pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies module

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

class AbstractChemenvStrategy(structure_environments=None, symmetry_measure_type='csm_wcs_ctwcc')[source]

Bases: monty.json.MSONable

Class used to define a Chemenv strategy for the neighbors and coordination environment to be applied to a StructureEnvironments object

Abstract constructor for the all chemenv strategies. :param structure_environments: StructureEnvironments object containing all the information on the

coordination of the sites in a structure

AC = <pymatgen.analysis.chemenv.utils.defs_utils.AdditionalConditions object>[source]
DEFAULT_SYMMETRY_MEASURE_TYPE = 'csm_wcs_ctwcc'[source]
STRATEGY_DESCRIPTION: str = None[source]
STRATEGY_INFO_FIELDS: List = [][source]
STRATEGY_OPTIONS: Dict[str, Dict] = {}[source]
abstract as_dict()[source]

Bson-serializable dict representation of the SimplestChemenvStrategy object. :return: Bson-serializable dict representation of the SimplestChemenvStrategy object.

equivalent_site_index_and_transform(psite)[source]

Get the equivalent site and corresponding symmetry+translation transformations.

Parameters

psite – Periodic site.

Returns

Equivalent site in the unit cell, translations and symmetry transformation.

classmethod from_dict(d)[source]

Reconstructs the SimpleAbundanceChemenvStrategy object from a dict representation of the SimpleAbundanceChemenvStrategy object created using the as_dict method. :param d: dict representation of the SimpleAbundanceChemenvStrategy object :return: StructureEnvironments object

get_site_ce_fractions_and_neighbors(site, full_ce_info=False, strategy_info=False)[source]

Applies the strategy to the structure_environments object in order to get coordination environments, their fraction, csm, geometry_info, and neighbors :param site: Site for which the above information is seeked :return: The list of neighbors of the site. For complex strategies, where one allows multiple solutions, this can return a list of list of neighbors

abstract get_site_coordination_environment(site)[source]

Applies the strategy to the structure_environments object in order to define the coordination environment of a given site. :param site: Site for which the coordination environment is looked for :return: The coordination environment of the site. For complex strategies, where one allows multiple

solutions, this can return a list of coordination environments for the site

abstract get_site_coordination_environments(site)[source]

Applies the strategy to the structure_environments object in order to define the coordination environment of a given site. :param site: Site for which the coordination environment is looked for :return: The coordination environment of the site. For complex strategies, where one allows multiple

solutions, this can return a list of coordination environments for the site

abstract get_site_coordination_environments_fractions(site, isite=None, dequivsite=None, dthissite=None, mysym=None, ordered=True, min_fraction=0.0, return_maps=True, return_strategy_dict_info=False)[source]

Applies the strategy to the structure_environments object in order to define the coordination environment of a given site. :param site: Site for which the coordination environment is looked for :return: The coordination environment of the site. For complex strategies, where one allows multiple

solutions, this can return a list of coordination environments for the site

abstract get_site_neighbors(site)[source]

Applies the strategy to the structure_environments object in order to get the neighbors of a given site. :param site: Site for which the neighbors are looked for :param structure_environments: StructureEnvironments object containing all the information needed to get the

neighbors of the site

Returns

The list of neighbors of the site. For complex strategies, where one allows multiple solutions, this can return a list of list of neighbors

prepare_symmetries()[source]

Prepare the symmetries for the structure contained in the structure environments.

set_option(option_name, option_value)[source]

Set up a given option for this strategy.

Parameters
  • option_name – Name of the option.

  • option_value – Value for this option.

Returns

None

set_structure_environments(structure_environments)[source]

Set the structure environments to this strategy.

Parameters

structure_environments – StructureEnvironments object.

Returns

None

setup_options(all_options_dict)[source]

Set up options for this strategy based on a dict.

Parameters

all_options_dict – Dict of option_name->option_value.

Returns

None

property symmetry_measure_type[source]

Type of symmetry measure.

property uniquely_determines_coordination_environments[source]

Returns True if the strategy leads to a unique coordination environment, False otherwise. :return: True if the strategy leads to a unique coordination environment, False otherwise.

class AdditionalConditionInt(integer)[source]

Bases: int, pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.StrategyOption

Integer representing an additional condition in a strategy.

Special int representing additional conditions.

allowed_values = 'Integer amongst :\n - 0 for "No additional condition"\n - 1 for "Only anion-cation bonds"\n - 2 for "No element-element bonds (same elements)"\n - 3 for "Only anion-cation bonds and no element-element bonds (same elements)"\n - 4 for "Only element-oxygen bonds"\n'[source]
as_dict()[source]

MSONable dict

description = 'Only element-oxygen bonds'[source]
classmethod from_dict(d)[source]

Initialize additional condition from dict.

Parameters

d – Dict representation of the additional condition.

integer = 4[source]
class AngleCutoffFloat(myfloat)[source]

Bases: float, pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.StrategyOption

Angle cutoff in a strategy

Special float that should be between 0.0 and 1.0.

Parameters

myfloat – Angle cutoff.

allowed_values = 'Real number between 0.0 and 1.0'[source]
as_dict()[source]

MSONAble dict

classmethod from_dict(d)[source]

Initialize angle cutoff from dict.

Parameters

d – Dict representation of the angle cutoff.

class AngleNbSetWeight(aa=1.0)[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.NbSetWeight

Weight of neighbors set based on the angle.

Initialize AngleNbSetWeight estimator.

Parameters

aa – Exponent of the angle for the estimator.

SHORT_NAME = 'AngleWeight'[source]
static angle_sum(nb_set)[source]

Sum of all angles in a neighbors set.

Parameters

nb_set – Neighbors set.

Returns

Sum of solid angles for the neighbors set.

angle_sumn(nb_set)[source]

Sum of all angles to a given power in a neighbors set.

Parameters

nb_set – Neighbors set.

Returns

Sum of solid angles to the power aa for the neighbors set.

as_dict()[source]

MSONAble dict

classmethod from_dict(dd)[source]

From dict :param dd: :return:

weight(nb_set, structure_environments, cn_map=None, additional_info=None)[source]

Get the weight of a given neighbors set.

Parameters
  • nb_set – Neighbors set.

  • structure_environments – Structure environments used to estimate weight.

  • cn_map – Mapping index for this neighbors set.

  • additional_info – Additional information.

Returns

Weight of the neighbors set.

class AnglePlateauNbSetWeight(angle_function=None, weight_function=None)[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.NbSetWeight

Weight of neighbors set based on the angle.

Initialize AnglePlateauNbSetWeight.

Parameters
  • angle_function – Angle function to use.

  • weight_function – Ratio function to use.

SHORT_NAME = 'AnglePlateauWeight'[source]
as_dict()[source]

MSONable dict

classmethod from_dict(dd)[source]

Initialize from dict.

Parameters

dd – Dict representation of AnglePlateauNbSetWeight.

Returns

AnglePlateauNbSetWeight.

weight(nb_set, structure_environments, cn_map=None, additional_info=None)[source]

Get the weight of a given neighbors set.

Parameters
  • nb_set – Neighbors set.

  • structure_environments – Structure environments used to estimate weight.

  • cn_map – Mapping index for this neighbors set.

  • additional_info – Additional information.

Returns

Weight of the neighbors set.

class CNBiasNbSetWeight(cn_weights, initialization_options)[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.NbSetWeight

Weight of neighbors set based on specific biases towards specific coordination numbers.

Initialize CNBiasNbSetWeight.

Parameters
  • cn_weights – Weights for each coordination.

  • initialization_options – Options for initialization.

SHORT_NAME = 'CNBiasWeight'[source]
as_dict()[source]

MSONable dict

classmethod explicit(cn_weights)[source]

Initializes weights explicitly for each coordination.

Parameters

cn_weights – Weights for each coordination.

Returns

CNBiasNbSetWeight.

classmethod from_description(dd)[source]

Initializes weights from description.

Parameters

dd – Dictionary description.

Returns

CNBiasNbSetWeight.

classmethod from_dict(dd)[source]

Initialize from dict.

Parameters

dd – Dict representation of CNBiasNbSetWeight.

Returns

CNBiasNbSetWeight.

classmethod geometrically_equidistant(weight_cn1, weight_cn13)[source]

Initializes geometrically equidistant weights for each coordination.

Parameters
  • weight_cn1 – Weight of coordination 1.

  • weight_cn13 – Weight of coordination 13.

Returns

CNBiasNbSetWeight.

classmethod linearly_equidistant(weight_cn1, weight_cn13)[source]

Initializes linearly equidistant weights for each coordination.

Parameters
  • weight_cn1 – Weight of coordination 1.

  • weight_cn13 – Weight of coordination 13.

Returns

CNBiasNbSetWeight.

weight(nb_set, structure_environments, cn_map=None, additional_info=None)[source]

Get the weight of a given neighbors set.

Parameters
  • nb_set – Neighbors set.

  • structure_environments – Structure environments used to estimate weight.

  • cn_map – Mapping index for this neighbors set.

  • additional_info – Additional information.

Returns

Weight of the neighbors set.

class CSMFloat(myfloat)[source]

Bases: float, pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.StrategyOption

Real number representing a Continuous Symmetry Measure

Special float that should be between 0.0 and 100.0.

Parameters

myfloat – CSM.

allowed_values = 'Real number between 0.0 and 100.0'[source]
as_dict()[source]

MSONable dict

classmethod from_dict(d)[source]

Initialize CSM from dict.

Parameters

d – Dict representation of the CSM.

class DeltaCSMNbSetWeight(effective_csm_estimator={'function': 'power2_inverse_decreasing', 'options': {'max_csm': 8.0}}, weight_estimator={'function': 'smootherstep', 'options': {'delta_csm_max': 3.0, 'delta_csm_min': 0.5}}, delta_cn_weight_estimators=None, symmetry_measure_type='csm_wcs_ctwcc')[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.NbSetWeight

Weight of neighbors set based on the differences of CSM.

