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

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


"""
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 collections import OrderedDict
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()
[docs] class NeighborsSet: """ Class used to store a given set of neighbors of a given site (based on the detailed_voronoi). """ def __init__(self, structure, isite, detailed_voronoi, site_voronoi_indices, sources=None): """ Constructor for NeighborsSet. Args: structure: Structure object. isite: Index of the site for which neighbors are stored in this NeighborsSet. detailed_voronoi: Corresponding DetailedVoronoiContainer object containing all the possible neighbors of the give site. site_voronoi_indices: Indices of the voronoi sites in the DetailedVoronoiContainer object that make up this NeighborsSet. sources: Sources for this NeighborsSet, i.e. how this NeighborsSet was generated. """ self.structure = structure self.isite = isite self.detailed_voronoi = detailed_voronoi self.voronoi = detailed_voronoi.voronoi_list2[isite] myset = set(site_voronoi_indices) if len(myset) != len(site_voronoi_indices): raise ValueError('Set of neighbors contains duplicates !') self.site_voronoi_indices = sorted(myset) if sources is None: self.sources = [{'origin': 'UNKNOWN'}] elif isinstance(sources, list): self.sources = sources else: self.sources = [sources]
[docs] def get_neighb_voronoi_indices(self, permutation): """ Return the indices in the detailed_voronoi corresponding to the current permutation. Args: permutation: Current permutation for which the indices in the detailed_voronoi are needed. Returns: List of indices in the detailed_voronoi. """ return [self.site_voronoi_indices[ii] for ii in permutation]
@property def neighb_coords(self): """ Coordinates of neighbors for this NeighborsSet. """ return [self.voronoi[inb]['site'].coords for inb in self.site_voronoi_indices] @property def neighb_coordsOpt(self): """ Optimized access to the coordinates of neighbors for this NeighborsSet. """ return self.detailed_voronoi.voronoi_list_coords[self.isite].take(self.site_voronoi_indices, axis=0) @property def neighb_sites(self): """ Neighbors for this NeighborsSet as pymatgen Sites. """ return [self.voronoi[inb]['site'] for inb in self.site_voronoi_indices] @property def neighb_sites_and_indices(self): """ List of neighbors for this NeighborsSet as pymatgen Sites and their index in the original structure. """ return [{'site': self.voronoi[inb]['site'], 'index': self.voronoi[inb]['index']} for inb in self.site_voronoi_indices] @property def coords(self): """ Coordinates of the current central atom and its neighbors for this NeighborsSet. """ coords = [self.structure[self.isite].coords] coords.extend(self.neighb_coords) return coords @property def normalized_distances(self): """ Normalized distances to each neighbor in this NeighborsSet. """ return [self.voronoi[inb]['normalized_distance'] for inb in self.site_voronoi_indices] @property def normalized_angles(self): """ Normalized angles for each neighbor in this NeighborsSet. """ return [self.voronoi[inb]['normalized_angle'] for inb in self.site_voronoi_indices] @property def distances(self): """ Distances to each neighbor in this NeighborsSet. """ return [self.voronoi[inb]['distance'] for inb in self.site_voronoi_indices] @property def angles(self): """ Angles for each neighbor in this NeighborsSet. """ return [self.voronoi[inb]['angle'] for inb in self.site_voronoi_indices] # @property # def sphere_fraction_angles(self): # return [0.25 * self.voronoi[inb]['angle'] / np.pi for inb in self.site_voronoi_indices] @property def info(self): """ Summarized information about this NeighborsSet. """ was = self.normalized_angles wds = self.normalized_distances angles = self.angles distances = self.distances return {'normalized_angles': was, 'normalized_distances': wds, 'normalized_angles_sum': np.sum(was), 'normalized_angles_mean': np.mean(was), 'normalized_angles_std': np.std(was), 'normalized_angles_min': np.min(was), 'normalized_angles_max': np.max(was), 'normalized_distances_mean': np.mean(wds), 'normalized_distances_std': np.std(wds), 'normalized_distances_min': np.min(wds), 'normalized_distances_max': np.max(wds), 'angles': angles, 'distances': distances, 'angles_sum': np.sum(angles), 'angles_mean': np.mean(angles), 'angles_std': np.std(angles), 'angles_min': np.min(angles), 'angles_max': np.max(angles), 'distances_mean': np.mean(distances), 'distances_std': np.std(distances), 'distances_min': np.min(distances), 'distances_max': np.max(distances) }
