Source code for pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies

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


"""
This module provides so-called "strategies" to determine the coordination environments of an atom in a structure.
Some strategies can favour larger or smaller environments. Some strategies uniquely identifies the environments while
some others can identify the environment as a "mix" of several environments, each of which is assigned with a given
fraction. The choice of the strategy depends on the purpose of the user.
"""

__author__ = "David Waroquiers"
__copyright__ = "Copyright 2012, The Materials Project"
__credits__ = "Geoffroy Hautier"
__version__ = "2.0"
__maintainer__ = "David Waroquiers"
__email__ = "david.waroquiers@gmail.com"
__date__ = "Feb 20, 2016"

import abc
import os
from collections import OrderedDict
from typing import Dict, List, Optional

import numpy as np
from monty.json import MSONable
from scipy.stats import gmean

from pymatgen.analysis.chemenv.coordination_environments.coordination_geometries import (
    AllCoordinationGeometries,
)
from pymatgen.analysis.chemenv.coordination_environments.voronoi import (
    DetailedVoronoiContainer,
)
from pymatgen.analysis.chemenv.utils.chemenv_errors import EquivalentSiteSearchError
from pymatgen.analysis.chemenv.utils.coordination_geometry_utils import (
    get_lower_and_upper_f,
)
from pymatgen.analysis.chemenv.utils.defs_utils import AdditionalConditions
from pymatgen.analysis.chemenv.utils.func_utils import (
    CSMFiniteRatioFunction,
    CSMInfiniteRatioFunction,
    DeltaCSMRatioFunction,
    RatioFunction,
)
from pymatgen.core.operations import SymmOp
from pymatgen.core.sites import PeriodicSite
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer

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

MPSYMBOL_TO_CN = AllCoordinationGeometries().get_symbol_cn_mapping()
ALLCG = AllCoordinationGeometries()


[docs]class StrategyOption(MSONable, metaclass=abc.ABCMeta): """Abstract class for the options of the chemenv strategies.""" allowed_values = None # type: Optional[str]
[docs] @abc.abstractmethod def as_dict(self): """ A JSON serializable dict representation of this strategy option. """ pass
[docs]class DistanceCutoffFloat(float, StrategyOption): """Distance cutoff in a strategy.""" allowed_values = "Real number between 1.0 and +infinity" def __new__(cls, myfloat): """Special float that should be between 1.0 and infinity. :param myfloat: Distance cutoff. """ flt = float.__new__(cls, myfloat) if flt < 1.0: raise ValueError("Distance cutoff should be between 1.0 and +infinity") return flt
[docs] def as_dict(self): """MSONAble dict""" return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "value": self, }
[docs] @classmethod def from_dict(cls, d): """Initialize distance cutoff from dict. :param d: Dict representation of the distance cutoff.""" return cls(d["value"])
[docs]class AngleCutoffFloat(float, StrategyOption): """Angle cutoff in a strategy""" allowed_values = "Real number between 0.0 and 1.0" def __new__(cls, myfloat): """Special float that should be between 0.0 and 1.0. :param myfloat: Angle cutoff.""" flt = float.__new__(cls, myfloat) if flt < 0.0 or flt > 1.0: raise ValueError("Angle cutoff should be between 0.0 and 1.0") return flt
[docs] def as_dict(self): """MSONAble dict""" return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "value": self, }
[docs] @classmethod def from_dict(cls, d): """Initialize angle cutoff from dict. :param d: Dict representation of the angle cutoff. """ return cls(d["value"])
[docs]class CSMFloat(float, StrategyOption): """Real number representing a Continuous Symmetry Measure""" allowed_values = "Real number between 0.0 and 100.0" def __new__(cls, myfloat): """Special float that should be between 0.0 and 100.0. :param myfloat: CSM.""" flt = float.__new__(cls, myfloat) if flt < 0.0 or flt > 100.0: raise ValueError("Continuous symmetry measure limits should be between 0.0 and 100.0") return flt
[docs] def as_dict(self): """MSONable dict""" return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "value": self, }
[docs] @classmethod def from_dict(cls, d): """Initialize CSM from dict. :param d: Dict representation of the CSM. """ return cls(d["value"])
[docs]class AdditionalConditionInt(int, StrategyOption): """Integer representing an additional condition in a strategy.""" allowed_values = "Integer amongst :\n" for integer, description in AdditionalConditions.CONDITION_DESCRIPTION.items(): allowed_values += ' - {:d} for "{}"\n'.format(integer, description) def __new__(cls, integer): """Special int representing additional conditions.""" if str(int(integer)) != str(integer): raise ValueError("Additional condition {} is not an integer".format(str(integer))) intger = int.__new__(cls, integer) if intger not in AdditionalConditions.ALL: raise ValueError("Additional condition {:d} is not allowed".format(integer)) return intger
[docs] def as_dict(self): """MSONable dict""" return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "value": self, }
[docs] @classmethod def from_dict(cls, d): """Initialize additional condition from dict. :param d: Dict representation of the additional condition. """ return cls(d["value"])
[docs]class AbstractChemenvStrategy(MSONable, metaclass=abc.ABCMeta): """ Class used to define a Chemenv strategy for the neighbors and coordination environment to be applied to a StructureEnvironments object """ AC = AdditionalConditions() STRATEGY_OPTIONS = OrderedDict() # type: Dict[str, Dict] STRATEGY_DESCRIPTION = None # type: str STRATEGY_INFO_FIELDS = [] # type: List DEFAULT_SYMMETRY_MEASURE_TYPE = "csm_wcs_ctwcc" def __init__( self, structure_environments=None, symmetry_measure_type=DEFAULT_SYMMETRY_MEASURE_TYPE, ): """ Abstract constructor for the all chemenv strategies. :param structure_environments: StructureEnvironments object containing all the information on the coordination of the sites in a structure """ self.structure_environments = None if structure_environments is not None: self.set_structure_environments(structure_environments) self._symmetry_measure_type = symmetry_measure_type @property def symmetry_measure_type(self): """Type of symmetry measure.""" return self._symmetry_measure_type
[docs] def set_structure_environments(self, structure_environments): """Set the structure environments to this strategy. :param structure_environments: StructureEnvironments object. :return: None """ self.structure_environments = structure_environments if not isinstance(self.structure_environments.voronoi, DetailedVoronoiContainer): raise ValueError('Voronoi Container not of type "DetailedVoronoiContainer"') self.prepare_symmetries()
[docs] def prepare_symmetries(self): """Prepare the symmetries for the structure contained in the structure environments.""" try: self.spg_analyzer = SpacegroupAnalyzer(self.structure_environments.structure) self.symops = self.spg_analyzer.get_symmetry_operations() except Exception: self.symops = []
[docs] def equivalent_site_index_and_transform(self, psite): """Get the equivalent site and corresponding symmetry+translation transformations. :param psite: Periodic site. :return: Equivalent site in the unit cell, translations and symmetry transformation. """ # Get the index of the site in the unit cell of which the PeriodicSite psite is a replica. try: isite = self.structure_environments.structure.index(psite) except ValueError: try: uc_psite = psite.to_unit_cell() isite = self.structure_environments.structure.index(uc_psite) except ValueError: for isite2, site2 in enumerate(self.structure_environments.structure): if psite.is_periodic_image(site2): isite = isite2 break # Get the translation between psite and its corresponding site in the unit cell (Translation I) thissite = self.structure_environments.structure[isite] dthissite = psite.frac_coords - thissite.frac_coords # Get the translation between the equivalent site for which the neighbors have been computed and the site in # the unit cell that corresponds to psite (Translation II) equivsite = self.structure_environments.structure[self.structure_environments.sites_map[isite]].to_unit_cell() # equivsite = self.structure_environments.structure[self.structure_environments.sites_map[isite]] dequivsite = ( self.structure_environments.structure[self.structure_environments.sites_map[isite]].frac_coords - equivsite.frac_coords ) found = False # Find the symmetry that applies the site in the unit cell to the equivalent site, as well as the translation # that gets back the site to the unit cell (Translation III) # TODO: check that these tolerances are needed, now that the structures are refined before analyzing envs tolerances = [1e-8, 1e-7, 1e-6, 1e-5, 1e-4] for tolerance in tolerances: for symop in self.symops: newsite = PeriodicSite( equivsite._species, symop.operate(equivsite.frac_coords), equivsite._lattice, ) if newsite.is_periodic_image(thissite, tolerance=tolerance): mysym = symop dthissite2 = thissite.frac_coords - newsite.frac_coords found = True break if not found: symops = [SymmOp.from_rotation_and_translation()] for symop in symops: newsite = PeriodicSite( equivsite._species, symop.operate(equivsite.frac_coords), equivsite._lattice, ) # if newsite.is_periodic_image(thissite): if newsite.is_periodic_image(thissite, tolerance=tolerance): mysym = symop dthissite2 = thissite.frac_coords - newsite.frac_coords found = True break if found: break if not found: raise EquivalentSiteSearchError(psite) return [ self.structure_environments.sites_map[isite], dequivsite, dthissite + dthissite2, mysym, ]
[docs] @abc.abstractmethod def get_site_neighbors(self, site): """ Applies the strategy to the structure_environments object in order to get the neighbors of a given site. :param site: Site for which the neighbors are looked for :param structure_environments: StructureEnvironments object containing all the information needed to get the neighbors of the site :return: The list of neighbors of the site. For complex strategies, where one allows multiple solutions, this can return a list of list of neighbors """ raise NotImplementedError()
@property def uniquely_determines_coordination_environments(self): """ Returns True if the strategy leads to a unique coordination environment, False otherwise. :return: True if the strategy leads to a unique coordination environment, False otherwise. """ raise NotImplementedError()
[docs] @abc.abstractmethod def get_site_coordination_environment(self, site): """ Applies the strategy to the structure_environments object in order to define the coordination environment of a given site. :param site: Site for which the coordination environment is looked for :return: The coordination environment of the site. For complex strategies, where one allows multiple solutions, this can return a list of coordination environments for the site """ raise NotImplementedError()
[docs] @abc.abstractmethod def get_site_coordination_environments(self, site): """ Applies the strategy to the structure_environments object in order to define the coordination environment of a given site. :param site: Site for which the coordination environment is looked for :return: The coordination environment of the site. For complex strategies, where one allows multiple solutions, this can return a list of coordination environments for the site """ raise NotImplementedError()
[docs] @abc.abstractmethod def get_site_coordination_environments_fractions( self, site, isite=None, dequivsite=None, dthissite=None, mysym=None, ordered=True, min_fraction=0.0, return_maps=True, return_strategy_dict_info=False, ): """ Applies the strategy to the structure_environments object in order to define the coordination environment of a given site. :param site: Site for which the coordination environment is looked for :return: The coordination environment of the site. For complex strategies, where one allows multiple solutions, this can return a list of coordination environments for the site """ raise NotImplementedError()
[docs] def get_site_ce_fractions_and_neighbors(self, site, full_ce_info=False, strategy_info=False): """ Applies the strategy to the structure_environments object in order to get coordination environments, their fraction, csm, geometry_info, and neighbors :param site: Site for which the above information is seeked :return: The list of neighbors of the site. For complex strategies, where one allows multiple solutions, this can return a list of list of neighbors """ [ isite, dequivsite, dthissite, mysym, ] = self.equivalent_site_index_and_transform(site) geoms_and_maps_list = self.get_site_coordination_environments_fractions( site=site, isite=isite, dequivsite=dequivsite, dthissite=dthissite, mysym=mysym, return_maps=True, return_strategy_dict_info=True, ) if geoms_and_maps_list is None: return None site_nbs_sets = self.structure_environments.neighbors_sets[isite] ce_and_neighbors = [] for fractions_dict in geoms_and_maps_list: ce_map = fractions_dict["ce_map"] ce_nb_set = site_nbs_sets[ce_map[0]][ce_map[1]] neighbors = [ {"site": nb_site_and_index["site"], "index": nb_site_and_index["index"]} for nb_site_and_index in ce_nb_set.neighb_sites_and_indices ] fractions_dict["neighbors"] = neighbors ce_and_neighbors.append(fractions_dict) return ce_and_neighbors
