A dashboard to show them all.

GammaBoard is a simple jupyter dashboard thought to display metrics assessing the reconstructions performances of Imaging Atmospheric Cherenkov Telescopes (IACTs).
Deep learning is a lot about bookkeeping and trials and errors. GammaBoard ease this bookkeeping and allows quick comparison of the reconstruction performances of your machine learning experiments.

It is a working prototype used in CTA, especially by the GammaLearn project

• ctaplot>=0.3.0
• pytables
• pandas
• scikit-learn
• jupyter
• ipywidgets

cd gammaboard
pip install .

export GAMMABOARD_DATA=path_to_the_data_directory

We recommend that you add this line to your bash source file ($HOME/.bashrc or $HOME/.bash_profile)

To launch the dashboard, you can simply try the command: gammaboard

This will run a temporary copy of the dashboard (a jupyter notebook). Local changes that you make will running the dashboard will be discarded afterwards.

GammaBoard is using data in a specific directory storing all your experiments files. This directory is known under $GAMMABOARD_DATA by default. However, you can change the path access at any time in the dashboard itself.

Here is a simple demo of GammaBoard.

• On top the plots (metrics) such as angular resolution and energy resolution.
• Below, the list of experiments in the user folder.

When an experiment is selected in the list, the data is automatically loaded, the metrics computed and displayed. A list of information provided during the training phase is also displayed.
As many experiments results can be overlaid.
When an experiment is deselected, it simply is removed from the plots.