Pynapple: a toolbox for data analysis in neuroscience

Abstract

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, 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 fully open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within in 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. An open-source framework for data analysis in systems neuroscience. Easy-to-use object-oriented programming for data manipulation. A lightweight and standalone package ensuring long-term backward compatibility.

Spyglass: a framework for reproducible and shareable neuroscience research

“…It uses DataJoint 6,18 to manage reproducible analysis pipelines with a relational database and incorporates novel software tools (Kachery and Figurl) for sharing data and web-based visualizations to enable collaboration within and across labs. It is Python-based and uses standard data types, and can thus include pipelines that use a wide array of analysis packages including SpikeInterface 1 , GhostiPy 19 , DeepLabCut 3 , and Pynapple 20 . Spyglass also offers ready-to-use pipelines for analyzing behavior and electrophysiological data, including spectral analysis of local field potential (LFP), spike sorting, video processing to extract position, and decoding neural data.…”

“…It uses DataJoint (Yatsenko et al, 2018, 2015) to manage reproducible analysis pipelines with a relational database and novel software tools such as Kachery and Figurl for sharing data and web-based visualizations to enable collaboration within and across labs. Being Python-based and using standard data types, it is compatible with a wide array of analysis packages including SpikeInterface (Buccino et al, 2020), GhostiPy (Chu and Kemere, 2021), and Pynapple (Viejo et al, 2023). Spyglass also offers ready-to-use pipelines for analyzing behavior and electrophysiology data, as well as extensive documentation and tutorials for training new users.…”