# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
from __future__ import division, unicode_literals
"""
This module contains objects that are used to describe the environments in a structure. The most detailed object
(StructureEnvironments) contains a very thorough analysis of the environments of a given atom but is difficult to
used as such. The LightStructureEnvironments object is a lighter version that is obtained by applying a "strategy"
on the StructureEnvironments object. Basically, the LightStructureEnvironments provides the coordination environment(s)
and possibly some fraction corresponding to these.
"""
__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 numpy as np
from pymatgen.core.sites import PeriodicSite
from monty.json import MSONable, MontyDecoder
from pymatgen.core.periodic_table import Element, Specie
from pymatgen.core.structure import Structure
from monty.json import jsanitize
from pymatgen.analysis.chemenv.coordination_environments.voronoi import DetailedVoronoiContainer
from pymatgen.analysis.chemenv.utils.chemenv_errors import ChemenvError
from pymatgen.analysis.chemenv.coordination_environments.coordination_geometries import AllCoordinationGeometries
from pymatgen.analysis.chemenv.utils.defs_utils import AdditionalConditions
allcg = AllCoordinationGeometries()
symbol_cn_mapping = allcg.get_symbol_cn_mapping()
[docs]class StructureEnvironments(MSONable):
"""
Class used to store the chemical environments of a given structure.
"""
AC = AdditionalConditions()
def __init__(self, voronoi, valences, sites_map, equivalent_sites,
ce_list, structure, neighbors_sets=None, info=None):
"""
Constructor for the StructureEnvironments object.
:param voronoi: VoronoiContainer object for the structure
:param valences: Valences provided
:param sites_map: Mapping of equivalent sites to the unequivalent sites that have been computed.
:param equivalent_sites: List of list of equivalent sites of the structure
:param struct_sites_to_irreducible_site_list_map: Maps the index of a site to the index of the item in the
list of equivalent sites to which the site belongs.
:param ce_list: List of chemical environments
:param structure: Structure object
"""
self.voronoi = voronoi
self.valences = valences
self.sites_map = sites_map
self.equivalent_sites = equivalent_sites
#self.struct_sites_to_irreducible_site_list_map = struct_sites_to_irreducible_site_list_map
self.ce_list = ce_list
self.structure = structure
if neighbors_sets is None:
self.neighbors_sets = [None] * len(self.structure)
else:
self.neighbors_sets = neighbors_sets
self.info = info
[docs] def init_neighbors_sets(self, isite, additional_conditions=None, valences=None):
site_voronoi = self.voronoi.voronoi_list2[isite]
if site_voronoi is None:
return
if additional_conditions is None:
additional_conditions = self.AC.ALL
if (self.AC.ONLY_ACB in additional_conditions or
self.AC.ONLY_ACB_AND_NO_E2SEB) and valences is None:
raise ChemenvError('StructureEnvironments', 'init_neighbors_sets',
'Valences are not given while only_anion_cation_bonds are allowed. Cannot continue')
site_distance_parameters = self.voronoi.neighbors_normalized_distances[isite]
site_angle_parameters = self.voronoi.neighbors_normalized_angles[isite]
# Precompute distance conditions
distance_conditions = []
for idp, dp_dict in enumerate(site_distance_parameters):
distance_conditions.append([])
for inb, voro_nb_dict in enumerate(site_voronoi):
cond = inb in dp_dict['nb_indices']
distance_conditions[idp].append(cond)
# Precompute angle conditions
angle_conditions = []
for iap, ap_dict in enumerate(site_angle_parameters):
angle_conditions.append([])
for inb, voro_nb_dict in enumerate(site_voronoi):
cond = inb in ap_dict['nb_indices']
angle_conditions[iap].append(cond)
# Precompute additional conditions
precomputed_additional_conditions = {ac: [] for ac in additional_conditions}
for inb, voro_nb_dict in enumerate(site_voronoi):
for ac in additional_conditions:
cond = self.AC.check_condition(condition=ac, structure=self.structure,
parameters={'valences': valences,
'neighbor_index': voro_nb_dict['index'],
'site_index': isite})
precomputed_additional_conditions[ac].append(cond)
# Add the neighbors sets based on the distance/angle/additional parameters
for idp, dp_dict in enumerate(site_distance_parameters):
for iap, ap_dict in enumerate(site_angle_parameters):
for iac, ac in enumerate(additional_conditions):
src = {'origin': 'dist_ang_ac_voronoi',
'idp': idp, 'iap': iap, 'dp_dict': dp_dict, 'ap_dict': ap_dict,
'iac': iac, 'ac': ac, 'ac_name': self.AC.CONDITION_DESCRIPTION[ac]}
site_voronoi_indices = [inb for inb, voro_nb_dict in enumerate(site_voronoi)
if (distance_conditions[idp][inb] and
angle_conditions[iap][inb] and
precomputed_additional_conditions[ac][inb])]
nb_set = self.NeighborsSet(structure=self.structure,
isite=isite,
detailed_voronoi=self.voronoi,
site_voronoi_indices=site_voronoi_indices,
sources=src)
self.add_neighbors_set(isite=isite, nb_set=nb_set)
[docs] def add_neighbors_set(self, isite, nb_set):
if self.neighbors_sets[isite] is None:
self.neighbors_sets[isite] = {}
self.ce_list[isite] = {}
cn = len(nb_set)
if cn not in self.neighbors_sets[isite]:
self.neighbors_sets[isite][cn] = []
self.ce_list[isite][cn] = []
try:
nb_set_index = self.neighbors_sets[isite][cn].index(nb_set)
self.neighbors_sets[isite][cn][nb_set_index].add_source(nb_set.source)
except ValueError:
self.neighbors_sets[isite][cn].append(nb_set)
self.ce_list[isite][cn].append(None)
[docs] def update_coordination_environments(self, isite, cn, nb_set, ce):
if self.ce_list[isite] is None:
self.ce_list[isite] = {}
if cn not in self.ce_list[isite]:
self.ce_list[isite][cn] = []
try:
nb_set_index = self.neighbors_sets[isite][cn].index(nb_set)
except ValueError:
raise ValueError('Neighbors set not found in the structure environments')
if nb_set_index == len(self.ce_list[isite][cn]):
self.ce_list[isite][cn].append(ce)
elif nb_set_index < len(self.ce_list[isite][cn]):
self.ce_list[isite][cn][nb_set_index] = ce
else:
raise ValueError('Neighbors set not yet in ce_list !')
