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# 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 the object used to describe the possible bonded atoms based on a Voronoi analysis 

""" 

 

__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" 

 

 

from operator import attrgetter 

 

import logging 

import numpy as np 

import time 

from pyhull.voronoi import VoronoiTess 

from pymatgen.core.structure import Structure 

from pymatgen.core.sites import PeriodicSite 

from monty.json import MSONable 

from pymatgen.analysis.structure_analyzer import solid_angle 

 

from pymatgen.analysis.chemenv.utils.chemenv_errors import ChemenvError 

from pymatgen.analysis.chemenv.utils.coordination_geometry_utils import my_solid_angle 

from pymatgen.analysis.chemenv.utils.defs_utils import AdditionalConditions 

 

 

def from_bson_voronoi_list(bson_nb_voro_list, structure): 

""" 

Returns the voronoi_list needed for the VoronoiContainer object from a bson-encoded voronoi_list (composed of 

vlist and bson_nb_voro_list). 

:param vlist: List of voronoi objects 

:param bson_nb_voro_list: List of periodic sites involved in the Voronoi 

:return: The voronoi_list needed for the VoronoiContainer (with PeriodicSites as keys of the dictionary - not 

allowed in the BSON format) 

""" 

voronoi_list = [None] * len(bson_nb_voro_list) 

for isite, voro in enumerate(bson_nb_voro_list): 

if voro is None or voro == 'None': 

continue 

voronoi_list[isite] = [] 

for psd, dd in voro: 

struct_site = structure[dd['index']] 

periodic_site = PeriodicSite(struct_site._species, struct_site.frac_coords + psd[1], 

struct_site._lattice, properties=struct_site._properties) 

voronoi_list[isite].append((periodic_site, dd)) 

return voronoi_list 

 

 

class DetailedVoronoiContainer(MSONable): 

""" 

Class used to store the full Voronoi of a given structure. 

""" 

AC = AdditionalConditions() 

default_voronoi_cutoff = 10.0 

 

def __init__(self, structure=None, voronoi_list=None, 

neighbors_lists=None, 

voronoi_cutoff=default_voronoi_cutoff, isites=None, 

weighted_distance_tolerance=1e-5, weighted_angle_tolerance=1e-3, 

additional_conditions=None, valences=None, 

maximum_distance_factor=None, minimum_angle_factor=None): 

""" 

Constructor for the VoronoiContainer object. Either a structure is given, in which case the Voronoi is 

computed, or the different components of the VoronoiContainer are given (used in the from_dict method) 

:param structure: Structure for which the Voronoi is computed 

:param voronoi_list: List of voronoi polyhedrons for each site 

:param neighbors_list: list of neighbors for each site 

:param voronoi_cutoff: cutoff used for the voronoi 

:param isites: indices of sites for which the Voronoi has to be computed 

:raise: RuntimeError if the Voronoi cannot be constructed 

""" 

self.weighted_distance_tolerance = weighted_distance_tolerance 

self.weighted_angle_tolerance = weighted_angle_tolerance 

if additional_conditions is None: 

self.additional_conditions = [self.AC.NONE, self.AC.ONLY_ACB] 

else: 

self.additional_conditions = additional_conditions 

self.valences = valences 

self.maximum_distance_factor = maximum_distance_factor 

self.minimum_angle_factor = minimum_angle_factor 

if isites is None: 

indices = list(range(len(structure))) 

else: 

indices = isites 

self.structure = structure 

logging.info('Setting Voronoi list') 

if voronoi_list is not None: 

self.voronoi_list = voronoi_list 

else: 

self.setup_voronoi_list(indices=indices, voronoi_cutoff=voronoi_cutoff) 

logging.info('Setting neighbors distances and angles') 

t1 = time.clock() 

self.setup_neighbors_distances_and_angles(indices=indices) 

t2 = time.clock() 

logging.info('Neighbors distances and angles set up in {:.2f} seconds'.format(t2-t1)) 

if neighbors_lists is None: 

self.setup_neighbors(additional_conditions=self.additional_conditions, valences=self.valences) 

else: 

self.neighbors_lists = neighbors_lists 

logging.info('Setting unique coordinations') 

t1 = time.clock() 

self.setup_unique_coordinations() 

t2 = time.clock() 

logging.info('Unique coordinations set up in {:.2f} seconds'.format(t2-t1)) 

 

def setup_voronoi_list(self, indices, voronoi_cutoff): 

""" 

