def figure_fractions(self, weights_options, morphing_factors=None):
if morphing_factors is None:
morphing_factors = np.linspace(1.0, 2.0, 21)
# Set up the local geometry finder
lgf = LocalGeometryFinder()
lgf.setup_parameters(structure_refinement=lgf.STRUCTURE_REFINEMENT_NONE)
# Set up the weights for the MultiWeights strategy
weights = self.get_weights(weights_options)
# Set up the strategy
strat = MultiWeightsChemenvStrategy(dist_ang_area_weight=weights['DistAngArea'],
self_csm_weight=weights['SelfCSM'],
delta_csm_weight=weights['DeltaCSM'],
cn_bias_weight=weights['CNBias'],
angle_weight=weights['Angle'],
normalized_angle_distance_weight=weights['NormalizedAngDist'])
fake_valences = [-1] * (self.coordination_geometry.coordination_number + 1)
fake_valences[0] = 1
fractions_initial_environment = np.zeros_like(morphing_factors)
fractions_final_environment = np.zeros_like(morphing_factors)
for ii, morphing_factor in enumerate(morphing_factors):
print(ii)
struct = self.get_structure(morphing_factor=morphing_factor)
print(struct)
# Get the StructureEnvironments
lgf.setup_structure(structure=struct)
se = lgf.compute_structure_environments(only_indices=[0], valences=fake_valences)
strat.set_structure_environments(structure_environments=se)
result = strat.get_site_coordination_environments_fractions(site=se.structure[0], isite=0,
return_strategy_dict_info=True,
return_all=True)
for res in result:
if res['ce_symbol'] == self.initial_environment_symbol:
fractions_initial_environment[ii] = res['ce_fraction']
elif res['ce_symbol'] == self.expected_final_environment_symbol:
fractions_final_environment[ii] = res['ce_fraction']
fig_width_cm = 8.25
fig_height_cm = 7.0
fig_width = fig_width_cm / 2.54
fig_height = fig_height_cm / 2.54
fig = plt.figure(num=1, figsize=(fig_width, fig_height))
subplot = fig.add_subplot(111)
subplot.plot(morphing_factors, fractions_initial_environment, 'b-',
label='{}'.format(self.initial_environment_symbol),
linewidth=1.5)
subplot.plot(morphing_factors, fractions_final_environment, 'g--',
label='{}'.format(self.expected_final_environment_symbol), linewidth=1.5)
plt.legend(fontsize=8.0, loc=7)
plt.show()
def analyse_local_coord_env(atoms=None, ):
"""
"""
#| - analyse_local_coord_env
out_dict = dict()
Ir_indices = []
for i, s in enumerate(atoms.get_chemical_symbols()):
if s == "Ir":
Ir_indices.append(i)
struct = AseAtomsAdaptor.get_structure(atoms)
lgf = LocalGeometryFinder()
lgf.setup_structure(structure=struct)
se = lgf.compute_structure_environments(maximum_distance_factor=1.41,
only_cations=False)
strategy = MultiWeightsChemenvStrategy.stats_article_weights_parameters()
lse = LightStructureEnvironments.from_structure_environments(
strategy=strategy, structure_environments=se)
isite = 0
cor_env = []
coor_env_dict = dict()
for isite in Ir_indices:
c_env = lse.coordination_environments[isite]
coor_env_dict[isite] = c_env
cor_env += [c_env[0]['ce_symbol']]
out_dict["coor_env_dict"] = coor_env_dict
return (out_dict)
def get_cesym(lgf, structure, site):
"""See module docstring."""
# doc: http://pymatgen.org/_modules/pymatgen/analysis/chemenv/
# coordination_environments/coordination_geometry_finder.html
lgf.setup_structure(structure)
# doc: http://pymatgen.org/_modules/pymatgen/analysis/chemenv/
# coordination_environments/
# chemenv_strategies.html#MultiWeightsChemenvStrategy.
# stats_article_weights_parameters
strategy = MultiWeightsChemenvStrategy.stats_article_weights_parameters()
# returns all information about the structure; se is a structure object
se = lgf.compute_structure_environments(maximum_distance_factor=1.2,
only_cations=False,
only_indices=[site])
lse = LightStructureEnvironments.\
from_structure_environments(strategy=strategy,
structure_environments=se)
