def test_fermi_isotropic():
filename = "$GAMMAPY_DATA/fermi_3fhl/iso_P8R2_SOURCE_V6_v06.txt"
model = create_fermi_isotropic_diffuse_model(filename)
coords = {"lon": 0 * u.deg, "lat": 0 * u.deg, "energy": 50 * u.GeV}
flux = model(**coords)
assert_allclose(flux.value, 1.463e-13, rtol=1e-3)
assert flux.unit == "MeV-1 cm-2 s-1 sr-1"
def extrapolate_iso(infile, outfile, logEc_extra):
tmp = np.loadtxt(make_path(infile), delimiter=" ")
Ecbd = tmp[:, 0]
qiso = tmp[:, 1]
finterp = interp1d(np.log10(Ecbd),
np.log10(qiso),
kind="linear",
fill_value="extrapolate")
qiso_extra = 10**finterp(logEc_extra)
np.savetxt(outfile, np.c_[10**logEc_extra, qiso_extra], delimiter=" ")
return create_fermi_isotropic_diffuse_model(
filename=outfile, norm=0.92,
interp_kwargs={"fill_value": None}) # norm=0.92 see paper appendix A
# In[ ]:
# TODO: show how one can fix the extrapolate to high energy
# by computing and padding an extra plane e.g. at 1e3 TeV
# that corresponds to a linear extrapolation
# ## Isotropic diffuse background
#
# To load the isotropic diffuse model with Gammapy, use the `~gammapy.modeling.models.TemplateSpectralModel`. We are using `'fill_value': 'extrapolate'` to extrapolate the model above 500 GeV:
# In[ ]:
filename = "$GAMMAPY_DATA/fermi_3fhl/iso_P8R2_SOURCE_V6_v06.txt"
diffuse_iso = create_fermi_isotropic_diffuse_model(
filename=filename, interp_kwargs={"fill_value": None})
# We can plot the model in the energy range between 50 GeV and 2000 GeV:
# In[ ]:
erange = [50, 2000] * u.GeV
diffuse_iso.spectral_model.plot(erange, flux_unit="1 / (cm2 MeV s)")
# ## PSF
#
# Next we will tke a look at the PSF. It was computed using ``gtpsf``, in this case for the Galactic center position. Note that generally for Fermi-LAT, the PSF only varies little within a given regions of the sky, especially at high energies like what we have here. We use the `~gammapy.irf.EnergyDependentTablePSF` class to load the PSF and use some of it's methods to get some information about it.
# In[ ]:
psf = EnergyDependentTablePSF.read(
def __init__(self, selection="short", savefig=True):
log.info("Executing __init__()")
self.resdir = BASE_PATH / "results"
self.savefig = savefig
# event list
self.events = EventList.read(
"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_events_selected.fits.gz"
)
# psf
self.psf = EnergyDependentTablePSF.read(
"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_psf_gc.fits.gz"
)
# mask margin
psf_r99max = self.psf.containment_radius(10 * u.GeV, fraction=0.99)
self.psf_margin = np.ceil(psf_r99max.value[0] * 10) / 10.0
# energies
self.El_flux = [10.0, 20.0, 50.0, 150.0, 500.0, 2000.0]
El_fit = 10 ** np.arange(1, 3.31, 0.1)
self.energy_axis = MapAxis.from_edges(
El_fit, name="energy", unit="GeV", interp="log"
)
# iso norm=0.92 see paper appendix A
self.model_iso = create_fermi_isotropic_diffuse_model(
filename="data/iso_P8R2_SOURCE_V6_v06_extrapolated.txt",
norm=0.92,
interp_kwargs={"fill_value": None},
)
# regions selection
file3fhl = "$GAMMAPY_DATA/catalogs/fermi/gll_psch_v13.fit.gz"
self.FHL3 = SourceCatalog3FHL(file3fhl)
hdulist = fits.open(make_path(file3fhl))
self.ROIs = hdulist["ROIs"].data
Scat = hdulist[1].data
order = np.argsort(Scat.Signif_Avg)[::-1]
ROIs_ord = Scat.ROI_num[order]
if selection == "short":
self.ROIs_sel = [430, 135, 118, 212, 277, 42, 272, 495]
# Crab, Vela, high-lat, +some fast regions
elif selection == "long":
# get small regions with few sources among the most significant
indexes = np.unique(ROIs_ord, return_index=True)[1]
ROIs_ord = [ROIs_ord[index] for index in sorted(indexes)]
self.ROIs_sel = [
kr
for kr in ROIs_ord
if sum(Scat.ROI_num == kr) <= 4 and self.ROIs.RADIUS[kr] < 6
][:100]
elif selection == "debug":
self.ROIs_sel = [135] # Vela region
else:
raise ValueError(f"Invalid selection: {selection!r}")
# fit options
self.optimize_opts = {
"backend": "minuit",
"optimize_opts": {"tol": 10.0, "strategy": 2},
}
# calculate flux points only for sources significant above this threshold
self.sig_cut = 8.0
# diagnostics stored to produce plots and outputs
self.diags = {
"message": [],
"stat": [],
"params": {},
"errel": {},
"compatibility": {},
"cat_fp_sel": [],
}
self.diags["errel"]["flux_points"] = []
keys = [
"PL_tags",
"PL_index",
"PL_amplitude",
"LP_tags",
"LP_alpha",
"LP_beta",
"LP_amplitude",
]
for key in keys:
self.diags["params"][key] = []
