VTNA is a Jupyter Notebook for the visualization of face-to-face networks collected over long time frames. This tool focuses specifically on data collected by the Sociopatterns project. Its main goal is to enable users to visually explore temporal graphs and create animations in shareable video formats.

• latest, python0.1 (Dockerfile)
• alpine0.1 (Dockerfile_alpine)
• minimal-notebook0.1 (Dockerfile_minimal-notebook)

Prerequisites:

• Internet connection
• Installed docker
  • Running docker service
  • Docker CLI

$ docker run -t -p 8888:8888 --name vtna marvinf/vtna:python0.1

After the start you can open the notebook by copying the shown link in the terminal to the browser.

$ docker stop vtna
$ docker start vtna

Delete the docker container and the image.

$ docker stop vtna && docker rm vtna
$ docker rmi marvinf/vtna:python0.1

You can create your own Dockerfile that adds a notebook from the context into frontend/, like so.

FROM marvinf/vtna:$TAG
USER vtna
WORKDIR /usr/src/vtna/
COPY $NOTEBOOK frontend/
CMD jupyter notebook --ip 0.0.0.0 --no-browser frontend/

$ docker build -t vtna_private_notebook:1 .
$ docker run -t -p 8888:8888 --name vtna_private_notebook vtna_private_notebook:1

Alternatively, you can specify something along the same lines with docker run options.

$ docker run -it -p 8888:8888 -u vtna -w /usr/src/vtna/ -v $(pwd)/notebook/$NOTEBOOK:/usr/src/vtna/frontend/$NOTEBOOK --name vtna_private_notebook marvinf/vtna:&TAG jupyter notebook --ip 0.0.0.0 --no-browser frontend/

(Notebook must be in a folder, cause otherwise the notebook is created not as a file in the container.) Using this method means that there is no need for you to have a Dockerfile for your vtna container.

The vtna images come in many flavors, each designed for a specific use case.

This is the defacto image. If you are unsure about what your needs are, you probably want to use this one. It is designed to be used both as a throw away container (mount your source code and start the container to start your app), as well as the base to build other images off of.

This image is based on the Jupyter Notebook minimal image. It is useful if you want to test and add some code to the application. For that, the most common python modules are already installed. Because of that it's larger than the other images.

This image is based on the popular Alpine Linux project, available in the alpine official image. Alpine Linux is much smaller than most distribution base images (~5MB), and thus leads to much slimmer images in general.

• Where to file issues:
  https://github.com/marvinf95/vtna_frontend/issues

• Developed by Students of the University Koblenz-Landau

  • Adam Mtarji
  • Alex Baier
  • Evgeny Sinderovich
  • Kim Ballmes
  • Marvin Forster
  • Philipp Töws

  during the Research Practical Computational Social Science (WS 17/18) under Prof. Claudia Wagner.

• Supported Docker versions:
  the latest release (down to 1.6 on a best-effort basis)

View license information for the software contained in this image.

As with all Docker images, these likely also contain other software which may be under other licenses (such as Bash, etc from the base distribution, along with any direct or indirect dependencies of the primary software being contained).

Some additional license information which was able to be auto-detected might be found in the repo-info repository's vtna/ directory.

As for any pre-built image usage, it is the image user's responsibility to ensure that any use of this image complies with any relevant licenses for all software contained within.