Sumo is a Python toolkit for plotting and analysis of ab initio solid-state calculation data, built on existing Python packages from the solid-state chemistry/physics community. It is hoped that these command-line tools will bring some of the benefits of these libraries to a wider user-base while providing publication-ready plotting (powered by Matplotlib.)
The main features include:
- An extensive framework for generating high-symmetry k-point paths.
- Plotting scripts for electronic and phonon band structures, density of states, and optical absorption diagrams.
- Analysis scripts to calculate parabolic and non-parabolic band
effective masses.
- Curve fitting is performed using Scipy.
The code currently primarily supports VASP calculations, and has partial support for LMTO calculations with Questaal. We would like to add support for additional solid-state codes in future releases. Code contributions to interface with these packages are welcome.
Sumo is free to use, however, we ask that you cite the code if you use it in your research. See the "contributing" section for information about reporting bugs and getting involved.
Sumo is intended to be used via the command-line, however, a
fully-documented python API is also provided. A manual, including
tutorials and API documentation, is available online. Additionally, the built-in
help (-h) option for each command provides a summary of the
available options.
A guide to using each command can be found on the Tutorial page.
For a preview of the functionality of sumo, see the Gallery.
Currently, the scripts provided are:
sumo-kgen: For generating VASP KPOINTS files along high-symmetry k-point paths.sumo-bandplot: For plotting publication-ready electronic band structure diagrams.sumo-dosplot: For plotting publication-ready electronic density of states diagrams.sumo-optplot: For plotting publication-ready optical absorption diagrams.sumo-phonon-bandplot: For plotting publication-ready phonon band structure diagrams.sumo-bandstats: For calculating electron and hole effective masses from a band structure.
Information on how to tweak the style of sumo plots is provided on the Customising Sumo Plots page.
Sumo is a Python 3 package and requires a typical scientific Python stack; we recommend using your main package manager if possible (e.g. apt, Homebrew), or Anaconda to install Python 3 with setuptools. It is a good idea to also use this package manager to install Numpy and Matplotlib, as building them with setuptools can be troublesome. Sumo can then be installed using the Python package manager "Pip", which will automatically setup other Python packages as required:
pip3 install --user sumoIf this is your first entry to the scientific Python ecosystem, be aware that the full stack including Scipy with need several hundred MB of disk space.
Regular users can skip this section!
Sumo can also be installed from a copy of the source repository (https://github.com/smtg-ucl/sumo); this will be preferred for development work or if using experimental code branches.
To clone the project from Github and make a local installation:
git clone https://github.com/smtg-ucl/sumo.git
cd sumo
pip3 install --user -e .The -e and --user options are recommended:
Instead of copying files, with -e pip will create links to the
source folder so that that tweaks to the code will be immediately
reflected on the PATH.
The --user flag installs to a directory in your home folder
(usually under the hidden directory ~/.local),
preventing interference with your root Python installation.
From a developer installation, the unit tests can be run (from the root directory of the project) using:
python3 -m unittest discover tests
Automatic testing is run on the master branch of Sumo and proposed features at https://travis-ci.org/SMTG-UCL/sumo .
To build the documentation from the project files, install
sumo with extra Sphinx dependencies before compiling with make:
pip3 install --user .[docs]
cd docs
make htmlThe user guide can then be explored from docs/build/html/index.html.
If you use sumo in your research, please consider citing the following work:
Alex M. Ganose, Adam J. Jackson, David O. Scanlon. sumo: Command-line tools for plotting and analysis of periodic ab initio calculations. Journal of Open Source Software, 2018 3 (28), 717, doi:10.21105/joss.00717.
Sumo is made available under the MIT License.
Sumo is currently compatible with Python 3.5+ and relies on a number of open-source python packages, specifically:
There are probably still some bugs. If you think you've found one, please report it on the Issue Tracker. This is also the place to propose ideas for new features or ask questions about the design of Sumo. Poor documentation is considered a bug, but please be as specific as possible when asking for improvements.
We welcome your help in improving and extending the package with your own contributions. This is managed through Github pull requests; for external contributions we prefer the "fork and pull" workflow while core developers use branches in the main repository:
- First open an Issue to discuss the proposed contribution. This discussion might include how the changes fit Sumo's scope and a general technical approach.
- Make your own project fork and implement the changes there. Please keep your code style compliant with PEP8.
- Open a pull request to merge the changes into the main project. A more detailed discussion can take place there before the changes are accepted.