coor = lse.coordination_environments
# ce = chemical environment
# csm = continuous symmetry measure
# from Waroquiers et al (verbatim)
# DOI: 10.1021/acs.chemmater.7b02766
# "The environment of the atom is then the model polyhedron for which
# the similarity is the highest, that is, for which the CSM is the lowest."
# in this case, it looks like O:6 (octahedral?)
return [coor[site][0]['ce_symbol'], coor[site][0]['csm']]
def setUp(self):
with open("mp-7000.json", "r") as f:
dict_lse = json.load(f)
lse = LightStructureEnvironments.from_dict(dict_lse)
struct = lse.structure
bva = BVAnalyzer()
valences = bva.get_valences(structure=struct)
lgf = LocalGeometryFinder()
lgf.setup_structure(structure=struct)
se = lgf.compute_structure_environments(maximum_distance_factor=1.41, only_cations=False, valences=valences)
strategy = MultiWeightsChemenvStrategy.stats_article_weights_parameters()
self.lse = LightStructureEnvironments.from_structure_environments(strategy=strategy, structure_environments=se)
with open("mp-5634.json", "r") as f:
dict_lse2 = json.load(f)
self.lse2 = LightStructureEnvironments.from_dict(dict_lse2)
def getMultiWeightsChemenvStrategy(struct):
# Setup the local geometry finder
lgf = LocalGeometryFinder()
lgf.setup_parameters(centering_type='centroid',
include_central_site_in_centroid=True)
#you can also save the logging to a file, just remove the comment
# logging.basicConfig(#filename='chemenv_structure_environments.log',
# format='%(levelname)s:%(module)s:%(funcName)s:%(message)s',
# level=logging.DEBUG)
lgf.setup_structure(structure=struct)
se = lgf.compute_structure_environments(maximum_distance_factor=1.41,
only_cations=False)
strategy = MultiWeightsChemenvStrategy.stats_article_weights_parameters()
lse = LightStructureEnvironments.from_structure_environments(
strategy=strategy, structure_environments=se)
return lse.coordination_environments
def from_preset(preset):
"""
Use a standard collection of CE types and
choose your ChemEnv neighbor-finding strategy.
Args:
preset (str): preset types ("simple" or
"multi_weights").
Returns:
ChemEnvSiteFingerprint object from a preset.
"""
cetypes = [
'S:1', 'L:2', 'A:2', 'TL:3', 'TY:3', 'TS:3', 'T:4', 'S:4', 'SY:4',
'SS:4', 'PP:5', 'S:5', 'T:5', 'O:6', 'T:6', 'PP:6', 'PB:7', 'ST:7',
'ET:7', 'FO:7', 'C:8', 'SA:8', 'SBT:8', 'TBT:8', 'DD:8', 'DDPN:8',
'HB:8', 'BO_1:8', 'BO_2:8', 'BO_3:8', 'TC:9', 'TT_1:9', 'TT_2:9',
'TT_3:9', 'HD:9', 'TI:9', 'SMA:9', 'SS:9', 'TO_1:9', 'TO_2:9',
'TO_3:9', 'PP:10', 'PA:10', 'SBSA:10', 'MI:10', 'S:10', 'H:10',
'BS_1:10', 'BS_2:10', 'TBSA:10', 'PCPA:11', 'H:11', 'SH:11',
'CO:11', 'DI:11', 'I:12', 'PBP:12', 'TT:12', 'C:12', 'AC:12',
'SC:12', 'S:12', 'HP:12', 'HA:12', 'SH:13', 'DD:20'
]
lgf = LocalGeometryFinder()
lgf.setup_parameters(
centering_type='centroid',
include_central_site_in_centroid=True,
structure_refinement=lgf.STRUCTURE_REFINEMENT_NONE)
if preset == "simple":
return ChemEnvSiteFingerprint(
cetypes,
SimplestChemenvStrategy(distance_cutoff=1.4, angle_cutoff=0.3),
lgf)
elif preset == "multi_weights":
return ChemEnvSiteFingerprint(
cetypes,
MultiWeightsChemenvStrategy.stats_article_weights_parameters(),
lgf)
else:
raise RuntimeError('unknown neighbor-finding strategy preset.')
