ctaplot

ctaplot provides low-level reconstruction quality-checks metrics computation and vizualisation for Imaging Atmospheric Cherenkov Telescopes such as CTA

You may find examples in the documentation and run them via mybinder.

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• Code : https://github.com/cta-observatory/ctaplot
• Documentation : https://ctaplot.readthedocs.io/en/latest/
• Author contact: Thomas Vuillaume - [email protected]
• License: MIT

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The CTA instrument response functions data used in ctaplot come from the CTA Consortium and Observatory and may be found on the cta-observatory website .

In cases for which the CTA instrument response functions are used in a research project, we ask to add the following acknowledgement in any resulting publication:

“This research has made use of the CTA instrument response functions provided by the CTA Consortium and Observatory, see http://www.cta-observatory.org/science/cta-performance/ (version prod3b-v2) for more details.”

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Install

Requirements packages:

• python >= 3.9
• numpy
• scipy>=0.19
• matplotlib>=3.6
• astropy

Optional LaTeX dependencies for enhanced plot rendering:

• A LaTeX distribution (e.g., TeX Live, MiKTeX)
• dvipng (for PNG output)
• cm-super (Computer Modern fonts)

Installation instructions:

On Ubuntu/Debian:

sudo apt-get install texlive-latex-base texlive-fonts-recommended dvipng cm-super

On macOS with Homebrew:

brew install --cask mactex
# dvipng and cm-super are included with MacTeX

On macOS with MacPorts:

sudo port install texlive +full

Note: LaTeX dependencies are optional. If not installed, ctaplot will automatically fall back to matplotlib's default text rendering without LaTeX support.

We recommend the use of anaconda

The package is available through pip:

pip install ctaplot

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)

GammaBoard

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 especially by the GammaLearn project.

Run GammaBoard

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 in 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.

Demo

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.

Cite

We would appreciate you cite the version of ctaplot you used using the corresponding Zenodo DOI that cound find here: https://doi.org/10.5281/zenodo.5833853