text: # Copyright (c) Pymatgen Development Team.
  # Distributed under the terms of the MIT License.
  
  
  """
  This module implements functions to perform various useful operations on
  entries, such as grouping entries by structure.
  """
  
  
  import collections
  import csv
  import datetime
  import itertools
  import json
  import logging
  import re
  from typing import Iterable, List, Set, Union
  
  from monty.json import MontyDecoder, MontyEncoder, MSONable
  from monty.string import unicode2str
  
  from pymatgen.analysis.phase_diagram import PDEntry
  from pymatgen.analysis.structure_matcher import SpeciesComparator, StructureMatcher
  from pymatgen.core.composition import Composition
  from pymatgen.core.periodic_table import Element
  from pymatgen.entries.computed_entries import ComputedEntry, ComputedStructureEntry
  
  logger = logging.getLogger(__name__)
  
  
  def _get_host(structure, species_to_remove):
      if species_to_remove:
          s = structure.copy()
          s.remove_species(species_to_remove)
          return s
      return structure
  
  
  def _perform_grouping(args):
      (
          entries_json,
          hosts_json,
          ltol,
          stol,
          angle_tol,
          primitive_cell,
          scale,
          comparator,
          groups,
      ) = args
  
      entries = json.loads(entries_json, cls=MontyDecoder)
      hosts = json.loads(hosts_json, cls=MontyDecoder)
      unmatched = list(zip(entries, hosts))
      while len(unmatched) > 0:
          ref_host = unmatched[0][1]
          logger.info(f"Reference tid = {unmatched[0][0].entry_id}, formula = {ref_host.formula}")
          ref_formula = ref_host.composition.reduced_formula
          logger.info(f"Reference host = {ref_formula}")
          matches = [unmatched[0]]
          for i in range(1, len(unmatched)):
              test_host = unmatched[i][1]
              logger.info(f"Testing tid = {unmatched[i][0].entry_id}, formula = {test_host.formula}")
              test_formula = test_host.composition.reduced_formula
              logger.info(f"Test host = {test_formula}")
              m = StructureMatcher(
                  ltol=ltol,
                  stol=stol,
                  angle_tol=angle_tol,
                  primitive_cell=primitive_cell,
                  scale=scale,
                  comparator=comparator,
              )
              if m.fit(ref_host, test_host):
                  logger.info("Fit found")
                  matches.append(unmatched[i])
          groups.append(json.dumps([m[0] for m in matches], cls=MontyEncoder))
          unmatched = list(filter(lambda x: x not in matches, unmatched))
          logger.info(f"{len(unmatched)} unmatched remaining")
  
  
  def group_entries_by_structure(
      entries,
      species_to_remove=None,
      ltol=0.2,
      stol=0.4,
      angle_tol=5,
      primitive_cell=True,
      scale=True,
      comparator=SpeciesComparator(),
      ncpus=None,
  ):
      """
      Given a sequence of ComputedStructureEntries, use structure fitter to group
      them by structural similarity.
  
      Args:
          entries: Sequence of ComputedStructureEntries.
          species_to_remove: Sometimes you want to compare a host framework
              (e.g., in Li-ion battery analysis). This allows you to specify
              species to remove before structural comparison.
          ltol (float): Fractional length tolerance. Default is 0.2.
          stol (float): Site tolerance in Angstrom. Default is 0.4 Angstrom.
          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. Defaults to True.
          scale: Input structures are scaled to equivalent volume if true;
              For exact matching, set to False.
          comparator: A comparator object implementing an equals method that
              declares equivalency of sites. Default is SpeciesComparator,
              which implies rigid species mapping.
          ncpus: Number of cpus to use. Use of multiple cpus can greatly improve
              fitting speed. Default of None means serial processing.
  
      Returns:
          Sequence of sequence of entries by structural similarity. e.g,
          [[ entry1, entry2], [entry3, entry4, entry5]]
      """
      start = datetime.datetime.now()
      logger.info(f"Started at {start}")
      entries_host = [(entry, _get_host(entry.structure, species_to_remove)) for entry in entries]
      if ncpus:
          symm_entries = collections.defaultdict(list)
          for entry, host in entries_host:
              symm_entries[comparator.get_structure_hash(host)].append((entry, host))
          import multiprocessing as mp
  
          logging.info(f"Using {ncpus} cpus")
          manager = mp.Manager()
          groups = manager.list()
          with mp.Pool(ncpus) as p:
              # Parallel processing only supports Python primitives and not objects.
              p.map(
                  _perform_grouping,
                  [
                      (
                          json.dumps([e[0] for e in eh], cls=MontyEncoder),
                          json.dumps([e[1] for e in eh], cls=MontyEncoder),
                          ltol,
                          stol,
                          angle_tol,
                          primitive_cell,
                          scale,
                          comparator,
                          groups,
                      )
                      for eh in symm_entries.values()
                  ],
              )
      else:
          groups = []
          hosts = [host for entry, host in entries_host]
          _perform_grouping(
              (
                  json.dumps(entries, cls=MontyEncoder),
                  json.dumps(hosts, cls=MontyEncoder),
                  ltol,
                  stol,
                  angle_tol,
                  primitive_cell,
                  scale,
                  comparator,
                  groups,
              )
          )
      entry_groups = []
      for g in groups:
          entry_groups.append(json.loads(g, cls=MontyDecoder))
      logging.info(f"Finished at {datetime.datetime.now()}")
      logging.info(f"Took {datetime.datetime.now() - start}")
      return entry_groups
  
