How Does py5 Work?#

This document explains py5’s basic architecture and how it is able to make the Java Processing jars available to the Python 3 interpreter using JPype.

The intended audience is intermediate to advanced coders with some experience using py5 and Processing in Java.

How py5 Doesn’t Work#

Before diving into the details of how py5 actually works, let’s first discuss how py5 doesn’t work.

You should know that py5 was not the first open source project that attempted to create a Python 3 version of Processing that leveraged the Processing Jar files, as does in Jython. The general approach of the other attempts I’ve seen, as well the first few iterations of py5’s architecture, followed this general pattern:

  1. Python code calls the user’s settings() function

  2. Python code calls Java code to initialize the Sketch window

  3. Python code calls the user’s setup() function

  4. Python code repeatedly calls the user’s draw() function in a loop

You can imagine implementing the critical run_sketch() function with code that looks like this:

import time

def run_sketch(settings, setup, draw):
    while True:
        time.sleep(1 / frame_rate)

In fact, if you look at the initial commit of the py5 project, you can see that this is very close to what py5 actually did. (Note that this early version of py5 also used modified Processing Library code that was never checked in.) Observe that with this approach, Python is managing the animation thread. Remarkably, this could in fact create runnable py5 Sketches. There were, however, many problems. Two of the most critical were:

  • There’s no way to trigger mouse and keyboard events

  • The OpenGL renderers P2D and P3D always crashed

The problem with the OpenGL renderers had to do with something called a “context thread.” The bottom line is only one thread is allowed to make OpenGL calls, and there is no way to make the context thread be the Python thread that this early version of py5 was using to call the user’s draw() function in a loop. I tried everything I could think of to get OpenGL to function correctly. As these explorations took place during the first month of COVID lockdowns, I had a lot of time to experiment. Nevertheless, this approach was not and could not be successful.

How py5 Actually Works#

The key insight behind py5 was to abandon the above approach and instead leverage JPype’s ability to call Python from Java. Calling Java from Python is obviously important for py5, but py5 wouldn’t exist if JPype couldn’t also make calls in the other direction, making calls from Java to Python.

The basic idea of py5 is to provide a Processing Sketch in Java that makes calls to Python to execute the user’s functions, which will in turn make calls back to Java to access Processing Library functionality. This approach lets the Processing Library manage the Sketch’s animation thread and the various OpenGL complexities. As far as the Processing Library is concerned, every py5 user is running the exact same Java code, py5.core.Sketch, found in This py5.core.Sketch class is an instance of the Processing Library’s processing.core.PApplet. The py5.core.Sketch code makes calls to Python to execute the user’s code.

The basic steps of a running py5 Sketch look like this:

  1. Python code uses JPype to create an instance of the Java class py5.core.Sketch, which itself extends processing.core.PApplet.

  2. Python code passes the instance of py5.core.Sketch to the Processing Library’s runSketch() method

  3. The Processing Library’s runSketch() method launches the Sketch, opening the Sketch window and starting the animation thread

  4. The Processing Library animation thread calls py5.core.Sketch’s setup() and draw() methods

  5. py5.core.Sketch’s setup() and draw() methods make calls from Java to Python, instructing it to call the user’s setup() and draw() functions

  6. Python code executes the user’s setup() and draw() functions, making calls to py5’s API methods such as rect(), begin_shape(), convert_shape(), random(), etc.

  7. Calls to py5’s API methods that leverage Processing Library code such as rect() and begin_shape() make corresponding calls to the Processing Library Java methods rect(), beginShape(), etc.

  8. Calls to py5’s API methods that are implemented in Python such as convert_shape() and random() provide their functionality without using the Processing Library

This approach is more complicated than the initial approach. However, the OpenGL renderers P2D and P3D work correctly. The mouse and keyboard event functions will also be triggered at the appropriate times.

Let’s review py5’s code in more detail, examining each of the above steps to understand how they work.

1. Python Creates Instance of the Java Class py5.core.Sketch#

The JPype library lets Python create instances of Java classes. It can create an instance of py5.core.Sketch with jpype.JClass.

There is a Python class called Sketch, found in, that py5 uses to manage the interaction with the Java py5.core.Sketch instance. The Java class is instantiated in the Python Sketch class’s __init__() method.

2. Pass py5.core.Sketch Instance to the Processing Library’s runSketch() Method#

The Python class Sketch has a method _run_sketch() that calls processing.core.PApplet’s runSketch() method.

