Compute and plot CTA IRFs
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.
Documentation : https://ctaplot.readthedocs.io/en/latest/
Author contact: Thomas Vuillaume - firstname.lastname@example.org
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.”
python > 3.6
We recommend the use of anaconda
The package is available through pip:
pip install ctaplot
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 especially by the GammaLearn project.
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.
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
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