# Source code for pymatgen.alchemy.materials

    # coding: utf-8
    # Copyright (c) Pymatgen Development Team.
    # Distributed under the terms of the MIT License.

    """
    This module provides various representations of transformed structures. A
    TransformedStructure is a structure that has been modified by undergoing a
    series of transformations.
    """

    import os
    import re
    import json
    import datetime

    from monty.json import MontyDecoder, jsanitize

    from pymatgen.core.structure import Structure
    from pymatgen.io.cif import CifParser
    from pymatgen.io.vasp.inputs import Poscar
    from monty.json import MSONable

    from pymatgen.io.vasp.sets import MPRelaxSet

    from warnings import warn

    __author__ = "Shyue Ping Ong, Will Richards"
    __copyright__ = "Copyright 2012, The Materials Project"
    __version__ = "1.0"
    __maintainer__ = "Shyue Ping Ong"
    __email__ = "shyuep@gmail.com"
    __date__ = "Mar 2, 2012"

    dec = MontyDecoder()

    class TransformedStructure(MSONable):
        """
        Container object for new structures that include history of
        transformations.

        Each transformed structure is made up of a sequence of structures with
        associated transformation history.
        """

        def __init__(self, structure, transformations=None, history=None,
                     other_parameters=None):
            """
            Initializes a transformed structure from a structure.

            Args:
                structure (Structure): Input structure
                transformations ([Transformations]): List of transformations to
                    apply.
                history (list): Previous history.
                other_parameters (dict): Additional parameters to be added.
            """
            self.final_structure = structure
            self.history = history or []
            self.other_parameters = other_parameters or {}
            self._undone = []

            transformations = transformations or []
            for t in transformations:
                self.append_transformation(t)

        def undo_last_change(self):
            """
            Undo the last change in the TransformedStructure.

            Raises:
                IndexError: If already at the oldest change.
            """
            if len(self.history) == 0:
                raise IndexError("Can't undo. Already at oldest change.")
            if 'input_structure' not in self.history[-1]:
                raise IndexError("Can't undo. Latest history has no "
                                 "input_structure")
            h = self.history.pop()
            self._undone.append((h, self.final_structure))
            s = h["input_structure"]
            if isinstance(s, dict):
                s = Structure.from_dict(s)
            self.final_structure = s

        def redo_next_change(self):
            """
            Redo the last undone change in the TransformedStructure.

            Raises:
                IndexError: If already at the latest change.
            """
            if len(self._undone) == 0:
                raise IndexError("Can't redo. Already at latest change.")
            h, s = self._undone.pop()
            self.history.append(h)
            self.final_structure = s

        def __getattr__(self, name):
            s = object.__getattribute__(self, 'final_structure')
            return getattr(s, name)

        def __len__(self):
            return len(self.history)

        def append_transformation(self, transformation, return_alternatives=False,
                                  clear_redo=True):
            """
            Appends a transformation to the TransformedStructure.

            Args:
                transformation: Transformation to append
                return_alternatives: Whether to return alternative
                    TransformedStructures for one-to-many transformations.
                    return_alternatives can be a number, which stipulates the
                    total number of structures to return.
                clear_redo: Boolean indicating whether to clear the redo list.
                    By default, this is True, meaning any appends clears the
                    history of undoing. However, when using append_transformation
                    to do a redo, the redo list should not be cleared to allow
                    multiple redos.
            """
            if clear_redo:
                self._undone = []

            if return_alternatives and transformation.is_one_to_many:
                ranked_list = transformation.apply_transformation(
                    self.final_structure, return_ranked_list=return_alternatives)

                input_structure = self.final_structure.as_dict()
                alts = []
                for x in ranked_list[1:]:
                    s = x.pop("structure")
                    actual_transformation = x.pop("transformation", transformation)
                    hdict = actual_transformation.as_dict()
                    hdict["input_structure"] = input_structure
                    hdict["output_parameters"] = x
                    self.final_structure = s
                    d = self.as_dict()
                    d['history'].append(hdict)
                    d['final_structure'] = s.as_dict()
                    alts.append(TransformedStructure.from_dict(d))

