def test_CN_bias_weight(self):
fake_nb_set = FakeNbSet()
dummy_se = DummyStructureEnvironments()
bias_weight1 = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0, weight_cn13=13.0)
bias_weight2 = CNBiasNbSetWeight.geometrically_equidistant(weight_cn1=1.0, weight_cn13=1.1**12)
bias_weight3 = CNBiasNbSetWeight.explicit(cn_weights={1: 1.0, 2: 3.0, 3: 3.2, 4: 4.0,
5: 4.1, 6: 4.2, 7: 4.3, 8: 4.4,
9: 4.5, 10: 4.6, 11: 4.6, 12: 4.7,
13: 4.8})
with self.assertRaisesRegexp(ValueError, 'Weights should be provided for CN 1 to 13'):
CNBiasNbSetWeight.explicit(cn_weights={1: 1.0, 13: 2.0})
fake_nb_set.cn = 1
w1 = bias_weight1.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w1, 1.0, delta=1e-8)
w2 = bias_weight2.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w2, 1.0, delta=1e-8)
w3 = bias_weight3.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w3, 1.0, delta=1e-8)
fake_nb_set.cn = 7
w1 = bias_weight1.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w1, 7.0, delta=1e-8)
w2 = bias_weight2.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w2, 1.1**6, delta=1e-8)
w3 = bias_weight3.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w3, 4.3, delta=1e-8)
fake_nb_set.cn = 13
w1 = bias_weight1.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w1, 13.0, delta=1e-8)
w2 = bias_weight2.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w2, 1.1**12, delta=1e-8)
w3 = bias_weight3.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w3, 4.8, delta=1e-8)
def test_CN_bias_weight(self):
fake_nb_set = FakeNbSet()
dummy_se = DummyStructureEnvironments()
bias_weight1 = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0, weight_cn13=13.0)
bias_weight2 = CNBiasNbSetWeight.geometrically_equidistant(weight_cn1=1.0, weight_cn13=1.1**12)
bias_weight3 = CNBiasNbSetWeight.explicit(cn_weights={1: 1.0, 2: 3.0, 3: 3.2, 4: 4.0,
5: 4.1, 6: 4.2, 7: 4.3, 8: 4.4,
9: 4.5, 10: 4.6, 11: 4.6, 12: 4.7,
13: 4.8})
with self.assertRaisesRegexp(ValueError, 'Weights should be provided for CN 1 to 13'):
CNBiasNbSetWeight.explicit(cn_weights={1: 1.0, 13: 2.0})
fake_nb_set.cn = 1
w1 = bias_weight1.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w1, 1.0, delta=1e-8)
w2 = bias_weight2.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w2, 1.0, delta=1e-8)
w3 = bias_weight3.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w3, 1.0, delta=1e-8)
fake_nb_set.cn = 7
w1 = bias_weight1.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w1, 7.0, delta=1e-8)
w2 = bias_weight2.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w2, 1.1**6, delta=1e-8)
w3 = bias_weight3.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w3, 4.3, delta=1e-8)
fake_nb_set.cn = 13
w1 = bias_weight1.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w1, 13.0, delta=1e-8)
w2 = bias_weight2.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w2, 1.1**12, delta=1e-8)
w3 = bias_weight3.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w3, 4.8, delta=1e-8)
def test_CN_bias_weight(self):
fake_nb_set = FakeNbSet()
dummy_se = DummyStructureEnvironments()
bias_weight1 = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0, weight_cn13=13.0)
bias_weight2 = CNBiasNbSetWeight.geometrically_equidistant(weight_cn1=1.0, weight_cn13=1.1 ** 12)
bias_weight3 = CNBiasNbSetWeight.explicit(cn_weights={1: 1.0, 2: 3.0, 3: 3.2, 4: 4.0,
5: 4.1, 6: 4.2, 7: 4.3, 8: 4.4,
9: 4.5, 10: 4.6, 11: 4.6, 12: 4.7,
13: 4.8})
with self.assertRaisesRegex(ValueError, 'Weights should be provided for CN 1 to 13'):
CNBiasNbSetWeight.explicit(cn_weights={1: 1.0, 13: 2.0})
fake_nb_set.cn = 1
w1 = bias_weight1.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w1, 1.0, delta=1e-8)
w2 = bias_weight2.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w2, 1.0, delta=1e-8)
w3 = bias_weight3.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w3, 1.0, delta=1e-8)
fake_nb_set.cn = 7
w1 = bias_weight1.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w1, 7.0, delta=1e-8)
w2 = bias_weight2.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w2, 1.1 ** 6, delta=1e-8)