[docs] def set_option(self, option_name, option_value): """Set up a given option for this strategy. :param option_name: Name of the option. :param option_value: Value for this option. :return: None """ self.__setattr__(option_name, option_value)
[docs] def setup_options(self, all_options_dict): """Set up options for this strategy based on a dict. :param all_options_dict: Dict of option_name->option_value. :return: None """ for option_name, option_value in all_options_dict.items(): self.set_option(option_name, option_value)
@abc.abstractmethod def __eq__(self, other): """ Equality method that should be implemented for any strategy :param other: strategy to be compared with the current one :return: """ raise NotImplementedError() def __str__(self): out = ' Chemenv Strategy "{}"\n'.format(self.__class__.__name__) out += " {}\n\n".format("=" * (19 + len(self.__class__.__name__))) out += " Description :\n {}\n".format("-" * 13) out += self.STRATEGY_DESCRIPTION out += "\n\n" out += " Options :\n {}\n".format("-" * 9) for option_name, option_dict in self.STRATEGY_OPTIONS.items(): out += " - {} : {}\n".format(option_name, str(getattr(self, option_name))) return out
[docs] @abc.abstractmethod def as_dict(self): """ Bson-serializable dict representation of the SimplestChemenvStrategy object. :return: Bson-serializable dict representation of the SimplestChemenvStrategy object. """ raise NotImplementedError()
[docs] @classmethod def from_dict(cls, d): """ Reconstructs the SimpleAbundanceChemenvStrategy object from a dict representation of the SimpleAbundanceChemenvStrategy object created using the as_dict method. :param d: dict representation of the SimpleAbundanceChemenvStrategy object :return: StructureEnvironments object """ raise NotImplementedError()
[docs]class SimplestChemenvStrategy(AbstractChemenvStrategy): """ Simplest ChemenvStrategy using fixed angle and distance parameters for the definition of neighbors in the Voronoi approach. The coordination environment is then given as the one with the lowest continuous symmetry measure """ # Default values for the distance and angle cutoffs DEFAULT_DISTANCE_CUTOFF = 1.4 DEFAULT_ANGLE_CUTOFF = 0.3 DEFAULT_CONTINUOUS_SYMMETRY_MEASURE_CUTOFF = 10.0 DEFAULT_ADDITIONAL_CONDITION = AbstractChemenvStrategy.AC.ONLY_ACB STRATEGY_OPTIONS = OrderedDict() # type: Dict[str, Dict] STRATEGY_OPTIONS["distance_cutoff"] = { "type": DistanceCutoffFloat, "internal": "_distance_cutoff", "default": DEFAULT_DISTANCE_CUTOFF, } STRATEGY_OPTIONS["angle_cutoff"] = { "type": AngleCutoffFloat, "internal": "_angle_cutoff", "default": DEFAULT_ANGLE_CUTOFF, } STRATEGY_OPTIONS["additional_condition"] = { "type": AdditionalConditionInt, "internal": "_additional_condition", "default": DEFAULT_ADDITIONAL_CONDITION, } STRATEGY_OPTIONS["continuous_symmetry_measure_cutoff"] = { "type": CSMFloat, "internal": "_continuous_symmetry_measure_cutoff", "default": DEFAULT_CONTINUOUS_SYMMETRY_MEASURE_CUTOFF, } STRATEGY_DESCRIPTION = ( " Simplest ChemenvStrategy using fixed angle and distance parameters \n" " for the definition of neighbors in the Voronoi approach. \n" " The coordination environment is then given as the one with the \n" " lowest continuous symmetry measure." ) def __init__( self, structure_environments=None, distance_cutoff=DEFAULT_DISTANCE_CUTOFF, angle_cutoff=DEFAULT_ANGLE_CUTOFF, additional_condition=DEFAULT_ADDITIONAL_CONDITION, continuous_symmetry_measure_cutoff=DEFAULT_CONTINUOUS_SYMMETRY_MEASURE_CUTOFF, symmetry_measure_type=AbstractChemenvStrategy.DEFAULT_SYMMETRY_MEASURE_TYPE, ): """ Constructor for this SimplestChemenvStrategy. :param distance_cutoff: Distance cutoff used :param angle_cutoff: Angle cutoff used """ AbstractChemenvStrategy.__init__(self, structure_environments, symmetry_measure_type=symmetry_measure_type) self.distance_cutoff = distance_cutoff self.angle_cutoff = angle_cutoff self.additional_condition = additional_condition self.continuous_symmetry_measure_cutoff = continuous_symmetry_measure_cutoff @property def uniquely_determines_coordination_environments(self): """Whether this strategy uniquely determines coordination environments.""" return True @property def distance_cutoff(self): """Distance cutoff used.""" return self._distance_cutoff @distance_cutoff.setter def distance_cutoff(self, distance_cutoff): """Set the distance cutoff for this strategy. :param distance_cutoff: Distance cutoff. :return: None """ self._distance_cutoff = DistanceCutoffFloat(distance_cutoff) @property def angle_cutoff(self): """Angle cutoff used.""" return self._angle_cutoff @angle_cutoff.setter def angle_cutoff(self, angle_cutoff): """Set the angle cutoff for this strategy. :param angle_cutoff: Angle cutoff. :return: None """ self._angle_cutoff = AngleCutoffFloat(angle_cutoff) @property def additional_condition(self): """Additional condition for this strategy.""" return self._additional_condition @additional_condition.setter def additional_condition(self, additional_condition): """Set the additional condition for this strategy. :param additional_condition: Additional condition. :return: None """ self._additional_condition = AdditionalConditionInt(additional_condition) @property def continuous_symmetry_measure_cutoff(self): """CSM cutoff used""" return self._continuous_symmetry_measure_cutoff @continuous_symmetry_measure_cutoff.setter def continuous_symmetry_measure_cutoff(self, continuous_symmetry_measure_cutoff): """Set the CSM cutoff for this strategy. :param continuous_symmetry_measure_cutoff: CSM cutoff :return: None """ self._continuous_symmetry_measure_cutoff = CSMFloat(continuous_symmetry_measure_cutoff)
[docs] def get_site_neighbors(self, site, isite=None, dequivsite=None, dthissite=None, mysym=None): """Get the neighbors of a given site. :param site: Site for which neighbors are needed. :param isite: Index of the site. :param dequivsite: Translation of the equivalent site. :param dthissite: Translation of this site. :param mysym: Symmetry to be applied. :return: List of coordinated neighbors of site. """ if isite is None: [ isite, dequivsite, dthissite, mysym, ] = self.equivalent_site_index_and_transform(site) ce, cn_map = self.get_site_coordination_environment( site=site, isite=isite, dequivsite=dequivsite, dthissite=dthissite, mysym=mysym, return_map=True, ) nb_set = self.structure_environments.neighbors_sets[isite][cn_map[0]][cn_map[1]] eqsite_ps = nb_set.neighb_sites coordinated_neighbors = [] for ips, ps in enumerate(eqsite_ps): coords = mysym.operate(ps.frac_coords + dequivsite) + dthissite ps_site = PeriodicSite(ps._species, coords, ps._lattice) coordinated_neighbors.append(ps_site) return coordinated_neighbors
[docs] def get_site_coordination_environment( self, site, isite=None, dequivsite=None, dthissite=None, mysym=None, return_map=False, ): """Get the coordination environment of a given site. :param site: Site for which coordination environment is needed. :param isite: Index of the site. :param dequivsite: Translation of the equivalent site. :param dthissite: Translation of this site. :param mysym: Symmetry to be applied. :param return_map: Whether to return cn_map (identifies the NeighborsSet used). :return: Coordination environment of site. """ if isite is None: [ isite, dequivsite, dthissite, mysym, ] = self.equivalent_site_index_and_transform(site) neighbors_normalized_distances = self.structure_environments.voronoi.neighbors_normalized_distances[isite] neighbors_normalized_angles = self.structure_environments.voronoi.neighbors_normalized_angles[isite] idist = None for iwd, wd in enumerate(neighbors_normalized_distances): if self.distance_cutoff >= wd["min"]: idist = iwd else: break iang = None for iwa, wa in enumerate(neighbors_normalized_angles): if self.angle_cutoff <= wa["max"]: iang = iwa else: break if idist is None or iang is None: raise ValueError("Distance or angle parameter not found ...") my_cn = None my_inb_set = None found = False for cn, nb_sets in self.structure_environments.neighbors_sets[isite].items(): for inb_set, nb_set in enumerate(nb_sets): sources = [ src for src in nb_set.sources if src["origin"] == "dist_ang_ac_voronoi" and src["ac"] == self.additional_condition ] for src in sources: if src["idp"] == idist and src["iap"] == iang: my_cn = cn my_inb_set = inb_set found = True break if found: break if found: break if not found: return None cn_map = (my_cn, my_inb_set) ce = self.structure_environments.ce_list[self.structure_environments.sites_map[isite]][cn_map[0]][cn_map[1]] if ce is None: return None coord_geoms = ce.coord_geoms if return_map: if coord_geoms is None: return cn_map[0], cn_map return ( ce.minimum_geometry(symmetry_measure_type=self._symmetry_measure_type), cn_map, ) if coord_geoms is None: return cn_map[0] return ce.minimum_geometry(symmetry_measure_type=self._symmetry_measure_type)
[docs] def get_site_coordination_environments_fractions( self, site, isite=None, dequivsite=None, dthissite=None, mysym=None, ordered=True, min_fraction=0.0, return_maps=True, return_strategy_dict_info=False, ): """Get the coordination environments of a given site and additional information. :param site: Site for which coordination environment is needed. :param isite: Index of the site. :param dequivsite: Translation of the equivalent site. :param dthissite: Translation of this site. :param mysym: Symmetry to be applied. :param ordered: Whether to order the list by fractions. :param min_fraction: Minimum fraction to include in the list :param return_maps: Whether to return cn_maps (identifies all the NeighborsSet used). :param return_strategy_dict_info: Whether to add the info about the strategy used. :return: List of Dict with coordination environment, fraction and additional info. """ if isite is None or dequivsite is None or dthissite is None or mysym is None: [ isite, dequivsite, dthissite, mysym, ] = self.equivalent_site_index_and_transform(site) site_nb_sets = self.structure_environments.neighbors_sets[isite] if site_nb_sets is None: return None ce_and_map = self.get_site_coordination_environment( site=site, isite=isite, dequivsite=dequivsite, dthissite=dthissite, mysym=mysym, return_map=True, ) if ce_and_map is None: return None ce, ce_map = ce_and_map if ce is None: ce_dict = { "ce_symbol": "UNKNOWN:{:d}".format(ce_map[0]), "ce_dict": None, "ce_fraction": 1.0, } else: ce_dict = {"ce_symbol": ce[0], "ce_dict": ce[1], "ce_fraction": 1.0} if return_maps: ce_dict["ce_map"] = ce_map if return_strategy_dict_info: ce_dict["strategy_info"] = {} fractions_info_list = [ce_dict] return fractions_info_list
[docs] def get_site_coordination_environments( self, site, isite=None, dequivsite=None, dthissite=None, mysym=None, return_maps=False, ): """Get the coordination environments of a given site. :param site: Site for which coordination environment is needed. :param isite: Index of the site. :param dequivsite: Translation of the equivalent site. :param dthissite: Translation of this site. :param mysym: Symmetry to be applied. :param return_maps: Whether to return cn_maps (identifies all the NeighborsSet used). :return: List of coordination environment. """ return [ self.get_site_coordination_environment( site=site, isite=isite, dequivsite=dequivsite, dthissite=dthissite, mysym=mysym, return_map=return_maps, ) ]
[docs] def add_strategy_visualization_to_subplot(self, subplot, visualization_options=None, plot_type=None): """Add a visual of the strategy on a distance-angle plot. :param subplot: Axes object onto the visual should be added. :param visualization_options: Options for the visual. :param plot_type: Type of distance-angle plot. :return: None """ subplot.plot( self._distance_cutoff, self._angle_cutoff, "o", mec=None, mfc="w", markersize=12, ) subplot.plot(self._distance_cutoff, self._angle_cutoff, "x", linewidth=2, markersize=12)
def __eq__(self, other): return ( self.__class__.__name__ == other.__class__.__name__ and self._distance_cutoff == other._distance_cutoff and self._angle_cutoff == other._angle_cutoff and self._additional_condition == other._additional_condition and self._continuous_symmetry_measure_cutoff == other._continuous_symmetry_measure_cutoff and self.symmetry_measure_type == other.symmetry_measure_type )