[docs] def distance_plateau(self): """ Returns the distances plateau's for this NeighborsSet. """ all_nbs_normalized_distances_sorted = sorted([nb['normalized_distance'] for nb in self.voronoi], reverse=True) maxdist = np.max(self.normalized_distances) plateau = None for idist, dist in enumerate(all_nbs_normalized_distances_sorted): if np.isclose(dist, maxdist, rtol=0.0, atol=self.detailed_voronoi.normalized_distance_tolerance): if idist == 0: plateau = np.inf else: plateau = all_nbs_normalized_distances_sorted[idist - 1] - maxdist break if plateau is None: raise ValueError('Plateau not found ...') return plateau
[docs] def angle_plateau(self): """ Returns the angles plateau's for this NeighborsSet. """ all_nbs_normalized_angles_sorted = sorted([nb['normalized_angle'] for nb in self.voronoi]) minang = np.min(self.normalized_angles) # print('minang', minang) # print('all_nbs_normalized_angles_sorted', all_nbs_normalized_angles_sorted) for nb in self.voronoi: print(nb) plateau = None for iang, ang in enumerate(all_nbs_normalized_angles_sorted): if np.isclose(ang, minang, rtol=0.0, atol=self.detailed_voronoi.normalized_angle_tolerance): if iang == 0: plateau = minang else: plateau = minang - all_nbs_normalized_angles_sorted[iang - 1] break if plateau is None: raise ValueError('Plateau not found ...') return plateau
[docs] def voronoi_grid_surface_points(self, additional_condition=1, other_origins='DO_NOTHING'): """ Get the surface points in the Voronoi grid for this neighbor from the sources. The general shape of the points should look like a staircase such as in the following figure : ^ 0.0| | | B----C | | | | | | a | k D-------E n | | | g | | | l | | | e | j F----n---------G | | | | | | | A----g-------h----i---------H | | 1.0+-------------------------------------------------> 1.0 distance 2.0 ->+Inf Args: additional_condition: Additional condition for the neighbors. other_origins: What to do with sources that do not come from the Voronoi grid (e.g. "from hints"). """ mysrc = [] for src in self.sources: if src['origin'] == 'dist_ang_ac_voronoi': if src['ac'] != additional_condition: continue mysrc.append(src) else: if other_origins == 'DO_NOTHING': continue else: raise NotImplementedError('Nothing implemented for other sources ...') if len(mysrc) == 0: return None dists = [src['dp_dict']['min'] for src in mysrc] angs = [src['ap_dict']['max'] for src in mysrc] next_dists = [src['dp_dict']['next'] for src in mysrc] next_angs = [src['ap_dict']['next'] for src in mysrc] points_dict = OrderedDict() pdists = [] pangs = [] for isrc in range(len(mysrc)): if not any(np.isclose(pdists, dists[isrc])): pdists.append(dists[isrc]) if not any(np.isclose(pdists, next_dists[isrc])): pdists.append(next_dists[isrc]) if not any(np.isclose(pangs, angs[isrc])): pangs.append(angs[isrc]) if not any(np.isclose(pangs, next_angs[isrc])): pangs.append(next_angs[isrc]) d1_indices = np.argwhere(np.isclose(pdists, dists[isrc])).flatten() if len(d1_indices) != 1: raise ValueError('Distance parameter not found ...') d2_indices = np.argwhere(np.isclose(pdists, next_dists[isrc])).flatten() if len(d2_indices) != 1: raise ValueError('Distance parameter not found ...') a1_indices = np.argwhere(np.isclose(pangs, angs[isrc])).flatten() if len(a1_indices) != 1: raise ValueError('Angle parameter not found ...') a2_indices = np.argwhere(np.isclose(pangs, next_angs[isrc])).flatten() if len(a2_indices) != 1: raise ValueError('Angle parameter not found ...') id1 = d1_indices[0] id2 = d2_indices[0] ia1 = a1_indices[0] ia2 = a2_indices[0] for id_ia in [(id1, ia1), (id1, ia2), (id2, ia1), (id2, ia2)]: if id_ia not in points_dict: points_dict[id_ia] = 0 points_dict[id_ia] += 1 new_pts = [] for pt, pt_nb in points_dict.items(): if pt_nb % 2 == 1: new_pts.append(pt) sorted_points = [(0, 0)] move_ap_index = True while True: last_pt = sorted_points[-1] if move_ap_index: # "Move" the angle parameter idp = last_pt[0] iap = None for pt in new_pts: if pt[0] == idp and pt != last_pt: iap = pt[1] break else: # "Move" the distance parameter idp = None iap = last_pt[1] for pt in new_pts: if pt[1] == iap and pt != last_pt: idp = pt[0] break if (idp, iap) == (0, 0): break if (idp, iap) in sorted_points: raise ValueError('Error sorting points ...') sorted_points.append((idp, iap)) move_ap_index = not move_ap_index points = [(pdists[idp], pangs[iap]) for (idp, iap) in sorted_points] return points
@property def source(self): """ Returns the source of this NeighborsSet (how it was generated, e.g. from which Voronoi cut-offs, or from hints). """ if len(self.sources) != 1: raise RuntimeError('Number of sources different from 1 !') return self.sources[0]