Initialize SelfCSMNbSetWeight.

Parameters
  • effective_csm_estimator – Ratio function used for the effective CSM (comparison between neighbors sets).

  • weight_estimator – Weight estimator within a given neighbors set.

  • delta_cn_weight_estimators – Specific weight estimators for specific cn

  • symmetry_measure_type – Type of symmetry measure to be used.

DEFAULT_EFFECTIVE_CSM_ESTIMATOR = {'function': 'power2_inverse_decreasing', 'options': {'max_csm': 8.0}}[source]
DEFAULT_SYMMETRY_MEASURE_TYPE = 'csm_wcs_ctwcc'[source]
DEFAULT_WEIGHT_ESTIMATOR = {'function': 'smootherstep', 'options': {'delta_csm_max': 3.0, 'delta_csm_min': 0.5}}[source]
SHORT_NAME = 'DeltaCSMWeight'[source]
as_dict()[source]

MSONable dict. :return:

classmethod delta_cn_specifics(delta_csm_mins=None, delta_csm_maxs=None, function='smootherstep', symmetry_measure_type='csm_wcs_ctwcc', effective_csm_estimator={'function': 'power2_inverse_decreasing', 'options': {'max_csm': 8.0}})[source]

Initializes DeltaCSMNbSetWeight from specific coordination number differences.

Parameters
  • delta_csm_mins – Minimums for each coordination number.

  • delta_csm_maxs – Maximums for each coordination number.

  • function – Ratio function used.

  • symmetry_measure_type – Type of symmetry measure to be used.

  • effective_csm_estimator – Ratio function used for the effective CSM (comparison between neighbors sets).

Returns

DeltaCSMNbSetWeight.

classmethod from_dict(dd)[source]

Initialize from dict.

Parameters

dd – Dict representation of DeltaCSMNbSetWeight.

Returns

DeltaCSMNbSetWeight.

weight(nb_set, structure_environments, cn_map=None, additional_info=None)[source]

Get the weight of a given neighbors set.

Parameters
  • nb_set – Neighbors set.

  • structure_environments – Structure environments used to estimate weight.

  • cn_map – Mapping index for this neighbors set.

  • additional_info – Additional information.

Returns

Weight of the neighbors set.

class DeltaDistanceNbSetWeight(weight_function=None, nbs_source='voronoi')[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.NbSetWeight

Weight of neighbors set based on the difference of distances.

Initialize DeltaDistanceNbSetWeight.

Parameters
  • weight_function – Ratio function to use.

  • nbs_source – Source of the neighbors.

SHORT_NAME = 'DeltaDistanceNbSetWeight'[source]
as_dict()[source]

MSONable dict

classmethod from_dict(dd)[source]

Initialize from dict.

Parameters

dd – Dict representation of DeltaDistanceNbSetWeight.

Returns

DeltaDistanceNbSetWeight.

weight(nb_set, structure_environments, cn_map=None, additional_info=None)[source]

Get the weight of a given neighbors set.

Parameters
  • nb_set – Neighbors set.

  • structure_environments – Structure environments used to estimate weight.

  • cn_map – Mapping index for this neighbors set.

  • additional_info – Additional information.

Returns

Weight of the neighbors set.

class DistanceAngleAreaNbSetWeight(weight_type='has_intersection', surface_definition={'angle_bounds': {'lower': 0.1, 'upper': 0.8}, 'distance_bounds': {'lower': 1.2, 'upper': 1.8}, 'type': 'standard_elliptic'}, nb_sets_from_hints='fallback_to_source', other_nb_sets='0_weight', additional_condition=1, smoothstep_distance=None, smoothstep_angle=None)[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.NbSetWeight

Weight of neighbors set based on the area in the distance-angle space.

Initialize CNBiasNbSetWeight.

Parameters
  • weight_type – Type of weight.

  • surface_definition – Definition of the surface.

  • nb_sets_from_hints – How to deal with neighbors sets obtained from “hints”.

  • other_nb_sets – What to do with other neighbors sets.

  • additional_condition – Additional condition to be used.

  • smoothstep_distance – Smoothstep distance.

  • smoothstep_angle – Smoothstep angle.

AC = <pymatgen.analysis.chemenv.utils.defs_utils.AdditionalConditions object>[source]
DEFAULT_SURFACE_DEFINITION = {'angle_bounds': {'lower': 0.1, 'upper': 0.8}, 'distance_bounds': {'lower': 1.2, 'upper': 1.8}, 'type': 'standard_elliptic'}[source]
SHORT_NAME = 'DistAngleAreaWeight'[source]
as_dict()[source]

MSONable dict

classmethod from_dict(dd)[source]

Initialize from dict.

Parameters

dd – Dict representation of DistanceAngleAreaNbSetWeight.

