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 here:
Getting Started. Remember that you can always use
gammapy info to check your setup.
For a quick introduction to Gammapy, go here:
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
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:
- Image analysis with Gammapy (individual steps) (H.E.S.S. data example) | image_analysis.ipynb
- 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
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
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: