pymatgen.analysis.chemenv.utils package
Utility package for chemenv.
Submodules
pymatgen.analysis.chemenv.utils.chemenv_config module
This module contains the classes for configuration of the chemenv package.
- class ChemEnvConfig(package_options=None)[source]
Bases:
objectStore the configuration of the chemenv package: - Materials project access - ICSD database access - Default options (strategies, …).
- Parameters:
package_options
pymatgen.analysis.chemenv.utils.chemenv_errors module
This module contains the error classes for the chemenv package.
- exception AbstractChemenvError(cls, method, msg)[source]
Bases:
ExceptionAbstract class for Chemenv errors.
- Parameters:
cls
method
msg
- exception ChemenvError(cls: str, method: str, msg: str)[source]
Bases:
ExceptionChemenv error.
- Parameters:
cls
method
msg
- exception EquivalentSiteSearchError(site)[source]
Bases:
AbstractChemenvErrorEquivalent site search error.
- Parameters:
site
- exception NeighborsNotComputedChemenvError(site)[source]
Bases:
AbstractChemenvErrorNeighbors not computed error.
- Parameters:
site
- exception SolidAngleError(cosinus)[source]
Bases:
AbstractChemenvErrorSolid angle error.
- Parameters:
cosinus
pymatgen.analysis.chemenv.utils.coordination_geometry_utils module
This module contains some utility functions and classes that are used in the chemenv package.
- class Plane(coefficients, p1=None, p2=None, p3=None)[source]
Bases:
objectDescribe a plane.
Initialize a plane from the 4 coefficients a, b, c and d of ax + by + cz + d = 0.
- Parameters:
coefficients – abcd coefficients of the plane.
- TEST_2D_POINTS = (array([0., 0.]), array([1., 0.]), array([0., 1.]), array([-1., 0.]), array([ 0., -1.]), array([0., 2.]), array([2., 0.]), array([ 0., -2.]), array([-2., 0.]), array([1., 1.]), array([2., 2.]), array([-1., -1.]), array([-2., -2.]), array([1., 2.]), array([ 1., -2.]), array([-1., 2.]), array([-1., -2.]), array([2., 1.]), array([ 2., -1.]), array([-2., 1.]), array([-2., -1.]))[source]
- distance_to_point(point)[source]
Compute the absolute distance from the plane to the point.
- Parameters:
point – Point for which distance is computed
- Returns:
Distance between the plane and the point.
- distances(points)[source]
Compute the distances from the plane to each of the points. Positive distances are on the side of the normal of the plane while negative distances are on the other side.
- Parameters:
points – Points for which distances are computed
- Returns:
Distances from the plane to the points (positive values on the side of the normal to the plane, negative values on the other side).
- distances_indices_groups(points, delta=None, delta_factor=0.05, sign=False)[source]
Compute the distances from the plane to each of the points. Positive distances are on the side of the normal of the plane while negative distances are on the other side. Indices sorting the points from closest to furthest is also computed. Grouped indices are also given, for which indices of the distances that are separated by less than delta are grouped together. The delta parameter is either set explicitly or taken as a fraction (using the delta_factor parameter) of the maximal point distance.
- Parameters:
points – Points for which distances are computed
delta – Distance interval for which two points are considered in the same group.
delta_factor – If delta is None, the distance interval is taken as delta_factor times the maximal
distance. (point)
sign – Whether to add sign information in the indices sorting the points distances
- Returns:
Distances from the plane to the points (positive values on the side of the normal to the plane, negative values on the other side), as well as indices of the points from closest to furthest and grouped indices of distances separated by less than delta. For the sorting list and the grouped indices, when the sign parameter is True, items are given as tuples of (index, sign).
- distances_indices_sorted(points, sign=False)[source]
Compute the distances from the plane to each of the points. Positive distances are on the side of the normal of the plane while negative distances are on the other side. Indices sorting the points from closest to furthest is also computed.
- Parameters:
points – Points for which distances are computed
sign – Whether to add sign information in the indices sorting the points distances
- Returns:
Distances from the plane to the points (positive values on the side of the normal to the plane, negative values on the other side), as well as indices of the points from closest to furthest. For the latter, when the sign parameter is True, items of the sorting list are given as tuples of (index, sign).