[docs] def get_coordination_environments(self, isite, cn, nb_set):
if self.ce_list[isite] is None:
return None
if cn not in self.ce_list[isite]:
return None
try:
nb_set_index = self.neighbors_sets[isite][cn].index(nb_set)
except ValueError:
return None
return self.ce_list[isite][cn][nb_set_index]
[docs] def get_csm(self, isite, mp_symbol):
csms = self.get_csms(isite, mp_symbol)
if len(csms) != 1:
raise ChemenvError('StructureEnvironments',
'get_csm',
'Number of csms for site #{} with '
'mp_symbol "{}" = {}'.format(str(isite),
mp_symbol,
str(len(csms))))
return csms[0]
[docs] def get_csms(self, isite, mp_symbol):
"""
Returns the continuous symmetry measure(s) of site with index isite with respect to the
perfect coordination environment with mp_symbol. For some environments, a given mp_symbol might not
be available (if there is no voronoi parameters leading to a number of neighbours corresponding to
the coordination number of environment mp_symbol). For some environments, a given mp_symbol might
lead to more than one csm (when two or more different voronoi parameters lead to different neighbours
but with same number of neighbours).
:param isite: Index of the site
:param mp_symbol: MP symbol of the perfect environment for which the csm has to be given
:return: List of csms for site isite with respect to geometry mp_symbol
"""
cn = symbol_cn_mapping[mp_symbol]
if cn not in self.ce_list[isite]:
return []
else:
return [envs[mp_symbol] for envs in self.ce_list[isite][cn]]
[docs] def plot_csm_and_maps(self, isite, max_csm=8.0):
"""
Plotting of the coordination numbers of a given site for all the distfactor/angfactor parameters. If the
chemical environments are given, a color map is added to the plot, with the lowest continuous symmetry measure
as the value for the color of that distfactor/angfactor set.
:param isite: Index of the site for which the plot has to be done
:param plot_type: How to plot the coordinations
:param title: Title for the figure
:param max_dist: Maximum distance to be plotted when the plotting of the distance is set to 'initial_normalized'
or 'initial_real' (Warning: this is not the same meaning in both cases! In the first case,
the closest atom lies at a "normalized" distance of 1.0 so that 2.0 means refers to this
normalized distance while in the second case, the real distance is used)
:param figsize: Size of the figure to be plotted
:return: Nothing returned, just plot the figure
"""
try:
import matplotlib.pyplot as plt
except ImportError:
print('Plotting Chemical Environments requires matplotlib ... exiting "plot" function')
return
fig = self.get_csm_and_maps(isite=isite, max_csm=max_csm)
if fig is None:
return
plt.show()
[docs] def get_csm_and_maps(self, isite, max_csm=8.0, figsize=None, symmetry_measure_type=None):
try:
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
except ImportError:
print('Plotting Chemical Environments requires matplotlib ... exiting "plot" function')
return
if symmetry_measure_type is None:
symmetry_measure_type = 'csm_wcs_ctwcc'
# Initializes the figure
if figsize is None:
fig = plt.figure()
else:
fig = plt.figure(figsize=figsize)
gs = GridSpec(2, 1, hspace=0.0, wspace=0.0)
subplot = fig.add_subplot(gs[:])
subplot_distang = subplot.twinx()
ix = 0
cn_maps = []
all_wds = []
all_was = []
max_wd = 0.0
for cn, nb_sets in self.neighbors_sets[isite].items():
for inb_set, nb_set in enumerate(nb_sets):
ce = self.ce_list[isite][cn][inb_set]
if ce is None:
continue
mingeoms = ce.minimum_geometries(max_csm=max_csm)
if len(mingeoms) == 0:
continue
wds = nb_set.normalized_distances
max_wd = max(max_wd, max(wds))
all_wds.append(wds)
all_was.append(nb_set.normalized_angles)
for mp_symbol, cg_dict in mingeoms:
csm = cg_dict['other_symmetry_measures'][symmetry_measure_type]
subplot.plot(ix, csm, 'ob')
subplot.annotate(mp_symbol, xy = (ix, csm))
cn_maps.append((cn, inb_set))
ix += 1
if max_wd < 1.225:
ymax_wd = 1.25
yticks_wd = np.linspace(1.0, ymax_wd, 6)
elif max_wd < 1.36:
ymax_wd = 1.4
yticks_wd = np.linspace(1.0, ymax_wd, 5)
elif max_wd < 1.45:
ymax_wd = 1.5
yticks_wd = np.linspace(1.0, ymax_wd, 6)
elif max_wd < 1.55:
ymax_wd = 1.6
yticks_wd = np.linspace(1.0, ymax_wd, 7)
elif max_wd < 1.75:
ymax_wd = 1.8
yticks_wd = np.linspace(1.0, ymax_wd, 5)
elif max_wd < 1.95:
ymax_wd = 2.0
yticks_wd = np.linspace(1.0, ymax_wd, 6)
elif max_wd < 2.35:
ymax_wd = 2.5
yticks_wd = np.linspace(1.0, ymax_wd, 7)
else:
ymax_wd = np.ceil(1.1*max_wd)
yticks_wd = np.linspace(1.0, ymax_wd, 6)
yticks_wa = np.linspace(0.0, 1.0, 6)
frac_bottom = 0.05
frac_top = 0.05
frac_middle = 0.1
yamin = frac_bottom
yamax = 0.5 - frac_middle / 2
ydmin = 0.5 + frac_middle / 2
ydmax = 1.0 - frac_top
def yang(wa):
return (yamax-yamin) * np.array(wa) + yamin
def ydist(wd):
return (np.array(wd) - 1.0) / (ymax_wd - 1.0) * (ydmax - ydmin) + ydmin
for ix, was in enumerate(all_was):
subplot_distang.plot(0.2+ix*np.ones_like(was), yang(was), '<g')
if np.mod(ix, 2) == 0:
alpha = 0.3
else:
alpha = 0.1
subplot_distang.fill_between([-0.5+ix, 0.5+ix],
[1.0, 1.0], 0.0,
facecolor='k', alpha=alpha, zorder=-1000)
for ix, wds in enumerate(all_wds):
subplot_distang.plot(0.2+ix*np.ones_like(wds), ydist(wds), 'sm')
subplot_distang.plot([-0.5, len(cn_maps)], [0.5, 0.5], 'k--', alpha=0.5)
yticks = yang(yticks_wa).tolist()
yticks.extend(ydist(yticks_wd).tolist())
yticklabels = yticks_wa.tolist()
yticklabels.extend(yticks_wd.tolist())
subplot_distang.set_yticks(yticks)
subplot_distang.set_yticklabels(yticklabels)
fake_subplot_ang = fig.add_subplot(gs[1], frame_on=False)
fake_subplot_dist = fig.add_subplot(gs[0], frame_on=False)
fake_subplot_ang.set_yticks([])
fake_subplot_dist.set_yticks([])
fake_subplot_ang.set_xticks([])
fake_subplot_dist.set_xticks([])
fake_subplot_ang.set_ylabel('Angle parameter', labelpad=45, rotation=-90)
fake_subplot_dist.set_ylabel('Distance parameter', labelpad=45, rotation=-90)
fake_subplot_ang.yaxis.set_label_position("right")
fake_subplot_dist.yaxis.set_label_position("right")
subplot_distang.set_ylim([0.0, 1.0])
subplot.set_xticks(range(len(cn_maps)))
subplot.set_ylabel('Continuous symmetry measure')
subplot.set_xlim([-0.5, len(cn_maps)-0.5])
subplot_distang.set_xlim([-0.5, len(cn_maps)-0.5])
subplot.set_xticklabels([str(cn_map) for cn_map in cn_maps])
[docs] def plot_environments(self, isite, plot_type=None, title='Coordination numbers', max_dist=2.0,
additional_condition=AC.ONLY_ACB, figsize=None, strategy=None):
"""
Plotting of the coordination numbers of a given site for all the distfactor/angfactor parameters. If the
chemical environments are given, a color map is added to the plot, with the lowest continuous symmetry measure
as the value for the color of that distfactor/angfactor set.
