ergodicity is an open-source Python library for computational work on stochastic dynamics, with particular emphasis on non-ergodicity, time-average behavior, heavy-tailed processes, and decision making under uncertainty. The package brings together three layers that are often split across ad hoc scripts: process definitions and simulators, analysis and fitting tools, and agent-based experimentation. This article documents the implemented software rather than presenting new stochastic theory. We describe the package architecture, the supported process families, the analysis workflow, and the practical boundaries of the current implementation. We also provide fully reproducible examples covering heavy-tailed ensemble spread, multiplicative Levy growth diagnostics, adaptive memory mean reversion, preasymptotic fluctuation analysis, and partial stochastic differential equation simulation. The package is positioned as an integration layer on top of the scientific Python stack, reducing the amount of glue code required to move from process specification to diagnostics and comparative experiments.

翻译：ergodicity 是一个面向随机动力学计算研究的开源Python库，尤其关注非遍历性、时间平均行为、重尾过程以及不确定性下的决策问题。该软件包整合了通常散落在临时脚本中的三个层次：过程定义与仿真器、分析与拟合工具，以及基于主体的实验。本文记录所实现的软件功能，而非提出新的随机理论。我们描述了该包的架构、所支持的过程族、分析工作流，以及当前实现的实际边界。同时提供了完全可复现的示例，涵盖重尾系综扩散、乘性Levy增长诊断、自适应记忆均值回归、前渐近波动分析及偏随机微分方程仿真。该软件包定位为科学Python栈之上的集成层，可减少从过程规格到诊断与对比实验所需的胶水代码量。