amep.evaluate.RDF#

class amep.evaluate.RDF(traj: ParticleTrajectory, skip: float = 0.0, nav: int = 10, ptype: int | None = None, other: int | None = None, **kwargs)#

Bases: BaseEvaluation

Radial pair distribution function.

__init__(traj: ParticleTrajectory, skip: float = 0.0, nav: int = 10, ptype: int | None = None, other: int | None = None, **kwargs) → None#

Calculate the radial pair-distribution function [1].

Works for a one-component system. Takes the time average over several time steps.

Notes

This version only works for one-component systems in 2D. The radial pair-distribution function is defined by (see Refs. [2] [3])

\[g(r) = \frac{1}{\rho N}\sum\limits_{k}\sum\limits_{l\neq k} \left\langle\frac{\delta\left(r-\left|\vec{r}_k(t) -\vec{r}_l(t)\right|\right)}{2\pi r}\right\rangle_t\]

References

Parameters:

traj (ParticleTrajectory) – Trajectory object of particle-based simulation data.
skip (float, optional) – Skip this fraction at the beginning of the trajectory. The default is 0.0.
nav (int, optional) – Maximum number of frames to consider for the time average. The default is 10.
ptype (float, optional) – Particle type. The default is None.
other (float, optional) – Other particle type (to calculate the correlation between different particle types). The default is None.
**kwargs – Other keyword arguments are forwarded to amep.spatialcor.rdf.

Examples

>>> import amep
>>> traj = amep.load.traj("../examples/data/lammps.h5amep")
>>> rdfcalc = amep.evaluate.RDF(
...     traj, nav=2, nbins=1000, skip=0.9, njobs=4
... )
>>> rdfcalc.save('./eval/rdf.h5')
>>> fig, axs = amep.plot.new()
>>> axs.plot(rdfcalc.r, rdfcalc.avg)
>>> axs.set_xlim(0,10)
>>> axs.set_xlabel(r'$r$')
>>> axs.set_ylabel(r'$g(r)$')
>>> fig.savefig("./figures/evaluate/evaluate-RDF.png")

Methods

`__init__`(traj[, skip, nav, ptype, other])	Calculate the radial pair-distribution function [1].
`items`()
`keys`()
`save`(path[, backup, database, name])	Stores the evaluation result in an HDF5 file.
`values`()

Attributes

`avg`	Time-averaged RDF (averaged over the given number of frames).
`frames`	RDF for each frame.
`indices`	Indices of all frames for which the RDF has been evaluated.
`name`
`r`	Distances.
`times`	Times at which the RDF is evaluated.

property avg#

Time-averaged RDF (averaged over the given number of frames).

Returns:: Time-averaged RDF.
Return type:: np.ndarray

property frames#

RDF for each frame.

Returns:: Function value for each frame.
Return type:: np.ndarray

property indices#

Indices of all frames for which the RDF has been evaluated.

Returns:: Frame indices.
Return type:: np.ndarray

property r#

Distances.

Returns:: Distances.
Return type:: np.ndarray

save(path: str, backup: bool = True, database: bool = False, name: str | None = None) → None#

Stores the evaluation result in an HDF5 file.

Parameters:

path (str) – Path of the ‘.h5’ file in which the data should be stored. If only a directory is given, the filename is chosen as self.name. Raises an error if the given directory does not exist or if the file extension is not ‘.h5’.
backup (bool, optional) – If True, an already existing file is backed up and not overwritten. This keyword is ignored if database=True. The default is True.
database (bool, optional) – If True, the results are appended to the given ‘.h5’ file if it already exists. If False, a new file is created and the old is backed up. If False and the given ‘.h5’ file contains multiple evaluation results, an error is raised. In this case, database has to be set to True. The default is False.
name (str or None, optional) – Name under which the data should be stored in the HDF5 file. If None, self.name is used. The default is None.

Return type:

None.

property times#

Times at which the RDF is evaluated.

Returns:: Times at which the RDF is evaluated.
Return type:: np.ndarray