Pymatgen is a community project. We welcome any contributions to either improve or expand its functionality. There are two main ways of contributing to pymatgen:
Direct contributions to pymatgen main distribution. For improvements to existing pymatgen code or new functionality that is expected to be of broad interest and usage to the materials science community, you may make direct contributions to the pymatgen main distribution.
Add-ons to pymatgen. With effect from v2022.0.3, pymatgen now supports the development of add-on packages under the pymatgen, pymatgen.analysis, pymatgen.ext and and pymatgen.io namespaces. If you are developing new functionality that is likely to be used by a sub-community of materials scientists, this is probably the best route going forward. Examples include functionality related to a specific technological application or io for a new quantum chemistry software, etc.
We understand that it is not always clear-cut what contributions should be in the main pymatgen distribution and what should be as an add-on. It is helpful to always submit a GitHub Issue with your proposed feature to get feedback from the pymatgen maintainers as well as the broader community. It is also possible that a package that initially was developed as an add-on subsequently gain enough broad interest and traction such that it is incorporated into the main pymatgen distribution.
Direct contributions to pymatgen main distribution¶
Create a free GitHub account (if you don’t already have one) and perform the necessary setup (e.g., install SSH keys etc.).
Fork the pymatgen GitHub repo, i.e., go to the main pymatgen GitHub repo and click fork to create a copy of the pymatgen code base on your own Github account.
Install git on your local machine (if you don’t already have it).
Clone your forked repo to your local machine. You will work mostly with your local repo and only publish changes when they are ready to be merged:
git clone firstname.lastname@example.org:YOURNAME/pymatgen.git
Note that the entire Github repo is fairly large because of the presence of test files, but these are absolutely necessary for rigorous testing of the code.
It is highly recommended you install all the optional dependencies as well.
Code (see Coding Guidelines). Commit early and commit often. Keep your code up to date. You need to add the main repository to the list of your remotes. Let’s name the upstream repo as mpmaster (materialsproject master):
git remote add mpmaster git://github.com/materialsproject/pymatgen.git
Make sure your repository is clean (no uncommitted changes) and is currently on the master branch. If not, commit or stash any changes and switch to the master:
git checkout master
Then you can pull all the new commits from the main line:
git pull mpmaster master
Remember, pull is a combination of the commands fetch and merge, so there may be merge conflicts that may need to be manually resolved.
Publish your contributions. Assuming that you now have a couple of commits that you would like to contribute to the main repository. Please follow the following steps:
If your change is based on a relatively old state of the main repository, then you should probably bring your repository up-to-date first to see if the change is not creating any merge conflicts.
Check that everything compiles cleanly and passes all tests. The pymatgen repo comes with a complete set of tests for all modules. If you have written new modules or methods, you must write tests for the new code as well (see Coding Guidelines). Install and run pytest in your local repo directory and fix all errors before continuing further.
If you have pre-commit installed you can use the provided
.pre-commit-config.yamlfile to perform automatic style checks before publishing your code. The pre-commit hooks can be installed using:
If everything is ok, publish the commits to your github repository:
git push origin master
Now that your commit is published, it does not mean that it has already been merged into the main repository. You should submit a pull request to the pymatgen main repository. A set of linting and unittests will be automatically performed. Fix all lint and test errors identified. Your PR will only be merged if these checks passed.
“Work-in-progress” pull requests are encouraged, especially if this is your first time contributing to pymatgen, and the maintainers will be happy to help or provide code review as necessary. Put “[WIP]” in the title of your pull request to indicate it’s not ready to be merged.
Writing add-ons for pymatgen¶
Add-on packages can be developed under the pymatgen, pymatgen.analysis, pymatgen.ext and and pymatgen.io namespaces. We ask that most add-ons be developed under the pymatgen.analysis, pymatgen.ext and and pymatgen.io namespaces and not the root pymatgen namespace. The pymatgen root namespace is meant for development of broad classes of functionality. If in doubt, please consult with the pymatgen maintainers. The benefits of writing an add-on for pymatgen are:
You control the development and distribution of the add-on. You also get full recognition of your work.
The add-on does not affect the main pymatgen distribution and end users have a choice of whether to install the add-on or not via pip install pymatgen-analysis-addon.
Once installed, the add-on functions exactly like a part of pymatgen in that the imports are still via from pymatgen.analysis.addon import *.
We will help your add-on gain recognition via our listing of pymatgen add-ons. We have plans to develop this into a full-fledged searchable database of add-ons.
The namespaces provide an important clue what kind of contributions are suitable for add-ons.
pymatgen.analysis.*: A new type of analysis, such as those for a specific application. E.g. superconductors, solar, etc. or an entire category of analysis, e.g., machine learning, diffusion, etc.
pymatgen.ext.*: A high-level API access to a new external resource, for example, a new database of crystal structure, molecules and/or properties.
pymatgen.io.*: Support for input/output from another code, e.g., some quantum chemistry software.
To help developers write add-ons, we have written a pymatgen add-on template with detailed instructions. For a real-world example using this template, check out Materials Virtual Lab’s `pymatgen-diffusion <http://github.com/materialsvirtuallab/pymatgen-diffusion`_.
It should be noted that while the pymatgen maintainers will attempt to help developers as far as possible, we provide no guarantees whatsoever on the quality or reliability of any code that is not part of the main pymatgen distribution. The add-on architecture therefore provides flexibility for broad expansion of scope in pymatgen functionality by the community by loosening up the tight control in the main repository, which is bottlenecked by the small team maintaining it.
Given that pymatgen is intended to be long-term code base, we adopt very strict quality control and coding guidelines for all contributions to pymatgen. The following must be satisfied for your contributions to be accepted into pymatgen.
Unittests are required for all new modules and methods. The only way to minimize code regression is to ensure that all code are well-tested. If the maintainer cannot test your code, the contribution will be rejected.
Python PEP 8 code style. We allow a few exceptions when they are well-justified (e.g., Element’s atomic number is given a variable name of capital Z, in line with accepted scientific convention), but generally, PEP 8 must be observed. Code style will be automatically checked for all PRs and must pass before any PR is merged. To aid you, you can copy the example pre-commit hook into your .git/hooks directly. This will automatically run pycodestyle and other linting services prior to any commits. At the very least, copy pre-commit to .git/hooks/pre-push.
Python 3. We only support Python 3.7+.
Documentation required for all modules, classes and methods. In particular, the method docstrings should make clear the arguments expected and the return values. For complex algorithms (e.g., an Ewald summation), a summary of the alogirthm should be provided, and preferably with a link to a publication outlining the method in detail.
IDE. We highly recommend the use of Pycharm. You should also set up pycodestyle and turn those on within the IDE setup. This will warn of any issues with coding styles. Many code style errors can be done by simply selecting the entire code and using the Code->Reformat Code within Pycharm.
For the above, if in doubt, please refer to the core classes in pymatgen for examples of what is expected.