# freud: A Software Suite for High Throughput Analysis of Particle Simulation Data

## TL;DR

freud is a versatile Python library that efficiently analyzes particle simulation data using parallelized C++ routines, supporting various methods and flexible data input for high-throughput and adaptive analysis.

## Contribution

It introduces a flexible, high-performance Python package that directly analyzes numerical data arrays, supporting a wide range of particle analysis methods with a uniform API.

## Key findings

- Supports standard and advanced particle analysis methods.
- Compatible with on-the-fly analysis in simulation workflows.
- Efficiently handles large datasets on various computing platforms.

## Abstract

The freud Python package is a powerful library for analyzing simulation data. Written with modern simulation and data analysis workflows in mind, freud provides a Python interface to fast, parallelized C++ routines that run efficiently on laptops, workstations, and supercomputing clusters. The package provides the core tools for finding particle neighbors in periodic systems, and offers a uniform API to a wide variety of methods implemented using these tools. As such, freud users can access standard methods such as the radial distribution function as well as newer, more specialized methods such as the potential of mean force and torque and local crystal environment analysis with equal ease. While many comparable tools place a heavy emphasis on reading and operating on trajectory file formats, freud instead accepts numerical arrays of data directly as inputs. By remaining agnostic to its data source, freud is suitable for analyzing any coarse-grained particle simulation, regardless of the original data representation or simulation method. When used for on-the-fly analysis in conjunction with scriptable simulation software such as HOOMD-blue, freud enables smart simulations that adapt to the current state of the system, allowing users to study phenomena such as nucleation and growth.