 '
s.input.ams.system.Atoms._2 = ' O 1.0000000000 0.0000000000 0.0000000000 '
t = s.copy()
mol = AMSJob.settings_to_mol(t)['']
assert AMSJob(settings=s).get_input() == AMSJob(settings=t,
molecule=mol).get_input()
s = Settings()
s.input.ams.system.Atoms._1 = [
' H 0.0000000000 0.0000000000 0.0000000000 ',
' O 1.0000000000 0.0000000000 0.0000000000 '
]
t = s.copy()
mol = AMSJob.settings_to_mol(t)['']
assert AMSJob(settings=s).get_input() == AMSJob(settings=t,
molecule=mol).get_input()
#test for headers:
s.input.ams.system.Atoms._h = '[A]'
s.input.ams.system.Atoms._1 = [
' O -0.0509000000 -0.2754000000 0.6371000000 ForceField.Charge=-0.834 ForceField.Type=OW',
' H 0.0157000000 0.5063000000 0.0531000000 ForceField.Charge=0.417 ForceField.Type=HW',
' H -0.0055000000 -1.0411000000 0.0658000000 ForceField.Charge=0.417 ForceField.Type=HW',
' O 0.0981000000 1.7960000000 -1.2550000000 ForceField.Charge=-0.834 ForceField.Type=OW',
' H -0.6686000000 2.2908000000 -1.5343000000 ForceField.Charge=0.417 ForceField.Type=HW',
' H 0.8128000000 2.3488000000 -1.5619000000 ForceField.Charge=0.417 ForceField.Type=HW',
]
t = s.copy()
#currently plams completely ignores the [A] unit specifier, it might also not print it if it does
#get implemented and instead just convert the values in the molecule.
del s.input.ams.system.Atoms._h
mol = AMSJob.settings_to_mol(t)['']
assert AMSJob(settings=s).get_input() == AMSJob(settings=t,
molecule=mol).get_input()
def get_recipe(self) -> Dict[Tuple[str], JobRecipe]:
"""Create a recipe for :meth:`WorkFlow.to_db`."""
settings_names = [i[1:] for i in self.export_columns if i[0] == 'settings']
uff_fallback = {
'key': f'RDKit_{rdkit.__version__}', 'value': f'{UFF.__module__}.{UFF.__name__}'
}
ret: Dict[Tuple[str], JobRecipe] = Settings()
for name, job, settings in zip(settings_names, self.jobs, self.settings):
# job is None, *i.e.* it's an RDKit UFF optimziation
if job is None:
ret[name].update(uff_fallback) # type: ignore
continue
settings = Settings(settings)
if self.read_template: # Update the settings using a QMFlows template
template = qmflows.geometry['specific'][self.type_to_string(job)].copy()
settings.soft_update(template)
ret[name]['key'] = job
ret[name]['value'] = settings
return ret
def get_pka(mol: Molecule, coskf_mol: Optional[str],
coskf_mol_conj: Optional[str], water: str, hydronium: str,
job: Type[Job], s: Settings) -> List[float]:
if coskf_mol is None:
return 5 * [np.nan]
elif coskf_mol_conj is None:
return 5 * [np.nan]
s = Settings(s)
s.input.compound[1]._h = water
s.ignore_molecule = True
s_dict = {}
for name, coskf in _iter_coskf(coskf_mol_conj, coskf_mol, water,
hydronium):
_s = s.copy()
_s.name = name
_s.input.compound[0]._h = coskf
s_dict[name] = _s
# Run the job
mol_name = mol.properties.name
job_list = [CRSJob(settings=s, name=name) for name, s in s_dict.items()]
results_list = [_crs_run(job, mol_name) for job in job_list]
# Extract solvation energies and activity coefficients
E_solv = {}
for name, results in zip(("acid", "base", "solvent", "solvent_conj"),
results_list):
results.wait()
try:
E_solv[name] = _E = results.get_energy()
assert _E is not None
logger.info(f'{results.job.__class__.__name__}: {mol_name} pKa '
f'calculation ({results.job.name}) is successful')
except Exception:
logger.error(f'{results.job.__class__.__name__}: {mol_name} pKa '
f'calculation ({results.job.name}) has failed')
E_solv[name] = np.nan
try:
mol.properties.job_path += [
join(job.path, job.name + '.in') for job in job_list
]
except IndexError: # The 'job_path' key is not available
mol.properties.job_path = [
join(job.path, job.name + '.in') for job in job_list
]
ret = [E_solv[k] for k in ("acid", "base", "solvent", "solvent_conj")]
ret.append(_get_pka(**E_solv))
return ret
def overlay_cp2k_settings(self, cp2k_settings: Settings) -> None:
"""Extract forcefield information from PLAMS-style CP2K settings.
