def get_npred_map():
position = SkyCoord(0.0, 0.0, frame="galactic", unit="deg")
energy_axis = MapAxis.from_bounds(1,
100,
nbin=30,
unit="TeV",
name="energy",
interp="log")
exposure = Map.create(
binsz=0.02,
map_type="wcs",
skydir=position,
width="2 deg",
axes=[energy_axis],
coordsys="GAL",
unit="cm2 s",
)
spatial_model = GaussianSpatialModel(lon_0="0 deg",
lat_0="0 deg",
sigma="0.2 deg",
frame="galactic")
spectral_model = PowerLawSpectralModel(amplitude="1e-11 cm-2 s-1 TeV-1")
skymodel = SkyModel(spatial_model=spatial_model,
spectral_model=spectral_model)
exposure.data = 1e14 * np.ones(exposure.data.shape)
evaluator = MapEvaluator(model=skymodel, exposure=exposure)
npred = evaluator.compute_npred()
return evaluator, npred
def calc_bk(self, lon0, lat0, sig, amp):
"""
returns the computed b_k and the diffuse model template.
"""
# Define sky model to fit the data
ind = 2.0
spatial_model = SkyGaussian(lon_0=lon0, lat_0=lat0, sigma=sig)
spectral_model = PowerLaw(index=ind, amplitude=amp, reference="1 TeV")
model = SkyModel(spatial_model=spatial_model,
spectral_model=spectral_model)
# For simulations, we can have the same npred map
b_k = []
Sk_list = []
for count, bkg, exp in zip(self.count_list, self.background_list,
self.exposure_list):
evaluator = MapEvaluator(model=model, exposure=exp)
npred = evaluator.compute_npred()
geom = exp.geom
diffuse_map = WcsNDMap(geom, npred) #This is Sk
Bk = bkg.data
Sk = diffuse_map.data
Nk = count.data
not_has_exposure = ~(exp.data > 0)
not_has_bkg = ~(Bk > 0)
S_B = np.divide(Sk, Bk)
S_B[not_has_exposure] = 0.0
S_B[not_has_bkg] = 0.0
#Sk is nan for large sep.. to be investigated. temp soln
#if np.isnan(np.sum(S_B)):
# S_B=np.zeros(S_B.shape)
delta = np.power(np.sum(Nk) / np.sum(Bk),
2.0) - 4.0 * np.sum(S_B) / np.sum(Bk)
#print(np.sum(Nk),np.sum(Bk),np.sum(Sk),np.sum(S_B), delta)
#print("delta is %f for obs no %s",delta,k)
#bk1=(np.sum(Nk)/np.sum(Bk) - np.sqrt(delta))/2.0
bk2 = (np.sum(Nk) / np.sum(Bk) + np.sqrt(delta)) / 2.0
b_k.append(bk2)
Sk_list.append(diffuse_map)
return Sk_list, b_k
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)
background_iso = counts.copy(data=evaluator.compute_npred())
background_iso.sum_over_axes().plot()
print("Background counts from isotropic diffuse: ", background_iso.data.sum())
)
# 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)
)
# 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)
def run_region(self, kr, lon, lat, radius):
# TODO: for now we have to read/create the allsky maps each in each job
# because we can't pickle <functools._lru_cache_wrapper object
# send this back to init when fixed
# exposure
exposure_hpx = Map.read(
self.datadir + "/fermi_3fhl/fermi_3fhl_exposure_cube_hpx.fits.gz")
exposure_hpx.unit = "cm2 s"
# background iem
infile = self.datadir + "/catalogs/fermi/gll_iem_v06.fits.gz"
outfile = self.resdir + "/gll_iem_v06_extra.fits"
model_iem = extrapolate_iem(infile, outfile, self.logEc_extra)
# ROI
roi_time = time()
ROI_pos = SkyCoord(lon, lat, frame="galactic", unit="deg")
width = 2 * (radius + self.psf_margin)
# Counts
counts = Map.create(
skydir=ROI_pos,
width=width,
proj="CAR",
coordsys="GAL",
binsz=self.dlb,
axes=[self.energy_axis],
dtype=float,
)
counts.fill_by_coord({
"skycoord": self.events.radec,
"energy": self.events.energy
})
axis = MapAxis.from_nodes(counts.geom.axes[0].center,
name="energy",
unit="GeV",
interp="log")
wcs = counts.geom.wcs
geom = WcsGeom(wcs=wcs, npix=counts.geom.npix, axes=[axis])
coords = counts.geom.get_coord()
# expo
data = exposure_hpx.interp_by_coord(coords)
exposure = WcsNDMap(geom, data, unit=exposure_hpx.unit, dtype=float)
# read PSF
psf_kernel = PSFKernel.from_table_psf(self.psf,
counts.geom,
max_radius=self.psf_margin *
u.deg)
# Energy Dispersion
e_true = exposure.geom.axes[0].edges
e_reco = counts.geom.axes[0].edges