[docs] def add_source(self, source): """ Add a source to this NeighborsSet. Args: source: Information about the generation of this NeighborsSet. """ if source not in self.sources: self.sources.append(source)
def __len__(self): return len(self.site_voronoi_indices) def __hash__(self): return len(self.site_voronoi_indices) def __eq__(self, other): return self.isite == other.isite and self.site_voronoi_indices == other.site_voronoi_indices def __ne__(self, other): return not self == other def __str__(self): out = 'Neighbors Set for site #{:d} :\n'.format(self.isite) out += ' - Coordination number : {:d}\n'.format(len(self)) out += ' - Voronoi indices : {}\n'.format(', '.join(['{:d}'.format(site_voronoi_index) for site_voronoi_index in self.site_voronoi_indices])) return out
[docs] def as_dict(self): """ A JSON serializable dict representation of the NeighborsSet. """ return {'isite': self.isite, 'site_voronoi_indices': self.site_voronoi_indices, 'sources': self.sources}
[docs] @classmethod def from_dict(cls, dd, structure, detailed_voronoi): """ Reconstructs the NeighborsSet algorithm from its JSON serializable dict representation, together with the structure and the DetailedVoronoiContainer. As an inner (nested) class, the NeighborsSet is not supposed to be used anywhere else that inside the StructureEnvironments. The from_dict method is thus using the structure and detailed_voronoi when reconstructing itself. These two are both in the StructureEnvironments object. Args: dd: a JSON serializable dict representation of a NeighborsSet. structure: The structure. detailed_voronoi: The Voronoi object containing all the neighboring atoms from which the subset of neighbors for this NeighborsSet is extracted. Returns: a NeighborsSet. """ return cls(structure=structure, isite=dd['isite'], detailed_voronoi=detailed_voronoi, site_voronoi_indices=dd['site_voronoi_indices'], sources=dd['sources'])
def __init__(self, voronoi, valences, sites_map, equivalent_sites, ce_list, structure, neighbors_sets=None, info=None): """ Constructor for the StructureEnvironments object. Args: voronoi: VoronoiContainer object for the structure. valences: Valences provided. sites_map: Mapping of equivalent sites to the unequivalent sites that have been computed. equivalent_sites: List of list of equivalent sites of the structure. ce_list: List of chemical environments. structure: Structure object. neighbors_sets: List of neighbors sets. info: Additional information for this StructureEnvironments 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): """ Initialize the list of neighbors sets for the current site. Args: isite: Index of the site under consideration. additional_conditions: Additional conditions to be used for the initialization of the list of neighbors sets, e.g. "Only anion-cation bonds", ... valences: List of valences for each site in the structure (needed if an additional condition based on the valence is used, e.g. only anion-cation bonds). """ 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): """ Adds a neighbor set to the list of neighbors sets for this site. Args: isite: Index of the site under consideration. nb_set: NeighborsSet to be added. """ 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): """ Updates the coordination environment for this site, coordination and neighbor set. Args: isite: Index of the site to be updated. cn: Coordination to be updated. nb_set: Neighbors set to be updated. ce: ChemicalEnvironments object for this neighbors set. """ 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 update_site_info(self, isite, info_dict): """ Update information about this site. Args: isite: Index of the site for which info has to be updated. info_dict: Dictionary of information to be added for this site. """ if 'sites_info' not in self.info: self.info['sites_info'] = [{} for _ in range(len(self.structure))] self.info['sites_info'][isite].update(info_dict)
[docs] def get_coordination_environments(self, isite, cn, nb_set): """ Get the ChemicalEnvironments for a given site, coordination and neighbors set. Args: isite: Index of the site for which the ChemicalEnvironments is looked for. cn: Coordination for which the ChemicalEnvironments is looked for. nb_set: Neighbors set for which the ChemicalEnvironments is looked for. Returns: a ChemicalEnvironments object. """ 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): """ Get the continuous symmetry measure for a given site in the given coordination environment. Args: isite: Index of the site. mp_symbol: Symbol of the coordination environment for which we want the continuous symmetry measure. Returns: Continuous symmetry measure of the given site in the given environment. """ 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). Args: isite: Index of the site. mp_symbol: MP symbol of the perfect environment for which the csm has to be given. Returns: 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. Args: isite: Index of the site for which the plot has to be done max_csm: Maximum continuous symmetry measure to be shown. """ 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): """ 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. Args: isite: Index of the site for which the plot has to be done. max_csm: Maximum continuous symmetry measure to be shown. figsize: Size of the figure. symmetry_measure_type: Type of continuous symmetry measure to be used. Returns: Matplotlib figure and axes representing the csm and maps. """ 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]) return fig, subplot
[docs] def get_environments_figure(self, isite, plot_type=None, title='Coordination numbers', max_dist=2.0, colormap=None, figsize=None, strategy=None): """ Plotting of the coordination environments of a given site for all the distfactor/angfactor regions. The chemical environments with the lowest continuous symmetry measure is shown for each distfactor/angfactor region as the value for the color of that distfactor/angfactor region (using a colormap). Args: isite: Index of the site for which the plot has to be done. plot_type: How to plot the coordinations. title: Title for the figure. 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). colormap: Color map to be used for the continuous symmetry measure. figsize: Size of the figure. strategy: Whether to plot information about one of the Chemenv Strategies. Returns: Matplotlib figure and axes representing the environments. """ try: import matplotlib.pyplot as mpl from matplotlib import cm from matplotlib.colors import Normalize from matplotlib.patches import Polygon except ImportError: print('Plotting Chemical Environments requires matplotlib ... exiting "plot" function') return # Initializes the figure if figsize is None: fig = mpl.figure() else: fig = mpl.figure(figsize=figsize) subplot = fig.add_subplot(111) # Initializes the distance and angle parameters if plot_type is None: plot_type = {'distance_parameter': ('initial_normalized', None), 'angle_parameter': ('initial_normalized_inverted', None)} if colormap is None: mycm = cm.jet else: mycm = colormap mymin = 0.0 mymax = 10.0 norm = Normalize(vmin=mymin, vmax=mymax) scalarmap = cm.ScalarMappable(norm=norm, cmap=mycm) dist_limits = [1.0, max_dist] ang_limits = [0.0, 1.0] if plot_type['distance_parameter'][0] == 'one_minus_inverse_alpha_power_n': if plot_type['distance_parameter'][1] is None: exponent = 3 else: exponent = plot_type['distance_parameter'][1]['exponent'] xlabel = 'Distance parameter : $1.0-\\frac{{1.0}}{{\\alpha^{{{:d}}}}}$'.format(exponent) def dp_func(dp): return 1.0 - 1.0 / np.power(dp, exponent) elif plot_type['distance_parameter'][0] == 'initial_normalized': xlabel = 'Distance parameter : $\\alpha$' def dp_func(dp): return dp else: raise ValueError('Wrong value for distance parameter plot type "{}"'. format(plot_type['distance_parameter'][0])) if plot_type['angle_parameter'][0] == 'one_minus_gamma': ylabel = 'Angle parameter : $1.0-\\gamma$' def ap_func(ap): return 1.0 - ap elif plot_type['angle_parameter'][0] in ['initial_normalized_inverted', 'initial_normalized']: ylabel = 'Angle parameter : $\\gamma$' def ap_func(ap): return ap else: raise ValueError('Wrong value for angle parameter plot type "{}"'. format(plot_type['angle_parameter'][0])) dist_limits = [dp_func(dp) for dp in dist_limits] ang_limits = [ap_func(ap) for ap in ang_limits] for cn, cn_nb_sets in self.neighbors_sets[isite].items(): for inb_set, nb_set in enumerate(cn_nb_sets): nb_set_surface_pts = nb_set.voronoi_grid_surface_points() if nb_set_surface_pts is None: continue ce = self.ce_list[isite][cn][inb_set] if ce is None: mycolor = 'w' myinvcolor = 'k' mytext = '{:d}'.format(cn) else: mingeom = ce.minimum_geometry() if mingeom is not None: mp_symbol = mingeom[0] csm = mingeom[1]['symmetry_measure'] mycolor = scalarmap.to_rgba(csm) myinvcolor = [1.0 - mycolor[0], 1.0 - mycolor[1], 1.0 - mycolor[2], 1.0] mytext = '{}'.format(mp_symbol) else: mycolor = 'w' myinvcolor = 'k' mytext = '{:d}'.format(cn) nb_set_surface_pts = [(dp_func(pt[0]), ap_func(pt[1])) for pt in nb_set_surface_pts] polygon = Polygon(nb_set_surface_pts, closed=True, edgecolor='k', facecolor=mycolor, linewidth=1.2) subplot.add_patch(polygon) myipt = len(nb_set_surface_pts) / 2 ipt = int(myipt) if myipt != ipt: raise RuntimeError('Number of surface points not even') patch_center = ((nb_set_surface_pts[0][0] + min(nb_set_surface_pts[ipt][0], dist_limits[1])) / 2, (nb_set_surface_pts[0][1] + nb_set_surface_pts[ipt][1]) / 2) if (np.abs(nb_set_surface_pts[-1][1] - nb_set_surface_pts[-2][1]) > 0.06 and np.abs(min(nb_set_surface_pts[-1][0], dist_limits[1]) - nb_set_surface_pts[0][0]) > 0.125): xytext = ((min(nb_set_surface_pts[-1][0], dist_limits[1]) + nb_set_surface_pts[0][0]) / 2, (nb_set_surface_pts[-1][1] + nb_set_surface_pts[-2][1]) / 2) subplot.annotate(mytext, xy=xytext, ha='center', va='center', color=myinvcolor, fontsize='x-small') elif (np.abs(nb_set_surface_pts[ipt][1] - nb_set_surface_pts[0][1]) > 0.1 and np.abs(min(nb_set_surface_pts[ipt][0], dist_limits[1]) - nb_set_surface_pts[0][0]) > 0.125): xytext = patch_center subplot.annotate(mytext, xy=xytext, ha='center', va='center', color=myinvcolor, fontsize='x-small') subplot.set_title(title) subplot.set_xlabel(xlabel) subplot.set_ylabel(ylabel) dist_limits.sort() ang_limits.sort() subplot.set_xlim(dist_limits) subplot.set_ylim(ang_limits) if strategy is not None: try: strategy.add_strategy_visualization_to_subplot(subplot=subplot) except Exception: pass if plot_type['angle_parameter'][0] == 'initial_normalized_inverted': subplot.axes.invert_yaxis() scalarmap.set_array([mymin, mymax]) cb = fig.colorbar(scalarmap, ax=subplot, extend='max') cb.set_label('Continuous symmetry measure') return fig, subplot
[docs] def plot_environments(self, isite, plot_type=None, title='Coordination numbers', max_dist=2.0, 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. Args: isite: Index of the site for which the plot has to be done. plot_type: How to plot the coordinations. title: Title for the figure. 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). figsize: Size of the figure. strategy: Whether to plot information about one of the Chemenv Strategies. """ fig, subplot = self.get_environments_figure(isite=isite, plot_type=plot_type, title=title, max_dist=max_dist, figsize=figsize, strategy=strategy) if fig is None: return fig.show()
[docs] def save_environments_figure(self, isite, imagename='image.png', plot_type=None, title='Coordination numbers', max_dist=2.0, figsize=None): """ Saves the environments figure to a given file. Args: isite: Index of the site for which the plot has to be done. imagename: Name of the file to which the figure has to be saved. plot_type: How to plot the coordinations. title: Title for the figure. 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). figsize: Size of the figure. """ fig, subplot = self.get_environments_figure(isite=isite, plot_type=plot_type, title=title, max_dist=max_dist, figsize=figsize) if fig is None: return fig.savefig(imagename)
[docs] def differences_wrt(self, other): """ Return differences found in the current StructureEnvironments with respect to another StructureEnvironments. Args: other: A StructureEnvironments object. Returns: List of differences between the two StructureEnvironments objects. """ 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] if self_site_nb_sets is None: if other_site_nb_sets is None: continue else: differences.append({'difference': 'neighbors_sets[isite={:d}]'.format(isite), 'comparison': 'has_neighbors', 'self': 'None', 'other': set(other_site_nb_sets.keys())}) continue elif other_site_nb_sets is None: differences.append({'difference': 'neighbors_sets[isite={:d}]'.format(isite), 'comparison': 'has_neighbors', 'self': set(self_site_nb_sets.keys()), 'other': 'None'}) continue 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. Returns: 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] info_dict = {key: val for key, val in self.info.items() if key not in ['sites_info']} info_dict['sites_info'] = [{'nb_sets_info': {str(cn): {str(inb_set): nb_set_info for inb_set, nb_set_info in cn_sets.items()} for cn, cn_sets in site_info['nb_sets_info'].items()}, 'time': site_info['time']} if 'nb_sets_info' in site_info else {} for site_info in self.info['sites_info']] 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": info_dict}
[docs] @classmethod def from_dict(cls, d): """ Reconstructs the StructureEnvironments object from a dict representation of the StructureEnvironments created using the as_dict method. Args: d: dict representation of the StructureEnvironments object. Returns: 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']] info = {key: val for key, val in d['info'].items() if key not in ['sites_info']} if 'sites_info' in d['info']: info['sites_info'] = [{'nb_sets_info': {int(cn): {int(inb_set): nb_set_info for inb_set, nb_set_info in cn_sets.items()} for cn, cn_sets in site_info['nb_sets_info'].items()}, 'time': site_info['time']} if 'nb_sets_info' in site_info else {} for site_info in d['info']['sites_info']] 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=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']
