# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
from astropy.coordinates import Angle
from ..maps import WcsNDMap
__all__ = ["make_map_background_irf"]
[docs]def make_map_background_irf(pointing, ontime, bkg, geom):
"""Compute background map from background IRFs.
Parameters
----------
pointing : `~astropy.coordinates.SkyCoord`
Pointing direction
ontime : `~astropy.units.Quantity`
Observation ontime. i.e. not corrected for deadtime
see https://gamma-astro-data-formats.readthedocs.io/en/stable/irfs/full_enclosure/bkg/index.html#notes)
bkg : `~gammapy.irf.Background3D`
Background rate model
geom : `~gammapy.maps.WcsGeom`
Reference geometry
Returns
-------
background : `~gammapy.maps.WcsNDMap`
Background predicted counts sky cube in reco energy
"""
# Compute FOV coordinates; at the moment assume symmetric background model
# TODO: implement FOV coordinates properly
map_coord = geom.to_image().get_coord()
fov_lon = map_coord.skycoord.separation(pointing)
fov_lat = Angle(np.zeros_like(fov_lon), fov_lon.unit)
energy_axis = geom.get_axis_by_name("energy")
energies = energy_axis.edges * energy_axis.unit
bkg_de = bkg.evaluate_integrate(
fov_lon=fov_lon,
fov_lat=fov_lat,
energy_reco=energies[:, np.newaxis, np.newaxis],
)
d_omega = geom.solid_angle()
data = (bkg_de * d_omega * ontime).to_value("")
return WcsNDMap(geom, data=data)
def _fov_background_norm(acceptance_map, counts_map, exclusion_mask=None):
"""Compute FOV background norm
This operation is normally performed on single observation maps.
An exclusion map is used to avoid using regions with significant gamma-ray emission.
All maps are assumed to follow the same WcsGeom.
Parameters
----------
acceptance_map : `~gammapy.maps.WcsNDMap`
Observation hadron acceptance map (i.e. predicted background map)
counts_map : `~gammapy.maps.WcsNDMap`
Observation counts map
exclusion_mask : `~gammapy.maps.WcsNDMap`
Exclusion mask
Returns
-------
norm_factor : array
Background normalisation factor as function of energy (1D vector)
"""
if exclusion_mask is None:
mask = np.ones_like(counts_map, dtype=bool)
else:
# We resize the mask
mask = np.resize(np.squeeze(exclusion_mask.data), acceptance_map.data.shape)
# We multiply the data with the mask to obtain normalization factors in each energy bin
integ_acceptance = np.sum(acceptance_map.data * mask, axis=(1, 2))
integ_counts = np.sum(counts_map.data * mask, axis=(1, 2))
norm_factor = integ_counts / integ_acceptance
return norm_factor