- fit_error(points, fit='least_square_distance')[source]
Evaluate the error for a list of points with respect to this plane.
- Parameters:
points – List of points.
fit – Type of fit error.
- Returns:
Error for a list of points with respect to this plane.
- fit_least_square_distance_error(points)[source]
Evaluate the sum of squared distances error for a list of points with respect to this plane.
- Parameters:
points – List of points.
- Returns:
Sum of squared distances error for a list of points with respect to this plane.
- fit_maximum_distance_error(points)[source]
Evaluate the max distance error for a list of points with respect to this plane.
- Parameters:
points – List of points.
- Returns:
Max distance error for a list of points with respect to this plane.
- classmethod from_2points_and_origin(p1, p2) Self[source]
Initialize plane from two points and the origin.
- Parameters:
p1 – First point.
p2 – Second point.
- Returns:
Plane.
- classmethod from_3points(p1, p2, p3) Self[source]
Initialize plane from three points.
- Parameters:
p1 – First point.
p2 – Second point.
p3 – Third point.
- Returns:
Plane.
- classmethod from_coefficients(a, b, c, d) Self[source]
Initialize plane from its coefficients.
- Parameters:
a – a coefficient of the plane.
b – b coefficient of the plane.
c – c coefficient of the plane.
d – d coefficient of the plane.
- Returns:
Plane.
- classmethod from_npoints(points, best_fit='least_square_distance') Self[source]
Initialize plane from a list of points.
If the number of points is larger than 3, will use a least square fitting or max distance fitting.
- Parameters:
points – List of points.
best_fit – Type of fitting procedure for more than 3 points.
- Returns:
Plane
- classmethod from_npoints_least_square_distance(points) Self[source]
Initialize plane from a list of points using a least square fitting procedure.
- Parameters:
points – List of points.
- Returns:
Plane.
- classmethod from_npoints_maximum_distance(points) Self[source]
Initialize plane from a list of points using a max distance fitting procedure.
- Parameters:
points – List of points.
- Returns:
Plane.
- indices_separate(points, dist_tolerance)[source]
Get three lists containing the indices of the points lying on one side of the plane, on the plane and on the other side of the plane. The dist_tolerance parameter controls the tolerance to which a point is considered to lie on the plane or not (distance to the plane).
- Parameters:
points – list of points
dist_tolerance – tolerance to which a point is considered to lie on the plane or not (distance to the plane)
- Returns:
The lists of indices of the points on one side of the plane, on the plane and on the other side of the plane.
- init_3points(non_zeros, zeros)[source]
Initialize three random points on this plane.
- Parameters:
non_zeros – Indices of plane coefficients ([a, b, c]) that are not zero.
zeros – Indices of plane coefficients ([a, b, c]) that are equal to zero.
- is_in_list(plane_list) bool[source]
Checks whether the plane is identical to one of the Planes in the plane_list list of Planes.
- Parameters:
plane_list – List of Planes to be compared to
- Returns:
True if the plane is in the list.
- Return type:
bool
- is_in_plane(pp, dist_tolerance) bool[source]
Determines if point pp is in the plane within the tolerance dist_tolerance.
- Parameters:
pp – point to be tested
dist_tolerance – tolerance on the distance to the plane within which point pp is considered in the plane
- Returns:
True if pp is in the plane.
- Return type:
bool
- is_same_plane_as(plane) bool[source]
Checks whether the plane is identical to another Plane “plane”.
- Parameters:
plane – Plane to be compared to
- Returns:
True if the two facets are identical.
- Return type:
bool
- orthonormal_vectors()[source]
Get a list of three orthogonal vectors, the two first being parallel to the plane and the third one is the normal vector of the plane.
- Returns:
List of orthogonal vectors
- Raise:
ValueError if all the coefficients are zero or if there is some other strange error.
- classmethod perpendicular_bisector(p1, p2) Self[source]
Initialize a plane from the perpendicular bisector of two points.
The perpendicular bisector of two points is the plane perpendicular to the vector joining these two points and passing through the middle of the segment joining the two points.
- Parameters:
p1 – First point.
p2 – Second point.
- Returns:
Plane.
