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https://github.com/nir/jupylet

Python game programming in Jupyter notebooks.
https://github.com/nir/jupylet

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Python game programming in Jupyter notebooks.

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README 

          
# Jupylet



*Jupylet* is a Python library for programming 2D and 3D games, graphics, music 

and sound synthesizers, interactively in a Jupyter notebook. It is intended 

for three types of audiences:



* Computer scientists, researchers, and students of deep reinforcement learning.

* Musicians interested in sound synthesis and live music coding.

* Kids and their parents interested in learning to program.



 





    

    




## Jupylet for Kids



A Jupyter notebook is in essence a laboratory for programming. It is the ideal

environment for playing around with code, experimenting, and exploring ideas.

It is used by professional machine learning scientists who come every day to

play at work, so why not by kids?



*Jupylet* is wonderfully easy to use for creating simple 2D and 3D games and 

music interactively and experimentally. Change a variable or a function and 

see how the game is affected immediately while running.



## Jupylet for Deep Reinforcement Learning



*Jupylet* makes it is super easy to create and modify environments in which to

experiment with deep reinforcement learning algorithms and it includes the API

to programmatically control multiple simultaneous games and render thousands 

of frames per second.



Consider for example the pong game included in this code base. With a few

lines of code you can modify the colors of the game to experiment with transfer 

learning, or turn the game into 4-way pong with agents on all four sides of the 

game court to experiment with cooperation between multiple agents. And since you 

can modify the game interactively in Jupyter this process is not only easy but 

also fun.  



Check out the [*Programming Graphics*](https://jupylet.readthedocs.io/en/latest/programmers_reference_guide/graphics.html) 

and the [*Reinforcement Learning*](https://jupylet.readthedocs.io/en/latest/programmers_reference_guide/rl.html) 

chapters in the Jupylet Programmer's Reference Guide.



## Jupylet for Musicians



*Jupylet* imports ideas and methods from machine learning into the domain

of sound synthesis to easily let you create sound synthesizers as wild as you

can dream up - it includes impulse response reverb effects, colored noise 

generators, resonant filters with cutoff frequency sweeping, oscillators with 

LFO modulation, multi sampled instruments, and much more... And all of it in 

pure Python for you to modify and experiment with.



In addition *Jupylet* draws inspiration from the wonderful [*Sonic Pi*](https://sonic-pi.net/)

and brings live loops and live music coding to Jupyter and Python. Hook up 

your MIDI keyboard and take off.



Check out the [*Programming Sound and Music*](https://jupylet.readthedocs.io/en/latest/programmers_reference_guide/sound.html) 

and the [*Programming Synthesizers*](https://jupylet.readthedocs.io/en/latest/programmers_reference_guide/synthesis.html)

chapters in the Jupylet Programmer's Reference Guide.



## Requirements



*Jupylet* should run on Python 3.9 and up on Windows, Mac, and Linux.



## How to Install and Run Jupylet



If you are new to Python, I recommend that you install and use the

[Miniconda Python](https://docs.conda.io/en/latest/miniconda.html)

distribution. 



**On Windows** – download and run the 64-bit installer for Python 3.11. Once 

Miniconda is installed press the `⊞ Winkey` and then type *Miniconda* and 

press the `Enter` key. This should open a small window that programmers call 

*console* or *shell* in which you can enter commands and run programs.



**On macOS with M1 processor** – download and run "Miniconda3 macOS Apple M1 64-bit pkg" 

for Python 3.11. Once installed click the Spotlight icon `🔍` and in the search 

field type *terminal* and press the `Enter` key to open the console. Then you need 

to run the following command:



    pip install --extra-index https://github.com/nir/jupylet/releases/download/v0.9.2/ moderngl glcontext



**On macOS with Intel processor** – download and run "Miniconda3 macOS Intel x86 64-bit pkg" 

for Python 3.11. Once installed click the Spotlight icon `🔍` and in the search 

field type *terminal* and press the `Enter` key to open the console.



**On Linux** – download "Miniconda3 Linux 64-bit". This should download the file

Miniconda3-latest-Linux-x86_64.sh. Install it by running the following command 

in a bash shell (once installed start a new bash shell):



    bash Miniconda3-latest-Linux-x86_64.sh



---



Once Miniconda is installed it is time to install *jupylet* by typing the 

following command in the console:



    pip install jupylet



Next, to run the example notebooks, download the *jupylet* source code. 

If you have [Git](https://git-scm.com/) installed type the following command:



    git clone https://github.com/nir/jupylet.git



Alternatively, you can download the source code with the following command:



    python -m jupylet download



Next, enter the *jupylet/examples/* directory with the change directory

command:



    cd jupylet/examples/



And start a jupyter notebook with:



    jupyter notebook 11-spaceship.ipynb



Run the notebook by following the instructions in the notebook and a game

canvas should appear with the spaceship example:






Alternatively, you can run the same game as a Python script from the console 

with:



    python spaceship.py



## Documentation



To get started with Jupylet head over to the *Jupylet Programmer's Reference 

Guide* which you can find at 

[jupylet.readthedocs.io](https://jupylet.readthedocs.io/).  



To complement the online guide check out the growing collection of 

[*example notebooks*](examples/) that you can download and run on your 

computer as explained above.



## Contact



For questions and feedback send an email to [Nir Aides](mailto:nir.8bit@gmail.com) or [join the discussion](https://github.com/nir/jupylet/discussions).



## Spread the Word



Jupylet is a new library and you can help it grow with a few clicks - 

if you like it let your friends know about it!



## Acknowledgements



* [Einar Forselv](https://github.com/einarf) - The programmer behind ModernGL 

for his endless help in the trenches of OpenGL programming.

* [Alban Fichet](https://afichet.github.io/) - For kindly licensing his 

sound visualizer Shadertoy as CC BY 4.0 license.



## What's New in Version 0.9.1



* Support for Python 3.10 and Python 3.11 with MIDI functionality.

* Seamlessly track changes to audio devices on macOS.

* Workaround PIL api change - thanks to [@misolietavec](https://github.com/misolietavec).

* Bug fixes. 



## What's New in Version 0.8.9



* Support for Python 3.10 and Python 3.11 - except for MIDI functionality.

* Support for macOS M1.

* Spectrum analyzer.

* Bug fixes. 






## What's New in Version 0.8.8



* Support for Python 3.9. 



## What's New in Version 0.8.7



* Workaround auto-completion bug in Jupyter notebooks. 



## What's New in Version 0.8.6



* Support for rendering Shadertoy OpenGL shaders. 

[Shadertoy](https://www.shadertoy.com/) is an awesome online platform for  

programming and sharing OpenGL shaders online, and now you can 

[use and render shadertoy shaders in Jupylet!](https://jupylet.readthedocs.io/en/latest/programmers_reference_guide/graphics-3d.html#shadertoys)