Post-processing tools for particle simulations
Project description
Postprocessing
Post-processing tools to compute static and dynamic correlation functions from simulations of interacting particles, such as molecular dynamics or Monte Carlo simulations.
Quick start
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. If you use a custom trajectory format, it is easy to add it.
As an example, we compute the structure factor S(k) for the trajectory
file trajectory.xyz contained in the data/ folder.
From the command line
pp.py --norigins 0.2 msd data/trajectory.xyz
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 the file data/trajectory.xyz.pp.sk.
From Python
The same calculation can be done from Python:
from atooms.trajectory import Trajectory
import atooms.postprocessing as pp
with Trajectory('data/trajectory.xyz') as t:
p = pp.StructureFactor(t)
p.do()
Features
Available correlation and distribution functions
- Real space
- radial distribution function
- mean square displacement
- velocity auto-correlation function
- self overlap functions
- collective overlap functions
- dynamic susceptibility of the self overlap function
- non-Gaussian parameter
- bond-angle distribution
- Fourier space
- structure factor
- spectral density
- self intermediate scattering functions
- collective intermediate scattering functions
- four-point dynamic susceptibility
Documentation
Check out the tutorial for more examples and the public API for full details.
The tutorial is also available as
- org-mode file
- jupyter notebook
- jupyter notebook on binder for interactive execution
- pdf file
Requirements
- numpy
- atooms
- [optional] argh (only needed when using
pp.py) - [optional] tqdm (enable progress bars)
- [optional] argcomplete (enable tab-completion for
pp.py) - [optional] fortran compiler for more efficient execution
Installation
Install with pip
pip install atooms-pp
If you cannot install the package system-wide, you can still install it in the user space
pip install --user atooms-pp
or cloning the project repo
git clone https://framagit.org/atooms/postprocessing.git
cd postprocessing
pip install --user .
The commands above will install pp.py under ~/.local/bin. Make sure this folder is in your $PATH.
Contributing
Contributions to the project are welcome. If you wish to contribute, check out these guidelines.
Authors
Daniele Coslovich: https://www.units.it/daniele.coslovich/
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