# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import numpy as np
from astropy import units as u
from . import EnergyDependentTablePSF, IRFStacker, EffectiveAreaTable
__all__ = [
"make_psf",
"make_mean_psf",
"make_mean_edisp",
"apply_containment_fraction",
"compute_energy_thresholds",
]
log = logging.getLogger(__name__)
[docs]def make_psf(observation, position, energy=None, rad=None):
"""Make energy-dependent PSF for a given source position.
Parameters
----------
observation : `~gammapy.data.DataStoreObservation`
Observation for which to compute the PSF
position : `~astropy.coordinates.SkyCoord`
Position at which to compute the PSF
energy : `~astropy.units.Quantity`
1-dim energy array for the output PSF.
If none is given, the energy array of the PSF from the observation is used.
rad : `~astropy.coordinates.Angle`
1-dim offset wrt source position array for the output PSF.
If none is given, the offset array of the PSF from the observation is used.
Returns
-------
psf : `~gammapy.irf.EnergyDependentTablePSF`
Energy dependent psf table
"""
offset = position.separation(observation.pointing_radec)
if energy is None:
energy = observation.psf.to_energy_dependent_table_psf(theta=offset).energy
if rad is None:
rad = observation.psf.to_energy_dependent_table_psf(theta=offset).rad
psf_value = observation.psf.to_energy_dependent_table_psf(
theta=offset, rad=rad
).evaluate(energy)
arf = observation.aeff.data.evaluate(offset=offset, energy=energy)
exposure = arf * observation.observation_live_time_duration
psf = EnergyDependentTablePSF(
energy=energy, rad=rad, exposure=exposure, psf_value=psf_value
)
return psf
[docs]def make_mean_psf(observations, position, energy=None, rad=None):
"""Compute mean energy-dependent PSF.
Parameters
----------
observations : `~gammapy.data.Observations`
Observations for which to compute the PSF
position : `~astropy.coordinates.SkyCoord`
Position at which to compute the PSF
energy : `~astropy.units.Quantity`
1-dim energy array for the output PSF.
If none is given, the energy array of the PSF from the first
observation is used.
rad : `~astropy.coordinates.Angle`
1-dim offset wrt source position array for the output PSF.
If none is given, the energy array of the PSF from the first
observation is used.
Returns
-------
psf : `~gammapy.irf.EnergyDependentTablePSF`
Mean PSF
"""
for idx, observation in enumerate(observations):
psf = make_psf(observation, position, energy, rad)
if idx == 0:
stacked_psf = psf
else:
stacked_psf = stacked_psf.stack(psf)
return stacked_psf
[docs]def make_mean_edisp(
observations,
position,
e_true,
e_reco,
low_reco_threshold="0.002 TeV",
high_reco_threshold="150 TeV",
):
"""Compute mean energy dispersion.
Compute the mean edisp of a set of observations j at a given position
The stacking is implemented in :func:`~gammapy.irf.IRFStacker.stack_edisp`
Parameters
----------
observations : `~gammapy.data.Observations`
Observations for which to compute the EDISP
position : `~astropy.coordinates.SkyCoord`
Position at which to compute the EDISP
e_true : `~astropy.units.Quantity`
True energy axis
e_reco : `~astropy.units.Quantity`
Reconstructed energy axis
low_reco_threshold : `~astropy.units.Quantity`
low energy threshold in reco energy
high_reco_threshold : `~astropy.units.Quantity`
high energy threshold in reco energy
Returns
-------
stacked_edisp : `~gammapy.irf.EnergyDispersion`
Stacked EDISP for a set of observation
"""
low_reco_threshold = u.Quantity(low_reco_threshold)
high_reco_threshold = u.Quantity(high_reco_threshold)
list_aeff = []
list_edisp = []
list_livetime = []
list_low_threshold = [low_reco_threshold] * len(observations)
list_high_threshold = [high_reco_threshold] * len(observations)
for obs in observations:
offset = position.separation(obs.pointing_radec)
list_aeff.append(obs.aeff.to_effective_area_table(offset, energy=e_true))
list_edisp.append(
obs.edisp.to_energy_dispersion(offset, e_reco=e_reco, e_true=e_true)
)
list_livetime.append(obs.observation_live_time_duration)
irf_stack = IRFStacker(
list_aeff=list_aeff,
list_edisp=list_edisp,
list_livetime=list_livetime,
list_low_threshold=list_low_threshold,
list_high_threshold=list_high_threshold,
)
irf_stack.stack_edisp()
return irf_stack.stacked_edisp
[docs]def apply_containment_fraction(aeff, psf, radius):
"""Estimate PSF containment inside a given radius and correct effective area for leaking flux.