w3 = bias_weight3.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w3, 4.3, delta=1e-8)
fake_nb_set.cn = 13
w1 = bias_weight1.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w1, 13.0, delta=1e-8)
w2 = bias_weight2.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w2, 1.1 ** 12, delta=1e-8)
w3 = bias_weight3.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w3, 4.8, delta=1e-8)
bias_weight4 = CNBiasNbSetWeight.from_description({'type': 'linearly_equidistant',
'weight_cn1': 2.0,
'weight_cn13': 26.0})
for cn in range(1, 14):
self.assertAlmostEqual(bias_weight4.cn_weights[cn], 2.0 * cn)
bias_weight5 = CNBiasNbSetWeight.from_description({'type': 'geometrically_equidistant',
'weight_cn1': 1.0,
'weight_cn13': 13.0})
self.assertAlmostEqual(bias_weight5.cn_weights[1], 1.0)
self.assertAlmostEqual(bias_weight5.cn_weights[3], 1.5334062370163877)
self.assertAlmostEqual(bias_weight5.cn_weights[9], 5.5287748136788739)
self.assertAlmostEqual(bias_weight5.cn_weights[12], 10.498197520079623)
cn_weights = {cn: 0.0 for cn in range(1, 14)}
cn_weights[6] = 2.0
cn_weights[4] = 1.0
bias_weight6 = CNBiasNbSetWeight.from_description({'type': 'explicit',
'cn_weights': cn_weights})
self.assertAlmostEqual(bias_weight6.cn_weights[1], 0.0)
self.assertAlmostEqual(bias_weight6.cn_weights[4], 1.0)
self.assertAlmostEqual(bias_weight6.cn_weights[6], 2.0)
def test_CN_bias_weight(self):
fake_nb_set = FakeNbSet()
dummy_se = DummyStructureEnvironments()
bias_weight1 = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0, weight_cn13=13.0)
bias_weight2 = CNBiasNbSetWeight.geometrically_equidistant(weight_cn1=1.0, weight_cn13=1.1**12)
bias_weight3 = CNBiasNbSetWeight.explicit(cn_weights={1: 1.0, 2: 3.0, 3: 3.2, 4: 4.0,
5: 4.1, 6: 4.2, 7: 4.3, 8: 4.4,
9: 4.5, 10: 4.6, 11: 4.6, 12: 4.7,
13: 4.8})
with self.assertRaisesRegex(ValueError, 'Weights should be provided for CN 1 to 13'):
CNBiasNbSetWeight.explicit(cn_weights={1: 1.0, 13: 2.0})
fake_nb_set.cn = 1
w1 = bias_weight1.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w1, 1.0, delta=1e-8)
w2 = bias_weight2.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w2, 1.0, delta=1e-8)
w3 = bias_weight3.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w3, 1.0, delta=1e-8)
fake_nb_set.cn = 7
w1 = bias_weight1.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w1, 7.0, delta=1e-8)
w2 = bias_weight2.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w2, 1.1**6, delta=1e-8)
w3 = bias_weight3.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w3, 4.3, delta=1e-8)
fake_nb_set.cn = 13
w1 = bias_weight1.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w1, 13.0, delta=1e-8)
w2 = bias_weight2.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w2, 1.1**12, delta=1e-8)
w3 = bias_weight3.weight(nb_set=fake_nb_set, structure_environments=dummy_se)
self.assertAlmostEqual(w3, 4.8, delta=1e-8)
bias_weight4 = CNBiasNbSetWeight.from_description({'type': 'linearly_equidistant',
'weight_cn1': 2.0,
'weight_cn13': 26.0})
for cn in range(1, 14):
self.assertAlmostEqual(bias_weight4.cn_weights[cn], 2.0*cn)
bias_weight5 = CNBiasNbSetWeight.from_description({'type': 'geometrically_equidistant',
'weight_cn1': 1.0,
'weight_cn13': 13.0})
self.assertAlmostEqual(bias_weight5.cn_weights[1], 1.0)
self.assertAlmostEqual(bias_weight5.cn_weights[3], 1.5334062370163877)
self.assertAlmostEqual(bias_weight5.cn_weights[9], 5.5287748136788739)
self.assertAlmostEqual(bias_weight5.cn_weights[12], 10.498197520079623)
cn_weights = {cn: 0.0 for cn in range(1, 14)}
cn_weights[6] = 2.0
cn_weights[4] = 1.0
bias_weight6 = CNBiasNbSetWeight.from_description({'type': 'explicit',
'cn_weights': cn_weights})
self.assertAlmostEqual(bias_weight6.cn_weights[1], 0.0)
self.assertAlmostEqual(bias_weight6.cn_weights[4], 1.0)
self.assertAlmostEqual(bias_weight6.cn_weights[6], 2.0)
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 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)
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 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)