:param isite: Index of the site for which the plot has to be done
:param plot_type: How to plot the coordinations
:param title: Title for the figure
:param max_dist: Maximum distance to be plotted when the plotting of the distance is set to 'initial_normalized'
or 'initial_real' (Warning: this is not the same meaning in both cases! In the first case,
the closest atom lies at a "normalized" distance of 1.0 so that 2.0 means refers to this
normalized distance while in the second case, the real distance is used)
:param figsize: Size of the figure to be plotted
:return: Nothing returned, just plot the figure
"""
fig = self.get_environments_figure(isite=isite, plot_type=plot_type, title=title, max_dist=max_dist,
additional_condition=additional_condition, figsize=figsize,
strategy=strategy)
if fig is None:
return
fig.show()
[docs] def to_bv_dict(self, isite):
out = {"neighbours_lists": [], "continuous_symmetry_measures": []}
for cn, coordnbs_list in self.voronoi._unique_coordinated_neighbors[isite].items():
for i_coordnbs, coordnbs in enumerate(coordnbs_list):
neighbours = []
for ineighb, neighb in enumerate(coordnbs[0]):
mydict = {'neighbour': neighb.as_dict(),
'distance': coordnbs[2][ineighb]['distance'],
'normalized_distance': coordnbs[2][ineighb]['normalized_distance'],
'angle': coordnbs[2][ineighb]['angle'],
'normalized_angle': coordnbs[2][ineighb]['normalized_angle']}
neighbours.append(mydict)
ce = self.ce_list[isite][cn][i_coordnbs]
mingeoms = ce.minimum_geometries()
csm_dict = {ce: ce_dict['symmetry_measure'] for ce, ce_dict in mingeoms}
out["neighbours_lists"].append(neighbours)
out["continuous_symmetry_measures"].append(csm_dict)
return out
[docs] def differences_wrt(self, other):
differences = []
if self.structure != other.structure:
differences.append({'difference': 'structure',
'comparison': '__eq__',
'self': self.structure,
'other': other.structure})
differences.append({'difference': 'PREVIOUS DIFFERENCE IS DISMISSIVE',
'comparison': 'differences_wrt'})
return differences
if self.valences != other.valences:
differences.append({'difference': 'valences',
'comparison': '__eq__',
'self': self.valences,
'other': other.valences})
if self.info != other.info:
differences.append({'difference': 'info',
'comparison': '__eq__',
'self': self.info,
'other': other.info})
if self.voronoi != other.voronoi:
if self.voronoi.is_close_to(other.voronoi):
differences.append({'difference': 'voronoi',
'comparison': '__eq__',
'self': self.voronoi,
'other': other.voronoi})
differences.append({'difference': 'PREVIOUS DIFFERENCE IS DISMISSIVE',
'comparison': 'differences_wrt'})
return differences
else:
differences.append({'difference': 'voronoi',
'comparison': 'is_close_to',
'self': self.voronoi,
'other': other.voronoi})
# TODO: make it possible to have "close" voronoi's
differences.append({'difference': 'PREVIOUS DIFFERENCE IS DISMISSIVE',
'comparison': 'differences_wrt'})
return differences
for isite, self_site_nb_sets in enumerate(self.neighbors_sets):
other_site_nb_sets = other.neighbors_sets[isite]
self_site_cns = set(self_site_nb_sets.keys())
other_site_cns = set(other_site_nb_sets.keys())
if self_site_cns != other_site_cns:
differences.append({'difference': 'neighbors_sets[isite={:d}]'.format(isite),
'comparison': 'coordination_numbers',
'self': self_site_cns,
'other': other_site_cns})
common_cns = self_site_cns.intersection(other_site_cns)
for cn in common_cns:
other_site_cn_nb_sets = other_site_nb_sets[cn]
self_site_cn_nb_sets = self_site_nb_sets[cn]
set_self_site_cn_nb_sets = set(self_site_cn_nb_sets)
set_other_site_cn_nb_sets = set(other_site_cn_nb_sets)
if set_self_site_cn_nb_sets != set_other_site_cn_nb_sets:
differences.append({'difference': 'neighbors_sets[isite={:d}][cn={:d}]'.format(isite, cn),
'comparison': 'neighbors_sets',
'self': self_site_cn_nb_sets,
'other': other_site_cn_nb_sets})
common_nb_sets = set_self_site_cn_nb_sets.intersection(set_other_site_cn_nb_sets)
for nb_set in common_nb_sets:
inb_set_self = self_site_cn_nb_sets.index(nb_set)
inb_set_other = other_site_cn_nb_sets.index(nb_set)
self_ce = self.ce_list[isite][cn][inb_set_self]
other_ce = other.ce_list[isite][cn][inb_set_other]
if self_ce != other_ce:
if self_ce.is_close_to(other_ce):
differences.append({'difference': 'ce_list[isite={:d}][cn={:d}]'
'[inb_set={:d}]'.format(isite, cn, inb_set_self),
'comparison': '__eq__',
'self': self_ce,
'other': other_ce})
else:
differences.append({'difference': 'ce_list[isite={:d}][cn={:d}]'
'[inb_set={:d}]'.format(isite, cn, inb_set_self),
'comparison': 'is_close_to',
'self': self_ce,
'other': other_ce})
return differences
def __eq__(self, other):
if len(self.ce_list) != len(other.ce_list):
return False
if self.voronoi != other.voronoi:
return False
if len(self.valences) != len(other.valences):
return False
if self.sites_map != other.sites_map:
return False
if self.equivalent_sites != other.equivalent_sites:
return False
if self.structure != other.structure:
return False
if self.info != other.info:
return False
for isite, site_ces in enumerate(self.ce_list):
site_nb_sets_self = self.neighbors_sets[isite]
site_nb_sets_other = other.neighbors_sets[isite]
if site_nb_sets_self != site_nb_sets_other:
return False
if site_ces != other.ce_list[isite]:
return False
return True
def __ne__(self, other):
return not self == other
[docs] def as_dict(self):
"""
Bson-serializable dict representation of the StructureEnvironments object.