def test_strategies(self):
simplest_strategy_1 = SimplestChemenvStrategy()
simplest_strategy_2 = SimplestChemenvStrategy(distance_cutoff=1.5,
angle_cutoff=0.5)
self.assertFalse(simplest_strategy_1 == simplest_strategy_2)
simplest_strategy_1_from_dict = SimplestChemenvStrategy.from_dict(
simplest_strategy_1.as_dict())
self.assertTrue(simplest_strategy_1, simplest_strategy_1_from_dict)
effective_csm_estimator = {
"function": "power2_inverse_decreasing",
"options": {
"max_csm": 8.0
},
}
self_csm_weight = SelfCSMNbSetWeight()
surface_definition = {
"type": "standard_elliptic",
"distance_bounds": {
"lower": 1.1,
"upper": 1.9
},
"angle_bounds": {
"lower": 0.1,
"upper": 0.9
},
}
surface_definition_2 = {
"type": "standard_elliptic",
"distance_bounds": {
"lower": 1.1,
"upper": 1.9
},
"angle_bounds": {
"lower": 0.1,
"upper": 0.95
},
}
da_area_weight = DistanceAngleAreaNbSetWeight(
weight_type="has_intersection",
surface_definition=surface_definition,
nb_sets_from_hints="fallback_to_source",
other_nb_sets="0_weight",
additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB,
)
da_area_weight_2 = DistanceAngleAreaNbSetWeight(
weight_type="has_intersection",
surface_definition=surface_definition_2,
nb_sets_from_hints="fallback_to_source",
other_nb_sets="0_weight",
additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB,
)
weight_estimator = {
"function": "smootherstep",
"options": {
"delta_csm_min": 0.5,
"delta_csm_max": 3.0
},
}
symmetry_measure_type = "csm_wcs_ctwcc"
delta_weight = DeltaCSMNbSetWeight(
effective_csm_estimator=effective_csm_estimator,
weight_estimator=weight_estimator,
symmetry_measure_type=symmetry_measure_type,
)
bias_weight = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0,
weight_cn13=4.0)
bias_weight_2 = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0,
weight_cn13=5.0)
angle_weight = AngleNbSetWeight()
nad_weight = NormalizedAngleDistanceNbSetWeight(
average_type="geometric", aa=1, bb=1)
multi_weights_strategy_1 = MultiWeightsChemenvStrategy(
dist_ang_area_weight=da_area_weight,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type,
)
multi_weights_strategy_2 = MultiWeightsChemenvStrategy(
dist_ang_area_weight=da_area_weight,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight_2,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type,
)
multi_weights_strategy_3 = MultiWeightsChemenvStrategy(
dist_ang_area_weight=da_area_weight_2,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type,
)
multi_weights_strategy_1_from_dict = MultiWeightsChemenvStrategy.from_dict(
multi_weights_strategy_1.as_dict())
self.assertTrue(
multi_weights_strategy_1 == multi_weights_strategy_1_from_dict)
self.assertFalse(simplest_strategy_1 == multi_weights_strategy_1)
self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_2)
self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_3)
self.assertFalse(multi_weights_strategy_2 == multi_weights_strategy_3)
def test_light_structure_environments(self):
with ScratchDir("."):
f = open("{}/{}".format(se_files_dir, 'se_mp-7000.json'), 'r')
dd = json.load(f)
f.close()
se = StructureEnvironments.from_dict(dd)
strategy = SimplestChemenvStrategy()
lse = LightStructureEnvironments.from_structure_environments(
structure_environments=se,
strategy=strategy,
valences='undefined')
isite = 6
nb_set = lse.neighbors_sets[isite][0]
neighb_coords = [
np.array([0.2443798, 1.80409653, -1.13218359]),
np.array([1.44020353, 1.11368738, 1.13218359]),
np.array([2.75513098, 2.54465207, -0.70467298]),
np.array([0.82616785, 3.65833945, 0.70467298])
]
neighb_indices = [0, 3, 5, 1]
neighb_images = [[0, 0, -1], [0, 0, 0], [0, 0, -1], [0, 0, 0]]
np.testing.assert_array_almost_equal(neighb_coords,
nb_set.neighb_coords)
np.testing.assert_array_almost_equal(
neighb_coords, [s.coords for s in nb_set.neighb_sites])
nb_sai = nb_set.neighb_sites_and_indices
np.testing.assert_array_almost_equal(
neighb_coords, [sai['site'].coords for sai in nb_sai])
np.testing.assert_array_almost_equal(
neighb_indices, [sai['index'] for sai in nb_sai])
nb_iai = nb_set.neighb_indices_and_images
np.testing.assert_array_almost_equal(
