| Name | freud-analysis JSON |
| Version |
3.4.0
JSON |
| download |
| home_page | None |
| Summary | Powerful, efficient trajectory analysis in scientific Python. |
| upload_time | 2025-08-05 20:42:38 |
| maintainer | freud Developers |
| docs_url | None |
| author | The Glotzer Lab |
| requires_python | >=3.9 |
| license | BSD 3-Clause License for freud
Copyright (c) 2010-2025 The Regents of the University of Michigan
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors
may be used to endorse or promote products derived from this software without
specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
| keywords |
simulation
analysis
molecular dynamics
soft matter
particle
system
computational
physics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
=====
freud
=====
|Citing freud|
|PyPI|
|conda-forge|
|ReadTheDocs|
|Binder|
|GitHub-Stars|
.. |Citing freud| image:: https://img.shields.io/badge/cite-freud-informational.svg
:target: https://freud.readthedocs.io/en/stable/reference/citing.html
.. |PyPI| image:: https://img.shields.io/pypi/v/freud-analysis.svg
:target: https://pypi.org/project/freud-analysis/
.. |conda-forge| image:: https://img.shields.io/conda/vn/conda-forge/freud.svg
:target: https://anaconda.org/conda-forge/freud
.. |ReadTheDocs| image:: https://readthedocs.org/projects/freud/badge/?version=latest
:target: https://freud.readthedocs.io/en/latest/?badge=latest
.. |Binder| image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/glotzerlab/freud-examples/master?filepath=index.ipynb
.. |GitHub-Stars| image:: https://img.shields.io/github/stars/glotzerlab/freud.svg?style=social
:target: https://github.com/glotzerlab/freud
Overview
========
The **freud** Python library provides a simple, flexible, powerful set of tools
for analyzing trajectories obtained from molecular dynamics or Monte Carlo
simulations. High performance, parallelized C++ is used to compute standard
tools such as radial distribution functions, correlation functions, order
parameters, and clusters, as well as original analysis methods including
potentials of mean force and torque (PMFTs) and local environment matching. The
**freud** library supports
`many input formats <https://freud.readthedocs.io/en/stable/topics/datainputs.html>`__
and outputs `NumPy arrays <https://numpy.org/>`__, enabling integration
with the scientific Python ecosystem for many typical materials science
workflows.
Resources
=========
- `Reference Documentation <https://freud.readthedocs.io/>`__: Examples, tutorials, topic guides, and package Python APIs.
- `Installation Guide <https://freud.readthedocs.io/en/stable/gettingstarted/installation.html>`__: Instructions for installing and compiling **freud**.
- `freud discussion board <https://github.com/glotzerlab/freud/discussions/>`__: Ask the **freud** user community for help.
- `GitHub repository <https://github.com/glotzerlab/freud>`__: Download the **freud** source code.
- `Issue tracker <https://github.com/glotzerlab/freud/issues>`__: Report issues or request features.
Related Tools
=============
- `HOOMD-blue <https://hoomd-blue.readthedocs.io/>`__: Perform MD / MC simulations that can be analyzed with **freud**.
- `signac <https://signac.readthedocs.io/>`__: Manage your workflow with **signac**.
Citation
========
When using **freud** to process data for publication, please `use this citation
<https://freud.readthedocs.io/en/stable/reference/citing.html>`__.
Installation
============
**freud** is available on conda-forge_ for the *linux-64*, *linux-aarch64*, *linux-ppc64le*,
*osx-64*, *osx-arm64* and *win-64* architectures. Install with:
.. code:: bash
mamba install freud
**freud** is also available on PyPI_:
.. code:: bash
python3 -m pip install freud-analysis
.. _conda-forge: https://conda-forge.org/
.. _PyPI: https://pypi.org/
If you need more detailed information or wish to install **freud** from source, please refer to the
`Installation Guide <https://freud.readthedocs.io/en/stable/gettingstarted/installation.html>`__ to
compile **freud** from source.
Examples
========
The **freud** library is called using Python scripts. Many core features are
`demonstrated in the freud documentation
<https://freud.readthedocs.io/en/stable/examples.html>`_. The examples come in
the form of Jupyter notebooks, which can also be downloaded from the `freud
examples repository <https://github.com/glotzerlab/freud-examples>`_ or
`launched interactively on Binder
<https://mybinder.org/v2/gh/glotzerlab/freud-examples/master?filepath=index.ipynb>`_.
Below is a sample script that computes the radial distribution function for a
simulation run with `HOOMD-blue <https://hoomd-blue.readthedocs.io/>`__ and
saved into a `GSD file <https://gsd.readthedocs.io/>`_.
.. code:: python
import freud
import gsd.hoomd
# Create a freud compute object (RDF is the canonical example)
rdf = freud.density.RDF(bins=50, r_max=5)
# Load a GSD trajectory (see docs for other formats)
traj = gsd.hoomd.open('trajectory.gsd', 'rb')
for frame in traj:
rdf.compute(system=frame, reset=False)
# Get bin centers, RDF data from attributes
r = rdf.bin_centers
y = rdf.rdf
Support and Contribution
========================
Please visit our repository on `GitHub <https://github.com/glotzerlab/freud>`__ for the library source code.
Any issues or bugs may be reported at our `issue tracker <https://github.com/glotzerlab/freud/issues>`__, while questions and discussion can be directed to our `discussion board <https://github.com/glotzerlab/freud/discussions/>`__.
All contributions to **freud** are welcomed via pull requests!
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