In addition, the _run_sketch() method builds a bridge between the Java class py5.core.Sketch and Python. This bridge implements the Java Interface py5.core.Py5Bridge but is coded in Python with the Python class Py5Bridge, found in This programming sorcery uses jpype.JImplements to create a Python object that can be “passed” to Java. This bridge is the mechanism through which py5.core.Sketch makes calls back to Python to run the user defined functions. The bridge will also inform the py5.core.Sketch instance which user functions are defined so it knows which calls to make to Python and which to skip.

The _run_sketch() method also has some special code needed to get py5 to run on macOS.

3. Processing Library’s runSketch() Method Launches the Sketch#

In processing.core.PApplet’s runSketch() method, the Processing Library opens the Sketch window and starts the animation thread. The mechanism behind this is exactly the same as it is for any ordinary Processing Sketch.

4. Processing Animation Thread Calls py5.core.Sketch’s User Methods#

The animation thread created by the Processing Library will make calls to py5.core.Sketch’s setup() and draw() methods. It will also call its mouse and keyboard methods. The py5.core.Sketch class implements every possible user method so it can call the user’s Python functions when needed.

5. Call User’s Python Functions from py5.core.Sketch’s User Methods#

When any of py5.core.Sketch’s user methods are called, it will check if there is a corresponding Python user function that needs to be executed. It gets the set of registered events that require calls to Python when the bridge between Python and Java is first created in step 2.

The code in each of py5.core.Sketch’s user functions looks very much like this:

    if (success) {
      if (py5RegisteredEvents.contains("draw")) {
        success = py5Bridge.run_method("draw");
      } else {
        // do nothing

The boolean success variable is a flag that is set to false if an exception is thrown in Python and py5 needs to terminate the Sketch.

6. Python Code Executes User Implemented Functions#

The user’s setup() and draw() functions are executed just like any other Python function.

Exceptions are always caught and handled in Python. Error handling in py5 is complex; Java and Python cannot throw or handle each other’s exceptions.

When a Python exception is thrown, py5 will make it look like the Sketch has stopped by pausing the py5.core.Sketch instance. It will also set the success variable mentioned in the previous step to false. Pausing the Sketch instead of stopping it by throwing a Java exception is necessary to ensure py5 can reliably dispose of the Sketch window without also shutting down the Java Virtual Machine. Thrown exceptions have an unpredictable impact on Processing’s internals.

In Processing, when an exception is thrown, the exception can put the Sketch into a weird state that would complicate code that attempts to dispose of the Sketch window resources cleanly. This doesn’t matter for a Processing Sketch because the Sketch window is terminated with a call System.exit(). This will shut down the Java Virtual Machine and as a consequence dispose of the Sketch window. In py5, calling System.exit() is not an option because it would make py5 unusable until you restarted your Python interpreter or Jupyter Notebook. JPype does not support restarting the Java Virtual Machine, so py5 cannot make calls to System.exit().

7. API Methods that Leverage the Processing Library Code#

Almost all of the code for py5’s methods that leverage the Processing Library are written dynamically using py5generator’s custom code template engine. The code for a typical function, minus the docstring, is brief:

    def rect(self, *args):
        return self._instance.rect(*args)

The self._instance attribute is the Java py5.core.Sketch instance created in step 1. Most of py5’s methods are actually thin wrappers of their underlying Processing Library methods. Except for Py5Vector, all py5 class instances have an _instance attribute referencing the underlying Processing object.

Critically, the API method calls back to Java are with the same thread as the call from py5.core.Sketch’s user methods to the user’s Python functions described in step 5. This means that the context thread issue that doomed the first few py5 prototypes is solved.

Code written with py5generator’s template engine can be customized with decorators when the above code is not adequate.

8. API Methods that are Implemented in Python#

Not all of py5’s methods are implemented in Java. API methods that draw to the Sketch window in some way must be implemented in Java. Generally, API methods that do not draw to the Sketch window are implemented in Python. This is done for performance reasons. Notable exceptions are the noise methods, which are implemented in Java.

What Else?#

The above description gives an overview of the core py5 architecture. All of this was rebuilt from scratch many times in the early days of py5. It took a lot of time, effort, research, and experimentation to figure out the right way to do this. Now, the core py5 code is stable and rarely needs updates.

There is much more to know about py5! There are other non-core but critical parts of py5 that are worth exploring for folks interested in looking through the source code. If you have any comments or questions about what you see, please join us on py5’s GitHub discussions forum.