text: # Copyright (c) Pymatgen Development Team.
  # Distributed under the terms of the MIT License.
  
  """
  This module provides classes to perform fitting of structures.
  """
  
  import abc
  import itertools
  
  import numpy as np
  from monty.json import MSONable
  
  from pymatgen.analysis.defects.core import Defect, Interstitial, Substitution, Vacancy
  from pymatgen.core import PeriodicSite
  from pymatgen.core.composition import Composition
  from pymatgen.core.lattice import Lattice
  from pymatgen.core.periodic_table import get_el_sp
  from pymatgen.core.structure import Structure
  from pymatgen.optimization.linear_assignment import LinearAssignment  # type: ignore
  from pymatgen.util.coord import lattice_points_in_supercell
  from pymatgen.util.coord_cython import (  # type: ignore
      is_coord_subset_pbc,
      pbc_shortest_vectors,
  )
  
  __author__ = "William Davidson Richards, Stephen Dacek, Shyue Ping Ong"
  __copyright__ = "Copyright 2011, The Materials Project"
  __version__ = "1.0"
  __maintainer__ = "William Davidson Richards"
  __email__ = "wrichard@mit.edu"
  __status__ = "Production"
  __date__ = "Dec 3, 2012"
  
  
  class AbstractComparator(MSONable, metaclass=abc.ABCMeta):
      """
      Abstract Comparator class. A Comparator defines how sites are compared in
      a structure.
      """
  
      @abc.abstractmethod
      def are_equal(self, sp1, sp2):
          """
          Defines how the species of two sites are considered equal. For
          example, one can consider sites to have the same species only when
          the species are exactly the same, i.e., Fe2+ matches Fe2+ but not
          Fe3+. Or one can define that only the element matters,
          and all oxidation state information are ignored.
  
          Args:
              sp1: First species. A dict of {specie/element: amt} as per the
                  definition in Site and PeriodicSite.
              sp2: Second species. A dict of {specie/element: amt} as per the
                  definition in Site and PeriodicSite.
  
          Returns:
              Boolean indicating whether species are considered equal.
          """
          return
  
      @abc.abstractmethod
      def get_hash(self, composition):
          """
          Defines a hash to group structures. This allows structures to be
          grouped efficiently for comparison. The hash must be invariant under
          supercell creation. (e.g. composition is not a good hash, but
          fractional_composition might be). Reduced formula is not a good formula,
          due to weird behavior with fractional occupancy.
  
          Composition is used here instead of structure because for anonymous
          matches it is much quicker to apply a substitution to a composition
          object than a structure object.
  
          Args:
              composition (Composition): composition of the structure
  
          Returns:
              A hashable object. Examples can be string formulas, integers etc.
          """
          return
  
      @classmethod
      def from_dict(cls, d):
          """
          :param d: Dict representation
          :return: Comparator.
          """
          for trans_modules in ["structure_matcher"]:
              mod = __import__(
                  "pymatgen.analysis." + trans_modules,
                  globals(),
                  locals(),
                  [d["@class"]],
                  0,
              )
              if hasattr(mod, d["@class"]):
                  trans = getattr(mod, d["@class"])
                  return trans()
          raise ValueError("Invalid Comparator dict")
  
      def as_dict(self):
          """
          :return: MSONable dict
          """
          return {
              "version": __version__,
              "@module": self.__class__.__module__,
              "@class": self.__class__.__name__,
          }
  
  
  class SpeciesComparator(AbstractComparator):
      """
      A Comparator that matches species exactly. The default used in
      StructureMatcher.
      """
  
      def are_equal(self, sp1, sp2):
          """
          True if species are exactly the same, i.e., Fe2+ == Fe2+ but not Fe3+.
  
          Args:
              sp1: First species. A dict of {specie/element: amt} as per the
                  definition in Site and PeriodicSite.
              sp2: Second species. A dict of {specie/element: amt} as per the
                  definition in Site and PeriodicSite.
  
          Returns:
              Boolean indicating whether species are equal.
          """
          return sp1 == sp2
  
      def get_hash(self, composition):
          """
          Returns: Fractional composition
          """
          return composition.fractional_composition
  
  
  class SpinComparator(AbstractComparator):
      """
      A Comparator that matches magnetic structures to their inverse spins.
      This comparator is primarily used to filter magnetically ordered
      structures with opposite spins, which are equivalent.
      """
  
      def are_equal(self, sp1, sp2):
          """
          True if species are exactly the same, i.e., Fe2+ == Fe2+ but not
          Fe3+. and the spins are reversed. i.e., spin up maps to spin down,
          and vice versa.
  
          Args:
              sp1: First species. A dict of {specie/element: amt} as per the
                  definition in Site and PeriodicSite.
              sp2: Second species. A dict of {specie/element: amt} as per the
                  definition in Site and PeriodicSite.
  
          Returns:
              Boolean indicating whether species are equal.
          """
          for s1 in sp1.keys():
              spin1 = getattr(s1, "spin", 0)
              oxi1 = getattr(s1, "oxi_state", 0)
              for s2 in sp2.keys():
                  spin2 = getattr(s2, "spin", 0)
                  oxi2 = getattr(s2, "oxi_state", 0)
                  if s1.symbol == s2.symbol and oxi1 == oxi2 and spin2 == -spin1:
                      break
              else:
                  return False
          return True
  
      def get_hash(self, composition):
          """
          Returns: Fractional composition
          """
          return composition.fractional_composition
  
  
  class ElementComparator(AbstractComparator):
      """
      A Comparator that matches elements. i.e. oxidation states are
      ignored.
      """
  
      def are_equal(self, sp1, sp2):
          """
          True if element:amounts are exactly the same, i.e.,
          oxidation state is not considered.
  
