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, and clusters, as well as original analysis methods including potentials of mean force and torque (PMFTs) and local environment matching. The freud library uses NumPy arrays for input and output, enabling integration with the scientific Python ecosystem for many typical materials science workflows.
When using freud to process data for publication, please use this citation.
The documentation is available online at https://freud.readthedocs.io. These pages include an installation guide, examples demonstrating many of freud's modules, an API reference, and development guides for adding new features.
Many core features are demonstrated in the freud documentation. Additional example Jupyter notebooks can be found in the freud-examples repository. These notebooks may be launched interactively on Binder or downloaded and run on your own system.
Install via conda
conda install -c conda-forge freud
Install via pip
pip install freud-analysis
Please refer to the installation documentation for help compiling freud from source.
Simple example script
The freud library is called using Python scripts.
Here is a simple example.
import freud # create a freud compute object (rdf is the canonical example) rdf = freud.density.rdf(rmax=5, dr=0.1) # load in your data (freud does not provide a data reader) box_data = np.load("path/to/box_data.npy") pos_data = np.load("path/to/pos_data.npy") # create freud box box = freud.box.Box(Lx=box_data["Lx"], Ly=box_data["Ly"], is2D=True) # compute RDF rdf.compute(box, pos_data, pos_data) # get bin centers, rdf data r = rdf.R y = rdf.RDF