[docs] class NeighborsSet: """ Class used to store a given set of neighbors of a given site (based on a list of sites, the voronoi container is not part of the LightStructureEnvironments object). """ def __init__(self, structure, isite, all_nbs_sites, all_nbs_sites_indices): """ Constructor for NeighborsSet. Args: structure: Structure object. isite: Index of the site for which neighbors are stored in this NeighborsSet. all_nbs_sites: All the possible neighbors for this site. all_nbs_sites_indices: Indices of the sites in all_nbs_sites that make up this NeighborsSet. """ self.structure = structure self.isite = isite self.all_nbs_sites = all_nbs_sites myset = set(all_nbs_sites_indices) if len(myset) != len(all_nbs_sites_indices): raise ValueError('Set of neighbors contains duplicates !') self.all_nbs_sites_indices = sorted(myset) self.all_nbs_sites_indices_unsorted = all_nbs_sites_indices self.all_nbs_sites_indices_and_image = [] @property def neighb_coords(self): """ Coordinates of neighbors for this NeighborsSet. """ return [self.all_nbs_sites[inb]['site'].coords for inb in self.all_nbs_sites_indices_unsorted] @property def neighb_sites(self): """ Neighbors for this NeighborsSet as pymatgen Sites. """ return [self.all_nbs_sites[inb]['site'] for inb in self.all_nbs_sites_indices_unsorted] @property def neighb_sites_and_indices(self): """ List of neighbors for this NeighborsSet as pymatgen Sites and their index in the original structure. """ return [{'site': self.all_nbs_sites[inb]['site'], 'index': self.all_nbs_sites[inb]['index']} for inb in self.all_nbs_sites_indices_unsorted] @property def neighb_indices_and_images(self): """ List of indices and images with respect to the original unit cell sites for this NeighborsSet. """ return [{'index': self.all_nbs_sites[inb]['index'], 'image_cell': self.all_nbs_sites[inb]['image_cell']} for inb in self.all_nbs_sites_indices_unsorted] def __len__(self): return len(self.all_nbs_sites_indices) def __hash__(self): return len(self.all_nbs_sites_indices) def __eq__(self, other): return self.isite == other.isite and self.all_nbs_sites_indices == other.all_nbs_sites_indices def __ne__(self, other): return not self == other def __str__(self): out = 'Neighbors Set for site #{:d} :\n'.format(self.isite) out += ' - Coordination number : {:d}\n'.format(len(self)) out += ' - Neighbors sites indices : {}' \ '\n'.format(', '.join(['{:d}'.format(nb_list_index) for nb_list_index in self.all_nbs_sites_indices])) return out
[docs] def as_dict(self): """ A JSON serializable dict representation of the NeighborsSet. """ return {'isite': self.isite, 'all_nbs_sites_indices': self.all_nbs_sites_indices_unsorted}
# 'all_nbs_sites_indices_unsorted': self.all_nbs_sites_indices_unsorted}
[docs] @classmethod def from_dict(cls, dd, structure, all_nbs_sites): """ Reconstructs the NeighborsSet algorithm from its JSON serializable dict representation, together with the structure and all the possible neighbors sites. As an inner (nested) class, the NeighborsSet is not supposed to be used anywhere else that inside the LightStructureEnvironments. The from_dict method is thus using the structure and all_nbs_sites when reconstructing itself. These two are both in the LightStructureEnvironments object. Args: dd: a JSON serializable dict representation of a NeighborsSet. structure: The structure. all_nbs_sites: The list of all the possible neighbors for a given site. Returns: a NeighborsSet. """ return cls(structure=structure, isite=dd['isite'], all_nbs_sites=all_nbs_sites, all_nbs_sites_indices=dd['all_nbs_sites_indices'])
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. Args: strategy: ChemEnv strategy used to get the environments. coordination_environments: The coordination environments identified. all_nbs_sites: All the possible neighbors for each site in the structure. neighbors_sets: The neighbors sets of each site in the structure. structure: The structure. valences: The valences used to get the environments (if needed). valences_origin: How the valences were obtained (e.g. from the Bond-valence analysis or from the original structure). """ 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): """ Construct a LightStructureEnvironments object from a strategy and a StructureEnvironments object. Args: strategy: ChemEnv strategy used. structure_environments: StructureEnvironments object from which to construct the LightStructureEnvironments. valences: The valences of each site in the structure. valences_origin: How the valences were obtained (e.g. from the Bond-valence analysis or from the original structure). Returns: a LightStructureEnvironments object. """ 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): """ Set up the statistics of environments for this LightStructureEnvironments. """ 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.