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A Jupyter kernel for CASA


Jupyter is a web-based application which allows users to create interactive notebooks which can include annotated text and graphics as well as executable code. The notebook format has the great advantage that all steps of the data reduction are preserved inside the notebook. This means that the whole data reduction process is self-documenting and fully repeatable. It also allows users to very easily make changes to their pipeline and then rerun the pipeline steps affected.

As part of the the EU funded ESCAPE project we have created a Jupyter kernel for CASA, a widely-used software package for processing astronomical data. The kernel allows all CASA tasks to be run from inside a Jupyter notebook, albeit non-interactively. Tasks which normally spawn a GUI window are wrapped so that their output is saved to an image instead, which is then displayed inside the notebook.


Because Jupyter requires a much more current python distibution than what is provided in NRAO's CASA releases, a custom build of CASA is required. We distribute a DOCKER image containing a version of CASA which uses the most recent (I)python, matplotlib, etc. Note that this version of CASA can only be used from within Jupyter.

Installation is as simple as executing: docker pull penngwyn/jupytercasa

Alternatively there is also a SINGULARITY image which may be a bit easier to use, it can be downloaded by executing:

singularity pull --name jupyter-casa.simg shub://aardk/jupyter-casa



The simplest way to start the Jupyter server is to execute:

singularity run jupyter-casa.simg

Unlike DOCKER, a SINGULARITY containter runs with UID of the current user (i.e. the user executing singularity run). The home directory of the user on the local filesystem will also be accessible inside the container, but by default only the home directory is shared with the container. Therefore any symbolic links which point to locations outside of the home directory will not be valid inside the container.

Fortunately, it is fairly straigthforward to make your local filesystem accessible to the container using the -B option. For example to mount a directory called /data inside the container execute:

singularity run -B /data:$HOME/data jupyter-casa.simg


Even though we wrap all CASA tasks so that they will not launch a GUI window, the QT based CASA tasks still require X11, unfortunately. Tasks such as plotms won't start unless X11 is working even when it doesn't even open a window. Therefore the local X11 socket needs to be shared with Docker container.

The simplest incantation to start JUPYTER on a recent Ubuntu:

docker run --rm -p 8888:8888 -i -t -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY penngwyn/jupytercasa

Note that the '--rm' option will make DOCKER delete the container after use.

Of course the above example is not very useful as the container will not be able to access locally stored measurement sets. To add a data directory to the DOCKER container is, fortunately, very simple using the -v option:

docker run --rm -p 8888:8888 -i -t -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v PATH_TO_DATA_DIR:/home/jupyter/data penngwyn/jupytercasa

Where PATH_TO_DATA_DIR should be replaced with the full path to your local data directory.

The above examples use a JUPYTER kernel which is baked into the DOCKER image. It is also possible to use the GITHUB development version within the CASA container, from the root of the source tree run:

docker run --rm -p 8888:8888 -i -t -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $PWD/jupyter:/home/jupyter/.local/share/jupyter -v $PWD/python/casapy:/home/jupyter/.local/lib/python2.7/site-packages/casapy -v PATH_TO_DATA_DIR:/home/jupyter/data penngwyn/jupytercasa


In the examples directory there is a notebook which contains the NRAO continuum VLA tutorial. To run that code locally be sure to download the data files from the NRAO wiki.

Also don't forget to make the directory available to the DOCKER container using the -v option as is explained above.


A Jupyter kernel for CASA







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