def test_generator(self):
"""Test the feature generation."""
atoms = molecule('HCOOH')
atoms.center(vacuum=5)
radii = [covalent_radii[z] for z in atoms.numbers]
atoms.connectivity = ase_connectivity(atoms, radii)
images = [atoms]
gen = FeatureGenerator()
features = gen.return_vec(images, [gen.get_autocorrelation])
np.testing.assert_allclose(features, truth)
def images_connectivity(images):
"""Return a list of atoms objects imported from an ase database.
Parameters
----------
fname : str
path/filename of ase database.
selection : list
search filters to limit the import.
"""
for atoms in tqdm(images):
if not hasattr(atoms, 'connectivity'):
radii = [default_catlearn_radius(z) for z in atoms.numbers]
atoms.connectivity = ase_connectivity(atoms, radii)
return images
def images_connectivity(images, check_cn_max=False):
"""Return a list of atoms objects imported from an ase database.
Parameters
----------
fname : str
path/filename of ase database.
selection : list
search filters to limit the import.
"""
for atoms in tqdm(images):
if not hasattr(atoms, 'connectivity'):
radii = [default_catlearn_radius(z) for z in atoms.numbers]
atoms.connectivity = ase_connectivity(atoms, cutoffs=radii)
if check_cn_max:
n_connections = np.sum(atoms.connectivity, axis=0)
if max(n_connections) > 12:
msg = 'atom has more than 12 connections.'
if 'id' in atoms.info:
msg += str(atoms.info['id'])
warnings.warn(msg)
return images