[docs] def as_dict(self): """ Bson-serializable dict representation of the SimplestChemenvStrategy object. :return: Bson-serializable dict representation of the SimplestChemenvStrategy object. """ return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "distance_cutoff": float(self._distance_cutoff), "angle_cutoff": float(self._angle_cutoff), "additional_condition": int(self._additional_condition), "continuous_symmetry_measure_cutoff": float(self._continuous_symmetry_measure_cutoff), "symmetry_measure_type": self._symmetry_measure_type, }
[docs] @classmethod def from_dict(cls, d): """ Reconstructs the SimplestChemenvStrategy object from a dict representation of the SimplestChemenvStrategy object created using the as_dict method. :param d: dict representation of the SimplestChemenvStrategy object :return: StructureEnvironments object """ return cls( distance_cutoff=d["distance_cutoff"], angle_cutoff=d["angle_cutoff"], additional_condition=d["additional_condition"], continuous_symmetry_measure_cutoff=d["continuous_symmetry_measure_cutoff"], symmetry_measure_type=d["symmetry_measure_type"], )
[docs]class SimpleAbundanceChemenvStrategy(AbstractChemenvStrategy): """ Simple ChemenvStrategy using the neighbors that are the most "abundant" in the grid of angle and distance parameters for the definition of neighbors in the Voronoi approach. The coordination environment is then given as the one with the lowest continuous symmetry measure """ DEFAULT_MAX_DIST = 2.0 DEFAULT_ADDITIONAL_CONDITION = AbstractChemenvStrategy.AC.ONLY_ACB STRATEGY_OPTIONS = OrderedDict() # type: Dict[str, Dict] STRATEGY_OPTIONS["additional_condition"] = { "type": AdditionalConditionInt, "internal": "_additional_condition", "default": DEFAULT_ADDITIONAL_CONDITION, } STRATEGY_OPTIONS["surface_calculation_type"] = {} STRATEGY_DESCRIPTION = ( ' Simple Abundance ChemenvStrategy using the most "abundant" neighbors map \n' " for the definition of neighbors in the Voronoi approach. \n" " The coordination environment is then given as the one with the \n" " lowest continuous symmetry measure." ) def __init__( self, structure_environments=None, additional_condition=AbstractChemenvStrategy.AC.ONLY_ACB, symmetry_measure_type=AbstractChemenvStrategy.DEFAULT_SYMMETRY_MEASURE_TYPE, ): """ Constructor for the SimpleAbundanceChemenvStrategy. :param structure_environments: StructureEnvironments object containing all the information on the coordination of the sites in a structure """ raise NotImplementedError("SimpleAbundanceChemenvStrategy not yet implemented") AbstractChemenvStrategy.__init__(self, structure_environments, symmetry_measure_type=symmetry_measure_type) self._additional_condition = additional_condition @property def uniquely_determines_coordination_environments(self): """Whether this strategy uniquely determines coordination environments.""" return True
[docs] def get_site_neighbors(self, site): """Get the neighbors of a given site with this strategy. :param site: Periodic site. :return: List of neighbors of site. """ [ isite, dequivsite, dthissite, mysym, ] = self.equivalent_site_index_and_transform(site) cn_map = self._get_map(isite) eqsite_ps = self.structure_environments.unique_coordinated_neighbors(isite, cn_map=cn_map) coordinated_neighbors = [] for ips, ps in enumerate(eqsite_ps): coords = mysym.operate(ps.frac_coords + dequivsite) + dthissite ps_site = PeriodicSite(ps._species, coords, ps._lattice) coordinated_neighbors.append(ps_site) return coordinated_neighbors
[docs] def get_site_coordination_environment( self, site, isite=None, dequivsite=None, dthissite=None, mysym=None, return_map=False, ): """Get the coordination environment of a given site. :param site: Site for which coordination environment is needed. :param isite: Index of the site. :param dequivsite: Translation of the equivalent site. :param dthissite: Translation of this site. :param mysym: Symmetry to be applied. :param return_map: Whether to return cn_map (identifies the NeighborsSet used). :return: Coordination environment of site. """ if isite is None: [ isite, dequivsite, dthissite, mysym, ] = self.equivalent_site_index_and_transform(site) cn_map = self._get_map(isite) if cn_map is None: return None coord_geoms = self.structure_environments.ce_list[self.structure_environments.sites_map[isite]][cn_map[0]][ cn_map[1] ] if return_map: if coord_geoms is None: return cn_map[0], cn_map return ( coord_geoms.minimum_geometry(symmetry_measure_type=self._symmetry_measure_type), cn_map, ) if coord_geoms is None: return cn_map[0] return coord_geoms.minimum_geometry(symmetry_measure_type=self._symmetry_measure_type)
[docs] def get_site_coordination_environments( self, site, isite=None, dequivsite=None, dthissite=None, mysym=None, return_maps=False, ): """Get the coordination environments of a given site. :param site: Site for which coordination environment is needed. :param isite: Index of the site. :param dequivsite: Translation of the equivalent site. :param dthissite: Translation of this site. :param mysym: Symmetry to be applied. :param return_maps: Whether to return cn_maps (identifies all the NeighborsSet used). :return: List of coordination environment. """ return [ self.get_site_coordination_environment( site=site, isite=isite, dequivsite=dequivsite, dthissite=dthissite, mysym=mysym, return_map=return_maps, ) ]
def _get_map(self, isite): maps_and_surfaces = self._get_maps_surfaces(isite) if maps_and_surfaces is None: return None surface_max = 0.0 imax = -1 for ii, map_and_surface in enumerate(maps_and_surfaces): all_additional_conditions = [ac[2] for ac in map_and_surface["parameters_indices"]] if self._additional_condition in all_additional_conditions and map_and_surface["surface"] > surface_max: surface_max = map_and_surface["surface"] imax = ii return maps_and_surfaces[imax]["map"] def _get_maps_surfaces(self, isite, surface_calculation_type=None): if surface_calculation_type is None: surface_calculation_type = { "distance_parameter": ("initial_normalized", None), "angle_parameter": ("initial_normalized", None), } return self.structure_environments.voronoi.maps_and_surfaces( isite=isite, surface_calculation_type=surface_calculation_type, max_dist=self.DEFAULT_MAX_DIST, ) def __eq__(self, other): return ( self.__class__.__name__ == other.__class__.__name__ and self._additional_condition == other.additional_condition )
[docs] def as_dict(self): """ Bson-serializable dict representation of the SimpleAbundanceChemenvStrategy object. :return: Bson-serializable dict representation of the SimpleAbundanceChemenvStrategy object. """ return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "additional_condition": self._additional_condition, }
[docs] @classmethod def from_dict(cls, d): """ Reconstructs the SimpleAbundanceChemenvStrategy object from a dict representation of the SimpleAbundanceChemenvStrategy object created using the as_dict method. :param d: dict representation of the SimpleAbundanceChemenvStrategy object :return: StructureEnvironments object """ return cls(additional_condition=d["additional_condition"])
[docs]class TargettedPenaltiedAbundanceChemenvStrategy(SimpleAbundanceChemenvStrategy): """ Simple ChemenvStrategy using the neighbors that are the most "abundant" in the grid of angle and distance parameters for the definition of neighbors in the Voronoi approach, with a bias for a given list of target environments. This can be useful in the case of, e.g. connectivity search of some given environment. The coordination environment is then given as the one with the lowest continuous symmetry measure """ DEFAULT_TARGET_ENVIRONMENTS = ["O:6"] def __init__( self, structure_environments=None, truncate_dist_ang=True, additional_condition=AbstractChemenvStrategy.AC.ONLY_ACB, max_nabundant=5, target_environments=DEFAULT_TARGET_ENVIRONMENTS, target_penalty_type="max_csm", max_csm=5.0, symmetry_measure_type=AbstractChemenvStrategy.DEFAULT_SYMMETRY_MEASURE_TYPE, ): """Initializes strategy. Not yet implemented. :param structure_environments: :param truncate_dist_ang: :param additional_condition: :param max_nabundant: :param target_environments: :param target_penalty_type: :param max_csm: :param symmetry_measure_type: """ raise NotImplementedError("TargettedPenaltiedAbundanceChemenvStrategy not yet implemented") SimpleAbundanceChemenvStrategy.__init__( self, structure_environments, additional_condition=additional_condition, symmetry_measure_type=symmetry_measure_type, ) self.max_nabundant = max_nabundant self.target_environments = target_environments self.target_penalty_type = target_penalty_type self.max_csm = max_csm
[docs] def get_site_coordination_environment( self, site, isite=None, dequivsite=None, dthissite=None, mysym=None, return_map=False, ): """Get the coordination environment of a given site. :param site: Site for which coordination environment is needed. :param isite: Index of the site. :param dequivsite: Translation of the equivalent site. :param dthissite: Translation of this site. :param mysym: Symmetry to be applied. :param return_map: Whether to return cn_map (identifies the NeighborsSet used). :return: Coordination environment of site. """ if isite is None: [ isite, dequivsite, dthissite, mysym, ] = self.equivalent_site_index_and_transform(site) cn_map = self._get_map(isite) if cn_map is None: return None chemical_environments = self.structure_environments.ce_list[self.structure_environments.sites_map[isite]][ cn_map[0] ][cn_map[1]] if return_map: if chemical_environments.coord_geoms is None or len(chemical_environments) == 0: return cn_map[0], cn_map return ( chemical_environments.minimum_geometry(symmetry_measure_type=self._symmetry_measure_type), cn_map, ) if chemical_environments.coord_geoms is None: return cn_map[0] return chemical_environments.minimum_geometry(symmetry_measure_type=self._symmetry_measure_type)
def _get_map(self, isite): maps_and_surfaces = SimpleAbundanceChemenvStrategy._get_maps_surfaces(self, isite) if maps_and_surfaces is None: return SimpleAbundanceChemenvStrategy._get_map(self, isite) current_map = None current_target_env_csm = 100.0 surfaces = [map_and_surface["surface"] for map_and_surface in maps_and_surfaces] order = np.argsort(surfaces)[::-1] target_cgs = [ AllCoordinationGeometries().get_geometry_from_mp_symbol(mp_symbol) for mp_symbol in self.target_environments ] target_cns = [cg.coordination_number for cg in target_cgs] for ii in range(min([len(maps_and_surfaces), self.max_nabundant])): my_map_and_surface = maps_and_surfaces[order[ii]] mymap = my_map_and_surface["map"] cn = mymap[0] if cn not in target_cns or cn > 12 or cn == 0: continue all_conditions = [params[2] for params in my_map_and_surface["parameters_indices"]] if self._additional_condition not in all_conditions: continue cg, cgdict = self.structure_environments.ce_list[self.structure_environments.sites_map[isite]][mymap[0]][ mymap[1] ].minimum_geometry(symmetry_measure_type=self._symmetry_measure_type) if ( cg in self.target_environments and cgdict["symmetry_measure"] <= self.max_csm and cgdict["symmetry_measure"] < current_target_env_csm ): current_map = mymap current_target_env_csm = cgdict["symmetry_measure"] if current_map is not None: return current_map return SimpleAbundanceChemenvStrategy._get_map(self, isite) @property def uniquely_determines_coordination_environments(self): """Whether this strategy uniquely determines coordination environments.""" return True
[docs] def as_dict(self): """ Bson-serializable dict representation of the TargettedPenaltiedAbundanceChemenvStrategy object. :return: Bson-serializable dict representation of the TargettedPenaltiedAbundanceChemenvStrategy object. """ return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "additional_condition": self._additional_condition, "max_nabundant": self.max_nabundant, "target_environments": self.target_environments, "target_penalty_type": self.target_penalty_type, "max_csm": self.max_csm, }
def __eq__(self, other): return ( self.__class__.__name__ == other.__class__.__name__ and self._additional_condition == other.additional_condition and self.max_nabundant == other.max_nabundant and self.target_environments == other.target_environments and self.target_penalty_type == other.target_penalty_type and self.max_csm == other.max_csm )
[docs] @classmethod def from_dict(cls, d): """ Reconstructs the TargettedPenaltiedAbundanceChemenvStrategy object from a dict representation of the TargettedPenaltiedAbundanceChemenvStrategy object created using the as_dict method. :param d: dict representation of the TargettedPenaltiedAbundanceChemenvStrategy object :return: TargettedPenaltiedAbundanceChemenvStrategy object """ return cls( additional_condition=d["additional_condition"], max_nabundant=d["max_nabundant"], target_environments=d["target_environments"], target_penalty_type=d["target_penalty_type"], max_csm=d["max_csm"], )
[docs]class NbSetWeight(MSONable, metaclass=abc.ABCMeta): """Abstract object for neighbors sets weights estimations."""
[docs] @abc.abstractmethod def as_dict(self): """ A JSON serializable dict representation of this neighbors set weight. """ pass
[docs] @abc.abstractmethod def weight(self, nb_set, structure_environments, cn_map=None, additional_info=None): """Get the weight of a given neighbors set. :param nb_set: Neighbors set. :param structure_environments: Structure environments used to estimate weight. :param cn_map: Mapping index for this neighbors set. :param additional_info: Additional information. :return: Weight of the neighbors set. """ pass
[docs]class AngleNbSetWeight(NbSetWeight): """Weight of neighbors set based on the angle.""" SHORT_NAME = "AngleWeight" def __init__(self, aa=1.0): """Initialize AngleNbSetWeight estimator. :param aa: Exponent of the angle for the estimator. """ self.aa = aa if self.aa == 1.0: self.aw = self.angle_sum else: self.aw = self.angle_sumn
[docs] def weight(self, nb_set, structure_environments, cn_map=None, additional_info=None): """Get the weight of a given neighbors set. :param nb_set: Neighbors set. :param structure_environments: Structure environments used to estimate weight. :param cn_map: Mapping index for this neighbors set. :param additional_info: Additional information. :return: Weight of the neighbors set. """ return self.aw(nb_set=nb_set)
[docs] @staticmethod def angle_sum(nb_set): """Sum of all angles in a neighbors set. :param nb_set: Neighbors set. :return: Sum of solid angles for the neighbors set. """ return np.sum(nb_set.angles) / (4.0 * np.pi)
[docs] def angle_sumn(self, nb_set): """Sum of all angles to a given power in a neighbors set. :param nb_set: Neighbors set. :return: Sum of solid angles to the power aa for the neighbors set. """ return np.power(self.angle_sum(nb_set=nb_set), self.aa)
def __eq__(self, other): return self.aa == other.aa def __ne__(self, other): return not self == other
[docs] def as_dict(self): """MSONAble dict""" return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "aa": self.aa, }
[docs] @classmethod def from_dict(cls, dd): """ From dict :param dd: :return: """ return cls(aa=dd["aa"])
[docs]class NormalizedAngleDistanceNbSetWeight(NbSetWeight): """Weight of neighbors set based on the normalized angle/distance.""" SHORT_NAME = "NormAngleDistWeight" def __init__(self, average_type, aa, bb): """Initialize NormalizedAngleDistanceNbSetWeight. :param average_type: Average function. :param aa: Exponent for the angle values. :param bb: Exponent for the distance values. """ self.average_type = average_type if self.average_type == "geometric": self.eval = self.gweight elif self.average_type == "arithmetic": self.eval = self.aweight else: raise ValueError( 'Average type is "{}" while it should be ' '"geometric" or "arithmetic"'.format(average_type) ) self.aa = aa self.bb = bb if self.aa == 0: if self.bb == 1: self.fda = self.invdist elif self.bb == 0: raise ValueError("Both exponents are 0.") else: self.fda = self.invndist elif self.bb == 0: if self.aa == 1: self.fda = self.ang else: self.fda = self.angn else: if self.aa == 1: if self.bb == 1: self.fda = self.anginvdist else: self.fda = self.anginvndist else: if self.bb == 1: self.fda = self.angninvdist else: self.fda = self.angninvndist def __eq__(self, other): return self.average_type == other.average_type and self.aa == other.aa and self.bb == other.bb def __ne__(self, other): return not self == other
[docs] def as_dict(self): """MSONable dict""" return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "average_type": self.average_type, "aa": self.aa, "bb": self.bb, }
[docs] @classmethod def from_dict(cls, dd): """Initialize from dict. :param dd: Dict representation of NormalizedAngleDistanceNbSetWeight. :return: NormalizedAngleDistanceNbSetWeight. """ return cls(average_type=dd["average_type"], aa=dd["aa"], bb=dd["bb"])
[docs] @staticmethod def invdist(nb_set): """Inverse distance weight. :param nb_set: Neighbors set. :return: List of inverse distances. """ return [1.0 / dist for dist in nb_set.normalized_distances]
[docs] def invndist(self, nb_set): """Inverse power distance weight. :param nb_set: Neighbors set. :return: List of inverse power distances. """ return [1.0 / dist ** self.bb for dist in nb_set.normalized_distances]
[docs] @staticmethod def ang(nb_set): """Angle weight. :param nb_set: Neighbors set. :return: List of angle weights. """ return nb_set.normalized_angles
[docs] def angn(self, nb_set): """Power angle weight. :param nb_set: Neighbors set. :return: List of power angle weights. """ return [ang ** self.aa for ang in nb_set.normalized_angles]
[docs] @staticmethod def anginvdist(nb_set): """Angle/distance weight. :param nb_set: Neighbors set. :return: List of angle/distance weights. """ nangles = nb_set.normalized_angles return [nangles[ii] / dist for ii, dist in enumerate(nb_set.normalized_distances)]
[docs] def anginvndist(self, nb_set): """Angle/power distance weight. :param nb_set: Neighbors set. :return: List of angle/power distance weights. """ nangles = nb_set.normalized_angles return [nangles[ii] / dist ** self.bb for ii, dist in enumerate(nb_set.normalized_distances)]
[docs] def angninvdist(self, nb_set): """Power angle/distance weight. :param nb_set: Neighbors set. :return: List of power angle/distance weights. """ nangles = nb_set.normalized_angles return [nangles[ii] ** self.aa / dist for ii, dist in enumerate(nb_set.normalized_distances)]
[docs] def angninvndist(self, nb_set): """Power angle/power distance weight. :param nb_set: Neighbors set. :return: List of power angle/power distance weights. """ nangles = nb_set.normalized_angles return [nangles[ii] ** self.aa / dist ** self.bb for ii, dist in enumerate(nb_set.normalized_distances)]
[docs] def weight(self, nb_set, structure_environments, cn_map=None, additional_info=None): """Get the weight of a given neighbors set. :param nb_set: Neighbors set. :param structure_environments: Structure environments used to estimate weight. :param cn_map: Mapping index for this neighbors set. :param additional_info: Additional information. :return: Weight of the neighbors set. """ fda_list = self.fda(nb_set=nb_set) return self.eval(fda_list=fda_list)
[docs] @staticmethod def gweight(fda_list): """Geometric mean of the weights. :param fda_list: List of estimator weights for each neighbor. :return: Geometric mean of the weights. """ return gmean(fda_list)
[docs] @staticmethod def aweight(fda_list): """Standard mean of the weights. :param fda_list: List of estimator weights for each neighbor. :return: Standard mean of the weights. """ return np.mean(fda_list)
[docs]def get_effective_csm( nb_set, cn_map, structure_environments, additional_info, symmetry_measure_type, max_effective_csm, effective_csm_estimator_ratio_function, ): """Get the effective continuous symmetry measure of a given neighbors set. :param nb_set: Neighbors set. :param cn_map: Mapping index of this neighbors set. :param structure_environments: Structure environments. :param additional_info: Additional information for the neighbors set. :param symmetry_measure_type: Type of symmetry measure to be used in the effective CSM. :param max_effective_csm: Max CSM to use for the effective CSM calculation. :param effective_csm_estimator_ratio_function: Ratio function to use to compute effective CSM. :return: Effective CSM of a given Neighbors set. """ try: effective_csm = additional_info["effective_csms"][nb_set.isite][cn_map] except KeyError: site_ce_list = structure_environments.ce_list[nb_set.isite] site_chemenv = site_ce_list[cn_map[0]][cn_map[1]] if site_chemenv is None: effective_csm = 100.0 else: mingeoms = site_chemenv.minimum_geometries( symmetry_measure_type=symmetry_measure_type, max_csm=max_effective_csm ) if len(mingeoms) == 0: effective_csm = 100.0 else: csms = [ ce_dict["other_symmetry_measures"][symmetry_measure_type] for mp_symbol, ce_dict in mingeoms if ce_dict["other_symmetry_measures"][symmetry_measure_type] <= max_effective_csm ] effective_csm = effective_csm_estimator_ratio_function.mean_estimator(csms) set_info( additional_info=additional_info, field="effective_csms", isite=nb_set.isite, cn_map=cn_map, value=effective_csm, ) return effective_csm
[docs]def set_info(additional_info, field, isite, cn_map, value): """Set additional information for the weights. :param additional_info: Additional information. :param field: Type of additional information. :param isite: Index of site to add info. :param cn_map: Mapping index of the neighbors set. :param value: Value of this additional information. :return: None """ try: additional_info[field][isite][cn_map] = value except KeyError: try: additional_info[field][isite] = {cn_map: value} except KeyError: additional_info[field] = {isite: {cn_map: value}}
[docs]class SelfCSMNbSetWeight(NbSetWeight): """Weight of neighbors set based on the Self CSM.""" SHORT_NAME = "SelfCSMWeight" DEFAULT_EFFECTIVE_CSM_ESTIMATOR = { "function": "power2_inverse_decreasing", "options": {"max_csm": 8.0}, } DEFAULT_WEIGHT_ESTIMATOR = { "function": "power2_decreasing_exp", "options": {"max_csm": 8.0, "alpha": 1.0}, } DEFAULT_SYMMETRY_MEASURE_TYPE = "csm_wcs_ctwcc" def __init__( self, effective_csm_estimator=DEFAULT_EFFECTIVE_CSM_ESTIMATOR, weight_estimator=DEFAULT_WEIGHT_ESTIMATOR, symmetry_measure_type=DEFAULT_SYMMETRY_MEASURE_TYPE, ): """Initialize SelfCSMNbSetWeight. :param effective_csm_estimator: Ratio function used for the effective CSM (comparison between neighbors sets). :param weight_estimator: Weight estimator within a given neighbors set. :param symmetry_measure_type: Type of symmetry measure to be used. """ self.effective_csm_estimator = effective_csm_estimator self.effective_csm_estimator_rf = CSMInfiniteRatioFunction.from_dict(effective_csm_estimator) self.weight_estimator = weight_estimator self.weight_estimator_rf = CSMFiniteRatioFunction.from_dict(weight_estimator) self.symmetry_measure_type = symmetry_measure_type self.max_effective_csm = self.effective_csm_estimator["options"]["max_csm"]
[docs] def weight(self, nb_set, structure_environments, cn_map=None, additional_info=None): """Get the weight of a given neighbors set. :param nb_set: Neighbors set. :param structure_environments: Structure environments used to estimate weight. :param cn_map: Mapping index for this neighbors set. :param additional_info: Additional information. :return: Weight of the neighbors set. """ effective_csm = get_effective_csm( nb_set=nb_set, cn_map=cn_map, structure_environments=structure_environments, additional_info=additional_info, symmetry_measure_type=self.symmetry_measure_type, max_effective_csm=self.max_effective_csm, effective_csm_estimator_ratio_function=self.effective_csm_estimator_rf, ) weight = self.weight_estimator_rf.evaluate(effective_csm) set_info( additional_info=additional_info, field="self_csms_weights", isite=nb_set.isite, cn_map=cn_map, value=weight, ) return weight
def __eq__(self, other): return ( self.effective_csm_estimator == other.effective_csm_estimator and self.weight_estimator == other.weight_estimator and self.symmetry_measure_type == other.symmetry_measure_type ) def __ne__(self, other): return not self == other
