In the realm of scientific computing, both Julia and Python have established themselves as powerful tools. Within the context of High Energy Physics (HEP) data analysis, Python has been traditionally favored, yet there exists a compelling case for migrating legacy software to Julia. This article focuses on language interoperability, specifically exploring how Awkward Array data structures can bridge the gap between Julia and Python. The talk offers insights into key considerations such as memory management, data buffer copies, and dependency handling. It delves into the performance enhancements achieved by invoking Julia from Python and vice versa, particularly for intensive array-oriented calculations involving large-scale, though not excessively dimensional, arrays of HEP data. The advantages and challenges inherent in achieving interoperability between Julia and Python in the domain of scientific computing are discussed.

翻译：在科学计算领域，Julia与Python均已确立为强大工具。在高能物理（HEP）数据分析背景下，Python传统上更受青睐，然而将遗留软件迁移至Julia存在令人信服的理由。本文聚焦于语言互操作性，具体探讨了不规则数组（Awkward Array）数据结构如何弥合Julia与Python之间的鸿沟。演讲深入剖析了内存管理、数据缓冲区拷贝及依赖处理等关键考量。它细致阐述了通过从Python调用Julia及反向调用所实现的性能提升，尤其针对涉及大规模（但维度不过高）HEP数据数组的密集型面向数组计算。本文讨论了在科学计算领域实现Julia与Python互操作性所固有的优势与挑战。