  
  class EntrySet(collections.abc.MutableSet, MSONable):
      """
      A convenient container for manipulating entries. Allows for generating
      subsets, dumping into files, etc.
      """
  
      def __init__(self, entries: Iterable[Union[PDEntry, ComputedEntry, ComputedStructureEntry]]):
          """
          Args:
              entries: All the entries.
          """
          self.entries = set(entries)
  
      def __contains__(self, item):
          return item in self.entries
  
      def __iter__(self):
          return self.entries.__iter__()
  
      def __len__(self):
          return len(self.entries)
  
      def add(self, element):
          """
          Add an entry.
  
          :param element: Entry
          """
          self.entries.add(element)
  
      def discard(self, element):
          """
          Discard an entry.
  
          :param element: Entry
          """
          self.entries.discard(element)
  
      @property
      def chemsys(self) -> set:
          """
          Returns:
              set representing the chemical system, e.g., {"Li", "Fe", "P", "O"}
          """
          chemsys = set()
          for e in self.entries:
              chemsys.update([el.symbol for el in e.composition.keys()])
          return chemsys
  
      def remove_non_ground_states(self):
          """
          Removes all non-ground state entries, i.e., only keep the lowest energy
          per atom entry at each composition.
          """
          entries = sorted(self.entries, key=lambda e: e.composition.reduced_formula)
          ground_states = set()
          for _, g in itertools.groupby(entries, key=lambda e: e.composition.reduced_formula):
              ground_states.add(min(g, key=lambda e: e.energy_per_atom))
          self.entries = ground_states
  
      def get_subset_in_chemsys(self, chemsys: List[str]):
          """
          Returns an EntrySet containing only the set of entries belonging to
          a particular chemical system (in this definition, it includes all sub
          systems). For example, if the entries are from the
          Li-Fe-P-O system, and chemsys=["Li", "O"], only the Li, O,
          and Li-O entries are returned.
  
          Args:
              chemsys: Chemical system specified as list of elements. E.g.,
                  ["Li", "O"]
  
          Returns:
              EntrySet
          """
          chem_sys = set(chemsys)
          if not chem_sys.issubset(self.chemsys):
              raise ValueError(f"{chem_sys} is not a subset of {self.chemsys}")
          subset = set()
          for e in self.entries:
              elements = [sp.symbol for sp in e.composition.keys()]
              if chem_sys.issuperset(elements):
                  subset.add(e)
          return EntrySet(subset)
  
      def as_dict(self):
          """
          :return: MSONable dict
          """
          return {"entries": list(self.entries)}
  
      def to_csv(self, filename: str, latexify_names: bool = False):
          """
          Exports PDEntries to a csv
  
          Args:
              filename: Filename to write to.
              entries: PDEntries to export.
              latexify_names: Format entry names to be LaTex compatible,
                  e.g., Li_{2}O
          """
  
          els = set()  # type: Set[Element]
          for entry in self.entries:
              els.update(entry.composition.elements)
          elements = sorted(list(els), key=lambda a: a.X)
          with open(filename, "w") as f:
              writer = csv.writer(
                  f,
                  delimiter=unicode2str(","),
                  quotechar=unicode2str('"'),
                  quoting=csv.QUOTE_MINIMAL,
              )
              writer.writerow(["Name"] + [el.symbol for el in elements] + ["Energy"])
              for entry in self.entries:
                  row = [entry.name if not latexify_names else re.sub(r"([0-9]+)", r"_{\1}", entry.name)]
                  row.extend([entry.composition[el] for el in elements])
                  row.append(str(entry.energy))
                  writer.writerow(row)
  
      @classmethod
      def from_csv(cls, filename: str):
          """
          Imports PDEntries from a csv.
  
          Args:
              filename: Filename to import from.
  
          Returns:
              List of Elements, List of PDEntries
          """
          with open(filename, encoding="utf-8") as f:
              reader = csv.reader(
                  f,
                  delimiter=unicode2str(","),
                  quotechar=unicode2str('"'),
                  quoting=csv.QUOTE_MINIMAL,
              )
              entries = []
              header_read = False
              elements = []  # type: List[str]
              for row in reader:
                  if not header_read:
                      elements = row[1 : (len(row) - 1)]
                      header_read = True
                  else:
                      name = row[0]
                      energy = float(row[-1])
                      comp = {}
                      for ind in range(1, len(row) - 1):
                          if float(row[ind]) > 0:
                              comp[Element(elements[ind - 1])] = float(row[ind])
                      entries.append(PDEntry(Composition(comp), energy, name))
          return cls(entries)
  