# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
from __future__ import division, unicode_literals
import logging
import numpy as np
import time
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 scipy.spatial import Voronoi
from pymatgen.analysis.chemenv.utils.coordination_geometry_utils import my_solid_angle
from pymatgen.analysis.chemenv.utils.coordination_geometry_utils import get_lower_and_upper_f
from pymatgen.analysis.chemenv.utils.coordination_geometry_utils import rectangle_surface_intersection
from pymatgen.analysis.chemenv.utils.defs_utils import AdditionalConditions
"""
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"
[docs]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
[docs]def from_bson_voronoi_list2(bson_nb_voro_list2, 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_list2)
for isite, voro in enumerate(bson_nb_voro_list2):
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)
dd['site'] = periodic_site
voronoi_list[isite].append(dd)
return voronoi_list
[docs]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, voronoi_list2=None,
# neighbors_lists=None,
voronoi_cutoff=default_voronoi_cutoff, isites=None,
normalized_distance_tolerance=1e-5, normalized_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.normalized_distance_tolerance = normalized_distance_tolerance
self.normalized_angle_tolerance = normalized_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_list2 is not None:
self.voronoi_list2 = voronoi_list2
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))
[docs] 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_list2 = [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 = Voronoi(points=qvoronoi_input, qhull_options="o Fv")
all_vertices = voro.vertices
results2 = []
maxangle = 0.0
mindist = 10000.0
for iridge, ridge_points in enumerate(voro.ridge_points):
if 0 in ridge_points:
ridge_vertices_indices = voro.ridge_vertices[iridge]
if -1 in ridge_vertices_indices:
raise RuntimeError("This structure is pathological,"
" infinite vertex in the voronoi "
"construction")
ridge_point2 = max(ridge_points)
facets = [all_vertices[i] for i in ridge_vertices_indices]
try:
sa = solid_angle(site.coords, facets)
except ValueError:
sa = my_solid_angle(site.coords, facets)
maxangle = max([sa, maxangle])
mindist = min([mindist, distances[ridge_point2]])
for iii, sss in enumerate(self.structure):
if neighbors[ridge_point2].is_periodic_image(sss):
myindex = iii
break
results2.append({'site': neighbors[ridge_point2],
'angle': sa,
'distance': distances[ridge_point2],
'index': myindex})
for dd in results2:
dd['normalized_angle'] = dd['angle'] / maxangle
dd['normalized_distance'] = dd['distance'] / mindist
self.voronoi_list2[isite] = results2
t2 = time.clock()
logging.info('Voronoi list set up in {:.2f} seconds'.format(t2-t1))
[docs] 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_normalized_distances = [None] * len(self.structure)
self.neighbors_angles = [None] * len(self.structure)
self.neighbors_normalized_angles = [None] * len(self.structure)
for isite in indices:
results = self.voronoi_list2[isite]
if results is None:
continue
#Initializes neighbors distances and normalized distances groups
self.neighbors_distances[isite] = []
self.neighbors_normalized_distances[isite] = []
normalized_distances = [nb_dict['normalized_distance'] for nb_dict in results]
isorted_distances = np.argsort(normalized_distances)