          Args:
              sp1: First species. A dict of {specie/element: amt} as per the
                  definition in Site and PeriodicSite.
              sp2: Second species. A dict of {specie/element: amt} as per the
                  definition in Site and PeriodicSite.
  
          Returns:
              Boolean indicating whether species are the same based on element
              and amounts.
          """
          comp1 = Composition(sp1)
          comp2 = Composition(sp2)
          return comp1.get_el_amt_dict() == comp2.get_el_amt_dict()
  
      def get_hash(self, composition):
          """
          Returns: Fractional element composition
          """
          return composition.element_composition.fractional_composition
  
  
  class FrameworkComparator(AbstractComparator):
      """
      A Comparator that matches sites, regardless of species.
      """
  
      def are_equal(self, sp1, sp2):
          """
          True if there are atoms on both sites.
  
          Args:
              sp1: First species. A dict of {specie/element: amt} as per the
                  definition in Site and PeriodicSite.
              sp2: Second species. A dict of {specie/element: amt} as per the
                  definition in Site and PeriodicSite.
  
          Returns:
              True always
          """
          return True
  
      def get_hash(self, composition):
          """
          No hash possible
          """
          return 1
  
  
  class OrderDisorderElementComparator(AbstractComparator):
      """
      A Comparator that matches sites, given some overlap in the element
      composition
      """
  
      def are_equal(self, sp1, sp2):
          """
          True if there is some overlap in composition between the species
  
          Args:
              sp1: First species. A dict of {specie/element: amt} as per the
                  definition in Site and PeriodicSite.
              sp2: Second species. A dict of {specie/element: amt} as per the
                  definition in Site and PeriodicSite.
  
          Returns:
              True always
          """
          set1 = set(sp1.elements)
          set2 = set(sp2.elements)
          return set1.issubset(set2) or set2.issubset(set1)
  
      def get_hash(self, composition):
          """
          Returns: Fractional composition
          """
          return composition.fractional_composition
  
  
  class OccupancyComparator(AbstractComparator):
      """
      A Comparator that matches occupancies on sites,
      irrespective of the species of those sites.
      """
  
      def are_equal(self, sp1, sp2):
          """
          Args:
              sp1: First species. A dict of {specie/element: amt} as per the
                  definition in Site and PeriodicSite.
              sp2: Second species. A dict of {specie/element: amt} as per the
                  definition in Site and PeriodicSite.
  
          Returns:
              True if sets of occupancies (amt) are equal on both sites.
          """
          return set(sp1.element_composition.values()) == set(sp2.element_composition.values())
  
      def get_hash(self, composition):
          """
          :param composition: Composition.
          :return: 1. Difficult to define sensible hash
          """
          return 1
  
  
  class StructureMatcher(MSONable):
      """
      Class to match structures by similarity.
  
      Algorithm:
  
      1. Given two structures: s1 and s2
      2. Optional: Reduce to primitive cells.
      3. If the number of sites do not match, return False
      4. Reduce to s1 and s2 to Niggli Cells
      5. Optional: Scale s1 and s2 to same volume.
      6. Optional: Remove oxidation states associated with sites
      7. Find all possible lattice vectors for s2 within shell of ltol.
      8. For s1, translate an atom in the smallest set to the origin
      9. For s2: find all valid lattices from permutations of the list
         of lattice vectors (invalid if: det(Lattice Matrix) < half
         volume of original s2 lattice)
      10. For each valid lattice:
  
          a. If the lattice angles of are within tolerance of s1,
             basis change s2 into new lattice.
          b. For each atom in the smallest set of s2:
  
              i. Translate to origin and compare fractional sites in
              structure within a fractional tolerance.
              ii. If true:
  
                  ia. Convert both lattices to cartesian and place
                  both structures on an average lattice
                  ib. Compute and return the average and max rms
                  displacement between the two structures normalized
                  by the average free length per atom
  
                  if fit function called:
                      if normalized max rms displacement is less than
                      stol. Return True
  
                  if get_rms_dist function called:
                      if normalized average rms displacement is less
                      than the stored rms displacement, store and
                      continue. (This function will search all possible
                      lattices for the smallest average rms displacement
                      between the two structures)
      """
  
      def __init__(
          self,
          ltol=0.2,
          stol=0.3,
          angle_tol=5,
          primitive_cell=True,
          scale=True,
          attempt_supercell=False,
          allow_subset=False,
          comparator=SpeciesComparator(),
          supercell_size="num_sites",
          ignored_species=None,
      ):
          """
          Args:
              ltol (float): Fractional length tolerance. Default is 0.2.
              stol (float): Site tolerance. Defined as the fraction of the
                  average free length per atom := ( V / Nsites ) ** (1/3)
                  Default is 0.3.
              angle_tol (float): Angle tolerance in degrees. Default is 5 degrees.
              primitive_cell (bool): If true: input structures will be reduced to
                  primitive cells prior to matching. Default to True.
              scale (bool): Input structures are scaled to equivalent volume if
                 true; For exact matching, set to False.
              attempt_supercell (bool): If set to True and number of sites in
                  cells differ after a primitive cell reduction (divisible by an
                  integer) attempts to generate a supercell transformation of the
                  smaller cell which is equivalent to the larger structure.
              allow_subset (bool): Allow one structure to match to the subset of
                  another structure. Eg. Matching of an ordered structure onto a
                  disordered one, or matching a delithiated to a lithiated
                  structure. This option cannot be combined with
                  attempt_supercell, or with structure grouping.
              comparator (Comparator): A comparator object implementing an equals
                  method that declares declaring equivalency of sites. Default is
                  SpeciesComparator, which implies rigid species
                  mapping, i.e., Fe2+ only matches Fe2+ and not Fe3+.
  
