def apply_transformation(self, structure):
# get the new sites
new_sites = self._voronoi_points(structure)
if self._midpoints:
dms = self._delaunay_midpoints(structure)
for i in range(len(new_sites)):
new_sites[i].extend(dms[i])
# insert the new sites with dummy species
se = structure
dummy = DummySpecie()
for i, sites_list in enumerate(new_sites):
for ns in sites_list:
if self._bl == {}:
se.append(self._sp, ns, coords_are_cartesian=True, validate_proximity=False)
elif structure[i].specie in self._bl.keys():
bv = ns - structure[i].coords
bv *= self._bl[structure[i].specie] / np.linalg.norm(bv)
coords = structure[i].coords + bv
se.append_site(self._sp, coords, coords_are_cartesian=True, validate_proximity=False)
logging.debug("Starting moving to unit cell")
se = IStructure.get_reduced_structure(se)
logging.debug("Finished moving to unit cell")
structure = se # .modified_structure
# delete sites that are closer than the bond distance
to_delete = []
for sp, d in self._bl.items():
neighbors = structure.get_all_neighbors(d * 0.999)
for i in range(len(structure)):
if structure[i].specie == dummy:
for n in neighbors[i]:
if n[0].specie == sp:
to_delete.append(i)
break
se.remove_sites(to_delete)
# replace the dummy species to get the correct composition
if self._n:
# amt = self._n / se.modified_structure.composition[dummy]
amt = self._n / se.composition[dummy]
se.replace_species({dummy: {self._sp: amt}})
return se # .modified_structure