[docs] def as_dict(self): """MSONable dict""" return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "effective_csm_estimator": self.effective_csm_estimator, "weight_estimator": self.weight_estimator, "symmetry_measure_type": self.symmetry_measure_type, }
[docs] @classmethod def from_dict(cls, dd): """Initialize from dict. :param dd: Dict representation of SelfCSMNbSetWeight. :return: SelfCSMNbSetWeight. """ return cls( effective_csm_estimator=dd["effective_csm_estimator"], weight_estimator=dd["weight_estimator"], symmetry_measure_type=dd["symmetry_measure_type"], )
[docs]class DeltaCSMNbSetWeight(NbSetWeight): """Weight of neighbors set based on the differences of CSM.""" SHORT_NAME = "DeltaCSMWeight" DEFAULT_EFFECTIVE_CSM_ESTIMATOR = { "function": "power2_inverse_decreasing", "options": {"max_csm": 8.0}, } DEFAULT_SYMMETRY_MEASURE_TYPE = "csm_wcs_ctwcc" DEFAULT_WEIGHT_ESTIMATOR = { "function": "smootherstep", "options": {"delta_csm_min": 0.5, "delta_csm_max": 3.0}, } def __init__( self, effective_csm_estimator=DEFAULT_EFFECTIVE_CSM_ESTIMATOR, weight_estimator=DEFAULT_WEIGHT_ESTIMATOR, delta_cn_weight_estimators=None, symmetry_measure_type=DEFAULT_SYMMETRY_MEASURE_TYPE, ): """Initialize SelfCSMNbSetWeight. :param effective_csm_estimator: Ratio function used for the effective CSM (comparison between neighbors sets). :param weight_estimator: Weight estimator within a given neighbors set. :param delta_cn_weight_estimators: Specific weight estimators for specific cn :param symmetry_measure_type: Type of symmetry measure to be used. """ self.effective_csm_estimator = effective_csm_estimator self.effective_csm_estimator_rf = CSMInfiniteRatioFunction.from_dict(effective_csm_estimator) self.weight_estimator = weight_estimator if self.weight_estimator is not None: self.weight_estimator_rf = DeltaCSMRatioFunction.from_dict(weight_estimator) self.delta_cn_weight_estimators = delta_cn_weight_estimators self.delta_cn_weight_estimators_rfs = {} if delta_cn_weight_estimators is not None: for delta_cn, dcn_w_estimator in delta_cn_weight_estimators.items(): self.delta_cn_weight_estimators_rfs[delta_cn] = DeltaCSMRatioFunction.from_dict(dcn_w_estimator) self.symmetry_measure_type = symmetry_measure_type self.max_effective_csm = self.effective_csm_estimator["options"]["max_csm"]
[docs] def weight(self, nb_set, structure_environments, cn_map=None, additional_info=None): """Get the weight of a given neighbors set. :param nb_set: Neighbors set. :param structure_environments: Structure environments used to estimate weight. :param cn_map: Mapping index for this neighbors set. :param additional_info: Additional information. :return: Weight of the neighbors set. """ effcsm = get_effective_csm( nb_set=nb_set, cn_map=cn_map, structure_environments=structure_environments, additional_info=additional_info, symmetry_measure_type=self.symmetry_measure_type, max_effective_csm=self.max_effective_csm, effective_csm_estimator_ratio_function=self.effective_csm_estimator_rf, ) cn = cn_map[0] isite = nb_set.isite delta_csm = None delta_csm_cn_map2 = None nb_set_weight = 1.0 for cn2, nb_sets in structure_environments.neighbors_sets[isite].items(): if cn2 < cn: continue for inb_set2, nb_set2 in enumerate(nb_sets): if cn == cn2: continue effcsm2 = get_effective_csm( nb_set=nb_set2, cn_map=(cn2, inb_set2), structure_environments=structure_environments, additional_info=additional_info, symmetry_measure_type=self.symmetry_measure_type, max_effective_csm=self.max_effective_csm, effective_csm_estimator_ratio_function=self.effective_csm_estimator_rf, ) this_delta_csm = effcsm2 - effcsm if cn2 == cn: if this_delta_csm < 0.0: set_info( additional_info=additional_info, field="delta_csms", isite=isite, cn_map=cn_map, value=this_delta_csm, ) set_info( additional_info=additional_info, field="delta_csms_weights", isite=isite, cn_map=cn_map, value=0.0, ) set_info( additional_info=additional_info, field="delta_csms_cn_map2", isite=isite, cn_map=cn_map, value=(cn2, inb_set2), ) return 0.0 else: dcn = cn2 - cn if dcn in self.delta_cn_weight_estimators_rfs: this_delta_csm_weight = self.delta_cn_weight_estimators_rfs[dcn].evaluate(this_delta_csm) else: this_delta_csm_weight = self.weight_estimator_rf.evaluate(this_delta_csm) if this_delta_csm_weight < nb_set_weight: delta_csm = this_delta_csm delta_csm_cn_map2 = (cn2, inb_set2) nb_set_weight = this_delta_csm_weight set_info( additional_info=additional_info, field="delta_csms", isite=isite, cn_map=cn_map, value=delta_csm, ) set_info( additional_info=additional_info, field="delta_csms_weights", isite=isite, cn_map=cn_map, value=nb_set_weight, ) set_info( additional_info=additional_info, field="delta_csms_cn_map2", isite=isite, cn_map=cn_map, value=delta_csm_cn_map2, ) return nb_set_weight
def __eq__(self, other): return ( self.effective_csm_estimator == other.effective_csm_estimator and self.weight_estimator == other.weight_estimator and self.delta_cn_weight_estimators == other.delta_cn_weight_estimators and self.symmetry_measure_type == other.symmetry_measure_type ) def __ne__(self, other): return not self == other
[docs] @classmethod def delta_cn_specifics( cls, delta_csm_mins=None, delta_csm_maxs=None, function="smootherstep", symmetry_measure_type="csm_wcs_ctwcc", effective_csm_estimator=DEFAULT_EFFECTIVE_CSM_ESTIMATOR, ): """Initializes DeltaCSMNbSetWeight from specific coordination number differences. :param delta_csm_mins: Minimums for each coordination number. :param delta_csm_maxs: Maximums for each coordination number. :param function: Ratio function used. :param symmetry_measure_type: Type of symmetry measure to be used. :param effective_csm_estimator: Ratio function used for the effective CSM (comparison between neighbors sets). :return: DeltaCSMNbSetWeight. """ if delta_csm_mins is None or delta_csm_maxs is None: delta_cn_weight_estimators = { dcn: { "function": function, "options": { "delta_csm_min": 0.25 + dcn * 0.25, "delta_csm_max": 5.0 + dcn * 0.25, }, } for dcn in range(1, 13) } else: delta_cn_weight_estimators = { dcn: { "function": function, "options": { "delta_csm_min": delta_csm_mins[dcn - 1], "delta_csm_max": delta_csm_maxs[dcn - 1], }, } for dcn in range(1, 13) } return cls( effective_csm_estimator=effective_csm_estimator, weight_estimator={ "function": function, "options": { "delta_csm_min": delta_cn_weight_estimators[12]["options"]["delta_csm_min"], "delta_csm_max": delta_cn_weight_estimators[12]["options"]["delta_csm_max"], }, }, delta_cn_weight_estimators=delta_cn_weight_estimators, symmetry_measure_type=symmetry_measure_type, )
[docs] def as_dict(self): """ MSONable dict. :return: """ return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "effective_csm_estimator": self.effective_csm_estimator, "weight_estimator": self.weight_estimator, "delta_cn_weight_estimators": self.delta_cn_weight_estimators, "symmetry_measure_type": self.symmetry_measure_type, }
[docs] @classmethod def from_dict(cls, dd): """Initialize from dict. :param dd: Dict representation of DeltaCSMNbSetWeight. :return: DeltaCSMNbSetWeight. """ return cls( effective_csm_estimator=dd["effective_csm_estimator"], weight_estimator=dd["weight_estimator"], delta_cn_weight_estimators={ int(dcn): dcn_estimator for dcn, dcn_estimator in dd["delta_cn_weight_estimators"].items() } if ("delta_cn_weight_estimators" in dd and dd["delta_cn_weight_estimators"] is not None) else None, symmetry_measure_type=dd["symmetry_measure_type"], )
[docs]class CNBiasNbSetWeight(NbSetWeight): """Weight of neighbors set based on specific biases towards specific coordination numbers.""" SHORT_NAME = "CNBiasWeight" def __init__(self, cn_weights, initialization_options): """Initialize CNBiasNbSetWeight. :param cn_weights: Weights for each coordination. :param initialization_options: Options for initialization. """ self.cn_weights = cn_weights self.initialization_options = initialization_options
[docs] def weight(self, nb_set, structure_environments, cn_map=None, additional_info=None): """Get the weight of a given neighbors set. :param nb_set: Neighbors set. :param structure_environments: Structure environments used to estimate weight. :param cn_map: Mapping index for this neighbors set. :param additional_info: Additional information. :return: Weight of the neighbors set. """ return self.cn_weights[len(nb_set)]
def __eq__(self, other): return self.cn_weights == other.cn_weights and self.initialization_options == other.initialization_options def __ne__(self, other): return not self == other
[docs] def as_dict(self): """MSONable dict""" return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "cn_weights": {str(cn): cnw for cn, cnw in self.cn_weights.items()}, "initialization_options": self.initialization_options, }
[docs] @classmethod def from_dict(cls, dd): """Initialize from dict. :param dd: Dict representation of CNBiasNbSetWeight. :return: CNBiasNbSetWeight. """ return cls( cn_weights={int(cn): cnw for cn, cnw in dd["cn_weights"].items()}, initialization_options=dd["initialization_options"], )
[docs] @classmethod def linearly_equidistant(cls, weight_cn1, weight_cn13): """Initializes linearly equidistant weights for each coordination. :param weight_cn1: Weight of coordination 1. :param weight_cn13: Weight of coordination 13. :return: CNBiasNbSetWeight. """ initialization_options = { "type": "linearly_equidistant", "weight_cn1": weight_cn1, "weight_cn13": weight_cn13, } dw = (weight_cn13 - weight_cn1) / 12.0 cn_weights = {cn: weight_cn1 + (cn - 1) * dw for cn in range(1, 14)} return cls(cn_weights=cn_weights, initialization_options=initialization_options)
[docs] @classmethod def geometrically_equidistant(cls, weight_cn1, weight_cn13): """Initializes geometrically equidistant weights for each coordination. :param weight_cn1: Weight of coordination 1. :param weight_cn13: Weight of coordination 13. :return: CNBiasNbSetWeight. """ initialization_options = { "type": "geometrically_equidistant", "weight_cn1": weight_cn1, "weight_cn13": weight_cn13, } factor = np.power(float(weight_cn13) / weight_cn1, 1.0 / 12.0) cn_weights = {cn: weight_cn1 * np.power(factor, cn - 1) for cn in range(1, 14)} return cls(cn_weights=cn_weights, initialization_options=initialization_options)
[docs] @classmethod def explicit(cls, cn_weights): """Initializes weights explicitly for each coordination. :param cn_weights: Weights for each coordination. :return: CNBiasNbSetWeight. """ initialization_options = {"type": "explicit"} if set(cn_weights.keys()) != set(range(1, 14)): raise ValueError("Weights should be provided for CN 1 to 13") return cls(cn_weights=cn_weights, initialization_options=initialization_options)
[docs] @classmethod def from_description(cls, dd): """Initializes weights from description. :param dd: Dictionary description. :return: CNBiasNbSetWeight. """ if dd["type"] == "linearly_equidistant": return cls.linearly_equidistant(weight_cn1=dd["weight_cn1"], weight_cn13=dd["weight_cn13"]) if dd["type"] == "geometrically_equidistant": return cls.geometrically_equidistant(weight_cn1=dd["weight_cn1"], weight_cn13=dd["weight_cn13"]) if dd["type"] == "explicit": return cls.explicit(cn_weights=dd["cn_weights"]) return None