                  Other comparators are provided, e.g., ElementComparator which
                  matches only the elements and not the species.
  
                  The reason why a comparator object is used instead of
                  supplying a comparison function is that it is not possible to
                  pickle a function, which makes it otherwise difficult to use
                  StructureMatcher with Python's multiprocessing.
              supercell_size (str or list): Method to use for determining the
                  size of a supercell (if applicable). Possible values are
                  num_sites, num_atoms, volume, or an element or list of elements
                  present in both structures.
              ignored_species (list): A list of ions to be ignored in matching.
                  Useful for matching structures that have similar frameworks
                  except for certain ions, e.g., Li-ion intercalation frameworks.
                  This is more useful than allow_subset because it allows better
                  control over what species are ignored in the matching.
          """
  
          self.ltol = ltol
          self.stol = stol
          self.angle_tol = angle_tol
          self._comparator = comparator
          self._primitive_cell = primitive_cell
          self._scale = scale
          self._supercell = attempt_supercell
          self._supercell_size = supercell_size
          self._subset = allow_subset
          self._ignored_species = [] if ignored_species is None else ignored_species[:]
  
      def _get_supercell_size(self, s1, s2):
          """
          Returns the supercell size, and whether the supercell should
          be applied to s1. If fu == 1, s1_supercell is returned as
          true, to avoid ambiguity.
          """
          if self._supercell_size == "num_sites":
              fu = s2.num_sites / s1.num_sites
          elif self._supercell_size == "num_atoms":
              fu = s2.composition.num_atoms / s1.composition.num_atoms
          elif self._supercell_size == "volume":
              fu = s2.volume / s1.volume
          elif not isinstance(self._supercell_size, str):
              s1comp, s2comp = 0, 0
              for el in self._supercell_size:
                  el = get_el_sp(el)
                  s1comp += s1.composition[el]
                  s2comp += s2.composition[el]
              fu = s2comp / s1comp
          else:
              el = get_el_sp(self._supercell_size)
              if (el in s2.composition) and (el in s1.composition):
                  fu = s2.composition[el] / s1.composition[el]
              else:
                  raise ValueError("Invalid argument for supercell_size.")
  
          if fu < 2 / 3:
              return int(round(1 / fu)), False
  
          return int(round(fu)), True
  
      def _get_lattices(self, target_lattice, s, supercell_size=1):
          """
          Yields lattices for s with lengths and angles close to the
          lattice of target_s. If supercell_size is specified, the
          returned lattice will have that number of primitive cells
          in it
  
          Args:
              s, target_s: Structure objects
          """
          lattices = s.lattice.find_all_mappings(
              target_lattice,
              ltol=self.ltol,
              atol=self.angle_tol,
              skip_rotation_matrix=True,
          )
          for l, _, scale_m in lattices:
              if abs(abs(np.linalg.det(scale_m)) - supercell_size) < 0.5:
                  yield l, scale_m
  
      def _get_supercells(self, struct1, struct2, fu, s1_supercell):
          """
          Computes all supercells of one structure close to the lattice of the
          other
          if s1_supercell == True, it makes the supercells of struct1, otherwise
          it makes them of s2
  
          yields: s1, s2, supercell_matrix, average_lattice, supercell_matrix
          """
  
          def av_lat(l1, l2):
              params = (np.array(l1.parameters) + np.array(l2.parameters)) / 2
              return Lattice.from_parameters(*params)
  
          def sc_generator(s1, s2):
              s2_fc = np.array(s2.frac_coords)
              if fu == 1:
                  cc = np.array(s1.cart_coords)
                  for l, sc_m in self._get_lattices(s2.lattice, s1, fu):
                      fc = l.get_fractional_coords(cc)
                      fc -= np.floor(fc)
                      yield fc, s2_fc, av_lat(l, s2.lattice), sc_m
              else:
                  fc_init = np.array(s1.frac_coords)
                  for l, sc_m in self._get_lattices(s2.lattice, s1, fu):
                      fc = np.dot(fc_init, np.linalg.inv(sc_m))
                      lp = lattice_points_in_supercell(sc_m)
                      fc = (fc[:, None, :] + lp[None, :, :]).reshape((-1, 3))
                      fc -= np.floor(fc)
                      yield fc, s2_fc, av_lat(l, s2.lattice), sc_m
  
          if s1_supercell:
              for x in sc_generator(struct1, struct2):
                  yield x
          else:
              for x in sc_generator(struct2, struct1):
                  # reorder generator output so s1 is still first
                  yield x[1], x[0], x[2], x[3]
  