Returns

DistanceAngleAreaNbSetWeight.

rectangle_crosses_area(d1, d2, a1, a2)[source]

Whether a given rectangle crosses the area defined by the upper and lower curves.

Parameters
  • d1 – lower d.

  • d2 – upper d.

  • a1 – lower a.

  • a2 – upper a.

Returns

w_area_has_intersection(nb_set, structure_environments, cn_map, additional_info)[source]

Get intersection of the neighbors set area with the surface.

Parameters
  • nb_set – Neighbors set.

  • structure_environments – Structure environments.

  • cn_map – Mapping index of the neighbors set.

  • additional_info – Additional information.

Returns

Area intersection between neighbors set and surface.

w_area_has_intersection_smoothstep(nb_set, structure_environments, cn_map, additional_info)[source]

Get intersection of the neighbors set area with the surface.

Parameters
  • nb_set – Neighbors set.

  • structure_environments – Structure environments.

  • cn_map – Mapping index of the neighbors set.

  • additional_info – Additional information.

Returns

Area intersection between neighbors set and surface.

w_area_intersection_nbsfh_fbs_onb0(nb_set, structure_environments, cn_map, additional_info)[source]

Get intersection of the neighbors set area with the surface.

Parameters
  • nb_set – Neighbors set.

  • structure_environments – Structure environments.

  • cn_map – Mapping index of the neighbors set.

  • additional_info – Additional information.

Returns

Area intersection between neighbors set and surface.

weight(nb_set, structure_environments, cn_map=None, additional_info=None)[source]

Get the weight of a given neighbors set.

Parameters
  • nb_set – Neighbors set.

  • structure_environments – Structure environments used to estimate weight.

  • cn_map – Mapping index for this neighbors set.

  • additional_info – Additional information.

Returns

Weight of the neighbors set.

class DistanceCutoffFloat(myfloat)[source]

Bases: float, pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.StrategyOption

Distance cutoff in a strategy.

Special float that should be between 1.0 and infinity.

Parameters

myfloat – Distance cutoff.

allowed_values = 'Real number between 1.0 and +infinity'[source]
as_dict()[source]

MSONAble dict

classmethod from_dict(d)[source]

Initialize distance cutoff from dict.

Parameters

d – Dict representation of the distance cutoff.

class DistanceNbSetWeight(weight_function=None, nbs_source='voronoi')[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.NbSetWeight

Weight of neighbors set based on the distance.

Initialize DistanceNbSetWeight.

Parameters
  • weight_function – Ratio function to use.

  • nbs_source – Source of the neighbors.

SHORT_NAME = 'DistanceNbSetWeight'[source]
as_dict()[source]

MSOnable dict

classmethod from_dict(dd)[source]

Initialize from dict.

Parameters

dd – Dict representation of DistanceNbSetWeight.

Returns

DistanceNbSetWeight.

weight(nb_set, structure_environments, cn_map=None, additional_info=None)[source]

Get the weight of a given neighbors set.

Parameters
  • nb_set – Neighbors set.

  • structure_environments – Structure environments used to estimate weight.

  • cn_map – Mapping index for this neighbors set.

  • additional_info – Additional information.

Returns

Weight of the neighbors set.

class DistancePlateauNbSetWeight(distance_function=None, weight_function=None)[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.NbSetWeight

Weight of neighbors set based on the distance.

Initialize DistancePlateauNbSetWeight.

Parameters
  • distance_function – Distance function to use.

  • weight_function – Ratio function to use.

SHORT_NAME = 'DistancePlateauWeight'[source]
as_dict()[source]

MSONable dict

classmethod from_dict(dd)[source]

Initialize from dict.

Parameters

dd – Dict representation of DistancePlateauNbSetWeight.

Returns

DistancePlateauNbSetWeight.

weight(nb_set, structure_environments, cn_map=None, additional_info=None)[source]

Get the weight of a given neighbors set.

Parameters
  • nb_set – Neighbors set.

  • structure_environments – Structure environments used to estimate weight.

  • cn_map – Mapping index for this neighbors set.

  • additional_info – Additional information.

Returns

Weight of the neighbors set.

class MultiWeightsChemenvStrategy(structure_environments=None, additional_condition=1, symmetry_measure_type='csm_wcs_ctwcc', dist_ang_area_weight=None, self_csm_weight=None, delta_csm_weight=None, cn_bias_weight=None, angle_weight=None, normalized_angle_distance_weight=None, ce_estimator={'function': 'power2_inverse_power2_decreasing', 'options': {'max_csm': 8.0}})[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.WeightedNbSetChemenvStrategy

Constructor for the MultiWeightsChemenvStrategy. :param structure_environments: StructureEnvironments object containing all the information on the

coordination of the sites in a structure

DEFAULT_CE_ESTIMATOR = {'function': 'power2_inverse_power2_decreasing', 'options': {'max_csm': 8.0}}[source]
DEFAULT_DIST_ANG_AREA_WEIGHT: Dict = {}[source]
STRATEGY_DESCRIPTION = ' Multi Weights ChemenvStrategy'[source]
as_dict()[source]

Bson-serializable dict representation of the MultiWeightsChemenvStrategy object. :return: Bson-serializable dict representation of the MultiWeightsChemenvStrategy object.

classmethod from_dict(d)[source]

Reconstructs the MultiWeightsChemenvStrategy object from a dict representation of the MultipleAbundanceChemenvStrategy object created using the as_dict method. :param d: dict representation of the MultiWeightsChemenvStrategy object :return: MultiWeightsChemenvStrategy object

classmethod stats_article_weights_parameters()[source]

Initialize strategy used in the statistics article.

property uniquely_determines_coordination_environments[source]

Whether this strategy uniquely determines coordination environments.

class NbSetWeight[source]

Bases: monty.json.MSONable

Abstract object for neighbors sets weights estimations.

abstract as_dict()[source]

A JSON serializable dict representation of this neighbors set weight.

abstract weight(nb_set, structure_environments, cn_map=None, additional_info=None)[source]

Get the weight of a given neighbors set.

Parameters
  • nb_set – Neighbors set.

  • structure_environments – Structure environments used to estimate weight.

  • cn_map – Mapping index for this neighbors set.

  • additional_info – Additional information.

Returns

Weight of the neighbors set.

class NormalizedAngleDistanceNbSetWeight(average_type, aa, bb)[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.NbSetWeight

Weight of neighbors set based on the normalized angle/distance.

Initialize NormalizedAngleDistanceNbSetWeight.

Parameters
  • average_type – Average function.

  • aa – Exponent for the angle values.

  • bb – Exponent for the distance values.

SHORT_NAME = 'NormAngleDistWeight'[source]
static ang(nb_set)[source]

Angle weight.

Parameters

nb_set – Neighbors set.

Returns

List of angle weights.

static anginvdist(nb_set)[source]

Angle/distance weight.

Parameters

nb_set – Neighbors set.

Returns

List of angle/distance weights.

anginvndist(nb_set)[source]

Angle/power distance weight.

Parameters

nb_set – Neighbors set.

Returns

List of angle/power distance weights.

angn(nb_set)[source]

Power angle weight.

Parameters

nb_set – Neighbors set.

Returns

List of power angle weights.

angninvdist(nb_set)[source]

Power angle/distance weight.

Parameters

nb_set – Neighbors set.

Returns

List of power angle/distance weights.

angninvndist(nb_set)[source]

Power angle/power distance weight.

Parameters

nb_set – Neighbors set.

Returns

List of power angle/power distance weights.

as_dict()[source]

MSONable dict

static aweight(fda_list)[source]

Standard mean of the weights.

Parameters

fda_list – List of estimator weights for each neighbor.

Returns

Standard mean of the weights.

classmethod from_dict(dd)[source]

Initialize from dict.

Parameters

dd – Dict representation of NormalizedAngleDistanceNbSetWeight.

Returns

NormalizedAngleDistanceNbSetWeight.

static gweight(fda_list)[source]

Geometric mean of the weights.

Parameters

fda_list – List of estimator weights for each neighbor.

Returns

Geometric mean of the weights.

static invdist(nb_set)[source]

Inverse distance weight.

Parameters

nb_set – Neighbors set.

Returns

List of inverse distances.

invndist(nb_set)[source]

Inverse power distance weight.

Parameters

nb_set – Neighbors set.

Returns

List of inverse power distances.

weight(nb_set, structure_environments, cn_map=None, additional_info=None)[source]

Get the weight of a given neighbors set.

Parameters
  • nb_set – Neighbors set.

  • structure_environments – Structure environments used to estimate weight.

  • cn_map – Mapping index for this neighbors set.

  • additional_info – Additional information.

Returns

Weight of the neighbors set.

class SelfCSMNbSetWeight(effective_csm_estimator={'function': 'power2_inverse_decreasing', 'options': {'max_csm': 8.0}}, weight_estimator={'function': 'power2_decreasing_exp', 'options': {'alpha': 1.0, 'max_csm': 8.0}}, symmetry_measure_type='csm_wcs_ctwcc')[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.NbSetWeight

Weight of neighbors set based on the Self CSM.

Initialize SelfCSMNbSetWeight.

Parameters
  • effective_csm_estimator – Ratio function used for the effective CSM (comparison between neighbors sets).

  • weight_estimator – Weight estimator within a given neighbors set.

  • symmetry_measure_type – Type of symmetry measure to be used.

DEFAULT_EFFECTIVE_CSM_ESTIMATOR = {'function': 'power2_inverse_decreasing', 'options': {'max_csm': 8.0}}[source]
DEFAULT_SYMMETRY_MEASURE_TYPE = 'csm_wcs_ctwcc'[source]
DEFAULT_WEIGHT_ESTIMATOR = {'function': 'power2_decreasing_exp', 'options': {'alpha': 1.0, 'max_csm': 8.0}}[source]
SHORT_NAME = 'SelfCSMWeight'[source]
as_dict()[source]

MSONable dict

classmethod from_dict(dd)[source]

Initialize from dict.

