Sample a source with energy-dependent temporal evolution#

This notebook shows how to sample events of sources whose model evolves in energy and time.


To understand how to generate a model and a MapDataset and how to fit the data, please refer to the SkyModel and 3D map simulation tutorial. To know how to sample events for standards sources, we suggest to visit the event sampler Event sampling tutorial.


Describe the process of sampling events of a source having an energy-dependent temporal model, and obtain an output event-list.

Proposed approach#

Here we will show how to create an energy dependent temporal model; then we also create an observation and define a Dataset object. Finally, we describe how to sample events from the given model.

We will work with the following functions and classes:


As usual, let’s start with some general imports…

from pathlib import Path
import astropy.units as u
from astropy.coordinates import Angle, SkyCoord
from astropy.time import Time
from regions import CircleSkyRegion, PointSkyRegion
import matplotlib.pyplot as plt
from import FixedPointingInfo, Observation, observatory_locations
from gammapy.datasets import MapDataset, MapDatasetEventSampler
from gammapy.irf import load_irf_dict_from_file
from gammapy.makers import MapDatasetMaker
from gammapy.maps import MapAxis, RegionNDMap, WcsGeom
from gammapy.modeling.models import (

Check setup#

from gammapy.utils.check import check_tutorials_setup


        python_executable      : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/bin/python
        python_version         : 3.9.18
        machine                : x86_64
        system                 : Linux

Gammapy package:

        version                : 1.3.dev60+ge5aecb334
        path                   : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy

Other packages:

        numpy                  : 1.26.4
        scipy                  : 1.12.0
        astropy                : 5.2.2
        regions                : 0.8
        click                  : 8.1.7
        yaml                   : 6.0.1
        IPython                : 8.18.1
        jupyterlab             : not installed
        matplotlib             : 3.8.3
        pandas                 : not installed
        healpy                 : 1.16.6
        iminuit                : 2.25.2
        sherpa                 : 4.16.0
        naima                  : 0.10.0
        emcee                  : 3.1.4
        corner                 : 2.2.2
        ray                    : 2.9.3

Gammapy environment variables:

        GAMMAPY_DATA           : /home/runner/work/gammapy-docs/gammapy-docs/gammapy-datasets/dev

Create the energy-dependent temporal model#

The source we want to simulate has a spectrum that varies as a function of the time. Here we show how to create an energy dependent temporal model. If you already have such a model, go directly to the corresponding section.

In the following example, the source spectrum will vary continuously with time. Here we define 5 time bins and represent the spectrum at the center of each bin as a powerlaw. The spectral evolution is also shown in the following plot:

amplitudes = u.Quantity(
    [2e-10, 8e-11, 5e-11, 3e-11, 1e-11], unit="cm-2s-1TeV-1"
)  # amplitude
indices = u.Quantity([2.2, 2.0, 1.8, 1.6, 1.4], unit="")  # index

for i in range(len(amplitudes)):
    spec = PowerLawSpectralModel(
        index=indices[i], amplitude=amplitudes[i], reference="1 TeV"
    spec.plot([0.2, 100] * u.TeV, label=f"Time bin {i+1}")
event sampling nrg depend models

Let’s now create the temporal model (if you already have this model, please go directly to the Read the energy-dependent model section), that will be defined as a LightCurveTemplateTemporalModel. The latter take as input a RegionNDMap with temporal and energy axes, on which the fluxes are stored.

To create such map, we first need to define a time axis with MapAxis: here we consider 5 time bins of 720 s (i.e. 1 hr in total). As a second step, we create an energy axis with 10 bins where the powerlaw spectral models will be evaluated.

# source position
pointing_position = SkyCoord("100 deg", "30 deg", frame="icrs")
position = FixedPointingInfo(fixed_icrs=pointing_position.icrs)

# time axis
time_axis = MapAxis.from_bounds(0 * u.s, 3600 * u.s, nbin=5, name="time", interp="lin")

# energy axis
energy_axis = MapAxis.from_energy_bounds(
    energy_min=0.2 * u.TeV, energy_max=100 * u.TeV, nbin=10

Now let’s create the RegionNDMap and fill it with the expected spectral values:

# create the RegionNDMap containing fluxes
m = RegionNDMap.create(
    axes=[energy_axis, time_axis],

# to compute the spectra as a function of time we extract the coordinates of the geometry
coords = m.geom.get_coord(sparse=True)

# We reshape the indices and amplitudes array to perform broadcasting
indices = indices.reshape(coords["time"].shape)
amplitudes = amplitudes.reshape(coords["time"].shape)

# evaluate the spectra and fill the RegionNDMap
m.quantity = PowerLawSpectralModel.evaluate(
    coords["energy"], indices, amplitudes, 1 * u.TeV

Create the temporal model and write it to disk#

Now, we define the LightCurveTemplateTemporalModel. It needs the map we created above and a reference time. The latter is crucial to evaluate the model as a function of time. We show also how to write the model on disk, noting that we explicitly set the format to map.

t_ref = Time(51544.00074287037, format="mjd", scale="tt")
filename = "./temporal_model_map.fits"
temp = LightCurveTemplateTemporalModel(m, t_ref=t_ref, filename=filename)
temp.write(filename, format="map", overwrite=True)

Read the energy-dependent model#

We read the map written on disc again with When the model is from a map, the arguments format="map" is mandatory. The map is fits file, with 3 extensions:

1) SKYMAP: a table with a CHANNEL and DATA column; the number of rows is given by the product of the energy and time bins. The DATA represent the values of the model at each energy;

2) SKYMAP_BANDS: a table with CHANNEL, ENERGY, E_MIN, E_MAX, TIME, TIME_MIN and TIME_MAX. ENERGY is the mean of E_MIN and E_MAX, as well as TIME is the mean of TIME_MIN and TIME_MAX; this extension should contain the reference time in the header, through the keywords MJDREFI and MJDREFF.

3) SKYMAP_REGION: it gives information on the spatial morphology, i.e. SHAPE (only point is accepted), X and Y (source position), R (the radius if extended; not used in our case) and ROTANG (the angular rotation of the spatial model, if extended; not used in our case).

We note that an interpolation scheme is also provided when loading a map: for an energy-dependent temporal model, the method and values_scale arguments by default are set to linear and log. We warn the reader to carefully check the interpolation method used for the time axis while creating the template model, as different methods provide different results. By default, we assume linear interpolation for the time, log for the energies and values. Users can modify the method and values_scale arguments but we warn that this should be done only when the user knows the consequences of the changes. Here, we show how to set them explicitly:

temporal_model.method = "linear"  # default
temporal_model.values_scale = "log"  # default

We can have a visual inspection of the temporal model at different energies:

event sampling nrg depend models

Prepare and run the event sampler#

Define the simulation source model#

Now that the temporal model is complete, we create the whole source SkyModel. We define its spatial morphology as point-like. This is a mandatory condition to simulate energy-dependent temporal model. Other morphologies will raise an error! Note also that the source spectral_model is a ConstantSpectralModel: this is necessary and mandatory, as the real source spectrum is actually passed through the map.

Define an observation and make a dataset#

In the following, we define an observation of 1 hr with CTA in the alpha-configuration for the south array, and we also create a dataset to be passed to the event sampler. The full SkyModel created above is passed to the dataset.

path = Path("$GAMMAPY_DATA/cta-caldb")
irf_filename = "Prod5-South-20deg-AverageAz-14MSTs37SSTs.180000s-v0.1.fits.gz"

pointing_position = SkyCoord(ra=100 * u.deg, dec=30 * u.deg)
pointing = FixedPointingInfo(fixed_icrs=pointing_position)
livetime = 1 *

irfs = load_irf_dict_from_file(path / irf_filename)
location = observatory_locations["cta_south"]

observation = Observation.create(
energy_axis = MapAxis.from_energy_bounds("0.2 TeV", "100 TeV", nbin=5, per_decade=True)
energy_axis_true = MapAxis.from_energy_bounds(
    "0.05 TeV", "150 TeV", nbin=10, per_decade=True, name="energy_true"
migra_axis = MapAxis.from_bounds(0.5, 2, nbin=150, node_type="edges", name="migra")

geom = WcsGeom.create(
    width=(2, 2),
empty = MapDataset.create(
maker = MapDatasetMaker(selection=["exposure", "background", "psf", "edisp"])
dataset =, observation)

dataset.models = models


Component 0: SkyModel

  Name                      : test-source
  Datasets names            : None
  Spectral model type       : ConstantSpectralModel
  Spatial  model type       : PointSpatialModel
  Temporal model type       : LightCurveTemplateTemporalModel
    const                         :      1.000   +/-    0.00 1 / (cm2 s TeV)
    lon_0                         :    100.000   +/-    0.00 deg
    lat_0                         :     30.000   +/-    0.00 deg
    t_ref                 (frozen):  51544.000       d

Component 1: FoVBackgroundModel

  Name                      : my-dataset-bkg
  Datasets names            : ['my-dataset']
  Spectral model type       : PowerLawNormSpectralModel
    norm                          :      1.000   +/-    0.00
    tilt                  (frozen):      0.000
    reference             (frozen):      1.000       TeV

Let’s simulate the model#

Initialize and run the MapDatasetEventSampler class. We also define the oversample_energy_factor arguments: this should be carefully considered by the user, as a higher oversample_energy_factor gives a more precise source flux estimate, at the expense of computational time. Here we adopt an oversample_energy_factor of 10:

sampler = MapDatasetEventSampler(random_state=0, oversample_energy_factor=10)
events =, observation)

Let’s inspect the simulated events in the source region:

event sampling nrg depend models

Let’s inspect the simulated events as a function of time:

event sampling nrg depend models


  • Try to create a temporal model with a more complex energy-dependent evolution;

  • Read your temporal model in Gammapy and simulate it;

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