I've chosen to archive this project since my other project denv can achieve the goals of this project in a cleaner fashion.

Use the latest Python3 container image and install the most recent versions of jupyterlab and scikit-hep to get the dependencies I want.

denv init python:3
denv python3 -m pip install --user --upgrade jupyterlab scikit-hep
denv jupyter lab

Note: One could also use python's virtual environments within the denv in order to use different versions of packages with the same base python version.

Use a more recent version of Python with JupyterLab and other HEP-focused Python analysis tools.

CentOS7 is stuck on Python 3.6 and I want better Python (and all the improvements to the packages I use that come with that).

I've tried building a custom version of Python myself but that took a long time and did not work on the first pass. Using images can make my environment identical across different systems where I have my notebooks.

Putting these packages into the image also makes the image usable with runners that treat the image as read-only (e.g. apptainer and singularity).

The command below will download the image and then run it. Subsequent runs will keep using the already downloaded image, so to get updates you will need to use docker pull.

docker run \
  --rm -it \
  -p 8888:8888 \
  -v $(pwd -P):/home/jovyan \
  ghcr.io/tomeichlersmith/jupyter-scikit-hep

Build the image from DockerHub into a local SIF image file.

apptainer build jupyter-scikit-hep.sif docker://ghcr.io/tomeichlersmith/jupyter-scikit-hep

Note: You may want to change APPTAINER_CACHEDIR so that apptainer doesn't fill up your home directory with layers.

Start JupyterLab from the container like normal defining your working directory as the home directory of the JupyterLab user jovyan.

apptainer run \
  --bind $(pwd -P):/home/jovyan \
  --bind ${TMPDIR:-/tmp/} \
  --home $(pwd -P) \
  --hostname jupyter-scikit-hep.$(uname -n) \
  --env "PS1=${PS1}" \
  jupyter-scikit-hep.sif

This is a very thin layer on top of the jupyter/scipy-notebook (documentation with packages included linked) that simply installs the scikit-hep metapackage. Just to list common packages that you may already use that are included: pandas, awkward, matplotlib, mplhep, hist, pylhe, h5py, scipy, sympy (and others - inspect links for details or give the notebook a try).

Since your working directory is mounted to the home directory within the container, you can add more python packages by installing them as a "user". Open a terminal in Jupyter Lab and enter your pip install command.

pip install --user <extra-package>

Additionally, if you feel like this package would be helpful for the wider community, feel free to open an issue in this repository to include it (or see if it can be included in the scikit-hep metapackage).

Following CalVer with just the date as the format. Check the release notes for a specific tag to learn why that release was made and what packages are included.