Set up of the voronoi list of neighbours by calling qhull 

:param indices: indices of the sites for which the Voronoi is needed 

:param voronoi_cutoff: Voronoi cutoff for the search of neighbours 

:raise RuntimeError: If an infinite vertex is found in the voronoi construction 

""" 

self.voronoi_list = [None] * len(self.structure) 

logging.info('Getting all neighbors in structure') 

struct_neighbors = self.structure.get_all_neighbors(voronoi_cutoff, include_index=True) 

t1 = time.clock() 

logging.info('Setting up Voronoi list :') 

 

for jj, isite in enumerate(indices): 

logging.info(' - Voronoi analysis for site #{:d} ({:d}/{:d})'.format(isite, jj+1, len(indices))) 

site = self.structure[isite] 

neighbors1 = [(site, 0.0, isite)] 

neighbors1.extend(struct_neighbors[isite]) 

distances = [i[1] for i in sorted(neighbors1, key=lambda s: s[1])] 

neighbors = [i[0] for i in sorted(neighbors1, key=lambda s: s[1])] 

qvoronoi_input = [s.coords for s in neighbors] 

voro = VoronoiTess(qvoronoi_input) 

all_vertices = voro.vertices 

 

results = [] 

maxangle = 0.0 

mindist = 10000.0 

for nn, vind in list(voro.ridges.items()): 

if 0 in nn: 

if 0 in vind: 

raise RuntimeError("This structure is pathological," 

" infinite vertex in the voronoi " 

"construction") 

 

facets = [all_vertices[i] for i in vind] 

try: 

sa = solid_angle(site.coords, facets) 

except ValueError: 

sa = my_solid_angle(site.coords, facets) 

maxangle = max([sa, maxangle]) 

mindist = min([mindist, distances[nn[1]]]) 

for iii, sss in enumerate(self.structure): 

if neighbors[nn[1]].is_periodic_image(sss): 

myindex = iii 

break 

results.append((neighbors[nn[1]], 

{'angle': sa, 

'distance': distances[nn[1]], 

'index': myindex})) 

for (nn, dd) in results: 

dd['weighted_angle'] = dd['angle'] / maxangle 

dd['weighted_distance'] = dd['distance'] / mindist 

self.voronoi_list[isite] = results 

t2 = time.clock() 

logging.info('Voronoi list set up in {:.2f} seconds'.format(t2-t1)) 

 

def setup_neighbors_distances_and_angles(self, indices): 

""" 

Initializes the angle and distance separations 

:param indices: indices of the sites for which the Voronoi is needed 

""" 

self.neighbors_distances = [None] * len(self.structure) 

self.neighbors_weighted_distances = [None] * len(self.structure) 

self.neighbors_angles = [None] * len(self.structure) 

self.neighbors_weighted_angles = [None] * len(self.structure) 

for isite in indices: 

results = self.voronoi_list[isite] 

if results is None: 

continue 

#Initializes neighbors distances and weighted distances groups 

self.neighbors_distances[isite] = [] 

self.neighbors_weighted_distances[isite] = [] 

weighted_distances = [dd['weighted_distance'] for (nn, dd) in results] 

isorted_distances = np.argsort(weighted_distances) 

#self.neighbors_weighted_distances[isite].append(weighted_distances[isorted_distances[0]]) 

self.neighbors_weighted_distances[isite].append({'min': weighted_distances[isorted_distances[0]], 

'max': weighted_distances[isorted_distances[0]]}) 

self.neighbors_distances[isite].append({'min': results[isorted_distances[0]][1]['distance'], 

'max': results[isorted_distances[0]][1]['distance']}) 

for idist in iter(isorted_distances): 

if self.maximum_distance_factor is not None: 

if weighted_distances[idist] > self.maximum_distance_factor: 

self.neighbors_weighted_distances[isite][-1]['max'] = weighted_distances[idist] 

self.neighbors_distances[isite][-1]['max'] = results[idist][1]['distance'] 

break 

if not np.isclose(weighted_distances[idist], self.neighbors_weighted_distances[isite][-1]['max'], 

rtol=0.0, atol=self.weighted_distance_tolerance): 

self.neighbors_weighted_distances[isite].append({'min': weighted_distances[idist], 

'max': weighted_distances[idist]}) 

self.neighbors_distances[isite].append({'min': results[idist][1]['distance'], 

'max': results[idist][1]['distance']}) 

else: 

self.neighbors_weighted_distances[isite][-1]['max'] = weighted_distances[idist] 

self.neighbors_distances[isite][-1]['max'] = results[idist][1]['distance'] 

#Initializes neighbors angles and weighted angles groups 

self.neighbors_angles[isite] = [] 

self.neighbors_weighted_angles[isite] = [] 

weighted_angles = [dd['weighted_angle'] for (nn, dd) in results] 

isorted_angles = np.argsort(weighted_angles) 

self.neighbors_weighted_angles[isite].append({'max': weighted_angles[isorted_angles[0]], 

