PSFKernel#

class gammapy.irf.PSFKernel(psf_kernel_map, normalize=True)[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 PSFMap.

Parameters:

psf_kernel_mapMap: PSF kernel stored in a Map.

Examples

from gammapy.maps import Map, WcsGeom, MapAxis
from gammapy.irf import PSFMap
from astropy import units as u

# Define energy axis
energy_axis_true = MapAxis.from_energy_bounds(
    "0.1 TeV", "10 TeV", nbin=3, name="energy_true"
)

# Create WcsGeom and map
geom = WcsGeom.create(binsz=0.02 * u.deg, width=2.0 * u.deg, axes=[energy_axis_true])
some_map = Map.from_geom(geom)

# Fill map at three positions
some_map.fill_by_coord([[0, 0, 0], [0, 0, 0], [0.3, 1, 3]])

psf = PSFMap.from_gauss(
    energy_axis_true=energy_axis_true, sigma=[0.3, 0.2, 0.1] * u.deg
)

kernel = psf.get_psf_kernel(geom=geom, max_radius=1*u.deg)

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

some_map_convolved.plot_grid() # doctest: +SKIP

Attributes Summary

`data`	Access the PSFKernel numpy array.
`psf_kernel_map`	The map object holding the kernel as a `Map`.

Methods Summary

`from_gauss`(geom, sigma[, max_radius, factor])	Create Gaussian PSF.
`from_spatial_model`(model, geom[, ...])	Create PSF kernel from spatial model.
`normalize`()	Force normalisation of the kernel.
`peek`([figsize])	Quick-look summary plots.
`plot_kernel`([ax, energy, add_cbar])	Plot PSF kernel.
`read`(args, *kwargs)	Read kernel Map from file.
`slice_by_idx`(slices)	Slice by index.
`to_image`([spectral_model, exposure, keepdims])	Transform 3D PSFKernel into a 2D PSFKernel.
`write`(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 as a Map.

Methods Documentation

classmethod from_gauss(geom, sigma, max_radius=None, 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:

geomWcsGeom: Map geometry.
sigmaAngle: Gaussian width.
max_radiusAngle, optional: Desired kernel map size. Default is None.
factorint, optional: Oversample factor to compute the PSF. Default is 4.

Returns:

kernelPSFKernel: The kernel Map with reduced geometry according to the max_radius.

classmethod from_spatial_model(model, geom, max_radius=None, factor=4)[source]#

Create PSF kernel from spatial model.

Parameters:

geomWcsGeom: Map geometry.
modelSpatiaModel: Gaussian width.
max_radiusAngle, optional: Desired kernel map size. Default is None.
factorint, optional: Oversample factor to compute the PSF. Default is 4.

Returns:

kernelPSFKernel: The kernel Map with reduced geometry according to the max_radius.

normalize()[source]#: Force normalisation of the kernel.

peek(figsize=(15, 5))[source]#

Quick-look summary plots.

Parameters:

figsizetuple, optional: Size of the figure. Default is (15, 5).

plot_kernel(ax=None, energy=None, add_cbar=False, **kwargs)[source]#

Plot PSF kernel.

Parameters:

axAxes, optional: Matplotlib axes. Default is None.
energyQuantity, optional: If None, the PSF kernel is summed over the energy axis. Otherwise, the kernel corresponding to the energy bin including the given energy is shown.
add_cbarbool, optional: Add a colorbar. Default is False.
kwargs: dict: Keyword arguments passed to imshow.

Returns:

axAxes: Matplotlib axes.

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

slice_by_idx(slices)[source]#: Slice by index.

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

Transform 3D PSFKernel into a 2D PSFKernel.

Parameters:

spectral_modelSpectralModel, optional: Spectral model to compute the weights. Default is power-law with spectral index of 2.
exposureQuantity or ndarray, optional: 1D array containing exposure in each true energy bin. It must have the same size as the PSFKernel energy axis. Default is uniform exposure over energy.
keepdimsbool, optional: If true, the resulting PSFKernel will keep an energy axis with one bin. Default is True.

Returns:

weighted_kernelPSFKernel: The weighted kernel summed over energy.

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

PSFKernel#

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