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_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)