# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import numpy as np
from astropy.coordinates import Angle
from gammapy.irf import EnergyDependentMultiGaussPSF
from gammapy.maps import Map
from gammapy.modeling.models import BackgroundModel
from .background import make_map_background_irf
from .edisp_map import make_edisp_map
from .exposure import make_map_exposure_true_energy
from .fit import MapDataset, MapDatasetOnOff
from .psf_map import make_psf_map
__all__ = ["MapDatasetMaker", "SafeMaskMaker"]
log = logging.getLogger(__name__)
[docs]class MapDatasetMaker:
"""Make maps for a single IACT observation.
Parameters
----------
background_oversampling : int
Background evaluation oversampling factor in energy.
selection : list
List of str, selecting which maps to make.
Available: 'counts', 'exposure', 'background', 'psf', 'edisp'
By default, all maps are made.
"""
available_selection = ["counts", "exposure", "background", "psf", "edisp"]
def __init__(self, background_oversampling=None, selection=None):
self.background_oversampling = background_oversampling
if selection is None:
selection = self.available_selection
selection = set(selection)
if not selection.issubset(self.available_selection):
difference = selection.difference(self.available_selection)
raise ValueError(f"{difference} is not a valid method.")
self.selection = selection
[docs] @staticmethod
def make_counts(geom, observation):
"""Make counts map.
Parameters
----------
geom : `~gammapy.maps.Geom`
Reference map geom.
observation : `~gammapy.data.DataStoreObservation`
Observation container.
Returns
-------
counts : `~gammapy.maps.Map`
Counts map.
"""
counts = Map.from_geom(geom)
counts.fill_events(observation.events)
return counts
[docs] @staticmethod
def make_exposure(geom, observation):
"""Make exposure map.
Parameters
----------
geom : `~gammapy.maps.Geom`
Reference map geom.
observation : `~gammapy.data.DataStoreObservation`
Observation container.
Returns
-------
exposure : `~gammapy.maps.Map`
Exposure map.
"""
return make_map_exposure_true_energy(
pointing=observation.pointing_radec,
livetime=observation.observation_live_time_duration,
aeff=observation.aeff,
geom=geom,
)
[docs] @staticmethod
def make_exposure_irf(geom, observation):
"""Make exposure map with irf geometry.
Parameters
----------
geom : `~gammapy.maps.Geom`
Reference geom.
observation : `~gammapy.data.DataStoreObservation`
Observation container.
Returns
-------
exposure : `~gammapy.maps.Map`
Exposure map.
"""
return make_map_exposure_true_energy(
pointing=observation.pointing_radec,
livetime=observation.observation_live_time_duration,
aeff=observation.aeff,
geom=geom,
)
[docs] def make_background(self, geom, observation):
"""Make background map.
Parameters
----------
geom : `~gammapy.maps.Geom`
Reference geom.
observation : `~gammapy.data.DataStoreObservation`
Observation container.
Returns
-------
background : `~gammapy.maps.Map`
Background map.
"""
bkg_coordsys = observation.bkg.meta.get("FOVALIGN", "RADEC")
if bkg_coordsys == "ALTAZ":
pointing = observation.fixed_pointing_info
elif bkg_coordsys == "RADEC":
pointing = observation.pointing_radec
else:
raise ValueError(
f"Invalid background coordinate system: {bkg_coordsys!r}\n"
"Options: ALTAZ, RADEC"
)
return make_map_background_irf(
pointing=pointing,
ontime=observation.observation_time_duration,
bkg=observation.bkg,
geom=geom,
oversampling=self.background_oversampling,
)
[docs] def make_edisp(self, geom, observation):
"""Make energy dispersion map.
Parameters
----------
geom : `~gammapy.maps.Geom`
Reference geom.
observation : `~gammapy.data.DataStoreObservation`
Observation container.
Returns
-------
edisp : `~gammapy.cube.EDispMap`
Edisp map.
"""
exposure = self.make_exposure_irf(geom.squash(axis="migra"), observation)
return make_edisp_map(
edisp=observation.edisp,
pointing=observation.pointing_radec,
geom=geom,
exposure_map=exposure,
)
[docs] def make_psf(self, geom, observation):
"""Make psf map.