[docs]class DistanceAngleAreaNbSetWeight(NbSetWeight): """Weight of neighbors set based on the area in the distance-angle space.""" SHORT_NAME = "DistAngleAreaWeight" AC = AdditionalConditions() DEFAULT_SURFACE_DEFINITION = { "type": "standard_elliptic", "distance_bounds": {"lower": 1.2, "upper": 1.8}, "angle_bounds": {"lower": 0.1, "upper": 0.8}, } def __init__( self, weight_type="has_intersection", surface_definition=DEFAULT_SURFACE_DEFINITION, nb_sets_from_hints="fallback_to_source", other_nb_sets="0_weight", additional_condition=AC.ONLY_ACB, smoothstep_distance=None, smoothstep_angle=None, ): """Initialize CNBiasNbSetWeight. :param weight_type: Type of weight. :param surface_definition: Definition of the surface. :param nb_sets_from_hints: How to deal with neighbors sets obtained from "hints". :param other_nb_sets: What to do with other neighbors sets. :param additional_condition: Additional condition to be used. :param smoothstep_distance: Smoothstep distance. :param smoothstep_angle: Smoothstep angle. """ self.weight_type = weight_type if weight_type == "has_intersection": self.area_weight = self.w_area_has_intersection elif weight_type == "has_intersection_smoothstep": raise NotImplementedError() # self.area_weight = self.w_area_has_intersection_smoothstep else: raise ValueError('Weight type is "{}" while it should be "has_intersection"'.format(weight_type)) self.surface_definition = surface_definition self.nb_sets_from_hints = nb_sets_from_hints self.other_nb_sets = other_nb_sets self.additional_condition = additional_condition self.smoothstep_distance = smoothstep_distance self.smoothstep_angle = smoothstep_angle if self.nb_sets_from_hints == "fallback_to_source": if self.other_nb_sets == "0_weight": self.w_area_intersection_specific = self.w_area_intersection_nbsfh_fbs_onb0 else: raise ValueError('Other nb_sets should be "0_weight"') else: raise ValueError("Nb_sets from hints should fallback to source") lower_and_upper_functions = get_lower_and_upper_f(surface_calculation_options=surface_definition) self.dmin = surface_definition["distance_bounds"]["lower"] self.dmax = surface_definition["distance_bounds"]["upper"] self.amin = surface_definition["angle_bounds"]["lower"] self.amax = surface_definition["angle_bounds"]["upper"] self.f_lower = lower_and_upper_functions["lower"] self.f_upper = lower_and_upper_functions["upper"]
[docs] def weight(self, nb_set, structure_environments, cn_map=None, additional_info=None): """Get the weight of a given neighbors set. :param nb_set: Neighbors set. :param structure_environments: Structure environments used to estimate weight. :param cn_map: Mapping index for this neighbors set. :param additional_info: Additional information. :return: Weight of the neighbors set. """ return self.area_weight( nb_set=nb_set, structure_environments=structure_environments, cn_map=cn_map, additional_info=additional_info, )
[docs] def w_area_has_intersection_smoothstep(self, nb_set, structure_environments, cn_map, additional_info): """Get intersection of the neighbors set area with the surface. :param nb_set: Neighbors set. :param structure_environments: Structure environments. :param cn_map: Mapping index of the neighbors set. :param additional_info: Additional information. :return: Area intersection between neighbors set and surface. """ w_area = self.w_area_intersection_specific( nb_set=nb_set, structure_environments=structure_environments, cn_map=cn_map, additional_info=additional_info, ) if w_area > 0.0: if self.smoothstep_distance is not None: w_area = w_area if self.smoothstep_angle is not None: w_area = w_area return w_area
[docs] def w_area_has_intersection(self, nb_set, structure_environments, cn_map, additional_info): """Get intersection of the neighbors set area with the surface. :param nb_set: Neighbors set. :param structure_environments: Structure environments. :param cn_map: Mapping index of the neighbors set. :param additional_info: Additional information. :return: Area intersection between neighbors set and surface. """ return self.w_area_intersection_specific( nb_set=nb_set, structure_environments=structure_environments, cn_map=cn_map, additional_info=additional_info, )
[docs] def w_area_intersection_nbsfh_fbs_onb0(self, nb_set, structure_environments, cn_map, additional_info): """Get intersection of the neighbors set area with the surface. :param nb_set: Neighbors set. :param structure_environments: Structure environments. :param cn_map: Mapping index of the neighbors set. :param additional_info: Additional information. :return: Area intersection between neighbors set and surface. """ dist_ang_sources = [ src for src in nb_set.sources if src["origin"] == "dist_ang_ac_voronoi" and src["ac"] == self.additional_condition ] if len(dist_ang_sources) > 0: for src in dist_ang_sources: d1 = src["dp_dict"]["min"] d2 = src["dp_dict"]["next"] a1 = src["ap_dict"]["next"] a2 = src["ap_dict"]["max"] if self.rectangle_crosses_area(d1=d1, d2=d2, a1=a1, a2=a2): return 1.0 return 0.0 from_hints_sources = [src for src in nb_set.sources if src["origin"] == "nb_set_hints"] if len(from_hints_sources) == 0: return 0.0 if len(from_hints_sources) != 1: raise ValueError("Found multiple hints sources for nb_set") cn_map_src = from_hints_sources[0]["cn_map_source"] nb_set_src = structure_environments.neighbors_sets[nb_set.isite][cn_map_src[0]][cn_map_src[1]] dist_ang_sources = [ src for src in nb_set_src.sources if src["origin"] == "dist_ang_ac_voronoi" and src["ac"] == self.additional_condition ] if len(dist_ang_sources) == 0: return 0.0 for src in dist_ang_sources: d1 = src["dp_dict"]["min"] d2 = src["dp_dict"]["next"] a1 = src["ap_dict"]["next"] a2 = src["ap_dict"]["max"] if self.rectangle_crosses_area(d1=d1, d2=d2, a1=a1, a2=a2): return 1.0 return 0.0
[docs] def rectangle_crosses_area(self, d1, d2, a1, a2): """Whether a given rectangle crosses the area defined by the upper and lower curves. :param d1: lower d. :param d2: upper d. :param a1: lower a. :param a2: upper a. :return: """ # Case 1 if d1 <= self.dmin and d2 <= self.dmin: return False # Case 6 if d1 >= self.dmax and d2 >= self.dmax: return False # Case 2 if d1 <= self.dmin and d2 <= self.dmax: ld2 = self.f_lower(d2) if a2 <= ld2 or a1 >= self.amax: return False return True # Case 3 if d1 <= self.dmin and d2 >= self.dmax: if a2 <= self.amin or a1 >= self.amax: return False return True # Case 4 if self.dmin <= d1 <= self.dmax and self.dmin <= d2 <= self.dmax: ld1 = self.f_lower(d1) ld2 = self.f_lower(d2) if a2 <= ld1 and a2 <= ld2: return False ud1 = self.f_upper(d1) ud2 = self.f_upper(d2) if a1 >= ud1 and a1 >= ud2: return False return True # Case 5 if self.dmin <= d1 <= self.dmax and d2 >= self.dmax: ud1 = self.f_upper(d1) if a1 >= ud1 or a2 <= self.amin: return False return True raise ValueError("Should not reach this point!")
def __eq__(self, other): return ( self.weight_type == other.weight_type and self.surface_definition == other.surface_definition and self.nb_sets_from_hints == other.nb_sets_from_hints and self.other_nb_sets == other.other_nb_sets and self.additional_condition == other.additional_condition ) def __ne__(self, other): return not self == other
[docs] def as_dict(self): """MSONable dict""" return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "weight_type": self.weight_type, "surface_definition": self.surface_definition, "nb_sets_from_hints": self.nb_sets_from_hints, "other_nb_sets": self.other_nb_sets, "additional_condition": self.additional_condition, }
[docs] @classmethod def from_dict(cls, dd): """Initialize from dict. :param dd: Dict representation of DistanceAngleAreaNbSetWeight. :return: DistanceAngleAreaNbSetWeight. """ return cls( weight_type=dd["weight_type"], surface_definition=dd["surface_definition"], nb_sets_from_hints=dd["nb_sets_from_hints"], other_nb_sets=dd["other_nb_sets"], additional_condition=dd["additional_condition"], )
[docs]class DistancePlateauNbSetWeight(NbSetWeight): """Weight of neighbors set based on the distance.""" SHORT_NAME = "DistancePlateauWeight" def __init__(self, distance_function=None, weight_function=None): """Initialize DistancePlateauNbSetWeight. :param distance_function: Distance function to use. :param weight_function: Ratio function to use. """ if distance_function is None: self.distance_function = {"type": "normalized_distance"} else: self.distance_function = distance_function if weight_function is None: self.weight_function = { "function": "inverse_smootherstep", "options": {"lower": 0.2, "upper": 0.4}, } else: self.weight_function = weight_function self.weight_rf = RatioFunction.from_dict(self.weight_function)
[docs] def weight(self, nb_set, structure_environments, cn_map=None, additional_info=None): """Get the weight of a given neighbors set. :param nb_set: Neighbors set. :param structure_environments: Structure environments used to estimate weight. :param cn_map: Mapping index for this neighbors set. :param additional_info: Additional information. :return: Weight of the neighbors set. """ return self.weight_rf.eval(nb_set.distance_plateau())
def __eq__(self, other): return self.__class__ == other.__class__ def __ne__(self, other): return not self == other
[docs] def as_dict(self): """MSONable dict""" return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "distance_function": self.distance_function, "weight_function": self.weight_function, }
[docs] @classmethod def from_dict(cls, dd): """Initialize from dict. :param dd: Dict representation of DistancePlateauNbSetWeight. :return: DistancePlateauNbSetWeight. """ return cls( distance_function=dd["distance_function"], weight_function=dd["weight_function"], )
[docs]class AnglePlateauNbSetWeight(NbSetWeight): """Weight of neighbors set based on the angle.""" SHORT_NAME = "AnglePlateauWeight" def __init__(self, angle_function=None, weight_function=None): """Initialize AnglePlateauNbSetWeight. :param angle_function: Angle function to use. :param weight_function: Ratio function to use. """ if angle_function is None: self.angle_function = {"type": "normalized_angle"} else: self.angle_function = angle_function if weight_function is None: self.weight_function = { "function": "inverse_smootherstep", "options": {"lower": 0.05, "upper": 0.15}, } else: self.weight_function = weight_function self.weight_rf = RatioFunction.from_dict(self.weight_function)
[docs] def weight(self, nb_set, structure_environments, cn_map=None, additional_info=None): """Get the weight of a given neighbors set. :param nb_set: Neighbors set. :param structure_environments: Structure environments used to estimate weight. :param cn_map: Mapping index for this neighbors set. :param additional_info: Additional information. :return: Weight of the neighbors set. """ return self.weight_rf.eval(nb_set.angle_plateau())
def __eq__(self, other): return self.__class__ == other.__class__ def __ne__(self, other): return not self == other
[docs] def as_dict(self): """MSONable dict""" return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "angle_function": self.angle_function, "weight_function": self.weight_function, }