                x = ranked_list[0]
                s = x.pop("structure")
                actual_transformation = x.pop("transformation", transformation)
                hdict = actual_transformation.as_dict()
                hdict["input_structure"] = self.final_structure.as_dict()
                hdict["output_parameters"] = x
                self.history.append(hdict)
                self.final_structure = s
                return alts
            else:
                s = transformation.apply_transformation(self.final_structure)
                hdict = transformation.as_dict()
                hdict["input_structure"] = self.final_structure.as_dict()
                hdict["output_parameters"] = {}
                self.history.append(hdict)
                self.final_structure = s

        def append_filter(self, structure_filter):
            """
            Adds a filter.

            Args:
                structure_filter (StructureFilter): A filter implementating the
                    AbstractStructureFilter API. Tells transmuter waht structures
                    to retain.
            """
            hdict = structure_filter.as_dict()
            hdict["input_structure"] = self.final_structure.as_dict()
            self.history.append(hdict)

        def extend_transformations(self, transformations,
                                   return_alternatives=False):
            """
            Extends a sequence of transformations to the TransformedStructure.

            Args:
                transformations: Sequence of Transformations
                return_alternatives: Whether to return alternative
                    TransformedStructures for one-to-many transformations.
                    return_alternatives can be a number, which stipulates the
                    total number of structures to return.
            """
            for t in transformations:
                self.append_transformation(t,
                                           return_alternatives=return_alternatives)

        def get_vasp_input(self, vasp_input_set=MPRelaxSet, **kwargs):
            """
            Returns VASP input as a dict of vasp objects.

            Args:
                vasp_input_set (pymatgen.io.vaspio_set.VaspInputSet): input set
                    to create vasp input files from structures
            """
            d = vasp_input_set(self.final_structure, **kwargs).get_vasp_input()
            d["transformations.json"] = json.dumps(self.as_dict())
            return d

        def write_vasp_input(self, vasp_input_set=MPRelaxSet, output_dir=".",
                             create_directory=True, **kwargs):
            r"""
            Writes VASP input to an output_dir.

            Args:
                vasp_input_set:
                    pymatgen.io.vaspio_set.VaspInputSet like object that creates
                    vasp input files from structures
                output_dir: Directory to output files
                create_directory: Create the directory if not present. Defaults to
                    True.
                **kwargs: All keyword args supported by the VASP input set.
            """
            vasp_input_set(self.final_structure, **kwargs).write_input(
                output_dir, make_dir_if_not_present=create_directory)
            with open(os.path.join(output_dir, "transformations.json"), "w") as fp:
                json.dump(self.as_dict(), fp)

        def __str__(self):
            output = ["Current structure", "------------",
                      str(self.final_structure),
                      "\nHistory",
                      "------------"]
            for h in self.history:
                h.pop('input_structure', None)
                output.append(str(h))
            output.append("\nOther parameters")
            output.append("------------")
            output.append(str(self.other_parameters))
            return "\n".join(output)

        def set_parameter(self, key, value):
            """
            Set a parameter

            :param key: The string key
            :param value: The value.
            """
            self.other_parameters[key] = value

        @property
        def was_modified(self):
            """
            Boolean describing whether the last transformation on the structure
            made any alterations to it one example of when this would return false
            is in the case of performing a substitution transformation on the
            structure when the specie to replace isn't in the structure.
            """
            return not self.final_structure == self.structures[-2]

        @property
        def structures(self):
            """
            Copy of all structures in the TransformedStructure. A
            structure is stored after every single transformation.
            """
            hstructs = [Structure.from_dict(s['input_structure'])
                        for s in self.history if 'input_structure' in s]
            return hstructs + [self.final_structure]

        @staticmethod
        def from_cif_string(cif_string, transformations=None, primitive=True,
                            occupancy_tolerance=1.):
            """
            Generates TransformedStructure from a cif string.

