def get_m_graph_from_site_data(s_data):
mol = Molecule.from_sites([Site.from_dict(isite)
for isite in s_data['local_graph']['sites']])
mg = MoleculeGraph.with_empty_graph(mol)
for i in range(1, len(mg)):
mg.add_edge(0, i)
return mg
def get_scene(data, site_index):
site_data = data['site_data'][site_index]
mol = Molecule.from_sites([Site.from_dict(isite)
for isite in site_data['local_graph']['sites']])
scene = get_m_graph_from_mol(mol).get_scene()
scene.name = "ref-site"
return scene
def compose_vasp_movie(self, diffusion_sites, empty_lattice):
for i in diffusion_sites:
site_from_dict = Site.from_dict(i)
species = str(site_from_dict.specie)
coords = site_from_dict.coords
empty_lattice.append(species, coords, coords_are_cartesian=True)
# writer.write_structure(empty_lattice, "POSCAR.path.vasp")
return
def get_closest_matched(ref_data, site_index, all_random_sites):
"""
Find the 10 most similar sites
Args:
ref_data:
site_index:
all_random_sites:
Returns:
"""
ref_soap_vec = ref_data['site_data'][site_index]['soap_vec']
def similarity(random_site_data):
return np.abs(1 - np.dot(random_site_data, ref_soap_vec) / np.dot(ref_soap_vec, ref_soap_vec))
all_sites_with_sim = [(isite, similarity(isite['soap_vec']))
for isite in all_random_sites]
all_sites_with_sim.sort(key=lambda x: x[1])
matched_res = []
for itr, (site_info, proj) in enumerate(all_sites_with_sim[:3]):
mol = Molecule.from_sites([Site.from_dict(isite)
for isite in site_info['local_graph']['sites']])
mg = get_m_graph_from_mol(mol)
scene = mg.get_scene()
scene.name = f"matched-site-{itr}"
matched_res.append(site_info["task_id"] + " " + f"{proj:0.4f}")
matched_res.append(Simple3DScene(
sceneSize=210,
inletSize=150,
inletPadding=0,
axisView='SW',
data=scene
))
return matched_res