# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import astropy.units as u
from astropy.table import Table
from regions import PointSkyRegion
from gammapy.irf import EDispKernelMap, PSFMap, RecoPSFMap
from gammapy.maps import Map
from .core import Maker
from .utils import (
make_counts_rad_max,
make_edisp_kernel_map,
make_edisp_map,
make_map_background_irf,
make_map_exposure_true_energy,
make_psf_map,
)
__all__ = ["MapDatasetMaker"]
log = logging.getLogger(__name__)
[docs]class MapDatasetMaker(Maker):
"""Make binned maps for a single IACT observation.
Parameters
----------
selection : list
List of str, selecting which maps to make.
Available: 'counts', 'exposure', 'background', 'psf', 'edisp'
By default, all maps are made.
background_oversampling : int
Background evaluation oversampling factor in energy.
background_interp_missing_data: bool
Interpolate missing values in background 3d map.
Default is True, have to be set to True for CTA IRF.
background_pad_offset: bool
Pad one bin in offset for 2d background map.
This avoid extrapolation at edges and use the nearest value.
Default is True.
Examples
--------
This example shows how to run the MapMaker for a single observation
>>> from gammapy.data import DataStore
>>> from gammapy.datasets import MapDataset
>>> from gammapy.maps import WcsGeom, MapAxis
>>> from gammapy.makers import MapDatasetMaker
>>> #load an observation
>>> data_store = DataStore.from_dir("$GAMMAPY_DATA/hess-dl3-dr1")
>>> obs = data_store.obs(23523)
>>> #prepare the geom
>>> energy_axis = MapAxis.from_energy_bounds(1.0, 10.0, 4, unit="TeV")
>>> energy_axis_true = MapAxis.from_energy_bounds( 0.5, 20, 10, unit="TeV", name="energy_true")
>>> geom = WcsGeom.create(
skydir=(83.633, 22.014),
binsz=0.02,
width=(2, 2),
frame="icrs",
proj="CAR",
axes=[energy_axis],
)
>>> #Run the maker
>>> empty = MapDataset.create(geom=geom, energy_axis_true=energy_axis_true, name="empty")
>>> maker = MapDatasetMaker()
>>> dataset = maker.run(empty, obs)
>>> print(dataset)
MapDataset
----------
<BLANKLINE>
Name : empty
<BLANKLINE>
Total counts : 787
Total background counts : 684.52
Total excess counts : 102.48
<BLANKLINE>
Predicted counts : 684.52
Predicted background counts : 684.52
Predicted excess counts : nan
<BLANKLINE>
Exposure min : 7.01e+07 m2 s
Exposure max : 1.10e+09 m2 s
<BLANKLINE>
Number of total bins : 40000
Number of fit bins : 40000
<BLANKLINE>
Fit statistic type : cash
Fit statistic value (-2 log(L)) : nan
<BLANKLINE>
Number of models : 0
Number of parameters : 0
Number of free parameters : 0
"""
tag = "MapDatasetMaker"
available_selection = ["counts", "exposure", "background", "psf", "edisp"]
def __init__(
self,
selection=None,
background_oversampling=None,
background_interp_missing_data=True,
background_pad_offset=True,
):
self.background_oversampling = background_oversampling
self.background_interp_missing_data = background_interp_missing_data
self.background_pad_offset = background_pad_offset
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.
**NOTE for 1D analysis:** if the `~gammapy.maps.Geom` is built from a
`~regions.CircleSkyRegion`, the latter will be directly used to extract
the counts. If instead the `~gammapy.maps.Geom` is built from a
`~regions.PointSkyRegion`, the size of the ON region is taken from
the `RAD_MAX_2D` table containing energy-dependent theta2 cuts.
Parameters
----------
geom : `~gammapy.maps.Geom`
Reference map geom.
observation : `~gammapy.data.Observation`
Observation container.
Returns
-------
counts : `~gammapy.maps.Map`
Counts map.
"""
if geom.is_region and isinstance(geom.region, PointSkyRegion):
counts = make_counts_rad_max(geom, observation.rad_max, observation.events)
else:
counts = Map.from_geom(geom)
counts.fill_events(observation.events)
return counts
[docs] @staticmethod
def make_exposure(geom, observation, use_region_center=True):
"""Make exposure map.
Parameters
----------
geom : `~gammapy.maps.Geom`
Reference map geom.
observation : `~gammapy.data.Observation`
Observation container.
Returns
-------
exposure : `~gammapy.maps.Map`
Exposure map.
"""
if isinstance(observation.aeff, Map):
return observation.aeff.interp_to_geom(
geom=geom,
)
return make_map_exposure_true_energy(
pointing=observation.get_pointing_icrs(observation.tmid),
livetime=observation.observation_live_time_duration,
aeff=observation.aeff,
geom=geom,
use_region_center=use_region_center,
)
[docs] @staticmethod
def make_exposure_irf(geom, observation, use_region_center=True):
"""Make exposure map with irf geometry.
