Python standard_transformations.SubstitutionTransformation类代码示例

 def apply_transformation(self, structure):
     charge = structure.charge
     specie = smart_element_or_specie(self._charge_balance_sp)
     num_to_remove = charge / specie.oxi_state
     num_in_structure = structure.composition[specie]
     removal_fraction = num_to_remove / num_in_structure
     if removal_fraction < 0:
         raise ValueError("addition of specie not yet supported by " "ChargeBalanceTransformation")
     trans = SubstitutionTransformation({self._charge_balance_sp: {self._charge_balance_sp: 1 - removal_fraction}})
     return trans.apply_transformation(structure)

    def test_apply_transformation(self):
        enum_trans = EnumerateStructureTransformation(refine_structure=True)
        enum_trans2 = EnumerateStructureTransformation(refine_structure=True,
                                                      sort_criteria="nsites")
        p = Poscar.from_file(os.path.join(test_dir, 'POSCAR.LiFePO4'),
                             check_for_POTCAR=False)
        struct = p.structure
        expected_ans = [1, 3, 1]
        for i, frac in enumerate([0.25, 0.5, 0.75]):
            trans = SubstitutionTransformation({'Fe': {'Fe': frac}})
            s = trans.apply_transformation(struct)
            oxitrans = OxidationStateDecorationTransformation(
                {'Li': 1, 'Fe': 2, 'P': 5, 'O': -2})
            s = oxitrans.apply_transformation(s)
            alls = enum_trans.apply_transformation(s, 100)
            self.assertEqual(len(alls), expected_ans[i])
            self.assertIsInstance(trans.apply_transformation(s), Structure)
            for ss in alls:
                self.assertIn("energy", ss)
            alls = enum_trans2.apply_transformation(s, 100)
            self.assertEqual(len(alls), expected_ans[i])
            self.assertIsInstance(trans.apply_transformation(s), Structure)
            for ss in alls:
                self.assertIn("num_sites", ss)

        # make sure it works for non-oxidation state decorated structure
        trans = SubstitutionTransformation({'Fe': {'Fe': 0.5}})
        s = trans.apply_transformation(struct)
        alls = enum_trans.apply_transformation(s, 100)
        self.assertEqual(len(alls), 3)
        self.assertIsInstance(trans.apply_transformation(s), Structure)
        for s in alls:
            self.assertNotIn("energy", s)

    def test_init(self):
        if not enumlib_present:
            raise SkipTest("enumlib not present. Skipping...")
        test_dir = os.path.join(os.path.dirname(__file__), "..", "..", "..",
                                'test_files')
        parser = CifParser(os.path.join(test_dir, "LiFePO4.cif"))
        struct = parser.get_structures(False)[0]
        subtrans = SubstitutionTransformation({'Li': {'Li': 0.5}})
        adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1, 2)
        adaptor.run()
        structures = adaptor.structures
        self.assertEqual(len(structures), 86)
        for s in structures:
            self.assertAlmostEqual(s.composition
                                   .get_atomic_fraction(Element("Li")),
                                   0.5 / 6.5)
        adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1, 2,
                                 refine_structure=True)
        adaptor.run()
        structures = adaptor.structures
        self.assertEqual(len(structures), 52)

        subtrans = SubstitutionTransformation({'Li': {'Li': 0.25}})
        adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1, 1,
                                 refine_structure=True)
        adaptor.run()
        structures = adaptor.structures
        self.assertEqual(len(structures), 1)
        for s in structures:
            self.assertAlmostEqual(s.composition
                                   .get_atomic_fraction(Element("Li")),
                                   0.25 / 6.25)

        #Make sure it works for completely disordered structures.
        struct = Structure([[10, 0, 0], [0, 10, 0], [0, 0, 10]], [{'Fe':0.5}],
                           [[0, 0, 0]])
        adaptor = EnumlibAdaptor(struct, 1, 2)
        adaptor.run()
        self.assertEqual(len(adaptor.structures), 3)

