  This paper describes a Python toolbox for active perception and control
synthesis of probabilistic signal temporal logic (PrSTL) formulas of switched
linear systems with additive Gaussian disturbances and measurement noises. We
implement a counterexample-guided synthesis strategy that combines Bounded
Model Checking, linear programming, and sampling-based motion planning
techniques. We illustrate our approach and the toolbox throughout the paper
with a motion planning example for a vehicle with noisy localization. The code
is available at \url{https://codeocean.com/capsule/0013534/tree}.

Systems and Control;Systems and Control