neighb_indices, [iai['index'] for iai in nb_iai])
np.testing.assert_array_equal(
neighb_images, [iai['image_cell'] for iai in nb_iai])
self.assertEqual(nb_set.__len__(), 4)
self.assertEqual(nb_set.__hash__(), 4)
self.assertFalse(nb_set.__ne__(nb_set))
self.assertEqual(
nb_set.__str__(), 'Neighbors Set for site #6 :\n'
' - Coordination number : 4\n'
' - Neighbors sites indices : 0, 1, 2, 3\n')
stats = lse.get_statistics()
neighbors = lse.strategy.get_site_neighbors(
site=lse.structure[isite])
self.assertArrayAlmostEqual(
neighbors[0].coords,
np.array([0.2443798, 1.80409653, -1.13218359]))
self.assertArrayAlmostEqual(
neighbors[1].coords,
np.array([1.44020353, 1.11368738, 1.13218359]))
self.assertArrayAlmostEqual(
neighbors[2].coords,
np.array([2.75513098, 2.54465207, -0.70467298]))
self.assertArrayAlmostEqual(
neighbors[3].coords,
np.array([0.82616785, 3.65833945, 0.70467298]))
equiv_site_index_and_transform = lse.strategy.equivalent_site_index_and_transform(
neighbors[0])
self.assertEqual(equiv_site_index_and_transform[0], 0)
self.assertArrayAlmostEqual(equiv_site_index_and_transform[1],
[0.0, 0.0, 0.0])
self.assertArrayAlmostEqual(equiv_site_index_and_transform[2],
[0.0, 0.0, -1.0])
equiv_site_index_and_transform = lse.strategy.equivalent_site_index_and_transform(
neighbors[1])
self.assertEqual(equiv_site_index_and_transform[0], 3)
self.assertArrayAlmostEqual(equiv_site_index_and_transform[1],
[0.0, 0.0, 0.0])
self.assertArrayAlmostEqual(equiv_site_index_and_transform[2],
[0.0, 0.0, 0.0])
self.assertEqual(stats['atom_coordination_environments_present'],
{'Si': {
'T:4': 3.0
}})
self.assertEqual(stats['coordination_environments_atom_present'],
{'T:4': {
'Si': 3.0
}})
self.assertEqual(
stats['fraction_atom_coordination_environments_present'],
{'Si': {
'T:4': 1.0
}})
site_info_ce = lse.get_site_info_for_specie_ce(specie=Species(
'Si', 4),
ce_symbol='T:4')
np.testing.assert_array_almost_equal(site_info_ce['fractions'],
[1.0, 1.0, 1.0])
np.testing.assert_array_almost_equal(site_info_ce['csms'], [
0.009887784240541068, 0.009887786546730826,
0.009887787384385317
])
self.assertEqual(site_info_ce['isites'], [6, 7, 8])
site_info_allces = lse.get_site_info_for_specie_allces(
specie=Species('Si', 4))
self.assertEqual(site_info_allces['T:4'], site_info_ce)
self.assertFalse(lse.contains_only_one_anion('I-'))
self.assertFalse(lse.contains_only_one_anion_atom('I'))
self.assertTrue(
lse.site_contains_environment(isite=isite, ce_symbol='T:4'))
self.assertFalse(
lse.site_contains_environment(isite=isite, ce_symbol='S:4'))
self.assertFalse(
lse.structure_contains_atom_environment(atom_symbol='Si',
ce_symbol='S:4'))
self.assertTrue(
lse.structure_contains_atom_environment(atom_symbol='Si',
ce_symbol='T:4'))
self.assertFalse(
lse.structure_contains_atom_environment(atom_symbol='O',
ce_symbol='T:4'))
self.assertTrue(lse.uniquely_determines_coordination_environments)
self.assertFalse(lse.__ne__(lse))
envs = lse.strategy.get_site_coordination_environments(
lse.structure[6])
self.assertEqual(len(envs), 1)
self.assertEqual(envs[0][0], 'T:4')
multi_strategy = MultiWeightsChemenvStrategy.stats_article_weights_parameters(
)
lse_multi = LightStructureEnvironments.from_structure_environments(
strategy=multi_strategy,
structure_environments=se,
valences='undefined')
self.assertAlmostEqual(
lse_multi.coordination_environments[isite][0]['csm'],
0.009887784240541068)
self.assertAlmostEqual(
lse_multi.coordination_environments[isite][0]['ce_fraction'],
1.0)
self.assertEqual(
lse_multi.coordination_environments[isite][0]['ce_symbol'],
'T:4')
def test_light_structure_environments(self):
f = open("{}/{}".format(se_files_dir, 'se_mp-7000.json'), 'r')
dd = json.load(f)
f.close()
se = StructureEnvironments.from_dict(dd)
strategy = SimplestChemenvStrategy()
lse = LightStructureEnvironments.from_structure_environments(structure_environments=se, strategy=strategy,
valences='undefined')
isite = 6
nb_set = lse.neighbors_sets[isite][0]
neighb_coords = [np.array([0.2443798, 1.80409653, -1.13218359]),
np.array([1.44020353, 1.11368738, 1.13218359]),
np.array([2.75513098, 2.54465207, -0.70467298]),
np.array([0.82616785, 3.65833945, 0.70467298])]