:return: Bson-serializable dict representation of the StructureEnvironments object.
"""
ce_list_dict = [{str(cn): [ce.as_dict() if ce is not None else None for ce in ce_dict[cn]]
for cn in ce_dict} if ce_dict is not None else None for ce_dict in self.ce_list]
nbs_sets_dict = [{str(cn): [nb_set.as_dict() for nb_set in nb_sets]
for cn, nb_sets in site_nbs_sets.items()}
if site_nbs_sets is not None else None
for site_nbs_sets in self.neighbors_sets]
return {"@module": self.__class__.__module__,
"@class": self.__class__.__name__,
"voronoi": self.voronoi.as_dict(),
"valences": self.valences,
"sites_map": self.sites_map,
"equivalent_sites": [[ps.as_dict() for ps in psl] for psl in self.equivalent_sites],
"ce_list": ce_list_dict,
"structure": self.structure.as_dict(),
"neighbors_sets": nbs_sets_dict,
"info": self.info}
[docs] @classmethod
def from_dict(cls, d):
"""
Reconstructs the StructureEnvironments object from a dict representation of the StructureEnvironments created
using the as_dict method.
:param d: dict representation of the StructureEnvironments object
:return: StructureEnvironments object
"""
ce_list = [None if (ce_dict == 'None' or ce_dict is None) else {
int(cn): [None if (ced is None or ced == 'None') else
ChemicalEnvironments.from_dict(ced) for ced in ce_dict[cn]]
for cn in ce_dict} for ce_dict in d['ce_list']]
voronoi = DetailedVoronoiContainer.from_dict(d['voronoi'])
structure = Structure.from_dict(d['structure'])
neighbors_sets = [{int(cn): [cls.NeighborsSet.from_dict(dd=nb_set_dict,
structure=structure,
detailed_voronoi=voronoi)
for nb_set_dict in nb_sets]
for cn, nb_sets in site_nbs_sets_dict.items()}
if site_nbs_sets_dict is not None else None
for site_nbs_sets_dict in d['neighbors_sets']]
return cls(voronoi=voronoi, valences=d['valences'],
sites_map=d['sites_map'],
equivalent_sites=[[PeriodicSite.from_dict(psd) for psd in psl] for psl in d['equivalent_sites']],
ce_list=ce_list, structure=structure,
neighbors_sets=neighbors_sets,
info=d['info'])
[docs]class LightStructureEnvironments(MSONable):
"""
Class used to store the chemical environments of a given structure obtained from a given ChemenvStrategy. Currently,
only strategies leading to the determination of a unique environment for each site is allowed
This class does not store all the information contained in the StructureEnvironments object, only the coordination
environment found
"""
DELTA_MAX_OXIDATION_STATE = 0.1
DEFAULT_STATISTICS_FIELDS = ['anion_list', 'anion_atom_list', 'cation_list', 'cation_atom_list',
'neutral_list', 'neutral_atom_list',
'atom_coordination_environments_present',
'ion_coordination_environments_present',
'fraction_atom_coordination_environments_present',
'fraction_ion_coordination_environments_present',
'coordination_environments_atom_present',
'coordination_environments_ion_present']
def __init__(self, strategy,
coordination_environments=None, all_nbs_sites=None, neighbors_sets=None,
structure=None, valences=None, valences_origin=None):
"""
Constructor for the LightStructureEnvironments object.
"""
self.strategy = strategy
self.statistics_dict = None
self.coordination_environments = coordination_environments
self._all_nbs_sites = all_nbs_sites
self.neighbors_sets = neighbors_sets
self.structure = structure
self.valences = valences
self.valences_origin = valences_origin
[docs] @classmethod
def from_structure_environments(cls, strategy, structure_environments, valences=None, valences_origin=None):
structure = structure_environments.structure
strategy.set_structure_environments(structure_environments=structure_environments)
coordination_environments = [None] * len(structure)
neighbors_sets = [None] * len(structure)
_all_nbs_sites = []
my_all_nbs_sites = []
if valences is None:
valences = structure_environments.valences
if valences_origin is None:
valences_origin = 'from_structure_environments'
else:
if valences_origin is None:
valences_origin = 'user-specified'
for isite, site in enumerate(structure):
site_ces_and_nbs_list = strategy.get_site_ce_fractions_and_neighbors(site, strategy_info=True)
if site_ces_and_nbs_list is None:
continue
coordination_environments[isite] = []
neighbors_sets[isite] = []
site_ces = []
site_nbs_sets = []
for ce_and_neighbors in site_ces_and_nbs_list:
_all_nbs_sites_indices = []
# Coordination environment
ce_dict = {'ce_symbol': ce_and_neighbors['ce_symbol'],
'ce_fraction': ce_and_neighbors['ce_fraction']}
if ce_and_neighbors['ce_dict'] is not None:
csm = ce_and_neighbors['ce_dict']['other_symmetry_measures'][strategy.symmetry_measure_type]
else:
csm = None
ce_dict['csm'] = csm
ce_dict['permutation'] = ce_and_neighbors['ce_dict']['permutation']
site_ces.append(ce_dict)
# Neighbors
neighbors = ce_and_neighbors['neighbors']
for nb_site_and_index in neighbors:
nb_site = nb_site_and_index['site']
try:
nb_allnbs_sites_index = my_all_nbs_sites.index(nb_site)
except ValueError:
nb_index_unitcell = nb_site_and_index['index']
diff = nb_site.frac_coords - structure[nb_index_unitcell].frac_coords
rounddiff = np.round(diff)
if not np.allclose(diff, rounddiff):
raise ValueError('Weird, differences between one site in a periodic image cell is not '
'integer ...')
