def test_ase_nl(self):
"""Function to test the ase wrapper."""
# Connect database generated by a GA search.
gadb = DataConnection('{}/data/gadb.db'.format(wkdir))
# Get all relaxed candidates from the db file.
all_cand = gadb.get_all_relaxed_candidates(use_extinct=False)
nl = ase_neighborlist(all_cand[0])
self.assertEqual(len(all_cand[0]), len(nl))
def test_feature_base(self):
"""Test the base feature generator."""
gadb = DataConnection('{}/data/gadb.db'.format(wkdir))
all_cand = gadb.get_all_relaxed_candidates()
f = BaseGenerator()
nl = ase_neighborlist(all_cand[0])
assert f.get_neighborlist(all_cand[0]) == nl
pos = all_cand[0].get_positions()
assert np.allclose(f.get_positions(all_cand[0]), pos)
def ase_to_networkx(atoms, cutoffs=None):
"""Make the NetworkX graph form ASE atoms object.
The graph is dependent on the generation of the neighborlist. Currently
this is handled by the version implemented in ASE.
Parameters
----------
atoms : object
An ASE atoms object.
cutoffs : list
A list of distance paramteres for each atom.
Returns
-------
atoms_graph : object
A networkx graph object.
"""
msg = 'Please pass an ASE atoms object, not a {}'.format(type(atoms))
assert isinstance(atoms, Atoms), msg
an = atoms.get_atomic_numbers()
ats = list(range(len(an)))
atoms_graph = nx.Graph()
atoms_graph.add_nodes_from(ats)
for n in atoms_graph:
atoms_graph.add_node(n, atomic_number=an[n])
extend_atoms_class(atoms)
nl = atoms.get_neighborlist()
if nl is None:
nl = ase_neighborlist(atoms, cutoffs)
for i in nl:
tup = ((i, nl[i][j]) for j in range(len(nl[i])))
atoms_graph.add_edges_from(tup)
return atoms_graph
def make_neighborlist(self, candidate, neighbor_number=1):
"""Function to generate the neighborlist.
Parameters
----------
candidate : object
Target data object on which to generate neighbor list.
dx : dict
Buffer to calculate nearest neighbor pairs in dict format:
dx = {atomic_number: buffer}.
neighbor_number : int
Neighbor shell.
"""
if self.dtype == 'atoms':
extend_atoms_class(candidate)
nl = ase_neighborlist(candidate)
else:
raise NotImplementedError('{} data type not implemented.'.format(
self.dtype))
candidate.set_neighborlist(nl)
return nl