def convert_cd_to_de( cd, b_cse):
"""
As of pymatgen v2.0, ComputedDefect objects were deprecated in favor
of DefectEntry objects in pymatgen.analysis.defects.core
This function takes a ComputedDefect (either as a dict or object) and
converts it into a DefectEntry object in order to handle legacy
PyCDT creation within the current paradigm of PyCDT.
:param cd (dict or ComputedDefect object): ComputedDefect as an object or as a dictionary
:params b_cse (dict or ComputedStructureEntry object): ComputedStructureEntry of bulk entry
associated with the ComputedDefect.
:return: de (DefectEntry): Resulting DefectEntry object
"""
if type(cd) != dict:
cd = cd.as_dict()
if type(b_cse) != dict:
b_cse = b_cse.as_dict()
bulk_sc_structure = Structure.from_dict( b_cse["structure"])
#modify defect_site as required for Defect object, confirming site exists in bulk structure
site_cls = cd["site"]
defect_site = PeriodicSite.from_dict( site_cls)
def_nom = cd["name"].lower()
if "sub_" in def_nom or "as_" in def_nom:
#modify site object for substitution site of Defect object
site_cls["species"][0]["element"] = cd["name"].split("_")[2]
defect_site = PeriodicSite.from_dict( site_cls)
poss_deflist = sorted(
bulk_sc_structure.get_sites_in_sphere(defect_site.coords, 0.1, include_index=True), key=lambda x: x[1])
if len(poss_deflist) != 1:
raise ValueError("ComputedDefect to DefectEntry conversion failed. "
"Could not determine periodic site position in bulk supercell.")
# create defect object
if "vac_" in def_nom:
defect_obj = Vacancy(bulk_sc_structure, defect_site, charge=cd["charge"])
elif "as_" in def_nom or "sub_" in def_nom:
defect_obj = Substitution(bulk_sc_structure, defect_site, charge=cd["charge"])
elif "int_" in def_nom:
defect_obj = Interstitial(bulk_sc_structure, defect_site, charge=cd["charge"])
else:
raise ValueError("Could not recognize defect type for {}".format( cd["name"]))
# assign proper energy and parameter metadata
uncorrected_energy = cd["entry"]["energy"] - b_cse["energy"]
def_path = os.path.split( cd["entry"]["data"]["locpot_path"])[0]
bulk_path = os.path.split( b_cse["data"]["locpot_path"])[0]
p = {"defect_path": def_path,
"bulk_path": bulk_path,
"encut": cd["entry"]["data"]["encut"]}
de = DefectEntry( defect_obj, uncorrected_energy, parameters = p)
return de
def get_connected_sites(self, n, jimage=(0, 0, 0)):
"""
Returns a named tuple of neighbors of site n:
periodic_site, jimage, index, weight.
Index is the index of the corresponding site
in the original structure, weight can be
None if not defined.
:param n: index of Site in Structure
:param jimage: lattice vector of site
:return: list of ConnectedSite tuples,
sorted by closest first
"""
connected_sites = set()
out_edges = [(u, v, d, 'out')
for u, v, d in self.graph.out_edges(n, data=True)]
in_edges = [(u, v, d, 'in')
for u, v, d in self.graph.in_edges(n, data=True)]
for u, v, d, dir in out_edges + in_edges:
to_jimage = d['to_jimage']
if dir == 'in':
u, v = v, u
to_jimage = np.multiply(-1, to_jimage)
site_d = self.structure[v].as_dict()
site_d['abc'] = np.add(site_d['abc'], to_jimage).tolist()
to_jimage = tuple(map(int, np.add(to_jimage, jimage)))
periodic_site = PeriodicSite.from_dict(site_d)
weight = d.get('weight', None)
# from_site if jimage arg != (0, 0, 0)
relative_jimage = np.subtract(to_jimage, jimage)
dist = self.structure[u].distance(self.structure[v],
jimage=relative_jimage)
connected_site = ConnectedSite(periodic_site=periodic_site,
jimage=to_jimage,
index=v,
weight=weight,
dist=dist)
connected_sites.add(connected_site)
# return list sorted by closest sites first
connected_sites = list(connected_sites)
connected_sites.sort(key=lambda x: x.dist)
return connected_sites
def get_connected_sites(self, n, jimage=(0, 0, 0)):
"""
Returns a named tuple of neighbors of site n:
periodic_site, jimage, index, weight.
Index is the index of the corresponding site
in the original structure, weight can be
None if not defined.
:param n: index of Site in Structure
:param jimage: lattice vector of site
:return: list of ConnectedSite tuples,
sorted by closest first
"""
connected_sites = set()
out_edges = [(u, v, d, 'out') for u, v, d in self.graph.out_edges(n, data=True)]
in_edges = [(u, v, d, 'in') for u, v, d in self.graph.in_edges(n, data=True)]
for u, v, d, dir in out_edges + in_edges:
to_jimage = d['to_jimage']
if dir == 'in':
u, v = v, u
to_jimage = np.multiply(-1, to_jimage)
site_d = self.structure[v].as_dict()
site_d['abc'] = np.add(site_d['abc'], to_jimage).tolist()
to_jimage = tuple(map(int, np.add(to_jimage, jimage)))
periodic_site = PeriodicSite.from_dict(site_d)
weight = d.get('weight', None)
# from_site if jimage arg != (0, 0, 0)
relative_jimage = np.subtract(to_jimage, jimage)
dist = self.structure[u].distance(self.structure[v], jimage=relative_jimage)
connected_site = ConnectedSite(periodic_site=periodic_site,
jimage=to_jimage,
index=v,
weight=weight,
dist=dist)
connected_sites.add(connected_site)
# return list sorted by closest sites first
connected_sites = list(connected_sites)
connected_sites.sort(key=lambda x: x.dist)
return connected_sites