hello-world

Hello World

Set up the development environment and print to the console.

• The template folder structure
• A minimal extension that prints to the browser console
• Building and Installing an Extension

The template folder structure

Writing a JupyterLab extension usually starts from a configurable template. It can be downloaded with the copier tool and the following command:

pip install "copier~=7.2" jinja2-time "pydantic<2.0.0"
mkdir my_extension
cd my_extension
copier copy https://github.com/jupyterlab/extension-template .

You will be asked for some basic information that could for example be setup like this:

🎤 What is your extension kind?
   frontend
🎤 Extension author name
   tuto
🎤 Extension author email
   tuto@help.you
🎤 JavaScript package name
   hello-world
🎤 Python package name
   hello_world
🎤 Extension short description
   Minimal JupyterLab example.
🎤 Does the extension have user settings?
   No
🎤 Do you want to set up Binder example?
   Yes
🎤 Do you want to set up tests for the extension?
   Yes
🎤 Git remote repository URL
   https://github.com/github_username/hello-world

The python name must be a valid Python module name (characters such -, @ or / are not allowed). It is nice for user to test your extension online, so the set up Binder was set to Yes.

The template creates creates files in the current director that looks like this:

.
├── babel.config.js
├── binder
│   ├── environment.yml
│   └── postBuild
├── CHANGELOG.md
├── .copier-answers.yml
├── .github
│   └── workflows
│       ├── build.yml
│       ├── check-release.yml
│       ├── enforce-label.yml
│       ├── prep-release.yml
│       ├── publish-release.yml
│       └── update-integration-tests.yml
├── .gitignore
├── hello_world
│   └── __init__.py
├── install.json
├── jest.config.js
├── LICENSE
├── package.json
├── .prettierignore
├── pyproject.toml
├── README.md
├── RELEASE.md
├── setup.py
├── src
│   ├── index.ts
│   └── __tests__
│       └── hello_world.spec.ts
├── style
│   ├── base.css
│   ├── index.css
│   └── index.js
├── tsconfig.json
├── tsconfig.test.json
├── ui-tests
│   ├── jupyter_server_test_config.py
│   ├── package.json
│   ├── playwright.config.js
│   ├── README.md
│   ├── tests
│   │   └── hello_world.spec.ts
│   └── yarn.lock
└── .yarnrc.yml

Those files can be separated in 5 groups:

• Extension code (those files are mandatory):
  • src/index.ts this contains the actual code of your extension
  • style/ folder contains style elements used by your extension
• Information about the extension:
  • CHANGELOG.md will be populated automatically by changes when using the suggested release tool.
  • .copier-answers.yml contains the answers given when generating the directory from the extension template.
  • LICENSE contains your extension code license; BSD-3 Clause by default (but you can change it).
  • package.json contains the JavaScript package metadata and configuration.
  • README.md contains some instructions to install and use the extension.
  • RELEASE.md contains instructions to release the extension.
• Configuration:
  • binder/ contains configuration to test online your extension using Binder.
  • .gitignore files to be ignored by Git (the recommended version control tool).
  • .prettierignore files to be ignored by JavaScript code formatter prettier.
  • package.json contains the JavaScript configuration for the linters: eslint, prettier and stylelint.
  • tsconfig.json contains the typescript compilation configuration.
  • .yarnrc.yml contains the configuration of the JavaScript package manager jlpm.
• Tests:
  • .github/workflows/build.yml sets the continuous integration tests of the code using GitHub Actions
  • .github/workflows/update-integration-tests.yml sets up a GitHub action to update integration test snapshots.
  • .github/workflows/<others>.yml set GitHub actions to check and handle release of the extension.
  • jest.config.js, babel.config.js and tsconfig.test.json configure the JavaScript test Framework jest.
  • ui-tests/ sets up integration tests using playwright.
• Packaging as a Python package:
  • pyproject.toml contains the configuration to create the Python package (using hatch) and to release it (using jupyter-releaser).
  • install.json contains information retrieved by JupyterLab to help users know how to manage the package
  • hello_world/ folder contains the final code to be distributed
  • setup.py is present for backward compatibility with tools not supporting pyproject.toml.

The following sections will walk you through the extension code files.