- project_and_to2dim(pps, plane_center)[source]
Projects the list of points pps to the plane and changes the basis from 3D to the 2D basis of the plane.
- Parameters:
pps – List of points to be projected
- Returns:
raise:
- project_and_to2dim_ordered_indices(pps, plane_center='mean')[source]
Projects each points in the point list pps on plane and returns the indices that would sort the list of projected points in anticlockwise order.
- Parameters:
pps – List of points to project on plane
- Returns:
List of indices that would sort the list of projected points.
- anticlockwise_sort(pps)[source]
Sort a list of 2D points in anticlockwise order.
- Parameters:
pps – List of points to be sorted
- Returns:
Sorted list of points.
- anticlockwise_sort_indices(pps)[source]
Get the indices that would sort a list of 2D points in anticlockwise order.
- Parameters:
pps – List of points to be sorted
- Returns:
Indices of the sorted list of points.
- changebasis(uu, vv, nn, pps)[source]
For a list of points given in standard coordinates (in terms of e1, e2 and e3), returns the same list expressed in the basis (uu, vv, nn), which is supposed to be orthonormal.
- Parameters:
uu – First vector of the basis
vv – Second vector of the basis
nn – Third vector of the basis
pps – List of points in basis (e1, e2, e3)
- Returns:
List of points in basis (uu, vv, nn).
- collinear(p1, p2, p3=None, tolerance=0.25)[source]
Checks if the three points p1, p2 and p3 are collinear or not within a given tolerance. The collinearity is checked by computing the area of the triangle defined by the three points p1, p2 and p3. If the area of this triangle is less than (tolerance x largest_triangle), then the three points are considered collinear. The largest_triangle is defined as the right triangle whose legs are the two smallest distances between the three
- Parameters:
p1 – First point
p2 – Second point
p3 – Third point (origin [0.0, 0.0, 0.0 if not given])
tolerance – Area tolerance for the collinearity test (0.25 gives about 0.125 deviation from the line)
- Returns:
True if the three points are considered as collinear within the given tolerance.
- Return type:
bool
- diamond_functions(xx, yy, y_x0, x_y0)[source]
Method that creates two upper and lower functions based on points xx and yy as well as intercepts defined by y_x0 and x_y0. The resulting functions form kind of a distorted diamond-like structure aligned from point xx to point yy.
Schematically :
xx is symbolized by x, yy is symbolized by y, y_x0 is equal to the distance from x to a, x_y0 is equal to the distance from x to b, the lines a-p and b-q are parallel to the line x-y such that points p and q are obtained automatically. In case of an increasing diamond the lower function is x-b-q and the upper function is a-p-y while in case of a decreasing diamond, the lower function is a-p-y and the upper function is x-b-q.
- Parameters:
xx – First point
yy – Second point
- Returns:
A dictionary with the lower and upper diamond functions.
- function_comparison(f1, f2, x1, x2, numpoints_check=500)[source]
Method that compares two functions.
- Parameters:
f1 – First function to compare
f2 – Second function to compare
x1 – Lower bound of the interval to compare
x2 – Upper bound of the interval to compare
numpoints_check – Number of points used to compare the functions
- Returns:
- ‘=’ if the functions are equal, ‘<’ if f1 is always lower than f2, ‘>’ if f1 is always larger than f2,
f1 is always lower than or equal to f2 (“<”), f1 is always larger than or equal to f2 (“>”) on the interval [x1, x2]. If the two functions cross, a RuntimeError is thrown (i.e. we expect to compare functions that do not cross…)
- Return type:
str
- get_lower_and_upper_f(surface_calculation_options)[source]
Get the lower and upper functions defining a surface in the distance-angle space of neighbors.
- Parameters:
surface_calculation_options – Options for the surface.
- Returns:
Dictionary containing the “lower” and “upper” functions for the surface.
- is_anion_cation_bond(valences, ii, jj) bool[source]
Checks if two given sites are an anion and a cation.
- Parameters:
valences – list of site valences
ii – index of a site
jj – index of another site
- Returns:
True if one site is an anion and the other is a cation (based on valences).
- Return type:
bool
- matrixTimesVector(MM, aa)[source]
- Parameters:
MM – A matrix of size 3x3
aa – A vector of size 3.
