# Source code for pymatgen.analysis.magnetism.analyzer

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

    """
    This module provides some useful functions for dealing with magnetic Structures
    (e.g. Structures with associated magmom tags).
    """

    import warnings
    import numpy as np
    import os
    import logging

    from enum import Enum, unique
    from collections import namedtuple

    from scipy.stats import gaussian_kde
    from scipy.signal import argrelextrema

    from pymatgen.core.structure import Specie, Structure, Element, DummySpecie
    from pymatgen.electronic_structure.core import Magmom
    from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
    from pymatgen.transformations.standard_transformations import (
        AutoOxiStateDecorationTransformation,
    )
    from pymatgen.transformations.advanced_transformations import (
        MagOrderingTransformation,
        MagOrderParameterConstraint,
    )
    from pymatgen.symmetry.groups import SpaceGroup
    from monty.serialization import loadfn

    from typing import Union, List, Dict, Tuple, Optional, Any
    from pymatgen.util.typing import Vector3Like

    """
    This module provides some useful functions for dealing with magnetic Structures
    (e.g. Structures with associated magmom tags).
    """

    __author__ = "Matthew Horton"
    __copyright__ = "Copyright 2017, The Materials Project"
    __version__ = "0.1"
    __maintainer__ = "Matthew Horton"
    __email__ = "mkhorton@lbl.gov"
    __status__ = "Development"
    __date__ = "Feb 2017"

    MODULE_DIR = os.path.dirname(os.path.abspath(__file__))

    try:
        DEFAULT_MAGMOMS = loadfn(os.path.join(MODULE_DIR, "default_magmoms.yaml"))
    except Exception:
        warnings.warn(
            "Could not load default_magmoms.yaml, falling back to VASPIncarBase.yaml"
        )
        DEFAULT_MAGMOMS = loadfn(
            os.path.join(MODULE_DIR, "../../io/vasp/VASPIncarBase.yaml")
        )
        DEFAULT_MAGMOMS = DEFAULT_MAGMOMS["MAGMOM"]

    @unique
    class Ordering(Enum):
        """
        Enumeration defining possible magnetic orderings.
        """

        FM = "FM"  # Ferromagnetic
        AFM = "AFM"  # Antiferromagnetic
        FiM = "FiM"  # Ferrimagnetic
        NM = "NM"  # Non-magnetic
        Unknown = "Unknown"

    @unique
    class OverwriteMagmomMode(Enum):
        """
        Enumeration defining different modes for analyzer.
        """

        none = "none"
        respect_sign = "respect_sign"
        respect_zero = "respect_zeros"
        replace_all = "replace_all"
        normalize = "normalize"

    class CollinearMagneticStructureAnalyzer:
        """
        A class which provides a few helpful methods to analyze
        collinear magnetic structures.
        """
        def __init__(
            self,
            structure: Structure,
            overwrite_magmom_mode: Union[OverwriteMagmomMode, str] = "none",
            round_magmoms: bool = False,
            detect_valences: bool = False,
            make_primitive: bool = True,
            default_magmoms: dict = None,
            set_net_positive: bool = True,
            threshold: float = 0.00,
            threshold_nonmag: float = 0.1,
        ):
            r"""
            If magnetic moments are not defined, moments will be
            taken either from default_magmoms.yaml (similar to the
            default magmoms in MPRelaxSet, with a few extra definitions)
            or from a specie:magmom dict provided by the default_magmoms
            kwarg.

            Input magmoms can be replaced using the 'overwrite_magmom_mode'
            kwarg. This can be:
            * "none" to do nothing,
            * "respect_sign" which will overwrite existing magmoms with
              those from default_magmoms but will keep sites with positive magmoms
              positive, negative magmoms negative and zero magmoms zero,
            * "respect_zeros", which will give a ferromagnetic structure
              (all positive magmoms from default_magmoms) but still keep sites with
              zero magmoms as zero,
            * "replace_all" which will try to guess initial magmoms for
              all sites in the structure irrespective of input structure
              (this is most suitable for an initial DFT calculation),
            * "replace_all_if_undefined" is the same as "replace_all" but only if
              no magmoms are defined in input structure, otherwise it will respect
              existing magmoms.
            * "normalize" will normalize magmoms to unity, but will respect sign
              (used for comparing orderings), magmoms < theshold will be set to zero

            Args:
                structure: input Structure object
                overwrite_magmom_mode: "respect_sign", "respect_zeros", "replace_all",
                    "replace_all_if_undefined", "normalize" (default "none")
                round_magmoms: will round input magmoms to
                    specified number of decimal places if integer is supplied, if set
                    to a float will try and group magmoms together using a kernel density
                    estimator of provided width, and extracting peaks of the estimator
                    detect_valences: if True, will attempt to assign valences
                    to input structure
                make_primitive: if True, will transform to primitive
                    magnetic cell
                default_magmoms: (optional) dict specifying default magmoms
                set_net_positive: if True, will change sign of magnetic
                    moments such that the net magnetization is positive. Argument will be
                    ignored if mode "respect_sign" is used.
                threshold: number (in Bohr magnetons) below which magmoms
                    will be rounded to zero
                threshold_nonmag: number (in Bohr magneton)
                    below which nonmagnetic ions (with no magmom specified
                    in default_magmoms) will be rounded to zero
            """

            if default_magmoms:
                self.default_magmoms = default_magmoms
            else:
                self.default_magmoms = DEFAULT_MAGMOMS

            structure = structure.copy()

