Build Process

The py5 library is different from the typical Python library in that the code itself is the output of the meta-programming project py5generator. This approach is necessary because of the size, depth, and complexity of Processing.


You will need to download and install Anaconda. You’ll also need build tools such as make and ant, as well as the source control program git.

Clone Source Repositories from GitHub

You will need to clone the py5generator repo from GitHub. If you don’t already have the processing4 repo on your computer, you’ll need to clone that one too.

On my computer I keep all of the relevant repos together in one directory.

$ mkdir pythonprocessing
$ cd pythonprocessing

$ git clone
$ git clone

No need to clone Processing again if you already have the repo available.

My preference is to check out specific tagged versions of the Processing code to match the latest release. As time moves forward you’ll need to remember to pull changes into this repo to keep the code current.

$ cd processing4
$ git checkout processing-1272-4.0a3
$ cd ..

Create Anaconda Environment

Next you will need to create the Anaconda environment. This will install Python packages that go beyond what is necessary for regular py5 use.

$ cd py5generator
$ conda env create -f environment.yml
$ conda activate py5
(py5) $ cd ..

The environment file will install a version of OpenJDK 11 that will provide the necessary Java tools used when the conda environment is active.

Build Processing Jars

You will need to build the Processing jars if you are starting with a fresh Processing repo. You will need to repeat these steps when the repo is updated.

Start with core.jar.

(py5) $ cd processing4
(py5) $ cd core
(py5) $ ant
(py5) $ cd ..

You’ll see it download some JOGL jars and then compile and build core.jar.

Now build the supporting libraries.

(py5) $ cd java/libraries/dxf
(py5) $ ant
(py5) $ cd ../pdf
(py5) $ ant
(py5) $ cd ../svg
(py5) $ ant
(py5) $ cd ../../../..

When the build process runs, Py5Generator will inspect and copy these jars into a specific location in the destination directory. Remember, it is your responsibility to keep the Processing code and jars current.

The Java classes need to be compiled with debug information because py5generator parses the output of javap to inspect the Processing jars and gather information about the relevant public fields and methods.

Run the py5 Makefile

Finally, run the Makefile. The processing_dir parameter needs to point to the location of the Processing repo. The py5_build_dir parameter is where the generated code will go.

(py5) $ cd py5generator
(py5) $ make processing_dir=../processing4 py5_build_dir=../py5code skip_autopep8=true

The optional skip_autopep8 argument will skip autopep8 formatting of the output code, accelerating the build process. Use this during development.

A full build takes under a minute to complete. When this runs you’ll see a lot of logging information appear on the screen. There will be a few warnings about skipping typehints for some Matrix functions, but everything else will be info messages. You should familiarize yourself with the logs so you can spot changes and identify when something goes wrong.

The final Makefile step installs the generated code into the active Anaconda environment. At this point the new code will be available for you to use.

When py5generator detects changes to Processing’s methods and fields, a message will appear in the logs. Py5generator is designed to adapt as the Processing library evolves. Be aware that signature changes to known methods will result in automatic updates to documentation files in the py5_docs/Reference/api_en directory.

There are additional make commands for generating website documentation or building the PyPI distribution files.

(py5) $ make generate_py5_docs py5_website_dir=../py5website py5_api_lang=api_en
(py5) $ make distributions py5_build_dir=../py5code