def diffuse_model():
axis = MapAxis.from_nodes([0.1, 100],
name="energy",
unit="TeV",
interp="log")
m = Map.create(npix=(4, 3),
binsz=2,
axes=[axis],
unit="cm-2 s-1 MeV-1 sr-1",
coordsys="GAL")
m.data += 42
return SkyDiffuseCube(m)
# In[ ]:
psf_kernel.psf_kernel_map.sum_over_axes().plot(stretch="log", add_cbar=True);
# ## Background
#
# Let's compute a background cube, with predicted number of background events per pixel from the diffuse Galactic and isotropic model components. For this, we use the use the [gammapy.cube.MapEvaluator](http://docs.gammapy.org/dev/api/gammapy.cube.MapEvaluator.html) to multiply with the exposure and apply the PSF. The Fermi-LAT energy dispersion at high energies is small, we neglect it here.
# In[ ]:
model = SkyDiffuseCube(diffuse_galactic)
evaluator = MapEvaluator(model=model, exposure=exposure, psf=psf_kernel)
background_gal = counts.copy(data=evaluator.compute_npred())
background_gal.sum_over_axes().plot()
print("Background counts from Galactic diffuse: ", background_gal.data.sum())
# In[ ]:
model = SkyModel(SkyDiffuseConstant(), diffuse_iso)
evaluator = MapEvaluator(model=model, exposure=exposure, psf=psf_kernel)