            # check for disorder
            if not structure.is_ordered:
                raise NotImplementedError(
                    "Not implemented for disordered structures, "
                    "make ordered approximation first."
                )

            if detect_valences:
                trans = AutoOxiStateDecorationTransformation()
                try:
                    structure = trans.apply_transformation(structure)
                except ValueError:
                    warnings.warn(
                        "Could not assign valences "
                        "for {}".format(structure.composition.reduced_formula)
                    )

            # check to see if structure has magnetic moments
            # on site properties or species spin properties,
            # prioritize site properties

            has_magmoms = bool(structure.site_properties.get("magmom", False))

            has_spin = False
            for comp in structure.species_and_occu:
                for sp, occu in comp.items():
                    if getattr(sp, "spin", False):
                        has_spin = True

            # perform input sanitation ...
            # rest of class will assume magnetic moments
            # are stored on site properties:
            # this is somewhat arbitrary, arguments can
            # be made for both approaches

            if has_magmoms and has_spin:
                raise ValueError(
                    "Structure contains magnetic moments on both "
                    "magmom site properties and spin species "
                    "properties. This is ambiguous. Remove one or "
                    "the other."
                )
            elif has_magmoms:
                if None in structure.site_properties["magmom"]:
                    warnings.warn(
                        "Be careful with mixing types in your magmom "
                        "site properties. Any 'None' magmoms have been "
                        "replaced with zero."
                    )
                magmoms = [m if m else 0 for m in structure.site_properties["magmom"]]
            elif has_spin:
                magmoms = [getattr(sp, "spin", 0) for sp in structure.species]
                structure.remove_spin()
            else:
                # no magmoms present, add zero magmoms for now
                magmoms = [0] * len(structure)
                # and overwrite magmoms with default magmoms later unless otherwise stated
                if overwrite_magmom_mode == "replace_all_if_undefined":
                    overwrite_magmom_mode = "replace_all"

            # test to see if input structure has collinear magmoms
            self.is_collinear = Magmom.are_collinear(magmoms)

            if not self.is_collinear:
                warnings.warn(
                    "This class is not designed to be used with "
                    "non-collinear structures. If your structure is "
                    "only slightly non-collinear (e.g. canted) may still "
                    "give useful results, but use with caution."
                )

            # this is for collinear structures only, make sure magmoms
            # are all floats
            magmoms = list(map(float, magmoms))

            # set properties that should be done /before/ we process input magmoms
            self.total_magmoms = sum(magmoms)
            self.magnetization = sum(magmoms) / structure.volume

            # round magmoms on magnetic ions below threshold to zero
            # and on non magnetic ions below threshold_nonmag
            magmoms = [
                m
                if abs(m) > threshold and a.species_string in self.default_magmoms
                else m
                if abs(m) > threshold_nonmag
                and a.species_string not in self.default_magmoms
                else 0
                for (m, a) in zip(magmoms, structure.sites)
            ]

            # overwrite existing magmoms with default_magmoms
            if overwrite_magmom_mode not in (
                "none",
                "respect_sign",
                "respect_zeros",
                "replace_all",
                "replace_all_if_undefined",
                "normalize",
            ):
                raise ValueError("Unsupported mode.")

            for idx, site in enumerate(structure):

                if site.species_string in self.default_magmoms:
                    # look for species first, e.g. Fe2+
                    default_magmom = self.default_magmoms[site.species_string]
                elif (
                    isinstance(site.specie, Specie)
                    and str(site.specie.element) in self.default_magmoms
                ):
                    # look for element, e.g. Fe
                    default_magmom = self.default_magmoms[str(site.specie.element)]
                else:
                    default_magmom = 0

                # overwrite_magmom_mode = "respect_sign" will change magnitude of
                # existing moments only, and keep zero magmoms as
                # zero: it will keep the magnetic ordering intact

                if overwrite_magmom_mode == "respect_sign":
                    set_net_positive = False
                    if magmoms[idx] > 0:
                        magmoms[idx] = default_magmom
                    elif magmoms[idx] < 0:
                        magmoms[idx] = -default_magmom

                # overwrite_magmom_mode = "respect_zeros" will give a ferromagnetic
                # structure but will keep zero magmoms as zero

                elif overwrite_magmom_mode == "respect_zeros":
                    if magmoms[idx] != 0:
                        magmoms[idx] = default_magmom

                # overwrite_magmom_mode = "replace_all" will ignore input magmoms
                # and give a ferromagnetic structure with magnetic
                # moments on *all* atoms it thinks could be magnetic

                elif overwrite_magmom_mode == "replace_all":
                    magmoms[idx] = default_magmom

                # overwrite_magmom_mode = "normalize" set magmoms magnitude to 1

                elif overwrite_magmom_mode == "normalize":
                    if magmoms[idx] != 0:
                        magmoms[idx] = int(magmoms[idx] / abs(magmoms[idx]))

