CONTRIBUTING.md

Contributing

Welcome to ac-training-lab contributor's guide.

This document focuses on getting any potential contributor familiarized with the development processes, but other kinds of contributions are also appreciated.

If you are new to using git or have never collaborated in a project previously, please have a look at contribution-guide.org. Other resources are also listed in the excellent guide created by FreeCodeCamp ¹.

Please notice, all users and contributors are expected to be open, considerate, reasonable, and respectful. When in doubt, Python Software Foundation's Code of Conduct is a good reference in terms of behavior guidelines.

Issue Reports

If you experience bugs or general issues with ac-training-lab, please have a look on the issue tracker. If you don't see anything useful there, please feel free to fire an issue report.

:::{tip} Please don't forget to include the closed issues in your search. Sometimes a solution was already reported, and the problem is considered solved. :::

New issue reports should include information about your programming environment (e.g., operating system, Python version) and steps to reproduce the problem. Please try also to simplify the reproduction steps to a very minimal example that still illustrates the problem you are facing. By removing other factors, you help us to identify the root cause of the issue.

Documentation Improvements

You can help improve ac-training-lab docs by making them more readable and coherent, or by adding missing information and correcting mistakes.

ac-training-lab documentation uses Sphinx as its main documentation compiler. This means that the docs are kept in the same repository as the project code, and that any documentation update is done in the same way was a code contribution. The documentation uses CommonMark with MyST extensions.

:::{tip} Please notice that the GitHub web interface provides a quick way of propose changes in ac-training-lab's files. While this mechanism can be tricky for normal code contributions, it works perfectly fine for contributing to the docs, and can be quite handy.

If you are interested in trying this method out, please navigate to the docs folder in the source repository, find which file you would like to propose changes and click in the little pencil icon at the top, to open GitHub's code editor. Once you finish editing the file, please write a message in the form at the bottom of the page describing which changes have you made and what are the motivations behind them and submit your proposal. :::

When working on documentation changes in your local machine, you can compile them using tox :

tox -e docs

and use Python's built-in web server for a preview in your web browser (http://localhost:8000):

python3 -m http.server --directory 'docs/_build/html'

Code Contributions

   An architecture description, design principles or at least a summary of the
   main concepts will make it easy for potential contributors to get started
   quickly.

A list of projects is available on the documentation homepage.

Project Organization

├── AUTHORS.md              <- List of developers and maintainers.
├── CHANGELOG.md            <- Changelog to keep track of new features and fixes.
├── CONTRIBUTING.md         <- Guidelines for contributing to this project.
├── Dockerfile              <- Build a docker container with `docker build .`.
├── LICENSE.txt             <- License as chosen on the command-line.
├── README.md               <- The top-level README for developers.
├── configs                 <- Directory for configurations of model & application.
├── data
│   ├── external            <- Data from third party sources.
│   ├── interim             <- Intermediate data that has been transformed.
│   ├── processed           <- The final, canonical data sets for modeling.
│   └── raw                 <- The original, immutable data dump.
├── docs                    <- Directory for Sphinx documentation in rst or md.
├── environment.yml         <- The conda environment file for reproducibility.
├── models                  <- Trained and serialized models, model predictions,
│                              or model summaries.
├── notebooks               <- Jupyter notebooks. Naming convention is a number (for
│                              ordering), the creator's initials and a description,
│                              e.g. `1.0-fw-initial-data-exploration`.
├── pyproject.toml          <- Build configuration. Don't change! Use `pip install -e .`
│                              to install for development or to build `tox -e build`.
├── references              <- Data dictionaries, manuals, and all other materials.
├── reports                 <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures             <- Generated plots and figures for reports.
├── scripts                 <- Analysis and production scripts which import the
│                              actual PYTHON_PKG, e.g. train_model.
├── setup.cfg               <- Declarative configuration of your project.
├── setup.py                <- [DEPRECATED] Use `python setup.py develop` to install for
│                              development or `python setup.py bdist_wheel` to build.
├── src
│   └── ac_training_lab     <- Actual Python package where the main functionality goes.
├── tests                   <- Unit tests which can be run with `pytest`.
├── .coveragerc             <- Configuration for coverage reports of unit tests.
├── .isort.cfg              <- Configuration for git hook that sorts imports.
└── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.

Submit an issue

Before you work on any non-trivial code contribution it's best to first create either a feature request in the issue tracker or a new discussion in the discussions page on the subject. This often provides additional considerations and avoids unnecessary work.

Create an environment

Before you start coding, we recommend creating an isolated virtual environment to avoid any problems with your installed Python packages. This can easily be done via Miniconda:

conda env create -f environment.yml
conda activate ac-training-lab

NOTE: The conda environment will have ac-training-lab installed in editable mode. Some changes, e.g. in setup.cfg, might require you to run pip install -e . again.