'min': weighted_angles[isorted_angles[0]]}) 

self.neighbors_angles[isite].append({'max': results[isorted_angles[0]][1]['angle'], 

'min': results[isorted_angles[0]][1]['angle']}) 

for iang in iter(isorted_angles[1:]): 

if self.minimum_angle_factor is not None: 

if weighted_angles[iang] < self.minimum_angle_factor: 

self.neighbors_weighted_angles[isite][-1]['min'] = weighted_angles[iang] 

self.neighbors_angles[isite][-1]['min'] = results[iang][1]['angle'] 

break 

if not np.isclose(weighted_angles[iang], self.neighbors_weighted_angles[isite][-1]['min'], 

rtol=0.0, atol=self.weighted_angle_tolerance): 

self.neighbors_weighted_angles[isite].append({'max': weighted_angles[iang], 

'min': weighted_angles[iang]}) 

self.neighbors_angles[isite].append({'max': results[iang][1]['angle'], 

'min': results[iang][1]['angle']}) 

else: 

self.neighbors_weighted_angles[isite][-1]['min'] = weighted_angles[iang] 

self.neighbors_angles[isite][-1]['min'] = results[iang][1]['angle'] 

 

def setup_neighbors(self, additional_conditions=None, valences=None): 

""" 

Compute the list of neighbors for each distfactor/angfactor set of parameters from the voronoi list. The set of 

distfactor/angfactor parameters is a "square" of all combinations of distfactors with angfactors. 

:param distfactors: list of distfactors 

:param angfactors: list of angfactors 

:param only_anion_cation_bonds: Allows only neighbors that are cations (resp. anions) when the current site is 

an anion (resp. a cation). 

:param valences: Valences of all the sites in the structure, needed to check the anion-cation bond when 

only_anion_cation_bonds is set to True. 

:raise: ChemenvError if only_anion_cation_bonds is set to True and valences are not given. 

""" 

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('VoronoiContainer', 'setup_neighbors', 

'Valences are not given while only_anion_cation_bonds are allowed. Cannot continue') 

self.neighbors_lists = [None] * len(self.voronoi_list) 

self.additional_conditions = additional_conditions 

 

for ivoronoi, voronoi in enumerate(self.voronoi_list): 

if voronoi is None: 

continue 

self.neighbors_lists[ivoronoi] = [] 

voronoi_ac = self._precompute_additional_conditions(ivoronoi, voronoi, valences) 

distance_conditions = self._precompute_distance_conditions(ivoronoi, voronoi) 

angle_conditions = self._precompute_angle_conditions(ivoronoi, voronoi) 

 

for idp, dp_dict in enumerate(self.neighbors_weighted_distances[ivoronoi]): 

self.neighbors_lists[ivoronoi].append([]) 

for iap, ap_dict in enumerate(self.neighbors_weighted_angles[ivoronoi]): 

self.neighbors_lists[ivoronoi][idp].append([]) 

for iac, ac in enumerate(self.additional_conditions): 

nlist = [(ips, vals['index'], {'weighted_distance': vals['weighted_distance'], 

'weighted_angle': vals['weighted_angle'], 

'distance': vals['distance'], 

'angle': vals['angle']}) 

for ips, (ps, vals) in enumerate(voronoi) 

if (distance_conditions[idp][ips]) and 

(angle_conditions[iap][ips]) and 

(voronoi_ac[ac][ips])] 

self.neighbors_lists[ivoronoi][idp][iap].append(nlist) 

 

def _precompute_additional_conditions(self, ivoronoi, voronoi, valences): 

additional_conditions = {ac: [] for ac in self.additional_conditions} 

for ips, (ps, vals) in enumerate(voronoi): 

for ac in self.additional_conditions: 

additional_conditions[ac].append(self.AC.check_condition(condition=ac, structure=self.structure, 

parameters={'valences': valences, 

'neighbor_index': vals['index'], 

'site_index': ivoronoi})) 

return additional_conditions 

 

def _precompute_distance_conditions(self, ivoronoi, voronoi): 

distance_conditions = [] 

for idp, dp_dict in enumerate(self.neighbors_weighted_distances[ivoronoi]): 

distance_conditions.append([]) 

dp = dp_dict['max'] 

for ips, (ps, vals) in enumerate(voronoi): 

distance_conditions[idp].append(vals['weighted_distance'] <= dp or 

np.isclose(vals['weighted_distance'], dp, 

rtol=0.0, atol=self.weighted_distance_tolerance/2.0)) 

return distance_conditions 

 

def _precompute_angle_conditions(self, ivoronoi, voronoi): 

angle_conditions = [] 

for iap, ap_dict in enumerate(self.neighbors_weighted_angles[ivoronoi]): 

angle_conditions.append([]) 

ap = ap_dict['max'] 

for ips, (ps, vals) in enumerate(voronoi): 

angle_conditions[iap].append(vals['weighted_angle'] >= ap or 

np.isclose(vals['weighted_angle'], ap, 

rtol=0.0, atol=self.weighted_angle_tolerance/2.0)) 

return angle_conditions 

 

def setup_unique_coordinations(self): 

