def test_all_nn_classes(self):
self.assertEqual(MinimumDistanceNN(cutoff=5, get_all_sites=True).get_cn(
self.cscl, 0), 14)
self.assertEqual(MinimumDistanceNN().get_cn(self.diamond, 0), 4)
self.assertEqual(MinimumDistanceNN().get_cn(self.nacl, 0), 6)
self.assertEqual(MinimumDistanceNN().get_cn(self.lifepo4, 0), 6)
self.assertEqual(MinimumDistanceNN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertEqual(MinimumDistanceNN(tol=0.1).get_cn(self.mos2, 0), 6)
for image in MinimumDistanceNN(tol=0.1).get_nn_images(self.mos2, 0):
self.assertTrue(image in [(0, 0, 0), (0, 1, 0), (-1, 0, 0),
(0, 0, 0), (0, 1, 0), (-1, 0, 0)])
okeeffe = MinimumOKeeffeNN(tol=0.01)
self.assertEqual(okeeffe.get_cn(self.diamond, 0), 4)
self.assertEqual(okeeffe.get_cn(self.nacl, 0), 6)
self.assertEqual(okeeffe.get_cn(self.cscl, 0), 8)
self.assertEqual(okeeffe.get_cn(self.lifepo4, 0), 2)
virenn = MinimumVIRENN(tol=0.01)
self.assertEqual(virenn.get_cn(self.diamond, 0), 4)
self.assertEqual(virenn.get_cn(self.nacl, 0), 6)
self.assertEqual(virenn.get_cn(self.cscl, 0), 8)
self.assertEqual(virenn.get_cn(self.lifepo4, 0), 2)
brunner_recip = BrunnerNN_reciprocal(tol=0.01)
self.assertEqual(brunner_recip.get_cn(self.diamond, 0), 4)
self.assertEqual(brunner_recip.get_cn(self.nacl, 0), 6)
self.assertEqual(brunner_recip.get_cn(self.cscl, 0), 14)
self.assertEqual(brunner_recip.get_cn(self.lifepo4, 0), 6)
brunner_rel = BrunnerNN_relative(tol=0.01)
self.assertEqual(brunner_rel.get_cn(self.diamond, 0), 4)
self.assertEqual(brunner_rel.get_cn(self.nacl, 0), 6)
self.assertEqual(brunner_rel.get_cn(self.cscl, 0), 14)
self.assertEqual(brunner_rel.get_cn(self.lifepo4, 0), 6)
brunner_real = BrunnerNN_real(tol=0.01)
self.assertEqual(brunner_real.get_cn(self.diamond, 0), 4)
self.assertEqual(brunner_real.get_cn(self.nacl, 0), 6)
self.assertEqual(brunner_real.get_cn(self.cscl, 0), 14)
self.assertEqual(brunner_real.get_cn(self.lifepo4, 0), 30)
econn = EconNN()
self.assertEqual(econn.get_cn(self.diamond, 0), 4)
self.assertEqual(econn.get_cn(self.nacl, 0), 6)
self.assertEqual(econn.get_cn(self.cscl, 0), 14)
self.assertEqual(econn.get_cn(self.lifepo4, 0), 6)
voroinn = VoronoiNN(tol=0.5)
self.assertEqual(voroinn.get_cn(self.diamond, 0), 4)
self.assertEqual(voroinn.get_cn(self.nacl, 0), 6)
self.assertEqual(voroinn.get_cn(self.cscl, 0), 8)
self.assertEqual(voroinn.get_cn(self.lifepo4, 0), 6)
crystalnn = CrystalNN()
self.assertEqual(crystalnn.get_cn(self.diamond, 0), 4)
self.assertEqual(crystalnn.get_cn(self.nacl, 0), 6)
self.assertEqual(crystalnn.get_cn(self.cscl, 0), 8)
self.assertEqual(crystalnn.get_cn(self.lifepo4, 0), 6)
def test_all_nn_classes(self):
self.assertAlmostEqual(MinimumDistanceNN().get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(MinimumDistanceNN().get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(
MinimumDistanceNN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
MinimumDistanceNN(tol=0.1).get_cn(self.mos2, 0), 6)
for image in MinimumDistanceNN(tol=0.1).get_nn_images(self.mos2, 0):
self.assertTrue(image in [(0, 0, 0), (0, 1,
0), (-1, 0,
0), (0, 0,
0), (0, 1,
0), (-1, 0,
0)])
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
MinimumVIRENN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(MinimumVIRENN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(MinimumVIRENN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
BrunnerNN_reciprocal(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(
BrunnerNN_reciprocal(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(
BrunnerNN_reciprocal(tol=0.01).get_cn(self.cscl, 0), 14)
self.assertAlmostEqual(
BrunnerNN_relative(tol=0.01).get_cn(self.diamond, 0), 16)
self.assertAlmostEqual(
BrunnerNN_relative(tol=0.01).get_cn(self.nacl, 0), 18)
self.assertAlmostEqual(
BrunnerNN_relative(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
BrunnerNN_real(tol=0.01).get_cn(self.diamond, 0), 16)
self.assertAlmostEqual(
BrunnerNN_real(tol=0.01).get_cn(self.nacl, 0), 18)
self.assertAlmostEqual(
BrunnerNN_real(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(EconNN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(EconNN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(EconNN(tol=0.01).get_cn(self.cscl, 0), 14)