Optional and needed only once after git clone:

1. install several pre-commit git hooks with:

   pre-commit install
   # You might also want to run `pre-commit autoupdate`

   and checkout the configuration under .pre-commit-config.yaml. The -n, --no-verify flag of git commit can be used to deactivate pre-commit hooks temporarily.

Clone the repository

The instructions below assume that you are using git's command line interface. Alternatively, you may use GitHub Desktop or the built-in git functionality of your favorite IDE, such as VS Code's Source Control extension.

1. Create an user account on GitHub if you do not already have one.

2. Fork the project repository: click on the Fork button near the top of the page. This creates a copy of the code under your account on GitHub.

3. Clone this copy to your local disk:

   git clone [email protected]:YourLogin/ac-training-lab.git
   cd ac-training-lab
   

4. You should run:

   pip install -U pip setuptools -e .
   

   to be able to import the package under development in the Python REPL.

5. Install pre-commit:

   pip install pre-commit
   pre-commit install
   

   ac-training-lab comes with a lot of hooks configured to automatically help the developer to check the code being written.

Implement your changes

1. Create a branch to hold your changes:

   git checkout -b my-feature
   

   and start making changes. Never work on the main branch!

2. Start your work on this branch. Don't forget to add docstrings to new functions, modules and classes, especially if they are part of public APIs.

3. Add yourself to the list of contributors in AUTHORS.md.

4. When you’re done editing, do:

   git add <MODIFIED FILES>
   git commit
   

   to record your changes in git.

   Please make sure to see the validation messages from pre-commit and fix any eventual issues. This should automatically use flake8/black to check/fix the code style in a way that is compatible with the project.

   :::{important} Don't forget to add unit tests and documentation in case your contribution adds an additional feature and is not just a bugfix.

   Moreover, writing a descriptive commit message is highly recommended. In case of doubt, you can check the commit history with:

   git log --graph --decorate --pretty=oneline --abbrev-commit --all
   

   to look for recurring communication patterns. :::

5. Please check that your changes don't break any unit tests with:

   tox
   

   (after having installed tox with pip install tox or pipx).

   You can also use tox to run several other pre-configured tasks in the repository. Try tox -av to see a list of the available checks.

Submit your contribution

1. If everything works fine, push your local branch to the remote server with:

   git push -u origin my-feature
   

2. Go to the web page of your fork and click "Create pull request" to send your changes for review.

Find more detailed information in creating a PR. You might also want to open the PR as a draft first and mark it as ready for review after the feedbacks from the continuous integration (CI) system or any required fixes.

Troubleshooting

The following tips can be used when facing problems to build or test the package:

1. Make sure to fetch all the tags from the upstream repository. The command git describe --abbrev=0 --tags should return the version you are expecting. If you are trying to run CI scripts in a fork repository, make sure to push all the tags. You can also try to remove all the egg files or the complete egg folder, i.e., .eggs, as well as the *.egg-info folders in the src folder or potentially in the root of your project.

2. Sometimes tox misses out when new dependencies are added, especially to setup.cfg and docs/requirements.txt. If you find any problems with missing dependencies when running a command with tox, try to recreate the tox environment using the -r flag. For example, instead of:

   tox -e docs
   

   Try running:

   tox -r -e docs
   

3. Make sure to have a reliable tox installation that uses the correct Python version (e.g., 3.7+). When in doubt you can run:

   tox --version
   # OR
   which tox
   

   If you have trouble and are seeing weird errors upon running tox, you can also try to create a dedicated virtual environment with a tox binary freshly installed. For example:

   virtualenv .venv
   source .venv/bin/activate
   .venv/bin/pip install tox
   .venv/bin/tox -e all
   

4. Pytest can drop you in an interactive session in the case an error occurs. In order to do that you need to pass a --pdb option (for example by running tox -- -k <NAME OF THE FALLING TEST> --pdb). You can also setup breakpoints manually instead of using the --pdb option.

Maintainer tasks

Releases

If you are part of the group of maintainers and have correct user permissions on PyPI, the following steps can be used to release a new version for ac-training-lab:

1. Make sure all unit tests are successful.
2. Tag the current commit on the main branch with a release tag, e.g., v1.2.3.
3. Push the new tag to the upstream repository, e.g., git push upstream v1.2.3
4. Clean up the dist and build folders with tox -e clean (or rm -rf dist build) to avoid confusion with old builds and Sphinx docs.
5. Run tox -e build and check that the files in dist have the correct version (no .dirty or git hash) according to the git tag. Also check the sizes of the distributions, if they are too big (e.g., > 500KB), unwanted clutter may have been accidentally included.
6. Run tox -e publish -- --repository pypi and check that everything was uploaded to PyPI correctly.

Footnotes

1. Even though, these resources focus on open source projects and communities, the general ideas behind collaborating with other developers to collectively create software are general and can be applied to all sorts of environments, including private companies and proprietary code bases. ↩