""" 

Setup of the unique coordinations and the mapping of distfactor/angfactor parameters 

to the unique coordinations. 

""" 

self._unique_coordinated_neighbors = [None] * len(self.voronoi_list) 

self._unique_coordinated_neighbors_parameters_indices = [None] * len(self.voronoi_list) 

self._parameters_to_unique_coordinated_neighbors_map = [None] * len(self.voronoi_list) 

ncond_params = len(self.additional_conditions) 

for isite, voronoi in enumerate(self.voronoi_list): 

 

if voronoi is None: 

continue 

ndist_params = len(self.neighbors_distances[isite]) 

nang_params = len(self.neighbors_angles[isite]) 

self._unique_coordinated_neighbors[isite] = {} 

self._unique_coordinated_neighbors_parameters_indices[isite] = {} 

self._parameters_to_unique_coordinated_neighbors_map[isite] = [None] * ndist_params 

for idp, dp in enumerate(self.neighbors_distances[isite]): 

self._parameters_to_unique_coordinated_neighbors_map[isite][idp] = [None] * nang_params 

for iap, ap in enumerate(self.neighbors_weighted_angles[isite]): 

self._parameters_to_unique_coordinated_neighbors_map[isite][idp][iap] = [None] * ncond_params 

for iac, ac in enumerate(self.additional_conditions): 

cn = len(self.neighbors_lists[isite][idp][iap][iac]) 

if not cn in self._unique_coordinated_neighbors[isite]: 

self._unique_coordinated_neighbors[isite][cn] = [] 

self._unique_coordinated_neighbors_parameters_indices[isite][cn] = [] 

indices_array = [nlist[1] for nlist in self.neighbors_lists[isite][idp][iap][iac]] 

nlist_dict_array = [nlist[2] for nlist in self.neighbors_lists[isite][idp][iap][iac]] 

ps_array = sorted([self.voronoi_list[isite][nlist[0]][0] for nlist in 

self.neighbors_lists[isite][idp][iap][iac]], 

key=attrgetter('a', 'b', 'c')) 

found = False 

for i_cn_neighblist, nlist_tuple in enumerate(self._unique_coordinated_neighbors[isite][cn]): 

if indices_array == nlist_tuple[1]: 

self._parameters_to_unique_coordinated_neighbors_map[isite][idp][iap][iac] = [cn, 

i_cn_neighblist] 

(self._unique_coordinated_neighbors_parameters_indices[isite][cn][i_cn_neighblist]. 

append((idp, iap, iac))) 

found = True 

break 

if not found: 

self._unique_coordinated_neighbors[isite][cn].append((ps_array, indices_array, 

nlist_dict_array)) 

i_parameters_list = [(idp, iap, iac)] 

self._unique_coordinated_neighbors_parameters_indices[isite][cn].append(i_parameters_list) 

self._parameters_to_unique_coordinated_neighbors_map[isite][idp][iap][iac] = [cn, 0] 

 

def unique_coordinated_neighbors(self, isite=None, cn_map=None): 

if isite is None: 

return self._unique_coordinated_neighbors 

else: 

if cn_map is None: 

return self._unique_coordinated_neighbors[isite] 

else: 

return self._unique_coordinated_neighbors[isite][cn_map[0]][cn_map[1]] 

 

@property 

def parameters_to_unique_coordinated_neighbors_map(self): 

return self._parameters_to_unique_coordinated_neighbors_map 

 

def angles(self, isite, include_index=True): 

if not include_index: 

return [result[1]['angle'] for result in self.voronoi_list[isite]] 

else: 

return [(result[1]['angle'], result[1]['index']) for result in self.voronoi_list[isite]] 