Parameters
----------
geom : `~gammapy.maps.Geom`
Reference geom.
observation : `~gammapy.data.DataStoreObservation`
Observation container.
Returns
-------
psf : `~gammapy.cube.PSFMap`
Psf map.
"""
psf = observation.psf
if isinstance(psf, EnergyDependentMultiGaussPSF):
rad_axis = geom.get_axis_by_name("theta")
psf = psf.to_psf3d(rad=rad_axis.center)
exposure = self.make_exposure_irf(geom.squash(axis="theta"), observation)
return make_psf_map(
psf=psf,
pointing=observation.pointing_radec,
geom=geom,
exposure_map=exposure,
)
[docs] def run(self, dataset, observation):
"""Make map dataset.
Parameters
----------
dataset : `~gammapy.cube.MapDataset`
Reference dataset.
observation : `~gammapy.data.DataStoreObservation`
Observation
Returns
-------
dataset : `~gammapy.cube.MapDataset`
Map dataset.
"""
kwargs = {"gti": observation.gti}
mask_safe = Map.from_geom(dataset.counts.geom, dtype=bool)
mask_safe.data |= True
kwargs["mask_safe"] = mask_safe
if "counts" in self.selection:
counts = self.make_counts(dataset.counts.geom, observation)
kwargs["counts"] = counts
if "exposure" in self.selection:
exposure = self.make_exposure(dataset.exposure.geom, observation)
kwargs["exposure"] = exposure
if "background" in self.selection:
background_map = self.make_background(dataset.counts.geom, observation)
kwargs["background_model"] = BackgroundModel(background_map)
if "psf" in self.selection:
psf = self.make_psf(dataset.psf.psf_map.geom, observation)
kwargs["psf"] = psf
if "edisp" in self.selection:
edisp = self.make_edisp(dataset.edisp.edisp_map.geom, observation)
kwargs["edisp"] = edisp
return MapDataset(name=dataset.name, **kwargs)
[docs]class SafeMaskMaker:
"""Make safe data range mask for a given observation.
Parameters
----------
methods : {"aeff-default", "aeff-max", "edisp-bias", "offset-max", "bkg-peak"}
Method to use for the safe energy range. Can be a
list with a combination of those. Resulting masks
are combined with logical `and`. "aeff-default"
uses the energy ranged specified in the DL3 data
files, if available.
aeff_percent : float
Percentage of the maximal effective area to be used
as lower energy threshold for method "aeff-max".
bias_percent : float
Percentage of the energy bias to be used as lower
energy threshold for method "edisp-bias"
position : `~astropy.coordinates.SkyCoord`
Position at which the `aeff_percent` or `bias_percent` are computed. By default,
it uses the position of the center of the map.
offset_max : str or `~astropy.units.Quantity`
Maximum offset cut.
"""
available_methods = {
"aeff-default",
"aeff-max",
"edisp-bias",
"offset-max",
"bkg-peak",
}
def __init__(
self,
methods=("aeff-default",),
aeff_percent=10,
bias_percent=10,
position=None,
offset_max="3 deg",
):
methods = set(methods)
if not methods.issubset(self.available_methods):
difference = methods.difference(self.available_methods)
raise ValueError(f"{difference} is not a valid method.")
self.methods = methods
self.aeff_percent = aeff_percent
self.bias_percent = bias_percent
self.position = position
self.offset_max = Angle(offset_max)
[docs] def make_mask_offset_max(self, dataset, observation):
"""Make maximum offset mask.
Parameters
----------
dataset : `~gammapy.modeling.Dataset`
Dataset to compute mask for.
observation: `~gammapy.data.DataStoreObservation`
Observation to compute mask for.
Returns
-------
mask_safe : `~numpy.ndarray`
Maximum offset mask.
"""
separation = dataset._geom.separation(observation.pointing_radec)
return separation < self.offset_max
[docs] @staticmethod
def make_mask_energy_aeff_default(dataset, observation):
"""Make safe energy mask from aeff default.
Parameters
----------
dataset : `~gammapy.modeling.Dataset`
Dataset to compute mask for.
observation: `~gammapy.data.DataStoreObservation`
Observation to compute mask for.
