def run_job(settings, mol, job_name='gamess_job', work_dir=None):
"""
Call the Cp2K binary using plams interface.
:param settings: Job Settings.
:type settings: :class:`~qmflows.Settings`
:param mol: molecular Geometry
:type mol: plams Molecule
:param input_file_name: Optional name for the input.
:type input_file_name: String
:param out_file_name: Optional name for the output.
:type out_file_name: String
:return: Package.Result
"""
gamess_settings = Settings()
gamess_settings.input = settings.specific.gamess
job = plams.interfaces.gamess.GamessJob(molecule=mol,
name=job_name,
settings=gamess_settings)
r = job.run()
# Relative job path
relative_plams_path = '/'.join(r.job.path.split('/')[-2:])
result = Gamess_Result(gamess_settings,
mol,
r.job.name,
plams_dir=relative_plams_path,
work_dir=work_dir,
status=job.status)
return result
def process_input(input_file: str, workflow_name: str) -> Dict:
"""
Read the `input_file` in YAML format, validate it against the
corresponding `workflow_name` schema and return a nested dictionary with the input.
:param str input_file: path to the input
:return: Input as dictionary
:raise SchemaError: If the input is not valid
"""
schema = schema_workflows[workflow_name]
with open(input_file, 'r') as f:
dict_input = yaml.load(f.read())
try:
d = schema.validate(dict_input)
# Convert cp2k definitions to settings
d['general_settings']['settings_main'] = Settings(
d['general_settings']['settings_main'])
d['general_settings']['settings_guess'] = Settings(
d['general_settings']['settings_guess'])
return d
except SchemaError as e:
msg = "There was an error in the input provided:\n{}".format(e)
raise RuntimeError(msg)
def get_constraint_settings(self, step):
s = Settings()
if isinstance(self.constraints, list):
for c in range(len(self.constraints)):
s.constraint.update(
self.constraints[c].get_settings(self.start[c] +
self.stepsize[c] * step))
else:
s.constraint = self.constraints.get_settings(self.start +
self.stepsize * step)
return s
def run_job(settings, mol, job_name="dirac_job"):
dirac_settings = Settings()
dirac_settings.input = settings.specific.dirac
dirac_settings.ignore_molecule
job = plams.interfaces.thirdparty.dirac.DiracJob(
name=job_name, settings=dirac_settings, molecule=mol)
result = job.run()
# Relative job path
relative_plams_path = '/'.join(result.job.path.split('/')[-2:])
return DIRAC_Result(dirac_settings, mol, result.job.name,
plams_dir=relative_plams_path, status=job.status)
def run_job(settings, mol, job_name='cp2k_job', work_dir=None, **kwargs):
"""
Call the Cp2K binary using plams interface.
:param settings: Job Settings.
:type settings: :class:`~qmflows.Settings`
:param mol: molecular Geometry
:type mol: plams Molecule
:param hdf5_file: Path to the HDF5 file that contains the
numerical results.
:type hdf5_file: String
:param input_file_name: Optional name for the input.
:type input_file_name: String
:param out_file_name: Optional name for the output.
:type out_file_name: String
:param store_in_hdf5: wether to store the output arrays in HDF5 format.
:type store_in_hdf5: Bool
"""
# Yet another work directory
# Input modifications
cp2k_settings = Settings()
cp2k_settings.input = settings.specific.cp2k
# Add molecular coordinates
m = format_coord_xyz(mol) + '{:>8}'.format('&END')
cp2k_settings.input.force_eval.subsys['&COORD'] = m
# Create a Plams job
job = plams.interfaces.thirdparty.cp2k.Cp2kJob(name=job_name,
settings=cp2k_settings,
molecule=mol)
r = job.run()
work_dir = work_dir if work_dir is not None else job.path
warnings = parse_output_warnings(job_name, r.job.path,
parse_cp2k_warnings, cp2k_warnings)
result = CP2K_Result(cp2k_settings,
mol,
job_name,
r.job.path,
work_dir,
status=job.status,
warnings=warnings)
return result
def freeze():
if not isinstance(value, list):
msg = 'selected_atoms ' + str(value) + ' is not a list'
raise RuntimeError(msg)
sel_coords = []
if isinstance(value[0], int):
for v in value:
a = v - 1
sel_coords += [str(i) for i in range(a * 3 + 1, a * 3 + 4)]
else:
for a in range(len(mol)):
if mol[a + 1].symbol in value:
sel_coords += [
str(i) for i in range(a * 3 + 1, a * 3 + 4)
]
ifreez = Settings()
ifreez.statpt = "IFREEZ(1)=" + ",".join(sel_coords)
settings.specific.gamess.update(ifreez)
def run_job(settings, mol, job_name='DFTBjob', nproc=None):
"""
Execute an DFTB job with the *ADF* quantum package.