[docs] @classmethod def from_dict(cls, dd): """Initialize from dict. :param dd: Dict representation of AnglePlateauNbSetWeight. :return: AnglePlateauNbSetWeight. """ return cls(angle_function=dd["angle_function"], weight_function=dd["weight_function"])
[docs]class DistanceNbSetWeight(NbSetWeight): """Weight of neighbors set based on the distance.""" SHORT_NAME = "DistanceNbSetWeight" def __init__(self, weight_function=None, nbs_source="voronoi"): """Initialize DistanceNbSetWeight. :param weight_function: Ratio function to use. :param nbs_source: Source of the neighbors. """ if weight_function is None: self.weight_function = { "function": "smootherstep", "options": {"lower": 1.2, "upper": 1.3}, } else: self.weight_function = weight_function self.weight_rf = RatioFunction.from_dict(self.weight_function) if nbs_source not in ["nb_sets", "voronoi"]: raise ValueError('"nbs_source" should be one of ["nb_sets", "voronoi"]') self.nbs_source = nbs_source
[docs] def weight(self, nb_set, structure_environments, cn_map=None, additional_info=None): """Get the weight of a given neighbors set. :param nb_set: Neighbors set. :param structure_environments: Structure environments used to estimate weight. :param cn_map: Mapping index for this neighbors set. :param additional_info: Additional information. :return: Weight of the neighbors set. """ cn = cn_map[0] isite = nb_set.isite voronoi = structure_environments.voronoi.voronoi_list2[isite] if self.nbs_source == "nb_sets": all_nbs_voro_indices = set() for cn2, nb_sets in structure_environments.neighbors_sets[isite].items(): for inb_set2, nb_set2 in enumerate(nb_sets): if cn == cn2: continue all_nbs_voro_indices.update(nb_set2.site_voronoi_indices) elif self.nbs_source == "voronoi": all_nbs_voro_indices = set(range(len(voronoi))) else: raise ValueError('"nbs_source" should be one of ["nb_sets", "voronoi"]') all_nbs_indices_except_nb_set = all_nbs_voro_indices.difference(nb_set.site_voronoi_indices) normalized_distances = [voronoi[inb]["normalized_distance"] for inb in all_nbs_indices_except_nb_set] if len(normalized_distances) == 0: return 1.0 return self.weight_rf.eval(min(normalized_distances))
def __eq__(self, other): return self.__class__ == other.__class__ def __ne__(self, other): return not self == other
[docs] def as_dict(self): """MSOnable dict""" return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "weight_function": self.weight_function, "nbs_source": self.nbs_source, }
[docs] @classmethod def from_dict(cls, dd): """Initialize from dict. :param dd: Dict representation of DistanceNbSetWeight. :return: DistanceNbSetWeight. """ return cls(weight_function=dd["weight_function"], nbs_source=dd["nbs_source"])
[docs]class DeltaDistanceNbSetWeight(NbSetWeight): """Weight of neighbors set based on the difference of distances.""" SHORT_NAME = "DeltaDistanceNbSetWeight" def __init__(self, weight_function=None, nbs_source="voronoi"): """Initialize DeltaDistanceNbSetWeight. :param weight_function: Ratio function to use. :param nbs_source: Source of the neighbors. """ if weight_function is None: self.weight_function = { "function": "smootherstep", "options": {"lower": 0.1, "upper": 0.2}, } else: self.weight_function = weight_function self.weight_rf = RatioFunction.from_dict(self.weight_function) if nbs_source not in ["nb_sets", "voronoi"]: raise ValueError('"nbs_source" should be one of ["nb_sets", "voronoi"]') self.nbs_source = nbs_source
[docs] def weight(self, nb_set, structure_environments, cn_map=None, additional_info=None): """Get the weight of a given neighbors set. :param nb_set: Neighbors set. :param structure_environments: Structure environments used to estimate weight. :param cn_map: Mapping index for this neighbors set. :param additional_info: Additional information. :return: Weight of the neighbors set. """ cn = cn_map[0] isite = nb_set.isite voronoi = structure_environments.voronoi.voronoi_list2[isite] if self.nbs_source == "nb_sets": all_nbs_voro_indices = set() for cn2, nb_sets in structure_environments.neighbors_sets[isite].items(): for inb_set2, nb_set2 in enumerate(nb_sets): if cn == cn2: continue all_nbs_voro_indices.update(nb_set2.site_voronoi_indices) elif self.nbs_source == "voronoi": all_nbs_voro_indices = set(range(len(voronoi))) else: raise ValueError('"nbs_source" should be one of ["nb_sets", "voronoi"]') all_nbs_indices_except_nb_set = all_nbs_voro_indices.difference(nb_set.site_voronoi_indices) normalized_distances = [voronoi[inb]["normalized_distance"] for inb in all_nbs_indices_except_nb_set] if len(normalized_distances) == 0: return 1.0 if len(nb_set) == 0: return 0.0 nb_set_max_normalized_distance = max(nb_set.normalized_distances) return self.weight_rf.eval(min(normalized_distances) - nb_set_max_normalized_distance)
def __eq__(self, other): return self.__class__ == other.__class__ def __ne__(self, other): return not self == other
[docs] def as_dict(self): """MSONable dict""" return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "weight_function": self.weight_function, "nbs_source": self.nbs_source, }
[docs] @classmethod def from_dict(cls, dd): """Initialize from dict. :param dd: Dict representation of DeltaDistanceNbSetWeight. :return: DeltaDistanceNbSetWeight. """ return cls(weight_function=dd["weight_function"], nbs_source=dd["nbs_source"])
[docs]class WeightedNbSetChemenvStrategy(AbstractChemenvStrategy): """ WeightedNbSetChemenvStrategy """ STRATEGY_DESCRIPTION = " WeightedNbSetChemenvStrategy" DEFAULT_CE_ESTIMATOR = { "function": "power2_inverse_power2_decreasing", "options": {"max_csm": 8.0}, } def __init__( self, structure_environments=None, additional_condition=AbstractChemenvStrategy.AC.ONLY_ACB, symmetry_measure_type=AbstractChemenvStrategy.DEFAULT_SYMMETRY_MEASURE_TYPE, nb_set_weights=None, ce_estimator=DEFAULT_CE_ESTIMATOR, ): """ Constructor for the WeightedNbSetChemenvStrategy. :param structure_environments: StructureEnvironments object containing all the information on the coordination of the sites in a structure """ AbstractChemenvStrategy.__init__(self, structure_environments, symmetry_measure_type=symmetry_measure_type) self._additional_condition = additional_condition if nb_set_weights is None: raise ValueError() self.nb_set_weights = nb_set_weights self.ordered_weights = [] for nb_set_weight in self.nb_set_weights: self.ordered_weights.append({"weight": nb_set_weight, "name": nb_set_weight.SHORT_NAME}) self.ce_estimator = ce_estimator self.ce_estimator_ratio_function = CSMInfiniteRatioFunction.from_dict(self.ce_estimator) self.ce_estimator_fractions = self.ce_estimator_ratio_function.fractions @property def uniquely_determines_coordination_environments(self): """Whether this strategy uniquely determines coordination environments.""" return False
[docs] def get_site_coordination_environments_fractions( self, site, isite=None, dequivsite=None, dthissite=None, mysym=None, ordered=True, min_fraction=0.0, return_maps=True, return_strategy_dict_info=False, return_all=False, ): """Get the coordination environments of a given site and additional information. :param site: Site for which coordination environment is needed. :param isite: Index of the site. :param dequivsite: Translation of the equivalent site. :param dthissite: Translation of this site. :param mysym: Symmetry to be applied. :param ordered: Whether to order the list by fractions. :param min_fraction: Minimum fraction to include in the list :param return_maps: Whether to return cn_maps (identifies all the NeighborsSet used). :param return_strategy_dict_info: Whether to add the info about the strategy used. :return: List of Dict with coordination environment, fraction and additional info. """ if isite is None or dequivsite is None or dthissite is None or mysym is None: [ isite, dequivsite, dthissite, mysym, ] = self.equivalent_site_index_and_transform(site) site_nb_sets = self.structure_environments.neighbors_sets[isite] if site_nb_sets is None: return None cn_maps = [] for cn, nb_sets in site_nb_sets.items(): for inb_set, nb_set in enumerate(nb_sets): # CHECK THE ADDITIONAL CONDITION HERE ? cn_maps.append((cn, inb_set)) weights_additional_info = {"weights": {isite: {}}} for wdict in self.ordered_weights: cn_maps_new = [] weight = wdict["weight"] weight_name = wdict["name"] for cn_map in cn_maps: nb_set = site_nb_sets[cn_map[0]][cn_map[1]] w_nb_set = weight.weight( nb_set=nb_set, structure_environments=self.structure_environments, cn_map=cn_map, additional_info=weights_additional_info, ) if cn_map not in weights_additional_info["weights"][isite]: weights_additional_info["weights"][isite][cn_map] = {} weights_additional_info["weights"][isite][cn_map][weight_name] = w_nb_set if return_all or w_nb_set > 0.0: cn_maps_new.append(cn_map) cn_maps = cn_maps_new for cn_map, weights in weights_additional_info["weights"][isite].items(): weights_additional_info["weights"][isite][cn_map]["Product"] = np.product(list(weights.values())) w_nb_sets = { cn_map: weights["Product"] for cn_map, weights in weights_additional_info["weights"][isite].items() } w_nb_sets_total = np.sum(list(w_nb_sets.values())) nb_sets_fractions = {cn_map: w_nb_set / w_nb_sets_total for cn_map, w_nb_set in w_nb_sets.items()} for cn_map in weights_additional_info["weights"][isite]: weights_additional_info["weights"][isite][cn_map]["NbSetFraction"] = nb_sets_fractions[cn_map] ce_symbols = [] ce_dicts = [] ce_fractions = [] ce_dict_fractions = [] ce_maps = [] site_ce_list = self.structure_environments.ce_list[isite] if return_all: for cn_map, nb_set_fraction in nb_sets_fractions.items(): cn = cn_map[0] inb_set = cn_map[1] site_ce_nb_set = site_ce_list[cn][inb_set] if site_ce_nb_set is None: continue mingeoms = site_ce_nb_set.minimum_geometries(symmetry_measure_type=self.symmetry_measure_type) if len(mingeoms) > 0: csms = [ ce_dict["other_symmetry_measures"][self.symmetry_measure_type] for ce_symbol, ce_dict in mingeoms ] fractions = self.ce_estimator_fractions(csms) if fractions is None: ce_symbols.append("UNCLEAR:{:d}".format(cn)) ce_dicts.append(None) ce_fractions.append(nb_set_fraction) all_weights = weights_additional_info["weights"][isite][cn_map] dict_fractions = dict(all_weights.items()) dict_fractions["CEFraction"] = None dict_fractions["Fraction"] = nb_set_fraction ce_dict_fractions.append(dict_fractions) ce_maps.append(cn_map) else: for ifraction, fraction in enumerate(fractions): ce_symbols.append(mingeoms[ifraction][0]) ce_dicts.append(mingeoms[ifraction][1]) ce_fractions.append(nb_set_fraction * fraction) all_weights = weights_additional_info["weights"][isite][cn_map] dict_fractions = dict(all_weights.items()) dict_fractions["CEFraction"] = fraction dict_fractions["Fraction"] = nb_set_fraction * fraction ce_dict_fractions.append(dict_fractions) ce_maps.append(cn_map) else: ce_symbols.append("UNCLEAR:{:d}".format(cn)) ce_dicts.append(None) ce_fractions.append(nb_set_fraction) all_weights = weights_additional_info["weights"][isite][cn_map] dict_fractions = dict(all_weights.items()) dict_fractions["CEFraction"] = None dict_fractions["Fraction"] = nb_set_fraction ce_dict_fractions.append(dict_fractions) ce_maps.append(cn_map) else: for cn_map, nb_set_fraction in nb_sets_fractions.items(): if nb_set_fraction > 0.0: cn = cn_map[0] inb_set = cn_map[1] site_ce_nb_set = site_ce_list[cn][inb_set] mingeoms = site_ce_nb_set.minimum_geometries(symmetry_measure_type=self._symmetry_measure_type) csms = [ ce_dict["other_symmetry_measures"][self._symmetry_measure_type] for ce_symbol, ce_dict in mingeoms ] fractions = self.ce_estimator_fractions(csms) for ifraction, fraction in enumerate(fractions): if fraction > 0.0: ce_symbols.append(mingeoms[ifraction][0]) ce_dicts.append(mingeoms[ifraction][1]) ce_fractions.append(nb_set_fraction * fraction) all_weights = weights_additional_info["weights"][isite][cn_map] dict_fractions = dict(all_weights.items()) dict_fractions["CEFraction"] = fraction dict_fractions["Fraction"] = nb_set_fraction * fraction ce_dict_fractions.append(dict_fractions) ce_maps.append(cn_map) if ordered: indices = np.argsort(ce_fractions)[::-1] else: indices = list(range(len(ce_fractions))) fractions_info_list = [ { "ce_symbol": ce_symbols[ii], "ce_dict": ce_dicts[ii], "ce_fraction": ce_fractions[ii], } for ii in indices if ce_fractions[ii] >= min_fraction ] if return_maps: for ifinfo, ii in enumerate(indices): if ce_fractions[ii] >= min_fraction: fractions_info_list[ifinfo]["ce_map"] = ce_maps[ii] if return_strategy_dict_info: for ifinfo, ii in enumerate(indices): if ce_fractions[ii] >= min_fraction: fractions_info_list[ifinfo]["strategy_info"] = ce_dict_fractions[ii] return fractions_info_list
