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Contributing

Welcome to ac-microcourses 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-microcourses, 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-microcourses docs by making them more readable and coherent, or by adding missing information and correcting mistakes.

ac-microcourses 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 markup language used is CommonMark with MyST extensions.

:::{tip} Please notice that the GitHub web interface provides a quick way of propose changes in ac-microcourses'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

Courses are divided into modules, where each module typically contains a tutorial, a quiz, and an assignment. Tutorials contain text and video resources and often a hands-on and bare bones implementation of the concept being covered. The tutorial also provides links to quizzes which are hosted on Quercus, and links to assignments which are hosted through GitHub Classroom.

Most of the content in this repo is contained within the docs folder. Each course has it's own folder within docs/courses/ (e.g., docs/courses/hello-world).

Index and overview files are generated automatically via Jinja2 templates contained within src/ac_microcourses via scripts/generate_overviews.py. This script is run automatically via the Makefile when building the docs.

Submit an issue

Before you work on any non-trivial code contribution it's best to first create a report in the issue tracker to start a discussion 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 either virtualenv:

virtualenv <PATH TO VENV>
source <PATH TO VENV>/bin/activate

or Miniconda:

conda create -n ac-microcourses python=3 six virtualenv pytest pytest-cov
conda activate ac-microcourses

Clone the repository

  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 git@github.com:YourLogin/ac-microcourses.git
cd ac-microcourses

  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-microcourses 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.rst.

  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-microcourses:

  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.

New Courses

Create a new GitHub organization (e.g., https://github.com/orgs/ACC-HelloWorld/ and https://github.com/ACC-DataScience). Free organization. Tied to personal account. Add corresponding emoji as profile picture (PPT --> fill white background, copy-paste as image, crop, save as image, org settings, upload profile picture). Organization display name of form AC Classroom - Hello World. URL is the specific microcourse URL (e.g., https://ac-microcourses.readthedocs.io/en/latest/courses/hello-world/overview.html).

There is also a parent-level organization: https://github.com/AC-Classroom. This is where base templates go.

Within https://classroom.github.com/, "New Classroom". Pick the corresponding organization. Of the form robotics-8a3a78 (i.e., can keep the random ID that's autogenerated).

"TAs" would need admin access to the organization, then send the TAs the classroom invitation URL.

Add roster entries manually (can upload a CSV). I use adj-noun-pairs.csv which has 500. It's a bit unwieldy, but scalable.

For the first assignment, go to the existing assignment from one of the courses and click "Edit" then "Reuse assignment". At minimum, do this for 0.1 (intro git and GitHub), 0.2 (intro to GitHub Classroom), and 0.3 (Python refresher). If the copy fails, just make a new assignment and manually mirror the settings for the assignment you're trying to copy. Note that when you "reuse an assignment" it creates templates in your new organization, so make sure that you actually want the same repo as the one that was used in the original assignment. Otherwise, start from scratch and copy over settings as needed.

Enable GitHub Codespaces on the organization level (free assuming academic organization). "Upgrade this organization to GitHub Team for free to use this feature". Also, on the GitHub organization settings, change to "Organization ownership" (e.g., https://github.com/organizations/ACC-Robotics/settings/codespaces).

Related, e.g.,: https://classroom.github.com/classrooms/180682108-robotics-8a3a78/settings.

You need to complete the above step for the organization before you can choose GitHub Codespaces as the supported editor for an assignment.

Typically, for each assignment, you'll need to create a new repository within the organization. Go to https://github.com/AC-Classroom/autograding-codespace-python and click "use this template" --> "create a new repository". Owner should be the organization. Name it e.g., 1-pumps-and-pipettes, keep it private. Then, go to settings and check the box for "template repository". This is necessary for it to be used by GitHub Classroom.

You'll add this repo as the starter code to the GitHub Classroom assignment. Update that repo with your assignment content. Typically, you'll also grant students admin access to the repositories so that they can do things like add repo secrets. Usually, copy the default branch only.

For autograding tests, typically I use sudo -H pip3 install -r requirements.txt for the setup command (even though it says don't use sudo, and use pip instead of pip3). This was due to some intricate things with installing local packages and getting both Codespaces and GitHub Actions to deal with these local installations appropriately. Perhaps there's a workaround. A typical name: Multi-task test suite (also Pumps and pipettes test suite). A timeout of 5 and a point value of 10 is typical.

Usually, you'll want to assign partial points. You can do this by using a run_command preset (instead of Python preset) with a command such as pytest bayesian_optimization_test.py::test_parameters_and_objectives. There may be some potential to refactor things to use Custom YAML instead.

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. ↩