Parameters

dd – Dict representation of SelfCSMNbSetWeight.

Returns

SelfCSMNbSetWeight.

weight(nb_set, structure_environments, cn_map=None, additional_info=None)[source]

Get the weight of a given neighbors set.

Parameters
  • nb_set – Neighbors set.

  • structure_environments – Structure environments used to estimate weight.

  • cn_map – Mapping index for this neighbors set.

  • additional_info – Additional information.

Returns

Weight of the neighbors set.

class SimpleAbundanceChemenvStrategy(structure_environments=None, additional_condition=1, symmetry_measure_type='csm_wcs_ctwcc')[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.AbstractChemenvStrategy

Simple ChemenvStrategy using the neighbors that are the most “abundant” in the grid of angle and distance parameters for the definition of neighbors in the Voronoi approach. The coordination environment is then given as the one with the lowest continuous symmetry measure

Constructor for the SimpleAbundanceChemenvStrategy. :param structure_environments: StructureEnvironments object containing all the information on the

coordination of the sites in a structure

DEFAULT_ADDITIONAL_CONDITION = 1[source]
DEFAULT_MAX_DIST = 2.0[source]
STRATEGY_DESCRIPTION = ' Simple Abundance ChemenvStrategy using the most "abundant" neighbors map \n for the definition of neighbors in the Voronoi approach. \n The coordination environment is then given as the one with the \n lowest continuous symmetry measure.'[source]
STRATEGY_OPTIONS: Dict[str, Dict] = {'additional_condition': {'default': 1, 'internal': '_additional_condition', 'type': <class 'pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.AdditionalConditionInt'>}, 'surface_calculation_type': {}}[source]
as_dict()[source]

Bson-serializable dict representation of the SimpleAbundanceChemenvStrategy object. :return: Bson-serializable dict representation of the SimpleAbundanceChemenvStrategy object.

classmethod from_dict(d)[source]

Reconstructs the SimpleAbundanceChemenvStrategy object from a dict representation of the SimpleAbundanceChemenvStrategy object created using the as_dict method. :param d: dict representation of the SimpleAbundanceChemenvStrategy object :return: StructureEnvironments object

get_site_coordination_environment(site, isite=None, dequivsite=None, dthissite=None, mysym=None, return_map=False)[source]

Get the coordination environment of a given site.

Parameters
  • site – Site for which coordination environment is needed.

  • isite – Index of the site.

  • dequivsite – Translation of the equivalent site.

  • dthissite – Translation of this site.

  • mysym – Symmetry to be applied.

  • return_map – Whether to return cn_map (identifies the NeighborsSet used).

Returns

Coordination environment of site.

get_site_coordination_environments(site, isite=None, dequivsite=None, dthissite=None, mysym=None, return_maps=False)[source]

Get the coordination environments of a given site.

Parameters
  • site – Site for which coordination environment is needed.

  • isite – Index of the site.

  • dequivsite – Translation of the equivalent site.

  • dthissite – Translation of this site.

  • mysym – Symmetry to be applied.

  • return_maps – Whether to return cn_maps (identifies all the NeighborsSet used).

Returns

List of coordination environment.

get_site_neighbors(site)[source]

Get the neighbors of a given site with this strategy.

Parameters

site – Periodic site.

Returns

List of neighbors of site.

property uniquely_determines_coordination_environments[source]

Whether this strategy uniquely determines coordination environments.

class SimplestChemenvStrategy(structure_environments=None, distance_cutoff=1.4, angle_cutoff=0.3, additional_condition=1, continuous_symmetry_measure_cutoff=10.0, symmetry_measure_type='csm_wcs_ctwcc')[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.AbstractChemenvStrategy

Simplest ChemenvStrategy using fixed angle and distance parameters for the definition of neighbors in the Voronoi approach. The coordination environment is then given as the one with the lowest continuous symmetry measure

Constructor for this SimplestChemenvStrategy. :param distance_cutoff: Distance cutoff used :param angle_cutoff: Angle cutoff used

DEFAULT_ADDITIONAL_CONDITION = 1[source]
DEFAULT_ANGLE_CUTOFF = 0.3[source]
DEFAULT_CONTINUOUS_SYMMETRY_MEASURE_CUTOFF = 10.0[source]
DEFAULT_DISTANCE_CUTOFF = 1.4[source]
STRATEGY_DESCRIPTION = ' Simplest ChemenvStrategy using fixed angle and distance parameters \n for the definition of neighbors in the Voronoi approach. \n The coordination environment is then given as the one with the \n lowest continuous symmetry measure.'[source]
STRATEGY_OPTIONS: Dict[str, Dict] = {'additional_condition': {'default': 1, 'internal': '_additional_condition', 'type': <class 'pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.AdditionalConditionInt'>}, 'angle_cutoff': {'default': 0.3, 'internal': '_angle_cutoff', 'type': <class 'pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.AngleCutoffFloat'>}, 'continuous_symmetry_measure_cutoff': {'default': 10.0, 'internal': '_continuous_symmetry_measure_cutoff', 'type': <class 'pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.CSMFloat'>}, 'distance_cutoff': {'default': 1.4, 'internal': '_distance_cutoff', 'type': <class 'pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.DistanceCutoffFloat'>}}[source]
add_strategy_visualization_to_subplot(subplot, visualization_options=None, plot_type=None)[source]

Add a visual of the strategy on a distance-angle plot.