            Args:
                cif_string (str): Input cif string. Should contain only one
                    structure. For cifs containing multiple structures, please use
                    CifTransmuter.
                transformations ([Transformations]): Sequence of transformations
                    to be applied to the input structure.
                primitive (bool): Option to set if the primitive cell should be
                    extracted. Defaults to True. However, there are certain
                    instances where you might want to use a non-primitive cell,
                    e.g., if you are trying to generate all possible orderings of
                    partial removals or order a disordered structure.
                occupancy_tolerance (float): If total occupancy of a site is
                    between 1 and occupancy_tolerance, the occupancies will be
                    scaled down to 1.

            Returns:
                TransformedStructure
            """
            parser = CifParser.from_string(cif_string, occupancy_tolerance)
            raw_string = re.sub(r"'", "\"", cif_string)
            cif_dict = parser.as_dict()
            cif_keys = list(cif_dict.keys())
            s = parser.get_structures(primitive)[0]
            partial_cif = cif_dict[cif_keys[0]]
            if "_database_code_ICSD" in partial_cif:
                source = partial_cif["_database_code_ICSD"] + "-ICSD"
            else:
                source = "uploaded cif"
            source_info = {"source": source,
                           "datetime": str(datetime.datetime.now()),
                           "original_file": raw_string,
                           "cif_data": cif_dict[cif_keys[0]]}
            return TransformedStructure(s, transformations, history=[source_info])

        @staticmethod
        def from_poscar_string(poscar_string, transformations=None):
            """
            Generates TransformedStructure from a poscar string.

            Args:
                poscar_string (str): Input POSCAR string.
                transformations ([Transformations]): Sequence of transformations
                    to be applied to the input structure.
            """
            p = Poscar.from_string(poscar_string)
            if not p.true_names:
                raise ValueError("Transformation can be craeted only from POSCAR "
                                 "strings with proper VASP5 element symbols.")
            raw_string = re.sub(r"'", "\"", poscar_string)
            s = p.structure
            source_info = {"source": "POSCAR",
                           "datetime": str(datetime.datetime.now()),
                           "original_file": raw_string}
            return TransformedStructure(s, transformations, history=[source_info])

        def as_dict(self):
            """
            Dict representation of the TransformedStructure.
            """
            d = self.final_structure.as_dict()
            d["@module"] = self.__class__.__module__
            d["@class"] = self.__class__.__name__
            d["history"] = jsanitize(self.history)
            d["version"] = __version__
            d["last_modified"] = str(datetime.datetime.utcnow())
            d["other_parameters"] = jsanitize(self.other_parameters)
            return d

        @classmethod
        def from_dict(cls, d):
            """
            Creates a TransformedStructure from a dict.
            """
            s = Structure.from_dict(d)
            return cls(s, history=d["history"],
                       other_parameters=d.get("other_parameters", None))

        def to_snl(self, authors, **kwargs):
            """
            Generate SNL from TransformedStructure.

            :param authors: List of authors
            :param **kwargs: All kwargs supported by StructureNL.
            :return: StructureNL
            """
            if self.other_parameters:
                warn('Data in TransformedStructure.other_parameters discarded '
                     'during type conversion to SNL')
            hist = []
            for h in self.history:
                snl_metadata = h.pop('_snl', {})
                hist.append({'name': snl_metadata.pop('name', 'pymatgen'),
                             'url': snl_metadata.pop(
                                 'url', 'http://pypi.python.org/pypi/pymatgen'),
                             'description': h})
            from pymatgen.util.provenance import StructureNL
            return StructureNL(self.final_structure, authors, history=hist, **kwargs)

        @classmethod
        def from_snl(cls, snl):
            """
            Create TransformedStructure from SNL.

            Args:
                snl (StructureNL): Starting snl

            Returns:
                TransformedStructure
            """
            hist = []
            for h in snl.history:
                d = h.description
                d['_snl'] = {'url': h.url, 'name': h.name}
                hist.append(d)
            return cls(snl.structure, history=hist)