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()
# ## Fit
#
# Finally, the big finale: let's do a 3D map fit for the source at the Galactic center, to measure it's position and spectrum.
# 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).
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()
spatial_model = SkyPointSource(lon_0="0.01 deg", lat_0="0.01 deg")
spectral_model = PowerLaw(index=2.2,
amplitude="3e-12 cm-2 s-1 TeV-1",
reference="1 TeV")
model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)
# Now we set up the `MapFit` object by passing the prepared maps, IRFs as well as the model:
# In[ ]:
fit = MapFit(
model=model,
counts=cmaps["counts"],
exposure=cmaps["exposure"],
background=cmaps["background"],
mask=mask,
psf=psf_kernel,
edisp=edisp,
)
# No we run the model fit:
# In[ ]:
get_ipython().run_cell_magic(
'time', '', 'result = fit.run(optimize_opts={"print_level": 1})')
# ### Check model fit
#
# Finally we check the model fit by cmputing a residual image. For this we first get the number of predicted counts from the fit evaluator:
# In[ ]:
spatial_model = SkyPointSource(lon_0="0.01 deg", lat_0="0.01 deg")
spectral_model = PowerLaw2(emin=emin,
emax=emax,
index=2.0,
amplitude="3e-12 cm-2 s-1")
model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)
model.parameters["index"].frozen = True
# In[ ]:
fit = MapFit(
model=model,
counts=maps2D["counts"],
exposure=maps2D["exposure"],
background=maps2D["background"],
mask=mask,
psf=psf_kernel,
)
# In[ ]:
get_ipython().run_cell_magic('time', '', 'result = fit.run()')
# To see the actual best-fit parameters, do a print on the result
# In[ ]:
print(model)
# ## Todo: Demonstrate plotting a flux map
spatial_model = SkyPointSource(lon_0="0.01 deg", lat_0="0.01 deg")
spectral_model = PowerLaw(index=2.2,
amplitude="3e-12 cm-2 s-1 TeV-1",
reference="1 TeV")
model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)
# Now we set up the `MapFit` object by passing the prepared maps, IRFs as well as the model:
# In[ ]:
fit = MapFit(
model=model,
counts=cmaps["counts"],
exposure=cmaps["exposure"],
background=cmaps["background"],
mask=mask,
psf=psf_kernel,
edisp=edisp,
)
# No we run the model fit:
# In[ ]:
get_ipython().run_cell_magic(
'time', '', 'result = fit.run(optimize_opts={"print_level": 1})')
# In[ ]:
print(model)
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()