PlaybookMC is a statistical sampling research code that bears intentional analogy to radiation transport codes and is designed for fundamental algorithm development and a low technical threshold to first use. To date, research has primarily focused on algorithms for radiation transport in stochastic media, though the code is amenable to use for other algorithmic research and to use as a black-box transport-like Monte Carlo solver or stochastic-media generator. The ubiquity of the Python language, the restriction of third-party libraries to only common Python packages, and the use of relative directory paths give PlaybookMC a low technical threshold to first use, making it approachable to both career scientists and budding STEM students. It is the primary author’s wish that PlaybookMC will be both a testbed and tool for state-of-the-art algorithmic research as well as a training ground and companion for aspiring engineering, applied math, and computer science students.

As an athletic sport playbook contains premeditated team strategies and plays that can be directly applied or quickly modified to pioneer new plays, PlaybookMC is intended to be an algorithmic playbook of premeditated classes, modules, and scripts that can be directly applied or quickly modified to pioneer new computational methods. While the particle and material physics are intentionally rudimentary, precluding the solving of physically real problems, PlaybookMC supports radiation transport-like simulations in 1D rod and slab geometries, a collection of 2D and 3D geometries, and several stochastic media transport algorithms including Chord Length Sampling and Conditional Point Sampling. For such computations, PlaybookMC tallies leakage and absorption and can tally internal particle flux profiles. Due to its modularity, some of the stochastic-media capabilities can be used independently of the transport-like capabilities to generate and export realizations of 1D, 2D, and 3D stochastic media.