A JupyterLab extension that tracks a user's current notebook and cell.
A JupyterLab frontend extension that tracks user awareness by adding activeCellId and notebookPath to each client's collaborative awareness state. This enables applications to know which notebook and cell each user is currently viewing.
The extension adds the following fields to each client's awareness state:
activeCellId(string | null): The unique ID of the currently active cell the user is viewingnotebookPath(string | null): The file path of the notebook the user has open
Here is an example of the complete awareness structure in JSON with multiple users:
{
"4233401820": {
"user": {
"username": "960fe034b7b847dfbee15eede12caac7",
"name": "Anonymous Kale",
"display_name": "Anonymous Kale",
"initials": "AK",
"avatar_url": null,
"color": "var(--jp-collaborator-color5)"
},
"activeCellId": "42768507-1132-43fb-86ba-980a4e73e490",
"notebookPath": "data_analysis.ipynb",
"cursors": [
{
"anchor": {
"type": {
"client": 2431406065,
"clock": 6
},
"tname": null,
"item": null,
"assoc": 0
},
"head": {
"type": {
"client": 2431406065,
"clock": 6
},
"tname": null,
"item": null,
"assoc": 0
},
"primary": true,
"empty": true
}
]
},
"1582940372": {
"user": {
"username": "alice_researcher",
"name": "Alice Smith",
"display_name": "Alice Smith",
"initials": "AS",
"avatar_url": "https://example.com/avatar.jpg",
"color": "var(--jp-collaborator-color2)"
},
"activeCellId": "b8f3e729-4891-4c2a-9876-543210fedcba",
"notebookPath": "machine_learning.ipynb",
"cursors": []
}
}The awareness state is automatically updated when:
- A user opens a notebook (sets
notebookPath) - A user clicks on a different cell (updates
activeCellId) - A user switches between notebooks (updates both fields)
- A user refreshes the page (restores state when notebook loads)
You can access the awareness data programmatically in JupyterLab:
// Get the current notebook's awareness
const notebook = notebookTracker.currentWidget;
const awareness = notebook?.model?.sharedModel?.awareness;
// Get all awareness states
const allStates = awareness?.getStates();
// Get local user's state
const localState = awareness?.getLocalState();- JupyterLab >= 4.0.0
To install the extension, execute:
pip install jupyterlab_notebook_awarenessTo remove the extension, execute:
pip uninstall jupyterlab_notebook_awarenessNote: You will need NodeJS to build the extension package.
The jlpm command is JupyterLab's pinned version of
yarn that is installed with JupyterLab. You may use
yarn or npm in lieu of jlpm below.
# Clone the repo to your local environment
# Change directory to the jupyterlab_notebook_awareness directory
# Install package in development mode
pip install -e "."
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Rebuild extension Typescript source after making changes
jlpm buildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.
# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm watch
# Run JupyterLab in another terminal
jupyter labWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).
By default, the jlpm build command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:
jupyter lab build --minimize=Falsepip uninstall jupyterlab_notebook_awarenessIn development mode, you will also need to remove the symlink created by jupyter labextension develop
command. To find its location, you can run jupyter labextension list to figure out where the labextensions
folder is located. Then you can remove the symlink named jupyterlab-notebook-awareness within that folder.
This extension is using Jest for JavaScript code testing.
To execute them, execute:
jlpm
jlpm testThis extension uses Playwright for the integration tests (aka user level tests). More precisely, the JupyterLab helper Galata is used to handle testing the extension in JupyterLab.
More information are provided within the ui-tests README.
See RELEASE