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.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): """ Get list of indices that have the given specie with a given Coordination environment. Args: specie: Specie to get. ce_symbol: Symbol of the coordination environment to get. Returns: Dictionary with the list of indices in the structure that have the given specie in the given environment, their fraction and continuous symmetry measures. """ 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]: if self.valences == 'undefined' or 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['csm']) fractions.append(ce_dict['ce_fraction']) return {'isites': isites, 'fractions': fractions, 'csms': csms}
[docs] def get_site_info_for_specie_allces(self, specie, min_fraction=0.0): """ Get list of indices that have the given specie. Args: specie: Specie to get. Returns: Dictionary with the list of coordination environments for the given species, the indices of the sites in which they appear, their fractions and continuous symmetry measures. """ 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]: if self.valences == 'undefined' or 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): """ Get the statistics of environments for this structure. Args: statistics_fields: Which statistics to get. bson_compatible: Whether to make the dictionary BSON-compatible. Returns: A dictionary with the requested statistics. """ 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): """ Whether this LightStructureEnvironments concerns a structure with only one given anion atom type. Args: anion_atom: Anion (e.g. O, ...). The structure could contain O2- and O- though. Returns: True if this LightStructureEnvironments concerns a structure with only one given 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): """ Whether this LightStructureEnvironments concerns a structure with only one given anion type. Args: anion: Anion (e.g. O2-, ...). Returns: True if this LightStructureEnvironments concerns a structure with only one given 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): """ Whether a given site contains a given coordination environment. Args: isite: Index of the site. ce_symbol: Symbol of the coordination environment. Returns: True if the site contains the given coordination environment. """ if self.coordination_environments[isite] is None: return False return ce_symbol in [ce_dict['ce_symbol'] for ce_dict in self.coordination_environments[isite]]
[docs] def site_has_clear_environment(self, isite, conditions=None): """ Whether a given site has a "clear" environments. A "clear" environment is somewhat arbitrary. You can pass (multiple) conditions, e.g. the environment should have a continuous symmetry measure lower than this, a fraction higher than that, ... Args: isite: Index of the site. conditions: Conditions to be checked for an environment to be "clear". Returns: True if the site has a clear environment. """ if self.coordination_environments[isite] is None: raise ValueError('Coordination environments have not been determined for site {:d}'.format(isite)) if conditions is None: return len(self.coordination_environments[isite]) == 1 ce = max(self.coordination_environments[isite], key=lambda x: x['ce_fraction']) for condition in conditions: target = condition['target'] if target == 'ce_fraction': if ce[target] < condition['minvalue']: return False elif target == 'csm': if ce[target] > condition['maxvalue']: return False elif target == 'number_of_ces': if ce[target] > condition['maxnumber']: return False else: raise ValueError('Target "{}" for condition of clear environment is not allowed'.format(target)) return True
[docs] def structure_has_clear_environments(self, conditions=None, skip_none=True, skip_empty=False): """ Whether all sites in a structure have "clear" environments. Args: conditions: Conditions to be checked for an environment to be "clear". skip_none: Whether to skip sites for which no environments have been computed. skip_empty: Whether to skip sites for which no environments could be found. Returns: True if all the sites in the structure have clear environments. """ for isite in range(len(self.structure)): if self.coordination_environments[isite] is None: if skip_none: continue else: return False if len(self.coordination_environments[isite]) == 0: if skip_empty: continue else: return False if not self.site_has_clear_environment(isite=isite, conditions=conditions): return False return True
