Render markdown cells using MyST 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 the
v0 branch.
Info This extension is composed of a Python package named
jupyterlab_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_mystjupyterlab-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:
import numpy as np
array = 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 with ipywidgets, and have inline interactive text:
Or with matplotlib 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 a jupyterlab-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 the jupyterlab-myst:executor plugin with:
jupyter labextension disable jupyterlab-myst:executorJupyter Notebooks implement a trust-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, the jupyterlab_myst server extension replaces the NotebookNotary from nbformat with MySTNotebookNotary. This can be disabled with
jupyter server extension disable jupyterlab-mystBy disabling this extension, it will not be possible to render unsafe expression results from inline expressions; the MySTNotebookNotary adds additional code that makes it possible to mark Markdown cells as trusted.
To remove the extension, execute:
pip uninstall jupyterlab_mystIf you are seeing the frontend extension, but it is not working, check that the server extension is enabled:
jupyter server extension listIf the server extension is installed and enabled, but you are not seeing the frontend extension, check the frontend extension is installed:
jupyter labextension listNote: 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_myst directory
# Install package in development mode
pip install -e ".[test]"
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Server extension must be manually installed in develop mode
jupyter server extension enable jupyterlab_myst
# 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=False# Server extension must be manually disabled in develop mode
jupyter server extension disable jupyterlab_myst
pip uninstall jupyterlab_mystIn 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-myst within that folder.
This extension is using Pytest 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 link
jupyter labextension develop . --overwriteTo execute them, run:
pytest -vv -r ap --cov jupyterlab_mystThis 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