[docs] def get_site_coordination_environment(self, site): """Get the coordination environment of a given site. Not implemented for this strategy """ pass
[docs] def get_site_neighbors(self, site): """Get the neighbors of a given site. Not implemented for this strategy. """ pass
[docs] def get_site_coordination_environments( self, site, isite=None, dequivsite=None, dthissite=None, mysym=None, return_maps=False, ): """Get the coordination environments of a given site. :param site: Site for which coordination environment is needed. :param isite: Index of the site. :param dequivsite: Translation of the equivalent site. :param dthissite: Translation of this site. :param mysym: Symmetry to be applied. :param return_maps: Whether to return cn_maps (identifies all the NeighborsSet used). :return: List of coordination environment. """ if isite is None or dequivsite is None or dthissite is None or mysym is None: [ isite, dequivsite, dthissite, mysym, ] = self.equivalent_site_index_and_transform(site) return [ self.get_site_coordination_environment( # pylint: disable=E1123 site=site, isite=isite, dequivsite=dequivsite, dthissite=dthissite, mysym=mysym, return_map=return_maps, ) ]
def __eq__(self, other): return ( self.__class__.__name__ == other.__class__.__name__ and self._additional_condition == other._additional_condition and self.symmetry_measure_type == other.symmetry_measure_type and self.nb_set_weights == other.nb_set_weights and self.ce_estimator == other.ce_estimator ) def __ne__(self, other): return not self == other
[docs] def as_dict(self): """ Bson-serializable dict representation of the WeightedNbSetChemenvStrategy object. :return: Bson-serializable dict representation of the WeightedNbSetChemenvStrategy object. """ return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "additional_condition": self._additional_condition, "symmetry_measure_type": self.symmetry_measure_type, "nb_set_weights": [nb_set_weight.as_dict() for nb_set_weight in self.nb_set_weights], "ce_estimator": self.ce_estimator, }
[docs] @classmethod def from_dict(cls, d): """ Reconstructs the WeightedNbSetChemenvStrategy object from a dict representation of the WeightedNbSetChemenvStrategy object created using the as_dict method. :param d: dict representation of the WeightedNbSetChemenvStrategy object :return: WeightedNbSetChemenvStrategy object """ return cls( additional_condition=d["additional_condition"], symmetry_measure_type=d["symmetry_measure_type"], nb_set_weights=d["nb_set_weights"], ce_estimator=d["ce_estimator"], )
[docs]class MultiWeightsChemenvStrategy(WeightedNbSetChemenvStrategy): """ MultiWeightsChemenvStrategy """ STRATEGY_DESCRIPTION = " Multi Weights ChemenvStrategy" # STRATEGY_INFO_FIELDS = ['cn_map_surface_fraction', 'cn_map_surface_weight', # 'cn_map_mean_csm', 'cn_map_csm_weight', # 'cn_map_delta_csm', 'cn_map_delta_csms_cn_map2', 'cn_map_delta_csm_weight', # 'cn_map_cn_weight', # 'cn_map_fraction', 'cn_map_ce_fraction', 'ce_fraction'] DEFAULT_CE_ESTIMATOR = { "function": "power2_inverse_power2_decreasing", "options": {"max_csm": 8.0}, } DEFAULT_DIST_ANG_AREA_WEIGHT = {} # type: Dict def __init__( self, structure_environments=None, additional_condition=AbstractChemenvStrategy.AC.ONLY_ACB, symmetry_measure_type=AbstractChemenvStrategy.DEFAULT_SYMMETRY_MEASURE_TYPE, dist_ang_area_weight=None, self_csm_weight=None, delta_csm_weight=None, cn_bias_weight=None, angle_weight=None, normalized_angle_distance_weight=None, ce_estimator=DEFAULT_CE_ESTIMATOR, ): """ Constructor for the MultiWeightsChemenvStrategy. :param structure_environments: StructureEnvironments object containing all the information on the coordination of the sites in a structure """ self._additional_condition = additional_condition self.dist_ang_area_weight = dist_ang_area_weight self.angle_weight = angle_weight self.normalized_angle_distance_weight = normalized_angle_distance_weight self.self_csm_weight = self_csm_weight self.delta_csm_weight = delta_csm_weight self.cn_bias_weight = cn_bias_weight self.ordered_weights = [] nb_sets_weights = [] if dist_ang_area_weight is not None: self.ordered_weights.append({"weight": dist_ang_area_weight, "name": "DistAngArea"}) nb_sets_weights.append(dist_ang_area_weight) if self_csm_weight is not None: self.ordered_weights.append({"weight": self_csm_weight, "name": "SelfCSM"}) nb_sets_weights.append(self_csm_weight) if delta_csm_weight is not None: self.ordered_weights.append({"weight": delta_csm_weight, "name": "DeltaCSM"}) nb_sets_weights.append(delta_csm_weight) if cn_bias_weight is not None: self.ordered_weights.append({"weight": cn_bias_weight, "name": "CNBias"}) nb_sets_weights.append(cn_bias_weight) if angle_weight is not None: self.ordered_weights.append({"weight": angle_weight, "name": "Angle"}) nb_sets_weights.append(angle_weight) if normalized_angle_distance_weight is not None: self.ordered_weights.append( { "weight": normalized_angle_distance_weight, "name": "NormalizedAngDist", } ) nb_sets_weights.append(normalized_angle_distance_weight) self.ce_estimator = ce_estimator self.ce_estimator_ratio_function = CSMInfiniteRatioFunction.from_dict(self.ce_estimator) self.ce_estimator_fractions = self.ce_estimator_ratio_function.fractions WeightedNbSetChemenvStrategy.__init__( self, structure_environments, additional_condition=additional_condition, symmetry_measure_type=symmetry_measure_type, nb_set_weights=nb_sets_weights, ce_estimator=ce_estimator, )
[docs] @classmethod def stats_article_weights_parameters(cls): """Initialize strategy used in the statistics article.""" self_csm_weight = SelfCSMNbSetWeight( weight_estimator={ "function": "power2_decreasing_exp", "options": {"max_csm": 8.0, "alpha": 1.0}, } ) surface_definition = { "type": "standard_elliptic", "distance_bounds": {"lower": 1.15, "upper": 2.0}, "angle_bounds": {"lower": 0.05, "upper": 0.75}, } da_area_weight = DistanceAngleAreaNbSetWeight( weight_type="has_intersection", surface_definition=surface_definition, nb_sets_from_hints="fallback_to_source", other_nb_sets="0_weight", additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB, ) symmetry_measure_type = "csm_wcs_ctwcc" delta_weight = DeltaCSMNbSetWeight.delta_cn_specifics() bias_weight = None angle_weight = None nad_weight = None return cls( dist_ang_area_weight=da_area_weight, self_csm_weight=self_csm_weight, delta_csm_weight=delta_weight, cn_bias_weight=bias_weight, angle_weight=angle_weight, normalized_angle_distance_weight=nad_weight, symmetry_measure_type=symmetry_measure_type, )
@property def uniquely_determines_coordination_environments(self): """Whether this strategy uniquely determines coordination environments.""" return False def __eq__(self, other): return ( self.__class__.__name__ == other.__class__.__name__ and self._additional_condition == other._additional_condition and self.symmetry_measure_type == other.symmetry_measure_type and self.dist_ang_area_weight == other.dist_ang_area_weight and self.self_csm_weight == other.self_csm_weight and self.delta_csm_weight == other.delta_csm_weight and self.cn_bias_weight == other.cn_bias_weight and self.angle_weight == other.angle_weight and self.normalized_angle_distance_weight == other.normalized_angle_distance_weight and self.ce_estimator == other.ce_estimator ) def __ne__(self, other): return not self == other
[docs] def as_dict(self): """ Bson-serializable dict representation of the MultiWeightsChemenvStrategy object. :return: Bson-serializable dict representation of the MultiWeightsChemenvStrategy object. """ return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "additional_condition": self._additional_condition, "symmetry_measure_type": self.symmetry_measure_type, "dist_ang_area_weight": self.dist_ang_area_weight.as_dict() if self.dist_ang_area_weight is not None else None, "self_csm_weight": self.self_csm_weight.as_dict() if self.self_csm_weight is not None else None, "delta_csm_weight": self.delta_csm_weight.as_dict() if self.delta_csm_weight is not None else None, "cn_bias_weight": self.cn_bias_weight.as_dict() if self.cn_bias_weight is not None else None, "angle_weight": self.angle_weight.as_dict() if self.angle_weight is not None else None, "normalized_angle_distance_weight": self.normalized_angle_distance_weight.as_dict() if self.normalized_angle_distance_weight is not None else None, "ce_estimator": self.ce_estimator, }
[docs] @classmethod def from_dict(cls, d): """ Reconstructs the MultiWeightsChemenvStrategy object from a dict representation of the MultipleAbundanceChemenvStrategy object created using the as_dict method. :param d: dict representation of the MultiWeightsChemenvStrategy object :return: MultiWeightsChemenvStrategy object """ if d["normalized_angle_distance_weight"] is not None: nad_w = NormalizedAngleDistanceNbSetWeight.from_dict(d["normalized_angle_distance_weight"]) else: nad_w = None return cls( additional_condition=d["additional_condition"], symmetry_measure_type=d["symmetry_measure_type"], dist_ang_area_weight=DistanceAngleAreaNbSetWeight.from_dict(d["dist_ang_area_weight"]) if d["dist_ang_area_weight"] is not None else None, self_csm_weight=SelfCSMNbSetWeight.from_dict(d["self_csm_weight"]) if d["self_csm_weight"] is not None else None, delta_csm_weight=DeltaCSMNbSetWeight.from_dict(d["delta_csm_weight"]) if d["delta_csm_weight"] is not None else None, cn_bias_weight=CNBiasNbSetWeight.from_dict(d["cn_bias_weight"]) if d["cn_bias_weight"] is not None else None, angle_weight=AngleNbSetWeight.from_dict(d["angle_weight"]) if d["angle_weight"] is not None else None, normalized_angle_distance_weight=nad_w, ce_estimator=d["ce_estimator"], )