def make_d_to_p(self):
result = []
for site in self._defect_structure:
distances = Distances(self._perfect_structure, site.frac_coords,
self.same_distance_criterion)
result.append(distances.atom_idx_at_center(specie=site.specie))
return result
def _atom_projection(self, structure_from, structure_to, specie=True):
result = []
for site in structure_from:
distances = Distances(structure_to, site.frac_coords,
self.dist_tol)
specie = site.specie if specie else None
result.append(distances.atom_idx_at_center(specie=specie))
return result
def test_distances_w_specie(ortho_conventional):
distances = Distances(ortho_conventional, center_coord=[0.25, 0.26, 0.0])
expected = [float("inf")] * 4 + [
4.030645109656765, 1.9068560512005097, 1.9990247622278219,
3.985737071107425
]
np.testing.assert_almost_equal(
distances.distances(remove_self=False, specie="He"), expected)
def test_distances_mapped_atom_idx(ortho_conventional):
distances = Distances(ortho_conventional, center_coord=[0.0, 0.0, 0.49])
assert distances.atom_idx_at_center(specie="He") == 4
distances = Distances(ortho_conventional, center_coord=[0.251, 0.25, 0.25])
assert distances.atom_idx_at_center(specie="H") is None
distances = Distances(ortho_conventional, center_coord=[0.0, 0.0, 0.5])
assert distances.atom_idx_at_center(specie="Li") == None
def test_coord_distances(mocker, ortho_conventional):
mock = mocker.patch("pydefect.util.structure_tools.defaults")
mock.cutoff_distance_factor = 3.9051248 / 2.5 + 1e-5 # = 1.562
distances = Distances(ortho_conventional, center_coord=[0.5, 0.5, 0.5])
actual = distances.coordination(include_on_site=True)
expected = Coordination({"H": [2.5, 3.0, 3.5], "He": [0.0, 3.91]},
3.905,
neighboring_atom_indices=[1, 2, 3, 4, 7])
assert actual == expected
actual = distances.coordination()
expected = Coordination({"H": [2.5, 3.0, 3.5], "He": [3.91]},
3.905,
neighboring_atom_indices=[1, 2, 3, 4])
assert actual == expected
def neighboring_atom_indices(self, cutoff_factor=None):
distances = []
for v in self.removed_indices:
distances.append(
Distances(self._defect_structure,
self._perfect_structure[v].frac_coords,
self.dist_tol))
for i in self.inserted_indices:
distances.append(
Distances(self._defect_structure,
self._defect_structure[i].frac_coords,
self.dist_tol))
result = set()
for d in distances:
result.update(
d.coordination(
cutoff_factor=cutoff_factor).neighboring_atom_indices)
return sorted(list(result))
def find_inequivalent_coords(structure: Structure,
df: DataFrame) -> List[CoordInfo]:
result = []
initial_sg = StructureSymmetrizer(structure).sg_number
added_structure = Structure.from_dict(structure.as_dict())
start_index = len(structure)
for _, column in df.iterrows():
coords = [column.a, column.b, column.c]
assert Element.Og not in structure.composition.elements
added_structure.append(Element.Og, coords)
end_index = len(added_structure)
symmetrizer = StructureSymmetrizer(added_structure)
if initial_sg != symmetrizer.sg_number:
logger.warning("The symmetry has changed, meaning all the symmetry "
"equivalent sites do not exist.")
sym_data = symmetrizer.spglib_sym_data
_indices = [i for i in range(start_index, end_index)]
repr_atom_pairs = zip(sym_data["equivalent_atoms"][_indices], _indices)
key = lambda x: x[0]
for _, equiv_sites in groupby(sorted(repr_atom_pairs, key=key), key=key):
coords, quantity = [], []
for repr_idx, atom_idx in equiv_sites:
fcoord = added_structure[atom_idx].frac_coords
coords.append(tuple(fcoord))
key = "ave_value" if "ave_value" in df else "value"
quantity.append(df[key][atom_idx - start_index])
if repr_idx == atom_idx:
site_sym = sym_data["site_symmetry_symbols"][atom_idx]
distances = Distances(structure, fcoord)
coordination = distances.coordination()
coord_info = CoordInfo(site_symmetry=remove_dot(site_sym),
coordination=coordination,
frac_coords=coords,
quantities=quantity)
result.append(coord_info)
return result
def test_distances(ortho_conventional):
distances = Distances(ortho_conventional, center_coord=[0.25, 0.25, 0.25])
np.testing.assert_almost_equal(distances.distances(), [5.2440442 / 2] * 8)
distances = Distances(ortho_conventional, center_coord=[0.5, 0.5, 0.5])
expected = [5.2440442, 3.5, 3.0, 2.5, 3.9051248, 4.3011626, 4.6097722]
np.testing.assert_almost_equal(distances.distances(), expected)
def neighboring_atom_indices(self):
result = set()
for v in self.vacancy_indices:
distances = Distances(self._defective_structure,
self._perfect_structure[v].frac_coords)
result.update(distances.coordination().neighboring_atom_indices)
for i in self.inserted_indices:
distances = Distances(self._defective_structure,
self._defective_structure[i].frac_coords)
result.update(distances.coordination().neighboring_atom_indices)
return sorted(list(result))
def interstitial_coords(self, idx: int):
interstitial = self.interstitials[idx]
distances = Distances(self.structure, interstitial.frac_coords)
return distances.coordination(include_on_site=True)
def coords(self, name):
site = self.sites[name]
coord = list(self.structure[site.equivalent_atoms[0]].frac_coords)
distances = Distances(self.structure, coord)
return distances.coordination()
def test_shortest_distances(ortho_conventional):
distances = Distances(ortho_conventional, center_coord=[0.5, 0.5, 0.5])
assert distances.shortest_distance == 2.5
def make_p_to_d(self):
result = []
for site in self._perfect_structure:
distances = Distances(self._defective_structure, site.frac_coords)
result.append(distances.atom_idx_at_center(specie=site.specie))
return result
