freud-analysis 2.9.0

Powerful, efficient trajectory analysis in scientific Python.

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-users Google Group: Ask questions to the freud user community.

• GitHub repository: Download the freud source code.

• Issue tracker: Report issues or request features.

Related Tools

• HOOMD-blue: Perform MD / MC simulations that can be analyzed with freud.

• signac: Manage your workflow with signac.

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 user forum. All contributions to freud are welcomed via pull requests!