blue-sampler
Generate large stealthy point patterns on the unit torus [0, 1)^D. Stealthy point patterns have vanishing density fluctuations at low 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. It run e.g. in under 15 minutes for 1 million 2D points on a standard CPU, and 30 times faster on a GPU.
📦 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=10_000, D=5)
# image stippling
x = blue.im2points(image="zebra.jpg")
🖼️ Example
📊 Supported dimensions
| Dimension | Notes |
|---|---|
| 2D | Fast, recommended |
| 3D | ~2× slower |
| 4–5D | Works, more iterations needed |
| ≥6D | Experimental (small N recommended) |
Extensions
blue_sampler was extended to sample and plot various type of point sets related to the blue noise.
- Sobol (quasi random low discrepency sequence)
- STIT (fair tesselation of the space)
- Clusters (fair partititions of a target distribution)
- Pinwheels (aperiodic tiling of the space)
📚 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
📄 License
MIT
1 maintainer
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