vasppy.rdf module

class RadialDistributionFunction(structures: list[Structure], indices_i: list[int], indices_j: list[int] | None = None, nbins: int = 500, r_min: float = 0.0, r_max: float = 10.0, weights: list[float] | None = None)

Bases: object

Class for computing radial distribution functions.

nbins

Number of bins.

Type:

int

range

Minimum and maximum values of r.

Type:

(float, float)

intervals

r values of the bin edges.

Type:

np.array(float)

dr

bin width.

Type:

float

r

mid-points of each bin.

Type:

float

rdf

RDF values.

Type:

np.array(float)

coordination_number

Volume integral of the RDF.

Type:

np.array(float)

Initialise a RadialDistributionFunction instance.

Parameters:

• structures (list(pymatgen.Structure)) – List of pymatgen Structure objects.

• indices_i (list(int)) – List of indices for species i.

• indices_j (list(int), optional) – List of indices for species j. Optional, default is None.

• nbins (int, optional) – Number of bins used for the RDF. Optional, default is 500.

• rmin (float, optional) – Minimum r value. Optional, default is 0.0.

• rmax (float, optional) – Maximum r value. Optional, default is 10.0.

• weights (list(float), optional) – List of weights for each structure. Optional, default is None.

Returns:

None

classmethod from_species_strings(structures: list[Structure], species_i: str, species_j: str | None = None, **kwargs) → RadialDistributionFunction

Initialise a RadialDistributionFunction instance by specifying species strings.

Parameters:

• structures (list(pymatgen.Structure)) – List of pymatgen Structure objects.

• species_i (str) – String for species i, e.g. "Na".

• species_j (str, optional) – String for species j, e.g. "Cl". Optional default is None.

• **kwargs – Variable length keyword argument list. See vasppy.rdf.RadialDistributionFunction() for the full list of accepted arguments.

Returns:

(RadialDistributionFunction)

smeared_rdf(sigma: float = 0.1) → ndarray[tuple[int, ...], dtype[floating[Any]]]

Smear the RDF with a Gaussian kernel.

Parameters:

sigma (float, optional) – Standard deviation for Gaussian kernel. Optional, default is 0.1.

Returns:

Smeared RDF data.

Return type:

(np.array)

class VanHoveAnalysis(structures: list[Structure], indices: list[int], d_steps: int, nbins: int = 500, r_min: float = 0.0, r_max: float = 10.0)

Bases: object

Class for computing Van Hove correlation functions.

nbins

Number of bins.

Type:

int

range

Minimum and maximum values of r.

Type:

(float, float)

intervals

r values of the bin edges.

Type:

np.array(float)

dr

bin width.

Type:

float

r

mid-points of each bin.

Type:

float

gsrt

Self part of the Van Hove correlation function.

Type:

np.array(float)

gdrt

Distinct part of the Van Hove correlation function.

Type:

np.array(float)

Initialise a VanHoveCorrelationFunction instance.

Parameters:

• structures (list(pymatgen.Structure)) – List of pymatgen Structure objects.

• indices (list(int)) – List of indices for species to consider.

• d_steps (int) – number of steps between structures at dt=0 and dt=t.

• nbins (int, optional) – Number of bins used for the RDF. Optional, default is 500.

• rmin (float, optional) – Minimum r value. Optional, default is 0.0.

• rmax (float, optional) – Maximum r value. Optional, default is 10.0.

Returns:

None

distinct(sigma: float | None = None) → ndarray

Returns the distinct part of the Van Hove correlation function.

Parameters:

sigma (float, optional) – Optional smearing width.

Returns:

(np.ndarray)

self(sigma: float | None = None) → ndarray

Returns the self part of the Van Hove correlation function.

Parameters:

sigma (float, optional) – Optional smearing width.

Returns:

(np.ndarray)

smeared_gdrt(sigma: float = 0.1) → ndarray[tuple[int, ...], dtype[floating[Any]]]

Smear the distinct part of the Van Hove correlation function with a Gaussian kernel.

Parameters:

sigma (float, optional) – Standard deviation for Gaussian kernel. Optional, default is 0.1.

Returns:

Smeared data.

Return type:

(np.array)

smeared_gsrt(sigma: float = 0.1) → ndarray[tuple[int, ...], dtype[floating[Any]]]

Smear the self part of the Van Hove correlation function with a Gaussian kernel.

Parameters:

sigma (float, optional) – Standard deviation for Gaussian kernel. Optional, default is 0.1.

Returns:

Smeared data.

Return type:

(np.array)

shell_volumes(intervals: ndarray[tuple[int, ...], dtype[floating[Any]]]) → ndarray[tuple[int, ...], dtype[floating[Any]]]

Volumes of concentric spherical shells.

Parameters:

intervals (np.array) – N radial boundaries used to define the set of N-1 shells.

Returns:

Volumes of each shell.

Return type:

np.array