[docs] def clear_environments(self, conditions=None): """ Get the clear environments in the structure. Args: conditions: Conditions to be checked for an environment to be "clear". Returns: Set of clear environments in this structure. """ clear_envs_list = set() for isite in range(len(self.structure)): if self.coordination_environments[isite] is None: continue if len(self.coordination_environments[isite]) == 0: continue if self.site_has_clear_environment(isite=isite, conditions=conditions): ce = max(self.coordination_environments[isite], key=lambda x: x['ce_fraction']) clear_envs_list.add(ce['ce_symbol']) return list(clear_envs_list)
[docs] def structure_contains_atom_environment(self, atom_symbol, ce_symbol): """ Checks whether the structure contains a given atom in a given environment. Args: atom_symbol: Symbol of the atom. ce_symbol: Symbol of the coordination environment. Returns: True if the coordination environment is found, False otherwise """ for isite, site in enumerate(self.structure): if (Element(atom_symbol) in site.species. element_composition and self.site_contains_environment(isite, ce_symbol)): return True return False
[docs] def environments_identified(self): """ Return the set of environments identified in this structure. Returns: Set of environments identified in this structure. """ return set(ce['ce_symbol'] for celist in self.coordination_environments if celist is not None for ce in celist)
@property def uniquely_determines_coordination_environments(self): """ True if the coordination environments are uniquely determined. """ return self.strategy.uniquely_determines_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. Args: other: LightStructureEnvironments object to compare with. Returns: 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. Returns: 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. Args: d: dict representation of the LightStructureEnvironments object. Returns: 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. Args: 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): return self.coord_geoms[mp_symbol] def __len__(self): """ Returns the number of coordination geometries in this ChemicalEnvironments object. Returns: 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. Returns: tuple (symbol, csm) with symbol being the geometry with the minimum continuous symmetry measure and csm being the continuous symmetry measure associated to it. Raises: 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. Args: n: Number of geometries to be included in the list. Returns: List of geometries with increasing continuous symmetry measure in this ChemicalEnvironments object. Raises: 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. Args: mp_symbol: Symbol of the coordination geometry added. symmetry_measure: Symmetry measure of the coordination geometry added. algo: Algorithm used for the search of the coordination geometry added. permutation: Permutation of the neighbors that leads to the csm stored. override: If set to True, the coordination geometry will override the existent one if present. local2perfect_map: Mapping of the local indices to the perfect indices. perfect2local_map: Mapping of the perfect indices to the local indices. detailed_voronoi_index: Index in the voronoi containing the neighbors set. other_symmetry_measures: Other symmetry measure of the coordination geometry added (with/without the central atom, centered on the central atom or on the centroid with/without the central atom). rotation_matrix: Rotation matrix mapping the local geometry to the perfect geometry. scaling_factor: Scaling factor mapping the local geometry to the perfect geometry. Raises: 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. Returns: 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): """ Whether this ChemicalEnvironments object is close to another one. Args: other: Another ChemicalEnvironments object. rtol: Relative tolerance for the comparison of Continuous Symmetry Measures. atol: Absolute tolerance for the comparison of Continuous Symmetry Measures. Returns: True if the two ChemicalEnvironments objects are close to each other. """ 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. Args: other: ChemicalEnvironments object to compare with. Returns: 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. Returns: A dictionary representation of the ChemicalEnvironments object. """ 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. Args: d: dict representation of the ChemicalEnvironments object. Returns: 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