blue-sampler 1.0.0

Large points sampling on the unit torus [0, 1)^D, either with uniform blue noise propriety (vanishing density fluctuations) or with target density (stippling)

blue-sampler

Generate large stealthy point patterns on the unit torus [0, 1)^D. Stealthy point patterns have vanishing density fluctuations at low ("blue") frequencies, making them useful for Monte Carlo integration, image stippling, and any application that needs well-spread, low-discrepancy points. The main blue noise sampler (RGBN) implemented here have linear complexity in the number of points and the dimension, and run in under 15 minutes for 1 million 2D points.

📦 Installation

pip install blue_sampler

🚀 Quick start

import blue_sampler as blue

# 10 000 points in 2D
x = blue.sample_points(N=10_000, D=2)
blue.plot(x, auto_zoom=True)

# structure factor visualization
blue.plot_structure_factor(x)

# higher dimensions
x = blue.sample_points(N=2_000, D=5)

# image stippling
x = blue.im2points(image="zebra.jpg")

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🖼️ Example

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📊 Supported dimensions

Dimension	Notes
2D	Fast, recommended
3D	~2× slower
4–5D	Works, more iterations needed
≥6D	Experimental (small N recommended)

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📚 Links

• 🌐 Project website: https://for-a-few-dpps-more.github.io/rgbn/
• 📦 PyPI: https://pypi.org/project/blue-sampler/
• 🐙 GitHub: https://github.com/For-a-few-DPPs-more/rgbn
• 📖 Documentation: https://blue-sampler.readthedocs.io

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📄 License

MIT