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Media Laboratory project

Data visualisation tool to share your visual analysis on the Web. Build on top of Dash and Gunicorn.

See docs.

Overview

The main use case proceed as follow:

  • analyse data, transform it and finalise the best visualisation using a Jupyter notebook
  • create a Dash application to represent your visualisation and save it
  • access it and share it using its unique identifier

Deploy

First, build the custom images:

docker build -t medialab-notebook notebook
docker-compose build

Launch the whole stack using Docker Compose:

docker-compose up -d
  • JupyterHub will be available at http://localhost:8000
  • visualizations will be available at http://localhost:8080/<uid>

HTTPS

docker-compose.secure.yml uses Caddy as reverse proxy to serve JupyterHub and the python webserver over HTTPS. You'll need to replace .your_domain.com inside caddy/Caddyfile with your registered domain.
Then, append this second configuration to the base one:

docker-compose -f docker-compose.yml -f docker-compose.secure.yml up -d

Usage

Access JupyterHub at http://localhost:8000 and login with your credentials. Start creating your visualizations in the form of Dash applications and save them using the DataViz python package (already included in the custom docker notebook image).
After saving, they'll be available on the webserver at http://localhost:8080/<uid>, with uid being the visualization unique identifier.

Check the notebook getting_started.ipynb for a first overview and dataviz.md for more information on the python package.

Contributions

Please use issues to suggest changes and pull requests to suggest implementations of changes.

See CONTRIBUTING.md for more details on contribution process.

License

This software is licensed under the terms of the GNU GPLv3. See the LICENSE file for more details.
Plotly Dash is Copyright (c) 2021 Plotly, Inc, and is not part of the Media Laboratory project.