def test_adsorption(self):
images = database_to_list('data/bajdichWO32018_ads.db')
images = images_connectivity(images)
slabs = database_to_list('data/bajdichWO32018_slabs.db')
slabs_dict = {}
for slab in slabs:
slabs_dict[slab.info['id']] = slab
for i, atoms in enumerate(images):
species = atoms.info['key_value_pairs']['species']
atoms.subsets['ads_atoms'] = \
slab_positions2ads_index(atoms, slabs[i], species)
if 'slab_atoms' not in atoms.subsets:
atoms.subsets['slab_atoms'] = slab_index(atoms)
if ('chemisorbed_atoms' not in atoms.subsets
or 'site_atoms' not in atoms.subsets
or 'ligand_atoms' not in atoms.subsets):
chemi, site, ligand = info2primary_index(atoms)
atoms.subsets['chemisorbed_atoms'] = chemi
atoms.subsets['site_atoms'] = site
atoms.subsets['ligand_atoms'] = ligand
attach_cations(atoms, anion_number=8)
charges = formal_charges(atoms)
atoms.set_initial_charges(charges)
gen = FeatureGenerator(nprocs=1)
train_fpv = default_fingerprinters(gen, 'chalcogenides')
matrix = gen.return_vec(images, train_fpv)
labels = gen.return_names(train_fpv)
if __name__ == '__main__':
for i, l in enumerate(labels):
print(i, l)
print(atoms.get_initial_charges())
self.assertTrue(len(labels) == np.shape(matrix)[1])
def test_recontruction(self):
images = database_to_list('data/ads_example.db')
slabs = []
for atoms in images:
slab = atoms.copy()
slab.pop(-1)
slabs.append(slab)
images = autogen_info(images)
slabs = images_connectivity(slabs)
image_pairs = zip(images, slabs)
r_index = check_reconstructions(image_pairs)
for i in range(len(images)):
species = images[i].info['key_value_pairs']['species']
connectivity2ads_index(images[i], species)
self.assertTrue(len(r_index) == 0)
def test_db_ads(self):
"""Test the feature generation."""
images = database_to_list('data/ads_example.db')
images = autogen_info(images)
print(str(len(images)) + ' training examples.')
gen = FeatureGenerator(nprocs=1)
train_fpv = default_fingerprinters(gen, 'adsorbates')
train_fpv += [gen.db_size, gen.ctime, gen.dbid, gen.delta_energy]
matrix = gen.return_vec(images, train_fpv)
labels = gen.return_names(train_fpv)
print(np.shape(matrix), type(matrix))
if __name__ == '__main__':
for i, l in enumerate(labels):
print(i, l)
self.assertTrue(len(labels) == np.shape(matrix)[1])
def test_db_ads(self):
"""Test the feature generation."""
images = database_to_list('data/ads_example.db')
[atoms.set_tags(np.zeros(len(atoms))) for atoms in images]
images = autogen_info(images)
print(str(len(images)) + ' training examples.')
gen = FeatureGenerator(nprocs=1)
train_fpv = default_fingerprinters(gen, 'adsorbates')
# Test db specific functions.
train_fpv += [gen.db_size, gen.ctime, gen.dbid, gen.delta_energy]
# Old CatApp AxBy fingerprints.
train_fpv += [gen.catapp_AB]
matrix = gen.return_vec(images, train_fpv)
labels = gen.return_names(train_fpv)
print(np.shape(matrix), type(matrix))
if __name__ == '__main__':
for i, l in enumerate(labels):
print(i, l)
self.assertTrue(len(labels) == np.shape(matrix)[1])