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]]

def group_entries_by_structure(entries, species_to_remove=None,
                               ltol=0.2, stol=.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("Started at {}".format(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("Using {} cpus".format(ncpus))
        manager = mp.Manager()
        groups = manager.list()
        p = mp.Pool(ncpus)
        # 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("Finished at {}".format(datetime.datetime.now()))
    logging.info("Took {}".format(datetime.datetime.now() - start))
    return entry_groups