Platform Issues: macOS and Windows

The main purpose of this release is to address a long-standing issue on macOS. The macOS issue was opened by me in issue #5 and has been a thorn in my side ever since. Recently a member of our py5 community, wissme, reported that my hack to alleviate the symptoms of the issue wasn't working on Apple Silicon computers. This prompted me to revisit the situation, and happily I was able to gain new insights into how to properly address py5's platform specific challenges. This resulted in a new approach to window focusing that not only fixes the macOS issue, but also improves code quality on macOS and Windows.

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New Feature: Live Coding

There's one big feature: Live Coding. The basic idea is to have a live coding environment where you can write and modify your py5 code and see the results in real time. This is a feature that I've wanted to add to py5 for a long time and I'm excited to finally have it in the library. It is designed to support rapid prototyping of ideas and experimentation. You'll be able to write code in an editor or Jupyter Notebook and see the results in a running Sketch immediately, without having to exit a running Sketch and then start a new one.

I'm not going to repeat all of the details here, but you can read more about it in the Live Coding documentation page.

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PyCon 2024

But before talking about the release, I want to share the py5 recordings from PyCon 2024. I delivered a presentation titled Creative Coding with py5, the Python version of Processing. Alexandre Villares led py5 tutorial Learning Python while making drawings and animations. Both were well attended and a lot of fun for both of us to organize and deliver. I'm grateful for the opportunity to meet so many people from the py5 community and the Python community! I hope to see you all again soon.

Please watch the videos and give both of them a thumbs up!

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Numpy Version

The main reason why this py5 release is happening now and not later is because numpy will soon have a major release that is not completely backwards compatible. Version 2.0 of numpy will be released soon and library maintainers have been advised to pin dependencies to numpy<2.0 and then test with numpy 2.0 when it is available. This is to ensure that py5 and every library py5 depends on is ready for the new version and does not cause users any problems.

Once the new numpy version is released and I verify py5 works well with it, I will do another release of py5 that removes the numpy<2.0 pin.

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New Features

The major theme of this release is expanding and improving py5's Python ecosystem integrations. This release includes new features for working with matplotlib, Shapely, and Trimesh through the new convert_shape() method and the upgraded convert_image() method. It also includes a new color mode called CMAP that is built around matplotlib colormaps.

The documentation for these new features is extensive. There is now a new section in the documentation called Python Ecosystem Integrations that explains how py5 integrates with other Python libraries. This covers the new features in this release as well as existing features that perhaps some people didn't know about.

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Imported Mode Code Importing feature

Python projects with moderate to large amounts of code will be typically split into multiple files that will be imported from from one file to another. However, due to the way py5's Imported Mode works, this couldn't be done with py5 Imported Mode code. This new feature changes that, supporting the ability to import Imported Mode code from one file to another. How to do this is described in more detail in the documentation page Importing Imported Mode Code. In addition, the documentation also describes General py5 Importing Guidelines for writing importable py5 code in that supports all of py5's programming modes.

Other Changes

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Hybrid Programming & Processing Mode

Hybrid Programming refers to the ability to add your own Java (Processing) code to py5. Think of it as a way to add custom Processing extensions to py5. Hybrid Programming is useful for boosting py5's performance and making Java libraries more accessible to py5.

Processing Mode is a completely new way to use py5. This feature is built for the Java Processing users. It allows py5 to function as a bridge, connecting the Java code users write for Processing sketches to the Python ecosystem. Processing Mode gives Processing users a new method callPython(), which gives everyone an easy way to call Python code from Processing.

Both Hybrid Programming and Processing Mode are documented extensively on the py5 documentation website. There's also a GitHub repo with example code. As these are new features, the documentation and example code will grow as questions come up and issues are raised. Stay tuned!

I'm thrilled to finally be able to share these new features with the py5 community. I hope you find them useful and easy to use. I'm looking forward to seeing what people do with them.

On the subject of documentation, there is now a page explaining how py5 actually works. I've wanted to write this documentation for a long time. This information will be useful for advanced coders who want to better understand how py5 works and for developers who want to contribute to py5. More documentation does need to be written about py5's internals, but this is a good start. The documentation will evolve as readers ask questions and I make improvements to the text.

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