jupyterlab/docs/extensions_dev.md

Extensions Developer Guide

JupyterLab can be extended in two ways via:

• application plugins (top level): Application plugins extend the functionality of JupyterLab itself, and this tutorial focuses on them.
• document widget extensions (lower level): Document widget extensions extend the functionality of document widgets added to the application, and we cover them in the "Documents" tutorial.

A JupyterLab application is comprised of:

• A core Application object
• Plugins

A full example of an application is contained here. Looking at the index.js file, you can see the extensions used in the tutorial example.

Plugins

A plugin adds a core functionality to the application:

• A plugin can require other plugins for operation.
• A plugin is activated when it is needed by other plugins, or when explicitly activated.
• Plugins require and provide Token objects, which are used to provide a typed value to the plugin's activate() method.
• The module providing plugin(s) must meet the JupyterLab.IPluginModule interface, by exporting a plugin object or array of plugin objects as the default export.

The default plugins in the JupyterLab application include:

• Terminal - Adds the ability to create command prompt terminals.
• Shortcuts - Sets the default set of shortcuts for the application.
• Images - Adds a widget factory for displaying image files.
• Help - Adds a side bar widget for displaying external documentation.
• File Browser - Creates the file browser and the document manager and the file browser to the side bar.
• Editor - Add a widget factory for displaying editable source files.
• Console - Adds the ability to launch Jupyter Console instances for interactive kernel console sessions.
• Services - An application-specific interface to @jupyterlab/services.
• RenderMime - The registry for adding kernel display_data renderers.
• Document Registry - Used to add functionality around widgets backed by files.

Application Object

The JupyterLab Application object is given to each plugin in its activate() function. The Application object has a:

• commands - used to add and execute commands in the application.
• keymap - used to add keyboard shortcuts to the application.
• shell - a JupyterLab shell instance.

JupyterLab Shell

The JupyterLab shell is used to add and interact with content in the application. The application consists of:

• A top area for things like top level menus and toolbars
• Left and right side bar areas for collapsable content
• A main area for user activity.
• A bottom area for things like status bars

Phosphor

The Phosphor library is used as the underlying architecture of JupyterLab and provides many of the low level primitives and widget structure used in the application. Phosphor provides a rich set of widgets for developing desktop-like applications in the browser, as well as patterns and objects for writing clean, well-abstracted code. The widgets in the application are primarily Phosphor widgets, and Phosphor concepts, like message passing and signals, are used throughout. Phosphor messages are a many-to-one interaction that allows information like resize events to flow through the widget hierarchy in the application. Phosphor signals are a one-to-many interaction that allow listeners to react to changes in an observed object.

Extension Authoring

An Extension is a valid npm package that meets the following criteria:

• Exports one or more JupyterLab plugins as the default export in its main file.
• Has a jupyterlab key in its package.json which has "extension": true metadata.

While authoring the extension, you can use the command:

jupyter labextension link <path>

This causes the builder to re-install the source folder before building the application files. You can re-build at any time using jupyter lab build and it will reinstall these packages. You can also link other npm packages that you are working on simultaneously; they will be re-installed but not considered as extensions if they lack the metadata.

You can see the list of linked extensions using:

jupyter labextension listlinked

You can also use jupyter labextension install <path>, but that will only copy the current contents of the source folder.

Note that the application is built against released versions of the core JupyterLab extensions. If your extension depends on JupyterLab extensions, it should be compatible with the versions used in the jupyterlab/package.template.json entry point file.

The package should export EMCAScript 5 compatible JavaScript. It can import CSS using the syntax require('foo.css'). The CSS files can also import CSS from other packages using the syntax @import url('~foo/index.css'), where foo is the name of the package.

The following file types are also supported (both in JavaScript and CSS): json, html, jpg, png, gif, svg, js.map, woff2, ttf, eot.

If your package uses any other file type it must be converted to one of the above types. If your JavaScript is written in any other dialect than EMCAScript 5 it must be converted using an appropriate tool.

If you publish your extension on npm.org, users will be able to install it as simply jupyter labextension install <foo>, where <foo> is the name of the published npm package. You can alternatively provide a script that runs jupyter labextension install against a local folder path on the user's machine or a provided tarball. Any valid npm install specifier can be used in jupyter labextension install (e.g. foo@latest, bar@3.0.0.0, path/to/folder, and path/to/tar.gz).