self.neighbors_normalized_distances[isite].append({'min': normalized_distances[isorted_distances[0]],
'max': normalized_distances[isorted_distances[0]]})
self.neighbors_distances[isite].append({'min': results[isorted_distances[0]]['distance'],
'max': results[isorted_distances[0]]['distance']})
icurrent = 0
nb_indices = {int(isorted_distances[0])}
dnb_indices = {int(isorted_distances[0])}
for idist in iter(isorted_distances):
wd = normalized_distances[idist]
if self.maximum_distance_factor is not None:
if wd > self.maximum_distance_factor:
self.neighbors_normalized_distances[isite][icurrent]['nb_indices'] = list(nb_indices)
self.neighbors_distances[isite][icurrent]['nb_indices'] = list(nb_indices)
self.neighbors_normalized_distances[isite][icurrent]['dnb_indices'] = list(dnb_indices)
self.neighbors_distances[isite][icurrent]['dnb_indices'] = list(dnb_indices)
break
if np.isclose(wd, self.neighbors_normalized_distances[isite][icurrent]['max'],
rtol=0.0, atol=self.normalized_distance_tolerance):
self.neighbors_normalized_distances[isite][icurrent]['max'] = wd
self.neighbors_distances[isite][icurrent]['max'] = results[idist]['distance']
dnb_indices.add(int(idist))
else:
self.neighbors_normalized_distances[isite][icurrent]['nb_indices'] = list(nb_indices)
self.neighbors_distances[isite][icurrent]['nb_indices'] = list(nb_indices)
self.neighbors_normalized_distances[isite][icurrent]['dnb_indices'] = list(dnb_indices)
self.neighbors_distances[isite][icurrent]['dnb_indices'] = list(dnb_indices)
dnb_indices = {int(idist)}
self.neighbors_normalized_distances[isite].append({'min': wd,
'max': wd})
self.neighbors_distances[isite].append({'min': results[idist]['distance'],
'max': results[idist]['distance']})
icurrent += 1
nb_indices.add(int(idist))
else:
self.neighbors_normalized_distances[isite][icurrent]['nb_indices'] = list(nb_indices)
self.neighbors_distances[isite][icurrent]['nb_indices'] = list(nb_indices)
self.neighbors_normalized_distances[isite][icurrent]['dnb_indices'] = list(dnb_indices)
self.neighbors_distances[isite][icurrent]['dnb_indices'] = list(dnb_indices)
for idist in range(len(self.neighbors_distances[isite]) - 1):
dist_dict = self.neighbors_distances[isite][idist]
dist_dict_next = self.neighbors_distances[isite][idist+1]
dist_dict['next'] = dist_dict_next['min']
ndist_dict = self.neighbors_normalized_distances[isite][idist]
ndist_dict_next = self.neighbors_normalized_distances[isite][idist + 1]
ndist_dict['next'] = ndist_dict_next['min']
if self.maximum_distance_factor is not None:
dfact = self.maximum_distance_factor
else:
dfact = self.default_voronoi_cutoff / self.neighbors_distances[isite][0]['min']
self.neighbors_normalized_distances[isite][-1]['next'] = dfact
self.neighbors_distances[isite][-1]['next'] = dfact * self.neighbors_distances[isite][0]['min']
#Initializes neighbors angles and normalized angles groups
self.neighbors_angles[isite] = []
self.neighbors_normalized_angles[isite] = []
normalized_angles = [nb_dict['normalized_angle'] for nb_dict in results]
isorted_angles = np.argsort(normalized_angles)[::-1]
self.neighbors_normalized_angles[isite].append({'max': normalized_angles[isorted_angles[0]],
'min': normalized_angles[isorted_angles[0]]})
self.neighbors_angles[isite].append({'max': results[isorted_angles[0]]['angle'],
'min': results[isorted_angles[0]]['angle']})
icurrent = 0
nb_indices = {int(isorted_angles[0])}
dnb_indices = {int(isorted_angles[0])}
for iang in iter(isorted_angles):
wa = normalized_angles[iang]
