freud-analysis

Powerful, efficient trajectory analysis in scientific Python.

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b-butler bdice joaander kelwang tcmoore3 tomwalt vramasub

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Project description

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 and outputs NumPy arrays, enabling integration with the scientific Python ecosystem for many typical materials science workflows.

Resources

Reference Documentation: Examples, tutorials, topic guides, and package Python APIs.
Installation Guide: Instructions for installing and compiling freud.
freud discussion board: Ask the freud user community for help.
GitHub repository: Download the freud source code.
Issue tracker: Report issues or request features.

Citation

When using freud to process data for publication, please use this citation.

Installation

The easiest ways to install freud are using pip:

pip install freud-analysis

or conda:

conda install -c conda-forge freud

freud is also available via containers for Docker or Singularity. If you need more detailed information or wish to install freud from source, please refer to the Installation Guide to compile freud from source.

Examples

The freud library is called using Python scripts. Many core features are demonstrated in the freud documentation. The examples come in the form of Jupyter notebooks, which can also be downloaded from the freud examples repository or launched interactively on Binder. Below is a sample script that computes the radial distribution function for a simulation run with HOOMD-blue and saved into a GSD file.

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 for the library source code. Any issues or bugs may be reported at our issue tracker, while questions and discussion can be directed to our discussion board. All contributions to freud are welcomed via pull requests!

Project details

These details have been verified by PyPI

Maintainers

b-butler bdice joaander kelwang tcmoore3 tomwalt vramasub

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

3.1.0

Jun 17, 2024

This version

3.0.0

Feb 23, 2024

2.13.2

Nov 28, 2023

2.13.1

Sep 15, 2023

2.13.0

May 9, 2023

2.12.1

Dec 5, 2022

2.12.0

Nov 9, 2022

2.11.0

Aug 9, 2022

2.10.0

May 18, 2022

2.9.0

Apr 19, 2022

2.8.0

Jan 25, 2022

2.7.0

Oct 1, 2021

2.6.2

Jun 26, 2021

2.6.1

Jun 23, 2021

2.6.0

Jun 22, 2021

2.5.1

Apr 7, 2021

2.5.0

Mar 24, 2021

2.4.1

Nov 17, 2020

2.4.0

Nov 10, 2020

2.3.0

Aug 3, 2020

2.2.0

Feb 25, 2020

2.1.0

Dec 20, 2019

2.0.1

Nov 8, 2019

2.0.0

Oct 31, 2019

1.2.2

Aug 19, 2019

1.2.1

Jul 27, 2019

1.2.0

Jun 28, 2019

1.1.0

May 30, 2019

1.0.0

Feb 20, 2019

0.11.4

Nov 9, 2018

0.11.3

Oct 18, 2018

0.10.0

Sep 10, 2018

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freud-analysis 3.0.0

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