nb_image_cell = np.array(rounddiff, np.int)
nb_allnbs_sites_index = len(_all_nbs_sites)
_all_nbs_sites.append({'site': nb_site,
'index': nb_index_unitcell,
'image_cell': nb_image_cell})
my_all_nbs_sites.append(nb_site)
_all_nbs_sites_indices.append(nb_allnbs_sites_index)
nb_set = cls.NeighborsSet(structure=structure, isite=isite,
all_nbs_sites=_all_nbs_sites,
all_nbs_sites_indices=_all_nbs_sites_indices)
site_nbs_sets.append(nb_set)
coordination_environments[isite] = site_ces
neighbors_sets[isite] = site_nbs_sets
return cls(strategy=strategy,
coordination_environments=coordination_environments,
all_nbs_sites=_all_nbs_sites,
neighbors_sets=neighbors_sets,
structure=structure, valences=valences,
valences_origin=valences_origin)
[docs] def setup_statistic_lists(self):
self.statistics_dict = {'valences_origin': self.valences_origin,
'anion_list': {}, # OK
'anion_number': None, # OK
'anion_atom_list': {}, # OK
'anion_atom_number': None, # OK
'cation_list': {}, # OK
'cation_number': None, # OK
'cation_atom_list': {}, # OK
'cation_atom_number': None, # OK
'neutral_list': {}, # OK
'neutral_number': None, # OK
'neutral_atom_list': {}, # OK
'neutral_atom_number': None, # OK
'atom_coordination_environments_present': {}, # OK
'ion_coordination_environments_present': {}, # OK
'coordination_environments_ion_present': {}, # OK
'coordination_environments_atom_present': {}, # OK
'fraction_ion_coordination_environments_present': {}, # OK
'fraction_atom_coordination_environments_present': {}, # OK
'fraction_coordination_environments_ion_present': {}, # OK
'fraction_coordination_environments_atom_present': {}, # OK
'count_ion_present': {}, # OK
'count_atom_present': {}, # OK
'count_coordination_environments_present': {}}
atom_stat = self.statistics_dict['atom_coordination_environments_present']
ce_atom_stat = self.statistics_dict['coordination_environments_atom_present']
fraction_atom_stat = self.statistics_dict['fraction_atom_coordination_environments_present']
fraction_ce_atom_stat = self.statistics_dict['fraction_coordination_environments_atom_present']
count_atoms = self.statistics_dict['count_atom_present']
count_ce = self.statistics_dict['count_coordination_environments_present']
for isite, site in enumerate(self.structure):
# Building anion and cation list
site_species = []
if self.valences != 'undefined':
for sp, occ in site.species_and_occu.items():
valence = self.valences[isite]
strspecie = str(Specie(sp.symbol, valence))
if valence < 0:
specielist = self.statistics_dict['anion_list']
atomlist = self.statistics_dict['anion_atom_list']
elif valence > 0:
specielist = self.statistics_dict['cation_list']
atomlist = self.statistics_dict['cation_atom_list']
else:
specielist = self.statistics_dict['neutral_list']
atomlist = self.statistics_dict['neutral_atom_list']
if strspecie not in specielist:
specielist[strspecie] = occ
else:
specielist[strspecie] += occ
if sp.symbol not in atomlist:
atomlist[sp.symbol] = occ
else:
atomlist[sp.symbol] += occ
site_species.append((sp.symbol, valence, occ))
# Building environments lists
if self.coordination_environments[isite] is not None:
site_envs = [(ce_piece_dict['ce_symbol'], ce_piece_dict['ce_fraction'])
for ce_piece_dict in self.coordination_environments[isite]]
for ce_symbol, fraction in site_envs:
if fraction is None:
continue
if ce_symbol not in count_ce:
count_ce[ce_symbol] = 0.0
count_ce[ce_symbol] += fraction
for sp, occ in site.species_and_occu.items():
elmt = sp.symbol
if elmt not in atom_stat:
atom_stat[elmt] = {}
count_atoms[elmt] = 0.0
count_atoms[elmt] += occ
for ce_symbol, fraction in site_envs:
if fraction is None:
continue
if ce_symbol not in atom_stat[elmt]:
atom_stat[elmt][ce_symbol] = 0.0
atom_stat[elmt][ce_symbol] += occ * fraction
if ce_symbol not in ce_atom_stat:
ce_atom_stat[ce_symbol] = {}
if elmt not in ce_atom_stat[ce_symbol]:
ce_atom_stat[ce_symbol][elmt] = 0.0
ce_atom_stat[ce_symbol][elmt] += occ * fraction
if self.valences != 'undefined':
ion_stat = self.statistics_dict['ion_coordination_environments_present']
ce_ion_stat = self.statistics_dict['coordination_environments_ion_present']
count_ions = self.statistics_dict['count_ion_present']
for elmt, oxi_state, occ in site_species:
if elmt not in ion_stat:
ion_stat[elmt] = {}
count_ions[elmt] = {}
if oxi_state not in ion_stat[elmt]:
ion_stat[elmt][oxi_state] = {}
count_ions[elmt][oxi_state] = 0.0
count_ions[elmt][oxi_state] += occ
for ce_symbol, fraction in site_envs:
if fraction is None:
continue
if ce_symbol not in ion_stat[elmt][oxi_state]:
ion_stat[elmt][oxi_state][ce_symbol] = 0.0
ion_stat[elmt][oxi_state][ce_symbol] += occ * fraction
if ce_symbol not in ce_ion_stat:
ce_ion_stat[ce_symbol] = {}
if elmt not in ce_ion_stat[ce_symbol]:
ce_ion_stat[ce_symbol][elmt] = {}
if oxi_state not in ce_ion_stat[ce_symbol][elmt]:
ce_ion_stat[ce_symbol][elmt][oxi_state] = 0.0
ce_ion_stat[ce_symbol][elmt][oxi_state] += occ * fraction
self.statistics_dict['anion_number'] = len(self.statistics_dict['anion_list'])
self.statistics_dict['anion_atom_number'] = len(self.statistics_dict['anion_atom_list'])
self.statistics_dict['cation_number'] = len(self.statistics_dict['cation_list'])
self.statistics_dict['cation_atom_number'] = len(self.statistics_dict['cation_atom_list'])
self.statistics_dict['neutral_number'] = len(self.statistics_dict['neutral_list'])
self.statistics_dict['neutral_atom_number'] = len(self.statistics_dict['neutral_atom_list'])
for elmt, envs in atom_stat.items():
sumelement = count_atoms[elmt]