            # round magmoms, used to smooth out computational data
            magmoms = (
                self._round_magmoms(magmoms, round_magmoms) if round_magmoms else magmoms
            )

            if set_net_positive:
                sign = np.sum(magmoms)
                if sign < 0:
                    magmoms = -np.array(magmoms)

            structure.add_site_property("magmom", magmoms)

            if make_primitive:
                structure = structure.get_primitive_structure(use_site_props=True)

            self.structure = structure

        @staticmethod
        def _round_magmoms(
            magmoms: Vector3Like, round_magmoms_mode: Union[int, float]
        ) -> np.ndarray:
            """If round_magmoms_mode is an integer, simply round to that number
            of decimal places, else if set to a float will try and round
            intelligently by grouping magmoms.
            """

            if isinstance(round_magmoms_mode, int):

                # simple rounding to number of decimal places
                magmoms = np.around(magmoms, decimals=round_magmoms_mode)

            elif isinstance(round_magmoms_mode, float):

                try:

                    # get range of possible magmoms, pad by 50% just to be safe
                    range_m = max([max(magmoms), abs(min(magmoms))]) * 1.5

                    # construct kde, here "round_magmoms_mode" is the width of the kde
                    kernel = gaussian_kde(magmoms, bw_method=round_magmoms_mode)

                    # with a linearly spaced grid 1000x finer than width
                    xgrid = np.linspace(
                        -range_m, range_m, int(1000 * range_m / round_magmoms_mode)
                    )

                    # and evaluate the kde on this grid, extracting the maxima of the kde peaks
                    kernel_m = kernel.evaluate(xgrid)
                    extrema = xgrid[argrelextrema(kernel_m, comparator=np.greater)]

                    # round magmoms to these extrema
                    magmoms = [extrema[(np.abs(extrema - m)).argmin()] for m in magmoms]

                except Exception as e:

                    # TODO: typically a singular matrix warning, investigate this
                    warnings.warn(
                        "Failed to round magmoms intelligently, "
                        "falling back to simple rounding."
                    )
                    warnings.warn(str(e))

                # and finally round roughly to the number of significant figures in our kde width
                num_decimals = len(str(round_magmoms_mode).split(".")[1]) + 1
                magmoms = np.around(magmoms, decimals=num_decimals)

            return magmoms

        def get_structure_with_spin(self) -> Structure:
            """Returns a Structure with species decorated with spin values instead
            of using magmom site properties.
            """

            structure = self.structure.copy()
            structure.add_spin_by_site(structure.site_properties["magmom"])
            structure.remove_site_property("magmom")

            return structure

        def get_structure_with_only_magnetic_atoms(
            self, make_primitive: bool = True
        ) -> Structure:
            """Returns a Structure with only magnetic atoms present.

            Args:
              make_primitive: Whether to make structure primitive after
                removing non-magnetic atoms (Default value = True)

            Returns: Structure
            """

            sites = [site for site in self.structure if abs(site.properties["magmom"]) > 0]

            structure = Structure.from_sites(sites)

            if make_primitive:
                structure = structure.get_primitive_structure(use_site_props=True)

            return structure

        def get_nonmagnetic_structure(self, make_primitive: bool = True) -> Structure:
            """Returns a Structure without magnetic moments defined.

            Args:
              make_primitive: Whether to make structure primitive after
                removing magnetic information (Default value = True)

            Returns:
              Structure

            """

            structure = self.structure.copy()
            structure.remove_site_property("magmom")

            if make_primitive:
                structure = structure.get_primitive_structure()

            return structure

        def get_ferromagnetic_structure(self, make_primitive: bool = True) -> Structure:
            """Returns a Structure with all magnetic moments positive
            or zero.

            Args:
              make_primitive: Whether to make structure primitive after
                making all magnetic moments positive (Default value = True)

            Returns:
              Structure

            """

            structure = self.structure.copy()

            structure.add_site_property("magmom", [abs(m) for m in self.magmoms])

            if make_primitive:
                structure = structure.get_primitive_structure(use_site_props=True)

            return structure

        @property
        def is_magnetic(self) -> bool:
            """Convenience property, returns True if any non-zero magmoms present.
            """
            return any(map(abs, self.structure.site_properties["magmom"]))

        @property
        def magmoms(self) -> np.ndarray:
            """Convenience property, returns magmoms as a numpy array.
            """

            return np.array(self.structure.site_properties["magmom"])

        @property
        def types_of_magnetic_specie(self) -> Tuple[Union[Element, Specie, DummySpecie], ...]:
            """Equivalent to Structure.types_of_specie but only returns
            magnetic species.

            Returns: types of Specie as a list

            """
            if self.number_of_magnetic_sites > 0:
                structure = self.get_structure_with_only_magnetic_atoms()
                return tuple(sorted(structure.types_of_specie))
            else:
                return tuple()

        @property
        def magnetic_species_and_magmoms(self) -> Dict[str, Any]:
            """Returns a dict of magnetic species and the magnitude of
            their associated magmoms. Will return a list if there are
            multiple magmoms per species.

