# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import warnings
import numpy as np
import astropy.units as u
from astropy.coordinates import Angle
from astropy.table import Table
from gammapy.data import FixedPointingInfo
from gammapy.irf import BackgroundIRF, EDispMap, FoVAlignment, PSFMap
from gammapy.maps import Map, RegionNDMap
from gammapy.maps.utils import broadcast_axis_values_to_geom
from gammapy.modeling.models import PowerLawSpectralModel
from gammapy.stats import WStatCountsStatistic
from gammapy.utils.coordinates import sky_to_fov
from gammapy.utils.regions import compound_region_to_regions
__all__ = [
"make_counts_rad_max",
"make_edisp_kernel_map",
"make_edisp_map",
"make_map_background_irf",
"make_map_exposure_true_energy",
"make_psf_map",
"make_theta_squared_table",
"make_effective_livetime_map",
"make_observation_time_map",
]
log = logging.getLogger(__name__)
def _get_fov_coords(pointing, irf, geom, use_region_center=True, obstime=None):
# TODO: create dedicated coordinate handling see #5041
coords = {}
if isinstance(pointing, FixedPointingInfo):
# for backwards compatibility, obstime should be required
if obstime is None:
if isinstance(obstime, BackgroundIRF):
warnings.warn(
"Future versions of gammapy will require the obstime keyword for this function",
DeprecationWarning,
)
obstime = pointing.obstime
pointing_icrs = pointing.get_icrs(obstime)
else:
pointing_icrs = pointing
if not use_region_center:
region_coord, weights = geom.get_wcs_coord_and_weights()
sky_coord = region_coord.skycoord
else:
image_geom = geom.to_image()
map_coord = image_geom.get_coord()
sky_coord = map_coord.skycoord
if irf.has_offset_axis:
coords["offset"] = sky_coord.separation(pointing_icrs)
else:
if irf.fov_alignment == FoVAlignment.ALTAZ:
if not isinstance(pointing, FixedPointingInfo) and isinstance(
irf, BackgroundIRF
):
raise TypeError(
"make_map_background_irf requires FixedPointingInfo if "
"BackgroundIRF.fov_alignement is ALTAZ",
)
# for backwards compatibility, obstime should be required
if obstime is None:
warnings.warn(
"Future versions of gammapy will require the obstime keyword for this function",
DeprecationWarning,
)
obstime = pointing.obstime
pointing_altaz = pointing.get_altaz(obstime)
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.az, altaz_coord.alt, pointing_altaz.az, pointing_altaz.alt
)
elif irf.fov_alignment in [FoVAlignment.RADEC, FoVAlignment.REVERSE_LON_RADEC]:
fov_lon, fov_lat = sky_to_fov(
sky_coord.icrs.ra,
sky_coord.icrs.dec,
pointing_icrs.icrs.ra,
pointing_icrs.icrs.dec,
)
if irf.fov_alignment == FoVAlignment.REVERSE_LON_RADEC:
fov_lon = -fov_lon
else:
raise ValueError(
f"Unsupported background coordinate system: {irf.fov_alignment!r}"
)
coords["fov_lon"] = fov_lon
coords["fov_lat"] = fov_lat
return coords
[docs]
def make_map_exposure_true_energy(
pointing, livetime, aeff, geom, use_region_center=True
):
"""Compute exposure map.
This map has a true energy axis, the exposure is not combined
with energy dispersion.
Parameters
----------
pointing : `~astropy.coordinates.SkyCoord`
Pointing direction.
livetime : `~astropy.units.Quantity`
Livetime.
aeff : `~gammapy.irf.EffectiveAreaTable2D`
Effective area.
geom : `~gammapy.maps.WcsGeom`
Map geometry (must have an energy axis).
use_region_center : bool, optional
For geom as a `~gammapy.maps.RegionGeom`. If True, consider the values at the region center.
If False, average over the whole region.
Default is True.
Returns
-------
map : `~gammapy.maps.WcsNDMap`
Exposure map.
