Exploring continuous beta-ensembles: A Python implementation for random matrix spectral statistics

Abstract: We present an open-source Python package for sampling the Gaussian, Circular, and Laguerre $β$-ensembles of random matrix theory. The package implements the Dumitriu-Edelman and Killip-Nenciu constructions, allowing efficient generation of random spectra for general $β> 0$. In addition to spectrum generation, it includes tools for the analysis of spectral statistics, from standard nearest-neighbor spacings and spacing ratios to non-adjacent $k$-spacings and the spectral form factor. These tools can be applied to generic spectral data, allowing users to compare them with and fit them to $β$-ensemble predictions. In this note, we review the $β$-ensembles, describe the package interface, and illustrate its use through several numerical experiments motivated by applications to quantum chaos. Our numerical results include an analysis of $β$ as a continuous fitting parameter in spacing ratio statistics, an examination of the numerical evidence for the conjectured $k$-spacing ratio distributions, and a study of the spectral form factor for general values of $β$.