Literate computing has emerged as an important tool for computational studies and open science, with growing folklore of best practices. In this work, we report two case studies—one in computational magnetism and another in computational mathematics—where domain-specific software was exposed to the Jupyter environment. This enables high level control of simulations and computation, interactive exploration of computational results, batch processing on HPC resources, and reproducible workflow documentation in Jupyter notebooks. In the first study, Ubermag drives existing computational micromagnetics software through a domain-specific language embedded in Python. In the second study, a dedicated Jupyter kernel interfaces with the GAP system for computational discrete algebra and its dedicated programming language. In light of these case studies, we discuss the benefits of this approach, including progress toward more reproducible and reusable research results and outputs, notably through the use of infrastructure such as JupyterHub and Binder.

中文翻译：

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使用 Jupyter 实现可重复的科学工作流程


随着最佳实践的民间传说不断增多，文字计算已成为计算研究和开放科学的重要工具。在这项工作中，我们报告了两个案例研究——一个是计算磁学，另一个是计算数学——其中特定领域的软件暴露在 Jupyter 环境中。这使得能够对模拟和计算进行高级控制、计算结果的交互式探索、HPC 资源的批处理以及 Jupyter 笔记本中的可重现的工作流程文档。在第一项研究中，Ubermag 通过嵌入 Python 的特定领域语言驱动现有的计算微磁学软件。在第二项研究中，专用的 Jupyter 内核与用于计算离散代数的 GAP 系统及其专用编程语言进行交互。根据这些案例研究，我们讨论了这种方法的好处，包括在更具可重复性和可重用性的研究结果和输出方面取得的进展，特别是通过使用 JupyterHub 和 Binder 等基础设施。