 

def satisfy_condition(self, isite, cn, i_coordn_neighb, additional_condition): 

parameters_indices = self._unique_coordinated_neighbors_parameters_indices[isite][cn][i_coordn_neighb] 

additional_conditions = [pp[2] for pp in parameters_indices] 

return additional_condition in additional_conditions 

 

def neighbors_map(self, isite, distfactor, angfactor, additional_condition): 

if self.neighbors_weighted_distances[isite] is None: 

return None 

dist_where = np.argwhere(np.array([wd['min'] for wd in self.neighbors_weighted_distances[isite]]) <= distfactor) 

if len(dist_where) == 0: 

return None 

idist = dist_where[-1][0] 

ang_where = np.argwhere(np.array([wa['max'] for wa in self.neighbors_weighted_angles[isite]]) >= angfactor) 

if len(ang_where) == 0: 

return None 

iang = ang_where[0][0] 

if self.additional_conditions.count(additional_condition) != 1: 

return None 

i_additional_condition = self.additional_conditions.index(additional_condition) 

return {'i_distfactor': idist, 'i_angfactor': iang, 'i_additional_condition': i_additional_condition} 

 

def neighbors_surfaces(self, isite, surface_calculation_type=None, max_dist=2.0): 

if self.voronoi_list[isite] is None: 

return None 

#surfaces = np.zeros((len(self.neighbors_weighted_distances), len(self.neighbors_weighted_angles)), np.float) 

bounds_and_limits = self.voronoi_parameters_bounds_and_limits(isite, surface_calculation_type, max_dist) 

distance_bounds = bounds_and_limits['distance_bounds'] 

angle_bounds = bounds_and_limits['angle_bounds'] 

surfaces = np.zeros((len(distance_bounds), len(angle_bounds)), np.float) 

for idp in range(len(distance_bounds) - 1): 

this_dist_plateau = distance_bounds[idp + 1] - distance_bounds[idp] 

for iap in range(len(angle_bounds) - 1): 

this_ang_plateau = angle_bounds[iap + 1] - angle_bounds[iap] 

surfaces[idp][iap] = np.absolute(this_dist_plateau*this_ang_plateau) 

return surfaces 

 

def maps_with_condition(self, isite, additional_condition, return_parameter_indices=False): 

cn_maps = [] 

ucnpi_isite = self._unique_coordinated_neighbors_parameters_indices[isite] 

if ucnpi_isite is None: 

return None 

for cn, coordnbs_list in ucnpi_isite.items(): 

for i_coordnbs, coordnbs in enumerate(coordnbs_list): 

if self.satisfy_condition(isite, cn, i_coordnbs, additional_condition): 

cn_maps.append((cn, i_coordnbs)) 

result = {'cn_maps': cn_maps} 

if return_parameter_indices: 

parameter_indices = [] 

for (cn, i_coordnbs) in cn_maps: 

cn_map_parameter_indices = [] 

for params in self._unique_coordinated_neighbors_parameters_indices[isite][cn][i_coordnbs]: 

if params[2] == additional_condition: 

cn_map_parameter_indices.append(params) 

parameter_indices.append(cn_map_parameter_indices) 

result['parameter_indices'] = parameter_indices 

return result 

 

def maps_and_surface_vertices(self, isite, additional_condition=AC.ONLY_ACB, plot_type=None, max_dist=2.0): 

cn_maps_parameter_indices = self.maps_with_condition(isite=isite, additional_condition=additional_condition, 

return_parameter_indices=True) 

if cn_maps_parameter_indices is None: 

return None 

bounds_and_limits = self.voronoi_parameters_bounds_and_limits(isite, plot_type, max_dist) 

vertices_dist_ang_indices_list = [] 

vertices_dist_ang_list = [] 

text_info_dist_ang_list = [] 

for i_cn_map, cn_map in enumerate(cn_maps_parameter_indices['cn_maps']): 

parameter_indices_list = cn_maps_parameter_indices['parameter_indices'][i_cn_map] 

vertices_dist_ang_indices = self._get_vertices_dist_ang_indices(parameter_indices_list) 

vertices_dist_ang = [] 

idist, iang = vertices_dist_ang_indices[0] 

dist = bounds_and_limits['distance_bounds'][idist] 

ang = bounds_and_limits['angle_bounds'][iang] 

vertices_dist_ang.append([dist, ang]) 

idist, iang = vertices_dist_ang_indices[1] 

dist = bounds_and_limits['distance_bounds'][idist+1] 

ang = bounds_and_limits['angle_bounds'][iang] 

vertices_dist_ang.append([dist, ang]) 

idist, iang = vertices_dist_ang_indices[2] 

dist = bounds_and_limits['distance_bounds'][idist+1] 

ang = bounds_and_limits['angle_bounds'][iang] 

vertices_dist_ang.append([dist, ang]) 

idist, iang = vertices_dist_ang_indices[3] 

dist = bounds_and_limits['distance_bounds'][idist+1] 

ang = bounds_and_limits['angle_bounds'][iang] 