Performs an inplace update of this instance.
Examples
--------
Example input value for **cp2k_settings**.
In the provided example the **cp2k_settings** are directly extracted from a CP2K .inp file.
.. code:: python
>>> import cp2kparser # https://github.com/nlesc-nano/CP2K-Parser
>>> filename = str(...)
>>> cp2k_settings: dict = cp2kparser.read_input(filename)
>>> print(cp2k_settings)
{'force_eval': {'mm': {'forcefield': {'nonbonded': {'lennard-jones': [...]}}}}}
Parameters
----------
cp2k_settings : :class:`~collections.abc.Mapping`
A Mapping with PLAMS-style CP2K settings.
"""
if 'input' not in cp2k_settings:
cp2k_settings = Settings({'input': cp2k_settings})
# If cp2k_settings is a Settings instance enable the `suppress_missing` context manager
# In this manner normal KeyErrors will be raised, just like with dict
if isinstance(cp2k_settings, Settings):
context_manager = cp2k_settings.suppress_missing
else:
context_manager = nullcontext
with context_manager():
for prm_map in self.CP2K_TO_PRM.values():
name = prm_map['name']
columns = list(prm_map['columns'])
key_path = prm_map['key_path']
key = prm_map['key']
unit = prm_map['unit']
default_unit = prm_map['default_unit']
post_process = prm_map['post_process']
self._overlay_cp2k_settings(cp2k_settings,
name, columns, key_path, key, unit,
default_unit, post_process)
def extract_args(args: Optional[List[str]] = None) -> Settings:
"""Extract and return all arguments."""
input_file = args.YAML[0]
if exists(input_file):
pass
elif exists(join(getcwd(), input_file)):
input_file = join(getcwd(), input_file)
else:
input_file2 = join(getcwd(), input_file)
raise FileNotFoundError(
f'No file found at {input_file} or {input_file2}')
with open(input_file, 'r') as file:
return Settings(yaml.load(file, Loader=yaml.FullLoader))
def update_ff_jobs(s: Settings) -> None:
"""Update forcefield settings."""
if NANO_CAT is not None:
raise NANO_CAT
ff = Settings()
set_cp2k_param(ff, s.optional.forcefield)
optimize = s.optional.qd.optimize
if optimize and optimize.use_ff:
if optimize.job1 and str(optimize.job1) == str(Cp2kJob):
optimize.s1 = Settings() if optimize.s1 is None else optimize.s1
optimize.s1 += get_template('qd.yaml')['CP2K_CHARM_opt']
optimize.s1 += ff
if optimize.job2 and str(optimize.job1) == str(Cp2kJob):
optimize.s2 = Settings() if optimize.s2 is None else optimize.s2
optimize.s2 += get_template('qd.yaml')['CP2K_CHARM_opt']
optimize.s2 += ff
dissociate = s.optional.qd.dissociate
if dissociate and dissociate.use_ff:
if dissociate.job1 and str(dissociate.job1) == str(Cp2kJob):
dissociate.s1 = Settings(
) if dissociate.s1 is None else dissociate.s1
dissociate.s1 += get_template('qd.yaml')['CP2K_CHARM_opt']
dissociate.s1 += ff
activation_strain = s.optional.qd.activation_strain
if activation_strain and activation_strain.use_ff:
if activation_strain.job1 and str(
activation_strain.job1) == str(Cp2kJob):
key = 'CP2K_CHARM_singlepoint' if not activation_strain.md else 'CP2K_CHARM_md'
activation_strain.s1 = Settings(
) if activation_strain.s1 is None else activation_strain.s1 # noqa
activation_strain.s1 += get_template('qd.yaml')[key]
activation_strain.s1 += ff
def DFT():
s = Settings()
s.input.ams.task = 'GeometryOptimization'
s.input.adf.basis.type = 'TZ2P'
s.input.adf.basis.core = 'None'
s.input.adf.xc.hybrid = 'B3LYP'
s.input.adf.xc.Dispersion = 'GRIMME3 BJDAMP'
s.input.adf.Relativity.Level = 'None'
s.input.adf.NumericalQuality = 'Good'
s.input.adf.Symmetry = 'NOSYM'
s.input.ams.UseSymmetry = 'No'
s.input.adf.Unrestricted = 'No'
s.input.adf.SpinPolarization = 0
s.input.ams.System.Charge = 0
return s
def read_mol_folder(mol_dict: Settings) -> Optional[List[Molecule]]:
"""Read all files (.xyz, .pdb, .mol, .txt or further subfolders) within a folder."""