Parameters
  • subplot – Axes object onto the visual should be added.

  • visualization_options – Options for the visual.

  • plot_type – Type of distance-angle plot.

Returns

None

property additional_condition[source]

Additional condition for this strategy.

property angle_cutoff[source]

Angle cutoff used.

as_dict()[source]

Bson-serializable dict representation of the SimplestChemenvStrategy object. :return: Bson-serializable dict representation of the SimplestChemenvStrategy object.

property continuous_symmetry_measure_cutoff[source]

CSM cutoff used

property distance_cutoff[source]

Distance cutoff used.

classmethod from_dict(d)[source]

Reconstructs the SimplestChemenvStrategy object from a dict representation of the SimplestChemenvStrategy object created using the as_dict method. :param d: dict representation of the SimplestChemenvStrategy object :return: StructureEnvironments object

get_site_coordination_environment(site, isite=None, dequivsite=None, dthissite=None, mysym=None, return_map=False)[source]

Get the coordination environment of a given site.

Parameters
  • site – Site for which coordination environment is needed.

  • isite – Index of the site.

  • dequivsite – Translation of the equivalent site.

  • dthissite – Translation of this site.

  • mysym – Symmetry to be applied.

  • return_map – Whether to return cn_map (identifies the NeighborsSet used).

Returns

Coordination environment of site.

get_site_coordination_environments(site, isite=None, dequivsite=None, dthissite=None, mysym=None, return_maps=False)[source]

Get the coordination environments of a given site.

Parameters
  • site – Site for which coordination environment is needed.

  • isite – Index of the site.

  • dequivsite – Translation of the equivalent site.

  • dthissite – Translation of this site.

  • mysym – Symmetry to be applied.

  • return_maps – Whether to return cn_maps (identifies all the NeighborsSet used).

Returns

List of coordination environment.

get_site_coordination_environments_fractions(site, isite=None, dequivsite=None, dthissite=None, mysym=None, ordered=True, min_fraction=0.0, return_maps=True, return_strategy_dict_info=False)[source]

Get the coordination environments of a given site and additional information.

Parameters
  • site – Site for which coordination environment is needed.

  • isite – Index of the site.

  • dequivsite – Translation of the equivalent site.

  • dthissite – Translation of this site.

  • mysym – Symmetry to be applied.

  • ordered – Whether to order the list by fractions.

  • min_fraction – Minimum fraction to include in the list

  • return_maps – Whether to return cn_maps (identifies all the NeighborsSet used).

  • return_strategy_dict_info – Whether to add the info about the strategy used.

Returns

List of Dict with coordination environment, fraction and additional info.

get_site_neighbors(site, isite=None, dequivsite=None, dthissite=None, mysym=None)[source]

Get the neighbors of a given site.

Parameters
  • site – Site for which neighbors are needed.

  • isite – Index of the site.

  • dequivsite – Translation of the equivalent site.

  • dthissite – Translation of this site.

  • mysym – Symmetry to be applied.

Returns

List of coordinated neighbors of site.

property uniquely_determines_coordination_environments[source]

Whether this strategy uniquely determines coordination environments.

class StrategyOption[source]

Bases: monty.json.MSONable

Abstract class for the options of the chemenv strategies.

allowed_values: Optional[str] = None[source]
abstract as_dict()[source]

A JSON serializable dict representation of this strategy option.

class TargettedPenaltiedAbundanceChemenvStrategy(structure_environments=None, truncate_dist_ang=True, additional_condition=1, max_nabundant=5, target_environments=['O:6'], target_penalty_type='max_csm', max_csm=5.0, symmetry_measure_type='csm_wcs_ctwcc')[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.SimpleAbundanceChemenvStrategy

Simple ChemenvStrategy using the neighbors that are the most “abundant” in the grid of angle and distance parameters for the definition of neighbors in the Voronoi approach, with a bias for a given list of target environments. This can be useful in the case of, e.g. connectivity search of some given environment. The coordination environment is then given as the one with the lowest continuous symmetry measure

Initializes strategy.