- Returns:
A vector of size 3 which is the product of the matrix by the vector
- quarter_ellipsis_functions(xx: ArrayLike, yy: ArrayLike) dict[str, Callable][source]
Method that creates two quarter-ellipse functions based on points xx and yy. The ellipsis is supposed to be aligned with the axes. The two ellipsis pass through the two points xx and yy.
- Parameters:
xx – First point
yy – Second point
- Returns:
A dictionary with the lower and upper quarter ellipsis functions.
- rectangle_surface_intersection(rectangle, f_lower, f_upper, bounds_lower=None, bounds_upper=None, check=True, numpoints_check=500)[source]
Method to calculate the surface of the intersection of a rectangle (aligned with axes) and another surface defined by two functions f_lower and f_upper.
- Parameters:
rectangle – Rectangle defined as : ((x1, x2), (y1, y2)).
f_lower – Function defining the lower bound of the surface.
f_upper – Function defining the upper bound of the surface.
bounds_lower – Interval in which the f_lower function is defined.
bounds_upper – Interval in which the f_upper function is defined.
check – Whether to check if f_lower is always lower than f_upper.
numpoints_check – Number of points used to check whether f_lower is always lower than f_upper
- Returns:
The surface of the intersection of the rectangle and the surface defined by f_lower and f_upper.
- rotateCoords(coords, R)[source]
Rotate the list of points using rotation matrix R.
- Parameters:
coords – List of points to be rotated
R – Rotation matrix
- Returns:
List of rotated points.
- rotateCoordsOpt(coords, R)[source]
Rotate the list of points using rotation matrix R.
- Parameters:
coords – List of points to be rotated
R – Rotation matrix
- Returns:
List of rotated points.
- separation_in_list(separation_indices, separation_indices_list)[source]
Checks if the separation indices of a plane are already in the list.
- Parameters:
separation_indices – list of separation indices (three arrays of integers)
separation_indices_list – list of the list of separation indices to be compared to
- Returns:
True if the separation indices are already in the list.
- Return type:
bool
- solid_angle(center, coords)[source]
Helper method to calculate the solid angle of a set of coords from the center.
- Parameters:
center – Center to measure solid angle from.
coords – List of coords to determine solid angle.
- Returns:
The solid angle.
- sort_separation(separation)[source]
Sort a separation.
- Parameters:
separation – Initial separation.
- Returns:
Sorted list of separation.
- sort_separation_tuple(separation)[source]
Sort a separation.
- Parameters:
separation – Initial separation
- Returns:
Sorted tuple of separation
- spline_functions(lower_points, upper_points, degree=3)[source]
Method that creates two (upper and lower) spline functions based on points lower_points and upper_points.
- Parameters:
lower_points – Points defining the lower function.
upper_points – Points defining the upper function.
degree – Degree for the spline function
- Returns:
A dictionary with the lower and upper spline functions.
- vectorsToMatrix(aa, bb)[source]
Performs the vector multiplication of the elements of two vectors, constructing the 3x3 matrix.
- Parameters:
aa – One vector of size 3
bb – Another vector of size 3
- Returns:
M_ij = aa_i * bb_j.
- Return type:
A 3x3 matrix M composed of the products of the elements of aa and bb
pymatgen.analysis.chemenv.utils.defs_utils module
This module contains the definition of some objects used in the chemenv package.
- class AdditionalConditions[source]
Bases:
objectAdditional conditions that can be used to filter coordination environments.
pymatgen.analysis.chemenv.utils.func_utils module
This module contains some utility functions and classes that are used in the chemenv package.
- class AbstractRatioFunction(function, options_dict=None)[source]
Bases:
objectAbstract class for all ratio functions.
Constructor for AbstractRatioFunction.
- Parameters:
function – Ration function name.
options_dict – Dictionary containing the parameters for the ratio function.
- evaluate(value)[source]
Evaluate the ratio function for the given value.
- Parameters:
value – Value for which ratio function has to be evaluated.
- Returns:
Ratio function corresponding to the value.
- class CSMFiniteRatioFunction(function, options_dict=None)[source]
Bases:
AbstractRatioFunctionConcrete implementation of a series of ratio functions applied to the continuous symmetry measure (CSM).