vertices_dist_ang.append([dist, ang]) 

idist, iang = vertices_dist_ang_indices[4] 

dist = bounds_and_limits['distance_bounds'][idist+1] 

ang = bounds_and_limits['angle_bounds'][iang+1] 

vertices_dist_ang.append([dist, ang]) 

idist, iang = vertices_dist_ang_indices[5] 

dist = bounds_and_limits['distance_bounds'][idist] 

ang = bounds_and_limits['angle_bounds'][iang+1] 

vertices_dist_ang.append([dist, ang]) 

vertices_dist_ang_indices_list.append(vertices_dist_ang_indices) 

vertices_dist_ang_list.append(vertices_dist_ang) 

text_info_dist_ang = ((vertices_dist_ang[2][0] + vertices_dist_ang[5][0]) / 2.0, 

(vertices_dist_ang[2][1] + vertices_dist_ang[5][1]) / 2.0) 

text_info_dist_ang_list.append(text_info_dist_ang) 

result = {'cn_maps': cn_maps_parameter_indices['cn_maps'], 

'bounds_and_limits': bounds_and_limits, 

'vertices_dist_ang_indices': vertices_dist_ang_indices_list, 

'vertices_dist_ang': vertices_dist_ang_list, 

'text_info_dist_ang': text_info_dist_ang_list 

} 

return result 

 

@staticmethod 

def _get_vertices_dist_ang_indices(parameter_indices_list): 

pp0 = [pp[0] for pp in parameter_indices_list] 

pp1 = [pp[1] for pp in parameter_indices_list] 

min_idist = min(pp0) 

min_iang = min(pp1) 

max_idist = max(pp0) 

max_iang = max(pp1) 

i_min_angs = np.argwhere(np.array(pp1) == min_iang) 

i_max_dists = np.argwhere(np.array(pp0) == max_idist) 

pp0_at_min_iang = [pp0[ii[0]] for ii in i_min_angs] 

pp1_at_max_idist = [pp1[ii[0]] for ii in i_max_dists] 

max_idist_at_min_iang = max(pp0_at_min_iang) 

min_iang_at_max_idist = min(pp1_at_max_idist) 

 

p1 = (min_idist, min_iang) 

p2 = (max_idist_at_min_iang, min_iang) 

p3 = (max_idist_at_min_iang, min_iang_at_max_idist) 

p4 = (max_idist, min_iang_at_max_idist) 

p5 = (max_idist, max_iang) 

p6 = (min_idist, max_iang) 

 

return [p1, p2, p3, p4, p5, p6] 

 

def maps_and_surfaces(self, isite, surface_calculation_type=None, max_dist=2.0, additional_conditions=None): 

if self.voronoi_list[isite] is None: 

return None 

if additional_conditions is None: 

additional_conditions = [self.AC.ONLY_ACB] 

surfaces = self.neighbors_surfaces(isite=isite, surface_calculation_type=surface_calculation_type, 

max_dist=max_dist) 

maps_and_surfaces = [] 

for cn, value in self._unique_coordinated_neighbors_parameters_indices[isite].items(): 

for imap, list_parameters_indices in enumerate(value): 

thissurf = 0.0 

for (idp, iap, iacb) in list_parameters_indices: 

if iacb in additional_conditions: 

thissurf += surfaces[idp, iap] 

maps_and_surfaces.append({'map': (cn, imap), 'surface': thissurf, 

'parameters_indices': list_parameters_indices}) 

return maps_and_surfaces 

 

def get_neighbors(self, isite, neighbors_map): 

""" 

Returns the list of neighbours of a given site given the neighbors_map indices (index of the distance and 

angle factors as well as index of the only_anion_cation_bonds). This will usually not be used outside Voronoi. 

:param isite: Index of the site for which the neighbors have to be given. 

:param neighbors_map: Mapping of the distance/angle/additional_condition as a dict with keys "i_distfactor", 

"i_angfactor" and "i_additional_condition" and corresponding values. 