fraction_atom_stat[elmt] = {env: fraction / sumelement for env, fraction in envs.items()}
for ce_symbol, atoms in ce_atom_stat.items():
sumsymbol = count_ce[ce_symbol]
fraction_ce_atom_stat[ce_symbol] = {atom: fraction / sumsymbol for atom, fraction in atoms.items()}
ion_stat = self.statistics_dict['ion_coordination_environments_present']
fraction_ion_stat = self.statistics_dict['fraction_ion_coordination_environments_present']
ce_ion_stat = self.statistics_dict['coordination_environments_ion_present']
fraction_ce_ion_stat = self.statistics_dict['fraction_coordination_environments_ion_present']
count_ions = self.statistics_dict['count_ion_present']
for elmt, oxi_states_envs in ion_stat.items():
fraction_ion_stat[elmt] = {}
for oxi_state, envs in oxi_states_envs.items():
sumspecie = count_ions[elmt][oxi_state]
fraction_ion_stat[elmt][oxi_state] = {env: fraction / sumspecie
for env, fraction in envs.items()}
for ce_symbol, ions in ce_ion_stat.items():
fraction_ce_ion_stat[ce_symbol] = {}
sum_ce = np.sum([np.sum(list(oxistates.values())) for elmt, oxistates in ions.items()])
for elmt, oxistates in ions.items():
fraction_ce_ion_stat[ce_symbol][elmt] = {oxistate: fraction / sum_ce
for oxistate, fraction in oxistates.items()}
[docs] def get_site_info_for_specie_ce(self, specie, ce_symbol, min_fraction=0.0):
element = specie.symbol
oxi_state = specie.oxi_state
isites = []
csms = []
fractions = []
for isite, site in enumerate(self.structure):
if element in [sp.symbol for sp in site.species_and_occu]:
if oxi_state == self.valences[isite]:
for ce_dict in self.coordination_environments[isite]:
if ce_symbol == ce_dict['ce_symbol']:
isites.append(isite)
csms.append(ce_dict['ce_symbol'])
fractions.append(ce_dict['ce_fraction'])
[docs] def get_site_info_for_specie_allces(self, specie, min_fraction=0.0):
allces = {}
element = specie.symbol
oxi_state = specie.oxi_state
for isite, site in enumerate(self.structure):
if element in [sp.symbol for sp in site.species_and_occu]:
if oxi_state == self.valences[isite]:
if self.coordination_environments[isite] is None:
continue
for ce_dict in self.coordination_environments[isite]:
if ce_dict['ce_fraction'] < min_fraction:
continue
if ce_dict['ce_symbol'] not in allces:
allces[ce_dict['ce_symbol']] = {'isites': [], 'fractions': [], 'csms': []}
allces[ce_dict['ce_symbol']]['isites'].append(isite)
allces[ce_dict['ce_symbol']]['fractions'].append(ce_dict['ce_fraction'])
allces[ce_dict['ce_symbol']]['csms'].append(ce_dict['csm'])
return allces
[docs] def get_statistics(self, statistics_fields=DEFAULT_STATISTICS_FIELDS, bson_compatible=False):
if self.statistics_dict is None:
self.setup_statistic_lists()
if statistics_fields == 'ALL':
statistics_fields = [key for key in self.statistics_dict]
if bson_compatible:
dd = jsanitize({field: self.statistics_dict[field] for field in statistics_fields})
else:
dd = {field: self.statistics_dict[field] for field in statistics_fields}
return dd
[docs] def contains_only_one_anion_atom(self, anion_atom):
return (len(self.statistics_dict['anion_atom_list']) == 1 and
anion_atom in self.statistics_dict['anion_atom_list'])
[docs] def contains_only_one_anion(self, anion):
return len(self.statistics_dict['anion_list']) == 1 and anion in self.statistics_dict['anion_list']
[docs] def site_contains_environment(self, isite, ce_symbol):
return ce_symbol in [ce_dict['ce_symbol'] for ce_dict in self.coordination_environments[isite]]
[docs] def structure_contains_atom_environment(self, atom_symbol, ce_symbol):
"""
Checks whether the structure contains a given atom in a given environment
:param atom_symbol: Symbol of the atom
:param ce_symbol: Symbol of the coordination environment
:return: True if the coordination environment is found, False otherwise
"""
for isite, site in enumerate(self.structure):
if (Element(atom_symbol) in site.species_and_occu.
element_composition and self.site_contains_environment(isite, ce_symbol)):
return True
return False
@property
def uniquely_determined_coordination_environments(self):
"""
True if the coordination environments are uniquely determined.
"""
return self.strategy.uniquely_determined_coordination_environments
def __eq__(self, other):
"""
Equality method that checks if the LightStructureEnvironments object is equal to another
LightStructureEnvironments object. Two LightStructureEnvironments objects are equal if the strategy used
is the same, if the structure is the same, if the valences used in the strategies are the same, if the
coordination environments and the neighbours determined by the strategy are the same
:param other: LightStructureEnvironments object to compare with
:return: True if both objects are equal, False otherwise
"""
is_equal = (self.strategy == other.strategy and
self.structure == other.structure and
self.coordination_environments == other.coordination_environments and
self.valences == other.valences and
self.neighbors_sets == other.neighbors_sets)
this_sites = [ss['site'] for ss in self._all_nbs_sites]
other_sites = [ss['site'] for ss in other._all_nbs_sites]
this_indices = [ss['index'] for ss in self._all_nbs_sites]
other_indices = [ss['index'] for ss in other._all_nbs_sites]
return (is_equal and this_sites == other_sites and this_indices == other_indices)
def __ne__(self, other):
return not self == other
[docs] def as_dict(self):
"""
Bson-serializable dict representation of the LightStructureEnvironments object.
:return: Bson-serializable dict representation of the LightStructureEnvironments object.
