eduray 0.2.4

Educational Python ray tracer with a modular architecture, visualization tools, example scenes, and support for shadows, reflections, refractions, and Blinn-Phong shading.

EduRay: Educational Ray Tracer in Python

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EduRay is an educational ray tracer implemented in Python. It is designed to be simple, readable, and modular, making it suitable for learning the fundamentals of ray tracing and computer graphics.

The project focuses on clarity rather than performance. It includes the source code of the ray tracer, visualization tools, example scenes, and educational Jupyter notebooks.

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Installation and setup

You can install the package from PyPI without the notebooks as rendering and visualization software:

pip install eduray

Recommended for educational use and for learning how to adapt the library

Clone the repository to use the educational notebooks and install all dependencies. The project uses pyproject.toml for dependency management, so the required dependencies can be installed with pip.

git clone https://github.com/HonzaSik/eduray.git
cd eduray
python3.13 -m venv .venv

# macOS/Linux
source .venv/bin/activate

# Windows
.venv\Scripts\activate

# Option A: Library only — for use in IDEs like PyCharm or VS Code
pip install -e .
# You need to set your interpreter to the virtual environment you just created for the editable install to work in your IDE.

# Option B: Library + Jupyter — for running educational notebooks in the browser
pip install -e ".[all]"

To run the educational notebooks in your browser, start a Jupyter server in the project directory:

jupyter notebook

Or open the notebooks directly in an IDE with Jupyter support, such as VS Code or PyCharm.

Then open educational_notebooks/1_data_visualizer.ipynb to start with the first lesson. The notebooks are numbered and meant to be followed in order.

Note The project was developed and tested on Python 3.13. Requires Python 3.11 or newer.

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Quick start

The smallest working example:

from eduray import Object, Sphere, PhongMaterial, Scene, PinholeCamera, PointLight, LinearRenderLoop

sphere = Object(
    geometry=Sphere(),
    material=PhongMaterial()
).translate(0, 0, -2)

scene = Scene(
    camera=PinholeCamera(),
    objects=[sphere],
    lights=[PointLight()],
)

rt = LinearRenderLoop(scene=scene)
rt.render("hello_world.png")

To try the library, open the Hello World notebook. It demonstrates how to set up a minimal scene and render an image using only a few lines of code.

jupyter notebook ./hello_world.ipynb

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Educational notebooks series

A series of educational Jupyter notebooks is available in the educational_notebooks directory.

The notebooks explain selected ray tracing concepts step by step, including:

• camera construction
• ray-object intersections
• shading
• rendering
• reflections and refractions
• procedural texturing
• visualization of ray tracing concepts

More detailed description of the notebooks can be found in the introduction notebook.

Documentation approach

EduRay does not provide a separate generated API documentation. Instead, the main user-facing documentation is provided through the educational Jupyter notebooks. The notebooks introduce the library step by step and show how its classes, methods, and rendering components are used in practice.

This approach fits the educational purpose of the project, because many parts of the library are best explained in context: by defining objects, modifying parameters, rendering scenes, and observing the results directly. Users who want to experiment with the library can use the final demo notebook as a practical sandbox for creating their own scenes.

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For those who want to experiment

1. Click "Fork" on https://github.com/HonzaSik/eduray
2. Clone your fork:

   git clone https://github.com/YOUR_USERNAME/eduray.git
   

3. Continue with the editable install steps from the Installation section above.

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Troubleshooting

ModuleNotFoundError: No module named 'eduray' in PyCharm or VS Code

Make sure your IDE is configured to use the .venv you created above, not the system Python:

• PyCharm: Settings → Project → Python Interpreter → Add Interpreter → Add Local → Virtualenv → Existing, then select <repo>/.venv/bin/python (macOS/Linux) or <repo>\.venv\Scripts\python.exe (Windows).
• VS Code: Cmd/Ctrl+Shift+P → Python: Select Interpreter, pick the .venv from this project (workspace).