npred = evaluator.compute_npred()
npred_map = WcsNDMap(geom, npred)
fig, ax, cbar = npred_map.sum_over_axes().plot(add_cbar=True)
ax.scatter(
[lon_0_1, lon_0_2, pointing.galactic.l.degree],
[lat_0_1, lat_0_2, pointing.galactic.b.degree],
transform=ax.get_transform("galactic"),
marker="+",
color="cyan",
)
# plt.show()
plt.clf()
rng = get_random_state(42)
counts = rng.poisson(npred)
counts_map = WcsNDMap(geom, counts)
counts_map.sum_over_axes().plot()
# plt.show()
plt.clf()
compound_model.parameters.set_error(2, 0.1 * u.deg)
compound_model.parameters.set_error(4, 1e-12 * u.Unit("cm-2 s-1 TeV-1"))
compound_model.parameters.set_error(8, 0.1 * u.deg)
compound_model.parameters.set_error(10, 1e-12 * u.Unit("cm-2 s-1 TeV-1"))
fit = MapFit(model=compound_model, counts=counts_map, exposure=exposure_map)
fit.run()
# In[ ]:
model = SkyModel(
SkyPointSource("0 deg", "0 deg"),
PowerLaw(index=2.5, amplitude="1e-11 cm-2 s-1 TeV-1", reference="100 GeV"),
)
fit = MapFit(
model=model,
counts=counts,
exposure=exposure,
background=background,
psf=psf_kernel,
)
result = fit.run()
# In[ ]:
print(result)
# ## Exercises
#
# - Fit the position and spectrum of the source [SNR G0.9+0.1](http://gamma-sky.net/#/cat/tev/110).
# - Make maps and fit the position and spectrum of the [Crab nebula](http://gamma-sky.net/#/cat/tev/25).
# ## Summary
#
npred = evaluator.compute_npred()
npred_map = WcsNDMap(geom, npred)
fig, ax, cbar = npred_map.sum_over_axes().plot(add_cbar=True)
ax.scatter(
[lon_0_1, lon_0_2, pointing.galactic.l.degree],
[lat_0_1, lat_0_2, pointing.galactic.b.degree],
transform=ax.get_transform("galactic"),
marker="+",
color="cyan",
)
# plt.show()
plt.clf()
rng = get_random_state(42)
counts = rng.poisson(npred)
counts_map = WcsNDMap(geom, counts)
counts_map.sum_over_axes().plot()
# plt.show()
plt.clf()
models.parameters.set_error(2, 0.1 * u.deg)
models.parameters.set_error(4, 1e-12 * u.Unit("cm-2 s-1 TeV-1"))
models.parameters.set_error(8, 0.1 * u.deg)
models.parameters.set_error(10, 1e-12 * u.Unit("cm-2 s-1 TeV-1"))
fit = MapFit(model=models, counts=counts_map, exposure=exposure_map)
fit.run()