:return: Neighbors for this neighbors_map 

""" 

this_site_this_map_neighbors_list = (self.neighbors_lists 

[isite] 

[neighbors_map['i_distfactor']] 

[neighbors_map['i_angfactor']] 

[neighbors_map['i_additional_condition']]) 

neighbors = [self.voronoi_list[isite][nlist[0]][0] for nlist in this_site_this_map_neighbors_list] 

return neighbors 

 

def neighbors(self, isite, distfactor, angfactor, additional_condition): 

neighbors_map = self.neighbors_map(isite=isite, distfactor=distfactor, angfactor=angfactor, 

additional_condition=additional_condition) 

if neighbors_map is None: 

return [] 

return self.get_neighbors(isite=isite, neighbors_map=neighbors_map) 

 

def get_coordination_numbers_figure(self, isite, plot_type=None, title='Coordination numbers', max_dist=2.0, 

figsize=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: The figure object to be plotted or saved to file 

""" 

try: 

import matplotlib.pyplot as mpl 

from matplotlib import cm 

from matplotlib.colors import Normalize, LinearSegmentedColormap, ListedColormap 

from matplotlib.patches import Rectangle, 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 

bounds_and_limits = self.voronoi_parameters_bounds_and_limits(isite, plot_type, max_dist) 

if bounds_and_limits is None: 

return None 

distance_bounds = bounds_and_limits['distance_bounds'] 

angle_bounds = bounds_and_limits['angle_bounds'] 

dist_limits = bounds_and_limits['distance_limits'] 

ang_limits = bounds_and_limits['angle_limits'] 

 

#Plot the rectangles and coordinations 

for idp in range(len(distance_bounds) - 1): 

this_dist_plateau = distance_bounds[idp + 1] - distance_bounds[idp] 

#print('Dist Plateau : ', this_dist_plateau) 

for iap in range(len(angle_bounds) - 1): 

this_ang_plateau = angle_bounds[iap + 1] - angle_bounds[iap] 

#print('Ang Plateau : ', this_ang_plateau) 

#print('Rectangle from xy = ', distance_bounds[idp], ' ', angle_bounds[iap], 'of width ', this_dist_plateau, 'and height ', this_ang_plateau) 

r = Rectangle((distance_bounds[idp], angle_bounds[iap]), 

this_dist_plateau, 

this_ang_plateau, edgecolor='k', facecolor='b') 

subplot.annotate('{:d}'.format(len(self.neighbors_lists[isite][idp][iap][0])), 

xy=(distance_bounds[idp] + this_dist_plateau / 2.0, 

angle_bounds[iap] + this_ang_plateau / 2.0), 

ha='center', va='center', color='y', fontsize='x-small') 

subplot.add_patch(r) 

title += '\nDist: {}, Ang: {}'.format(plot_type['distance_parameter'][0], plot_type['angle_parameter'][0]) 

subplot.set_title(title) 

subplot.set_xlabel('Distance factor') 

subplot.set_ylabel('Angle factor') 

subplot.set_xlim(dist_limits) 

subplot.set_ylim(ang_limits) 

return fig 

 

def voronoi_parameters_bounds_and_limits(self, isite, plot_type, max_dist): 

#Initializes the distance and angle parameters 

if self.voronoi_list[isite] is None: 

return None 

if plot_type is None: 

plot_type = {'distance_parameter': ('initial_inverse_opposite', None), 

'angle_parameter': ('initial_opposite', None)} 

if plot_type['distance_parameter'][0] == 'initial_normalized': 

#dd = [dist['min'] for dist in self.neighbors_weighted_distances[isite] if dist['min'] <= max_dist] 

dd = [dist['min'] for dist in self.neighbors_weighted_distances[isite]] 

else: 

dd = [dist['min'] for dist in self.neighbors_weighted_distances[isite]] 

dd[0] = 1.0 

if plot_type['distance_parameter'][0] == 'initial_normalized': 

dd.append(max_dist) 

distance_bounds = np.array(dd) 

dist_limits = [1.0, max_dist] 

elif plot_type['distance_parameter'][0] == 'initial_inverse_opposite': 

ddinv = [1.0 / dist for dist in dd] 

ddinv.append(0.0) 

distance_bounds = np.array([1.0 - invdist for invdist in ddinv]) 

dist_limits = [0.0, 1.0] 

elif plot_type['distance_parameter'][0] == 'initial_inverse3_opposite': 

ddinv = [1.0 / dist**3.0 for dist in dd] 

ddinv.append(0.0) 

distance_bounds = np.array([1.0 - invdist for invdist in ddinv]) 

dist_limits = [0.0, 1.0] 

else: 

raise NotImplementedError('Plotting type "{}" ' 

'for the distance is not implemented'.format(plot_type['distance_parameter'])) 

if plot_type['angle_parameter'][0] == 'initial_normalized': 

aa = [0.0] 

aa.extend([ang['max'] for ang in self.neighbors_weighted_angles[isite]]) 

angle_bounds = np.array(aa) 

elif plot_type['angle_parameter'][0] == 'initial_opposite': 

aa = [0.0] 

aa.extend([ang['max'] for ang in self.neighbors_weighted_angles[isite]]) 

aa = [1.0 - ang for ang in aa] 

angle_bounds = np.array(aa) 

else: 

raise NotImplementedError('Plotting type "{}" ' 

