def make_map_dataset(observations,
target_pos,
geom,
geom_true,
offset_max=2 * u.deg):
maker = MapMaker(geom, offset_max, geom_true=geom_true)
maps = maker.run(observations)
table_psf = make_mean_psf(observations, target_pos)
# PSF kernel used for the model convolution
psf_kernel = PSFKernel.from_table_psf(table_psf,
geom_true,
max_radius="0.3 deg")
edisp = make_mean_edisp(
observations,
target_pos,
e_true=geom_true.axes[0].edges,
e_reco=geom.axes[0].edges,
)
background_model = BackgroundModel(maps["background"])
background_model.parameters["norm"].frozen = False
background_model.parameters["tilt"].frozen = True
dataset = MapDataset(
counts=maps["counts"],
exposure=maps["exposure"],
background_model=background_model,
psf=psf_kernel,
edisp=edisp,
)
return dataset
def test_make_mean_psf(data_store):
position = SkyCoord(83.63, 22.01, unit="deg")
observations = data_store.get_observations([23523, 23526])
psf = make_mean_psf(observations, position=position)
assert not np.isnan(psf.psf_value.value).any()
assert_allclose(psf.psf_value.value[22, 22], 12206.1665)
# In[ ]:
get_ipython().run_cell_magic('time', '', 'maker = MapMaker(geom, offset_max=4.0 * u.deg)\nmaps = maker.run(observations)')
# ### Making a PSF Map
#
# Make a PSF map and weigh it with the exposure at the source position to get a 2D PSF
# In[ ]:
# mean PSF
src_pos = SkyCoord(0, 0, unit="deg", frame="galactic")
table_psf = make_mean_psf(observations, src_pos)
# PSF kernel used for the model convolution
psf_kernel = PSFKernel.from_table_psf(table_psf, geom, max_radius="0.3 deg")
# get the exposure at the source position
exposure_at_pos = maps["exposure"].get_by_coord(
{
"lon": src_pos.l.value,
"lat": src_pos.b.value,
"energy": energy_axis.center,
}
)
# now compute the 2D PSF
psf2D = psf_kernel.make_image(exposures=exposure_at_pos)
maps.keys()
# In[ ]:
images["counts"].smooth(3).plot(stretch="sqrt", vmax=2)
# ## PSF
#
# Compute the mean PSF for these observations at the Crab position.
# In[ ]:
from gammapy.irf import make_mean_psf
table_psf = make_mean_psf(observations, pos_crab)
# In[ ]:
psf_kernel = PSFKernel.from_table_psf(table_psf, geom, max_radius="0.3 deg")
psf_kernel_array = psf_kernel.psf_kernel_map.sum_over_axes().data
# psf_kernel.psf_kernel_map.slice_by_idx({'energy': 0}).plot()
# plt.imshow(psf_kernel_array)
# ## Map fit
#
# Let's fit this source assuming a Gaussian spatial shape and a power-law spectral shape, and a background with a flexible normalisation
# In[ ]:
spatial_model = SkyPointSource(lon_0="83.6 deg",