        #Make sure it works properly when symmetry is broken by ordered sites.
        parser = CifParser(os.path.join(test_dir, "LiFePO4.cif"))
        struct = parser.get_structures(False)[0]
        subtrans = SubstitutionTransformation({'Li': {'Li': 0.25}})
        s = subtrans.apply_transformation(struct)
        #REmove some ordered sites to break symmetry.
        removetrans = RemoveSitesTransformation([4, 7])
        s = removetrans.apply_transformation(s)
        adaptor = EnumlibAdaptor(s, 1, 1, enum_precision_parameter=0.01)
        adaptor.run()
        structures = adaptor.structures
        self.assertEqual(len(structures), 4)

        struct = Structure([[3, 0, 0], [0, 3, 0], [0, 0, 3]],
                           [{"Si": 0.5}] * 2, [[0, 0, 0], [0.5, 0.5, 0.5]])
        adaptor = EnumlibAdaptor(struct, 1, 3, enum_precision_parameter=0.01)
        adaptor.run()
        structures = adaptor.structures
        self.assertEqual(len(structures), 10)

    def apply_transformation(self, structure, return_ranked_list=False):
        if not return_ranked_list:
            raise ValueError("SubstitutionPredictorTransformation doesn't"
                             " support returning 1 structure")

        preds = self._substitutor.pred_from_comp(structure.composition)
        preds.sort(key=lambda x: x['probability'], reverse=True)

        outputs = []
        for pred in preds:
            st = SubstitutionTransformation(pred['substitutions'])
            output = {'structure': st.apply_transformation(structure),
                      'probability': pred['probability'],
                      'threshold': self._threshold, 'substitutions': {}}
            #dictionary keys have to be converted to strings for JSON
            for key, value in pred['substitutions'].items():
                output['substitutions'][str(key)] = str(value)
            outputs.append(output)
        return outputs

    def test_init(self):
        test_dir = os.path.join(os.path.dirname(__file__), "..", "..", "..", "test_files")
        struct = self.get_structure("LiFePO4")
        subtrans = SubstitutionTransformation({"Li": {"Li": 0.5}})
        adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1, 2)
        adaptor.run()
        structures = adaptor.structures
        self.assertEqual(len(structures), 86)
        for s in structures:
            self.assertAlmostEqual(s.composition.get_atomic_fraction(Element("Li")), 0.5 / 6.5)
        adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1, 2, refine_structure=True)
        adaptor.run()
        structures = adaptor.structures
        self.assertEqual(len(structures), 52)

        subtrans = SubstitutionTransformation({"Li": {"Li": 0.25}})
        adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1, 1, refine_structure=True)
        adaptor.run()
        structures = adaptor.structures
        self.assertEqual(len(structures), 1)
        for s in structures:
            self.assertAlmostEqual(s.composition.get_atomic_fraction(Element("Li")), 0.25 / 6.25)

        # Make sure it works for completely disordered structures.
        struct = Structure([[10, 0, 0], [0, 10, 0], [0, 0, 10]], [{"Fe": 0.5}], [[0, 0, 0]])
        adaptor = EnumlibAdaptor(struct, 1, 2)
        adaptor.run()
        self.assertEqual(len(adaptor.structures), 3)

        # Make sure it works properly when symmetry is broken by ordered sites.
        struct = self.get_structure("LiFePO4")
        subtrans = SubstitutionTransformation({"Li": {"Li": 0.25}})
        s = subtrans.apply_transformation(struct)
        # REmove some ordered sites to break symmetry.
        removetrans = RemoveSitesTransformation([4, 7])
        s = removetrans.apply_transformation(s)
        adaptor = EnumlibAdaptor(s, 1, 1, enum_precision_parameter=0.01)
        adaptor.run()
        structures = adaptor.structures
        self.assertEqual(len(structures), 4)