"""
coords = _get_fov_coords(
pointing=pointing,
geom=geom,
use_region_center=use_region_center,
irf=aeff,
obstime=None,
)
coords["energy_true"] = broadcast_axis_values_to_geom(geom, "energy_true")
exposure = aeff.evaluate(**coords)
data = (exposure * livetime).to("m2 s")
meta = {"livetime": livetime, "is_pointlike": aeff.is_pointlike}
if not use_region_center:
_, weights = geom.get_wcs_coord_and_weights()
data = np.average(data, axis=-1, weights=weights, keepdims=True)
return Map.from_geom(geom=geom, data=data.value, unit=data.unit, meta=meta)
def _map_spectrum_weight(map, spectrum=None):
"""Weight a map with a spectrum.
This requires map to have an "energy" axis.
The weights are normalised so that they sum to 1.
The mean and unit of the output image is the same as of the input cube.
At the moment this is used to get a weighted exposure image.
Parameters
----------
map : `~gammapy.maps.Map`
Input map with an "energy" axis.
spectrum : `~gammapy.modeling.models.SpectralModel`, optional
Spectral model to compute the weights.
Default is None, which is a power-law with spectral index of 2.
Returns
-------
map_weighted : `~gammapy.maps.Map`
Weighted image.
"""
if spectrum is None:
spectrum = PowerLawSpectralModel(index=2.0)
# Compute weights vector
for name in map.geom.axes.names:
if "energy" in name:
energy_name = name
energy_edges = map.geom.axes[energy_name].edges
weights = spectrum.integral(
energy_min=energy_edges[:-1], energy_max=energy_edges[1:]
)
weights /= weights.sum()
shape = np.ones(len(map.geom.data_shape))
shape[0] = -1
return map * weights.reshape(shape.astype(int))
[docs]
def make_map_background_irf(
pointing,
ontime,
bkg,
geom,
oversampling=None,
use_region_center=True,
obstime=None,
):
"""Compute background map from background IRFs.
Parameters
----------
pointing : `~gammapy.data.FixedPointingInfo` or `~astropy.coordinates.SkyCoord`
Observation pointing.
- If a `~gammapy.data.FixedPointingInfo` is passed, FOV coordinates
are properly computed.
- If a `~astropy.coordinates.SkyCoord` is passed, FOV frame rotation
is not taken into account.
ontime : `~astropy.units.Quantity`
Observation ontime. i.e. not corrected for deadtime
see https://gamma-astro-data-formats.readthedocs.io/en/latest/irfs/full_enclosure/bkg/index.html#notes) # noqa: E501
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.
use_region_center : bool, optional
For geom as a `~gammapy.maps.RegionGeom`. If True, consider the values at the region center.
If False, average over the whole region.
Default is True.
obstime : `~astropy.time.Time`
Observation time to use.
Returns
-------
background : `~gammapy.maps.WcsNDMap`
Background predicted counts sky cube in reconstructed 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_name="energy")
if not use_region_center:
image_geom = geom.to_wcs_geom().to_image()
region_coord, weights = geom.get_wcs_coord_and_weights()
idx = image_geom.coord_to_idx(region_coord)
d_omega = image_geom.solid_angle().T[idx]
else:
image_geom = geom.to_image()
d_omega = image_geom.solid_angle()
coords = _get_fov_coords(
pointing=pointing,
irf=bkg,
geom=geom,
use_region_center=use_region_center,
obstime=obstime,
)
coords["energy"] = broadcast_axis_values_to_geom(geom, "energy", False)
bkg_de = bkg.integrate_log_log(**coords, axis_name="energy")
data = (bkg_de * d_omega * ontime).to_value("")
if not use_region_center:
region_coord, weights = geom.get_wcs_coord_and_weights()
data = np.sum(weights * data, axis=2, keepdims=True)
bkg_map = Map.from_geom(geom, data=data)
if oversampling is not None:
bkg_map = bkg_map.downsample(factor=oversampling, axis_name="energy")
return bkg_map
[docs]
def make_psf_map(psf, pointing, geom, exposure_map=None):
"""Make a PSF map for a single observation.
Expected axes : rad and true energy in this specific order.
The name of the rad MapAxis is expected to be 'rad'.
Parameters
----------
psf : `~gammapy.irf.PSF3D`
The PSF IRF.
pointing : `~astropy.coordinates.SkyCoord`
The pointing direction.
geom : `~gammapy.maps.Geom`
The map geometry to be used. It provides the target geometry.
rad and true energy axes should be given in this specific order.
exposure_map : `~gammapy.maps.Map`, optional
The associated exposure map.
Default is None.
Returns
-------
psfmap : `~gammapy.irf.PSFMap`
The resulting PSF map.
"""
coords = _get_fov_coords(
pointing=pointing,
irf=psf,
geom=geom,
use_region_center=True,
obstime=None,
)
coords["energy_true"] = broadcast_axis_values_to_geom(geom, "energy_true")
coords["rad"] = broadcast_axis_values_to_geom(geom, "rad")
# Compute PSF values
data = psf.evaluate(**coords)
# Create Map and fill relevant entries
psf_map = Map.from_geom(geom, data=data.value, unit=data.unit)
psf_map.normalize(axis_name="rad")
return PSFMap(psf_map, exposure_map)
[docs]
def make_edisp_map(edisp, pointing, geom, exposure_map=None, use_region_center=True):
"""Make an edisp map for a single observation.
Expected axes : migra and true energy in this specific order.
The name of the migra MapAxis is expected to be 'migra'.
Parameters
----------
edisp : `~gammapy.irf.EnergyDispersion2D`
The 2D energy dispersion IRF.
pointing : `~astropy.coordinates.SkyCoord`
The pointing direction.
geom : `~gammapy.maps.Geom`
The map geometry to be used. It provides the target geometry.
migra and true energy axes should be given in this specific order.
exposure_map : `~gammapy.maps.Map`, optional
The associated exposure map.
Default is None.
use_region_center : bool, optional
For geom as a `~gammapy.maps.RegionGeom`. If True, consider the values at the region center.
If False, average over the whole region.
Default is True.
Returns
-------
edispmap : `~gammapy.irf.EDispMap`
The resulting energy dispersion map.
"""
coords = _get_fov_coords(pointing, edisp, geom, use_region_center=use_region_center)
coords["energy_true"] = broadcast_axis_values_to_geom(geom, "energy_true")
coords["migra"] = broadcast_axis_values_to_geom(geom, "migra")
# Compute EDisp values
data = edisp.evaluate(**coords)
if not use_region_center:
_, weights = geom.get_wcs_coord_and_weights()
data = np.average(data, axis=-1, weights=weights, keepdims=True)
# Create Map and fill relevant entries
edisp_map = Map.from_geom(geom, data=data.to_value(""), unit="")
edisp_map.normalize(axis_name="migra")
return EDispMap(edisp_map, exposure_map)
[docs]
def make_edisp_kernel_map(
edisp, pointing, geom, exposure_map=None, use_region_center=True
):
"""Make an edisp kernel map for a single observation.
Expected axes : (reco) energy and true energy in this specific order.
The name of the reco energy MapAxis is expected to be 'energy'.
The name of the true energy MapAxis is expected to be 'energy_true'.
Parameters
----------
edisp : `~gammapy.irf.EnergyDispersion2D`
The 2D energy dispersion IRF.
pointing : `~astropy.coordinates.SkyCoord`
The pointing direction.
geom : `~gammapy.maps.Geom`
The map geometry to be used. It provides the target geometry.
energy and true energy axes should be given in this specific order.
exposure_map : `~gammapy.maps.Map`, optional
The associated exposure map.
Default is None.
use_region_center : bool, optional
For geom as a `~gammapy.maps.RegionGeom`. If True, consider the values at the region center.
If False, average over the whole region.
Default is True.
Returns
-------
edispmap : `~gammapy.irf.EDispKernelMap`
the resulting EDispKernel map
"""
coords = _get_fov_coords(
pointing=pointing,
irf=edisp,
geom=geom,
use_region_center=use_region_center,
)
coords["energy_true"] = geom.axes["energy_true"].edges.reshape((-1, 1, 1, 1))
# Use EnergyDispersion2D migra axis.
migra_axis = edisp.axes["migra"]
# Create temporary EDispMap Geom
new_geom = geom.to_image().to_cube([migra_axis, geom.axes["energy_true"]])
edisp_map = make_edisp_map(
edisp, pointing, new_geom, exposure_map, use_region_center
)
return edisp_map.to_edisp_kernel_map(geom.axes["energy"])
[docs]
def make_theta_squared_table(
observations, theta_squared_axis, position, position_off=None
):
"""Make theta squared distribution in the same FoV for a list of `Observation` objects.
The ON theta2 profile is computed from a given distribution, on_position.
By default, the OFF theta2 profile is extracted from a mirror position
radially symmetric in the FOV to pos_on.
The ON and OFF regions are assumed to be of the same size, so the normalisation
factor between both region alpha = 1.
Parameters
----------
observations: `~gammapy.data.Observations`
List of observations.
theta_squared_axis : `~gammapy.maps.geom.MapAxis`
Axis of edges of the theta2 bin used to compute the distribution.
position : `~astropy.coordinates.SkyCoord`
Position from which the on theta^2 distribution is computed.
position_off : `astropy.coordinates.SkyCoord`
Position from which the OFF theta^2 distribution is computed.
Default is reflected position w.r.t. to the pointing position.
Returns
-------
table : `~astropy.table.Table`
Table containing the on counts, the off counts, acceptance, off acceptance and alpha
for each theta squared bin.
"""
if not theta_squared_axis.edges.unit.is_equivalent("deg2"):
raise ValueError("The theta2 axis should be equivalent to deg2")
table = Table()
table["theta2_min"] = theta_squared_axis.edges[:-1]
table["theta2_max"] = theta_squared_axis.edges[1:]
table["counts"] = 0
table["counts_off"] = 0
table["acceptance"] = 0.0
table["acceptance_off"] = 0.0
alpha_tot = np.zeros(len(table))
livetime_tot = 0
create_off = position_off is None
for observation in observations:
event_position = observation.events.radec
pointing = observation.get_pointing_icrs(observation.tmid)
separation = position.separation(event_position)
counts, _ = np.histogram(separation**2, theta_squared_axis.edges)
table["counts"] += counts
if create_off:
# Estimate the position of the mirror position
pos_angle = pointing.position_angle(position)
sep_angle = pointing.separation(position)
position_off = pointing.directional_offset_by(
pos_angle + Angle(np.pi, "rad"), sep_angle
)
# Angular distance of the events from the mirror position
separation_off = position_off.separation(event_position)
# Extract the ON and OFF theta2 distribution from the two positions.
counts_off, _ = np.histogram(separation_off**2, theta_squared_axis.edges)
table["counts_off"] += counts_off
# Normalisation between ON and OFF is one
acceptance = np.ones(theta_squared_axis.nbin)
acceptance_off = np.ones(theta_squared_axis.nbin)
table["acceptance"] += acceptance
table["acceptance_off"] += acceptance_off
alpha = acceptance / acceptance_off
alpha_tot += alpha * observation.observation_live_time_duration.to_value("s")
livetime_tot += observation.observation_live_time_duration.to_value("s")
alpha_tot /= livetime_tot
table["alpha"] = alpha_tot
stat = WStatCountsStatistic(table["counts"], table["counts_off"], table["alpha"])
table["excess"] = stat.n_sig
table["sqrt_ts"] = stat.sqrt_ts
table["excess_errn"] = stat.compute_errn()
table["excess_errp"] = stat.compute_errp()
table.meta["ON_RA"] = position.icrs.ra
table.meta["ON_DEC"] = position.icrs.dec
return table
[docs]
def make_counts_rad_max(geom, rad_max, events):
"""Extract the counts using for the ON region size the values in the `RAD_MAX_2D` table.
Parameters
----------
geom : `~gammapy.maps.RegionGeom`
Reference map geometry.
rad_max : `~gammapy.irf.RadMax2D`
Rhe RAD_MAX_2D table IRF.
events : `~gammapy.data.EventList`
Event list to be used to compute the ON counts.
Returns
-------
counts : `~gammapy.maps.RegionNDMap`
Counts vs estimated energy extracted from the ON region.
"""
selected_events = events.select_rad_max(
rad_max=rad_max, position=geom.region.center
)
counts = Map.from_geom(geom=geom)
counts.fill_events(selected_events)
return counts
def make_counts_off_rad_max(geom_off, rad_max, events):
"""Extract the OFF counts from a list of point regions and given rad max.
This method does **not** check for overlap of the regions defined by rad_max.