"""
return {"@module": self.__class__.__module__,
"@class": self.__class__.__name__,
"strategy": self.strategy.as_dict(),
"structure": self.structure.as_dict(),
"coordination_environments": self.coordination_environments,
"all_nbs_sites": [{'site': nb_site['site'].as_dict(),
'index': nb_site['index'],
'image_cell': [int(ii) for ii in nb_site['image_cell']]}
for nb_site in self._all_nbs_sites],
"neighbors_sets": [[nb_set.as_dict() for nb_set in site_nb_sets] if site_nb_sets is not None else None
for site_nb_sets in self.neighbors_sets],
"valences": self.valences}
[docs] @classmethod
def from_dict(cls, d):
"""
Reconstructs the LightStructureEnvironments object from a dict representation of the
LightStructureEnvironments created using the as_dict method.
:param d: dict representation of the LightStructureEnvironments object
:return: LightStructureEnvironments object
"""
dec = MontyDecoder()
structure = dec.process_decoded(d['structure'])
all_nbs_sites = []
for nb_site in d['all_nbs_sites']:
site = dec.process_decoded(nb_site['site'])
if 'image_cell' in nb_site:
image_cell = np.array(nb_site['image_cell'], np.int)
else:
diff = site.frac_coords - structure[nb_site['index']].frac_coords
rounddiff = np.round(diff)
if not np.allclose(diff, rounddiff):
raise ValueError('Weird, differences between one site in a periodic image cell is not '
'integer ...')
image_cell = np.array(rounddiff, np.int)
all_nbs_sites.append({'site': site,
'index': nb_site['index'],
'image_cell': image_cell})
neighbors_sets = [[cls.NeighborsSet.from_dict(dd=nb_set, structure=structure,
all_nbs_sites=all_nbs_sites)
for nb_set in site_nb_sets] if site_nb_sets is not None else None
for site_nb_sets in d['neighbors_sets']]
return cls(strategy=dec.process_decoded(d['strategy']),
coordination_environments=d['coordination_environments'],
all_nbs_sites=all_nbs_sites,
neighbors_sets=neighbors_sets,
structure=structure,
valences=d['valences'])
[docs]class ChemicalEnvironments(MSONable):
"""
Class used to store all the information about the chemical environment of a given site for a given list of
coordinated neighbours (internally called "cn_map")
"""
def __init__(self, coord_geoms=None):
"""
Initializes the ChemicalEnvironments object containing all the information about the chemical
environment of a given site
:param coord_geoms: coordination geometries to be added to the chemical environment.
"""
if coord_geoms is None:
self.coord_geoms = {}
else:
raise NotImplementedError('Constructor for ChemicalEnvironments with the coord_geoms argument is not'
'yet implemented')
def __getitem__(self, mp_symbol):
if not mp_symbol in self.coord_geoms:
raise IndexError()
return self.coord_geoms[mp_symbol]
def __len__(self):
"""
Returns the number of coordination geometries in this ChemicalEnvironments object
:return: Number of coordination geometries in this ChemicalEnvironments object
"""
return len(self.coord_geoms)
def __iter__(self):
for cg, cg_dict in self.coord_geoms.items():
yield (cg, cg_dict)
[docs] def minimum_geometry(self, symmetry_measure_type=None, max_csm=None):
"""
Returns the geometry with the minimum continuous symmetry measure of this ChemicalEnvironments
:return: tuple (symbol, csm) with symbol being the geometry with the minimum continuous symmetry measure and
csm being the continuous symmetry measure associted to it
:raise: ValueError if no coordination geometry is found in this ChemicalEnvironments object
"""
if len(self.coord_geoms) == 0:
return None
cglist = [cg for cg in self.coord_geoms]
if symmetry_measure_type is None:
csms = np.array([self.coord_geoms[cg]['other_symmetry_measures']['csm_wcs_ctwcc'] for cg in cglist])
else:
csms = np.array([self.coord_geoms[cg]['other_symmetry_measures'][symmetry_measure_type] for cg in cglist])
csmlist = [self.coord_geoms[cg] for cg in cglist]
imin = np.argmin(csms)
if max_csm is not None:
if csmlist[imin] > max_csm:
return None
return cglist[imin], csmlist[imin]
[docs] def minimum_geometries(self, n=None, symmetry_measure_type=None, max_csm=None):
"""
Returns a list of geometries with increasing continuous symmetry measure in this ChemicalEnvironments object
:param n: Number of geometries to be included in the list
:return: list of geometries with increasing continuous symmetry measure in this ChemicalEnvironments object
:raise: ValueError if no coordination geometry is found in this ChemicalEnvironments object
"""
cglist = [cg for cg in self.coord_geoms]
if symmetry_measure_type is None:
csms = np.array([self.coord_geoms[cg]['other_symmetry_measures']['csm_wcs_ctwcc'] for cg in cglist])
else:
csms = np.array([self.coord_geoms[cg]['other_symmetry_measures'][symmetry_measure_type] for cg in cglist])
csmlist = [self.coord_geoms[cg] for cg in cglist]
isorted = np.argsort(csms)
if max_csm is not None:
if n is None:
return [(cglist[ii], csmlist[ii]) for ii in isorted if csms[ii] <= max_csm]
else:
return [(cglist[ii], csmlist[ii]) for ii in isorted[:n] if csms[ii] <= max_csm]
else:
if n is None:
return [(cglist[ii], csmlist[ii]) for ii in isorted]
else:
return [(cglist[ii], csmlist[ii]) for ii in isorted[:n]]
[docs] def add_coord_geom(self, mp_symbol, symmetry_measure, algo='UNKNOWN', permutation=None, override=False,
local2perfect_map=None, perfect2local_map=None, detailed_voronoi_index=None,
other_symmetry_measures=None, rotation_matrix=None, scaling_factor=None):
"""
Adds a coordination geometry to the ChemicalEnvironments object
:param mp_symbol: Symbol (internal) of the coordination geometry added
:param symmetry_measure: Symmetry measure of the coordination geometry added
:param algo: Algorithm used for the search of the coordination geometry added
