def get_structure(job):
atoms = pyiron_to_ase(job.load_object().get_structure())
atoms_dict = {
"symbols": atoms.get_chemical_symbols(),
"positions": atoms.get_positions().tolist(),
"cell": atoms.get_cell().tolist(),
"pbc": atoms.get_pbc().tolist(),
"celldisp": atoms.get_celldisp().tolist(),
}
if atoms.has("tags"):
atoms_dict["tags"] = atoms.get_tags().tolist()
if atoms.has("masses"):
atoms_dict["masses"] = atoms.get_masses().tolist()
if atoms.has("momenta"):
atoms_dict["momenta"] = atoms.get_momenta().tolist()
if atoms.has("initial_magmoms"):
atoms_dict["magmoms"] = atoms.get_initial_magnetic_moments().tolist()
if atoms.has("initial_charges"):
atoms_dict["charges"] = atoms.get_initial_charges().tolist()
if not atoms.__dict__["_calc"] == None:
warnings.warn("Found calculator: " + str(atoms.__dict__["_calc"]))
if not atoms.__dict__["_constraints"] == []:
warnings.warn("Found constraint: " +
str(atoms.__dict__["_constraints"]))
return {"structure": json.dumps(atoms_dict)}
def get_structure(job):
atoms = pyiron_to_ase(job.load_object().get_structure())
atoms_dict = {
'symbols': atoms.get_chemical_symbols(),
'positions': atoms.get_positions().tolist(),
'cell': atoms.get_cell().tolist(),
'pbc': atoms.get_pbc().tolist(),
'celldisp': atoms.get_celldisp().tolist()
}
if atoms.has('tags'):
atoms_dict['tags'] = atoms.get_tags().tolist()
if atoms.has('masses'):
atoms_dict['masses'] = atoms.get_masses().tolist()
if atoms.has('momenta'):
atoms_dict['momenta'] = atoms.get_momenta().tolist()
if atoms.has('initial_magmoms'):
atoms_dict['magmoms'] = atoms.get_initial_magnetic_moments().tolist()
if atoms.has('initial_charges'):
atoms_dict['charges'] = atoms.get_initial_charges().tolist()
if not atoms.__dict__['_calc'] == None:
warnings.warn('Found calculator: ' + str(atoms.__dict__['_calc']))
if not atoms.__dict__['_constraints'] == []:
warnings.warn('Found constraint: ' +
str(atoms.__dict__['_constraints']))
return {'structure': json.dumps(atoms_dict)}
def get_steinhardt_parameter_structure(structure,
neighbor_method="cutoff",
cutoff=0,
n_clusters=2,
q=(4, 6),
averaged=False,
clustering=True):
"""
Calculate Steinhardts parameters
Args:
job (job): pyiron job
neighbor_method (str) : can be ['cutoff', 'voronoi']
cutoff (float) : can be 0 for adaptive cutoff or any other value
n_clusters (int) : number of clusters for K means clustering
q (list) : can be from 2-12, the required q values to be calculated
averaged (bool) : If True, calculates the averaged versions of the parameter
clustering (bool) : If True, cluster based on the q values
Returns:
q (list) : calculated q parameters
"""
sys = pc.System()
sys.read_inputfile(
pyiron_to_ase(structure),
format='ase',
is_triclinic=not UnfoldingPrism(structure.cell, digits=15).is_skewed())
sys.find_neighbors(method=neighbor_method, cutoff=cutoff)
sys.calculate_q(q, averaged=averaged)
sysq = sys.get_qvals(q, averaged=averaged)
if clustering:
cl = cluster.KMeans(n_clusters=n_clusters)
ind = cl.fit(list(zip(*sysq))).labels_ == 0
return sysq, ind
else:
return sysq
def get_steinhardt_parameter_structure(structure, cutoff=3.50, n_clusters=2, q=[4, 6]):
sys = pc.System()
sys.read_inputfile(
pyiron_to_ase(structure),
format='ase',
is_triclinic=not UnfoldingPrism(structure.cell, digits=15).is_skewed()
)
sys.find_neighbors(
method='cutoff',
cutoff=cutoff
)
sys.calculate_q(
q,
averaged=True
)
sysq = sys.get_qvals(
q,
averaged=True
)
cl = cluster.KMeans(
n_clusters=n_clusters
)
ind = cl.fit(list(zip(*sysq))).labels_ == 0
return sysq, ind
def pyiron_to_pymatgen(structure):
return AseAtomsAdaptor.get_structure(pyiron_to_ase(structure))
def structure(self, basis):
if not isinstance(basis, Atoms):
basis = pyiron_to_ase(basis)
self._structure = basis
def structure(self, structure):
if isinstance(structure, PAtoms):
structure = pyiron_to_ase(structure)
GenericInteractive.structure.fset(self, structure)
