Source code for pymatgen.alchemy.filters

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

"""
This module defines filters for Transmuter object.
"""

import abc

from pymatgen.core.periodic_table import get_el_sp
from monty.json import MSONable
from pymatgen.analysis.structure_matcher import StructureMatcher, \
    ElementComparator
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer

from collections import defaultdict

__author__ = "Will Richards, Shyue Ping Ong, Stephen Dacek"
__copyright__ = "Copyright 2011, The Materials Project"
__version__ = "1.0"
__maintainer__ = "Will Richards"
__email__ = "wrichards@mit.edu"
__date__ = "Sep 25, 2012"


[docs]class AbstractStructureFilter(MSONable, metaclass=abc.ABCMeta): """ AbstractStructureFilter that defines an API to perform testing of Structures. Structures that return True to a test are retained during transmutation while those that return False are removed. """
[docs] @abc.abstractmethod def test(self, structure): """ Method to execute the test. Args: structure (Structure): Input structure to test Returns: (bool) Structures that return true are kept in the Transmuter object during filtering. """ return
[docs]class ContainsSpecieFilter(AbstractStructureFilter): """ Filter for structures containing certain elements or species. By default compares by atomic number. """ def __init__(self, species, strict_compare=False, AND=True, exclude=False): """ Args: species ([Specie/Element]): list of species to look for AND: whether all species must be present to pass (or fail) filter. strict_compare: if true, compares objects by specie or element object if false, compares atomic number exclude: If true, returns false for any structures with the specie (excludes them from the Transmuter) """ self._species = list(map(get_el_sp, species)) self._strict = strict_compare self._AND = AND self._exclude = exclude
[docs] def test(self, structure): """ Method to execute the test. Returns: True if structure do not contain specified species. """ # set up lists to compare if not self._strict: # compare by atomic number filter_set = set([sp.Z for sp in self._species]) structure_set = set([sp.Z for sp in structure.composition.elements]) else: # compare by specie or element object filter_set = set(self._species) structure_set = set(structure.composition.elements) if self._AND and filter_set <= structure_set: # return true if we aren't excluding since all are in structure return not self._exclude elif (not self._AND) and filter_set & structure_set: # return true if we aren't excluding since one is in structure return not self._exclude else: # return false if we aren't excluding otherwise return self._exclude
def __repr__(self): return "\n".join(["ContainsSpecieFilter with parameters:", "species = {}".format(self._species), "strict_compare = {}".format(self._strict), "AND = {}".format(self._AND), "exclude = {}".format(self._exclude)])
[docs] def as_dict(self): """ Returns: MSONAble dict """ return {"version": __version__, "@module": self.__class__.__module__, "@class": self.__class__.__name__, "init_args": {"species": [str(sp) for sp in self._species], "strict_compare": self._strict, "AND": self._AND, "exclude": self._exclude}}
[docs] @classmethod def from_dict(cls, d): """ Args: d (dict): Dict representation Returns: Filter """ return cls(**d["init_args"])
[docs]class SpecieProximityFilter(AbstractStructureFilter): """ This filter removes structures that have certain species that are too close together. """ def __init__(self, specie_and_min_dist_dict): """ Args: specie_and_min_dist_dict (dict): A species string to float mapping. For example, {"Na+": 1} means that all Na+ ions must be at least 1 Angstrom away from each other. Multiple species criteria can be applied. Note that the testing is done based on the actual object . If you have a structure with Element, you must use {"Na":1} instead to filter based on Element and not Specie. """ self.specie_and_min_dist = {get_el_sp(k): v for k, v in specie_and_min_dist_dict.items()}
[docs] def test(self, structure): """ Method to execute the test. Args: structure (Structure): Input structure to test Returns: True if structure does not contain species within specified distances. """ all_species = set(self.specie_and_min_dist.keys()) for site in structure: species = site.species.keys() sp_to_test = set(species).intersection(all_species) if sp_to_test: max_r = max([self.specie_and_min_dist[sp] for sp in sp_to_test]) nn = structure.get_neighbors(site, max_r) for sp in sp_to_test: for nnsite, dist, *_ in nn: if sp in nnsite.species.keys(): if dist < self.specie_and_min_dist[sp]: return False return True
[docs] def as_dict(self): """ Returns: MSONable dict """ return {"version": __version__, "@module": self.__class__.__module__, "@class": self.__class__.__name__, "init_args": {"specie_and_min_dist_dict": {str(sp): v for sp, v in self.specie_and_min_dist.items()}}}
[docs] @classmethod def from_dict(cls, d): """ Args: d (dict): Dict representation Returns: Filter """ return cls(**d["init_args"])