            Returns: dict of magnetic species and magmoms
            """

            structure = self.get_ferromagnetic_structure()

            magtypes: Dict = {
                str(site.specie): set()
                for site in structure
                if site.properties["magmom"] != 0
            }

            for site in structure:
                if site.properties["magmom"] != 0:
                    magtypes[str(site.specie)].add(site.properties["magmom"])

            for sp, magmoms in magtypes.items():
                if len(magmoms) == 1:
                    magtypes[sp] = magmoms.pop()
                else:
                    magtypes[sp] = sorted(list(magmoms))

            return magtypes

        @property
        def number_of_magnetic_sites(self) -> int:
            """Number of magnetic sites present in structure."""
            return int(np.sum([abs(m) > 0 for m in self.magmoms]))

        def number_of_unique_magnetic_sites(
            self, symprec: float = 1e-3, angle_tolerance: float = 5
        ) -> int:
            """

            Args:
              symprec: same as in SpacegroupAnalyzer (Default value = 1e-3)
              angle_tolerance: same as in SpacegroupAnalyzer (Default value = 5)

            Returns: Number of symmetrically-distinct magnetic sites present
            in structure.

            """

            structure = self.get_nonmagnetic_structure()

            sga = SpacegroupAnalyzer(
                structure, symprec=symprec, angle_tolerance=angle_tolerance
            )

            symm_structure = sga.get_symmetrized_structure()

            num_unique_mag_sites = 0

            for group_of_sites in symm_structure.equivalent_sites:
                if group_of_sites[0].specie in self.types_of_magnetic_specie:
                    num_unique_mag_sites += 1

            return num_unique_mag_sites

        @property
        def ordering(self) -> Ordering:
            """Applies heuristics to return a magnetic ordering for a collinear
            magnetic structure. Result is not guaranteed for correctness.

            Returns: Ordering Enum ('FiM' is used as the abbreviation for
            ferrimagnetic)
            """

            if not self.is_collinear:
                warnings.warn(
                    "Detecting ordering in non-collinear structures not yet implemented."
                )
                return Ordering.Unknown

            if "magmom" not in self.structure.site_properties:
                # maybe this was a non-spin-polarized calculation, or we've
                # lost the magnetic moment information
                return Ordering.Unknown

            magmoms = self.magmoms

            max_magmom = max(magmoms)

            total_magnetization = abs(sum(magmoms))

            is_potentially_ferromagnetic = np.all(magmoms >= 0) or np.all(magmoms <= 0)

            if total_magnetization > 0 and is_potentially_ferromagnetic:
                return Ordering.FM
            elif total_magnetization > 0:
                return Ordering.FiM
            elif max_magmom > 0:
                return Ordering.AFM
            else:
                return Ordering.NM

        def get_exchange_group_info(
            self, symprec: float = 1e-2, angle_tolerance: float = 5.0
        ) -> Tuple[str, int]:
            """Returns the information on the symmetry of the Hamiltonian
            describing the exchange energy of the system, taking into
            account relative direction of magnetic moments but not their
            absolute direction.

            This is not strictly accurate (e.g. some/many atoms will
            have zero magnetic moments), but defining symmetry this
            way is a useful way of keeping track of distinct magnetic
            orderings within pymatgen.

            Args:
              symprec: same as SpacegroupAnalyzer (Default value = 1e-2)
              angle_tolerance: same as SpacegroupAnalyzer (Default value = 5.0)

            Returns:
              spacegroup_symbol, international_number

            """

            structure = self.get_structure_with_spin()

            return structure.get_space_group_info(
                symprec=symprec, angle_tolerance=angle_tolerance
            )

        def matches_ordering(self, other: Structure) -> bool:
            """Compares the magnetic orderings of one structure with another.

            Args:
              other: Structure to compare

            Returns: True or False
            """

            a = CollinearMagneticStructureAnalyzer(
                self.structure, overwrite_magmom_mode="normalize"
            ).get_structure_with_spin()

            # sign of spins doesn't matter, so we're comparing both
            # positive and negative versions of the structure
            # this code is possibly redundant, but is included out of
            # an abundance of caution
            b_positive = CollinearMagneticStructureAnalyzer(
                other, overwrite_magmom_mode="normalize", make_primitive=False
            )

            b_negative = b_positive.structure.copy()
            b_negative.add_site_property(
                "magmom", np.multiply(-1, b_negative.site_properties["magmom"])
            )

            b_negative = CollinearMagneticStructureAnalyzer(
                b_negative, overwrite_magmom_mode="normalize", make_primitive=False
            )

            b_positive = b_positive.get_structure_with_spin()
            b_negative = b_negative.get_structure_with_spin()

            if a.matches(b_positive) or a.matches(
                b_negative
            ):  # sometimes returns None (bug?)
                return True
            else:
                return False

        def __str__(self):
            """
            Sorts a Structure (by fractional co-ordinate), and
            prints sites with magnetic information. This is
            useful over Structure.__str__ because sites are in
            a consistent order, which makes visual comparison between
            two identical Structures with different magnetic orderings
            easier.
            """

            frac_coords = self.structure.frac_coords
            sorted_indices = np.lexsort(
                (frac_coords[:, 2], frac_coords[:, 1], frac_coords[:, 0])
            )
            s = Structure.from_sites([self.structure[idx] for idx in sorted_indices])

