def get_weights(self, weights_options):
effective_csm_estimator = {"function": "power2_inverse_decreasing", "options": {"max_csm": 8.0}}
self_weight_estimator = {
"function": "power2_decreasing_exp",
"options": {"max_csm": 5.4230949041608305, "alpha": 1.0},
}
self_csm_weight = SelfCSMNbSetWeight(
effective_csm_estimator=effective_csm_estimator, weight_estimator=self_weight_estimator
)
surface_definition = {
"type": "standard_elliptic",
"distance_bounds": {"lower": 1.05, "upper": 2.0},
"angle_bounds": {"lower": 0.05, "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,
)
weight_estimator = {"function": "smootherstep", "options": {"delta_csm_min": 0.5, "delta_csm_max": 3.0}}
symmetry_measure_type = "csm_wcs_ctwcc"
delta_csm_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)
angle_weight = AngleNbSetWeight()
nad_weight = NormalizedAngleDistanceNbSetWeight(average_type="geometric", aa=1, bb=1)
weights = {
"DistAngArea": da_area_weight,
"SelfCSM": self_csm_weight,
"DeltaCSM": delta_csm_weight,
"CNBias": bias_weight,
"Angle": angle_weight,
"NormalizedAngDist": nad_weight,
}
return weights
def test_self_csms_weight(self):
# Get the StructureEnvironments for K2NaNb2Fe7Si8H4O31 (mp-743972)
f = open(os.path.join(se_files_dir, 'se_mp-743972.json'), 'r')
dd = json.load(f)
f.close()
se = StructureEnvironments.from_dict(dd)
# Get neighbors sets for which we get the weights
cn_maps = [(12, 3), (12, 2), (13, 2), (12, 0), (12, 1)]
nbsets = {cn_map: se.neighbors_sets[0][cn_map[0]][cn_map[1]] for cn_map in cn_maps}
effective_csm_estimator = {'function': 'power2_inverse_decreasing',
'options': {'max_csm': 8.0}}
weight_estimator = {'function': 'power2_decreasing_exp',
'options': {'max_csm': 8.0,
'alpha': 1.0}}
weight_estimator2 = {'function': 'power2_decreasing_exp',
'options': {'max_csm': 8.1,
'alpha': 1.0}}
symmetry_measure_type = 'csm_wcs_ctwcc'
self_weight = SelfCSMNbSetWeight(effective_csm_estimator=effective_csm_estimator,
weight_estimator=weight_estimator,
symmetry_measure_type=symmetry_measure_type)
self_weight2 = SelfCSMNbSetWeight(effective_csm_estimator=effective_csm_estimator,
weight_estimator=weight_estimator2,
symmetry_measure_type=symmetry_measure_type)
self.assertNotEqual(self_weight, self_weight2)
additional_info = {}
cn_map = (12, 3)
self_w = self_weight.weight(nb_set=nbsets[cn_map], structure_environments=se,
cn_map=cn_map, additional_info=additional_info)
self.assertAlmostEqual(self_w, 0.11671945916431022, delta=1e-8)
cn_map = (12, 2)
self_w = self_weight.weight(nb_set=nbsets[cn_map], structure_environments=se,
cn_map=cn_map, additional_info=additional_info)
self.assertAlmostEqual(self_w, 0.0, delta=1e-8)
cn_map = (12, 0)
self_w = self_weight.weight(nb_set=nbsets[cn_map], structure_environments=se,
cn_map=cn_map, additional_info=additional_info)
self.assertAlmostEqual(self_w, 0.0, delta=1e-8)
cn_map = (12, 1)
self_w = self_weight.weight(nb_set=nbsets[cn_map], structure_environments=se,
cn_map=cn_map, additional_info=additional_info)
self.assertAlmostEqual(self_w, 0.0, delta=1e-8)
cn_map = (13, 2)
self_w = self_weight.weight(nb_set=nbsets[cn_map], structure_environments=se,
cn_map=cn_map, additional_info=additional_info)
self.assertAlmostEqual(self_w, 0.14204073172729198, delta=1e-8)
# Get the StructureEnvironments for SiO2 (mp-7000)
f = open(os.path.join(se_files_dir, 'se_mp-7000.json'), 'r')
dd = json.load(f)
f.close()
se = StructureEnvironments.from_dict(dd)
# Get neighbors sets for which we get the weights
cn_maps = [(2, 0), (4, 0)]
nbsets = {cn_map: se.neighbors_sets[6][cn_map[0]][cn_map[1]] for cn_map in cn_maps}
effective_csm_estimator = {'function': 'power2_inverse_decreasing',
'options': {'max_csm': 8.0}}
weight_estimator = {'function': 'power2_decreasing_exp',
'options': {'max_csm': 8.0,
'alpha': 1.0}}
symmetry_measure_type = 'csm_wcs_ctwcc'
self_weight = SelfCSMNbSetWeight(effective_csm_estimator=effective_csm_estimator,
weight_estimator=weight_estimator,
symmetry_measure_type=symmetry_measure_type)
additional_info = {}
cn_map = (2, 0)
self_w = self_weight.weight(nb_set=nbsets[cn_map], structure_environments=se,
cn_map=cn_map, additional_info=additional_info)
self.assertAlmostEqual(self_w, 0.8143992162836029, delta=1e-8)
cn_map = (4, 0)
self_w = self_weight.weight(nb_set=nbsets[cn_map], structure_environments=se,
cn_map=cn_map, additional_info=additional_info)
self.assertAlmostEqual(self_w, 0.99629742352359496, delta=1e-8)
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_self_csms_weight(self):
