sumo

sumo: Command-line tools for plotting and analysis of periodic *ab initio* calculations - Published in JOSS (2018)

https://github.com/SMTG-Bham/sumo

Science Score: 59.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 6 DOI reference(s) in README
  • Academic publication links
    Links to: joss.theoj.org, zenodo.org
  • Committers with academic emails
    12 of 23 committers (52.2%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (19.7%) to scientific vocabulary

Keywords

computational-chemistry materials-science plotting python

Keywords from Contributors

materials-design defects electronic-structure semiconductors vasp computational-materials-science solar-cells

Scientific Fields

Mathematics Computer Science - 84% confidence
Last synced: 6 months ago · JSON representation

Repository

Heavyweight plotting tools for ab initio calculations

Basic Info
Statistics
  • Stars: 227
  • Watchers: 13
  • Forks: 86
  • Open Issues: 59
  • Releases: 38
Topics
computational-chemistry materials-science plotting python
Created almost 8 years ago · Last pushed 10 months ago
Metadata Files
Readme Changelog License

README.rst

Sumo
====


.. image:: https://img.shields.io/github/actions/workflow/status/smtg-ucl/sumo/tests.yml?branch=master
    :target: https://github.com/SMTG-Bham/sumo/actions?query=workflow%3A%22Run+tests%22
    :alt: Build Status

.. image:: http://joss.theoj.org/papers/d12ca1f4198dffa2642a30b2ab01e16d/status.svg
    :target: http://joss.theoj.org/papers/d12ca1f4198dffa2642a30b2ab01e16d
    :alt: JOSS Paper

.. image:: https://img.shields.io/pypi/v/sumo
    :target: https://pypi.org/project/sumo/
    :alt: Pypi Repository


.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1338124.svg
    :target: https://doi.org/10.5281/zenodo.1338124
    :alt: Zenodo Repository

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:

1. **An extensive framework for generating high-symmetry k-point paths.**

   - Crystallographic spacegroups are determined using Spglib_.
   - Conventional crystallographic paths are built in as well as interfaces to
     the SeeK-path_ and
     Pymatgen_ implementations.

2. **Plotting scripts for electronic and phonon band structures, density
   of states, and optical absorption diagrams.**

   - VASP calculations are imported using Pymatgen_.
   - The Phonopy_ framework is supported for phonon band structures.

3. **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 CASTEP and 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.

Usage
-----

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
`_.

Feature support for different codes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

+------------------+----------+--------+----------+
| Features         |  VASP    | CASTEP | Questaal |
+==================+==========+========+==========+
| k-point path     |  **Y**   | **Y**  |  **Y**   |
| generation       |          | (1)    |          |
+------------------+----------+--------+----------+
| band plotting    | **Y**    | **Y**  |  **Y**   |
+------------------+----------+--------+----------+
| band projections | **Y**    |  *N*   |   *N*    |
+------------------+----------+--------+----------+
| band analysis    | **Y**    |  *N*   |   *N*    |
+------------------+----------+--------+----------+
| total DOS plot   | **Y**    | **Y**  |  **Y**   |
+------------------+----------+--------+----------+
| projected DOS    | **Y**    | **Y**  |  **Y**   |
+------------------+----------+--------+----------+
| phonon band plot | **Y** (2)| **Y**  |   *N*    |
+------------------+----------+--------+----------+

(1) Brillouin-zone path can also be written for CASTEP phonon calculation
(2) VASP phonons are plotted from Phonopy output files

Installation
------------

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:

.. code-block:: bash

    pip install --user sumo

If 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.


Developer installation
~~~~~~~~~~~~~~~~~~~~~~

*Regular users can skip this section!*

Sumo can also be installed from a copy of the source repository
(https://github.com/smtg-bham/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:

.. code-block:: bash

    git clone https://github.com/smtg-bham/sumo.git
    cd sumo
    pip 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.

Tests
^^^^^

From a developer installation, the unit tests can be
run (from the root directory of the project) using::

  pytest

Automatic testing is run on the master branch of Sumo and proposed
features using GitHub Actions.

Documentation
^^^^^^^^^^^^^

To build the documentation from the project files, install
sumo with extra Sphinx dependencies before compiling with ``sphinx-build``.

.. code-block:: bash

    pip install --user .[docs]
    sphinx-build docs/src docs_build

The user guide can then be explored from *docs/build/html/index.html*.

How to cite sumo
----------------

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 `_.

License
-------

Sumo is made available under the MIT License.


Detailed requirements
---------------------

Sumo is currently compatible with Python 3.5+ and relies on a number of
open-source python packages, specifically:

- Pymatgen_ (version >= 2017.12.30)
- Numpy_
- Scipy_
- Matplotlib_
- Spglib_
- Phonopy_
- SeeK-path_
- `H5py `_

.. _matplotlib: https://matplotlib.org
.. _numpy: http://www.numpy.org
.. _phonopy: https://atztogo.github.io/phonopy
.. _pymatgen: http://pymatgen.org
.. _scipy: https://www.scipy.org
.. _seek-path: https://github.com/giovannipizzi/seekpath
.. _spglib: https://atztogo.github.io/spglib


Contributing
------------

Bugs reports and feature requests
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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.

Code contributions
~~~~~~~~~~~~~~~~~~