[docs]class RemoveDuplicatesFilter(AbstractStructureFilter): """ This filter removes exact duplicate structures from the transmuter. """ def __init__(self, structure_matcher=StructureMatcher(comparator=ElementComparator()), symprec=None): """ Remove duplicate structures based on the structure matcher and symmetry (if symprec is given). Args: structure_matcher: Provides a structure matcher to be used for structure comparison. symprec: The precision in the symmetry finder algorithm if None ( default value), no symmetry check is performed and only the structure matcher is used. A recommended value is 1e-5. """ self.symprec = symprec self.structure_list = defaultdict(list) if isinstance(structure_matcher, dict): self.structure_matcher = StructureMatcher.from_dict(structure_matcher) else: self.structure_matcher = structure_matcher
[docs] def test(self, structure): """ Args: structure (Structure): Input structure to test Returns: True if structure is not in list. """ h = self.structure_matcher._comparator.get_hash(structure.composition) if not self.structure_list[h]: self.structure_list[h].append(structure) return True def get_sg(s): finder = SpacegroupAnalyzer(s, symprec=self.symprec) return finder.get_space_group_number() for s in self.structure_list[h]: if self.symprec is None or \ get_sg(s) == get_sg(structure): if self.structure_matcher.fit(s, structure): return False self.structure_list[h].append(structure) return True
[docs]class RemoveExistingFilter(AbstractStructureFilter): """ This filter removes structures existing in a given list from the transmuter. """ def __init__(self, existing_structures, structure_matcher=StructureMatcher(comparator=ElementComparator()), symprec=None): """ Remove existing structures based on the structure matcher and symmetry (if symprec is given). Args: existing_structures: List of existing structures to compare with structure_matcher: Provides a structure matcher to be used for structure comparison. symprec: The precision in the symmetry finder algorithm if None ( default value), no symmetry check is performed and only the structure matcher is used. A recommended value is 1e-5. """ self.symprec = symprec self.structure_list = [] self.existing_structures = existing_structures if isinstance(structure_matcher, dict): self.structure_matcher = StructureMatcher.from_dict(structure_matcher) else: self.structure_matcher = structure_matcher
[docs] def test(self, structure): """ Method to execute the test. Args: structure (Structure): Input structure to test Returns: True if structure is not in existing list. """ def get_sg(s): finder = SpacegroupAnalyzer(s, symprec=self.symprec) return finder.get_space_group_number() for s in self.existing_structures: if self.structure_matcher._comparator.get_hash(structure.composition) == \ self.structure_matcher._comparator.get_hash(s.composition): if self.symprec is None or \ get_sg(s) == get_sg(structure): if self.structure_matcher.fit(s, structure): return False self.structure_list.append(structure) return True
[docs] def as_dict(self): """ Returns: MSONable dict """ return {"version": __version__, "@module": self.__class__.__module__, "@class": self.__class__.__name__, "init_args": {"structure_matcher": self.structure_matcher.as_dict()}}
[docs]class ChargeBalanceFilter(AbstractStructureFilter): """ This filter removes structures that are not charge balanced from the transmuter. This only works if the structure is oxidation state decorated, as structures with only elemental sites are automatically assumed to have net charge of 0. """ def __init__(self): """ No args required. """ pass
[docs] def test(self, structure): """ Method to execute the test. Args: structure (Structure): Input structure to test Returns: True if structure is neutral. """ if structure.charge == 0.0: return True else: return False
[docs]class SpeciesMaxDistFilter(AbstractStructureFilter): """ This filter removes structures that do have two particular species that are not nearest neighbors by a predefined max_dist. For instance, if you are analyzing Li battery materials, you would expect that each Li+ would be nearest neighbor to lower oxidation state transition metal for electrostatic reasons. This only works if the structure is oxidation state decorated, as structures with only elemental sites are automatically assumed to have net charge of 0. """ def __init__(self, sp1, sp2, max_dist): """ Args: sp1 (Specie): First specie sp2 (Specie): Second specie max_dist (float): Maximum distance between species. """ self.sp1 = get_el_sp(sp1) self.sp2 = get_el_sp(sp2) self.max_dist = max_dist
[docs] def test(self, structure): """ Method to execute the test. Args: structure (Structure): Input structure to test Returns: True if structure does not contain the two species are distances greater than max_dist. """ sp1_indices = [i for i, site in enumerate(structure) if site.specie == self.sp1] sp2_indices = [i for i, site in enumerate(structure) if site.specie == self.sp2] fcoords = structure.frac_coords fcoords1 = fcoords[sp1_indices, :] fcoords2 = fcoords[sp2_indices, :] lattice = structure.lattice dists = lattice.get_all_distances(fcoords1, fcoords2) return all([any(row) for row in dists < self.max_dist])