mpl-animators 1.2.4

Testing and CI Notes

Because this repo is heavily dependent on figure tests, most of the CI jobs (other than publish, and one windows and one macos build) run on Circle CI, there are no test runs on GH Actions. The -figure test tox jobs run all tests, figure and non-figure.

Usage of Generative AI

We expect authentic engagement in our community. Be wary of posting output from Large Language Models or similar generative AI as comments on GitHub or any other platform, as such comments tend to be formulaic and low quality content. If you use generative AI tools as an aid in developing code or documentation changes, ensure that you fully understand the proposed changes and can explain why they are the correct approach and an improvement to the current state.

License

This project is Copyright (c) The SunPy Developers and licensed under the terms of the BSD 3-Clause license. This package is based upon the Openastronomy packaging guide which is licensed under the BSD 3-clause licence. See the licenses folder for more information.

Contributing

We love contributions! mpl-animators 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 mpl-animators based on its use in the README file for the MetPy project.