Uniform SAmplINg with BOLTZmann

USAIN BOLTZ is a fast Python library for the uniform random generation of tree-like structures. It allows the user to specify both (1) the data structure they wish to sample, using simple combinators similar to those of context-free grammars, and (2) their memory representation. The underlying algorithms are optimised Boltzmann samplers allowing to get approximate-size uniform random generation in linear time. Experimental results show that USAIN BOLTZ matches the performance of the experimental Arbogen package for OCaml, and out-performs the Boltzmann brain Haskell library, while being easier to integrate into existing scientific tools such as Sagemath.