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Compute and plot CTA IRFs

Project description

ctaplot is a collection of functions to produce instrument response functions (IRF) and reconstruction quality-checks metrics and plots for Imaging Atmospheric Cherenkov Telescopes such as CTA

Given a list of reconstructed and simulated quantities, compute and plot metric and Instrument Response Functions such as:

  • charge resolution
  • ROC curves
  • angular resolution
  • energy resolution
  • effective surface
  • impact point resolution

You may find examples in the documentation. Or you can run a simple one here:

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 (version prod3b-v2) for more details.”

Travis CI Documentation Status License: MIT


Requirements packages:

  • python > 3.6
  • numpy
  • scipy>=0.19
  • matplotlib>=2.0
  • astropy

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)


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.

Run GammaBoard

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


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.


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.


Project details

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