minimal lab example
Project description
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 cookiecutter
tool and the following command:
cookiecutter https://github.com/jupyterlab/extension-cookiecutter-ts
cookiecutter
asks for some basic information that could for example be setup
like this:
author_name []: tuto
author_email []: tuto@help.you
labextension_name [myextension]: hello-world
python_name [hello_world]:
project_short_description [A JupyterLab extension.]: Minimal JupyterLab example
has_settings [n]:
has_server_extension [n]:
has_binder [n]: y
repository [https://github.com/github_username/hello-world]:
The python name should not contain
-
. It is nice for user to test your extension online, so thehas_binder
was set to yes.
The cookiecutter creates the directory hello_world
[or your extension name]
that looks like this:
hello_world/
│ .eslintignore
│ .eslintrc.js
│ .gitignore
│ .prettierignore
│ .prettierrc
│ install.json
│ LICENSE
│ MANIFEST.in
│ package.json
│ pyproject.toml
│ README.md
│ setup.py
│ tsconfig.json
│
├───.github
│ └───workflows
│ build.yml
│
├───binder
│ environment.yml
│ postBuild
│
├───hello_world
│ __init__.py
│ _version.py
│
├───src
│ index.ts
│
└───style
base.css
index.css
index.js
Those files can be separated in 4 groups:
- Information about the extension:
README.md
contains some instructionsLICENSE
contains your extension code license; BSD-3 Clause by default (but you can change it).
- Extension code (those files are mandatory):
package.json
contains information about the extension such as dependenciestsconfig.json
contains information for the typescript compilationsrc/index.ts
this contains the actual code of your extensionstyle/
folder contains style elements that you can use
- Validation:
.prettierrc
and.prettierignore
specify the code formatterprettier
configuration.eslintrc.js
and.eslintignore
specify the code lintereslint
configuration.github/workflows/build.yml
sets the continuous integration tests of the code using GitHub Actions
- Packaging as a Python package:
setup.py
contains information about the Python package such as what to packagepyproject.toml
contains the dependencies to create the Python packageMANIFEST.in
contains list of non-Python files to include in the Python packageinstall.json
contains information retrieved by JupyterLab to help users know how to manage the packagehello_world/
folder contains the final code to be distributed
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#L49-L51
"dependencies": {
"@jupyterlab/application": "^3.1.0"
},
With this basic import setup, you can move on to construct a new instance
of the JupyterFrontEndPlugin
class:
// src/index.ts#L9-L12
const plugin: JupyterFrontEndPlugin<void> = {
id: 'hello-world:plugin',
autoStart: true,
activate: (app: JupyterFrontEnd) => {
console.log('JupyterLab extension hello-world is activated!');
// src/index.ts#L14-L17
},
};
export default plugin;
A JupyterFrontEndPlugin
contains a few attributes:
id
: the unique id of the extensionautoStart
: a flag to start the extension automatically or notactivate
: a function (() => {}
notation) that takes one argumentapp
of typeJupyterFrontEnd
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 useyarn
ornpm
in lieu ofjlpm
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 [can take a while]:
jupyter lab --watch
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#L12-L14
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. Since you used the --watch
option when starting
JupyterLab, you just 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.
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
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