# Get the StructureEnvironments for K2NaNb2Fe7Si8H4O31 (mp-743972)
f = open(os.path.join(se_files_dir, 'se_mp-743972.json'), 'r')
dd = json.load(f)
f.close()
se = StructureEnvironments.from_dict(dd)
# Get neighbors sets for which we get the weights
cn_maps = [(12, 3), (12, 2), (13, 2), (12, 0), (12, 1)]
nbsets = {cn_map: se.neighbors_sets[0][cn_map[0]][cn_map[1]] for cn_map in cn_maps}
effective_csm_estimator = {'function': 'power2_inverse_decreasing',
'options': {'max_csm': 8.0}}
weight_estimator = {'function': 'power2_decreasing_exp',
'options': {'max_csm': 8.0,
'alpha': 1.0}}
weight_estimator2 = {'function': 'power2_decreasing_exp',
'options': {'max_csm': 8.1,
'alpha': 1.0}}
symmetry_measure_type = 'csm_wcs_ctwcc'
self_weight = SelfCSMNbSetWeight(effective_csm_estimator=effective_csm_estimator,
weight_estimator=weight_estimator,
symmetry_measure_type=symmetry_measure_type)
self_weight2 = SelfCSMNbSetWeight(effective_csm_estimator=effective_csm_estimator,
weight_estimator=weight_estimator2,
symmetry_measure_type=symmetry_measure_type)
self.assertNotEqual(self_weight, self_weight2)
additional_info = {}
cn_map = (12, 3)
self_w = self_weight.weight(nb_set=nbsets[cn_map], structure_environments=se,
cn_map=cn_map, additional_info=additional_info)
self.assertAlmostEqual(self_w, 0.11671945916431022, delta=1e-8)
cn_map = (12, 2)
self_w = self_weight.weight(nb_set=nbsets[cn_map], structure_environments=se,
cn_map=cn_map, additional_info=additional_info)
self.assertAlmostEqual(self_w, 0.0, delta=1e-8)
cn_map = (12, 0)
self_w = self_weight.weight(nb_set=nbsets[cn_map], structure_environments=se,
cn_map=cn_map, additional_info=additional_info)
self.assertAlmostEqual(self_w, 0.0, delta=1e-8)
cn_map = (12, 1)
self_w = self_weight.weight(nb_set=nbsets[cn_map], structure_environments=se,
cn_map=cn_map, additional_info=additional_info)
self.assertAlmostEqual(self_w, 0.0, delta=1e-8)
cn_map = (13, 2)
self_w = self_weight.weight(nb_set=nbsets[cn_map], structure_environments=se,
cn_map=cn_map, additional_info=additional_info)
self.assertAlmostEqual(self_w, 0.14204073172729198, delta=1e-8)
# Get the StructureEnvironments for SiO2 (mp-7000)
f = open(os.path.join(se_files_dir, 'se_mp-7000.json'), 'r')
dd = json.load(f)
f.close()
se = StructureEnvironments.from_dict(dd)
# Get neighbors sets for which we get the weights
cn_maps = [(2, 0), (4, 0)]
nbsets = {cn_map: se.neighbors_sets[6][cn_map[0]][cn_map[1]] for cn_map in cn_maps}
effective_csm_estimator = {'function': 'power2_inverse_decreasing',
'options': {'max_csm': 8.0}}
weight_estimator = {'function': 'power2_decreasing_exp',
'options': {'max_csm': 8.0,
'alpha': 1.0}}
symmetry_measure_type = 'csm_wcs_ctwcc'
self_weight = SelfCSMNbSetWeight(effective_csm_estimator=effective_csm_estimator,
weight_estimator=weight_estimator,
symmetry_measure_type=symmetry_measure_type)
additional_info = {}
cn_map = (2, 0)
self_w = self_weight.weight(nb_set=nbsets[cn_map], structure_environments=se,
cn_map=cn_map, additional_info=additional_info)
self.assertAlmostEqual(self_w, 0.8143992162836029, delta=1e-8)
cn_map = (4, 0)
self_w = self_weight.weight(nb_set=nbsets[cn_map], structure_environments=se,
cn_map=cn_map, additional_info=additional_info)
self.assertAlmostEqual(self_w, 0.99629742352359496, delta=1e-8)
def get_weights(self, weights_options):
effective_csm_estimator = {
'function': 'power2_inverse_decreasing',
'options': {
'max_csm': 8.0
}
}
self_weight_estimator = {
'function': 'power2_decreasing_exp',
'options': {
'max_csm': 5.4230949041608305,
'alpha': 1.0
}
}
self_csm_weight = SelfCSMNbSetWeight(
effective_csm_estimator=effective_csm_estimator,
weight_estimator=self_weight_estimator)
surface_definition = {
'type': 'standard_elliptic',
'distance_bounds': {
'lower': 1.05,
'upper': 2.0
},
'angle_bounds': {
'lower': 0.05,
'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)
weight_estimator = {
'function': 'smootherstep',
'options': {
'delta_csm_min': 0.5,
'delta_csm_max': 3.0
}
}
symmetry_measure_type = 'csm_wcs_ctwcc'
delta_csm_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)
angle_weight = AngleNbSetWeight()
nad_weight = NormalizedAngleDistanceNbSetWeight(
average_type='geometric', aa=1, bb=1)
weights = {
'DistAngArea': da_area_weight,
'SelfCSM': self_csm_weight,
'DeltaCSM': delta_csm_weight,
'CNBias': bias_weight,
'Angle': angle_weight,
'NormalizedAngDist': nad_weight
}
return weights
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)