Not yet implemented. :param structure_environments: :param truncate_dist_ang: :param additional_condition: :param max_nabundant: :param target_environments: :param target_penalty_type: :param max_csm: :param symmetry_measure_type:

DEFAULT_TARGET_ENVIRONMENTS = ['O:6'][source]
as_dict()[source]

Bson-serializable dict representation of the TargettedPenaltiedAbundanceChemenvStrategy object. :return: Bson-serializable dict representation of the TargettedPenaltiedAbundanceChemenvStrategy object.

classmethod from_dict(d)[source]

Reconstructs the TargettedPenaltiedAbundanceChemenvStrategy object from a dict representation of the TargettedPenaltiedAbundanceChemenvStrategy object created using the as_dict method. :param d: dict representation of the TargettedPenaltiedAbundanceChemenvStrategy object :return: TargettedPenaltiedAbundanceChemenvStrategy object

get_site_coordination_environment(site, isite=None, dequivsite=None, dthissite=None, mysym=None, return_map=False)[source]

Get the coordination environment of a given site.

Parameters
  • site – Site for which coordination environment is needed.

  • isite – Index of the site.

  • dequivsite – Translation of the equivalent site.

  • dthissite – Translation of this site.

  • mysym – Symmetry to be applied.

  • return_map – Whether to return cn_map (identifies the NeighborsSet used).

Returns

Coordination environment of site.

property uniquely_determines_coordination_environments[source]

Whether this strategy uniquely determines coordination environments.

class WeightedNbSetChemenvStrategy(structure_environments=None, additional_condition=1, symmetry_measure_type='csm_wcs_ctwcc', nb_set_weights=None, ce_estimator={'function': 'power2_inverse_power2_decreasing', 'options': {'max_csm': 8.0}})[source]

Bases: pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies.AbstractChemenvStrategy

Constructor for the WeightedNbSetChemenvStrategy. :param structure_environments: StructureEnvironments object containing all the information on the

coordination of the sites in a structure

DEFAULT_CE_ESTIMATOR = {'function': 'power2_inverse_power2_decreasing', 'options': {'max_csm': 8.0}}[source]
STRATEGY_DESCRIPTION = ' WeightedNbSetChemenvStrategy'[source]
as_dict()[source]

Bson-serializable dict representation of the WeightedNbSetChemenvStrategy object. :return: Bson-serializable dict representation of the WeightedNbSetChemenvStrategy object.

classmethod from_dict(d)[source]

Reconstructs the WeightedNbSetChemenvStrategy object from a dict representation of the WeightedNbSetChemenvStrategy object created using the as_dict method. :param d: dict representation of the WeightedNbSetChemenvStrategy object :return: WeightedNbSetChemenvStrategy object

get_site_coordination_environment(site)[source]

Get the coordination environment of a given site.

Not implemented for this strategy

get_site_coordination_environments(site, isite=None, dequivsite=None, dthissite=None, mysym=None, return_maps=False)[source]

Get the coordination environments of a given site.

Parameters
  • site – Site for which coordination environment is needed.

  • isite – Index of the site.

  • dequivsite – Translation of the equivalent site.

  • dthissite – Translation of this site.

  • mysym – Symmetry to be applied.

  • return_maps – Whether to return cn_maps (identifies all the NeighborsSet used).

Returns

List of coordination environment.

get_site_coordination_environments_fractions(site, isite=None, dequivsite=None, dthissite=None, mysym=None, ordered=True, min_fraction=0.0, return_maps=True, return_strategy_dict_info=False, return_all=False)[source]

Get the coordination environments of a given site and additional information.

Parameters
  • site – Site for which coordination environment is needed.

  • isite – Index of the site.

  • dequivsite – Translation of the equivalent site.

  • dthissite – Translation of this site.

  • mysym – Symmetry to be applied.

  • ordered – Whether to order the list by fractions.

  • min_fraction – Minimum fraction to include in the list

  • return_maps – Whether to return cn_maps (identifies all the NeighborsSet used).

  • return_strategy_dict_info – Whether to add the info about the strategy used.

Returns

List of Dict with coordination environment, fraction and additional info.

get_site_neighbors(site)[source]

Get the neighbors of a given site.

Not implemented for this strategy.

property uniquely_determines_coordination_environments[source]

Whether this strategy uniquely determines coordination environments.

get_effective_csm(nb_set, cn_map, structure_environments, additional_info, symmetry_measure_type, max_effective_csm, effective_csm_estimator_ratio_function)[source]

Get the effective continuous symmetry measure of a given neighbors set.

Parameters
  • nb_set – Neighbors set.

  • cn_map – Mapping index of this neighbors set.

  • structure_environments – Structure environments.

  • additional_info – Additional information for the neighbors set.

  • symmetry_measure_type – Type of symmetry measure to be used in the effective CSM.

  • max_effective_csm – Max CSM to use for the effective CSM calculation.

  • effective_csm_estimator_ratio_function – Ratio function to use to compute effective CSM.

Returns

Effective CSM of a given Neighbors set.

set_info(additional_info, field, isite, cn_map, value)[source]

Set additional information for the weights.

Parameters
  • additional_info – Additional information.

  • field – Type of additional information.

  • isite – Index of site to add info.

  • cn_map – Mapping index of the neighbors set.

  • value – Value of this additional information.

Returns

None