Parameters
----------
geom_off : `~gammapy.maps.RegionGeom`
Reference map geometry for the on region.
rad_max : `~gammapy.irf.RadMax2D`
The RAD_MAX_2D table IRF.
events : `~gammapy.data.EventList`
Event list to be used to compute the OFF counts.
Returns
-------
counts_off : `~gammapy.maps.RegionNDMap`
OFF Counts vs estimated energy extracted from the ON region.
"""
if not geom_off.is_all_point_sky_regions:
raise ValueError(
f"Only supports PointSkyRegions, got {geom_off.region} instead"
)
counts_off = RegionNDMap.from_geom(geom=geom_off)
for off_region in compound_region_to_regions(geom_off.region):
selected_events = events.select_rad_max(
rad_max=rad_max, position=off_region.center
)
counts_off.fill_events(selected_events)
return counts_off
[docs]
def make_observation_time_map(observations, geom, offset_max=None):
"""
Compute the total observation time on the target geometry
for a list of observations.
Parameters
----------
observations : `~gammapy.data.Observations`
Observations container containing list of observations.
geom : `~gammapy.maps.Geom`
Reference geometry.
offset_max : `~astropy.units.quantity.Quantity`, optional
The maximum offset FoV. Default is None.
If None, it will be taken from the IRFs.
Returns
-------
exposure : `~gammapy.maps.Map`
Effective livetime.
"""
geom = geom.to_image()
stacked = Map.from_geom(geom, unit=u.h)
for obs in observations:
if offset_max is None:
offset_max = guess_instrument_fov(obs)
coords = geom.get_coord(sparse=True)
offset = coords.skycoord.separation(obs.get_pointing_icrs(obs.tmid))
mask = offset < offset_max
c1 = coords.apply_mask(mask)
weights = np.ones(c1.shape) * obs.observation_live_time_duration
stacked.fill_by_coord(coords=c1, weights=weights)
return stacked
[docs]
def make_effective_livetime_map(observations, geom, offset_max=None):
"""
Compute the acceptance corrected livetime map
for a list of observations.
Parameters
----------
observations : `~gammapy.data.Observations`
Observations container containing list of observations.
geom : `~gammapy.maps.Geom`
Reference geometry.
offset_max : `~astropy.units.quantity.Quantity`, optional
The maximum offset FoV. Default is None.
Returns
-------
exposure : `~gammapy.maps.Map`
Effective livetime.
"""
livetime = Map.from_geom(geom, unit=u.hr)
for obs in observations:
if offset_max is None:
offset_max = guess_instrument_fov(obs)
geom_obs = geom.cutout(
position=obs.get_pointing_icrs(obs.tmid), width=2.0 * offset_max
)
coords = geom_obs.get_coord()
offset = coords.skycoord.separation(obs.get_pointing_icrs(obs.tmid))
mask = offset < offset_max
exposure = make_map_exposure_true_energy(
pointing=geom.center_skydir,
livetime=obs.observation_live_time_duration,
aeff=obs.aeff,
geom=geom_obs,
use_region_center=True,
)
on_axis = obs.aeff.evaluate(
offset=0.0 * u.deg, energy_true=geom.axes["energy_true"].center
)
on_axis = on_axis.reshape((on_axis.shape[0], 1, 1))
lv_obs = exposure * mask / on_axis
livetime.stack(lv_obs)
return livetime
def guess_instrument_fov(obs):
"""
Guess the camera field of view for the given observation
from the IRFs. This simply takes the maximum offset of the
effective area IRF.
TODO: This logic will break for more complex IRF models.
A better option would be to compute the offset at which
the effective area is above 10% of the maximum.
Parameters
----------
obs : `~gammapy.data.Observation`
Observation container.
Returns
-------
offset_max : `~astropy.units.quantity.Quantity`
The maximum offset of the effective area IRF.
"""
if "aeff" not in obs.available_irfs:
raise ValueError("No Effective area IRF to infer the FoV from")
if obs.aeff.is_pointlike:
raise ValueError("Cannot guess FoV from pointlike IRFs")
if "offset" not in obs.aeff.axes.names:
raise ValueError("Offset axis not present!")
return obs.aeff.axes["offset"].center[-1]
