def get_pair_distances(structure: Structure, r_max: float = 8.0) -> List[dict]:
"""
Get pair distances from structure.
The output will be a list of of dictionary, for example
[{"specie": "Mo",
"neighbors": {"S": [1.0, 2.0, ...], "Fe": [1.2, 3.0, ...]}},
{"specie": "Fe",
"neighbors": {"Mo": [1.0, 3.0, ...]}}]
it will be fairly easy to construct radial distribution func, etc
from here
Args:
structure (Structure): pymatgen Structure
r_max (float): maximum radius to consider
Returns:
"""
index1, index2, _, distances = structure.get_neighbor_list(r_max)
species = np.array([str(i.specie) for i in structure.sites])
res = [{"specie": i, "neighbors": {}} for i in species]
neighbor_species = species[index2]
tuples = np.array(list(zip(index1, neighbor_species)), dtype=[("index", "i4"), ("specie", "<U10")])
unique_tuples, indices = np.unique(tuples, return_inverse=True)
for index, unique_tuple in enumerate(unique_tuples):
res[unique_tuple[0]]["neighbors"][unique_tuple[1]] = distances[tuples == unique_tuple]
return res
def _get_nbr_data(
self,
crystal: Structure) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Get neighbours for every site.
Args:
crystal (Structure): pymatgen Structure to get neighbours for
Returns:
tuple containing:
- np.ndarray: Site indices
- np.ndarray: Neighbour indices
- np.ndarray: Distances between sites and neighbours
"""
self_idx, nbr_idx, _, nbr_dist = crystal.get_neighbor_list(
self.radius, numerical_tol=1e-8)
if self.max_num_nbr is not None:
_self_idx, _nbr_idx, _nbr_dist = [], [], []
for i, g in groupby(zip(self_idx, nbr_idx, nbr_dist),
key=lambda x: x[0]):
s, n, d = zip(*sorted(g, key=lambda x: x[2]))
_self_idx.extend(s[:self.max_num_nbr])
_nbr_idx.extend(n[:self.max_num_nbr])
_nbr_dist.extend(d[:self.max_num_nbr])
self_idx = np.array(_self_idx)
nbr_idx = np.array(_nbr_idx)
nbr_dist = np.array(_nbr_dist)
return self_idx, nbr_idx, nbr_dist
