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Contributing to NEPC

nepc is an open source project, and you are welcome to contribute to its development. Contributions can come in any areas: writing code, contributing Jupyter Notebooks, writing documentation, improving tests (including providing interesting yet realistic fictitious electron scattering data), cleaning up code (i.e. improving the pylint score), etc.

Version control of nepc is done with git. We use a continuous integration system and monitor code quality.

Reporting Issues

When opening an issue to report a problem, please try to provide a minimal code example that reproduces the issue along with details of the operating system and the Python, NumPy, and nepc versions you are using.

Contributing Code

So you are interested in contributing code to the NEPC Project? Excellent! We love contributions! NEPC is open source, built on open source, and we'd love to have you hang out in our community.

Imposter syndrome disclaimer: We want your help. No, really.

There may be a little voice inside your head that is telling you that you're not ready to be an open source contributor; that your skills aren't nearly good enough to contribute. What could you possibly offer a project like this one?

We assure you - the little voice in your head is wrong. If you can write code at all, you can contribute code to open source. Contributing to open source projects is a fantastic way to advance one's coding skills. Writing perfect code isn't the measure of a good developer (that would disqualify all of us!); it's trying to create something, making mistakes, and learning from those mistakes. That's how we all improve, and we are happy to help others learn.

Being an open source contributor doesn't just mean writing code, either. You can help out by writing documentation, tests, or even giving feedback about the project (and yes - that includes giving feedback about the contribution process). Some of these contributions may be the most valuable to the project as a whole, because you're coming to the project with fresh eyes, so you can see the errors and assumptions that seasoned contributors have glossed over.

Note: This disclaimer was originally written by Adrienne Lowe for a PyCon talk, and was adapted by Astropy based on its use in the README file for the MetPy project and then further adapted for NEPC.

Most contributions to NEPC are done via pull requests from GitHub users' forks of the nepc repository.

Once you open a pull request (which should be opened against the master branch, not against any of the other branches), please make sure to include the following:

  • Code: the code you are adding

  • Tests: these are usually tests to ensure code that previously failed now works (regression tests), or tests that cover as much as possible of the new functionality to make sure it does not break in the future.

  • Documentation: documentation is generated using Sphinx, so all source code should be commented with docstrings. Follow the NumPy conventions outlined in the numpydoc docstring guide for all docstrings. Build and check documentation locally before making pull requests.

  • Changelog entry: whether you are fixing a bug or adding new functionality, you should add an entry to the CHANGES.rst file that includes the PR number. If you are opening a pull request, you may not know the PR number yet, but you can add it once the pull request is open. If you are not sure where to put the changelog entry, wait until a maintainer has reviewed your PR and assigned it to a milestone.

    You do not need to include a changelog entry for fixes to bugs introduced in the developer version and therefore are not present in the stable releases. In general you do not need to include a changelog entry for minor documentation or test updates. Only user-visible changes (new features/API changes, fixed issues) need to be mentioned. If in doubt, ask the core maintainer reviewing your changes.

Checklist for Contributed Code

A pull request for a new feature will be reviewed to see if it meets the following requirements. For any pull request, a nepc maintainer can help to make sure that the pull request meets the requirements for inclusion in the package.

Scientific Quality (when applicable)

  • Is the submission relevant?
  • Are references included to the origin source?
  • Does the code perform as expected?
  • Has the code been tested against previously existing implementations?

Code Quality

  • Is the code compatible with Python >=3.6?
  • Are there dependencies other than the nepc core, the Python Standard Library, and NumPy 1.16.0 or later?
    • Is the package importable?
    • Are additional dependencies handled appropriately?
    • Do functions that require additional dependencies raise an ImportError if they are not present?
  • Run a linter before making pull requests. nepc CI uses pylint. See note below on running pylint in your conda environment.

Testing

  • Are the inputs to the functions sufficiently tested?
  • Are there tests for any exceptions raised?
  • Are there tests for the expected performance?
  • Are the sources for the tests documented?
  • Have tests that require an optional dependency been marked as such?
  • Does pytest --local run without failures? See notes about MySQL for setting up the nepc_test database.

Documentation

  • Is there a docstring in numpydoc format in the function describing:
    • What the code does?
    • The format of the inputs of the function?
    • The format of the outputs of the function?
    • Any exceptions which are raised?
    • An example of running the code?
  • Is there any information needed to be added to the docs to describe the function?
  • Does the documentation build with sphinx without errors or warnings? See note below on using livereload.

License

  • Is the nepc license included at the top of the file?
  • Are there any conflicts with this code and existing codes?

NEPC requirements

  • Do all the CI tests pass?
  • If applicable, has an entry been added into the changelog?
  • Can you check out the pull request and repeat the examples and tests?

Nuts and Bolts

Development Environment

Developing nepc works best within a conda environment (environment.yml file provided). Also, if you are going to build a database yourself, you will need MySQL. To get started:

$ git clone predator.nrl.navy.mil/padamson/nepc/
$ cd nepc
$ conda env create -f environment.yml #create the nepc conda environment 
$ conda activate nepc
$ pip install -e . # install the nepc package and sub-packages into the nepc conda environment
$ export NEPC_HOME=/path/to/cloned/nepc/repo/ # put this in your `~/.bashrc or ~/.bash_profile`
$ pytest # if on the NRL network (otherwise see MYSQL.md for building the `nepc_test` database)

pylint in a conda environment

It's probably best to make sure pylint is not in your base conda environment. Otherwise, when you run pylint, the linter will give you errors and warnings for your base environment, not your nepc conda environment. (i.e. run conda uninstall pylint in your base conda environment, if necessary.)

livereload

The very useful tool livereload for monitoring changes in Sphinx documentation in real-time is included in the nepc conda environment (environment.yml file). Here’s a simple script that detects changes in any *.rst files in doc or *.py files in nepc, rebuilds the Sphinx documentation, and starts a livereload server:

#!/usr/bin/env python
from livereload import Server, shell
import formic

PATTERNS = ["doc/**.rst", "nepc/**.py"]

SERVER = Server()
for filepath in formic.FileSet(include=PATTERNS):
    SERVER.watch(filepath, shell('make html', cwd='doc'))
SERVER.serve(root='doc/_build/html')

Run it inside the root directory of the nepc repo, then open http://localhost:5500/, and you can see any documentation changes you are making in real time.

Sphinx cross-references

If you are having trouble determining the syntax for a Sphinx cross-reference (internal or external), try the command-line tool https://github.com/bskinn/sphobjinv, which is included in the nepc conda environment (environment.yml file).