:param settings: user input settings.
:type settings: |Settings|
:param mol: Molecule to run the simulation
:type mol: Plams Molecule
:parameter input_file_name: The user can provide a name for the
job input.
:type input_file_name: String
:parameter out_file_name: The user can provide a name for the
job output.
:type out_file_name: String
:returns: :class:`~qmflows.packages.SCM.DFTB_Result`
"""
dftb_settings = Settings()
if nproc:
dftb_settings.runscript.nproc = nproc
dftb_settings.input = settings.specific.dftb
job = plams.DFTBJob(name=job_name,
molecule=mol,
settings=dftb_settings)
# Check RKF status
try:
result = job.run()
name = result.job.name
path = result.job.path
except struct.error:
job.status = 'failed'
name = job_name
path = None
msg = "job:{} has failed.\nRKF is corrupted"
print(msg.format(job_name))
if job.status in ['failed', 'crashed']:
builtins.config.jm.remove_job(job)
return DFTB_Result(dftb_settings,
mol,
name,
plams_dir=path,
status=job.status)
def create_settings(d: Dict) -> Dict:
"""
Transform the input dict into Cp2K settings.
:param d: input dict
:return: dictionary with Settings to call Cp2k
"""
# Convert cp2k definitions to settings
general = d['cp2k_general_settings']
general['cp2k_settings_main'] = Settings(general['cp2k_settings_main'])
general['cp2k_settings_guess'] = Settings(general['cp2k_settings_guess'])
apply_templates(general, d['path_traj_xyz'])
input_parameters = add_missing_keywords(d)
print_final_input(input_parameters)
return input_parameters
def generate_kinds(elements: Iterable[str], basis: str, potential: str) -> Settings:
"""Generate the kind section for cp2k basis."""
s = Settings()
subsys = s.cp2k.force_eval.subsys
for e in elements:
q = valence_electrons['-'.join((e, basis))]
subsys.kind[e]['basis_set'] = [f"{basis}-q{q}"]
subsys.kind[e]['potential'] = f"{potential}-q{q}"
return s
def get_settings(self, value=None, mol=None):
s = Settings()
if value is None:
if mol is None:
msg = 'Distance constraint settings requires a value or molecule'
raise RuntimeError(msg)
else:
value = self.get_current_value(mol)
s["dist {:d} {:d}".format(self.atom1, self.atom2)] = value
return s
def run_job(settings, mol, job_name="ORCAjob", work_dir=None, **kwargs):
orca_settings = Settings()
orca_settings.input = settings.specific.orca
# Running Orca with Plams
job = plams.interfaces.orca.ORCAJob(molecule=mol,
settings=orca_settings,
name=job_name)
result = job.run()
# Relative job path
relative_plams_path = '/'.join(result.job.path.split('/')[-2:])
return ORCA_Result(orca_settings,
mol,
result.job.name,
plams_dir=relative_plams_path,
status=job.status)
def generic2specific(self, settings, mol=None):
"""
Traverse all the key, value pairs of the ``settings``, translating
the generic keys into package specific keys as defined in the specific
dictionary. If one key is not in the specific dictionary an error
is raised. These new specific settings take preference over existing
specific settings.
:parameter settings: Settings provided by the user.
:type settings: Settings
:parameter mol: Molecule to run the calculation.
:type mol: plams Molecule
"""
generic_dict = self.get_generic_dict()
specific_from_generic_settings = Settings()
for k, v in settings.items():
if k != "specific":
key = generic_dict.get(k)
if key:
if isinstance(key, list):
if isinstance(key[1], dict):
value = key[1][v]
else:
value = {key[1]: v}
if value:
v = value
key = key[0]
if v:
if isinstance(v, dict):
v = Settings(v)
specific_from_generic_settings \
.specific[self.pkg_name][key] = v
else:
specific_from_generic_settings \
.specific[self.pkg_name][key]
else:
self.handle_special_keywords(
specific_from_generic_settings, k, v, mol)
return settings.overlay(specific_from_generic_settings)
def get_settings(self, value=None, mol=None):
s = Settings()
if value is None:
if mol is None:
msg = 'Dihedral constraint settings requires a value or molecule'
raise RuntimeError(msg)
else:
value = self.get_current_value(mol)
key = "dihed {:d} {:d} {:d} {:d}".format(self.atom1, self.atom2,
self.atom3, self.atom4)
s[key] = value
return s
def get_settings(self, value=None, mol=None):
s = Settings()
if value is None and mol is None:
msg = 'coordinate constraint settings requires a value or molecule'
raise RuntimeError(msg)
elif value is None:
value = self.get_current_value(mol)
# create settings entry
data = self.fmt.format(*self.atoms)
s[data] = value
return s
def dict2Setting(d):
"""
Transform recursively a dict into a Settings object.
