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 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

freud is available on conda-forge for the linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64 and win-64 architectures. Install with:

mamba install freud

freud is also available on PyPI:

python3 -m pip install freud-analysis

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!

Release files for freud-analysis 3.6.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for freud-analysis 3.6.0
File	Size	Uploaded
freud_analysis-3.6.0.tar.gz	5.8 MB	Sep 18, 2026	Details

Built distributions (wheels)

Table of built distributions (wheels) for freud-analysis 3.6.0
File	Interpreter	ABI	Platform	Reset
freud_analysis-3.6.0-cp312-abi3-win_amd64.whl 1.8 MB Sep 18, 2026	CPython 3.12	abi3	Windows x86-64	Details
freud_analysis-3.6.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 2.0 MB Sep 18, 2026	CPython 3.12	abi3	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
freud_analysis-3.6.0-cp312-abi3-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl 1.8 MB Sep 18, 2026	CPython 3.12	abi3	Linux glibc 2.28+ ARM64, Linux glibc 2.27+ ARM64	Details
freud_analysis-3.6.0-cp312-abi3-macosx_11_0_arm64.whl 1.4 MB Sep 18, 2026	CPython 3.12	abi3	macOS 11.0+ ARM64	Details

Total release size: 12.8 MB

Release files / freud_analysis-3.6.0.tar.gz

Download URL	freud_analysis-3.6.0.tar.gz
Size	5.8 MB
Tags	Source
SHA-256 checksum How to use checksums	`63722485f33dd97773d3c38cbe5e4fa11aa086a55a21b75df22897054de00016`
BLAKE2b-256 checksum How to use checksums	`e2c55527d5822ebaf96ce9c275672f54e700adaeca97d923c9f5f9acc3d1bd9a`
Upload date	Sep 18, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/glotzerlab/freud
Publishing commit: github.com/glotzerlab/freud/tree/144c7a2c0c420e39a3bdcb64693dd5c3f560b147

Workflow

Publishing configuration: github.com/glotzerlab/freud/blob/144c7a2c0c420e39a3bdcb64693dd5c3f560b147/.github/workflows/release.yaml
Publishing logs: github.com/glotzerlab/freud/actions/runs/35384649408/attempts/1

Artifact

Subject: freud_analysis-3.6.0.tar.gz
SHA-256 checksum: 63722485f33dd97773d3c38cbe5e4fa11aa086a55a21b75df22897054de00016 Verifying attestations

Release files / freud_analysis-3.6.0-cp312-abi3-win_amd64.whl

Download URL	freud_analysis-3.6.0-cp312-abi3-win_amd64.whl
Size	1.8 MB
Tags	CPython 3.12 Windows x86-64 abi3
SHA-256 checksum How to use checksums	`4ab9a95bf233f621e3f6c84a0775cdb6c80cc438efb4ad7ba2225c6fc01346c2`
BLAKE2b-256 checksum How to use checksums	`6d35762a10ca363940d845e3f467a409984e2d4147fb3fab83a06f9769a2d700`
Upload date	Sep 18, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/glotzerlab/freud
Publishing commit: github.com/glotzerlab/freud/tree/144c7a2c0c420e39a3bdcb64693dd5c3f560b147

Workflow

Publishing configuration: github.com/glotzerlab/freud/blob/144c7a2c0c420e39a3bdcb64693dd5c3f560b147/.github/workflows/release.yaml
Publishing logs: github.com/glotzerlab/freud/actions/runs/35384649408/attempts/1

Artifact

Subject: freud_analysis-3.6.0-cp312-abi3-win_amd64.whl
SHA-256 checksum: 4ab9a95bf233f621e3f6c84a0775cdb6c80cc438efb4ad7ba2225c6fc01346c2 Verifying attestations

Release files / freud_analysis-3.6.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl

Download URL	freud_analysis-3.6.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Size	2.0 MB
Tags	CPython 3.12 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64 abi3
SHA-256 checksum How to use checksums	`644ed31c5cd414990c67fa88e66413e25f0fc221dc1c496e039c5b708b3833af`
BLAKE2b-256 checksum How to use checksums	`1882709bc9fccfacd707dde5c490baa6c17642b72bb632cb598a275f23e9d73b`
Upload date	Sep 18, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/glotzerlab/freud
Publishing commit: github.com/glotzerlab/freud/tree/144c7a2c0c420e39a3bdcb64693dd5c3f560b147

Workflow

Publishing configuration: github.com/glotzerlab/freud/blob/144c7a2c0c420e39a3bdcb64693dd5c3f560b147/.github/workflows/release.yaml
Publishing logs: github.com/glotzerlab/freud/actions/runs/35384649408/attempts/1

Artifact

Subject: freud_analysis-3.6.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
SHA-256 checksum: 644ed31c5cd414990c67fa88e66413e25f0fc221dc1c496e039c5b708b3833af Verifying attestations

Release files / freud_analysis-3.6.0-cp312-abi3-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl

Download URL	freud_analysis-3.6.0-cp312-abi3-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
Size	1.8 MB
Tags	CPython 3.12 Linux glibc 2.27+ ARM64 Linux glibc 2.28+ ARM64 abi3
SHA-256 checksum How to use checksums	`823e09ef89779a4240e4cf4e9526b72800618d5acf2057df9b723f72d9b86bd2`
BLAKE2b-256 checksum How to use checksums	`e6b2418df60076b4a81f98dfc1537c8fd7e7fa59ec0e4f272cbe188cf9cadb2e`
Upload date	Sep 18, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/glotzerlab/freud
Publishing commit: github.com/glotzerlab/freud/tree/144c7a2c0c420e39a3bdcb64693dd5c3f560b147

Workflow

Publishing configuration: github.com/glotzerlab/freud/blob/144c7a2c0c420e39a3bdcb64693dd5c3f560b147/.github/workflows/release.yaml
Publishing logs: github.com/glotzerlab/freud/actions/runs/35384649408/attempts/1

Artifact

Subject: freud_analysis-3.6.0-cp312-abi3-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
SHA-256 checksum: 823e09ef89779a4240e4cf4e9526b72800618d5acf2057df9b723f72d9b86bd2 Verifying attestations

Release files / freud_analysis-3.6.0-cp312-abi3-macosx_11_0_arm64.whl

Download URL	freud_analysis-3.6.0-cp312-abi3-macosx_11_0_arm64.whl
Size	1.4 MB
Tags	CPython 3.12 abi3 macOS 11.0+ ARM64
SHA-256 checksum How to use checksums	`09fc93fc681b1c0982879f717a503b1b0156c357f5495b60e8e27b219a534620`
BLAKE2b-256 checksum How to use checksums	`028d48864f044e126d3527df2ae19e5a16115773354efd81c15bf18a5df98ac3`
Upload date	Sep 18, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/glotzerlab/freud
Publishing commit: github.com/glotzerlab/freud/tree/144c7a2c0c420e39a3bdcb64693dd5c3f560b147

Workflow

Publishing configuration: github.com/glotzerlab/freud/blob/144c7a2c0c420e39a3bdcb64693dd5c3f560b147/.github/workflows/release.yaml
Publishing logs: github.com/glotzerlab/freud/actions/runs/35384649408/attempts/1

Artifact

Subject: freud_analysis-3.6.0-cp312-abi3-macosx_11_0_arm64.whl
SHA-256 checksum: 09fc93fc681b1c0982879f717a503b1b0156c357f5495b60e8e27b219a534620 Verifying attestations