            # adapted from Structure.__repr__
            outs = ["Structure Summary", repr(s.lattice)]
            outs.append("Magmoms Sites")
            for site in s:
                if site.properties["magmom"] != 0:
                    prefix = "{:+.2f}   ".format(site.properties["magmom"])
                else:
                    prefix = "        "
                outs.append(prefix + repr(site))
            return "\n".join(outs)

    class MagneticStructureEnumerator:
        """Combines MagneticStructureAnalyzer and MagOrderingTransformation to
        automatically generate a set of transformations for a given structure
        and produce a list of plausible magnetic orderings.
        """

        available_strategies = (
            "ferromagnetic",
            "antiferromagnetic",
            "ferrimagnetic_by_motif",
            "ferrimagnetic_by_species",
            "antiferromagnetic_by_motif",
            "nonmagnetic",
        )

        def __init__(
            self,
            structure: Structure,
            default_magmoms: Optional[Dict[str, float]] = None,
            strategies: Union[List[str], Tuple[str, ...]] = ("ferromagnetic", "antiferromagnetic"),
            automatic: bool = True,
            truncate_by_symmetry: bool = True,
            transformation_kwargs: Optional[Dict] = None,
        ):
            """
            This class will try generated different collinear
            magnetic orderings for a given input structure.

            If the input structure has magnetic moments defined, it
            is possible to use these as a hint as to which elements are
            magnetic, otherwise magnetic elements will be guessed
            (this can be changed using default_magmoms kwarg).

            Args:
                structure: input structure
                default_magmoms: (optional, defaults provided) dict of
                    magnetic elements to their initial magnetic moments in µB, generally
                    these are chosen to be high-spin since they can relax to a low-spin
                    configuration during a DFT electronic configuration
                strategies: different ordering strategies to use, choose from:
                    ferromagnetic, antiferromagnetic, antiferromagnetic_by_motif,
                    ferrimagnetic_by_motif and ferrimagnetic_by_species (here, "motif",
                    means to use a different ordering parameter for symmetry inequivalent
                    sites)
                automatic: if True, will automatically choose sensible strategies
                truncate_by_symmetry: if True, will remove very unsymmetrical
                    orderings that are likely physically implausible
                transformation_kwargs: keyword arguments to pass to
                    MagOrderingTransformation, to change automatic cell size limits, etc.
            """

            self.logger = logging.getLogger(self.__class__.__name__)

            self.structure = structure

            # decides how to process input structure, which sites are magnetic
            self.default_magmoms = default_magmoms

            # different strategies to attempt, default is usually reasonable
            self.strategies = list(strategies)
            # and whether to automatically add strategies that may be appropriate
            self.automatic = automatic

            # and whether to discard low symmetry structures
            self.truncate_by_symmetry = truncate_by_symmetry

            # other settings
            self.num_orderings = 64
            self.max_unique_sites = 8

            # kwargs to pass to transformation (ultimately to enumlib)
            default_transformation_kwargs = {"check_ordered_symmetry": False, "timeout": 5}
            transformation_kwargs = transformation_kwargs or {}
            transformation_kwargs.update(default_transformation_kwargs)
            self.transformation_kwargs = transformation_kwargs

            # our magnetically ordered structures will be
            # stored here once generated and also store which
            # transformation created them, this is used for
            # book-keeping/user interest, and
            # is be a list of strings in ("fm", "afm",
            # "ferrimagnetic_by_species", "ferrimagnetic_by_motif",
            # "afm_by_motif", "input_structure")
            self.ordered_structures: List[Structure] = []
            self.ordered_structure_origins: List[str] = []

            formula = structure.composition.reduced_formula

            # to process disordered magnetic structures, first make an
            # ordered approximation
            if not structure.is_ordered:
                raise ValueError(
                    "Please obtain an ordered approximation of the "
                    "input structure ({}).".format(formula)
                )

            # CollinearMagneticStructureAnalyzer is used throughout:
            # it can tell us whether the input is itself collinear (if not,
            # this workflow is not appropriate), and has many convenience
            # methods e.g. magnetic structure matching, etc.
            self.input_analyzer = CollinearMagneticStructureAnalyzer(
                structure, default_magmoms=default_magmoms, overwrite_magmom_mode="none"
            )

            # this workflow enumerates structures with different combinations
            # of up and down spin and does not include spin-orbit coupling:
            # if your input structure has vector magnetic moments, this
            # workflow is not appropriate
            if not self.input_analyzer.is_collinear:
                raise ValueError("Input structure ({}) is non-collinear.".format(formula))

            self.sanitized_structure = self._sanitize_input_structure(structure)

            # we will first create a set of transformations
            # and then apply them to our input structure
            self.transformations = self._generate_transformations(self.sanitized_structure)
            self._generate_ordered_structures(
                self.sanitized_structure, self.transformations
            )

        @staticmethod
        def _sanitize_input_structure(input_structure: Structure) -> Structure:
            """Sanitize our input structure by removing magnetic information
            and making primitive.

            Args:
              input_structure: Structure

            Returns: Structure

            """

            input_structure = input_structure.copy()

            # remove any annotated spin
            input_structure.remove_spin()

            # sanitize input structure: first make primitive ...
            input_structure = input_structure.get_primitive_structure(use_site_props=False)

            # ... and strip out existing magmoms, which can cause conflicts
            # with later transformations otherwise since sites would end up
            # with both magmom site properties and Specie spins defined
            if "magmom" in input_structure.site_properties:
                input_structure.remove_site_property("magmom")

            return input_structure

        def _generate_transformations(
            self, structure: Structure
        ) -> Dict[str, MagOrderingTransformation]:
            """The central problem with trying to enumerate magnetic orderings is
            that we have to enumerate orderings that might plausibly be magnetic
            ground states, while not enumerating orderings that are physically
            implausible. The problem is that it is not always obvious by e.g.
            symmetry arguments alone which orderings to prefer. Here, we use a
            variety of strategies (heuristics) to enumerate plausible orderings,
            and later discard any duplicates that might be found by multiple
            strategies. This approach is not ideal, but has been found to be
            relatively robust over a wide range of magnetic structures.

