Stochastic Petri Nets (SPNs) are a powerful formalism, widely used for modeling complex systems in various domains, ranging from manufacturing and logistics to healthcare and computer networks. In this paper, we introduce PySPN, a flexible and easily extendable Python library for Modeling and Simulation of SPNs. Besides the simulation of SPNs, we further extended PySPN with the functionality of generating synthetic data in the form of event logs from SPNs’ simulations. Event logs in simulation models are essential for ensuring model accuracy, evaluating performance, debugging, and facilitating decision-making processes. Event logs offer a comprehensive record of simulated events, which can be analyzed to gain insights into systems’ behaviors and performance. PySPN aims to provide researchers, engineers, and simulation practitioners with a user-friendly and efficient toolset to model, simulate, and analyze SPNs, facilitating the understanding and optimization of stochastic processes in dynamic systems.
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