edisp = EnergyDispersion.from_diagonal_response(e_true=e_true,
e_reco=e_reco)
# fit mask
if coords["lon"].min() < 90 * u.deg and coords["lon"].max(
) > 270 * u.deg:
coords["lon"][coords["lon"].value > 180] -= 360 * u.deg
mask = (
(coords["lon"] >= coords["lon"].min() + self.psf_margin * u.deg)
& (coords["lon"] <= coords["lon"].max() - self.psf_margin * u.deg)
& (coords["lat"] >= coords["lat"].min() + self.psf_margin * u.deg)
& (coords["lat"] <= coords["lat"].max() - self.psf_margin * u.deg))
mask_fermi = WcsNDMap(counts.geom, mask)
# IEM
eval_iem = MapEvaluator(model=model_iem,
exposure=exposure,
psf=psf_kernel,
edisp=edisp)
bkg_iem = eval_iem.compute_npred()
# ISO
eval_iso = MapEvaluator(model=self.model_iso,
exposure=exposure,
edisp=edisp)
bkg_iso = eval_iso.compute_npred()
# merge iem and iso, only one local normalization is fitted
background_total = bkg_iem + bkg_iso
background_model = BackgroundModel(background_total)
background_model.parameters["norm"].min = 0.0
# Sources model
in_roi = self.FHL3.positions.galactic.contained_by(wcs)
FHL3_roi = []
for ks in range(len(self.FHL3.table)):
if in_roi[ks] == True:
model = self.FHL3[ks].sky_model()
model.spatial_model.parameters.freeze_all() # freeze spatial
model.spectral_model.parameters["amplitude"].min = 0.0
if isinstance(model.spectral_model, PowerLawSpectralModel):
model.spectral_model.parameters["index"].min = 0.1
model.spectral_model.parameters["index"].max = 10.0
else:
model.spectral_model.parameters["alpha"].min = 0.1
model.spectral_model.parameters["alpha"].max = 10.0
FHL3_roi.append(model)
model_total = SkyModels(FHL3_roi)
# Dataset
dataset = MapDataset(
model=model_total,
counts=counts,
exposure=exposure,
psf=psf_kernel,
edisp=edisp,
background_model=background_model,
mask_fit=mask_fermi,
name="3FHL_ROI_num" + str(kr),
)
cat_stat = dataset.stat_sum()
datasets = Datasets([dataset])
fit = Fit(datasets)
results = fit.run(optimize_opts=self.optimize_opts)
print("ROI_num", str(kr), "\n", results)
fit_stat = datasets.stat_sum()
if results.message == "Optimization failed.":
pass
else:
datasets.to_yaml(path=Path(self.resdir),
prefix=dataset.name,
overwrite=True)
np.save(
self.resdir + "/3FHL_ROI_num" + str(kr) + "_covariance.npy",
results.parameters.covariance,
)
np.savez(
self.resdir + "/3FHL_ROI_num" + str(kr) + "_fit_infos.npz",
message=results.message,
stat=[cat_stat, fit_stat],
)
exec_time = time() - roi_time
print("ROI", kr, " time (s): ", exec_time)
for model in FHL3_roi:
if (self.FHL3[model.name].data["ROI_num"] == kr
and self.FHL3[model.name].data["Signif_Avg"] >=
self.sig_cut):
flux_points = FluxPointsEstimator(
datasets=datasets,
e_edges=self.El_flux,
source=model.name,
sigma_ul=2.0,
).run()
filename = self.resdir + "/" + model.name + "_flux_points.fits"
flux_points.write(filename, overwrite=True)
exec_time = time() - roi_time - exec_time
print("ROI", kr, " Flux points time (s): ", exec_time)
e_reco = counts.geom.axes[0].edges
edisp = EnergyDispersion.from_diagonal_response(e_true=e_true, e_reco=e_reco)
# ## 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](https://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_diffuse = SkyDiffuseCube(diffuse_galactic, name="diffuse")
eval_diffuse = MapEvaluator(model=model_diffuse,
exposure=exposure,
psf=psf_kernel,
edisp=edisp)
background_gal = eval_diffuse.compute_npred()
background_gal.sum_over_axes().plot()
print("Background counts from Galactic diffuse: ", background_gal.data.sum())
# In[ ]:
model_iso = SkyModel(ConstantSpatialModel(), diffuse_iso, name="diffuse-iso")
eval_iso = MapEvaluator(model=model_iso, exposure=exposure, edisp=edisp)
background_iso = eval_iso.compute_npred()
background_iso.sum_over_axes().plot(add_cbar=True)
print("Background counts from isotropic diffuse: ", background_iso.data.sum())