try:
mol_type = 'input_cores' if mol_dict.is_core else 'input_ligands'
_file_list = os.listdir(mol_dict.mol)
optional_dict = Settings(
{k: v
for k, v in mol_dict.items() if k not in ('mol', 'path')})
file_list = [{i: optional_dict} for i in _file_list]
validate_mol(file_list, mol_type, mol_dict.path)
return read_mol(file_list)
except Exception as ex:
print_exception('read_mol_folder', mol_dict.name, ex)
def start_crs_jobs(mol_list: Iterable[Molecule], jobs: Iterable[Type[Job]],
settings: Iterable[Settings],
**kwargs: Any) -> List[pd.Series]:
# Parse the job type
job, *_ = jobs
if job is not ADFJob:
raise NotImplementedError(f"job: {job.__class__.__name__} = {job!r}")
# Parse and update the input settings
_s, *_ = settings
s = Settings(_s)
s.input += cdft.specific.adf
ret = []
for mol in mol_list:
ret.append(run_cdft_job(mol, job, s))
return ret
def _get_logp(solutes: _NDArray[np.object_]) -> _NDArray[np.float64]:
"""Perform a LogP calculation."""
ret = np.full_like(solutes, np.nan, dtype=np.float64)
mask = solutes != None
count = np.count_nonzero(mask)
if count == 0:
return ret
s = copy.deepcopy(LOGP_SETTINGS)
for v in s.input.compound[:2]:
v._h = v._h.format(os.environ["AMSRESOURCES"])
s.input.compound += [Settings({"_h": f'"{sol}"', "_1": "compkffile"}) for sol in solutes[mask]]
ret[mask] = _run_crs(
s, count,
logp=lambda r: r.readkf('LOGP', 'logp')[2:],
)
return ret
def _start_ligand_jobs_plams(ligand_list: Iterable[Molecule], job: Type[Job],
settings: Settings,
charge_func: ChargeFunc) -> None:
"""Loop over all molecules in ``ligand_df.loc[idx]`` and perform geometry optimizations."""
for ligand in ligand_list:
try:
optimize_ligand(ligand)
except Exception as ex:
logger.debug(f'{ex.__class__.__name__}: {ex}', exc_info=True)
ligand.properties.is_opt = False
continue
s = Settings(settings)
charge_func(s,
int(sum(at.properties.get('charge', 0) for at in ligand)))
ligand.job_geometry_opt(job, s, name='ligand_opt')
ligand.round_coords(
) # `.is_opt = True` is set by `ligand.job_geometry_opt()``
return None
def finalize_lj(mol: Molecule, s: List[Settings]) -> None:
"""Assign UFF Lennard-Jones parameters to all missing non-bonded core/ligand interactions.
.. _LENNARD_JONES: https://manual.cp2k.org/trunk/CP2K_INPUT/FORCE_EVAL/MM/FORCEFIELD/NONBONDED/LENNARD-JONES.html
Parameters
----------
mol : |plams.Molecule|_
A PLAMS molecule containing a core and ligand(s).
s : |list|_ [|plams.Settings|_]
A list of settings constructed from the
CP2K FORCE_EVAL/MM/FORCEFIELD/NONBONDED/`LENNARD-JONES`_ block.
The settings are expected to contain the ``"atoms"`` keys.