Returns
-------
mask_safe : `~numpy.ndarray`
Safe data range mask.
"""
try:
e_max = observation.aeff.high_threshold
e_min = observation.aeff.low_threshold
except KeyError:
log.warning(f"No thresholds defined for obs {observation}")
e_min, e_max = None, None
# TODO: introduce RegionNDMap and simplify the code below
try:
mask = dataset.counts.energy_mask(emin=e_min, emax=e_max)
except AttributeError:
mask = dataset.counts.geom.energy_mask(emin=e_min, emax=e_max)
return mask
[docs] def make_mask_energy_aeff_max(self, dataset):
"""Make safe energy mask from aeff max.
Parameters
----------
dataset : `~gammapy.spectrum.SpectrumDataset` or `~gammapy.spectrum.SpectrumDatasetOnOff`
Dataset to compute mask for.
Returns
-------
mask_safe : `~numpy.ndarray`
Safe data range mask.
"""
if isinstance(dataset, (MapDataset, MapDatasetOnOff)):
raise NotImplementedError(
"'aeff-max' method currently only supported for spectral datasets"
)
aeff_thres = self.aeff_percent / 100 * dataset.aeff.max_area
e_min = dataset.aeff.find_energy(aeff_thres)
return dataset.counts.energy_mask(emin=e_min)
[docs] def make_mask_energy_edisp_bias(self, dataset):
"""Make safe energy mask from aeff max.
Parameters
----------
dataset : `~gammapy.modeling.Dataset`
Dataset to compute mask for.
Returns
-------
mask_safe : `~numpy.ndarray`
Safe data range mask.
"""
edisp = dataset.edisp
if isinstance(dataset, (MapDataset, MapDatasetOnOff)):
position = self.position
if position is None:
position = dataset.counts.geom.center_skydir
e_reco = dataset.counts.geom.get_axis_by_name("energy").edges
edisp = edisp.get_edisp_kernel(position, e_reco)
counts = dataset.counts.geom
else:
counts = dataset.counts
e_min = edisp.get_bias_energy(self.bias_percent / 100)
return counts.energy_mask(emin=e_min)
[docs] @staticmethod
def make_mask_energy_bkg_peak(dataset):
"""Make safe energy mask based on the binned background.
The energy threshold is defined as the upper edge of the energy
bin with the highest predicted background rate. This method is motivated
by its use in the HESS DL3 validation paper: https://arxiv.org/pdf/1910.08088.pdf
Parameters
----------
dataset : `~gammapy.modeling.Dataset`
Dataset to compute mask for.
Returns
-------
mask_safe : `~numpy.ndarray`
Safe data range mask.
"""
if isinstance(dataset, (MapDataset, MapDatasetOnOff)):
background_spectrum = dataset.background_model.map.get_spectrum()
counts = dataset.counts.geom
else:
background_spectrum = dataset.background
counts = dataset.counts
idx = np.argmax(background_spectrum.data)
e_min = background_spectrum.energy.edges[idx + 1]
return counts.energy_mask(emin=e_min)
[docs] def run(self, dataset, observation=None):
"""Make safe data range mask.
Parameters
----------
dataset : `~gammapy.modeling.Dataset`
Dataset to compute mask for.
observation: `~gammapy.data.DataStoreObservation`
Observation to compute mask for.
Returns
-------
dataset : `Dataset`
Dataset with defined safe range mask.
"""
mask_safe = np.ones(dataset.data_shape, dtype=bool)
if "offset-max" in self.methods:
mask_safe &= self.make_mask_offset_max(dataset, observation)
if "aeff-default" in self.methods:
mask_safe &= self.make_mask_energy_aeff_default(dataset, observation)
if "aeff-max" in self.methods:
mask_safe &= self.make_mask_energy_aeff_max(dataset)
if "edisp-bias" in self.methods:
mask_safe &= self.make_mask_energy_edisp_bias(dataset)
if "bkg-peak" in self.methods:
mask_safe &= self.make_mask_energy_bkg_peak(dataset)
if isinstance(dataset, (MapDataset, MapDatasetOnOff)):
mask_safe = Map.from_geom(dataset._geom, data=mask_safe)
dataset.mask_safe = mask_safe
return dataset