def test_wcsndmap_sum_over_axes(npix, binsz, frame, proj, skydir, axes, keepdims):
geom = WcsGeom.create(npix=npix, binsz=binsz, proj=proj, frame=frame, axes=axes)
m = WcsNDMap(geom)
coords = m.geom.get_coord()
m.fill_by_coord(coords, coords[0].value)
msum = m.sum_over_axes(keepdims=keepdims)
if m.geom.is_regular:
assert_allclose(np.nansum(m.data), np.nansum(msum.data))
npred.sum_over_axes().plot(add_cbar=True)
# In[ ]:
# This one line is the core of how to simulate data when
# using binned simulation / analysis: you Poisson fluctuate
# npred to obtain simulated observed counts.
# Compute counts as a Poisson fluctuation
rng = np.random.RandomState(seed=42)
counts = rng.poisson(npred.data)
counts_map = WcsNDMap(geom, counts)
# In[ ]:
counts_map.sum_over_axes().plot()
# ## Fit
#
# Now let's analyse the simulated data.
# Here we just fit it again with the same model we had before, but you could do any analysis you like here, e.g. fit a different model, or do a region-based analysis, ...
# In[ ]:
# Define sky model to fit the data
spatial_model = SkyGaussian(lon_0="0.1 deg", lat_0="0.1 deg", sigma="0.5 deg")
spectral_model = PowerLaw(index=2,
amplitude="1e-11 cm-2 s-1 TeV-1",
reference="1 TeV")
model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)
print(model)
# Define some observation parameters
# we are not simulating many pointings / observations
pointing = SkyCoord(0.2, 0.5, unit="deg", frame="galactic")
livetime = 20 * u.hour
exposure_map = make_map_exposure_true_energy(
pointing=pointing, livetime=livetime, aeff=aeff, geom=geom
)
evaluator = MapEvaluator(model=compound_model, exposure=exposure_map)
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()
# Define some observation parameters
# we are not simulating many pointings / observations
pointing = SkyCoord(0.2, 0.5, unit="deg", frame="galactic")
livetime = 20 * u.hour
exposure_map = make_map_exposure_true_energy(
pointing=pointing, livetime=livetime, aeff=aeff, geom=geom
)
evaluator = MapEvaluator(model=models, exposure=exposure_map)
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()
{
"skycoord":
coord.skycoord,
"energy":
coord["energy"] * maps["counts"].geom.get_axis_by_name("energy").unit,
},
interp=3,
)
diffuse_galactic = WcsNDMap(maps["counts"].geom, data)
print("Before: \n", diffuse_gal.geom)
print("Now (same as maps): \n", diffuse_galactic.geom)
# In[ ]:
# diffuse_galactic.slice_by_idx({"energy": 0}).plot(add_cbar=True); # this can be used to check image at different energy bins
diffuse = diffuse_galactic.sum_over_axes()
diffuse.smooth(5).plot(stretch="sqrt", add_cbar=True)
print(diffuse)
# We now multiply the exposure for this diffuse emission to subtract the result from the counts along with the background.
# In[ ]:
combination = diffuse * exposure
combination.unit = ""
combination.smooth(5).plot(stretch="sqrt", add_cbar=True)
# We can plot then the excess image subtracting now the effect of the diffuse galactic emission.
# In[ ]:
'time', '',
'# The idea is that we have this class that can compute `npred`\n# maps, i.e. "predicted counts per pixel" given the model and\n# the observation infos: exposure, background, PSF and EDISP\nevaluator = MapEvaluator(\n model=sky_model, exposure=exposure, background=background, psf=psf_kernel\n)'
)
# In[ ]:
# Accessing and saving a lot of the following maps is for debugging.
# Just for a simulation one doesn't need to store all these things.
# dnde = evaluator.compute_dnde()
# flux = evaluator.compute_flux()
npred = evaluator.compute_npred()
npred_map = WcsNDMap(geom, npred)
# In[ ]:
npred_map.sum_over_axes().plot(add_cbar=True)
# In[ ]:
# This one line is the core of how to simulate data when
# using binned simulation / analysis: you Poisson fluctuate
# npred to obtain simulated observed counts.
# Compute counts as a Poisson fluctuation
rng = np.random.RandomState(seed=42)
counts = rng.poisson(npred)
counts_map = WcsNDMap(geom, counts)
# In[ ]:
counts_map.sum_over_axes().plot()
