def plot_source_spectra(name):
plot_source_spectrum(source=SourceCatalog3FGL()[name],
label="3FGL",
color="r")
plot_source_spectrum(source=SourceCatalog2FHL()[name],
label="2FHL",
color="g")
plot_source_spectrum(source=SourceCatalog3FHL()[name],
label="3FHL",
color="b")
ax = plt.gca()
ax.set_ylim(1.0e-12, 7.0e-11)
ax.set_xlim(1.0e-4, 2.0)
ax.set_xlabel("Energy (TeV)")
ax.set_ylabel("E^2 dN/dE (erg cm-2 s-1])")
plt.legend(loc=0)
def plot_source_spectra(name):
plot_source_spectrum(source=SourceCatalog3FGL()[name],
label='Fermi 3FGL',
color='r')
plot_source_spectrum(source=SourceCatalog2FHL()[name],
label='Fermi 2FHL',
color='g')
plot_source_spectrum(source=SourceCatalog1FHL()[name],
label='Fermi 1FHL',
color='c')
plot_source_spectrum(source=SourceCatalog3FHL()[name],
label='Fermi 3FHL',
color='b')
ax = plt.gca()
ax.set_ylim(1.e-12, 7.e-11)
ax.set_xlim(1.e-4, 2.)
ax.set_xlabel('Energy (TeV)')
ax.set_ylabel('E^2 dN/dE (erg cm-2 s-1])')
plt.legend(loc=0)
def setup_class(cls):
cls.cat = SourceCatalog3FHL()
def setup_class(cls):
cls.cat = SourceCatalog3FHL()
# Use 3FHL J0534.5+2201 (Crab) as a test source
cls.source_name = "3FHL J0534.5+2201"
cls.source = cls.cat[cls.source_name]
from gammapy.spectrum import FluxPointsDataset, FluxPoints
from gammapy.catalog import (
SourceCatalog3FGL,
SourceCatalogGammaCat,
SourceCatalog3FHL,
)
from gammapy.utils.fitting import Fit
# ## Load spectral points
#
# For this analysis we choose to work with the source 'HESS J1507-622' and the associated Fermi-LAT sources '3FGL J1506.6-6219' and '3FHL J1507.9-6228e'. We load the source catalogs, and then access source of interest by name:
# In[ ]:
fermi_3fgl = SourceCatalog3FGL()
fermi_3fhl = SourceCatalog3FHL()
gammacat = SourceCatalogGammaCat("$GAMMAPY_DATA/gamma-cat/gammacat.fits.gz")
# In[ ]:
source_gammacat = gammacat["HESS J1507-622"]
source_fermi_3fgl = fermi_3fgl["3FGL J1506.6-6219"]
source_fermi_3fhl = fermi_3fhl["3FHL J1507.9-6228e"]
# The corresponding flux points data can be accessed with `.flux_points` attribute:
# In[ ]:
flux_points_gammacat = source_gammacat.flux_points
flux_points_gammacat.table
def __init__(self, selection="short", savefig=True):
self.datadir = "$GAMMAPY_DATA"
self.resdir = "./res"
self.savefig = savefig
# event list
self.events = EventList.read(
self.datadir + "/fermi_3fhl/fermi_3fhl_events_selected.fits.gz")
# psf
self.psf = EnergyDependentTablePSF.read(
self.datadir + "/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.dlb = 1 / 8.0
El_extra = 10**np.arange(3.8, 6.51, 0.1) # MeV
self.logEc_extra = (np.log10(El_extra)[1:] +
np.log10(El_extra)[:-1]) / 2.0
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")
# background iso
infile = Path(self.datadir + "/fermi_3fhl/iso_P8R2_SOURCE_V6_v06.txt")
outfile = Path(self.resdir + "/iso_P8R2_SOURCE_V6_v06_extra.txt")
self.model_iso = extrapolate_iso(infile, outfile, self.logEc_extra)
# regions selection
file3fhl = self.datadir + "/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
# fit options
self.optimize_opts = {
"backend": "minuit",
"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] = []
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] = []