Parameters
----------
geom : `~gammapy.maps.Geom`
Reference geom.
observation : `~gammapy.data.Observation`
Observation container.
Returns
-------
exposure : `~gammapy.maps.Map`
Exposure map.
"""
return make_map_exposure_true_energy(
pointing=observation.get_pointing_icrs(observation.tmid),
livetime=observation.observation_live_time_duration,
aeff=observation.aeff,
geom=geom,
use_region_center=use_region_center,
)
[docs] def make_background(self, geom, observation):
"""Make background map.
Parameters
----------
geom : `~gammapy.maps.Geom`
Reference geom.
observation : `~gammapy.data.Observation`
Observation container.
Returns
-------
background : `~gammapy.maps.Map`
Background map.
"""
bkg = observation.bkg
if isinstance(bkg, Map):
return bkg.interp_to_geom(geom=geom, preserve_counts=True)
use_region_center = getattr(self, "use_region_center", True)
if self.background_interp_missing_data:
bkg.interp_missing_data(axis_name="energy")
if self.background_pad_offset and bkg.has_offset_axis:
bkg = bkg.pad(1, mode="edge", axis_name="offset")
return make_map_background_irf(
pointing=observation.pointing,
ontime=observation.observation_time_duration,
bkg=bkg,
geom=geom,
oversampling=self.background_oversampling,
use_region_center=use_region_center,
obstime=observation.tmid,
)
[docs] def make_edisp(self, geom, observation):
"""Make energy dispersion map.
Parameters
----------
geom : `~gammapy.maps.Geom`
Reference geom.
observation : `~gammapy.data.Observation`
Observation container.
Returns
-------
edisp : `~gammapy.irf.EDispMap`
Edisp map.
"""
exposure = self.make_exposure_irf(geom.squash(axis_name="migra"), observation)
use_region_center = getattr(self, "use_region_center", True)
return make_edisp_map(
edisp=observation.edisp,
pointing=observation.get_pointing_icrs(observation.tmid),
geom=geom,
exposure_map=exposure,
use_region_center=use_region_center,
)
[docs] def make_edisp_kernel(self, geom, observation):
"""Make energy dispersion kernel map.
Parameters
----------
geom : `~gammapy.maps.Geom`
Reference geom. Must contain "energy" and "energy_true" axes in that order.
observation : `~gammapy.data.Observation`
Observation container.
Returns
-------
edisp : `~gammapy.irf.EDispKernelMap`
EdispKernel map.
"""
if isinstance(observation.edisp, EDispKernelMap):
exposure = None
interp_map = observation.edisp.edisp_map.interp_to_geom(geom)
return EDispKernelMap(edisp_kernel_map=interp_map, exposure_map=exposure)
exposure = self.make_exposure_irf(geom.squash(axis_name="energy"), observation)
use_region_center = getattr(self, "use_region_center", True)
return make_edisp_kernel_map(
edisp=observation.edisp,
pointing=observation.get_pointing_icrs(observation.tmid),
geom=geom,
exposure_map=exposure,
use_region_center=use_region_center,
)
[docs] def make_psf(self, geom, observation):
"""Make psf map.
Parameters
----------
geom : `~gammapy.maps.Geom`
Reference geom.
observation : `~gammapy.data.Observation`
Observation container.
Returns
-------
psf : `~gammapy.irf.PSFMap`
Psf map.
"""
psf = observation.psf
if isinstance(psf, RecoPSFMap):
return RecoPSFMap(psf.psf_map.interp_to_geom(geom))
elif isinstance(psf, PSFMap):
return PSFMap(psf.psf_map.interp_to_geom(geom))
exposure = self.make_exposure_irf(geom.squash(axis_name="rad"), observation)
return make_psf_map(
psf=psf,
pointing=observation.get_pointing_icrs(observation.tmid),
geom=geom,
exposure_map=exposure,
)
[docs] def run(self, dataset, observation):
"""Make map dataset.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Reference dataset.
observation : `~gammapy.data.Observation`
Observation
Returns
-------
dataset : `~gammapy.datasets.MapDataset`
Map dataset.
"""
kwargs = {"gti": observation.gti}
kwargs["meta_table"] = self.make_meta_table(observation)
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)
else:
counts = Map.from_geom(dataset.counts.geom, data=0)
kwargs["counts"] = counts
if "exposure" in self.selection:
exposure = self.make_exposure(dataset.exposure.geom, observation)
kwargs["exposure"] = exposure
if "background" in self.selection:
kwargs["background"] = self.make_background(
dataset.counts.geom, observation
)
if "psf" in self.selection:
psf = self.make_psf(dataset.psf.psf_map.geom, observation)
kwargs["psf"] = psf
if "edisp" in self.selection:
if dataset.edisp.edisp_map.geom.axes[0].name.upper() == "MIGRA":
edisp = self.make_edisp(dataset.edisp.edisp_map.geom, observation)
else:
edisp = self.make_edisp_kernel(
dataset.edisp.edisp_map.geom, observation
)
kwargs["edisp"] = edisp
return dataset.__class__(name=dataset.name, **kwargs)