            Args:
              structure: A sanitized input structure (_sanitize_input_structure)
            Returns: A dict of a transformation class instance (values) and name of
            enumeration strategy (keys)

            Returns: dict of Transformations keyed by strategy

            """

            formula = structure.composition.reduced_formula
            transformations: Dict[str, MagOrderingTransformation] = {}

            # analyzer is used to obtain information on sanitized input
            analyzer = CollinearMagneticStructureAnalyzer(
                structure,
                default_magmoms=self.default_magmoms,
                overwrite_magmom_mode="replace_all",
            )

            if not analyzer.is_magnetic:
                raise ValueError(
                    "Not detected as magnetic, add a new default magmom for the "
                    "element you believe may be magnetic?"
                )

            # now we can begin to generate our magnetic orderings
            self.logger.info("Generating magnetic orderings for {}".format(formula))

            mag_species_spin = analyzer.magnetic_species_and_magmoms
            types_mag_species = sorted(
                analyzer.types_of_magnetic_specie,
                key=lambda sp: analyzer.default_magmoms.get(str(sp), 0),
                reverse=True,
            )
            num_mag_sites = analyzer.number_of_magnetic_sites
            num_unique_sites = analyzer.number_of_unique_magnetic_sites()

            # enumerations become too slow as number of unique sites (and thus
            # permutations) increase, 8 is a soft limit, this can be increased
            # but do so with care
            if num_unique_sites > self.max_unique_sites:
                raise ValueError("Too many magnetic sites to sensibly perform enumeration.")

            # maximum cell size to consider: as a rule of thumb, if the primitive cell
            # contains a large number of magnetic sites, perhaps we only need to enumerate
            # within one cell, whereas on the other extreme if the primitive cell only
            # contains a single magnetic site, we have to create larger supercells
            if "max_cell_size" not in self.transformation_kwargs:
                # TODO: change to 8 / num_mag_sites ?
                self.transformation_kwargs["max_cell_size"] = max(1, int(4 / num_mag_sites))
            self.logger.info(
                "Max cell size set to {}".format(
                    self.transformation_kwargs["max_cell_size"]
                )
            )

            # when enumerating ferrimagnetic structures, it's useful to detect
            # symmetrically distinct magnetic sites, since different
            # local environments can result in different magnetic order
            # (e.g. inverse spinels)
            # initially, this was done by co-ordination number, but is
            # now done by a full symmetry analysis
            sga = SpacegroupAnalyzer(structure)
            structure_sym = sga.get_symmetrized_structure()
            wyckoff = ["n/a"] * len(structure)
            for indices, symbol in zip(
                structure_sym.equivalent_indices, structure_sym.wyckoff_symbols
            ):
                for index in indices:
                    wyckoff[index] = symbol
            is_magnetic_sites = [
                True if site.specie in types_mag_species else False for site in structure
            ]
            # we're not interested in sites that we don't think are magnetic,
            # set these symbols to None to filter them out later
            wyckoff = [
                symbol if is_magnetic_site else "n/a"
                for symbol, is_magnetic_site in zip(wyckoff, is_magnetic_sites)
            ]
            structure.add_site_property("wyckoff", wyckoff)
            wyckoff_symbols = set(wyckoff) - {"n/a"}

            # if user doesn't specifically request ferrimagnetic orderings,
            # we apply a heuristic as to whether to attempt them or not
            if self.automatic:
                if (
                    "ferrimagnetic_by_motif" not in self.strategies
                    and len(wyckoff_symbols) > 1
                    and len(types_mag_species) == 1
                ):
                    self.strategies += ["ferrimagnetic_by_motif"]

                if (
                    "antiferromagnetic_by_motif" not in self.strategies
                    and len(wyckoff_symbols) > 1
                    and len(types_mag_species) == 1
                ):
                    self.strategies += ["antiferromagnetic_by_motif"]

                if (
                    "ferrimagnetic_by_species" not in self.strategies
                    and len(types_mag_species) > 1
                ):
                    self.strategies += ["ferrimagnetic_by_species"]

            # we start with a ferromagnetic ordering
            if "ferromagnetic" in self.strategies:
                # TODO: remove 0 spins !

                fm_structure = analyzer.get_ferromagnetic_structure()
                # store magmom as spin property, to be consistent with output from
                # other transformations
                fm_structure.add_spin_by_site(fm_structure.site_properties["magmom"])
                fm_structure.remove_site_property("magmom")

                # we now have our first magnetic ordering...
                self.ordered_structures.append(fm_structure)
                self.ordered_structure_origins.append("fm")

            # we store constraint(s) for each strategy first,
            # and then use each to perform a transformation later
            all_constraints: Dict[str, Any] = {}

