freud-analysis 0.11.3

Perform various analyses of particle simulations.

freud

The freud library provides users the ability to analyze molecular dynamics and Monte Carlo simulation trajectories for advanced metrics such as the radial distribution function and various order parameters. Its modules work with and return NumPy arrays, and are able to process both 2D and 3D data. Features in freud include computing the radial distribution function, local density, hexagonal order parameter and local bond order parameters, potentials of mean force and torque (PMFTs), Voronoi tessellations, and more.

When using freud to process data for publication, please use this citation.

Mailing List

If you have a question, please consider posting to the freud-users mailing list.

Examples

Example Jupyter notebooks can be found in a separate repository. These notebooks may be launched interactively on Binder or downloaded and run on your own system.

Installing freud

Official binaries of freud are available via conda through conda-forge. To install freud, first download and install miniconda following conda's instructions. Then install freud:

$ conda install -c conda-forge freud

Compiling freud

Building freud from source follows the typical setuptools pattern for Python packages:

git clone --recurse-submodules https://bitbucket.org/glotzer/freud.git
cd freud
python setup.py install

By default, freud installs to the USER_SITE directory, which is in ~/.local on Linux and in ~/Library on macOS. USER_SITE is on the Python search path by default, so there is no need to modify PYTHONPATH.

If you want to test freud without installing it for your system, you can instead build it in place.

python setup.py build_ext --inplace

For more detailed instructions, see the documentation.

Note

The freud library makes use of submodules. If you would like to update submodules yourself, please execute the following command after cloning:

git submodule update --init

Requirements

• Required:
  • Python >= 2.7 (3.5+ recommended)
  • NumPy >= 1.7
  • C++11 capable compiler (tested with gcc >= 4.8.5, clang 3.5)
  • Intel Threading Building Blocks
• Optional:
  • Cython >= 0.23 (to compile your own _freud.cpp)

Job scripts

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[0]["Lx"], Ly=box_data[0]["Ly"], is2D=True)
# compute RDF
rdf.compute(box, pos_data[0], pos_data[0])
# get bin centers, rdf data
r = rdf.getR()
y = rdf.getRDF()

Documentation

The documentation is available online at https://freud.readthedocs.io.

To build the documentation yourself, please install sphinx:

conda install sphinx

OR

pip install sphinx

To view the full documentation run the following commands in the source directory:

# Linux
cd doc
make html
xdg-open build/html/index.html

# Mac
cd doc
make html
open build/html/index.html

If you have latex and/or pdflatex, you may also build a pdf of the documentation:

# Linux
cd doc
make latexpdf
xdg-open build/latex/freud.pdf

# Mac
cd doc
make latexpdf
open build/latex/freud.pdf

Unit Tests

The unit tests for freud use the Python unittest framework:

# Run tests from the tests directory
cd tests
python -m unittest discover .