      @classmethod
      def _cmp_fstruct(cls, s1, s2, frac_tol, mask):
          """
          Returns true if a matching exists between s2 and s2
          under frac_tol. s2 should be a subset of s1
          """
          if len(s2) > len(s1):
              raise ValueError("s1 must be larger than s2")
          if mask.shape != (len(s2), len(s1)):
              raise ValueError("mask has incorrect shape")
  
          return is_coord_subset_pbc(s2, s1, frac_tol, mask)
  
      @classmethod
      def _cart_dists(cls, s1, s2, avg_lattice, mask, normalization, lll_frac_tol=None):
          """
          Finds a matching in cartesian space. Finds an additional
          fractional translation vector to minimize RMS distance
  
          Args:
              s1, s2: numpy arrays of fractional coordinates. len(s1) >= len(s2)
              avg_lattice: Lattice on which to calculate distances
              mask: numpy array of booleans. mask[i, j] = True indicates
                  that s2[i] cannot be matched to s1[j]
              normalization (float): inverse normalization length
  
          Returns:
              Distances from s2 to s1, normalized by (V/Natom) ^ 1/3
              Fractional translation vector to apply to s2.
              Mapping from s1 to s2, i.e. with numpy slicing, s1[mapping] => s2
          """
          if len(s2) > len(s1):
              raise ValueError("s1 must be larger than s2")
          if mask.shape != (len(s2), len(s1)):
              raise ValueError("mask has incorrect shape")
  
          # vectors are from s2 to s1
          vecs, d_2 = pbc_shortest_vectors(avg_lattice, s2, s1, mask, return_d2=True, lll_frac_tol=lll_frac_tol)
          lin = LinearAssignment(d_2)
          s = lin.solution  # pylint: disable=E1101
          short_vecs = vecs[np.arange(len(s)), s]
          translation = np.average(short_vecs, axis=0)
          f_translation = avg_lattice.get_fractional_coords(translation)
          new_d2 = np.sum((short_vecs - translation) ** 2, axis=-1)
  
          return new_d2 ** 0.5 * normalization, f_translation, s
  
      def _get_mask(self, struct1, struct2, fu, s1_supercell):
          """
          Returns mask for matching struct2 to struct1. If struct1 has sites
          a b c, and fu = 2, assumes supercells of struct2 will be ordered
          aabbcc (rather than abcabc)
  
          Returns:
          mask, struct1 translation indices, struct2 translation index
          """
          mask = np.zeros((len(struct2), len(struct1), fu), dtype=np.bool)
  
          inner = []
          for sp2, i in itertools.groupby(enumerate(struct2.species_and_occu), key=lambda x: x[1]):
              i = list(i)
              inner.append((sp2, slice(i[0][0], i[-1][0] + 1)))
  
          for sp1, j in itertools.groupby(enumerate(struct1.species_and_occu), key=lambda x: x[1]):
              j = list(j)
              j = slice(j[0][0], j[-1][0] + 1)
              for sp2, i in inner:
                  mask[i, j, :] = not self._comparator.are_equal(sp1, sp2)
  
          if s1_supercell:
              mask = mask.reshape((len(struct2), -1))
          else:
              # supercell is of struct2, roll fu axis back to preserve
              # correct ordering
              mask = np.rollaxis(mask, 2, 1)
              mask = mask.reshape((-1, len(struct1)))
  
          # find the best translation indices
          i = np.argmax(np.sum(mask, axis=-1))
          inds = np.where(np.invert(mask[i]))[0]
          if s1_supercell:
              # remove the symmetrically equivalent s1 indices
              inds = inds[::fu]
          return np.array(mask, dtype=np.int_), inds, i
  
      def fit(self, struct1, struct2, symmetric=False):
          """
          Fit two structures.
  
          Args:
              struct1 (Structure): 1st structure
              struct2 (Structure): 2nd structure
              symmetric (Bool): Defaults to False
                  If True, check the equality both ways.
                  This only impacts a small percentage of structures
  
          Returns:
              True or False.
          """
          struct1, struct2 = self._process_species([struct1, struct2])
  
          if not self._subset and self._comparator.get_hash(struct1.composition) != self._comparator.get_hash(
              struct2.composition
          ):
              return None
  
          if not symmetric:
              struct1, struct2, fu, s1_supercell = self._preprocess(struct1, struct2)
              match = self._match(struct1, struct2, fu, s1_supercell, break_on_match=True)
              if match is None:
                  return False
  
              return match[0] <= self.stol
  
          struct1, struct2, fu, s1_supercell = self._preprocess(struct1, struct2)
          match1 = self._match(struct1, struct2, fu, s1_supercell, break_on_match=True)
          struct1, struct2 = struct2, struct1
          struct1, struct2, fu, s1_supercell = self._preprocess(struct1, struct2)
          match2 = self._match(struct1, struct2, fu, s1_supercell, break_on_match=True)
  
          if match1 is None or match2 is None:
              return False
  
          return max(match1[0], match2[0]) <= self.stol
  
      def get_rms_dist(self, struct1, struct2):
          """
          Calculate RMS displacement between two structures
  
          Args:
              struct1 (Structure): 1st structure
              struct2 (Structure): 2nd structure
  