Uses “finite” ratio functions.
See the following reference for details: ChemEnv: a fast and robust coordination environment identification tool, D. Waroquiers et al., Acta Cryst. B 76, 683 (2020).
Constructor for AbstractRatioFunction.
- Parameters:
function – Ration function name.
options_dict – Dictionary containing the parameters for the ratio function.
- ALLOWED_FUNCTIONS: ClassVar = {'power2_decreasing_exp': ['max_csm', 'alpha'], 'smootherstep': ['lower_csm', 'upper_csm'], 'smoothstep': ['lower_csm', 'upper_csm']}[source]
- fractions(data)[source]
Get the fractions from the CSM ratio function applied to the data.
- Parameters:
data – List of CSM values to estimate fractions.
- Returns:
Corresponding fractions for each CSM.
- mean_estimator(data)[source]
Get the weighted CSM using this CSM ratio function applied to the data.
- Parameters:
data – List of CSM values to estimate the weighted CSM.
- Returns:
Weighted CSM from this ratio function.
- power2_decreasing_exp(vals)[source]
Get the evaluation of the ratio function f(x)=exp(-a*x)*(x-1)^2.
The CSM values (i.e. “x”), are scaled to the “max_csm” parameter. The “a” constant correspond to the “alpha” parameter.
- Parameters:
vals – CSM values for which the ratio function has to be evaluated.
- Returns:
Result of the ratio function applied to the CSM values.
- ratios(data)[source]
Get the fractions from the CSM ratio function applied to the data.
- Parameters:
data – List of CSM values to estimate fractions.
- Returns:
Corresponding fractions for each CSM.
- smootherstep(vals)[source]
Get the evaluation of the smootherstep ratio function: f(x)=6*x^5-15*x^4+10*x^3.
The CSM values (i.e. “x”), are scaled between the “lower_csm” and “upper_csm” parameters.
- Parameters:
vals – CSM values for which the ratio function has to be evaluated.
- Returns:
Result of the ratio function applied to the CSM values.
- smoothstep(vals)[source]
Get the evaluation of the smoothstep ratio function: f(x)=3*x^2-2*x^3.
The CSM values (i.e. “x”), are scaled between the “lower_csm” and “upper_csm” parameters.
- Parameters:
vals – CSM values for which the ratio function has to be evaluated.
- Returns:
Result of the ratio function applied to the CSM values.
- class CSMInfiniteRatioFunction(function, options_dict=None)[source]
Bases:
AbstractRatioFunctionConcrete implementation of a series of ratio functions applied to the continuous symmetry measure (CSM).
Uses “infinite” ratio functions.
See the following reference for details: ChemEnv: a fast and robust coordination environment identification tool, D. Waroquiers et al., Acta Cryst. B 76, 683 (2020).
Constructor for AbstractRatioFunction.
- Parameters:
function – Ration function name.
options_dict – Dictionary containing the parameters for the ratio function.
- ALLOWED_FUNCTIONS: ClassVar = {'power2_inverse_decreasing': ['max_csm'], 'power2_inverse_power2_decreasing': ['max_csm']}[source]
- fractions(data)[source]
Get the fractions from the CSM ratio function applied to the data.
- Parameters:
data – List of CSM values to estimate fractions.
- Returns:
Corresponding fractions for each CSM.
- mean_estimator(data)[source]
Get the weighted CSM using this CSM ratio function applied to the data.
- Parameters:
data – List of CSM values to estimate the weighted CSM.
- Returns:
Weighted CSM from this ratio function.
- power2_inverse_decreasing(vals)[source]
Get the evaluation of the ratio function f(x)=(x-1)^2 / x.
The CSM values (i.e. “x”), are scaled to the “max_csm” parameter. The “a” constant correspond to the “alpha” parameter.
- Parameters:
vals – CSM values for which the ratio function has to be evaluated.
- Returns:
Result of the ratio function applied to the CSM values.
- power2_inverse_power2_decreasing(vals)[source]
Get the evaluation of the ratio function f(x)=(x-1)^2 / x^2.
The CSM values (i.e. “x”), are scaled to the “max_csm” parameter. The “a” constant correspond to the “alpha” parameter.
