Source code for gammapy.makers.safe

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import numpy as np
from astropy import units as u
from astropy.coordinates import Angle
from gammapy.irf import EDispKernelMap
from gammapy.maps import Map
from gammapy.modeling.models import TemplateSpectralModel
from .core import Maker

__all__ = ["SafeMaskMaker"]


log = logging.getLogger(__name__)


[docs]class SafeMaskMaker(Maker): """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. fixed_offset : `~astropy.coordinates.Angle` offset, calculated from the pointing position, at which the `aeff_percent` or `bias_percent` are computed. If neither the position nor fixed_offset is specified, it uses the position of the center of the map by default. offset_max : str or `~astropy.units.Quantity` Maximum offset cut. """ tag = "SafeMaskMaker" 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, fixed_offset=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.fixed_offset = fixed_offset self.offset_max = Angle(offset_max) if self.position and self.fixed_offset: raise ValueError( "`position` and `fixed_offset` attributes are mutually exclusive" )
[docs] def make_mask_offset_max(self, dataset, observation): """Make maximum offset mask. Parameters ---------- dataset : `~gammapy.datasets.MapDataset` or `~gammapy.datasets.SpectrumDataset` Dataset to compute mask for. observation: `~gammapy.data.Observation` Observation to compute mask for. Returns ------- mask_safe : `~numpy.ndarray` Maximum offset mask. """ if observation is None: raise ValueError("Method 'offset-max' requires an observation object.") 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.datasets.MapDataset` or `~gammapy.datasets.SpectrumDataset` Dataset to compute mask for. observation: `~gammapy.data.Observation` Observation to compute mask for. Returns ------- mask_safe : `~numpy.ndarray` Safe data range mask. """ if observation is None: raise ValueError("Method 'offset-max' requires an observation object.") energy_max = observation.aeff.meta.get("HI_THRES", None) if energy_max: energy_max = energy_max * u.TeV else: log.warning( f"No default upper safe energy threshold defined for obs {observation.obs_id}" ) energy_min = observation.aeff.meta.get("LO_THRES", None) if energy_min: energy_min = energy_min * u.TeV else: log.warning( f"No default lower safe energy threshold defined for obs {observation.obs_id}" ) return dataset._geom.energy_mask(energy_min=energy_min, energy_max=energy_max)
[docs] def make_mask_energy_aeff_max(self, dataset, observation=None): """Make safe energy mask from effective area maximum value. Parameters ---------- dataset : `~gammapy.datasets.MapDataset` or `~gammapy.datasets.SpectrumDataset` Dataset to compute mask for. observation: `~gammapy.data.Observation` Observation to compute mask for. It is a mandatory argument when fixed_offset is set. Returns ------- mask_safe : `~numpy.ndarray` Safe data range mask. """ geom, exposure = dataset._geom, dataset.exposure if self.fixed_offset: if observation: position = observation.pointing_radec.directional_offset_by( position_angle=0.0 * u.deg, separation=self.fixed_offset ) else: raise ValueError( f"observation argument is mandatory with {self.fixed_offset}" ) elif self.position: position = self.position else: position = geom.center_skydir aeff = exposure.get_spectrum(position) / exposure.meta["livetime"] model = TemplateSpectralModel.from_region_map(aeff) energy_true = model.energy energy_min = energy_true[np.where(model.values > 0)[0][0]] energy_max = energy_true[-1] aeff_thres = (self.aeff_percent / 100) * aeff.quantity.max() inversion = model.inverse( aeff_thres, energy_min=energy_min, energy_max=energy_max ) if not np.isnan(inversion[0]): energy_min = inversion[0] return geom.energy_mask(energy_min=energy_min)
[docs] def make_mask_energy_edisp_bias(self, dataset, observation=None): """Make safe energy mask from energy dispersion bias. Parameters ---------- dataset : `~gammapy.datasets.MapDataset` or `~gammapy.datasets.SpectrumDataset` Dataset to compute mask for. observation: `~gammapy.data.Observation` Observation to compute mask for. It is a mandatory argument when fixed_offset is set. Returns ------- mask_safe : `~numpy.ndarray` Safe data range mask. """ edisp, geom = dataset.edisp, dataset._geom position = None if self.fixed_offset: if observation: position = observation.pointing_radec.directional_offset_by( position_angle=0 * u.deg, separation=self.fixed_offset ) else: raise ValueError( f"{observation} argument is mandatory with {self.fixed_offset}" ) if isinstance(edisp, EDispKernelMap): if position: edisp = edisp.get_edisp_kernel(position=position) else: edisp = edisp.get_edisp_kernel(position=self.position) else: if position: e_reco = dataset._geom.axes["energy"].edges edisp = edisp.get_edisp_kernel(position=position, energy_axis=e_reco) else: e_reco = dataset._geom.axes["energy"].edges edisp = edisp.get_edisp_kernel( position=self.position, energy_axis=e_reco ) energy_min = edisp.get_bias_energy(self.bias_percent / 100) return geom.energy_mask(energy_min=energy_min[0])
[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 lower edge of the energy bin with the highest predicted background rate. This is to ensure analysis in a region where a Powerlaw approximation to the background spectrum is valid. The is motivated by its use in the HESS DL3 validation paper: https://arxiv.org/pdf/1910.08088.pdf Parameters ---------- dataset : `~gammapy.datasets.MapDataset` or `~gammapy.datasets.SpectrumDataset` Dataset to compute mask for. Returns ------- mask_safe : `~numpy.ndarray` Safe data range mask. """ geom = dataset._geom background_spectrum = dataset.npred_background().get_spectrum() idx = np.argmax(background_spectrum.data, axis=0) energy_axis = geom.axes["energy"] energy_min = energy_axis.edges[idx] return geom.energy_mask(energy_min=energy_min)
[docs] @staticmethod def make_mask_bkg_invalid(dataset): """Mask non-finite values and zeros values in background maps. Parameters ---------- dataset : `~gammapy.datasets.MapDataset` or `~gammapy.datasets.SpectrumDataset` Dataset to compute mask for. Returns ------- mask_safe : `~numpy.ndarray` Safe data range mask. """ bkg = dataset.background.data mask = np.isfinite(bkg) if not dataset.stat_type == "wstat": mask &= bkg > 0.0 return mask
[docs] def run(self, dataset, observation=None): """Make safe data range mask. Parameters ---------- dataset : `~gammapy.datasets.MapDataset` or `~gammapy.datasets.SpectrumDataset` Dataset to compute mask for. observation: `~gammapy.data.Observation` Observation to compute mask for. Returns ------- dataset : `Dataset` Dataset with defined safe range mask. """ if dataset.mask_safe: mask_safe = dataset.mask_safe.data else: mask_safe = np.ones(dataset._geom.data_shape, dtype=bool) if dataset.background is not None: # apply it first so only clipped values are removed for "bkg-peak" mask_safe &= self.make_mask_bkg_invalid(dataset) 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) dataset.mask_safe = Map.from_geom(dataset._geom, data=mask_safe, dtype=bool) return dataset