Tutorial notebooks¶

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 and download the tutorial notebooks and example datasets. The setup steps are described in Getting Started. Once Gammapy installed, remember that you can always use gammapy info to check your setup.

Notebooks¶

For a quick introduction to Gammapy, go here:

First steps with Gammapy | first_steps.ipynb
Introduction to gammapy.maps | intro_maps.ipynb

Interested to do a first analysis of simulated CTA data?

CTA first data challenge (1DC) with Gammapy | cta_1dc_introduction.ipynb
CTA data analysis with Gammapy | cta_data_analysis.ipynb

To get started with H.E.S.S. data analysis see here:

H.E.S.S. with Gammapy | hess.ipynb

3-dimensional cube analysis:

3D analysis | analysis_3d.ipynb
3D simulation and fitting | simulate_3d.ipynb
Fermi-LAT data with Gammapy | fermi_lat.ipynb

2-dimensional sky image analysis:

Source detection with Gammapy (Fermi-LAT data example) | detect_ts.ipynb
CTA 2D source fitting with Sherpa | image_fitting_with_sherpa.ipynb

1-dimensional spectral analysis:

Spectral models in Gammapy | spectrum_models.ipynb
Spectral analysis with Gammapy (run pipeline) (H.E.S.S. data example) | spectrum_pipe.ipynb
Spectral analysis with Gammapy (individual steps) (H.E.S.S. data example) | spectrum_analysis.ipynb
Fitting gammapy spectra with sherpa | spectrum_fitting_with_sherpa.ipynb
Flux point fitting with Gammapy | sed_fitting_gammacat_fermi.ipynb

Time-dependent analysis:

Light curves | light_curve.ipynb

Sensitivity:

Compute the CTA sensitivity | cta_sensitivity.ipynb

Extra topics¶

These notebooks contain examples on some more specialised functionality in Gammapy.

H.E.S.S. Galactic plane survey (HGPS) data | hgps.ipynb
Astrophysical source population modeling with Gammapy | source_population_model.ipynb
Continuous wavelet transform on gamma-ray images | cwt.ipynb
Dark matter spatial and spectral models | astro_dark_matter.ipynb
Make template background model | background_model.ipynb

Basics¶

Gammapy is a Python package built on Numpy and Astropy, so for now you have to learn a bit of Python, Numpy and Astropy to be able to use Gammapy. To make plots you have to learn a bit of matplotlib.

We plan to add a very simple to use high-level interface to Gammapy where you just have to adjust a config file, but that isn’t available yet.

Here are some great resources:

Python: A Whirlwind tour of Python
IPython, Jupyter, Numpy, matplotlib: Python data science handbook
Astropy introduction for Gammapy users | astropy_introduction.ipynb
Astropy Hands On (1st ASTERICS-OBELICS International School)

Other useful resources: