jupyterlab-courselevels 0.6.1

Jlab extension for showing course levels and structure

jupyterlab_courselevels

JupyterLab extension to display cells in colors based on their intended audience level; the color codes follows the logic of ski tracks

• green : basic - all students should know that
• blue : intermediate - if you want to dig a little more
• red : advanced - for the geeks

in addition some cells may show up with a surrounding frame, to emphasize the course structure

Requirements

• JupyterLab >= 4.0.0

Install

To install the extension, execute:

pip install jupyterlab_courselevels

Uninstall

To remove the extension, execute:

pip uninstall jupyterlab_courselevels

misc commands

command	keybinding	comment
courselevels:toggle-basic	Ctrl-\ Ctrl-X
courselevels:toggle-intermediate	Ctrl-\ Ctrl-Y
courselevels:toggle-advanced	Ctrl-\ Ctrl-Z
courselevels:toggle-frame	Ctrl-\ Ctrl-M
courselevels:toggle-licence	Ctrl-\ Ctrl-L
courselevels:metadata-clean-selected	Alt-Cmd-7	clean metadata on selected cells
courselevels:metadata-clean-all	Ctrl-Alt-7	clean metadata on all cells

as well as for adding/removing an admonition around a cell

command	keybinding
courselevels:toggle-admonition	Ctrl-\ Ctrl-A
courselevels:toggle-admonition-tip	Ctrl-\ Ctrl-T
courselevels:toggle-admonition-note	Ctrl-\ Ctrl-N
courselevels:toggle-attention
courselevels:toggle-caution
courselevels:toggle-danger
courselevels:toggle-error
courselevels:toggle-hint
courselevels:toggle-important
courselevels:toggle-seealso
courselevels:toggle-warning

persistence

this is done by adding the following tags in each cell

• level_basic
• level_intermediate
• level_advanced
• framed_cell

rendering

for the record, in nb-courselevels - i.e. in the classic notebook - we had added e.g. data-tag-basic=true in the DOM element; here we rely on the jupyterlab-celltagsclasses extension, which will instead set the cell-tag-level_basic class, but it does not matter that we don't use the same means here

Development

Development install

Note: 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_courselevels directory
# 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 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 needed
jlpm watch
# Run JupyterLab in another terminal
jupyter 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, 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

Development uninstall

pip uninstall jupyterlab_courselevels

In 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-courselevels within that folder.

Packaging the extension

See RELEASE