def test_nbors():
box = [[5.493642, 0, 0], [0, 5.493642, 0], [0, 0, 5.493642]]
elements = ["Si", "Si", "Si", "Si", "Si", "Si", "Si", "Si"]
coords = [
[0, 0, 0],
[0.25, 0.25, 0.25],
[0.000000, 0.500000, 0.500000],
[0.250000, 0.750000, 0.750000],
[0.500000, 0.000000, 0.500000],
[0.750000, 0.250000, 0.750000],
[0.500000, 0.500000, 0.000000],
[0.750000, 0.750000, 0.250000],
]
coords = np.array(coords)
# box = [[2.715, 2.715, 0], [0, 2.715, 2.715], [2.715, 0, 2.715]]
# coords = [[0, 0, 0], [0.25, 0.25, 0.25]]
# elements = ["Si", "Si"]
Si = Atoms(lattice_mat=box, coords=coords, elements=elements)
nbr = NeighborsAnalysis(Si)
nb = nbr.get_all_distributions
tmp = round((nb["rdf"][-3]), 2)
assert (tmp) == (4.08)
nbr.get_rdf(plot=True)
# nbr.ang_dist(nbor_info=info,plot=True)
nbr.ang_dist_first(plot=True)
nbr.ang_dist_second(plot=True)
nbr.get_ddf(plot=True)
angs = nbr.atomwise_angle_dist()
ardf = nbr.atomwise_radial_dist()
cmd = "rm *.png"
os.system(cmd)
def from_atoms(
atoms=None,
get_prim=False,
zero_diag=False,
node_atomwise_angle_dist=False,
node_atomwise_rdf=False,
features="basic",
enforce_c_size=10.0,
max_n=100,
max_cut=5.0,
verbose=False,
make_colormap=True,
):
"""
Get Networkx graph. Requires Networkx installation.
Args:
atoms: jarvis.core.Atoms object.
rcut: cut-off after which distance will be set to zero
in the adjacency matrix.
features: Node features.
'atomic_number': graph with atomic numbers only.
'cfid': 438 chemical descriptors from CFID.
'basic':10 features
'atomic_fraction': graph with atomic fractions
in 103 elements.
array: array with CFID chemical descriptor names.
See: jarvis/core/specie.py
enforce_c_size: minimum size of the simulation cell in Angst.
"""
if get_prim:
atoms = atoms.get_primitive_atoms
dim = get_supercell_dims(atoms=atoms, enforce_c_size=enforce_c_size)
atoms = atoms.make_supercell(dim)
adj = np.array(atoms.raw_distance_matrix.copy())
# zero out edges with bond length greater than threshold
adj[adj >= max_cut] = 0
if zero_diag:
np.fill_diagonal(adj, 0.0)
nodes = np.arange(atoms.num_atoms)
if features == "atomic_number":
node_attributes = np.array(
[[np.array(Specie(i).Z)] for i in atoms.elements],
dtype="float",
)
if features == "atomic_fraction":
node_attributes = []
fracs = atoms.composition.atomic_fraction_array
for i in fracs:
node_attributes.append(np.array([float(i)]))
node_attributes = np.array(node_attributes)
elif features == "basic":
feats = [
"Z",
"coulmn",
"row",
"X",
"atom_rad",
"nsvalence",
"npvalence",
"ndvalence",
"nfvalence",
"first_ion_en",
"elec_aff",
]
node_attributes = []
for i in atoms.elements:
tmp = []
for j in feats:
tmp.append(Specie(i).element_property(j))
node_attributes.append(tmp)
node_attributes = np.array(node_attributes, dtype="float")
elif features == "cfid":
node_attributes = np.array(
[np.array(Specie(i).get_descrp_arr) for i in atoms.elements],
dtype="float",
)
elif isinstance(features, list):
node_attributes = []
for i in atoms.elements:
tmp = []
for j in features:
tmp.append(Specie(i).element_property(j))
node_attributes.append(tmp)
node_attributes = np.array(node_attributes, dtype="float")
else:
print("Please check the input options.")
if node_atomwise_rdf or node_atomwise_angle_dist:
nbr = NeighborsAnalysis(atoms,
max_n=max_n,
verbose=verbose,
max_cut=max_cut)
if node_atomwise_rdf:
node_attributes = np.concatenate(
(node_attributes, nbr.atomwise_radial_dist()), axis=1)
node_attributes = np.array(node_attributes, dtype="float")
if node_atomwise_angle_dist:
node_attributes = np.concatenate(
(node_attributes, nbr.atomwise_angle_dist()), axis=1)
node_attributes = np.array(node_attributes, dtype="float")
# construct edge list
uv = []
edge_features = []
for ii, i in enumerate(atoms.elements):
for jj, j in enumerate(atoms.elements):
bondlength = adj[ii, jj]
if bondlength > 0:
uv.append((ii, jj))
edge_features.append(bondlength)
edge_attributes = edge_features
if make_colormap:
sps = atoms.uniq_species
color_dict = random_colors(number_of_colors=len(sps))
new_colors = {}
for i, j in color_dict.items():
new_colors[sps[i]] = j
color_map = []
for ii, i in enumerate(atoms.elements):
color_map.append(new_colors[i])
return Graph(
nodes=nodes,
edges=uv,
node_attributes=np.array(node_attributes),
edge_attributes=np.array(edge_attributes),
color_map=color_map,
)