atooms-pp 2.1.0

Post-processing tools for particle simulations

Post processing

Python post processing tools to compute static and dynamic correlation functions from particle simulations

• Real space: radial distribution function, mean square displacement, time-dependent overlap functions, non-Gaussian parameter
• Fourier space: structure factor, intermediate scattering functions, four-point dynamic susceptibility

This package relies on atooms <https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing.git>__ to read trajectory files.

Quick start

Installation is easy (see Installation <#installation>__ for more details)

::

pip install atooms-pp

We can now compute correlation functions from trajectories produced by particle simulation codes. Any trajectory format recognized by atooms can be processed, for instance most “xyz” files should work fine.

As an example, we compute the structure factor S(k) for the trajectory file trajectory.xyz contained in the data/ directory.

.. figure:: https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing/raw/develop/docs/anim.gif :alt: https://www.coulomb.univ-montp2.fr/perso/daniele.coslovich/anim.gif

https://www.coulomb.univ-montp2.fr/perso/daniele.coslovich/anim.gif

In the example above, we used 20% of the available time frames to compute the averages using the --norigins flag. Without it, atooms-pp applies an heuristics to determine the number of time frames required to achieve a reasonable data quality.

The results of the calculation are stored in data/trajectory.xyz.pp.sk. If the system is a mixture of different types of particles, say A and B, the program will create additional files for partial correlations, named trajectory.xyz.pp.sk.A-A, trajectory.xyz.pp.sk.B-B and trajectory.xyz.pp.sk.A-B.

The same calculation can be done from python:

.. code:: python

from atooms.trajectory import Trajectory import atooms.postprocessing as pp

with Trajectory('data/trajectory.xyz') as t: p = pp.StructureFactor(t) p.do()

Checkout the tutorial <https://www.coulomb.univ-montp2.fr/perso/daniele.coslovich/pp_notebook/>__ and notebook <https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing/raw/develop/docs/tutorial.ipynb>__ for more details.

Requirements

• numpy <https://pypi.org/project/numpy/>__
• atooms <https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing.git>__
• [optional] argh <https://pypi.org/project/argh/>__ (only needed when using pp.py)
• [optional] tqdm <https://pypi.org/project/tqdm/>__ (enable progress bars)
• [optional] argcomplete <https://pypi.org/project/argcomplete/>__ (enable tab-completion for pp.py)

Installation

If you cannot install the package system-wide, you can still install it in the user space. Either from pypi

::

pip install --user atooms-pp

or cloning the project repo

::

git clone https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing.git cd postprocessing make user

The commands above will install pp.py under ~/.local/bin. Make sure this folder is in your $PATH. To install system-wide, sudo make install.