- Parameters:
vals – CSM values for which the ratio function has to be evaluated.
- Returns:
Result of the ratio function applied to the CSM values.
- class DeltaCSMRatioFunction(function, options_dict=None)[source]
Bases:
AbstractRatioFunctionConcrete implementation of a series of ratio functions applied to differences of continuous symmetry measures (DeltaCSM).
Uses “finite” ratio functions.
See the following reference for details: ChemEnv: a fast and robust coordination environment identification tool, D. Waroquiers et al., Acta Cryst. B 76, 683 (2020).
Constructor for AbstractRatioFunction.
- Parameters:
function – Ration function name.
options_dict – Dictionary containing the parameters for the ratio function.
- smootherstep(vals)[source]
Get the evaluation of the smootherstep ratio function: f(x)=6*x^5-15*x^4+10*x^3.
The DeltaCSM values (i.e. “x”), are scaled between the “delta_csm_min” and “delta_csm_max” parameters.
- Parameters:
vals – DeltaCSM values for which the ratio function has to be evaluated.
- Returns:
Result of the ratio function applied to the DeltaCSM values.
- class RatioFunction(function, options_dict=None)[source]
Bases:
AbstractRatioFunctionConcrete implementation of a series of ratio functions.
Constructor for AbstractRatioFunction.
- Parameters:
function – Ration function name.
options_dict – Dictionary containing the parameters for the ratio function.
- ALLOWED_FUNCTIONS: ClassVar = {'inverse_smootherstep': ['lower', 'upper'], 'inverse_smoothstep': ['lower', 'upper'], 'power2_decreasing_exp': ['max', 'alpha'], 'power2_inverse_decreasing': ['max'], 'power2_inverse_power2_decreasing': ['max'], 'smootherstep': ['lower', 'upper'], 'smoothstep': ['lower', 'upper']}[source]
- inverse_smootherstep(vals)[source]
Get the evaluation of the “inverse” smootherstep ratio function: f(x)=1-(6*x^5-15*x^4+10*x^3).
The values (i.e. “x”), are scaled between the “lower” and “upper” parameters.
- Parameters:
vals – Values for which the ratio function has to be evaluated.
- Returns:
Result of the ratio function applied to the values.
- inverse_smoothstep(vals)[source]
Get the evaluation of the “inverse” smoothstep ratio function: f(x)=1-(3*x^2-2*x^3).
The values (i.e. “x”), are scaled between the “lower” and “upper” parameters.
- Parameters:
vals – Values for which the ratio function has to be evaluated.
- Returns:
Result of the ratio function applied to the values.
- power2_decreasing_exp(vals)[source]
Get the evaluation of the ratio function f(x)=exp(-a*x)*(x-1)^2.
The values (i.e. “x”), are scaled to the “max” parameter. The “a” constant correspond to the “alpha” parameter.
- Parameters:
vals – Values for which the ratio function has to be evaluated.
- Returns:
Result of the ratio function applied to the values.
- power2_inverse_decreasing(vals)[source]
Get the evaluation of the ratio function f(x)=(x-1)^2 / x.
The values (i.e. “x”), are scaled to the “max” parameter.
- Parameters:
vals – Values for which the ratio function has to be evaluated.
- Returns:
Result of the ratio function applied to the values.
- power2_inverse_power2_decreasing(vals)[source]
Get the evaluation of the ratio function f(x)=(x-1)^2 / x^2.
The values (i.e. “x”), are scaled to the “max” parameter.
- Parameters:
vals – Values for which the ratio function has to be evaluated.
- Returns:
Result of the ratio function applied to the values.
- smootherstep(vals)[source]
Get the evaluation of the smootherstep ratio function: f(x)=6*x^5-15*x^4+10*x^3.
The values (i.e. “x”), are scaled between the “lower” and “upper” parameters.
- Parameters:
vals – Values for which the ratio function has to be evaluated.
- Returns:
Result of the ratio function applied to the values.
- smoothstep(vals)[source]
Get the evaluation of the smoothstep ratio function: f(x)=3*x^2-2*x^3.
The values (i.e. “x”), are scaled between the “lower” and “upper” parameters.
- Parameters:
vals – Values for which the ratio function has to be evaluated.