if self.minimum_angle_factor is not None:
if wa < self.minimum_angle_factor:
self.neighbors_normalized_angles[isite][icurrent]['nb_indices'] = list(nb_indices)
self.neighbors_angles[isite][icurrent]['nb_indices'] = list(nb_indices)
self.neighbors_normalized_angles[isite][icurrent]['dnb_indices'] = list(dnb_indices)
self.neighbors_angles[isite][icurrent]['dnb_indices'] = list(dnb_indices)
break
if np.isclose(wa, self.neighbors_normalized_angles[isite][icurrent]['min'],
rtol=0.0, atol=self.normalized_angle_tolerance):
self.neighbors_normalized_angles[isite][icurrent]['min'] = wa
self.neighbors_angles[isite][icurrent]['min'] = results[iang]['angle']
dnb_indices.add(int(iang))
else:
self.neighbors_normalized_angles[isite][icurrent]['nb_indices'] = list(nb_indices)
self.neighbors_angles[isite][icurrent]['nb_indices'] = list(nb_indices)
self.neighbors_normalized_angles[isite][icurrent]['dnb_indices'] = list(dnb_indices)
self.neighbors_angles[isite][icurrent]['dnb_indices'] = list(dnb_indices)
dnb_indices = {int(iang)}
self.neighbors_normalized_angles[isite].append({'max': wa,
'min': wa})
self.neighbors_angles[isite].append({'max': results[iang]['angle'],
'min': results[iang]['angle']})
icurrent += 1
nb_indices.add(int(iang))
else:
self.neighbors_normalized_angles[isite][icurrent]['nb_indices'] = list(nb_indices)
self.neighbors_angles[isite][icurrent]['nb_indices'] = list(nb_indices)
self.neighbors_normalized_angles[isite][icurrent]['dnb_indices'] = list(dnb_indices)
self.neighbors_angles[isite][icurrent]['dnb_indices'] = list(dnb_indices)
for iang in range(len(self.neighbors_angles[isite]) - 1):
ang_dict = self.neighbors_angles[isite][iang]
ang_dict_next = self.neighbors_angles[isite][iang + 1]
ang_dict['next'] = ang_dict_next['max']
nang_dict = self.neighbors_normalized_angles[isite][iang]
nang_dict_next = self.neighbors_normalized_angles[isite][iang + 1]
nang_dict['next'] = nang_dict_next['max']
if self.minimum_angle_factor is not None:
afact = self.minimum_angle_factor
else:
afact = 0.0
self.neighbors_normalized_angles[isite][-1]['next'] = afact
self.neighbors_angles[isite][-1]['next'] = afact * self.neighbors_angles[isite][0]['max']
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_normalized_distances[ivoronoi]):
distance_conditions.append([])
dp = dp_dict['max']
for ips, (ps, vals) in enumerate(voronoi):
distance_conditions[idp].append(vals['normalized_distance'] <= dp or
np.isclose(vals['normalized_distance'], dp,
rtol=0.0, atol=self.normalized_distance_tolerance/2.0))
return distance_conditions
def _precompute_angle_conditions(self, ivoronoi, voronoi):
angle_conditions = []
for iap, ap_dict in enumerate(self.neighbors_normalized_angles[ivoronoi]):
angle_conditions.append([])
ap = ap_dict['max']
for ips, (ps, vals) in enumerate(voronoi):
angle_conditions[iap].append(vals['normalized_angle'] >= ap or
np.isclose(vals['normalized_angle'], ap,
rtol=0.0, atol=self.normalized_angle_tolerance/2.0))
return angle_conditions
[docs] def neighbors_map(self, isite, distfactor, angfactor, additional_condition):
if self.neighbors_normalized_distances[isite] is None:
return None
dist_where = np.argwhere(np.array([wd['min'] for wd in self.neighbors_normalized_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_normalized_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}
[docs] def neighbors_surfaces(self, isite, surface_calculation_type=None, max_dist=2.0):
if self.voronoi_list2[isite] is None:
return None
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
[docs] def neighbors_surfaces_bounded(self, isite, surface_calculation_options=None):
if self.voronoi_list2[isite] is None:
return None
if surface_calculation_options is None:
surface_calculation_options = {'type': 'standard_elliptic',
'distance_bounds': {'lower': 1.2, 'upper': 1.8},
'angle_bounds': {'lower': 0.1, 'upper': 0.8}}
if surface_calculation_options['type'] in ['standard_elliptic', 'standard_diamond', 'standard_spline']:
plot_type = {'distance_parameter': ('initial_normalized', None),
'angle_parameter': ('initial_normalized', None)}
else:
raise ValueError('Type "{}" for the surface calculation in DetailedVoronoiContainer '
'is invalid'.format(surface_calculation_options['type']))
max_dist = surface_calculation_options['distance_bounds']['upper'] + 0.1
bounds_and_limits = self.voronoi_parameters_bounds_and_limits(isite=isite,
plot_type=plot_type,
max_dist=max_dist)
distance_bounds = bounds_and_limits['distance_bounds']
angle_bounds = bounds_and_limits['angle_bounds']
lower_and_upper_functions = get_lower_and_upper_f(surface_calculation_options=surface_calculation_options)
mindist = surface_calculation_options['distance_bounds']['lower']
maxdist = surface_calculation_options['distance_bounds']['upper']
minang = surface_calculation_options['angle_bounds']['lower']
maxang = surface_calculation_options['angle_bounds']['upper']
f_lower = lower_and_upper_functions['lower']
f_upper = lower_and_upper_functions['upper']
surfaces = np.zeros((len(distance_bounds), len(angle_bounds)), np.float)
for idp in range(len(distance_bounds) - 1):
dp1 = distance_bounds[idp]
dp2 = distance_bounds[idp+1]
if dp2 < mindist or dp1 > maxdist:
continue
if dp1 < mindist:
d1 = mindist
else:
d1 = dp1
if dp2 > maxdist:
d2 = maxdist
else:
d2 = dp2
for iap in range(len(angle_bounds) - 1):
ap1 = angle_bounds[iap]
ap2 = angle_bounds[iap+1]
if ap1 > ap2:
ap1 = angle_bounds[iap + 1]
ap2 = angle_bounds[iap]
if ap2 < minang or ap1 > maxang:
continue
intersection, interror = rectangle_surface_intersection(rectangle=((d1, d2),
(ap1, ap2)),
f_lower=f_lower,
f_upper=f_upper,
bounds_lower=[mindist, maxdist],
bounds_upper=[mindist, maxdist],
check=False)
surfaces[idp][iap] = intersection
return surfaces
@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]
[docs] def maps_and_surfaces(self, isite, surface_calculation_type=None, max_dist=2.0, additional_conditions=None):
if self.voronoi_list2[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
[docs] def maps_and_surfaces_bounded(self, isite, surface_calculation_options=None, additional_conditions=None):
if self.voronoi_list2[isite] is None:
return None
if additional_conditions is None:
additional_conditions = [self.AC.ONLY_ACB]
surfaces = self.neighbors_surfaces_bounded(isite=isite, surface_calculation_options=surface_calculation_options)
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
[docs] def neighbors(self, isite, distfactor, angfactor, additional_condition=None):
idist = None
dfact = None
for iwd, wd in enumerate(self.neighbors_normalized_distances[isite]):
if distfactor >= wd['min']:
idist = iwd
dfact = wd['max']
else:
break
iang = None
afact = None
for iwa, wa in enumerate(self.neighbors_normalized_angles[isite]):
if angfactor <= wa['max']:
iang = iwa
afact = wa['min']
else:
break
if idist is None or iang is None:
raise ValueError('Distance or angle parameter not found ...')
