A framework for simulations of interacting particles
Project description
Atooms
|pypi| |version| |license| |DOI|
atooms is a Python framework for simulations of interacting particles. It makes it easy to develop simulation and analysis tools using an expressive language, without sacrificing efficiency. To achieve this, atooms relies on backends written in C, CUDA or Fortran.
Quick start
The goal of atooms is to provide a coherent interface to the basic
objects of particle simulations, such as molecular dynamics <https://en.wikipedia.org/wiki/Molecular_dynamics>
__ or Monte Carlo <https://en.wikipedia.org/wiki/Monte_Carlo_method_in_statistical_physics>
__
simulations. The simulation data are usually stored in trajectory files,
which atooms makes it easy to analyze, manipulate and convert.
In this simple example, we read a trajectory file in xyz format <https://en.wikipedia.org/wiki/XYZ_format>
__ composed by
multiple frames. Each frame holds the state of the system at a given
instant of time during the simulation. Accessing the coordinates of the
particles in a trajectory file goes like this:
.. code:: python
from atooms.trajectory import Trajectory
with Trajectory('input.xyz') as trajectory:
for system in trajectory:
print('The position of particle 0 is', system.particle[0].position)
Note that trajectories support iteration and indexing, just like lists.
Here we pick the last frame of the trajectory, change the density of the
system to unity and write this new configuration to a trajectory format
suitable for the RUMD <http://rumd.org>
__ simulation package:
.. code:: python
with Trajectory('input.xyz') as trajectory:
system = trajectory[-1]
system.density = 1.0
print('The new density is', len(system.particle) / system.cell.volume)
from atooms.trajectory import TrajectoryRUMD
with TrajectoryRUMD('rescaled.xyz.gz', 'w') as trajectory:
trajectory.write(system, step=0)
We can now run 1000 molecular dynamics steps using the Lennard-Jones potential:
.. code:: python
from atooms.backends.rumd import RUMD
from atooms.simulation import Simulation
backend = RUMD('rescaled.xyz.gz', forcefield_file='lj_rumd.ff',
output_path='/tmp/outdir', integrator='nve')
sim = Simulation(backend)
sim.run(1000)
print('Final temperature and density', sim.system.temperature, sim.system.density)
The forcefield file lj_rumd.ff
(available in data/
) defines the
interaction potential.
Documentation
See the
tutorial <https://www.coulomb.univ-montp2.fr/perso/daniele.coslovich/atooms/>
__
for a step-by-step introduction to atooms objects and the public API documentation <https://www.coulomb.univ-montp2.fr/perso/daniele.coslovich/docs/api/atooms/>
__
for full details.
Installation
From the python package index
::
pip install atooms
From the code repository
::
git clone https://gitlab.info-ufr.univ-montp2.fr/atooms/atooms.git
cd atooms
make install
Simulation backends
atooms has a generic simulation interface that abstracts out most of the
common parts of particle-based simulations. The actual simulation code
is wrapped by a simulation backend that exposes a minimal but consistent
interface. This enables one to develop more complex simulation
frameworks (e.g., parallel tempering <https://gitlab.info-ufr.univ-montp2.fr/atooms/parallel_tempering>
__)
that are essentially decoupled from the underlying simulation code.
Trajectory conversion
atooms provides a command line tool to convert between various
trajectory formats. The following command will convert a trajectory file
produced by RUMD <http://rumd.org>
__ into a simpler xyz format
.. code:: bash
$ trj.py convert -i rumd -o xyz trajectory.xyz.gz output.xyz
If you don't specify the output path, the trajectory is written to standard output. This is useful for quick inspection of complex trajectory formats or for piping into sed / awk.
trj.py
provides means to fine tune the format of the output file.
Type trj.py --help
to get a list of options and supported trajectory
formats.
Custom trajectory formats
It is easy to add new trajectory formats by subclassing existing
trajectory classes. Just create a package called atooms_plugins
and
add your trajectory modules there. They will be automatically available
to all client codes that use atooms.
Suppose you wrote a custom trajectory class TrajectoryABC
in
atooms_plugins/test.py
(the last path is relative to the current
directory). You can now convert an existing xyz trajectory to your
custom format:
.. code:: bash
$ trj.py convert output.xyz output.abc
Remember to add an empty __init__.py
file at the root of
atooms_plugins
. Actually, the atooms_plugins
package can be put
anywhere in your PYTHONPATH
.
Additional packages
Atooms is composable: it is easy to add new functionalities, and just
those you actually need. Additional packages are available from the
atooms main repository <https://gitlab.info-ufr.univ-montp2.fr/atooms>
__. These
packages will be installed in the atooms namespace to prevent name
clashing.
If you want to add your own package to the atooms namespace, structure it this way
.. code:: bash
atooms/your_package
atooms/your_package/__init__.py
where __init__.py
contains
.. code:: python
from pkgutil import extend_path
__path__ = extend_path(__path__, __name__)
Add the package root folder to $PYTHONPATH. You can now import your package as
.. code:: python
import atooms.your_package
Authors
Daniele Coslovich: http://www.coulomb.univ-montp2.fr/perso/daniele.coslovich/
.. |pypi| image:: https://img.shields.io/pypi/v/atooms.svg :target: https://pypi.python.org/pypi/atooms/ .. |version| image:: https://img.shields.io/pypi/pyversions/atooms.svg :target: https://pypi.python.org/pypi/atooms/ .. |license| image:: https://img.shields.io/pypi/l/atooms.svg :target: https://en.wikipedia.org/wiki/GNU_General_Public_License .. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1183301.svg :target: https://doi.org/10.5281/zenodo.1183301
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