:param permutation: Permutation of the neighbors that leads to the csm stored
:param override: If set to True, the coordination geometry will override the existent one if present
:return: :raise: ChemenvError if the coordination geometry is already added and override is set to False
"""
if not allcg.is_a_valid_coordination_geometry(mp_symbol=mp_symbol):
raise ChemenvError(self.__class__,
'add_coord_geom',
'Coordination geometry with mp_symbol "{mp}" is not valid'
.format(mp=mp_symbol))
if mp_symbol in list(self.coord_geoms.keys()) and not override:
raise ChemenvError(self.__class__,
"add_coord_geom",
"This coordination geometry is already present and override is set to False")
else:
self.coord_geoms[mp_symbol] = {'symmetry_measure': float(symmetry_measure), 'algo': algo,
'permutation': [int(i) for i in permutation],
'local2perfect_map': local2perfect_map,
'perfect2local_map': perfect2local_map,
'detailed_voronoi_index': detailed_voronoi_index,
'other_symmetry_measures': other_symmetry_measures,
'rotation_matrix': rotation_matrix,
'scaling_factor': scaling_factor}
def __str__(self):
"""
Returns a string representation of the ChemicalEnvironments object
:return: String representation of the ChemicalEnvironments object
"""
out = 'Chemical environments object :\n'
if len(self.coord_geoms) == 0:
out += ' => No coordination in it <=\n'
return out
for key in self.coord_geoms.keys():
mp_symbol = key
break
cn = symbol_cn_mapping[mp_symbol]
out += ' => Coordination {} <=\n'.format(cn)
mp_symbols = list(self.coord_geoms.keys())
csms_wcs = [self.coord_geoms[mp_symbol]['other_symmetry_measures']['csm_wcs_ctwcc'] for mp_symbol in mp_symbols]
icsms_sorted = np.argsort(csms_wcs)
mp_symbols = [mp_symbols[ii] for ii in icsms_sorted]
for mp_symbol in mp_symbols:
csm_wcs = self.coord_geoms[mp_symbol]['other_symmetry_measures']['csm_wcs_ctwcc']
csm_wocs = self.coord_geoms[mp_symbol]['other_symmetry_measures']['csm_wocs_ctwocc']
out += ' - {}\n'.format(mp_symbol)
out += ' csm1 (with central site) : {}'.format(csm_wcs)
out += ' csm2 (without central site) : {}'.format(csm_wocs)
out += ' algo : {}'.format(self.coord_geoms[mp_symbol]['algo'])
out += ' perm : {}\n'.format(self.coord_geoms[mp_symbol]['permutation'])
out += ' local2perfect : {}\n'.format(str(self.coord_geoms[mp_symbol]['local2perfect_map']))
out += ' perfect2local : {}\n'.format(str(self.coord_geoms[mp_symbol]['perfect2local_map']))
return out
[docs] def is_close_to(self, other, rtol=0.0, atol=1e-8):
if set(self.coord_geoms.keys()) != set(other.coord_geoms.keys()):
return False
for mp_symbol, cg_dict_self in self.coord_geoms.items():
cg_dict_other = other[mp_symbol]
other_csms_self = cg_dict_self['other_symmetry_measures']
other_csms_other = cg_dict_other['other_symmetry_measures']
for csmtype in ['csm_wcs_ctwcc', 'csm_wcs_ctwocc', 'csm_wcs_csc',
'csm_wocs_ctwcc', 'csm_wocs_ctwocc', 'csm_wocs_csc']:
if not np.isclose(other_csms_self[csmtype], other_csms_other[csmtype], rtol=rtol, atol=atol):
return False
return True
def __eq__(self, other):
"""
Equality method that checks if the ChemicalEnvironments object is equal to another ChemicalEnvironments
object.
:param other: ChemicalEnvironments object to compare with
:return: True if both objects are equal, False otherwise
"""
if set(self.coord_geoms.keys()) != set(other.coord_geoms.keys()):
return False
for mp_symbol, cg_dict_self in self.coord_geoms.items():
cg_dict_other = other.coord_geoms[mp_symbol]
if cg_dict_self['symmetry_measure'] != cg_dict_other['symmetry_measure']:
return False
if cg_dict_self['algo'] != cg_dict_other['algo']:
return False
if cg_dict_self['permutation'] != cg_dict_other['permutation']:
return False
if cg_dict_self['detailed_voronoi_index'] != cg_dict_other['detailed_voronoi_index']:
return False
other_csms_self = cg_dict_self['other_symmetry_measures']
other_csms_other = cg_dict_other['other_symmetry_measures']
for csmtype in ['csm_wcs_ctwcc', 'csm_wcs_ctwocc', 'csm_wcs_csc',
'csm_wocs_ctwcc', 'csm_wocs_ctwocc', 'csm_wocs_csc']:
if other_csms_self[csmtype] != other_csms_other[csmtype]:
return False
return True
def __ne__(self, other):
return not self == other
[docs] def as_dict(self):
"""
Returns a dictionary representation of the ChemicalEnvironments object
:return:
"""
return {"@module": self.__class__.__module__,
"@class": self.__class__.__name__,
"coord_geoms": jsanitize(self.coord_geoms)}
[docs] @classmethod
def from_dict(cls, d):
"""
Reconstructs the ChemicalEnvironments object from a dict representation of the ChemicalEnvironments created
using the as_dict method.
:param d: dict representation of the ChemicalEnvironments object
:return: ChemicalEnvironments object
"""
ce = cls()
for cg in d['coord_geoms'].keys():
if d['coord_geoms'][cg]['local2perfect_map'] is None:
l2p_map = None
else:
l2p_map = {int(key): int(val) for key, val in d['coord_geoms'][cg]['local2perfect_map'].items()}
if d['coord_geoms'][cg]['perfect2local_map'] is None:
p2l_map = None
else:
p2l_map = {int(key): int(val) for key, val in d['coord_geoms'][cg]['perfect2local_map'].items()}
if ('other_symmetry_measures' in d['coord_geoms'][cg] and
d['coord_geoms'][cg]['other_symmetry_measures'] is not None):
other_csms = d['coord_geoms'][cg]['other_symmetry_measures']
else:
other_csms = None
ce.add_coord_geom(cg,
d['coord_geoms'][cg]['symmetry_measure'],
d['coord_geoms'][cg]['algo'],
permutation=d['coord_geoms'][cg]['permutation'],
local2perfect_map=l2p_map,
perfect2local_map=p2l_map,
detailed_voronoi_index=d['coord_geoms'][cg]['detailed_voronoi_index'],
other_symmetry_measures=other_csms,
rotation_matrix=d['coord_geoms'][cg]['rotation_matrix'],
scaling_factor=d['coord_geoms'][cg]['scaling_factor'])
return ce