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 the same 3D cube analysis of the Crab with the high level interface and with the lower level API:
What data can I analyse?
CTA with Gammapy | cta.ipynb
H.E.S.S. with Gammapy | hess.ipynb
Fermi-LAT with Gammapy | fermi_lat.ipynb
3-dim sky cube analysis
CTA data analysis with Gammapy | cta_data_analysis.ipynb
3D analysis | analysis_3d.ipynb
Multi instrument joint 3D and 1D analysis | analysis_mwl.ipynb
2-dim sky image analysis
1-dim spectral analysis
Simulations, Sensitivity, Observability
3D map simulation | simulate_3d.ipynb
1D spectrum simulation | spectrum_simulation.ipynb
Point source sensitivity | cta_sensitivity.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