How To#

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 check the Getting started section and the User guide 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!

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.

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 or also select observations based on other information available using the select_observations method.

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.

Observations can be grouped depending on a number of various quantities. The two methods to do so are manual grouping and hierarchical clustering. The quantity you group by can be adjusted according to each science case.

The DataStore provides access to a summary table of all observations available. It can be used to obtain various quantities from your Observations list, such as livetime. The on-axis equivalent number of observation hours on the source can be calculated.

The resample_energy_edges provides a way to resample the energy bins t o satisfy a minimum number of counts of significance per bin.

Units for plotting are handled with a combination of matplotlib and astropy.units. The methods ax.xaxis.set_units() and ax.yaxis.set_units() allow you to define the x and y axis units using astropy.units. Here is a minimal example:

import matplotlib.pyplot as plt
from gammapy.estimators import FluxPoints
from astropy import units as u

filename = "$GAMMAPY_DATA/hawc_crab/HAWC19_flux_points.fits"
fp =

ax = plt.subplot()
ax.yaxis.set_units(u.Unit("erg cm-2 s-1"))
fp.plot(ax=ax, sed_type="e2dnde")

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.

There are two ways for the data reduction steps to be implemented. Either a loop is used to run the full reduction chain, or the reduction is performed with multi-processing tools by utilising the DatasetsMaker to perform the loop internally.

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.

Gammapy allows the flexibility of using user-defined models for analysis.

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

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).

Temporal models can be directly fit on available lightcurves, or on the reduced datasets. This is done through a joint fitting of the datasets, one for each time bin.

It happens that a 3D fit does not converge with warning messages indicating that the scanned positions of the model are outside the valid IRF map range. The type of warning message is:

Position <SkyCoord (ICRS): (ra, dec) in deg
  (329.71693826, -33.18392464)> is outside valid IRF map range, using nearest IRF defined within

This issue might happen when the position of a model has no defined range. The minimizer might scan positions outside the spatial range in which the IRFs are computed and then it gets lost.

The simple solution is to add a physically-motivated range on the model’s position, e.g. within the field of view or around an excess position. Most of the time, this tip solves the issue. The documentation of the models sub-package explains how to add a validity range of a model parameter.

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.

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.

To interpolate maps onto a different geometry use interp_to_geom.

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

Gammapy provides the possibility of displaying a progress bar to monitor the advancement of time-consuming processes. To activate this functionality, make sure that tqdm is installed and add the following code snippet to your code:

from gammapy.utils import pbar

The progress bar is available within the following:

As the Gammapy visualisations are using the library matplotlib that provides color styles, it is possible to change the default colors map of the Gammapy plots. Using using the style sheet of matplotlib, you should add into your notebooks or scripts the following lines after the Gammapy imports:

import as style
# with XXXX from `print(`

Note that you can create your own style with matplotlib (see here and here)

The CTA observatory released a document describing best practices for data visualisation in a way friendly to color-blind people: CTAO document. To use them, you should add into your notebooks or scripts the following lines after the Gammapy imports:

import as style


import as style

For doing pulsar analysis, you must compute the phase associated to each event and then create a new EventList and a new Observation. Modifying the EventList of an Observation in-place is prohibited because of the underlying lazy loading implemented in reading observations. Code for computing phases is NOT provided within gammapy, and you must use an external s/w like PINT or TEMPO2. For brevity, this code example shows the only technical implementation using a dummy phase column.

import numpy as np
from import DataStore, Observation, EventList

# read the observation
datastore = DataStore.from_dir("$GAMMAPY_DATA/hess-dl3-dr1/")
obs = datastore.obs(23523)

# use the phase information - dummy in this example
phase = np.random.random(len(

# create a new `EventList`
table =
table["PHASE"] = phase
events_new = EventList(table)

# copy the observation in memory, changing the events
o2 = obs.copy(events=events_new, in_memory=True)

# The new observation and the new events table can be serialised independently
o2.write("new_obs.fits.gz", overwrite=True)
events_new.write("events.fits.gz", gti=obs.gti, overwrite=True)