""" # noqa
# Create a set of all core atom types
core_at, lig_at = _gather_core_lig_symbols(mol)
# Create a set of all user-specified core/ligand LJ pairs
if not s:
s = []
elif isinstance(s, dict):
s = [s]
atom_pairs = {frozenset(s.atoms.split()) for s in s}
# Check if LJ parameters are present for all atom pairs.
# If not, supplement them with UFF parameters.
for at1, symbol1 in core_at.items():
for at2, symbol2 in lig_at.items():
at1_at2 = {at1, at2}
if at1_at2 in atom_pairs:
continue
s.append(
Settings({
'atoms':
f'{at1} {at2}',
'epsilon':
f'[kcalmol] {round(combine_epsilon(symbol1, symbol2), 4)}',
'sigma':
f'[angstrom] {round(combine_sigma(symbol1, symbol2), 4)}'
}))
def init_ff_assignment(ligand_df: SettingsDataFrame,
forcefield: str = 'top_all36_cgenff_new') -> None:
"""Initialize the forcefield assignment procedure using MATCH_.
.. _MATCH: http://brooks.chem.lsa.umich.edu/index.php?page=match&subdir=articles/resources/software
Parameters
----------
ligand_df : |CAT.SettingsDataFrame|_
A DataFrame of ligands.
forcefield : str
The type of to-be assigned forcefield atom types.
See the ``-Forcefield`` paramater in the MATCH_ user guide for more details.
By default the allowed values are:
* ``"top_all22_prot"``
* ``"top_all27_na"``
* ``"top_all35_carb"``
* ``"top_all35_ether"``
* ``"top_all36_cgenff"``
* ``"top_all36_cgenff_new"``
* ``"top_all36_lipid"``
See Also
--------
MATCH publication:
`MATCH: An atom-typing toolset for molecular mechanics force fields,
J.D. Yesselman, D.J. Price, J.L. Knight and C.L. Brooks III,
J. Comput. Chem., 2011 <https://doi.org/10.1002/jcc.21963>`_
:func:`.run_match_job`:
Assign atom types and charges to **mol** based on the results of MATCH_.
:class:`.MatchJob`:
A :class:`Job` subclass for interfacing with MATCH_: Multipurpose Atom-Typer for CHARMM.
""" # noqa
workflow = WorkFlow.from_template(ligand_df, name='forcefield')
workflow.jobs = (MatchJob, )
workflow.settings = (Settings({'input': {'forcefield': forcefield}}), )
workflow(start_ff_assignment, ligand_df, columns=[])
def get_settings(values):
"""
Retrieve settings from command line, and set them up
"""
settings = Settings()
settings.input.XC.GGA = values['functional']
if values['dispersion'] is not None:
settings.input.XC.DISPERSION = values['dispersion']
settings.input.BASIS.type = values['basisset']
settings.input.BASIS.core = values['frozencore']
settings.input.BASIS.createoutput = 'None'
settings.input.NumericalQuality = values['integrationquality']
settings.input.RELATIVISTIC = values['relativistic'] + " ZORA"
settings.input.AOMAT2FILE = ''
settings.input.SAVE = 'TAPE15'
settings.input.FULLFOCK = ''
settings.input.NOPRINT = "LOGFILE"
settings.input.SYMMETRY = "NOSYM"
return settings
def _run_match(
mol_list: Iterable[Molecule],
forcefield: str = 'top_all36_cgenff_new'
) -> Tuple[List[str], PRMContainer]:
"""Run a :class:`MatchJob`, using **forcefield**, on all molecules in **mol_list**."""
s = Settings()
s.input.forcefield = forcefield
rtf_list = []
prm_list = []
for i, mol in enumerate(mol_list):
job = MatchJob(molecule=mol, settings=s, name=f'match_job.{i}')
results = job.run()
rtf = results['$JN.rtf']
prm = results['$JN.prm']
rtf_list.append(rtf)
prm_list.append(prm)
prm_ = _concatenate_prm(prm_list)
return rtf_list, prm_
def read_mol_txt(mol_dict: Settings) -> Optional[List[Molecule]]:
"""Read a plain text file containing one or more SMILES strings."""
try:
row = 0 if 'row' not in mol_dict else mol_dict.row
column = 0 if 'column' not in mol_dict else mol_dict.column
mol_type = 'input_cores' if mol_dict.is_core else 'input_ligands'
with open(mol_dict.mol, 'r') as f:
iterator = itertools.islice(f, row, None)
_file_list = [
i.rstrip('\n').split()[column] for i in iterator if i
]
optional_dict = Settings(
{k: v
for k, v in mol_dict.items() if k not in ('mol', 'path')})
file_list = [{i: optional_dict} for i in _file_list]
validate_mol(file_list, mol_type, mol_dict.path)
return read_mol(file_list)
except Exception as ex:
print_exception('read_mol_txt', mol_dict.name, ex)
def pre_process_settings(mol: Molecule, s: Settings, job_type: Type[Job],
template_name: str) -> Settings:
"""Update all :class:`Settings`, **s**, with those from a QMFlows template (see **job**)."""