            # ...to which we can add simple AFM cases first...
            if "antiferromagnetic" in self.strategies:

                constraint = MagOrderParameterConstraint(
                    0.5,
                    # TODO: update MagOrderParameterConstraint in
                    # pymatgen to take types_mag_species directly
                    species_constraints=list(map(str, types_mag_species)),
                )
                all_constraints["afm"] = [constraint]

                # allows for non-magnetic sublattices
                if len(types_mag_species) > 1:
                    for sp in types_mag_species:
                        constraints = [
                            MagOrderParameterConstraint(0.5, species_constraints=str(sp))
                        ]

                        all_constraints["afm_by_{}".format(sp)] = constraints

            # ...and then we also try ferrimagnetic orderings by motif if a
            # single magnetic species is present...
            if "ferrimagnetic_by_motif" in self.strategies and len(wyckoff_symbols) > 1:

                # these orderings are AFM on one local environment, and FM on the rest
                for symbol in wyckoff_symbols:
                    constraints = [
                        MagOrderParameterConstraint(
                            0.5, site_constraint_name="wyckoff", site_constraints=symbol
                        ),
                        MagOrderParameterConstraint(
                            1.0,
                            site_constraint_name="wyckoff",
                            site_constraints=list(wyckoff_symbols - {symbol}),
                        ),
                    ]

                    all_constraints["ferri_by_motif_{}".format(symbol)] = constraints

            # and also try ferrimagnetic when there are multiple magnetic species
            if "ferrimagnetic_by_species" in self.strategies:

                sp_list = [str(site.specie) for site in structure]
                num_sp = {sp: sp_list.count(str(sp)) for sp in types_mag_species}
                total_mag_sites = sum(num_sp.values())

                for sp in types_mag_species:
                    # attempt via a global order parameter
                    all_constraints["ferri_by_{}".format(sp)] = num_sp[sp] / total_mag_sites

                    # attempt via afm on sp, fm on remaining species

                    constraints = [
                        MagOrderParameterConstraint(0.5, species_constraints=str(sp)),
                        MagOrderParameterConstraint(
                            1.0,
                            species_constraints=list(
                                map(str, set(types_mag_species) - {sp})
                            ),
                        ),
                    ]

                    all_constraints["ferri_by_{}_afm".format(sp)] = constraints

            # ...and finally, we can try orderings that are AFM on one local
            # environment, and non-magnetic on the rest -- this is less common
            # but unless explicitly attempted, these states are unlikely to be found
            if "antiferromagnetic_by_motif" in self.strategies:

                for symbol in wyckoff_symbols:
                    constraints = [
                        MagOrderParameterConstraint(
                            0.5, site_constraint_name="wyckoff", site_constraints=symbol
                        )
                    ]

                    all_constraints["afm_by_motif_{}".format(symbol)] = constraints

            # and now construct all our transformations for each strategy
            transformations = {}
            for name, constraints in all_constraints.items():
                trans = MagOrderingTransformation(
                    mag_species_spin,
                    order_parameter=constraints,
                    **self.transformation_kwargs
                )

                transformations[name] = trans

            return transformations

        def _generate_ordered_structures(
            self,
            sanitized_input_structure: Structure,
            transformations: Dict[str, MagOrderingTransformation],
        ):
            """Apply our input structure to our list of transformations and output a list
            of ordered structures that have been pruned for duplicates and for those
            with low symmetry (optional).

            Args:
                sanitized_input_structure: A sanitized input structure
                (_sanitize_input_structure)
                transformations: A dict of transformations (values) and name of
                enumeration strategy (key), the enumeration strategy name is just
                for record keeping

            Returns: None (sets self.ordered_structures
            and self.ordered_structures_origins instance variables)

            Returns: List of Structures
            """

            ordered_structures = self.ordered_structures
            ordered_structures_origins = self.ordered_structure_origins

            # utility function to combine outputs from several transformations
            def _add_structures(
                ordered_structures, ordered_structures_origins, structures_to_add, origin=""
            ):
                """Transformations with return_ranked_list can return either
                just Structures or dicts (or sometimes lists!) -- until this
                is fixed, we use this function to concat structures given
                by the transformation.
                """
                if structures_to_add:
                    # type conversion
                    if isinstance(structures_to_add, Structure):
                        structures_to_add = [structures_to_add]
                    structures_to_add = [
                        s["structure"] if isinstance(s, dict) else s
                        for s in structures_to_add
                    ]
                    # concatenation
                    ordered_structures += structures_to_add
                    ordered_structures_origins += [origin] * len(structures_to_add)
                    self.logger.info(
                        "Adding {} ordered structures: {}".format(
                            len(structures_to_add), origin
                        )
                    )

                return ordered_structures, ordered_structures_origins

            for origin, trans in self.transformations.items():
                structures_to_add = trans.apply_transformation(
                    self.sanitized_structure, return_ranked_list=self.num_orderings
                )
                ordered_structures, ordered_structures_origins = _add_structures(
                    ordered_structures,
                    ordered_structures_origins,
                    structures_to_add,
                    origin=origin,
                )

