Return either a single ordered structure or a sequence of all ordered
        structures.

        Args:
            structure: Structure to order.
            return_ranked_list (bool): Whether or not multiple structures are
                returned. If return_ranked_list is a number, that number of
                structures is returned.

        Returns:
            Depending on returned_ranked list, either a transformed structure
            or a list of dictionaries, where each dictionary is of the form
            {"structure" = .... , "other_arguments"}

            The list of ordered structures is ranked by ewald energy / atom, if
            the input structure is an oxidation state decorated structure.
            Otherwise, it is ranked by number of sites, with smallest number of
            sites first.

def apply_transformation(self, structure, return_ranked_list=False):
        """
        Return either a single ordered structure or a sequence of all ordered
        structures.

        Args:
            structure: Structure to order.
            return_ranked_list (bool): Whether or not multiple structures are
                returned. If return_ranked_list is a number, that number of
                structures is returned.

        Returns:
            Depending on returned_ranked list, either a transformed structure
            or a list of dictionaries, where each dictionary is of the form
            {"structure" = .... , "other_arguments"}

            The list of ordered structures is ranked by ewald energy / atom, if
            the input structure is an oxidation state decorated structure.
            Otherwise, it is ranked by number of sites, with smallest number of
            sites first.
        """
        try:
            num_to_return = int(return_ranked_list)
        except ValueError:
            num_to_return = 1

        if self.refine_structure:
            finder = SpacegroupAnalyzer(structure, self.symm_prec)
            structure = finder.get_refined_structure()

        contains_oxidation_state = all(
            [hasattr(sp, "oxi_state") and sp.oxi_state != 0 for sp in
             structure.composition.elements]
        )

        structures = None

        if structure.is_ordered:
            warn("Enumeration skipped for structure with composition {} "
                 "because it is ordered".format(structure.composition))
            structures = [structure.copy()]

        if self.max_disordered_sites:
            ndisordered = sum([1 for site in structure if not site.is_ordered])
            if ndisordered > self.max_disordered_sites:
                raise ValueError(
                    "Too many disordered sites! ({} > {})".format(
                        ndisordered, self.max_disordered_sites))
            max_cell_sizes = range(self.min_cell_size, int(
                math.floor(self.max_disordered_sites / ndisordered)) + 1)
        else:
            max_cell_sizes = [self.max_cell_size]

        for max_cell_size in max_cell_sizes:
            adaptor = EnumlibAdaptor(
                structure, min_cell_size=self.min_cell_size,
                max_cell_size=max_cell_size,
                symm_prec=self.symm_prec, refine_structure=False,
                enum_precision_parameter=self.enum_precision_parameter,
                check_ordered_symmetry=self.check_ordered_symmetry,
                timeout=self.timeout)
            try:
                adaptor.run()
            except EnumError:
                warn("Unable to enumerate for max_cell_size = %d".format(
                    max_cell_size))
            structures = adaptor.structures
            if structures:
                break

        if structures is None:
            raise ValueError("Unable to enumerate")

        original_latt = structure.lattice
        inv_latt = np.linalg.inv(original_latt.matrix)
        ewald_matrices = {}
        all_structures = []
        for s in structures:
            new_latt = s.lattice
            transformation = np.dot(new_latt.matrix, inv_latt)
            transformation = tuple([tuple([int(round(cell)) for cell in row])
                                    for row in transformation])
            if contains_oxidation_state and self.sort_criteria == "ewald":
                if transformation not in ewald_matrices:
                    s_supercell = structure * transformation
                    ewald = EwaldSummation(s_supercell)
                    ewald_matrices[transformation] = ewald
                else:
                    ewald = ewald_matrices[transformation]
                energy = ewald.compute_sub_structure(s)
                all_structures.append({"num_sites": len(s), "energy": energy,
                                       "structure": s})
            else:
                all_structures.append({"num_sites": len(s), "structure": s})

        def sort_func(s):
            return s["energy"] / s["num_sites"] \
                if contains_oxidation_state and self.sort_criteria == "ewald" \
                else s["num_sites"]

        self._all_structures = sorted(all_structures, key=sort_func)

        if return_ranked_list:
            return self._all_structures[0:num_to_return]
        else:
            return self._all_structures[0]["structure"]