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Render markdown cells usingMyST Markdown, including support for rich frontmatter, interactive references, admonitions, figure numbering, tabs, proofs, exercises, glossaries, cards, and grids!

Note: If you are looking for the version of this repository based on jupyterlab-markup,see thev0 branch.

InfoThis extension is composed of a Python package namedjupyterlab_mystfor the server extension and a NPM package namedjupyterlab-mystfor the frontend extension.

• JupyterLab >= 4.0.0

To install the extension, execute:

pip install jupyterlab_myst

jupyterlab-myst is a fully featured markdown renderer for technical documents,get started with MyST Markdown. It supports the MyST{eval} inline role, which facilitates the interweaving of code outputs and prose. For example, we can use inline expressions to explore the properties of a NumPy array.

In the code cell:

importnumpyasnparray=np.arange(4)

In the markdown cell:

Let's consider the following array: {eval}`array`.We can compute the total: {eval}`array.sum()` and the maximum value is {eval}`array.max()`.

This will evaluate inline, and show:

Let's consider the following array: array([0, 1, 2, 3]).We can compute the total: 6 and the maximum value is 3.

You can also use this withipywidgets, and have inline interactive text:

Or withmatplotlib to show inline spark-lines:

You can also edit task lists directly in the rendered markdown.

MyST is a flavour of Markdown, which combines the fluid experience of writing Markdown with the programmable extensibility of reStructuredText. This extension for JupyterLab makes it easier to develop rich, computational narratives, technical documentation, and open scientific communication.

To facilitate inline expressions,jupyterlab-myst defines ajupyterlab-myst:executor plugin. This plugin sends expression code fragments to the active kernel when the user "executes" a Markdown cell. To disable this functionality, disable thejupyterlab-myst:executor plugin with:

jupyter labextension disable jupyterlab-myst:executor

Jupyter Notebooks implement atrust-based security model. With the addition of inline expressions, Markdown cells are now considered when determining whether a given notebook is "trusted". Any Markdown cell with inline-expression metadata (with display data) is considered "untrusted". Like outputs, expression results are rendered using safe renderers if the cell is not considered trusted.Executing the notebook will cause each cell to be considered trusted.

To facilitate this extension of the trust model, thejupyterlab_myst server extension replaces theNotebookNotary fromnbformat withMySTNotebookNotary. This can be disabled with

jupyter server extension disable jupyterlab-myst

By disabling this extension, it will not be possible to render unsafe expression results from inline expressions; theMySTNotebookNotary adds additional code that makes it possible to mark Markdown cells as trusted.

To remove the extension, execute:

pip uninstall jupyterlab_myst

If you are seeing the frontend extension, but it is not working, checkthat the server extension is enabled:

jupyter server extension list

If the server extension is installed and enabled, but you are not seeingthe frontend extension, check the frontend extension is installed:

jupyter labextension list

Note: You will need NodeJS to build the extension package.

Thejlpm command is JupyterLab's pinned version ofyarn that is installed with JupyterLab. You may useyarn ornpm in lieu ofjlpm below.

# Clone the repo to your local environment# Change directory to the jupyterlab_myst directory# Install package in development modepip install -e".[test]"# Link your development version of the extension with JupyterLabjupyter labextension develop. --overwrite# Server extension must be manually installed in develop modejupyter server extensionenable jupyterlab_myst# Rebuild extension Typescript source after making changesjlpm build

You 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 neededjlpm watch# Run JupyterLab in another terminaljupyter lab

With 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, thejlpm 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=False

# Server extension must be manually disabled in develop modejupyter server extension disable jupyterlab_mystpip uninstall jupyterlab_myst

In development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension list to figure out where thelabextensionsfolder is located. Then you can remove the symlink namedjupyterlab-myst within that folder.

This extension is usingPytest for Python code testing.

Install test dependencies (needed only once):

pip install -e".[test]"# Each time you install the Python package, you need to restore the front-end extension linkjupyter labextension develop. --overwrite

To execute them, run:

pytest -vv -r ap --cov jupyterlab_myst

This extension is usingJest for JavaScript code testing.

To execute them, execute:

jlpmjlpmtest

This extension usesPlaywright for the integration tests (aka user level tests).More precisely, the JupyterLab helperGalata is used to handle testing the extension in JupyterLab.

More information are provided within theui-tests README.

SeeRELEASE