def extract_aeff(exposure, target_position, energy):
energy_log_ctr = np.sqrt(energy[1:] * energy[:-1])
lon = target_position.galactic.l
lat = target_position.galactic.b
expo_values = exposure.get_by_coord((lon.value, lat.value, energy_log_ctr.value))
table = Table(
[energy[:-1], energy[1:], expo_values],
names=("ENERG_LO", "ENERG_HI", "SPECRESP"),
dtype=("float64", "float64", "float64"),
meta={"name": "Fermi-LAT exposure"},
)
table["ENERG_LO"].unit = str(energy.unit)
table["ENERG_HI"].unit = str(energy.unit)
table["SPECRESP"].unit = "cm2"
table.meta["EXTNAME"] = "SPECRESP"
table.meta["TELESCOP"] = "Fermi"
table.meta["INSTRUME"] = "LAT"
table.meta["EXPOSURE"] = "1"
table.meta["FILTER"] = ""
table.meta["HDUCLASS"] = "OGIP"
table.meta["HDUCLAS1"] = "RESPONSE"
table.meta["HDUCLAS2"] = "SPECRESP"
table.meta["HDUVERS"] = "1.1.0"
return EffectiveAreaTable.from_table(table)
def extract_aeff(exposure, target_position, energy):
"""Function to extract the effective area of Fermi-LAT starting from a
file containing the exposure.
We rely on the following assumption:
1) the exposure is flat in the ROI we are considering, so we assume the
value at the source position is valid all over the ROI
2) since the exposure is the convolution of the effective area and time
what we do is simply to store the exposure as it is [cm2 s] and then
put a mock livetime of 1 s in the PHACountsSpectrum definition later on
Parameters
----------
exposure : `~gammapy.maps.HpxMapND`
Fermi-LAT exposure
target_position : `~astropy.coordinates.SkyCoord`
position of the source of interest
energy : `~astropy.units.Quantity`
energy binning to calculate the effective area
Returns
-------
`~gammapy.irf.EffectiveAreaTable`
table containing the effective area
"""
energy_log_ctr = np.sqrt(energy[1:] * energy[:-1])
# the exposure is defined pixel-wise, fetch the value at source position
lon = target_position.galactic.l
lat = target_position.galactic.b
expo_values = exposure.get_by_coord((lon.value, lat.value, energy_log_ctr.value))
# generate an ARF table for the effective area
# according to the format defined in
# https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/memos/cal_gen_92_002/cal_gen_92_002.html#Sec:ARF-format
# we will create an astropy table that can be read by EffectiveAreaTable
# via from_table method
# we need low and high bin edges
table = Table(
[energy[:-1], energy[1:], expo_values],
names=("ENERG_LO", "ENERG_HI", "SPECRESP"),
dtype=("float64", "float64", "float64"),
meta={"name": "Fermi-LAT exposure"},
)
# declare units
table["ENERG_LO"].unit = str(energy.unit)
table["ENERG_HI"].unit = str(energy.unit)
table["SPECRESP"].unit = "cm2"
# add mandatory keywords in header
table.meta["EXTNAME"] = "SPECRESP"
table.meta["TELESCOP"] = "Fermi"
table.meta["INSTRUME"] = "LAT"
table.meta["EXPOSURE"] = "1"
table.meta["FILTER"] = ""
table.meta["HDUCLASS"] = "OGIP"
table.meta["HDUCLAS1"] = "RESPONSE"
table.meta["HDUCLAS2"] = "SPECRESP"
table.meta["HDUVERS"] = "1.1.0"
return EffectiveAreaTable.from_table(table)
