Examples are provided as Jupyter notebooks in a separate freud-examples repository. These notebooks may be launched interactively on Binder or downloaded and run on your own system. Visualization of data is done via Matplotlib and Bokeh, unless otherwise noted.

Key concepts

There are a few critical concepts, algorithms, and data structures that are central to all of freud. The freud.box.Box class defines the concept of a periodic simulation box, and the freud.locality module defines methods for finding nearest neighbors of particles. Since both of these are used throughout freud, we recommend reading the Tutorial first, before delving into the workings of specific freud analysis modules.

Analysis Modules

These introductory examples showcase the functionality of specific modules in freud, showing how they can be used to perform specific types of analyses of simulations.

Example Analyses

The examples below go into greater detail about specific applications of freud and use cases that its analysis methods enable, such as user-defined analyses, machine learning, and data visualization.


Performance is a central consideration for freud. Below are some benchmarks comparing freud to other tools offering similar analysis methods.