This page lists the Gammapy tutorials that are available as Jupyter notebooks.
You can read them here, or execute them using a temporary cloud server in Binder.
To execute them locally, you have to first install Gammapy locally (see
Installation) and download the tutorial notebooks and example datasets (see
Getting Started). Once Gammapy installed, remember that you can always
gammapy info to check your setup.
Gammapy is a Python package built on Numpy and Astropy, so to use it effectively, you have to learn the basics. Many good free resources are available, e.g. A Whirlwind tour of Python, the Python data science handbook and the Astropy Hands-On Tutorial.
The following tutorials show how to use gammapy to perform a complete data analysis, here a simple 3D cube analysis of the Crab. They show the gammapy workflow from data selection to data reduction and finally modeling and fitting.
First, we show how to do it with the high level interface in configuration-driven approach. The second tutorial exposes the same analysis, this time using the medium level API, showing what is happening ‘under-the-hood’:
The following tutorials expose common analysis tasks.
Accessing and exploring DL3 data
1-dim spectral analysis
2-dim sky image analysis
3-dim sky cube analysis
The following tutorials expose how to perform more complex analyses or they demonstrate how to use the Gammapy API.
Source detection and significance maps | detect.ipynb
Spectral analysis of extended sources | extended_source_spectral_analysis.ipynb
Multi instrument joint 3D and 1D analysis | analysis_mwl.ipynb
A Fermi-LAT analysis with Gammapy | fermi_lat.ipynb
Point source sensitivity | cta_sensitivity.ipynb
Modeling and fitting in gammapy
Working with catalogs
Source catalogs | catalog.ipynb
Working with gammapy maps
Maps | maps.ipynb
These notebooks contain examples on some more specialised functionality in Gammapy.
Dark matter spatial and spectral models | astro_dark_matter.ipynb
Make template background model | background_model.ipynb
MCMC sampling of Gammapy models using the emcee package | mcmc_sampling.ipynb
Pulsar analysis with Gammapy | pulsar_analysis.ipynb