Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
Flatiron Institute, Center for Computational Neuroscience, New York, United States.
Elife. 2023 Oct 16;12:RP85786. doi: 10.7554/eLife.85786.
Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high-dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, the PYthon Neural Analysis Package, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience.
神经科学研究中收集的数据集越来越复杂，通常将来自多种数据采集模式的高维时间序列数据结合在一起。在适当的编程环境中处理和操作这些各种数据流对于确保可靠的分析以及促进可重复分析管道的共享至关重要。在这里，我们介绍了 Pynapple，即 PYthon Neural Analysis Package，这是一个轻量级的 Python 包，旨在处理系统神经科学中广泛的时间分辨数据。该软件包的核心功能是一些功能多样的对象，它们支持对任何数据流和任务参数的操作。该软件包包括一组用于读取常见数据格式的方法，并允许用户轻松编写自己的方法。生成的代码易于阅读和编写，避免了低级别的数据处理和其他易出错的步骤，并且是开源的。更高级别的分析库是在 Pynapple 框架内开发的，但包含在专门的、不断更新的分析例程的协作存储库中。这提供了灵活性，同时确保了核心软件包的长期稳定性。总之，Pynapple 为神经科学中的数据分析提供了一个通用框架。
