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# NWB Widgets
A library of widgets for visualization NWB data in a Jupyter notebook (or lab). The widgets allow you to navigate through the hierarchical structure of the NWB file and visualize specific data elements. It is designed to work out-of-the-box with NWB 2.0 files and to be easy to extend.

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[![codecov](https://codecov.io/gh/NeurodataWithoutBorders/nwbwidgets/branch/master/graph/badge.svg)](https://codecov.io/gh/NeurodataWithoutBorders/nwbwidgets)
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<p align="center">
<img src="https://github.com/NeurodataWithoutBorders/nwbwidgets/assets/844306/f20b8c26-79c7-4c1c-a3b5-b49ecf8cce5d" width="350" alt="NWB Widgets"/>
<h3 align="center">Explore NWB data in Jupyter</h3>
</p>
<p align="center">
<a href="https://nwbwidgets.readthedocs.io/"><strong>Explore our documentation »</strong></a>
</p>

<!-- TABLE OF CONTENTS -->

## Table of Contents

- [About](#about)
- [Installation](#installation)
- [Usage](#usage)
- [Demo](#demo)
- [Documentation](#documentation)

## About
A library of widgets for visualization NWB data in a Jupyter notebook (or lab). The widgets allow you to navigate through the hierarchical structure of the NWB file and visualize specific data elements. It is designed to work out-of-the-box with NWB 2.0 files and to be easy to extend.


## Installation
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## Demo
![](https://drive.google.com/uc?export=download&id=1JtI2KtT8MielIMvvtgxRzFfBTdc41LiE)

## Documentation
See our [ReadTheDocs page](https://nwbwidgets.readthedocs.io/en/main/) for full documentation, including a gallery of all supported formats.

## How it works
All visualizations are controlled by the dictionary `neurodata_vis_spec`. The keys of this dictionary are pynwb neurodata types, and the values are functions that take as input that neurodata_type and output a visualization. The visualizations may be of type `Widget` or `matplotlib.Figure`. When you enter a neurodata_type instance into `nwb2widget`, it searches the `neurodata_vis_spec` for that instance's neurodata_type, progressing backwards through the parent classes of the neurodata_type to find the most specific neurodata_type in `neurodata_vis_spec`. Some of these types are containers for other types, and create accordian UI elements for its contents, which are then passed into the `neurodata_vis_spec` and rendered accordingly.

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