Tutorials#

Notice : it is advised to first read Gammapy analysis workflow and package structure of the User Guide before using the tutorials.

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 is installed, remember that you can always use 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.

Introduction#

The following three tutorials show different ways of how to use Gammapy to perform a complete data analysis, from data selection to data reduction and finally modeling and fitting.

The first tutorial is an overview on how to perform a standard analysis workflow using the high level interface in a configuration-driven approach, whilst the second deals with the same use-case using the low level API and showing what is happening under-the-hood. The third tutorial shows a glimpse of how to handle different basic data structures like event lists, source catalogs, sky maps, spectral models and flux points tables.

High level interface

High level interface

Low level API

Low level API

Data structures

Data structures

Data exploration#

These three tutorials show how to perform data exploration with Gammapy, providing an introduction to the CTA, H.E.S.S. and Fermi-LAT data and instrument response functions (IRFs). You will be able to explore and filter event lists according to different criteria, as well as to get a quick look of the multidimensional IRFs files.

CTA with Gammapy

CTA with Gammapy

Fermi-LAT with Gammapy

Fermi-LAT with Gammapy

H.E.S.S. with Gammapy

H.E.S.S. with Gammapy

Data analysis#

The following set of tutorials are devoted to data analysis, and grouped according to the specific covered use cases in spectral analysis and flux fitting, image and cube analysis modelling and fitting, as well as time-dependent analysis with light-curves.

1D Spectral#

Point source sensitivity

Point source sensitivity

Spectral analysis of extended sources

Spectral analysis of extended sources

Flux point fitting

Flux point fitting

Spectral analysis

Spectral analysis

Spectral analysis with the HLI

Spectral analysis with the HLI

Spectral analysis with energy-dependent directional cuts

Spectral analysis with energy-dependent directional cuts

1D spectrum simulation

1D spectrum simulation

2D Image#

Source detection and significance maps

Source detection and significance maps

2D map fitting

2D map fitting

Ring background map

Ring background map

3D Cube#

3D detailed analysis

3D detailed analysis

Multi instrument joint 3D and 1D analysis

Multi instrument joint 3D and 1D analysis

Basic image exploration and fitting

Basic image exploration and fitting

Event sampling

Event sampling

Flux Profile Estimation

Flux Profile Estimation

3D map simulation

3D map simulation

Time#

Light curves

Light curves

Light curves for flares

Light curves for flares

Simulating and fitting a time varying source

Simulating and fitting a time varying source

Pulsar analysis

Pulsar analysis

Package / API#

The following tutorials demonstrate different dimensions of the Gammapy API or expose how to perform more specific use cases.

Dark matter spatial and spectral models

Dark matter spatial and spectral models

Source catalogs

Source catalogs

Datasets - Reduced data, IRFs, models

Datasets - Reduced data, IRFs, models

Fitting

Fitting

Makers - Data reduction

Makers - Data reduction

Maps

Maps

Mask maps

Mask maps

Modelling

Modelling

Models

Models

Scripts#

For interactive use, IPython and Jupyter are great, and most Gammapy examples use those. However, for long-running, non-interactive tasks like data reduction or survey maps, you might prefer a Python script.

The following example shows how to run Gammapy within a Python script.

Survey Map Script

Survey Map Script

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