neighb_indices = [0, 3, 5, 1]
neighb_images = [[0, 0, -1], [0, 0, 0], [0, 0, -1], [0, 0, 0]]
np.testing.assert_array_almost_equal(neighb_coords, nb_set.neighb_coords)
np.testing.assert_array_almost_equal(neighb_coords, [s.coords for s in nb_set.neighb_sites])
nb_sai = nb_set.neighb_sites_and_indices
np.testing.assert_array_almost_equal(neighb_coords, [sai['site'].coords for sai in nb_sai])
np.testing.assert_array_almost_equal(neighb_indices, [sai['index'] for sai in nb_sai])
nb_iai = nb_set.neighb_indices_and_images
np.testing.assert_array_almost_equal(neighb_indices, [iai['index'] for iai in nb_iai])
np.testing.assert_array_equal(neighb_images, [iai['image_cell'] for iai in nb_iai])
self.assertEqual(nb_set.__len__(), 4)
self.assertEqual(nb_set.__hash__(), 4)
self.assertFalse(nb_set.__ne__(nb_set))
self.assertEqual(nb_set.__str__(), 'Neighbors Set for site #6 :\n'
' - Coordination number : 4\n'
' - Neighbors sites indices : 0, 1, 2, 3\n')
stats = lse.get_statistics()
neighbors = lse.strategy.get_site_neighbors(site=lse.structure[isite])
self.assertArrayAlmostEqual(neighbors[0].coords, np.array([ 0.2443798, 1.80409653, -1.13218359]))
self.assertArrayAlmostEqual(neighbors[1].coords, np.array([ 1.44020353, 1.11368738, 1.13218359]))
self.assertArrayAlmostEqual(neighbors[2].coords, np.array([ 2.75513098, 2.54465207, -0.70467298]))
self.assertArrayAlmostEqual(neighbors[3].coords, np.array([ 0.82616785, 3.65833945, 0.70467298]))
equiv_site_index_and_transform = lse.strategy.equivalent_site_index_and_transform(neighbors[0])
self.assertEqual(equiv_site_index_and_transform[0], 0)
self.assertArrayAlmostEqual(equiv_site_index_and_transform[1], [0.0, 0.0, 0.0])
self.assertArrayAlmostEqual(equiv_site_index_and_transform[2], [0.0, 0.0, -1.0])
equiv_site_index_and_transform = lse.strategy.equivalent_site_index_and_transform(neighbors[1])
self.assertEqual(equiv_site_index_and_transform[0], 3)
self.assertArrayAlmostEqual(equiv_site_index_and_transform[1], [0.0, 0.0, 0.0])
self.assertArrayAlmostEqual(equiv_site_index_and_transform[2], [0.0, 0.0, 0.0])
self.assertEqual(stats['atom_coordination_environments_present'], {'Si': {'T:4': 3.0}})
self.assertEqual(stats['coordination_environments_atom_present'], {'T:4': {'Si': 3.0}})
self.assertEqual(stats['fraction_atom_coordination_environments_present'], {'Si': {'T:4': 1.0}})
site_info_ce = lse.get_site_info_for_specie_ce(specie=Specie('Si', 4), ce_symbol='T:4')
np.testing.assert_array_almost_equal(site_info_ce['fractions'], [1.0, 1.0, 1.0])
np.testing.assert_array_almost_equal(site_info_ce['csms'],
[0.009887784240541068, 0.009887786546730826, 0.009887787384385317])
self.assertEqual(site_info_ce['isites'], [6, 7, 8])
site_info_allces = lse.get_site_info_for_specie_allces(specie=Specie('Si', 4))
self.assertEqual(site_info_allces['T:4'], site_info_ce)
self.assertFalse(lse.contains_only_one_anion('I-'))
self.assertFalse(lse.contains_only_one_anion_atom('I'))
self.assertTrue(lse.site_contains_environment(isite=isite, ce_symbol='T:4'))
self.assertFalse(lse.site_contains_environment(isite=isite, ce_symbol='S:4'))
self.assertFalse(lse.structure_contains_atom_environment(atom_symbol='Si', ce_symbol='S:4'))
self.assertTrue(lse.structure_contains_atom_environment(atom_symbol='Si', ce_symbol='T:4'))
self.assertFalse(lse.structure_contains_atom_environment(atom_symbol='O', ce_symbol='T:4'))
self.assertTrue(lse.uniquely_determines_coordination_environments)
self.assertFalse(lse.__ne__(lse))
envs = lse.strategy.get_site_coordination_environments(lse.structure[6])
self.assertEqual(len(envs), 1)
self.assertEqual(envs[0][0], 'T:4')
multi_strategy = MultiWeightsChemenvStrategy.stats_article_weights_parameters()
lse_multi = LightStructureEnvironments.from_structure_environments(strategy=multi_strategy,
structure_environments=se,
valences='undefined')
self.assertAlmostEqual(lse_multi.coordination_environments[isite][0]['csm'], 0.009887784240541068)
self.assertAlmostEqual(lse_multi.coordination_environments[isite][0]['ce_fraction'], 1.0)
self.assertEqual(lse_multi.coordination_environments[isite][0]['ce_symbol'], 'T:4')
print("RUNNING!")
atoms = row.toatoms()
Ir_indices = [
i for i, s in enumerate(atoms.get_chemical_symbols()) if s == 'Ir'
]
struct = AseAtomsAdaptor.get_structure(atoms)
lgf = LocalGeometryFinder()
lgf.setup_structure(structure=struct)
se = lgf.compute_structure_environments(maximum_distance_factor=1.41,
only_cations=False)
strategy = MultiWeightsChemenvStrategy.stats_article_weights_parameters(
)
lse = LightStructureEnvironments.from_structure_environments(
strategy=strategy, structure_environments=se)
isite = 0
cor_env = []
for isite in Ir_indices:
c_env = lse.coordination_environments[isite]
if len(c_env) == 0:
continue
cor_env += [c_env[0]['ce_symbol']]
if len(cor_env) == 0:
continue
def test_strategies(self):
simplest_strategy_1 = SimplestChemenvStrategy()
simplest_strategy_2 = SimplestChemenvStrategy(distance_cutoff=1.5, angle_cutoff=0.5)
self.assertFalse(simplest_strategy_1 == simplest_strategy_2)
simplest_strategy_1_from_dict = SimplestChemenvStrategy.from_dict(simplest_strategy_1.as_dict())
self.assertTrue(simplest_strategy_1, simplest_strategy_1_from_dict)
effective_csm_estimator = {'function': 'power2_inverse_decreasing',
'options': {'max_csm': 8.0}}
self_csm_weight = SelfCSMNbSetWeight()
surface_definition = {'type': 'standard_elliptic',
'distance_bounds': {'lower': 1.1, 'upper': 1.9},
'angle_bounds': {'lower': 0.1, 'upper': 0.9}}
surface_definition_2 = {'type': 'standard_elliptic',
'distance_bounds': {'lower': 1.1, 'upper': 1.9},
'angle_bounds': {'lower': 0.1, 'upper': 0.95}}
da_area_weight = DistanceAngleAreaNbSetWeight(weight_type='has_intersection',
surface_definition=surface_definition,
nb_sets_from_hints='fallback_to_source',
other_nb_sets='0_weight',
additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB)
da_area_weight_2 = DistanceAngleAreaNbSetWeight(weight_type='has_intersection',
surface_definition=surface_definition_2,
nb_sets_from_hints='fallback_to_source',
other_nb_sets='0_weight',
additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB)
weight_estimator = {'function': 'smootherstep',
'options': {'delta_csm_min': 0.5,
'delta_csm_max': 3.0}}
symmetry_measure_type = 'csm_wcs_ctwcc'
delta_weight = DeltaCSMNbSetWeight(effective_csm_estimator=effective_csm_estimator,
weight_estimator=weight_estimator,
symmetry_measure_type=symmetry_measure_type)
bias_weight = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0, weight_cn13=4.0)
bias_weight_2 = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0, weight_cn13=5.0)
angle_weight = AngleNbSetWeight()
nad_weight = NormalizedAngleDistanceNbSetWeight(average_type='geometric', aa=1, bb=1)
multi_weights_strategy_1 = MultiWeightsChemenvStrategy(dist_ang_area_weight=da_area_weight,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type)
multi_weights_strategy_2 = MultiWeightsChemenvStrategy(dist_ang_area_weight=da_area_weight,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight_2,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type)
multi_weights_strategy_3 = MultiWeightsChemenvStrategy(dist_ang_area_weight=da_area_weight_2,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type)
multi_weights_strategy_1_from_dict = MultiWeightsChemenvStrategy.from_dict(multi_weights_strategy_1.as_dict())
self.assertTrue(multi_weights_strategy_1 == multi_weights_strategy_1_from_dict)
self.assertFalse(simplest_strategy_1 == multi_weights_strategy_1)
self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_2)
self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_3)
self.assertFalse(multi_weights_strategy_2 == multi_weights_strategy_3)
def figure_fractions(self, weights_options, morphing_factors=None):
if morphing_factors is None:
morphing_factors = np.linspace(1.0, 2.0, 21)
# Set up the local geometry finder
lgf = LocalGeometryFinder()
lgf.setup_parameters(
structure_refinement=lgf.STRUCTURE_REFINEMENT_NONE)
# Set up the weights for the MultiWeights strategy
weights = self.get_weights(weights_options)
# Set up the strategy
strat = MultiWeightsChemenvStrategy(
dist_ang_area_weight=weights['DistAngArea'],
self_csm_weight=weights['SelfCSM'],
delta_csm_weight=weights['DeltaCSM'],
cn_bias_weight=weights['CNBias'],
angle_weight=weights['Angle'],
normalized_angle_distance_weight=weights['NormalizedAngDist'])
fake_valences = [-1] * (
self.coordination_geometry.coordination_number + 1)
fake_valences[0] = 1
fractions_initial_environment = np.zeros_like(morphing_factors)
fractions_final_environment = np.zeros_like(morphing_factors)
for ii, morphing_factor in enumerate(morphing_factors):
