Source code for gammapy.cube.background

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
from astropy.coordinates import SkyOffsetFrame
from import FixedPointingInfo
from gammapy.maps import WcsNDMap
from gammapy.utils.coordinates import sky_to_fov

__all__ = ["make_map_background_irf"]

[docs]def make_map_background_irf(pointing, ontime, bkg, geom, oversampling=None): """Compute background map from background IRFs. Parameters ---------- pointing : `` or `~astropy.coordinates.SkyCoord` Observation pointing - If a ``FixedPointingInfo`` is passed, FOV coordinates are properly computed. - If a ``SkyCoord`` is passed, FOV frame rotation is not taken into account. ontime : `~astropy.units.Quantity` Observation ontime. i.e. not corrected for deadtime see bkg : `~gammapy.irf.Background3D` Background rate model geom : `~gammapy.maps.WcsGeom` Reference geometry oversampling: int Oversampling factor in energy, used for the background model evaluation. Returns ------- background : `~gammapy.maps.WcsNDMap` Background predicted counts sky cube in reco energy """ # TODO: # This implementation can be improved in two ways: # 1. Create equal time intervals between TSTART and TSTOP and sum up the # background IRF for each interval. This is instead of multiplying by # the total ontime. This then handles the rotation of the FoV. # 2. Use the pointing table (does not currently exist in CTA files) to # obtain the RA DEC and time for each interval. This then considers that # the pointing might change slightly over the observation duration # Get altaz coords for map if oversampling is not None: geom = geom.upsample(factor=oversampling, axis="energy") map_coord = geom.to_image().get_coord() sky_coord = map_coord.skycoord if isinstance(pointing, FixedPointingInfo): altaz_coord = sky_coord.transform_to(pointing.altaz_frame) # Compute FOV coordinates of map relative to pointing fov_lon, fov_lat = sky_to_fov(, altaz_coord.alt,, pointing.altaz.alt ) else: # Create OffsetFrame frame = SkyOffsetFrame(origin=pointing) pseudo_fov_coord = sky_coord.transform_to(frame) fov_lon = pseudo_fov_coord.lon fov_lat = energies = geom.get_axis_by_name("energy").edges bkg_de = bkg.evaluate_integrate( fov_lon=fov_lon, fov_lat=fov_lat, energy_reco=energies[:, np.newaxis, np.newaxis], ) d_omega = geom.to_image().solid_angle() data = (bkg_de * d_omega * ontime).to_value("") bkg_map = WcsNDMap(geom, data=data) if oversampling is not None: bkg_map = bkg_map.downsample(factor=oversampling, axis="energy") return bkg_map