The PSF and effective area must have the same binning in energy.
Parameters
----------
aeff : `~gammapy.irf.EffectiveAreaTable`
the input 1D effective area
psf : `~gammapy.irf.EnergyDependentTablePSF`
the input 1D PSF
radius : `~astropy.coordinates.Angle`
the maximum angle
Returns
-------
correct_aeff : `~gammapy.irf.EffectiveAreaTable`
the output corrected 1D effective area
"""
energy_center = aeff.energy.center
energy_edges = aeff.energy.edges
containment = psf.containment(energy_center, radius)
corrected_aeff = EffectiveAreaTable(
energy_lo=energy_edges[:-1],
energy_hi=energy_edges[1:],
data=aeff.data.data * np.squeeze(containment),
meta=aeff.meta,
)
return corrected_aeff
[docs]def compute_energy_thresholds(
aeff, edisp, method_lo="none", method_hi="none", **kwargs
):
"""Compute safe energy thresholds from 1D energy dispersion and effective area.
Set the high and low energy threshold based on a chosen method.
For now the methods return thresholds assuming true and reco energy are comparable.
Available methods for setting the low energy threshold:
* area_max : Set energy threshold at x percent of the maximum effective
area (x given as kwargs['area_percent_lo'])
* energy_bias : Set energy threshold at energy where the energy bias
exceeds a value of x percent (given as kwargs['bias_percent_lo'])
* none : Do not apply a lower threshold
Available methods for setting the high energy threshold:
* area_max : Set energy threshold at x percent of the maximum effective
area (x given as kwargs['area_percent_hi']). The threshold is searched
in the last true energy decade of the effective area.
* energy_bias : Set energy threshold at energy where the energy bias
exceeds a value of x percent (given as kwargs['bias_percent_hi']).
The threshold is searched in the last true energy decade of the
energy dispersion.
* none : Do not apply a higher energy threshold
Parameters
----------
aeff : `~gammapy.irf.EffectiveAreaTable`
the 1D effective area
edisp : `~gammapy.irf.EnergyDispersion`
the energy dispersion used
method_lo : {'area_max', 'energy_bias', 'none'}
Method for defining the low energy threshold
method_hi : {'area_max', 'energy_bias', 'none'}
Method for defining the high energy threshold
"""
# Low threshold
if method_lo == "area_max":
aeff_thres = kwargs["area_percent_lo"] / 100 * aeff.max_area
thres_lo = aeff.find_energy(aeff_thres)
elif method_lo == "energy_bias":
thres_lo = edisp.get_bias_energy(kwargs["bias_percent_lo"] / 100)
elif method_lo == "none":
thres_lo = aeff.energy.edges[0]
else:
raise ValueError("Invalid method_lo: {}".format(method_lo))
# High threshold
if method_hi == "area_max":
aeff_thres = kwargs["area_percent_hi"] / 100 * aeff.max_area
e_max = aeff.energy.edges[-1]
try:
thres_hi = aeff.find_energy(aeff_thres, emin=0.1 * e_max, emax=e_max)
except ValueError:
thres_hi = e_max
elif method_hi == "energy_bias":
e_max = aeff.energy.edges[-1]
try:
thres_hi = edisp.get_bias_energy(
kwargs["bias_percent_hi"] / 100, emin=0.1 * e_max, emax=e_max
)
except ValueError:
thres_hi = e_max
elif method_hi == "none":
thres_hi = aeff.energy.edges[-1]
else:
raise ValueError("Invalid method_hi: {}".format(method_hi))
return thres_lo, thres_hi