  Reachability analysis is used to determine all possible states that a system
acting under uncertainty may reach. It is a critical component to obtain
guarantees of various safety-critical systems both for safety verification and
controller synthesis. Though traditional approaches to reachability analysis
provide formal guarantees of the reachable set, they involve complex algorithms
that require full system information, which is impractical for use in real
world settings. We present DaDRA, a Python library that allows for data-driven
reachability analysis with arbitrarily robust probabilistic guarantees. We
demonstrate the practical functionality of DaDRA on various systems including:
an analytically intractable chaotic system, benchmarks for systems with
nonlinear dynamics, and a realistic system acting under complex disturbance
signals and controlled with an intricate controller across multiple dimensions.