        struct = Structure([[3, 0, 0], [0, 3, 0], [0, 0, 3]], [{"Si": 0.5}] * 2, [[0, 0, 0], [0.5, 0.5, 0.5]])
        adaptor = EnumlibAdaptor(struct, 1, 3, enum_precision_parameter=0.01)
        adaptor.run()
        structures = adaptor.structures
        self.assertEqual(len(structures), 10)

        struct = Structure.from_file(os.path.join(test_dir, "EnumerateTest.json"))
        adaptor = EnumlibAdaptor(struct, 1, 1)
        adaptor.run()
        structures = adaptor.structures
        self.assertEqual(len(structures), 2)

    def test_apply_transformation(self):
        enum_trans = EnumerateStructureTransformation(refine_structure=True)
        p = Poscar.from_file(os.path.join(test_dir, "POSCAR.LiFePO4"), check_for_POTCAR=False)
        struct = p.structure
        expected_ans = [1, 3, 1]
        for i, frac in enumerate([0.25, 0.5, 0.75]):
            trans = SubstitutionTransformation({"Fe": {"Fe": frac}})
            s = trans.apply_transformation(struct)
            oxitrans = OxidationStateDecorationTransformation({"Li": 1, "Fe": 2, "P": 5, "O": -2})
            s = oxitrans.apply_transformation(s)
            alls = enum_trans.apply_transformation(s, 100)
            self.assertEqual(len(alls), expected_ans[i])
            self.assertIsInstance(trans.apply_transformation(s), Structure)
            for s in alls:
                self.assertIn("energy", s)

        # make sure it works for non-oxidation state decorated structure
        trans = SubstitutionTransformation({"Fe": {"Fe": 0.5}})
        s = trans.apply_transformation(struct)
        alls = enum_trans.apply_transformation(s, 100)
        self.assertEqual(len(alls), 3)
        self.assertIsInstance(trans.apply_transformation(s), Structure)
        for s in alls:
            self.assertNotIn("energy", s)

    def pred_from_structures(self, target_species, structures_list,
                             remove_duplicates=True):
        """
        performs a structure prediction targeting compounds containing the
        target_species and based on a list of structure (those structures
        can for instance come from a database like the ICSD). It will return
        all the structures formed by ionic substitutions with a probability
        higher than the threshold

        Args:
            target_species:
                a list of species with oxidation states
                e.g., [Specie('Li',1),Specie('Ni',2), Specie('O',-2)]

            structures_list:
                a list of dictionnary of the form {'structure':Structure object
                ,'id':some id where it comes from}
                the id can for instance refer to an ICSD id

        Returns:
            a list of TransformedStructure objects.
        """
        result = []
        transmuter = StandardTransmuter([])
        if len(list(set(target_species) & set(self.get_allowed_species()))) \
                != len(target_species):
            raise ValueError("the species in target_species are not allowed"
                              + "for the probability model you are using")

        for permut in itertools.permutations(target_species):
            for s in structures_list:
                #check if: species are in the domain,
                #and the probability of subst. is above the threshold
                els = s['structure'].composition.elements
                if len(els) == len(permut) and \
                    len(list(set(els) & set(self.get_allowed_species()))) == \
                        len(els) and self._sp.cond_prob_list(permut, els) > \
                        self._threshold:

                    clean_subst = {els[i]: permut[i]
                                   for i in xrange(0, len(els))
                                   if els[i] != permut[i]}

                    if len(clean_subst) == 0:
                        continue

                    transf = SubstitutionTransformation(clean_subst)

                    if Substitutor._is_charge_balanced(
                            transf.apply_transformation(s['structure'])):
                        ts = TransformedStructure(
                            s['structure'], [transf], history=[s['id']],
                            other_parameters={
                                'type': 'structure_prediction',
                                'proba': self._sp.cond_prob_list(permut, els)}
                        )
                        result.append(ts)
                        transmuter.append_transformed_structures([ts])

        if remove_duplicates:
            transmuter.apply_filter(RemoveDuplicatesFilter(
                symprec=self._symprec))
        return transmuter.transformed_structures