PSFMap

class gammapy.irf.PSFMap(psf_map, exposure_map=None)

Bases: gammapy.irf.irf_map.IRFMap

Class containing the Map of PSFs and allowing to interact with it.

Parameters

psf_mapMap

the input PSF Map. Should be a Map with 2 non spatial axes. rad and true energy axes should be given in this specific order.

exposure_mapMap

Associated exposure map. Needs to have a consistent map geometry.

Examples

import numpy as np
from astropy import units as u
from astropy.coordinates import SkyCoord
from gammapy.maps import Map, WcsGeom, MapAxis
from gammapy.irf import EnergyDependentMultiGaussPSF, EffectiveAreaTable2D
from gammapy.makers.utils import make_psf_map, PSFMap, make_map_exposure_true_energy

# Define energy axis. Note that the name is fixed.
energy_axis = MapAxis.from_edges(np.logspace(-1., 1., 4), unit='TeV', name='energy')
# Define rad axis. Again note the axis name
rads = np.linspace(0., 0.5, 100) * u.deg
rad_axis = MapAxis.from_edges(rads, unit='deg', name='theta')

# Define parameters
pointing = SkyCoord(0., 0., unit='deg')
max_offset = 4 * u.deg

# Create WcsGeom
geom = WcsGeom.create(binsz=0.25*u.deg, width=10*u.deg, skydir=pointing, axes=[rad_axis, energy_axis])

# Extract EnergyDependentTablePSF from CTA 1DC IRF
filename = '$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits'
psf = EnergyDependentMultiGaussPSF.read(filename, hdu='POINT SPREAD FUNCTION')
psf3d = psf.to_psf3d(rads)
aeff2d = EffectiveAreaTable2D.read(filename, hdu='EFFECTIVE AREA')

# Create the exposure map
exposure_geom = geom.to_image().to_cube([energy_axis])
exposure_map = make_map_exposure_true_energy(pointing, "1 h", aeff2d, exposure_geom)

# create the PSFMap for the specified pointing
psf_map = make_psf_map(psf3d, pointing, geom, max_offset, exposure_map)

# Get an EnergyDependentTablePSF at any position in the image
psf_table = psf_map.get_energy_dependent_table_psf(SkyCoord(2., 2.5, unit='deg'))

# Write map to disk
psf_map.write('psf_map.fits')

Attributes Summary

psf_map
tag

Methods Summary

containment_radius_map(energy[, fraction])	Containment radius map.
copy()	Copy IRF map
cutout(position, width[, mode])	Cutout IRF map.
downsample(factor[, axis, weights])	Downsample the spatial dimension by a given factor.
from_energy_dependent_table_psf(table_psf[, …])	Create PSF map from table PSF object.
from_gauss(energy_axis_true[, rad_axis, sigma])	Create all -sky PSF map from Gaussian width.
from_geom(geom)	Create psf map from geom.
from_hdulist(hdulist[, hdu, hdu_bands, …])	Create from HDUList.
get_energy_dependent_table_psf([position])	Get energy-dependent PSF at a given position.
get_psf_kernel(position, geom[, max_radius, …])	Returns a PSF kernel at the given position.
read(filename[, hdu])	Read an IRF_map from file and create corresponding object
sample_coord(map_coord[, random_state])	Apply PSF corrections on the coordinates of a set of simulated events.
slice_by_idx(slices)	Slice sub dataset.
stack(other[, weights])	Stack IRF map with another one in place.
to_hdulist()	Convert to HDUList.
to_image([spectrum, keepdims])	Reduce to a 2-D map after weighing with the associated exposure and a spectrum
write(filename[, overwrite])	Write IRF map to fits

Attributes Documentation

psf_map

tag = 'psf_map'

Methods Documentation

containment_radius_map(energy, fraction=0.68)

Containment radius map.

Parameters

energyQuantity

Scalar energy at which to compute the containment radius

fractionfloat

the containment fraction (range: 0 to 1)

Returns

containment_radius_mapMap

Containment radius map

copy()

Copy IRF map

cutout(position, width, mode='trim')

Cutout IRF map.

Parameters

positionSkyCoord

Center position of the cutout region.

widthtuple of Angle

Angular sizes of the region in (lon, lat) in that specific order. If only one value is passed, a square region is extracted.

mode{‘trim’, ‘partial’, ‘strict’}

Mode option for Cutout2D, for details see Cutout2D.

