Python One go: Bootstrapped uncertainty quantification given observation matrix
Leymosun is a Python package designed for randomness-based research, featuring high-entropy random number generation with non-deterministic seeds to enhance computational science experiments. It supports uncertainty quantification through bootstrapped methods and provides tools for spectral analysis and random matrix generation. The package emphasizes scientific correctness over speed and includes educational lecture notebooks as functional tests.
- ▪Leymosun provides High-Entropy Random Number Generation (HE-RNG) with non-deterministic seeds for improved simulation quality.
- ▪The package includes tools for bootstrapped uncertainty quantification, spectral unfolding, and random matrix ensembles like GOE and Rosenzweig-Porter.
- ▪It supports various statistical and quantum computing functionalities, including nearest-neighbour spacing densities and spread complexity calculations.
- ▪Educational lecture notebooks are included to demonstrate concepts and serve as functional tests.
- ▪The package prioritizes scientific correctness over computational speed and recommends installation via PyPI.
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| Original publisher | GitHub |
| Canonical URL | https://github.com/msuzen/leymosun |
| Publication time | Wed, 29 Apr 2026 09:27:12 +0000 |
| Retrieval time | 2026-04-29T10:06:15.560Z |
| Last seen | 2026-04-29T10:06:15.560Z |
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Leymosun: High-Entropy Randomness Research Toolkit A package for randomness based research: Collection of reference implementations. Figure Empirical spectral density for mixed ensemble at $\mu=0.8$, so called Wigner's Cats with error bars. (See the lecture.) This is also known as Wigner Cat Phases, see video. suzen25. Approach and features The package provides tools and utilities for randomness based research with High-Entropy Random Number Generation (HE-RNG). It means generation is performed with non-deterministic seeds every time a random library function is called. Having non-reproducible and unpredictable RNGs could improve Monte Carlo and similar randomness based computational science experimentation. Non-reproducible RNGs can still generate reproducible research.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.