NeuroConv#
NeuroConv is a Python package for converting neurophysiology data in a variety of proprietary formats to the Neurodata Without Borders (NWB) standard.
Features:
Reads data from 42 popular neurophysiology data formats and writes to NWB using best practices.
Extracts relevant metadata from each format.
Handles large data volume by reading datasets piece-wise.
Minimizes the size of the NWB files by automatically applying chunking and lossless compression.
Supports ensembles of multiple data streams, and supports common methods for temporal alignment of streams.
How to use the documentation#
Our documentation is structured to cater to users ranging from beginners to advanced developers and contributors. Below is an overview of the key sections to help you navigate our documentation effectively
Getting Started: Conversion Examples Gallery
If you’re new to NeuroConv or NWB, start with the Conversion Examples Gallery. This section provides concise scripts for converting data from common formats (e.g., Blackrock, Plexon, Neuralynx) to NWB. It’s designed to get you up and running quickly.
User Guide
The User Guide offers a comprehensive overview of NeuroConv’s data model and functionalities. It is recommended for users who wish to understand the underlying concepts and extend their scripts beyond basic conversions.
Catalogue of Projects
The Catalogue of Neuroconv Projects section showcases a collection of successful conversion projects utilizing NeuroConv. It serves as both inspiration and a practical reference for what can be achieved with our library.
Developer Guide
For developers interested in contributing to NeuroConv, the Developer Guide provides essential information such as instructions for building your own classes, our coding style, instructions on how to build the documentation, run the testing suite, etc.
API Reference
Detailed documentation of the NeuroConv API can be found in the API section.
Do you find that some information is missing or some section lacking or unclear? Reach out with an issue or pull request on our GitHub repository. We are happy to help and appreciate your feedback.