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 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)