            # in case we've introduced duplicates, let's remove them
            self.logger.info("Pruning duplicate structures.")
            structures_to_remove: List[int] = []
            for idx, ordered_structure in enumerate(ordered_structures):
                if idx not in structures_to_remove:
                    duplicate_checker = CollinearMagneticStructureAnalyzer(
                        ordered_structure, overwrite_magmom_mode="none"
                    )
                    for check_idx, check_structure in enumerate(ordered_structures):
                        if check_idx not in structures_to_remove and check_idx != idx:
                            if duplicate_checker.matches_ordering(check_structure):
                                structures_to_remove.append(check_idx)

            if len(structures_to_remove):
                self.logger.info(
                    "Removing {} duplicate ordered structures".format(
                        len(structures_to_remove)
                    )
                )
                ordered_structures = [
                    s
                    for idx, s in enumerate(ordered_structures)
                    if idx not in structures_to_remove
                ]
                ordered_structures_origins = [
                    o
                    for idx, o in enumerate(ordered_structures_origins)
                    if idx not in structures_to_remove
                ]

            # also remove low symmetry structures
            if self.truncate_by_symmetry:

                # by default, keep structures with 5 most symmetric space groups
                if not isinstance(self.truncate_by_symmetry, int):
                    self.truncate_by_symmetry = 5

                self.logger.info("Pruning low symmetry structures.")

                # first get a list of symmetries present
                symmetry_int_numbers = [
                    s.get_space_group_info()[1] for s in ordered_structures
                ]

                # then count the number of symmetry operations for that space group
                num_sym_ops = [
                    len(SpaceGroup.from_int_number(n).symmetry_ops)
                    for n in symmetry_int_numbers
                ]

                # find the largest values...
                max_symmetries = sorted(list(set(num_sym_ops)), reverse=True)

                # ...and decide which ones to keep
                if len(max_symmetries) > self.truncate_by_symmetry:
                    max_symmetries = max_symmetries[0:5]
                structs_to_keep = [
                    (idx, num)
                    for idx, num in enumerate(num_sym_ops)
                    if num in max_symmetries
                ]

                # sort so that highest symmetry structs are first
                structs_to_keep = sorted(
                    structs_to_keep, key=lambda x: (x[1], -x[0]), reverse=True
                )

                self.logger.info(
                    "Removing {} low symmetry "
                    "ordered structures".format(
                        len(ordered_structures) - len(structs_to_keep)
                    )
                )

                ordered_structures = [ordered_structures[i] for i, _ in structs_to_keep]
                ordered_structures_origins = [
                    ordered_structures_origins[i] for i, _ in structs_to_keep
                ]

                # and ensure fm is always at index 0
                fm_index = ordered_structures_origins.index("fm")
                ordered_structures.insert(0, ordered_structures.pop(fm_index))
                ordered_structures_origins.insert(
                    0, ordered_structures_origins.pop(fm_index)
                )

            # if our input structure isn't in our generated structures,
            # let's add it manually and also keep a note of which structure
            # is our input: this is mostly for book-keeping/benchmarking
            self.input_index = None
            self.input_origin = None
            if self.input_analyzer.ordering != Ordering.NM:
                matches = [
                    self.input_analyzer.matches_ordering(s) for s in ordered_structures
                ]
                if not any(matches):
                    ordered_structures.append(self.input_analyzer.structure)
                    ordered_structures_origins.append("input")
                    self.logger.info(
                        "Input structure not present in enumerated structures, adding..."
                    )
                else:
                    self.logger.info(
                        "Input structure was found in enumerated "
                        "structures at index {}".format(matches.index(True))
                    )
                    self.input_index = matches.index(True)
                    self.input_origin = ordered_structures_origins[self.input_index]

            self.ordered_structures = ordered_structures
            self.ordered_structure_origins = ordered_structures_origins

    MagneticDeformation = namedtuple("MagneticDeformation", "type deformation")

    def magnetic_deformation(
        structure_A: Structure, structure_B: Structure
    ) -> MagneticDeformation:
        """Calculates 'magnetic deformation proxy',
        a measure of deformation (norm of finite strain)
        between 'non-magnetic' (non-spin-polarized) and
        ferromagnetic structures.

        Adapted from Bocarsly et al. 2017,
        doi: 10.1021/acs.chemmater.6b04729

        Args:
          structure_A: Structure
          structure_B: Structure

        Returns: Magnetic deformation
        """

        # retrieve orderings of both input structures
        ordering_a = CollinearMagneticStructureAnalyzer(
            structure_A, overwrite_magmom_mode="none"
        ).ordering
        ordering_b = CollinearMagneticStructureAnalyzer(
            structure_B, overwrite_magmom_mode="none"
        ).ordering

        # get a type string, this is either 'NM-FM' for between non-magnetic
        # and ferromagnetic, as in Bocarsly paper, or e.g. 'FM-AFM'
        type_str = "{}-{}".format(ordering_a.value, ordering_b.value)

        lattice_a = structure_A.lattice.matrix.T
        lattice_b = structure_B.lattice.matrix.T
        lattice_a_inv = np.linalg.inv(lattice_a)
        p = np.dot(lattice_a_inv, lattice_b)
        eta = 0.5 * (np.dot(p.T, p) - np.identity(3))
        w, v = np.linalg.eig(eta)
        deformation = 100 * (1.0 / 3.0) * np.sqrt(w[0] ** 2 + w[1] ** 2 + w[2] ** 2)

        return MagneticDeformation(deformation=deformation, type=type_str)