print(ii)
struct = self.get_structure(morphing_factor=morphing_factor)
print(struct)
# Get the StructureEnvironments
lgf.setup_structure(structure=struct)
se = lgf.compute_structure_environments(only_indices=[0],
valences=fake_valences)
strat.set_structure_environments(structure_environments=se)
result = strat.get_site_coordination_environments_fractions(
site=se.structure[0],
isite=0,
return_strategy_dict_info=True,
return_all=True)
for res in result:
if res['ce_symbol'] == self.initial_environment_symbol:
fractions_initial_environment[ii] = res['ce_fraction']
elif res[
'ce_symbol'] == self.expected_final_environment_symbol:
fractions_final_environment[ii] = res['ce_fraction']
fig_width_cm = 8.25
fig_height_cm = 7.0
fig_width = fig_width_cm / 2.54
fig_height = fig_height_cm / 2.54
fig = plt.figure(num=1, figsize=(fig_width, fig_height))
subplot = fig.add_subplot(111)
subplot.plot(morphing_factors,
fractions_initial_environment,
'b-',
label='{}'.format(self.initial_environment_symbol),
linewidth=1.5)
subplot.plot(morphing_factors,
fractions_final_environment,
'g--',
label='{}'.format(self.expected_final_environment_symbol),
linewidth=1.5)
plt.legend(fontsize=8.0, loc=7)
plt.show()
def test_strategies(self):
simplest_strategy_1 = SimplestChemenvStrategy()
simplest_strategy_2 = SimplestChemenvStrategy(distance_cutoff=1.5,
angle_cutoff=0.5)
self.assertFalse(simplest_strategy_1 == simplest_strategy_2)
simplest_strategy_1_from_dict = SimplestChemenvStrategy.from_dict(
simplest_strategy_1.as_dict())
self.assertTrue(simplest_strategy_1, simplest_strategy_1_from_dict)
effective_csm_estimator = {
'function': 'power2_inverse_decreasing',
'options': {
'max_csm': 8.0
}
}
self_csm_weight = SelfCSMNbSetWeight()
surface_definition = {
'type': 'standard_elliptic',
'distance_bounds': {
'lower': 1.1,
'upper': 1.9
},
'angle_bounds': {
'lower': 0.1,
'upper': 0.9
}
}
surface_definition_2 = {
'type': 'standard_elliptic',
'distance_bounds': {
'lower': 1.1,
'upper': 1.9
},
'angle_bounds': {
'lower': 0.1,
'upper': 0.95
}
}
da_area_weight = DistanceAngleAreaNbSetWeight(
weight_type='has_intersection',
surface_definition=surface_definition,
nb_sets_from_hints='fallback_to_source',
other_nb_sets='0_weight',
additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB)
da_area_weight_2 = DistanceAngleAreaNbSetWeight(
weight_type='has_intersection',
surface_definition=surface_definition_2,
nb_sets_from_hints='fallback_to_source',
other_nb_sets='0_weight',
additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB)
weight_estimator = {
'function': 'smootherstep',
'options': {
'delta_csm_min': 0.5,
'delta_csm_max': 3.0
}
}
symmetry_measure_type = 'csm_wcs_ctwcc'
delta_weight = DeltaCSMNbSetWeight(
effective_csm_estimator=effective_csm_estimator,
weight_estimator=weight_estimator,
symmetry_measure_type=symmetry_measure_type)
bias_weight = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0,
weight_cn13=4.0)
bias_weight_2 = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0,
weight_cn13=5.0)
angle_weight = AngleNbSetWeight()
nad_weight = NormalizedAngleDistanceNbSetWeight(
average_type='geometric', aa=1, bb=1)
multi_weights_strategy_1 = MultiWeightsChemenvStrategy(
dist_ang_area_weight=da_area_weight,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type)
multi_weights_strategy_2 = MultiWeightsChemenvStrategy(
dist_ang_area_weight=da_area_weight,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight_2,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type)
multi_weights_strategy_3 = MultiWeightsChemenvStrategy(
dist_ang_area_weight=da_area_weight_2,
self_csm_weight=self_csm_weight,
delta_csm_weight=delta_weight,
cn_bias_weight=bias_weight,
angle_weight=angle_weight,
normalized_angle_distance_weight=nad_weight,
symmetry_measure_type=symmetry_measure_type)
multi_weights_strategy_1_from_dict = MultiWeightsChemenvStrategy.from_dict(
multi_weights_strategy_1.as_dict())
self.assertTrue(
multi_weights_strategy_1 == multi_weights_strategy_1_from_dict)
self.assertFalse(simplest_strategy_1 == multi_weights_strategy_1)
self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_2)
self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_3)
self.assertFalse(multi_weights_strategy_2 == multi_weights_strategy_3)
def test_light_structure_environments(self):
with ScratchDir("."):
with open(f"{se_files_dir}/se_mp-7000.json") as f:
dd = json.load(f)
se = StructureEnvironments.from_dict(dd)
strategy = SimplestChemenvStrategy()
