NaaVRE-workflow-jupyterlab 0.1.1

NaaVRE workflow editor frontend on Jupyter Lab

NaaVRE_workflow_jupyterlab

NaaVRE workflow editor frontend on Jupyter Lab

Requirements

• JupyterLab >= 4.0.0

Install

To install the extension, execute:

pip install NaaVRE_workflow_jupyterlab

Uninstall

To remove the extension, execute:

pip uninstall NaaVRE_workflow_jupyterlab

Contributing

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 NaaVRE_workflow_jupyterlab directory
# Create a virtual environment and activate it
virtualenv venv
. venv/bin/activate
# Install jupyterlab and refresh the virtual lab
pip install 'jupyterlab>=4.0.0,<5'
. venv/bin/activate
# 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

This extension communicates with external NaaVRE services. During development, you can run a local version of those services with Docker compose. Initial setup:

0. Build the NaaVRE-workflow-service service (TODO: this should be removed once NaaVRE-workflow-service is released):

   [e-mail]:NaaVRE/NaaVRE-workflow-service.git
   cd NaaVRE-workflow-service
   docker build . -f docker/Dockerfile -t ghcr.io/naavre/naavre-workflow-service:dev
   

1. Create a file ./dev/workflow-config.json by copying ./dev/workflow-config-example.json and fill-in values for api_endpoint and access_token. To obtain these values, either use an existing argo instance, or run NaaVRE/NaaVRE-dev-integration, and run echo "Bearer $(kubectl get secret vre-api.service-account-token -o=jsonpath='{.data.token}' | base64 --decode)" to get the access token. (TODO: this should be simplified in the future, after addressing NaaVRE/NaaVRE-workflow-service#1.)
2. Copy the Jupyter Lab configuration

   mkdir venv/share/jupyter/lab/settings/
   cp dev/overrides.json venv/share/jupyter/lab/settings/
   

3. Start docker compose

   docker compose -f dev/docker-compose.yaml up
   

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 NaaVRE_workflow_jupyterlab

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 @naavre/workflow-jupyterlab within that folder.

Testing the extension

Frontend tests

This extension is using Jest for JavaScript code testing.

To execute them, execute:

jlpm
jlpm test

Integration tests

This 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.

Packaging the extension

See RELEASE