PSFMap#

class gammapy.irf.PSFMap(psf_map, exposure_map=None)[source]#

Bases: 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

from astropy.coordinates import SkyCoord
from gammapy.maps import WcsGeom, MapAxis
from gammapy.data import Observation, FixedPointingInfo
from gammapy.irf import load_irf_dict_from_file
from gammapy.makers import MapDatasetMaker

# Define observation
pointing_position = SkyCoord(0, 0, unit="deg", frame="galactic")
pointing = FixedPointingInfo(
    fixed_icrs=pointing_position.icrs,
)
filename = "$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits"
irfs = load_irf_dict_from_file(filename)
obs = Observation.create(pointing=pointing, irfs=irfs, livetime="1h")

# Define energy axis. Note that the name is fixed.
energy_axis = MapAxis.from_energy_bounds("0.1 TeV", "10 TeV", nbin=3, name="energy_true")

# Define rad axis. Again note the axis name
rad_axis = MapAxis.from_bounds(0, 0.5, nbin=100, name="rad", unit="deg")

# Create WcsGeom
geom = WcsGeom.create(
    binsz=0.25, width="5 deg", skydir=pointing_position, axes=[rad_axis, energy_axis]
)

maker = MapDatasetMaker()
psf = maker.make_psf(geom=geom, observation=obs)

# Get a PSF kernel at the center of the image
upsample_geom = geom.upsample(factor=10).drop("rad")
psf_kernel = psf.get_psf_kernel(geom=upsample_geom)

Attributes Summary

`energy_name`
`has_single_spatial_bin`
`mask_safe_image`	Mask safe for the map.
`psf_map`
`required_axes`
`tag`

Methods Summary

`containment`(rad, energy_true[, position])	Containment at given coordinates.
`containment_radius`(fraction, energy_true[, ...])	Containment at given coordinates.
`containment_radius_map`(energy_true[, fraction])	Containment radius map.
`copy`()	Copy IRF map.
`cutout`(position, width[, mode, min_npix])	Cutout IRF map.
`downsample`(factor[, axis_name, weights])	Downsample the spatial dimension by a given factor.
`from_gauss`(energy_axis_true[, rad_axis, ...])	Create all-sky PSF map from Gaussian width.
`from_geom`(geom)	Create PSF map from geometry.
`from_hdulist`(hdulist[, hdu, hdu_bands, ...])	Create from `HDUList`.
`get_psf_kernel`(geom[, position, max_radius, ...])	Return a PSF kernel at the given position.
`normalize`()	Normalize PSF map.
`peek`([figsize])	Quick-look summary plots.
`plot_containment_radius_vs_energy`([ax, fraction])	Plot containment fraction as a function of energy.
`plot_psf_vs_rad`([ax, energy_true])	Plot PSF vs radius.
`read`(filename[, format, hdu, checksum])	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, nan_to_num])	Stack IRF map with another one in place.
`to_hdulist`([format])	Convert to `HDUList`.
`to_image`([spectral_model, keepdims])	Reduce to a 2D map after weighing with the associated exposure and a spectrum.
`to_region_nd_map`(region)	Extract IRFMap in a given region or position.
`write`(filename[, overwrite, format, checksum])	Write IRF map to fits.

Attributes Documentation

energy_name#

has_single_spatial_bin#

mask_safe_image#: Mask safe for the map.

psf_map#

required_axes = ['rad', 'energy_true']#

tag = 'psf_map'#

Methods Documentation

containment(rad, energy_true, position=None)[source]#

Containment at given coordinates.

Parameters:

radQuantity: Rad value.
energy_trueQuantity: Energy true value.
positionSkyCoord, optional: Sky position. If None, the center of the map is chosen. Default is None.

Returns:

containmentQuantity: Containment values.

containment_radius(fraction, energy_true, position=None)[source]#

Containment at given coordinates.

Parameters:

fractionfloat: Containment fraction.
energy_trueQuantity: Energy true value.
positionSkyCoord, optional: Sky position. If None, the center of the map is chosen. Default is None.

Returns:

containmentQuantity: Containment values.

containment_radius_map(energy_true, fraction=0.68)[source]#

Containment radius map.

Parameters:

energy_trueQuantity: Energy true at which to compute the containment radius.
fractionfloat, optional: Containment fraction (range: 0 to 1). Default is 0.68.

Returns:

containment_radius_mapMap: Containment radius map.

copy()#: Copy IRF map.

cutout(position, width, mode='trim', min_npix=3)#

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’}, optional: Mode option for Cutout2D, for details see Cutout2D. Default is “trim”.
min_npixbool, optional: Force width to a minimmum number of pixels. Default is 3. The default is 3 pixels so interpolation is done correctly if the binning of the IRF is larger than the width of the analysis region.

Returns:

cutoutIRFMap: Cutout IRF map.

downsample(factor, axis_name=None, weights=None)#

Downsample the spatial dimension by a given factor.

Parameters:

factorint: Downsampling factor.
axis_namestr: Axis to downsample. By default, spatial axes are downsampled.
weightsMap, optional: Map with weights downsampling. Default is None.

