PSFKernel¶

class gammapy.cube.PSFKernel(psf_kernel_map)[source]¶

Bases: object

PSF kernel for Map.

This is a container class to store a PSF kernel that can be used to convolve WcsNDMap objects. It is usually computed from an EnergyDependentTablePSF.

Parameters:	psf_kernel_map : `Map` PSF kernel stored in a Map

Examples

import numpy as np
from gammapy.maps import Map, WcsGeom, MapAxis
from gammapy.irf import EnergyDependentMultiGaussPSF
from gammapy.cube import PSFKernel
from astropy import units as u

# Define energy axis
energy_axis = MapAxis.from_edges(np.logspace(-1., 1., 4), unit='TeV', name='energy')

# Create WcsGeom and map
geom = WcsGeom.create(binsz=0.02*u.deg, width=2.0*u.deg, axes=[energy_axis])
some_map = Map.from_geom(geom)
# Fill map at two positions
some_map.fill_by_coord([[0.2,0.4],[-0.1,0.6],[0.5,3.6]])

# 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')
table_psf = psf.to_energy_dependent_table_psf(theta=0.5*u.deg)

psf_kernel = PSFKernel.from_table_psf(table_psf,geom, max_radius=1*u.deg)

# Do the convolution
some_map_convolved = some_map.convolve(psf_kernel)

some_map_convolved.get_image_by_coord(dict(energy=0.6*u.TeV)).plot()

Attributes Summary

`data`	Access the PSFKernel numpy array
`psf_kernel_map`	The map object holding the kernel (`Map`)

Methods Summary

`from_gauss`(geom, sigma[, max_radius, …])	Create Gaussian PSF.
`from_table_psf`(table_psf, geom[, …])	Create a PSF kernel from a TablePSF or an EnergyDependentTablePSF on a given Geom.
`make_image`(self, exposures[, spectrum])	Make a 2D PSF from a PSF kernel.
`read`(\args, \\*kwargs)	Read kernel Map from file.
`write`(self, \args, \\*kwargs)	Write the Map object which contains the PSF kernel to file.

Attributes Documentation

data¶: Access the PSFKernel numpy array

psf_kernel_map¶: The map object holding the kernel (Map)

Methods Documentation

classmethod from_gauss(geom, sigma, max_radius=None, containment_fraction=0.99, factor=4)[source]¶

Create Gaussian PSF.

This is used for testing and examples. The map geometry parameters (pixel size, energy bins) are taken from geom. The Gaussian width sigma is a scalar,

TODO : support array input if it should vary along the energy axis.

Parameters:	geom : `WcsGeom` Map geometry sigma : `Angle` Gaussian width. max_radius : `Angle` Desired kernel map size. factor : int Oversample factor to compute the PSF
Returns:	kernel : `PSFKernel` the kernel Map with reduced geometry according to the max_radius

classmethod from_table_psf(table_psf, geom, max_radius=None, factor=4)[source]¶

Create a PSF kernel from a TablePSF or an EnergyDependentTablePSF on a given Geom.

If the Geom is not an image, the same kernel will be present on all axes.

The PSF is estimated by oversampling defined by a given factor.

Parameters:	table_psf : `TablePSF` or `EnergyDependentTablePSF` the input table PSF geom : `WcsGeom` the target geometry. The PSF kernel will be centered on the central pixel. The geometry axes should contain an axis with name “energy” max_radius : `Angle` the maximum radius of the PSF kernel. factor : int the oversample factor to compute the PSF
Returns:	kernel : `PSFKernel` the kernel Map with reduced geometry according to the max_radius

make_image(self, exposures, spectrum=None)[source]¶

Make a 2D PSF from a PSF kernel.

The PSF Kernel is first weighed with a spectrum and an array of exposures. The PSF is also normalised after summation.

Parameters:	exposures : `ndarray` An array of exposures for the same true energies as the PSF kernel spectrum : `SpectralModel` Spectral model to compute the weights. Default is power-law with spectral index of 2.
Returns:	psf2D : `Map` Weighted 2D psf

classmethod read(*args, **kwargs)[source]¶: Read kernel Map from file.

write(self, *args, **kwargs)[source]¶: Write the Map object which contains the PSF kernel to file.