          Returns:
              rms displacement normalized by (Vol / nsites) ** (1/3)
              and maximum distance between paired sites. If no matching
              lattice is found None is returned.
          """
          struct1, struct2 = self._process_species([struct1, struct2])
          struct1, struct2, fu, s1_supercell = self._preprocess(struct1, struct2)
          match = self._match(struct1, struct2, fu, s1_supercell, use_rms=True, break_on_match=False)
  
          if match is None:
              return None
  
          return match[0], max(match[1])
  
      def _process_species(self, structures):
          copied_structures = []
          for s in structures:
              # We need the copies to be actual Structure to work properly, not
              # subclasses. So do type(s) == Structure.
              ss = Structure.from_sites(s)
              if self._ignored_species:
                  ss.remove_species(self._ignored_species)
              copied_structures.append(ss)
          return copied_structures
  
      def _preprocess(self, struct1, struct2, niggli=True):
          """
          Rescales, finds the reduced structures (primitive and niggli),
          and finds fu, the supercell size to make struct1 comparable to
          s2
          """
          struct1 = struct1.copy()
          struct2 = struct2.copy()
  
          if niggli:
              struct1 = struct1.get_reduced_structure(reduction_algo="niggli")
              struct2 = struct2.get_reduced_structure(reduction_algo="niggli")
  
          # primitive cell transformation
          if self._primitive_cell:
              struct1 = struct1.get_primitive_structure()
              struct2 = struct2.get_primitive_structure()
  
          if self._supercell:
              fu, s1_supercell = self._get_supercell_size(struct1, struct2)
          else:
              fu, s1_supercell = 1, True
          mult = fu if s1_supercell else 1 / fu
  
          # rescale lattice to same volume
          if self._scale:
              ratio = (struct2.volume / (struct1.volume * mult)) ** (1 / 6)
              nl1 = Lattice(struct1.lattice.matrix * ratio)
              struct1.lattice = nl1
              nl2 = Lattice(struct2.lattice.matrix / ratio)
              struct2.lattice = nl2
  
          return struct1, struct2, fu, s1_supercell
  
      def _match(
          self,
          struct1,
          struct2,
          fu,
          s1_supercell=True,
          use_rms=False,
          break_on_match=False,
      ):
          """
          Matches one struct onto the other
          """
          ratio = fu if s1_supercell else 1 / fu
          if len(struct1) * ratio >= len(struct2):
              return self._strict_match(
                  struct1,
                  struct2,
                  fu,
                  s1_supercell=s1_supercell,
                  break_on_match=break_on_match,
                  use_rms=use_rms,
              )
          return self._strict_match(
              struct2,
              struct1,
              fu,
              s1_supercell=(not s1_supercell),
              break_on_match=break_on_match,
              use_rms=use_rms,
          )
  
      def _strict_match(
          self,
          struct1,
          struct2,
          fu,
          s1_supercell=True,
          use_rms=False,
          break_on_match=False,
      ):
          """
          Matches struct2 onto struct1 (which should contain all sites in
          struct2).
  
          Args:
              struct1, struct2 (Structure): structures to be matched
              fu (int): size of supercell to create
              s1_supercell (bool): whether to create the supercell of
                  struct1 (vs struct2)
              use_rms (bool): whether to minimize the rms of the matching
              break_on_match (bool): whether to stop search at first
                  valid match
          """
          if fu < 1:
              raise ValueError("fu cannot be less than 1")
  
          mask, s1_t_inds, s2_t_ind = self._get_mask(struct1, struct2, fu, s1_supercell)
  
          if mask.shape[0] > mask.shape[1]:
              raise ValueError("after supercell creation, struct1 must have more sites than struct2")
  
          # check that a valid mapping exists
          if (not self._subset) and mask.shape[1] != mask.shape[0]:
              return None
  
          if LinearAssignment(mask).min_cost > 0:  # pylint: disable=E1101
              return None
  
          best_match = None
          # loop over all lattices
          for s1fc, s2fc, avg_l, sc_m in self._get_supercells(struct1, struct2, fu, s1_supercell):
              # compute fractional tolerance
              normalization = (len(s1fc) / avg_l.volume) ** (1 / 3)
              inv_abc = np.array(avg_l.reciprocal_lattice.abc)
              frac_tol = inv_abc * self.stol / (np.pi * normalization)
              # loop over all translations
              for s1i in s1_t_inds:
                  t = s1fc[s1i] - s2fc[s2_t_ind]
                  t_s2fc = s2fc + t
                  if self._cmp_fstruct(s1fc, t_s2fc, frac_tol, mask):
                      inv_lll_abc = np.array(avg_l.get_lll_reduced_lattice().reciprocal_lattice.abc)
                      lll_frac_tol = inv_lll_abc * self.stol / (np.pi * normalization)
                      dist, t_adj, mapping = self._cart_dists(s1fc, t_s2fc, avg_l, mask, normalization, lll_frac_tol)
                      if use_rms:
                          val = np.linalg.norm(dist) / len(dist) ** 0.5
                      else:
                          val = max(dist)
                      # pylint: disable=E1136
                      if best_match is None or val < best_match[0]:
                          total_t = t + t_adj
                          total_t -= np.round(total_t)
                          best_match = val, dist, sc_m, total_t, mapping
                          if (break_on_match or val < 1e-5) and val < self.stol:
                              return best_match
  
          if best_match and best_match[0] < self.stol:
              return best_match
  
          return None
  
      def group_structures(self, s_list, anonymous=False):
          """
          Given a list of structures, use fit to group
          them by structural equality.
  
          Args:
              s_list ([Structure]): List of structures to be grouped
              anonymous (bool): Whether to use anonymous mode.
  
