This page contains short “how to” or “frequently asked question” entries for Gammapy. Each entry is for a very specific task, with a short answer, and links to examples and documentation.

If you’re new to Gammapy, please read the Overview and have a look at the list of Tutorials. The information below is in addition to those pages, it’s not a complete list of how to do everything in Gammapy.

Please give feedback and suggest additions to this page!

Spell and pronounce Gammapy

The recommended spelling is “Gammapy” as proper name. The recommended pronunciation is [ɡæməpaɪ] where the syllable “py” is pronounced like the english word “pie”. You can listen to it here.

Access IACT DL3 data

To access IACT data in the DL3 format, use the DataStore. It allows easy access to observations stored in the DL3 data library. You can create it directly as in `this example<tutorials/data/hess.html#Datastore>`__. It is also internally used by the high level interface Analysis. You can see how to properly set it here.

Select observations

The DataStore provides access to a summary table of all observations available. It can be used to select observations with various criterion. You can for instance apply a cone search as shown here. You can also select observations based on other information available using the select_observations method.

Make a on-axis equivalent livetime map

IACT detection efficiency varies in the FoV. To have an estimate of the effective exposure with respect to the on-axis one, it can be useful to build an on-axis equivalent lifetime map.

Check IRFs

Gammapy offers a number of methods to explore the content of the various IRFs contained in an observation. This is usually done thanks to their peek() methods. See example for CTA here and for H.E.S.S. here.

Model 2D images

Gammapy treats 2D maps as 3D cubes with one bin in energy. To see an example of the relevant data reduction, see 2-dim sky image analysis

Sometimes, you might want to use previously obtained images lacking an energy axis (eg: reduced using traditional IACT tools) for modeling and fitting inside gammapy. In this case, it is necessary to attach an energy axis on

Extract 1D spectra

The Analysis class can perform spectral extraction. The AnalysisConfig must be defined to produce ‘1d’ datasets. Alternatively, you can follow the spectrum extraction notebook.

Extract a lightcurve

The Light curve estimation tutorial shows how to extract a run-wise lightcurve.

To perform an analysis in a time range smaller than that of an observation, it is necessary to filter the latter with its select_time method. This produces an new observation containing events in the specified time range. With the new Observations it is then possible to perform the usual data reduction which will produce datasets in the correct time range. The light curve extraction can then be performed as usual with the LightCurveEstimator. This is demonstrated in the Light curve - Flare tutorial.

Choose units for plotting

Units for plotting are handled with a combination of matplotlib and astropy.units. For most plotting methods Gammapy forwards additional keywords to the corresponding matplotlib plot method, including the xunits and yunits keywords, which allows you to define the x and y axis units using astropy.units. Here is a minimal example:

from gammapy.estimators import FluxPoints
from astropy import units as u

filename = "$GAMMAPY_DATA/hawc_crab/HAWC19_flux_points.fits"
fp =
fp.plot(sed_type="e2dnde", xunits=u.erg, yunits=u.Unit("erg cm-2 s-1"))

Compute source significance

Estimate the significance of a source, or more generally of an additional model component (such as e.g. a spectral line on top of a power-law spectrum), is done via a hypothesis test. You fit two models, with and without the extra source or component, then use the test statistic values from both fits to compute the significance or p-value. To obtain the test statistic, call stat_sum for the model corresponding to your two hypotheses (or take this value from the print output when running the fit), and take the difference. Note that in Gammapy, the fit statistic is defined as S = - 2 * log(L) for likelihood L, such that TS = S_0 - S_1. See Datasets (DL4) for an overview of fit statistics used.

Compute cumulative significance

A classical plot in gamma-ray astronomy is the cumulative significance of a source as a function of observing time. In Gammapy, you can produce it with 1D (spectral) analysis. Once datasets are produced for a given ON region, you can access the total statistics with the info_table(cumulative=True) method of Datasets. See example here.

Detect sources in a map

Gammapy provides methods to perform source detection in a 2D map. First step is to produce a significance map, i.e. a map giving the probability that the flux measured at each position is a background fluctuation. For a MapDataset, the class TSMapEstimator can be used. A simple correlated Li & Ma significance can be used, in particular for ON-OFF datasets. The second step consists in applying a peak finer algorithm, such as find_peaks. This is demonstrated in the Source detection tutorial.

Astrophysical source modeling

It is possible to combine Gammapy with astrophysical modeling codes, if they provide a Python interface. Usually this requires some glue code to be written, e.g. NaimaSpectralModel is an example of a Gammapy wrapper class around the Naima spectral model and radiation classes, which then allows modeling and fitting of Naima models within Gammapy (e.g. using CTA, H.E.S.S. or Fermi-LAT data).

Implement a custom model

Gammapy allows the flexibility of using user-defined models for analysis. For an example, see Implementing a Custom Model.

Implement an energy dependent spatial models

While Gammapy does not ship energy dependent spatial models, it is possible to define such models within the modeling framework. For an example, see here.

Reduce memory budget for large datasets

When dealing with surveys and large sky regions, the amount of memory required might become problematic, in particular because of the default settings of the IRF maps stored in the MapDataset used for the data reduction. Several options can be used to reduce the required memory: - Reduce the spatial sampling of the PSFMap and the EDispKernelMap using the binsz_irf argument of the create method. This will reduce the accuracy of the IRF kernels used for model counts predictions. - Change the default IRFMap axes, in particular the rad_axis argument of create This axis is used to define the geometry of the PSFMap and controls the distribution of error angles used to sample the PSF. This will reduce the quality of the PSF description. - If one or several IRFs are not required for the study at hand, it is possible not to build them by removing it from the list of options passed to the MapDatasetMaker.

Copy part of a data store

To share specific data from a database, it might be necessary to create a new data storage with a limited set of observations and summary files following the scheme described in gadf. This is possible with the method copy_obs provided by the DataStore. It allows to copy individual observations files in a given directory and build the associated observation and HDU tables.

Interpolate maps onto a different geometry

To interpolate maps onto a different geometry, use Map.interp_to_geom, see here.

Suppress warnings

In general it is not recommended to suppress warnings from code because they might point to potential issues or help debugging a non-working script. However in some cases the cause of the warning is known and the warnings clutter the logging output. In this case it can be useful to locally suppress a specific warning like so:

from import VerifyWarning
import warnings

with warnings.catch_warnings():
    warnings.simplefilter('ignore', VerifyWarning)
    # do stuff here