self.assertAlmostEqual(VoronoiNN(tol=0.5).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(VoronoiNN(tol=0.5).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(VoronoiNN(tol=0.5).get_cn(self.cscl, 0), 8)
def test_all_nn_classes(self):
self.assertAlmostEqual(MinimumDistanceNN().get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(MinimumDistanceNN().get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(
MinimumDistanceNN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
MinimumDistanceNN(tol=0.1).get_cn(self.mos2, 0), 6)
for image in MinimumDistanceNN(tol=0.1).get_nn_images(self.mos2, 0):
self.assertTrue(image in [[0, 0, 0], [0, 1, 0], [-1, 0, 0], \
[0, 0, 0], [0, 1, 0], [-1, 0, 0]])
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
MinimumVIRENN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(MinimumVIRENN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(MinimumVIRENN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(BrunnerNN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(BrunnerNN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(BrunnerNN(tol=0.01).get_cn(self.cscl, 0), 14)
self.assertAlmostEqual(
BrunnerNN(mode="real", tol=0.01).get_cn(self.diamond, 0), 16)
self.assertAlmostEqual(
BrunnerNN(mode="real", tol=0.01).get_cn(self.nacl, 0), 18)
self.assertAlmostEqual(
BrunnerNN(mode="real", tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
BrunnerNN(mode="relative", tol=0.01).get_cn(self.diamond, 0), 16)
self.assertAlmostEqual(
BrunnerNN(mode="relative", tol=0.01).get_cn(self.nacl, 0), 18)
self.assertAlmostEqual(
BrunnerNN(mode="relative", tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(EconNN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(EconNN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(EconNN(tol=0.01).get_cn(self.cscl, 0), 14)
self.assertAlmostEqual(VoronoiNN_modified().get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(VoronoiNN_modified().get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(VoronoiNN_modified().get_cn(self.cscl, 0), 8)
def test_all_nn_classes(self):
self.assertAlmostEqual(MinimumDistanceNN().get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(MinimumDistanceNN().get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(
MinimumDistanceNN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
MinimumDistanceNN(tol=0.1).get_cn(self.mos2, 0), 6)
for image in MinimumDistanceNN(tol=0.1).get_nn_images(self.mos2, 0):
self.assertTrue(image in [[0, 0, 0], [0, 1, 0], [-1, 0, 0], \
[0, 0, 0], [0, 1, 0], [-1, 0, 0]])
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(
MinimumOKeeffeNN(tol=0.01).get_cn(self.cscl, 0), 8)
self.assertAlmostEqual(
MinimumVIRENN(tol=0.01).get_cn(self.diamond, 0), 4)
self.assertAlmostEqual(MinimumVIRENN(tol=0.01).get_cn(self.nacl, 0), 6)
self.assertAlmostEqual(MinimumVIRENN(tol=0.01).get_cn(self.cscl, 0), 8)
def test_from_local_env_and_equality_and_diff(self):
nn = MinimumDistanceNN()
sg = StructureGraph.with_local_env_strategy(self.structure, nn)
self.assertEqual(sg.graph.number_of_edges(), 6)
nn2 = MinimumOKeeffeNN()
sg2 = StructureGraph.with_local_env_strategy(self.structure, nn2)
self.assertTrue(sg == sg2)
self.assertTrue(sg == self.mos2_sg)
# TODO: find better test case where graphs are different
diff = sg.diff(sg2)
self.assertEqual(diff['dist'], 0)
def test_extract_molecules(self):
structure_file = os.path.join(os.path.dirname(__file__), "..", "..", "..",
'test_files/C26H16BeN2O2S2.cif')
s = Structure.from_file(structure_file)
nn = MinimumOKeeffeNN()
sg = StructureGraph.with_local_env_strategy(s, nn)
molecules = sg.get_subgraphs_as_molecules()
self.assertEqual(molecules[0].composition.formula, "Be1 H16 C26 S2 N2 O2")
self.assertEqual(len(molecules), 1)
molecules = self.mos2_sg.get_subgraphs_as_molecules()
self.assertEqual(len(molecules), 0)
def get_neighbors_of_site_with_index_future(struct, n, approach="min_dist", \
delta=0.1, cutoff=10.0):
"""
Returns the neighbors of a given site using a specific neighbor-finding
method.
Args:
struct (Structure): input structure.
n (int): index of site in Structure object for which motif type
is to be determined.
approach (str): type of neighbor-finding approach, where
"min_dist" will use the MinimumDistanceNN class,
"voronoi" the VoronoiNN class, "min_OKeeffe" the
MinimumOKeeffe class, and "min_VIRE" the MinimumVIRENN class.
delta (float): tolerance involved in neighbor finding.
cutoff (float): (large) radius to find tentative neighbors.
Returns: neighbor sites.
"""
warnings.warn('This function will go into Pymatgen very soon.')
if approach == "min_dist":
return MinimumDistanceNN(tol=delta, cutoff=cutoff).get_nn(
struct, n)
elif approach == "voronoi":
return VoronoiNN(tol=delta, cutoff=cutoff).get_nn(
struct, n)
elif approach == "min_OKeeffe":
return MinimumOKeeffeNN(tol=delta, cutoff=cutoff).get_nn(
struct, n)
elif approach == "min_VIRE":
return MinimumVIRENN(tol=delta, cutoff=cutoff).get_nn(
struct, n)
else:
raise RuntimeError("unsupported neighbor-finding method ({}).".format(
approach))
def get_NNs_pm(atoms, site_idx, NN_method):
"""
Get coordinating atoms to the adsorption site
Args:
atoms (Atoms object): atoms object of MOF
site_idx (int): ASE index of adsorption site
NN_method (string): string representing the desired Pymatgen
nearest neighbor algorithm:
refer to http://pymatgen.org/_modules/pymatgen/analysis/local_env.html
Returns:
neighbors_idx (list of ints): ASE indices of coordinating atoms
"""
#Convert ASE Atoms object to Pymatgen Structure object
bridge = pm_ase.AseAtomsAdaptor()
struct = bridge.get_structure(atoms)
#Select Pymatgen NN algorithm
NN_method = NN_method.lower()
if NN_method == 'vire':
nn_object = MinimumVIRENN()
elif NN_method == 'voronoi':
nn_object = VoronoiNN()
elif NN_method == 'jmol':
nn_object = JmolNN()
elif NN_method == 'min_dist':
nn_object = MinimumDistanceNN()
elif NN_method == 'okeeffe':
nn_object = MinimumOKeeffeNN()
elif NN_method == 'brunner_real':
nn_object = BrunnerNN_real()
elif NN_method == 'brunner_recpirocal':
nn_object = BrunnerNN_reciprocal()
elif NN_method == 'brunner_relative':
nn_object = BrunnerNN_relative()
elif NN_method == 'econ':
nn_object = EconNN()
elif NN_method == 'dict':
#requires a cutoff dictionary located in the pwd
nn_object = CutOffDictNN(cut_off_dict='cut_off_dict.txt')
elif NN_method == 'critic2':
nn_object = Critic2NN()
elif NN_method == 'openbabel':
nn_object = OpenBabelNN()
elif NN_method == 'covalent':
nn_object = CovalentBondNN()
elif NN_method == 'crystal':
nn_object = CrystalNN(porous_adjustment=True)
elif NN_method == 'crystal_nonporous':
nn_object = CrystalNN(porous_adjustment=False)
else:
raise ValueError('Invalid NN algorithm specified')
#Find coordinating atoms
with warnings.catch_warnings():
warnings.simplefilter('ignore')
neighbors = nn_object.get_nn_info(struct, site_idx)
neighbors_idx = []
for neighbor in neighbors:
neighbors_idx.append(neighbor['site_index'])
return neighbors_idx