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.
nwbwidgets requires Python >= 3.7.
The latest published version can be installed by running:
pip install nwbwidgetsNote that there are some optional dependencies required for some widgets. If an NWB data file contains a data type that requires additional dependencies, you will see a list of extra modules needed for that specific widget. All other widgets in the file will still work.
The easiest way to use NWB widgets is with the interactive Panel:
from nwbwidgets.panel import Panel
Panel()If you’re working directly with a NWB file object in your Jupyter notebook, you can also explore it with NWB Widgets using
from pynwb import NWBHDF5IO
from nwbwidgets import nwb2widget
io = NWBHDF5IO('path/to/file.nwb', mode='r')
nwb = io.read()
nwb2widget(nwb)You can also run the NWB Widgets Panel using Docker:
$ docker run -p 8866:8866 ghcr.io/NeurodataWithoutBorders/nwbwidgets-panel:latestSee our ReadTheDocs page for full documentation, including a gallery of all supported formats.
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.
Instead of supplying a function for the value of the neurodata_vis_spec dict, you may provide a dict or OrderedDict with string keys and function values. In this case, a tab structure is rendered, with each of the key/value pairs as an individual tab. All accordian and tab structures are rendered lazily- they are only called with that tab is selected. As a result, you can provide may tabs for a single data type without a worry. They will only be run if they are selected.
To extend NWBWidgets, all you need to a function that takes as input an instance of a specific neurodata_type class, and outputs a matplotlib figure or a jupyter widget.