Returns:

mapIRFMap: Downsampled IRF map.

classmethod from_gauss(energy_axis_true, rad_axis=None, sigma=<Quantity 0.1 deg>, geom=None)[source]#

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.
geomGeom: Image geometry. By default, an all-sky geometry is created.

Returns:

psf_mapPSFMap: Point spread function map.

classmethod from_geom(geom)[source]#

Create PSF map from geometry.

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, format='gadf')#

Create from HDUList.

Parameters:

hdulistHDUList: HDU list.
hdustr, optional: Name or index of the HDU with the IRF map. Default is None.
hdu_bandsstr, optional: Name or index of the HDU with the IRF map BANDS table. Default is None.
exposure_hdustr, optional: Name or index of the HDU with the exposure map data. Default is None.
exposure_hdu_bandsstr, optional: Name or index of the HDU with the exposure map BANDS table. Default is None.
format{“gadf”, “gtpsf”}, optional: File format. Default is “gadf”.

Returns:

irf_mapIRFMap: IRF map.

get_psf_kernel(geom, position=None, max_radius=None, containment=0.999, precision_factor=12)[source]#

Return a PSF kernel at the given position.

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

Parameters:

geomGeom: Target geometry to use.
positionSkyCoord, optional: Target position. Should be a single coordinate. By default, the center position is used.
max_radiusAngle, optional: Maximum angular size of the kernel map. Default is None and it will be computed for the containment fraction set.
containmentfloat, optional: Containment fraction to use as size of the kernel. The radius can be overwritten using the max_radius argument. Default is 0.999.
precision_factorint, optional: Factor between the bin half-width of the geom and the median R68% containment radius. Used only for the oversampling method. Default is 10.

Returns:

kernelPSFKernel or list of PSFKernel: The resulting kernel.

normalize()[source]#: Normalize PSF map.

peek(figsize=(12, 10))[source]#

Quick-look summary plots.

Parameters:

figsizetuple: Size of figure.

plot_containment_radius_vs_energy(ax=None, fraction=(0.68, 0.95), **kwargs)[source]#

Plot containment fraction as a function of energy.

The method plots the containment radius at the center of the map.

Parameters:

axAxes, optional: Matplotlib axes. Default is None.
fractionlist of float or ndarray: Containment fraction between 0 and 1.
**kwargsdict: Keyword arguments passed to plot

Returns:

axAxes: Matplotlib axes.

plot_psf_vs_rad(ax=None, energy_true=<Quantity [ 0.1, 1., 10. ] TeV>, **kwargs)[source]#

Plot PSF vs radius.

The method plots the profile at the center of the map.

Parameters:

axAxes, optional: Matplotlib axes. Default is None.
energyQuantity: Energies where to plot the PSF.
**kwargsdict: Keyword arguments pass to plot.

Returns:

axAxes: Matplotlib axes.

classmethod read(filename, format='gadf', hdu=None, checksum=False)#

Read an IRF_map from file and create corresponding object.

Parameters:

filenamestr or Path: File name.
format{“gadf”, “gtpsf”}, optional: File format. Default is “gadf”.
hdustr or int: HDU location. Default is None.
checksumbool: If True checks both DATASUM and CHECKSUM cards in the file headers. Default is False.

Returns:

irf_mapPSFMap, EDispMap or EDispKernelMap: IRF map.

sample_coord(map_coord, random_state=0, chunk_size=10000)[source]#

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}, optional: Defines random number generator initialisation. Passed to get_random_state. Default is 0.
chunk_sizeint: If set, this will slice the input MapCoord into smaller chunks of chunk_size elements. Default is 10000.

Returns:

corr_coordMapCoord: 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: Dictionary 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 dictionary are kept unchanged.

Returns:

map_outIRFMap: Sliced IRF map object.

stack(other, weights=None, nan_to_num=True)#

Stack IRF map with another one in place.

Parameters:

otherIRFMap: IRF map to be stacked with this one.
weightsMap, optional: Map with stacking weights. Default is None.
nan_to_num: bool, optional: Non-finite values are replaced by zero if True. Default is True.

to_hdulist(format='gadf')#

Convert to HDUList.

Parameters:

format{“gadf”, “gtpsf”}, optional: File format. Default is “gadf”.

Returns:

hdu_listHDUList: HDU list.

to_image(spectral_model=None, keepdims=True)[source]#

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

Parameters:

spectral_modelSpectralModel, 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.

to_region_nd_map(region)#

Extract IRFMap in a given region or position.

If a region is given a mean IRF is computed, if a position is given the IRF is interpolated.

Parameters:

regionSkyRegion or SkyCoord: Region or position where to get the map.

Returns:

irfIRFMap: IRF map with region geometry.

write(filename, overwrite=False, format='gadf', checksum=False)#

Write IRF map to fits.

Parameters:

filenamestr or Path: Filename to write to.
overwritebool, optional: Overwrite existing file. Default is False.
format{“gadf”, “gtpsf”}, optional: File format. Default is “gadf”.
checksumbool, optional: When True adds both DATASUM and CHECKSUM cards to the headers written to the file. Default is False.