"""
r = Settings()
for k, v in d.items():
if isinstance(v, dict):
r[k] = dict2Setting(v)
else:
r[k] = v
return r
def run_job(settings, mol, job_name='ADFjob', nproc=None):
"""
Execute ADF job.
:param settings: user input settings.
:type settings: |Settings|
:param mol: Molecule to run the simulation
:type mol: Plams Molecule
:parameter input_file_name: The user can provide a name for the
job input.
:type input_file_name: String
:parameter out_file_name: The user can provide a name for the
job output.
:type out_file_name: String
:returns: :class:`~qmflows.packages.SCM.ADF_Result`
"""
adf_settings = Settings()
if nproc:
adf_settings.runscript.nproc = nproc
adf_settings.input = settings.specific.adf
job = plams.ADFJob(name=job_name, molecule=mol, settings=adf_settings)
result = job.run()
# Path to the tape 21 file
path_t21 = result._kf.path
# Relative path to the CWD
relative_path_t21 = '/'.join(path_t21.split('/')[-3:])
# Relative job path
relative_plams_path = '/'.join(result.job.path.split('/')[-2:])
adf_result = ADF_Result(adf_settings,
mol,
result.job.name,
relative_path_t21,
plams_dir=relative_plams_path,
status=job.status)
return adf_result
def create_settings_from_template(
general: Dict[str, Any], template_name: str, path_traj_xyz: PathLike) -> Settings:
"""Create a job Settings using the name provided by the user."""
setts = templates_dict[template_name]
elements = read_unique_atomic_labels(path_traj_xyz)
kinds = generate_kinds(elements, general['basis'], general['potential'])
if 'pbe0' in template_name:
s = Settings()
return generate_auxiliar_basis(setts + s + kinds, general['basis'], general['aux_fit'])
elif 'hse06' in template_name:
return generate_auxiliar_basis(setts + kinds, general['basis'], general['aux_fit'])
else:
return setts + kinds
def constraint():
if isinstance(value, Settings):
s = Settings()
if len(mol) == 2:
degr = 1
# s['izmat(1)'] = '1,1,2'
else:
degr = 3 * len(mol) - 6
s.auto = ".TRUE."
s.dlc = ".TRUE."
settings.specific.gamess.contrl.nzvar = degr
i = 1
for k, v in value.items():
ks = k.split()
# print('--->', ks, type(ks[2]), type(value), v)
if ks[0] == 'dist' and len(ks) == 3:
n = 'ifzmat({:d})'.format(i)
s[n] = "1,{},{}".format(int(ks[1]), int(ks[2]))
n = 'fvalue({:d})'.format(i)
s[n] = v
elif ks[0] == 'angle' and len(ks) == 4:
n = 'ifzmat({:d})'.format(i)
s[n] = "2,{},{},{}".format(int(ks[1]), int(ks[2]),
int(ks[3]))
n = 'fvalue({:d})'.format(i)
s[n] = v
elif ks[0] == 'dihed' and len(ks) == 5:
n = 'ifzmat({:d})'.format(i)
s[n] = "3,{},{},{},{}".format(int(ks[1]), int(ks[2]),
int(ks[3]), int(ks[4]))
n = 'fvalue({:d})'.format(i)
s[n] = v
else:
warn('Invalid constraint key: ' + k)
i += 1
settings.specific.gamess.zmat = s
def extract_properties_rkf(path_rkf, key=None, subkey=None):
"""
Read result from a DFTB computation using the job_name.rkf file.
