JupyterLab cis-extension

Show a Crops in Silico Model Composer in a JupyterLab panel

Prerequisites

• JupyterLab
• Docker (optional)

With Docker

Building and running with Docker is recommended.

Build

Build the Docker image:

docker build -t cropsinsilico/jupyterlab .

Or use the helper script:

./build.sh

Start JupyterLab

Run a Docker container from the built image:

docker run --name=jupyterlab -itd -p 8888:8888 cropsinsilico/jupyterlab

Or use the helper script:

./start.sh

Run docker logs -f jupyterlab to retrieve the URL / token to access your server.

Stop JupyterLab

Stop and remove the running container:

docker rm -f jupyterlab

Or use the helper script:

./stop.sh

Develop

Map the extension source code into the running container using -v <src>:<dest>:

docker run --name=jupyterlab -itd -v $(pwd):/home/jovyan/work/jupyterlab_cis -p 8888:8888 cropsinsilico/jupyterlab

IDE

You can then use any IDE on your host to edit the files locally.

If you don't have an IDE locally, you can run Cloud9 in a container and map in the same source.

Choose a USERNAME and PASSWORD, then run:

docker run --name=cloud9 -itd -v $(pwd):/workspace -p 8080:8080 ndslabs/cloud9-all node /cloud9/server.js -p 8080 -l 0.0.0.0 -a <USERNAME>:<PASSWORD> -w /workspace

You should then be able to browser to localhost:8080 in your browser to access your Cloud9 IDE.

NOTE: You should be prompted for the USERNAME / PASSWORD you chose prior.

WARNING: This authentication method is INSECURE over HTTP. Use HTTPS if you want to keep your credentials safe.

Without Docker

Although running in Docker is recommended, it should still be possible to use the extension without it.

Installation

git clone https://github.com/cropsinsilico/jupyterlab_cis && cd jupyterlab_cis/
jupyter labextension install

Development

For a development install (requires npm version 4 or later), do the following in the repository directory:

npm install
npm run build
jupyter labextension link .

To rebuild the package and the JupyterLab app:

npm run build
jupyter lab build