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:

   1. 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.
   2. Make your own project fork and implement the changes
      there. Please keep your code style compliant with PEP8.
   3. 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.

Owner

  • Name: Scanlon Materials Theory Group
  • Login: SMTG-Bham
  • Kind: organization
  • Location: UCL, UK

GitHub Events

Total
  • Create event: 4
  • Release event: 3
  • Issues event: 16
  • Watch event: 25
  • Delete event: 1
  • Issue comment event: 34
  • Push event: 23
  • Pull request review event: 3
  • Pull request review comment event: 3
  • Pull request event: 4
  • Fork event: 7
Last Year
  • Create event: 4
  • Release event: 3
  • Issues event: 16
  • Watch event: 25
  • Delete event: 1
  • Issue comment event: 34
  • Push event: 23
  • Pull request review event: 3
  • Pull request review comment event: 3
  • Pull request event: 4
  • Fork event: 7

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 639
  • Total Committers: 23
  • Avg Commits per committer: 27.783
  • Development Distribution Score (DDS): 0.498
Past Year
  • Commits: 21
  • Committers: 3
  • Avg Commits per committer: 7.0
  • Development Distribution Score (DDS): 0.524
Top Committers
Name Email Commits
Alex Ganose a****e@g****m 321
Adam J. Jackson a****n@p****g 199
Seán Kavanagh 5****e 61
Omar Ashour o****2@g****m 12
Bonan Zhu z****n@o****m 10
yw-fang f****n@g****m 8
Keith Butler k****r@s****k 6
utf a****e@g****m 3
shyamd s****d@l****v 2
Matthew Horton m****n@l****v 2
Alex Zanre z****x@g****m 2
Zongda Xing u****i@l****k 2
Arthur B. Youd a****3@u****k 1
Benjamin Morgan b****n@b****k 1
Benjamin Williamson b****0@u****k 1
Chris Savory c****4@u****k 1
Kieran B. Spooner z****s@u****k 1
Pezhman Zarabadi-Poor p****p@g****m 1
Sunghyun Kim f****m@g****m 1
Zongda Xing u****i@l****k 1
Rachel Woods-Robinson r****n@l****v 1
Ethan Rubinstein e****3@u****k 1
alexsquires a****s@g****m 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 39
  • Total pull requests: 18
  • Average time to close issues: 6 months
  • Average time to close pull requests: about 1 month
  • Total issue authors: 30
  • Total pull request authors: 7
  • Average comments per issue: 3.15
  • Average comments per pull request: 1.5
  • Merged pull requests: 15
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 11
  • Pull requests: 4
  • Average time to close issues: 4 days
  • Average time to close pull requests: about 17 hours
  • Issue authors: 9
  • Pull request authors: 2
  • Average comments per issue: 1.36
  • Average comments per pull request: 2.5
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • OkiPVP (3)
  • 20221116688 (2)
  • hongyi-zhao (2)
  • Moon121212 (2)
  • ekkowdg (2)
  • oashour (2)
  • FPrith (2)
  • ajjackson (2)
  • WalterWhite1611 (1)
  • Ah-Abd-Ellatife (1)
  • Asif-Iqbal-Bhatti (1)
  • brlec (1)
  • kuber05 (1)
  • joerivan (1)
  • giacsport (1)
Pull Request Authors
  • kavanase (5)
  • utf (5)
  • ajjackson (3)
  • oashour (2)
  • brlec (1)
  • kbspooner (1)
  • alexsquires (1)
Top Labels
Issue Labels
enhancement (1) wontfix (1)
Pull Request Labels
bug (1)

Dependencies

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