Agardpy: Analysis Software for GAmma-Ray Data in Python

'User-friendly' configuration-centred pipeline built over Gammapy to allow for easy simultaneous analysis of various datasets of different formats. Example: 3D Fermi-LAT (with various source models in the Region of Interest stored in XML file) + 1D energy-dependent directional cuts MAGIC/LST [PointSkyRegion geometry for ON region] + 1D global directional cut VERITAS [CircleSkyRegion geometry for ON region].

Follow the documentation at https://asgardpy.readthedocs.io/en/latest/ for the main functionality of this pipeline. Follow the Gammapy v1.3 documentation for understanding the core Gammapy objects.

Check this documentation page for seeing the list of reasons to use Asgardpy over Gammapy v1.3 and this documentation page for seeing an extended example of the usage of Asgardpy in analyzing multi-instrument data of Crab Nebula.

The various Data Levels used here follow the descriptions suggested by GADF v0.3 and CTAO Data Model.

NOTE

For requiring support only for Gammapy v1.1, one may follow the latest Hotfix release v0.4.4 which benefits from the correct usage of getting EBL-deabsorbed data products as included in v0.5.0. This can be done by using

git fetch --tags
git switch -c tags/v0.4.4

For creating a conda environment, for this Hotfix release, one can use

conda env create -f environment_0.4.4.yml

and in general, for the latest release,

conda env create -f environment.yml

This method was included after v0.5.0, and for earlier (<v0.4.4) releases, one can simply use the gammapy conda environment and install asgardpy on top of it.

Pipeline development

The pipeline was developed with first testing with Fermi-LAT (enrico and fermipy) files and LST-1 (cta-lstchain) DL3 files (with energy-dependent and global directional cuts) for point-like sources. It also allows for a preliminary analysis of HAWC datasets (stored in Gammapy-readable data). The pipeline can be further expanded to support more types of DL3 files of gamma-ray instruments.

Examples with Data

An example of configuration file that can be used with asgardpy can be found at asgardpy/config/template.yaml

For working with some public data to check the pipeline functionality, one should first download the public dataset available with Gammapy as indicated in Gammapy v1.3 Introduction and then run the scripts/download_asgardpy_data.sh script to add datasets for the full usage of the pipeline.

Examples of usage of Asgardpy is shown in jupyter notebooks.

Cite

If you use Asgardpy in a publication, please cite the exact version you used from Zenodo Cite as https://doi.org/10.5281/zenodo.8106369

Pipeline Template

Pipeline generated based on the template by python-package-template with additional standards being followed -

• PEP 517
• PEP 518
• PyPA specs following PEP 621
• PEP 660