"""
kf = plams.tools.kftools.KFFile(path_rkf).read
props = Settings()
for i in range(kf('Properties', 'nEntries')):
typ = kf('Properties', 'Type(' + str(i + 1) + ')').strip()
subtype = kf('Properties', 'Subtype(' + str(i + 1) + ')').strip()
value = kf('Properties', 'Value(' + str(i + 1) + ')')
props[typ][subtype] = value
ret = props[key][subkey]
if isinstance(ret, list):
ret = np.array(ret)
return ret
def create_settings_from_template(general: dict, template_name: str,
path_traj_xyz: str) -> Settings:
"""
Create a job Settings using the name provided by the user
"""
setts = templates_dict[template_name]
elements = read_unique_atomic_labels(path_traj_xyz)
kinds = generate_kinds(elements, general['basis'], general['potential'])
if 'pbe0' in template_name:
s = Settings()
s.cp2k.force_eval.dft.xc.hf.interaction_potential.t_c_g_data = os.path.abspath(
join(general['path_basis'], "t_c_g.dat"))
return generate_auxiliar_basis(setts + s + kinds, general['basis'],
general['aux_fit'])
elif 'hse06' in template_name:
return generate_auxiliar_basis(setts + kinds, general['basis'],
general['aux_fit'])
else:
return setts + kinds
def adf_fragmentsjob(settings,
frags,
caps=None,
fragment_settings=None,
job_name='fde'):
mol_tot = Molecule()
frag_settings = Settings()
cap_ids = {}
if caps:
for i, cap in enumerate(caps):
cap_id = 'cap' + str(i + 1)
for a in cap.mol:
cap_ids[a.coords] = cap_id
path = cap.result.kf.path
key = cap_id + ' ' + path + ' type=FDEsubstract &'
frag_settings.specific.adf.fragments[key] = fragment_settings
for i, frag in enumerate(frags):
frag_id = 'frag' + str(i + 1)
if frag.result:
for a in frag.mol:
a.properties.adf.fragment = frag_id
if a.coords in cap_ids:
a.properties.adf.fragment += ' fs=' + cap_ids[a.coords]
path = frag.result.kf.path
key = frag_id + ' ' + path + ' subfrag=active'
if frag.isfrozen:
key += ' type=FDE'
if fragment_settings:
key += ' &'
frag_settings.specific.adf.fragments[
key] = fragment_settings
else:
frag_settings.specific.adf.fragments[key] = ""
else:
frag_settings.specific.adf.fragments[key] = ""
mol_tot += frag.mol
frag_settings.specific.adf.fde.PW91k = ""
return adf(settings.overlay(frag_settings), mol_tot, job_name=job_name)
def decode(self, cls, data):
return Settings(data)
singlepoint = Settings(
yaml.load("""
specific:
adf:
basis:
type: SZ
xc:
__block_replace: true
lda: ""
numericalquality:
normal
scf:
converge: 1e-6
iterations: 100
ams:
ams:
Task: SinglePoint
dftb:
dftb:
resourcesdir:
"DFTB.org/3ob-3-1"
task:
runtype: SP
cp2k:
force_eval:
dft:
mgrid:
cutoff: 400
ngrids: 4
print:
mo:
add_last: numeric
each:
qs_scf: 0
eigenvalues: ""
eigenvectors: ""
filename: "./mo.data"
ndigits: 36
occupation_numbers: ""
qs:
method: gpw
scf:
eps_scf: 1e-06
max_scf: 200
scf_guess: restart
subsys:
cell:
periodic: xyz
global:
print_level: low
project: cp2k
run_type: energy
dirac:
DIRAC: WAVEFUNCTION
HAMILTONIAN: "LEVY-LEBLOND"
WAVE FUNCTION: SCF
gamess:
basis:
gbasis: sto
ngauss: 3
contrl:
scftyp: rhf
dfttyp: pbe
orca:
method:
method: dft
functional: lda
basis:
basis: sto_sz
""",
Loader=yaml.FullLoader))
cfit = aux_fit[atom][index]
kind[atom]["BASIS_SET"] = "AUX_FIT " + f"CFIT{cfit}"
return sett
cp2k_pbe_guess = Settings(
yaml.load("""
cp2k:
force_eval:
subsys:
cell:
periodic: "None"
dft:
xc:
xc_functional pbe: {}
scf:
eps_scf: 1e-6
added_mos: 0
scf_guess: "restart"
ot:
minimizer: "DIIS"
n_diis: 7
preconditioner: "FULL_SINGLE_INVERSE"
""",
Loader=yaml.FullLoader))
cp2k_pbe_main = Settings(
yaml.load("""
cp2k:
force_eval:
subsys:
def create_settings(self) -> None:
"""Transform the CP2K input dict into :class:`QMFLows.Settings`."""
self.general['cp2k_settings_main'] = Settings(
self.general['cp2k_settings_main'])
self.general['cp2k_settings_guess'] = Settings(
self.general['cp2k_settings_guess'])