return [nb for nb in self.voronoi_list2[isite] if
nb['normalized_distance'] <= dfact and nb['normalized_angle'] >= afact]
[docs] def voronoi_parameters_bounds_and_limits(self, isite, plot_type, max_dist):
#Initializes the distance and angle parameters
if self.voronoi_list2[isite] is None:
return None
if plot_type is None:
plot_type = {'distance_parameter': ('initial_inverse_opposite', None),
'angle_parameter': ('initial_opposite', None)}
dd = [dist['min'] for dist in self.neighbors_normalized_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_normalized_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_normalized_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}
[docs] def is_close_to(self, other, rtol=0.0, atol=1e-8):
isclose = (np.isclose(self.normalized_angle_tolerance, other.normalized_angle_tolerance,
rtol=rtol, atol=atol) and
np.isclose(self.normalized_distance_tolerance, other.normalized_distance_tolerance,
rtol=rtol, atol=atol) and
self.additional_conditions == other.additional_conditions and
self.valences == other.valences)
if not isclose:
return isclose
for isite, site_voronoi in enumerate(self.voronoi_list2):
self_to_other_nbs = {}
for inb, nb in enumerate(site_voronoi):
if nb is None:
if other.voronoi_list2[isite] is None:
continue
else:
return False
else:
if other.voronoi_list2[isite] is None:
return False
nb_other = None
for inb2, nb2 in enumerate(other.voronoi_list2[isite]):
if nb['site'] == nb2['site']:
self_to_other_nbs[inb] = inb2
nb_other = nb2
break
if nb_other is None:
return False
if not np.isclose(nb['distance'], nb_other['distance'],
rtol=rtol, atol=atol):
return False
if not np.isclose(nb['angle'], nb_other['angle'],
rtol=rtol, atol=atol):
return False
if not np.isclose(nb['normalized_distance'], nb_other['normalized_distance'],
rtol=rtol, atol=atol):
return False
if not np.isclose(nb['normalized_angle'], nb_other['normalized_angle'],
rtol=rtol, atol=atol):
return False
if nb['index'] != nb_other['index']:
return False
if nb['site'] != nb_other['site']:
return False
return True
def __eq__(self, other):
return (self.normalized_angle_tolerance == other.normalized_angle_tolerance and
self.normalized_distance_tolerance == other.normalized_distance_tolerance and
self.additional_conditions == other.additional_conditions and
self.valences == other.valences and
self.voronoi_list2 == other.voronoi_list2 and
self.structure == other.structure)
def __ne__(self, other):
return not self == other
[docs] def to_bson_voronoi_list2(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_list2 = [None] * len(self.voronoi_list2)
for ivoro, voro in enumerate(self.voronoi_list2):
if voro is None or voro == 'None':
continue
site_voro = []
# {'site': neighbors[nn[1]],
# 'angle': sa,
# 'distance': distances[nn[1]],
# 'index': myindex}
for nb_dict in voro:
site = nb_dict['site']
site_dict = {key: val for key, val in nb_dict.items() if key not in ['site']}
#site_voro.append([ps.as_dict(), dd]) [float(c) for c in self._fcoords]
diff = site._fcoords - self.structure[nb_dict['index']]._fcoords
site_voro.append([[nb_dict['index'], [float(c) for c in diff]],
site_dict])
bson_nb_voro_list2[ivoro] = site_voro
return bson_nb_voro_list2
[docs] def as_dict(self):
"""
Bson-serializable dict representation of the VoronoiContainer.
:return: dictionary that is BSON-encodable
"""
bson_nb_voro_list2 = self.to_bson_voronoi_list2()
return {"@module": self.__class__.__module__,
"@class": self.__class__.__name__,
"bson_nb_voro_list2": bson_nb_voro_list2,
# "neighbors_lists": self.neighbors_lists,
"structure": self.structure.as_dict(),
"normalized_angle_tolerance": self.normalized_angle_tolerance,
"normalized_distance_tolerance": self.normalized_distance_tolerance,
"additional_conditions": self.additional_conditions,
"valences": self.valences,
"maximum_distance_factor": self.maximum_distance_factor,
"minimum_angle_factor": self.minimum_angle_factor}
[docs] @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_list2 = from_bson_voronoi_list2(d['bson_nb_voro_list2'], structure)
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_list2=voronoi_list2,
# neighbors_lists=neighbors_lists,
normalized_angle_tolerance=d['normalized_angle_tolerance'],
normalized_distance_tolerance=d['normalized_distance_tolerance'],
additional_conditions=d['additional_conditions'],
valences=d['valences'],
maximum_distance_factor=maximum_distance_factor,
minimum_angle_factor=minimum_angle_factor)