          Returns:
              A list of lists of matched structures
              Assumption: if s1 == s2 but s1 != s3, than s2 and s3 will be put
              in different groups without comparison.
          """
          if self._subset:
              raise ValueError("allow_subset cannot be used with group_structures")
  
          original_s_list = list(s_list)
          s_list = self._process_species(s_list)
  
          # Use structure hash to pre-group structures
          if anonymous:
  
              def c_hash(c):
                  return c.anonymized_formula
  
          else:
              c_hash = self._comparator.get_hash
  
          def s_hash(s):
              return c_hash(s[1].composition)
  
          sorted_s_list = sorted(enumerate(s_list), key=s_hash)
          all_groups = []
  
          # For each pre-grouped list of structures, perform actual matching.
          for k, g in itertools.groupby(sorted_s_list, key=s_hash):
              unmatched = list(g)
              while len(unmatched) > 0:
                  i, refs = unmatched.pop(0)
                  matches = [i]
                  if anonymous:
                      inds = filter(
                          lambda i: self.fit_anonymous(refs, unmatched[i][1]),
                          list(range(len(unmatched))),
                      )
                  else:
                      inds = filter(
                          lambda i: self.fit(refs, unmatched[i][1]),
                          list(range(len(unmatched))),
                      )
                  inds = list(inds)
                  matches.extend([unmatched[i][0] for i in inds])
                  unmatched = [unmatched[i] for i in range(len(unmatched)) if i not in inds]
                  all_groups.append([original_s_list[i] for i in matches])
  
          return all_groups
  
      def as_dict(self):
          """
          :return: MSONable dict
          """
          return {
              "version": __version__,
              "@module": self.__class__.__module__,
              "@class": self.__class__.__name__,
              "comparator": self._comparator.as_dict(),
              "stol": self.stol,
              "ltol": self.ltol,
              "angle_tol": self.angle_tol,
              "primitive_cell": self._primitive_cell,
              "scale": self._scale,
              "attempt_supercell": self._supercell,
              "allow_subset": self._subset,
              "supercell_size": self._supercell_size,
              "ignored_species": self._ignored_species,
          }
  
      @classmethod
      def from_dict(cls, d):
          """
          :param d: Dict representation
          :return: StructureMatcher
          """
          return StructureMatcher(
              ltol=d["ltol"],
              stol=d["stol"],
              angle_tol=d["angle_tol"],
              primitive_cell=d["primitive_cell"],
              scale=d["scale"],
              attempt_supercell=d["attempt_supercell"],
              allow_subset=d["allow_subset"],
              comparator=AbstractComparator.from_dict(d["comparator"]),
              supercell_size=d["supercell_size"],
              ignored_species=d["ignored_species"],
          )
  
      def _anonymous_match(
          self,
          struct1,
          struct2,
          fu,
          s1_supercell=True,
          use_rms=False,
          break_on_match=False,
          single_match=False,
      ):
          """
          Tries all permutations of matching struct1 to struct2.
          Args:
              struct1, struct2 (Structure): Preprocessed input structures
          Returns:
              List of (mapping, match)
          """
          if not isinstance(self._comparator, SpeciesComparator):
              raise ValueError("Anonymous fitting currently requires SpeciesComparator")
  
          # check that species lists are comparable
          sp1 = struct1.composition.elements
          sp2 = struct2.composition.elements
          if len(sp1) != len(sp2):
              return None
  
          ratio = fu if s1_supercell else 1 / fu
          swapped = len(struct1) * ratio < len(struct2)
  
          s1_comp = struct1.composition
          s2_comp = struct2.composition
          matches = []
          for perm in itertools.permutations(sp2):
              sp_mapping = dict(zip(sp1, perm))
  
              # do quick check that compositions are compatible
              mapped_comp = Composition({sp_mapping[k]: v for k, v in s1_comp.items()})
              if (not self._subset) and (self._comparator.get_hash(mapped_comp) != self._comparator.get_hash(s2_comp)):
                  continue
  
              mapped_struct = struct1.copy()
              mapped_struct.replace_species(sp_mapping)
              if swapped:
                  m = self._strict_match(
                      struct2,
                      mapped_struct,
                      fu,
                      (not s1_supercell),
                      use_rms,
                      break_on_match,
                  )
              else:
                  m = self._strict_match(mapped_struct, struct2, fu, s1_supercell, use_rms, break_on_match)
              if m:
                  matches.append((sp_mapping, m))
                  if single_match:
                      break
          return matches
  
      def get_rms_anonymous(self, struct1, struct2):
          """
          Performs an anonymous fitting, which allows distinct species in one
          structure to map to another. E.g., to compare if the Li2O and Na2O
          structures are similar.
  
          Args:
              struct1 (Structure): 1st structure
              struct2 (Structure): 2nd structure
  
          Returns:
              (min_rms, min_mapping)
              min_rms is the minimum rms distance, and min_mapping is the
              corresponding minimal species mapping that would map
              struct1 to struct2. (None, None) is returned if the minimax_rms
              exceeds the threshold.
          """
          struct1, struct2 = self._process_species([struct1, struct2])
          struct1, struct2, fu, s1_supercell = self._preprocess(struct1, struct2)
  
          matches = self._anonymous_match(struct1, struct2, fu, s1_supercell, use_rms=True, break_on_match=False)
          if matches:
              best = sorted(matches, key=lambda x: x[1][0])[0]
              return best[1][0], best[0]
  
          return None, None
  
      def get_best_electronegativity_anonymous_mapping(self, struct1, struct2):
          """
          Performs an anonymous fitting, which allows distinct species in one
          structure to map to another. E.g., to compare if the Li2O and Na2O
          structures are similar. If multiple substitutions are within tolerance
          this will return the one which minimizes the difference in
          electronegativity between the matches species.
  