- Returns:
Result of the ratio function applied to the values.
pymatgen.analysis.chemenv.utils.graph_utils module
This module contains some graph utils that are used in the chemenv package.
- class MultiGraphCycle(nodes, edge_indices, validate=True, ordered=None)[source]
Bases:
MSONableDescribe a cycle in a multigraph.
nodes are the nodes of the cycle and edge_indices are the indices of the edges in the cycle. The nth index in edge_indices corresponds to the edge index between the nth node in nodes and the (n+1)th node in nodes with the exception of the last one being the edge index between the last node in nodes and the first node in nodes
- Example: A cycle
nodes: 1 - 3 - 4 - 0 - 2 - (1) edge_indices: 0 . 1 . 0 . 2 . 0 . (0)
- Parameters:
nodes – List of nodes in the cycle.
edge_indices – List of edge indices in the cycle.
validate – If True, will validate the cycle.
ordered – If True, will order the cycle.
- order(raise_on_fail: bool = True)[source]
Orders the SimpleGraphCycle.
The ordering is performed such that the first node is the “lowest” one and the second node is the lowest one of the two neighbor nodes of the first node. If raise_on_fail is set to True a RuntimeError will be raised if the ordering fails.
- Parameters:
raise_on_fail – If set to True, will raise a RuntimeError if the ordering fails.
- class SimpleGraphCycle(nodes, validate=True, ordered=None)[source]
Bases:
MSONableDescribe a cycle in a simple graph (graph without multiple edges).
Note that the convention used here is the networkx convention for which simple graphs allow to have self-loops in a simple graph. No simple graph cycle with two nodes is possible in a simple graph. The graph will not be validated if validate is set to False. By default, the “ordered” parameter is None, in which case the SimpleGraphCycle will be ordered. If the user explicitly sets ordered to False, the SimpleGraphCycle will not be ordered.
- Parameters:
nodes
validate
ordered
- classmethod from_dict(dct: dict, validate: bool = False) Self[source]
Serialize from dict.
- Parameters:
dct (dict) – Dict representation.
validate – If True, will validate the cycle.
- classmethod from_edges(edges, edges_are_ordered: bool = True) Self[source]
Construct SimpleGraphCycle from a list edges.
By default, the edges list is supposed to be ordered as it will be much faster to construct the cycle. If edges_are_ordered is set to False, the code will automatically try to find the corresponding edge order in the list.
- order(raise_on_fail=True)[source]
Orders the SimpleGraphCycle.
The ordering is performed such that the first node is the “lowest” one and the second node is the lowest one of the two neighbor nodes of the first node. If raise_on_fail is set to True a RuntimeError will be raised if the ordering fails.
- Parameters:
raise_on_fail (bool) – If set to True, will raise a RuntimeError if the ordering fails.
pymatgen.analysis.chemenv.utils.math_utils module
This module contains some math utils that are used in the chemenv package.
- divisors(n)[source]
From a given natural integer, returns the list of divisors in ascending order.
- Parameters:
n – Natural integer
- Returns:
List of divisors of n in ascending order.
- get_linearly_independent_vectors(vectors: list[ArrayLike]) list[np.ndarray][source]
- Parameters:
vectors (list[ArrayLike]) – List of vectors.
- power2_inverse_power2_decreasing(xx, edges=None, prefactor=None)[source]
- Parameters:
xx
edges
prefactor
- power2_inverse_powern_decreasing(xx, edges=None, prefactor=None, powern=2.0)[source]
- Parameters:
xx
edges
prefactor
powern
pymatgen.analysis.chemenv.utils.scripts_utils module
This module contains some script utils that are used in the chemenv package.
- compute_environments(chemenv_configuration)[source]
Compute the environments.
- Parameters:
chemenv_configuration
- draw_cg(vis, site, neighbors, cg=None, perm=None, perfect2local_map=None, show_perfect=False, csm_info=None, symmetry_measure_type='csm_wcs_ctwcc', perfect_radius=0.1, show_distorted=True, faces_color_override=None)[source]
Draw cg.
- Parameters:
site
vis
neighbors
cg
perm
perfect2local_map
show_perfect
csm_info
symmetry_measure_type
perfect_radius
show_distorted
faces_color_override