def get_psf():
filename = (
"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits"
)
psf = EnergyDependentMultiGaussPSF.read(filename,
hdu="POINT SPREAD FUNCTION")
table_psf = psf.to_energy_dependent_table_psf(theta=0.5 * u.deg)
psf_map = PSFMap.from_energy_dependent_table_psf(table_psf)
return psf_map
def simulate_dataset(
skymodel,
geom,
pointing,
irfs,
livetime=1 * u.h,
offset=1 * u.deg,
max_radius=0.8 * u.deg,
random_state="random-seed",
):
"""Simulate a 3D dataset.
Simulate a source defined with a sky model for a given pointing,
geometry and irfs for a given exposure time.
This will return a dataset object which includes the counts cube,
the exposure cube, the psf cube, the background model and the sky model.
Parameters
----------
skymodel : `~gammapy.modeling.models.SkyModel`
Background model map
geom : `~gammapy.maps.WcsGeom`
Geometry object for the observation
pointing : `~astropy.coordinates.SkyCoord`
Pointing position
irfs : dict
Irfs used for simulating the observation
livetime : `~astropy.units.Quantity`
Livetime exposure of the simulated observation
offset : `~astropy.units.Quantity`
Offset from the center of the pointing position.
This is used for the PSF and Edisp estimation
max_radius : `~astropy.coordinates.Angle`
The maximum radius of the PSF kernel.
random_state: {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`}
Defines random number generator initialisation.
Returns
-------
dataset : `~gammapy.cube.MapDataset`
A dataset of the simulated observation.
"""
background = make_map_background_irf(pointing=pointing,
ontime=livetime,
bkg=irfs["bkg"],
geom=geom)
background_model = BackgroundModel(background)
psf = irfs["psf"].to_energy_dependent_table_psf(theta=offset)
psf_map = PSFMap.from_energy_dependent_table_psf(psf)
exposure = make_map_exposure_true_energy(pointing=pointing,
livetime=livetime,
aeff=irfs["aeff"],
geom=geom)
if "edisp" in irfs:
energy = geom.axes[0].edges
edisp = irfs["edisp"].to_energy_dispersion(offset,
e_reco=energy,
e_true=energy)
else:
edisp = None
dataset = MapDataset(
models=skymodel,
exposure=exposure,
background_model=background_model,
psf=psf_map,
edisp=edisp,
)
npred_map = dataset.npred()
rng = get_random_state(random_state)
counts = rng.poisson(npred_map.data)
dataset.counts = WcsNDMap(geom, counts)
return dataset