          Args:
              struct1 (Structure): 1st structure
              struct2 (Structure): 2nd structure
  
          Returns:
              min_mapping (Dict): Mapping of struct1 species to struct2 species
          """
          struct1, struct2 = self._process_species([struct1, struct2])
          struct1, struct2, fu, s1_supercell = self._preprocess(struct1, struct2)
  
          matches = self._anonymous_match(struct1, struct2, fu, s1_supercell, use_rms=True, break_on_match=True)
  
          if matches:
              min_X_diff = np.inf
              for m in matches:
                  X_diff = 0
                  for k, v in m[0].items():
                      X_diff += struct1.composition[k] * (k.X - v.X) ** 2
                  if X_diff < min_X_diff:
                      min_X_diff = X_diff
                      best = m[0]
              return best
  
          return None
  
      def get_all_anonymous_mappings(self, struct1, struct2, niggli=True, include_dist=False):
          """
          Performs an anonymous fitting, which allows distinct species in one
          structure to map to another. Returns a dictionary of species
          substitutions that are within tolerance
  
          Args:
              struct1 (Structure): 1st structure
              struct2 (Structure): 2nd structure
              niggli (bool): Find niggli cell in preprocessing
              include_dist (bool): Return the maximin distance with each mapping
  
          Returns:
              list of species mappings that map struct1 to struct2.
          """
          struct1, struct2 = self._process_species([struct1, struct2])
          struct1, struct2, fu, s1_supercell = self._preprocess(struct1, struct2, niggli)
  
          matches = self._anonymous_match(struct1, struct2, fu, s1_supercell, break_on_match=not include_dist)
          if matches:
              if include_dist:
                  return [(m[0], m[1][0]) for m in matches]
  
              return [m[0] for m in matches]
  
          return None
  
      def fit_anonymous(self, struct1, struct2, niggli=True):
          """
          Performs an anonymous fitting, which allows distinct species in one
          structure to map to another. E.g., to compare if the Li2O and Na2O
          structures are similar.
  
          Args:
              struct1 (Structure): 1st structure
              struct2 (Structure): 2nd structure
  
          Returns:
              True/False: Whether a species mapping can map struct1 to stuct2
          """
          struct1, struct2 = self._process_species([struct1, struct2])
          struct1, struct2, fu, s1_supercell = self._preprocess(struct1, struct2, niggli)
  
          matches = self._anonymous_match(struct1, struct2, fu, s1_supercell, break_on_match=True, single_match=True)
  
          return bool(matches)
  
      def get_supercell_matrix(self, supercell, struct):
          """
          Returns the matrix for transforming struct to supercell. This
          can be used for very distorted 'supercells' where the primitive cell
          is impossible to find
          """
          if self._primitive_cell:
              raise ValueError("get_supercell_matrix cannot be used with the primitive cell option")
          struct, supercell, fu, s1_supercell = self._preprocess(struct, supercell, False)
  
          if not s1_supercell:
              raise ValueError("The non-supercell must be put onto the basis of the supercell, not the other way around")
  
          match = self._match(struct, supercell, fu, s1_supercell, use_rms=True, break_on_match=False)
  
          if match is None:
              return None
  
          return match[2]
  
      def get_transformation(self, struct1, struct2):
          """
          Returns the supercell transformation, fractional translation vector,
          and a mapping to transform struct2 to be similar to struct1.
  
          Args:
              struct1 (Structure): Reference structure
              struct2 (Structure): Structure to transform.
  
          Returns:
              supercell (numpy.ndarray(3, 3)): supercell matrix
              vector (numpy.ndarray(3)): fractional translation vector
              mapping (list(int or None)):
                  The first len(struct1) items of the mapping vector are the
                  indices of struct1's corresponding sites in struct2 (or None
                  if there is no corresponding site), and the other items are
                  the remaining site indices of struct2.
          """
          if self._primitive_cell:
              raise ValueError("get_transformation cannot be used with the primitive cell option")
  
          struct1, struct2 = self._process_species((struct1, struct2))
  
          s1, s2, fu, s1_supercell = self._preprocess(struct1, struct2, False)
          ratio = fu if s1_supercell else 1 / fu
          if s1_supercell and fu > 1:
              raise ValueError("Struct1 must be the supercell, not the other way around")
  
          if len(s1) * ratio >= len(s2):
              # s1 is superset
              match = self._strict_match(s1, s2, fu=fu, s1_supercell=False, use_rms=True, break_on_match=False)
              if match is None:
                  return None
              # invert the mapping, since it needs to be from s1 to s2
              mapping = [list(match[4]).index(i) if i in match[4] else None for i in range(len(s1))]
              return match[2], match[3], mapping
          # s2 is superset
          match = self._strict_match(s2, s1, fu=fu, s1_supercell=True, use_rms=True, break_on_match=False)
          if match is None:
              return None
          # add sites not included in the mapping
          not_included = list(range(len(s2) * fu))
          for i in match[4]:
              not_included.remove(i)
          mapping = list(match[4]) + not_included
          return match[2], -match[3], mapping
  
      def get_s2_like_s1(self, struct1, struct2, include_ignored_species=True):
          """
          Performs transformations on struct2 to put it in a basis similar to
          struct1 (without changing any of the inter-site distances)
  
          Args:
              struct1 (Structure): Reference structure
              struct2 (Structure): Structure to transform.
              include_ignored_species (bool): Defaults to True,
                  the ignored_species is also transformed to the struct1
                  lattice orientation, though obviously there is no direct
                  matching to existing sites.
  
          Returns:
              A structure object similar to struct1, obtained by making a
              supercell, sorting, and translating struct2.
          """
          s1, s2 = self._process_species([struct1, struct2])
          trans = self.get_transformation(s1, s2)
          if trans is None:
              return None
          sc, t, mapping = trans
          sites = list(s2)
          # Append the ignored sites at the end.
          sites.extend([site for site in struct2 if site not in s2])
          temp = Structure.from_sites(sites)
  
          temp.make_supercell(sc)
          temp.translate_sites(list(range(len(temp))), t)
          # translate sites to correct unit cell
          for i, j in enumerate(mapping[: len(s1)]):
              if j is not None:
                  vec = np.round(struct1[i].frac_coords - temp[j].frac_coords)
                  temp.translate_sites(j, vec, to_unit_cell=False)
  
          sites = [temp.sites[i] for i in mapping if i is not None]
  
          if include_ignored_species:
              start = int(round(len(temp) / len(struct2) * len(s2)))
              sites.extend(temp.sites[start:])
  
          return Structure.from_sites(sites)
  
      def get_mapping(self, superset, subset):
          """
          Calculate the mapping from superset to subset.
  
          Args:
              superset (Structure): Structure containing at least the sites in
                  subset (within the structure matching tolerance)
              subset (Structure): Structure containing some of the sites in
                  superset (within the structure matching tolerance)
  
          Returns:
              numpy array such that superset.sites[mapping] is within matching
              tolerance of subset.sites or None if no such mapping is possible
          """
          if self._supercell:
              raise ValueError("cannot compute mapping to supercell")
          if self._primitive_cell:
              raise ValueError("cannot compute mapping with primitive cell option")
          if len(subset) > len(superset):
              raise ValueError("subset is larger than superset")
  
          superset, subset, _, _ = self._preprocess(superset, subset, True)
          match = self._strict_match(superset, subset, 1, break_on_match=False)
  
          if match is None or match[0] > self.stol:
              return None
  
          return match[4]
  
  
  class PointDefectComparator(MSONable):
      """
      A class that matches pymatgen Point Defect objects even if their
      cartesian co-ordinates are different (compares sublattices for the defect)
  
      NOTE: for defect complexes (more than a single defect),
      this comparator will break.
      """
  
      def __init__(self, check_charge=False, check_primitive_cell=False, check_lattice_scale=False):
          """
          Args:
              check_charge (bool): Gives option to check
                  if charges are identical.
                  Default is False (different charged defects can be same)
              check_primitive_cell (bool): Gives option to
                  compare different supercells of bulk_structure,
                  rather than directly compare supercell sizes
                  Default is False (requires bulk_structure in each defect to be same size)
              check_lattice_scale (bool): Gives option to scale volumes of
                  structures to each other identical lattice constants.
                  Default is False (enforces same
                  lattice constants in both structures)
          """
          self.check_charge = check_charge
          self.check_primitive_cell = check_primitive_cell
          self.check_lattice_scale = check_lattice_scale
  
      def are_equal(self, d1, d2):
          """
          Args:
              d1: First defect. A pymatgen Defect object.
              d2: Second defect. A pymatgen Defect object.
  
          Returns:
              True if defects are identical in type and sublattice.
          """
          possible_defect_types = (Defect, Vacancy, Substitution, Interstitial)
  
          if not isinstance(d1, possible_defect_types) or not isinstance(d2, possible_defect_types):
              raise ValueError("Cannot use PointDefectComparator to compare non-defect objects...")
  
          if not isinstance(d1, d2.__class__):
              return False
          if d1.site.specie != d2.site.specie:
              return False
          if self.check_charge and (d1.charge != d2.charge):
              return False
  
          sm = StructureMatcher(
              ltol=0.01,
              primitive_cell=self.check_primitive_cell,
              scale=self.check_lattice_scale,
          )
  
          if not sm.fit(d1.bulk_structure, d2.bulk_structure):
              return False
  
          d1 = d1.copy()
          d2 = d2.copy()
          if self.check_primitive_cell or self.check_lattice_scale:
              # if allowing for base structure volume or supercell modifications,
              # then need to preprocess defect objects to allow for matching
              d1_mod_bulk_structure, d2_mod_bulk_structure, _, _ = sm._preprocess(d1.bulk_structure, d2.bulk_structure)
              d1_defect_site = PeriodicSite(
                  d1.site.specie,
                  d1.site.coords,
                  d1_mod_bulk_structure.lattice,
                  to_unit_cell=True,
                  coords_are_cartesian=True,
              )
              d2_defect_site = PeriodicSite(
                  d2.site.specie,
                  d2.site.coords,
                  d2_mod_bulk_structure.lattice,
                  to_unit_cell=True,
                  coords_are_cartesian=True,
              )
  
              d1._structure = d1_mod_bulk_structure
              d2._structure = d2_mod_bulk_structure
              d1._defect_site = d1_defect_site
              d2._defect_site = d2_defect_site
  
          return sm.fit(d1.generate_defect_structure(), d2.generate_defect_structure())
  