lse = LightStructureEnvironments.from_structure_environments(
structure_environments=se,
strategy=strategy,
valences="undefined")
isite = 6
nb_set = lse.neighbors_sets[isite][0]
neighb_coords = [
np.array([0.2443798, 1.80409653, -1.13218359]),
np.array([1.44020353, 1.11368738, 1.13218359]),
np.array([2.75513098, 2.54465207, -0.70467298]),
np.array([0.82616785, 3.65833945, 0.70467298]),
]
neighb_indices = [0, 3, 5, 1]
neighb_images = [[0, 0, -1], [0, 0, 0], [0, 0, -1], [0, 0, 0]]
np.testing.assert_array_almost_equal(neighb_coords,
nb_set.neighb_coords)
np.testing.assert_array_almost_equal(
neighb_coords, [s.coords for s in nb_set.neighb_sites])
nb_sai = nb_set.neighb_sites_and_indices
np.testing.assert_array_almost_equal(
neighb_coords, [sai["site"].coords for sai in nb_sai])
np.testing.assert_array_almost_equal(
neighb_indices, [sai["index"] for sai in nb_sai])
nb_iai = nb_set.neighb_indices_and_images
np.testing.assert_array_almost_equal(
neighb_indices, [iai["index"] for iai in nb_iai])
np.testing.assert_array_equal(
neighb_images, [iai["image_cell"] for iai in nb_iai])
self.assertEqual(nb_set.__len__(), 4)
self.assertEqual(nb_set.__hash__(), 4)
self.assertFalse(nb_set.__ne__(nb_set))
self.assertEqual(
nb_set.__str__(),
"Neighbors Set for site #6 :\n"
" - Coordination number : 4\n"
" - Neighbors sites indices : 0, 1, 2, 3\n",
)
stats = lse.get_statistics()
neighbors = lse.strategy.get_site_neighbors(
site=lse.structure[isite])
self.assertArrayAlmostEqual(
neighbors[0].coords,
np.array([0.2443798, 1.80409653, -1.13218359]))
self.assertArrayAlmostEqual(
neighbors[1].coords,
np.array([1.44020353, 1.11368738, 1.13218359]))
self.assertArrayAlmostEqual(
neighbors[2].coords,
np.array([2.75513098, 2.54465207, -0.70467298]))
self.assertArrayAlmostEqual(
neighbors[3].coords,
np.array([0.82616785, 3.65833945, 0.70467298]))
equiv_site_index_and_transform = lse.strategy.equivalent_site_index_and_transform(
neighbors[0])
self.assertEqual(equiv_site_index_and_transform[0], 0)
self.assertArrayAlmostEqual(equiv_site_index_and_transform[1],
[0.0, 0.0, 0.0])
self.assertArrayAlmostEqual(equiv_site_index_and_transform[2],
[0.0, 0.0, -1.0])
equiv_site_index_and_transform = lse.strategy.equivalent_site_index_and_transform(
neighbors[1])
self.assertEqual(equiv_site_index_and_transform[0], 3)
self.assertArrayAlmostEqual(equiv_site_index_and_transform[1],
[0.0, 0.0, 0.0])
self.assertArrayAlmostEqual(equiv_site_index_and_transform[2],
[0.0, 0.0, 0.0])
self.assertEqual(stats["atom_coordination_environments_present"],
{"Si": {
"T:4": 3.0
}})
self.assertEqual(stats["coordination_environments_atom_present"],
{"T:4": {
"Si": 3.0
}})
self.assertEqual(
stats["fraction_atom_coordination_environments_present"],
{"Si": {
"T:4": 1.0
}},
)
site_info_ce = lse.get_site_info_for_specie_ce(specie=Species(
"Si", 4),
ce_symbol="T:4")
np.testing.assert_array_almost_equal(site_info_ce["fractions"],
[1.0, 1.0, 1.0])
np.testing.assert_array_almost_equal(
site_info_ce["csms"],
[
0.009887784240541068, 0.009887786546730826,
0.009887787384385317
],
)
self.assertEqual(site_info_ce["isites"], [6, 7, 8])
site_info_allces = lse.get_site_info_for_specie_allces(
specie=Species("Si", 4))
self.assertEqual(site_info_allces["T:4"], site_info_ce)
self.assertFalse(lse.contains_only_one_anion("I-"))
self.assertFalse(lse.contains_only_one_anion_atom("I"))
self.assertTrue(
lse.site_contains_environment(isite=isite, ce_symbol="T:4"))
self.assertFalse(
lse.site_contains_environment(isite=isite, ce_symbol="S:4"))
self.assertFalse(
lse.structure_contains_atom_environment(atom_symbol="Si",
ce_symbol="S:4"))
self.assertTrue(
lse.structure_contains_atom_environment(atom_symbol="Si",
ce_symbol="T:4"))
self.assertFalse(
lse.structure_contains_atom_environment(atom_symbol="O",
ce_symbol="T:4"))
self.assertTrue(lse.uniquely_determines_coordination_environments)
self.assertFalse(lse.__ne__(lse))
envs = lse.strategy.get_site_coordination_environments(
lse.structure[6])
self.assertEqual(len(envs), 1)
self.assertEqual(envs[0][0], "T:4")
multi_strategy = MultiWeightsChemenvStrategy.stats_article_weights_parameters(
)
lse_multi = LightStructureEnvironments.from_structure_environments(
strategy=multi_strategy,
structure_environments=se,
valences="undefined")
self.assertAlmostEqual(
lse_multi.coordination_environments[isite][0]["csm"],
0.009887784240541068,
)
self.assertAlmostEqual(
lse_multi.coordination_environments[isite][0]["ce_fraction"],
1.0)
self.assertEqual(
lse_multi.coordination_environments[isite][0]["ce_symbol"],
"T:4")