ret = Settings()
type_key = type_to_string(job_type)
ret.input = getattr(qmflows, template_name)['specific'][type_key].copy()
ret.update(s)
if job_type is AMSJob:
mol.properties.pop('charge', None)
# ret.input.ams.system.BondOrders._1 = adf_connectivity(mol)
if 'uff' not in s.input:
ret.input.ams.system.charge = int(
sum(at.properties.get('charge', 0) for at in mol))
elif job_type is ADFJob:
mol.properties.pop('charge', None)
if not ret.input.charge:
ret.input.charge = int(
sum(at.properties.get('charge', 0) for at in mol))
return ret
def _get_kwarg_dict2(armc: ARMC) -> Settings:
ret = Settings() # type: ignore[var-annotated]
shape = armc.iter_len // armc.sub_iter_len, armc.sub_iter_len
err_shape = shape + (len(
armc.molecule), len(armc.pes_validation) // len(armc.molecule))
ret.aux_error.shape = err_shape
ret.aux_error.dtype = np.float64
ret.aux_error.fillvalue = np.nan
for key, partial in armc.pes_validation.items():
ref: pd.DataFrame = partial.ref # type: ignore[attr-defined]
ret[key].shape = shape + get_shape(ref)
ret[key].dtype = np.float64
ret[key].fillvalue = np.nan
ret[f'{key}.ref'].shape = get_shape(ref)
ret[f'{key}.ref'].dtype = np.float64
ret[f'{key}.ref'].data = ref
ret[f'{key}.ref'].fillvalue = np.nan
return ret
def _get_gamma_e(
solutes: _NDArray[np.object_],
solvent: str,
solvent_name: str,
) -> _NDArray[np.float64]:
"""Perform an activity coefficient and solvation energy calculation."""
ret = np.full((len(solutes), 2), np.nan, dtype=np.float64)
mask = solutes != None
count = np.count_nonzero(mask)
if count == 0:
return ret
s = copy.deepcopy(GAMMA_E_SETTINGS)
s.input.compound[0]._h = f'"{solvent}"'
s.input.compound += [Settings({"_h": f'"{sol}"', "_1": "compkffile"}) for sol in solutes[mask]]
ret[mask] = _run_crs(
s, count, solvent_name,
activity_coefficient=lambda r: r.readkf('ACTIVITYCOEF', 'gamma')[1:],
solvation_energy=lambda r: r.readkf('ACTIVITYCOEF', 'deltag')[1:],
)
return ret
def test_get_non_bonded() -> None:
"""Test :func:`FOX.functions.lj_calculate.get_non_bonded`."""
psf = PATH / 'Cd68Se55_26COO_MD_trajec.psf'
prm = PATH / 'Cd68Se55_26COO_MD_trajec.prm'
mol = MultiMolecule.from_xyz(PATH / 'Cd68Se55_26COO_MD_trajec.xyz')
s = Settings()
s.input.force_eval.mm.forcefield.charge = [
{'atom': 'Cd', 'charge': 0.933347},
{'atom': 'Se', 'charge': -0.923076}
]
s.input.force_eval.mm.forcefield.nonbonded['lennard-jones'] = [
{'atoms': 'Cd Cd', 'epsilon': '[kjmol] 0.310100', 'sigma': '[nm] 0.118464'},
{'atoms': 'Se Se', 'epsilon': '[kjmol] 0.426600', 'sigma': '[nm] 0.485200'},
{'atoms': 'Se Cd', 'epsilon': '[kjmol] 1.522500', 'sigma': '[nm] 0.294000'}
]
elstat_df, lj_df = get_non_bonded(mol, psf, prm=prm, cp2k_settings=s)
elstat_ref = np.load(PATH / 'get_non_bonded_elstat.npy')
lj_ref = np.load(PATH / 'get_non_bonded_lj.npy')
np.testing.assert_allclose(elstat_df, elstat_ref)
np.testing.assert_allclose(lj_df, lj_ref)