'for the angle is not implemented'.format(plot_type['angle_parameter'])) 

ang_limits = [0.0, 1.0] 

return {'distance_bounds': distance_bounds, 'distance_limits': dist_limits, 

'angle_bounds': angle_bounds, 'angle_limits': ang_limits} 

 

def save_coordination_numbers_figure(self, isite, imagename='image.png', plot_type=None, 

title='Coordination numbers', max_dist=2.0, 

figsize=None): 

fig = self.get_coordination_numbers_figure(isite=isite, plot_type=plot_type, title=title, max_dist=max_dist, 

figsize=figsize) 

if fig is None: 

return 

fig.savefig(imagename) 

 

def plot_coordination_numbers(self, isite, plot_type=None, title='Coordination numbers', max_dist=2.0, 

figsize=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_coordination_numbers_figure(isite=isite, plot_type=plot_type, title=title, max_dist=max_dist, 

figsize=figsize) 

if fig is None: 

return 

fig.show() 

 

def unique_coordinations(self, isite): 

""" 

Returns all the unique coordinations of a given site (two different sets of distfactor/angfactor parameters 

can lead to the same coordination). 

:param isite: Site for which the unique coordinations are needed 

:return: unique coordinations for this site. 

""" 

return self._unique_coordinated_neighbors[isite] 

 

def unique_coordinated_neighbors_parameters_indices(self, isite): 

return self._unique_coordinated_neighbors_parameters_indices[isite] 

 

def __eq__(self, other): 

return (self.weighted_angle_tolerance == other.weighted_angle_tolerance and 

self.weighted_distance_tolerance == other.weighted_distance_tolerance and 

self.additional_conditions == other.additional_conditions and 

self.valences == other.valences and 

self.voronoi_list == other.voronoi_list and 

self.structure == other.structure) 

 

def to_bson_voronoi_list(self): 

""" 

Transforms the voronoi_list into a vlist + bson_nb_voro_list, that are BSON-encodable. 

:return: [vlist, bson_nb_voro_list], to be used in the as_dict method 

""" 

bson_nb_voro_list = [None] * len(self.voronoi_list) 

for ivoro, voro in enumerate(self.voronoi_list): 

if voro is None or voro == 'None': 

continue 

site_voro = [] 

for (ps, dd) in voro: 

#site_voro.append([ps.as_dict(), dd]) [float(c) for c in self._fcoords] 

diff = ps._fcoords - self.structure[dd['index']]._fcoords 

site_voro.append([[dd['index'], [float(c) for c in diff]], 

dd]) 

bson_nb_voro_list[ivoro] = site_voro 

return bson_nb_voro_list 

 

def as_dict(self): 

""" 

Bson-serializable dict representation of the VoronoiContainer. 

:return: dictionary that is BSON-encodable 

""" 

bson_nb_voro_list = self.to_bson_voronoi_list() 

return {"@module": self.__class__.__module__, 

"@class": self.__class__.__name__, 

"bson_nb_voro_list": bson_nb_voro_list, 

"neighbors_lists": self.neighbors_lists, 

"structure": self.structure.as_dict(), 

"weighted_angle_tolerance": self.weighted_angle_tolerance, 

"weighted_distance_tolerance": self.weighted_distance_tolerance, 

"additional_conditions": self.additional_conditions, 

"valences": self.valences, 

"maximum_distance_factor": self.maximum_distance_factor, 

"minimum_angle_factor": self.minimum_angle_factor} 

 

@classmethod 

def from_dict(cls, d): 

""" 

Reconstructs the VoronoiContainer object from a dict representation of the VoronoiContainer created using 

the as_dict method. 

:param d: dict representation of the VoronoiContainer object 

:return: VoronoiContainer object 

""" 

structure = Structure.from_dict(d['structure']) 

voronoi_list = from_bson_voronoi_list(d['bson_nb_voro_list'], structure) 

neighbors_lists = d['neighbors_lists'] if 'neighbors_lists' in d else None 

maximum_distance_factor = d['maximum_distance_factor'] if 'maximum_distance_factor' in d else None 

minimum_angle_factor = d['minimum_angle_factor'] if 'minimum_angle_factor' in d else None 

return cls(structure=structure, voronoi_list=voronoi_list, 

neighbors_lists=neighbors_lists, 

weighted_angle_tolerance=d['weighted_angle_tolerance'], 

weighted_distance_tolerance=d['weighted_distance_tolerance'], 

additional_conditions=d['additional_conditions'], 

valences=d['valences'], 

maximum_distance_factor=maximum_distance_factor, 

minimum_angle_factor=minimum_angle_factor)