A minimal extension that prints to the browser console

Start with the file src/index.ts. This typescript file contains the main logic of the extension. It begins with the following import section:

// src/index.ts#L1-L4

import {
  JupyterFrontEnd,
  JupyterFrontEndPlugin
} from '@jupyterlab/application';

JupyterFrontEnd is the main Jupyterlab application class. It allows you to access and modify some of its main components. JupyterFrontEndPlugin is the class of the extension that you are building. Both classes are imported from a package called @jupyterlab/application. The dependency of your extension on this package is declared in the file package.json:

// package.json#L54-L56

"dependencies": {
    "@jupyterlab/application": "^4.0.0"
},

With this basic import setup, you can move on to construct a new instance of the JupyterFrontEndPlugin class:

// src/index.ts#L9-L13

const plugin: JupyterFrontEndPlugin<void> = {
  id: '@jupyterlab-examples/hello-world:plugin',
  description: 'Minimal JupyterLab extension.',
  autoStart: true,
  activate: (app: JupyterFrontEnd) => {

    console.log('JupyterLab extension hello-world is activated!');

// src/index.ts#L15-L18

  }
};

export default plugin;

A JupyterFrontEndPlugin contains a few attributes:

• id: the unique id of the extension
• autoStart: a flag to start the extension automatically or not
• activate: a function (() => {} notation) that takes one argument app of type JupyterFrontEnd and will be called by the main application to activate the extension.

app is the main JupyterLab application. The activate function acts as an entry point into the extension. In this example, it calls the console.log function to output something into the browser developer tools console.

Your new JupyterFrontEndPlugin instance has to be finally exported to be visible to JupyterLab, which is done with the line export default plugin.

Now that the extension code is ready, you need to install it within JupyterLab.

Building and Installing an Extension

These are the instructions on how your extension can be installed for development:

You will need NodeJS to build the extension package.

# 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 run build

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.

The first command installs the dependencies that are specified in the setup.py file and in package.json. Among the dependencies are also all of the JupyterLab components that you want to use in your project.

It then runs the build script. In that step, the TypeScript code gets converted to javascript using the compiler tsc and stored in a lib directory. And a condensed form of the Javascript is copied in the Python package (in the folder hello_world/labextension). This is the code that would be installed by the user in JupyterLab.

The second command create a symbolic link to the folder hello_world/labextension so that extension is installed in development mode in JupyterLab.

The third command allows you to update the Javascript code each time you modify your extension code.

After all of these steps are done, running jupyter labextension list should show something like:

   local extensions:
        @jupyterlab-examples/hello-world: [...]/hello-world

Now let's check inside of JupyterLab if it works. Run:

jupyter lab

Your extension writes something to the browser console. In most web browsers you can open the console pressing the F12 key. You should see something like:

JupyterLab extension hello-world is activated

Your extension works but it is not doing much. Let's modify the source code a bit. Simply replace the activate function with the following lines:

// src/index.ts#L13-L15

activate: (app: JupyterFrontEnd) => {
  console.log('The JupyterLab main application:', app);
}

To update the module, simply go to the extension directory and run jlpm build again. You have to refresh the JupyterLab website in the browser and should see in the browser console:

the JupyterLab main application:
Object { _started: true, _pluginMap: {…}, _serviceMap: Map(...), _delegate: {…}, commands: {…}, contextMenu: {…}, shell: {…}, registerPluginErrors: [], _dirtyCount: 0, _info: {…}, … }

This is the main application JupyterLab object and you will see how to interact with it in the other examples.

If you wish to avoid running jlpm run build after each change, executes jlpm run watch command from your extension directory. That command will automatically compile the TypeScript files as they are changed and saved.

Checkout how the core packages of JupyterLab are defined on this page. Each package is structured similarly to the extension that you are writing. This modular structure makes JupyterLab very adaptable.

An overview of the classes and their attributes and methods can be found in the JupyterLab documentation. The @jupyterlab/application module documentation is here and here is the JupyterFrontEnd class documentation.

Where to Go Next

JupyterLab is built on top of three major concepts. It is advised to look through the corresponding examples in the following order:

• command: Function to be executed from UI elements. See the commands example
• widget: UI based brick. See the widgets example
• signal: Observer pattern between JupyterLab elements. See the signals example