Returns

cutoutIRFMap

Cutout IRF map.

downsample(factor, axis=None, weights=None)

Downsample the spatial dimension by a given factor.

Parameters

factorint

Downsampling factor.

axisstr

Which axis to downsample. By default spatial axes are downsampled.

weightsMap

Map with weights downsampling.

Returns

mapIRFMap

Downsampled irf map.

classmethod from_energy_dependent_table_psf(table_psf, geom=None)

Create PSF map from table PSF object.

Helper function to create an allsky PSF map from table PSF, which does not depend on position.

Parameters

table_psfEnergyDependentTablePSF

Table PSF

Returns

psf_mapPSFMap

Point spread function map.

classmethod from_gauss(energy_axis_true, rad_axis=None, sigma=<Quantity 0.1 deg>)

Create all -sky PSF map from Gaussian width.

This is used for testing and examples.

The width can be the same for all energies or be an array with one value per energy node. It does not depend on position.

Parameters

energy_axis_trueMapAxis

True energy axis.

rad_axisMapAxis

Offset angle wrt source position axis.

sigmaAngle

Gaussian width.

Returns

psf_mapPSFMap

Point spread function map.

classmethod from_geom(geom)

Create psf map from geom.

Parameters

geomGeom

PSF map geometry.

Returns

psf_mapPSFMap

Point spread function map.

classmethod from_hdulist(hdulist, hdu=None, hdu_bands=None, exposure_hdu=None, exposure_hdu_bands=None)

Create from HDUList.

Parameters

hdulistHDUList

HDU list.

hdustr

Name or index of the HDU with the IRF map.

hdu_bandsstr

Name or index of the HDU with the IRF map BANDS table.

exposure_hdustr

Name or index of the HDU with the exposure map data.

exposure_hdu_bandsstr

Name or index of the HDU with the exposure map BANDS table.

Returns

irf_mapIRFMap

IRF map.

get_energy_dependent_table_psf(position=None)

Get energy-dependent PSF at a given position.

By default the PSF at the center of the map is returned.

Parameters

positionSkyCoord

the target position. Should be a single coordinates

Returns

psf_tableEnergyDependentTablePSF

the table PSF

get_psf_kernel(position, geom, max_radius=None, factor=4)

Returns a PSF kernel at the given position.

The PSF is returned in the form a WcsNDMap defined by the input Geom.

Parameters

positionSkyCoord

the target position. Should be a single coordinate

geomGeom

the target geometry to use

max_radiusAngle

maximum angular size of the kernel map

factorint

oversampling factor to compute the PSF

Returns

kernelPSFKernel

the resulting kernel

classmethod read(filename, hdu=None)

Read an IRF_map from file and create corresponding object

sample_coord(map_coord, random_state=0)

Apply PSF corrections on the coordinates of a set of simulated events.

Parameters

map_coordMapCoord object.

Sequence of coordinates and energies of sampled events.

random_state{int, ‘random-seed’, ‘global-rng’, RandomState}

Defines random number generator initialisation. Passed to get_random_state.

Returns

corr_coordMapCoord object.

Sequence of PSF-corrected coordinates of the input map_coord map.

slice_by_idx(slices)

Slice sub dataset.

The slicing only applies to the maps that define the corresponding axes.

Parameters

slicesdict

Dict of axes names and integers or slice object pairs. Contains one element for each non-spatial dimension. For integer indexing the corresponding axes is dropped from the map. Axes not specified in the dict are kept unchanged.

Returns

map_outIRFMap

Sliced irf map object.

stack(other, weights=None)

Stack IRF map with another one in place.

Parameters

otherIRFMap

IRF map to be stacked with this one.

weightsMap

Map with stacking weights.

to_hdulist()

Convert to HDUList.

Returns

hdu_listHDUList

HDU list.

to_image(spectrum=None, keepdims=True)

Reduce to a 2-D map after weighing with the associated exposure and a spectrum

Parameters

spectrumSpectralModel, optional

Spectral model to compute the weights. Default is power-law with spectral index of 2.

keepdimsbool, optional

If True, the energy axis is kept with one bin. If False, the axis is removed

Returns

psf_outPSFMap

PSFMap with the energy axis summed over

write(filename, overwrite=False, **kwargs)

Write IRF map to fits