# Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
import astropy.units as u
from astropy.coordinates import Angle
from gammapy.maps import Map, WcsGeom
from gammapy.modeling.models import PowerLawSpectralModel
from gammapy.utils.gauss import Gauss2DPDF
from .psf_table import EnergyDependentTablePSF, TablePSF
__all__ = ["PSFKernel"]
def _make_kernel_geom(geom, max_radius):
# Create a new geom object with an odd number of pixel and a maximum size
# This is useful for PSF kernel creation.
center = geom.center_skydir
binsz = Angle(np.abs(geom.wcs.wcs.cdelt[0]), "deg")
max_radius = Angle(max_radius)
npix = 2 * int(max_radius.deg / binsz.deg) + 1
return WcsGeom.create(
skydir=center,
binsz=binsz,
npix=npix,
proj=geom.projection,
frame=geom.frame,
axes=geom.axes,
)
[docs]class PSFKernel:
"""PSF kernel for `~gammapy.maps.Map`.
This is a container class to store a PSF kernel
that can be used to convolve `~gammapy.maps.WcsNDMap` objects.
It is usually computed from an `~gammapy.irf.EnergyDependentTablePSF`.
Parameters
----------
psf_kernel_map : `~gammapy.maps.Map`
PSF kernel stored in a Map
Examples
--------
::
import numpy as np
from gammapy.maps import Map, WcsGeom, MapAxis
from gammapy.irf import EnergyDependentMultiGaussPSF, 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()
"""
def __init__(self, psf_kernel_map, normalize=True):
self._psf_kernel_map = psf_kernel_map
if normalize:
self.normalize()
[docs] def normalize(self):
"""Force normalisation of the kernel"""
data = self.psf_kernel_map.data
if self.psf_kernel_map.geom.is_image:
axis = (0, 1)
else:
axis = (1, 2)
with np.errstate(divide="ignore", invalid="ignore"):
data = np.nan_to_num(data / data.sum(axis=axis, keepdims=True))
self.psf_kernel_map.data = data
@property
def data(self):
"""Access the PSFKernel numpy array"""
return self._psf_kernel_map.data
@property
def psf_kernel_map(self):
"""The map object holding the kernel (`~gammapy.maps.Map`)"""
return self._psf_kernel_map
[docs] @classmethod
def read(cls, *args, **kwargs):
"""Read kernel Map from file."""
psf_kernel_map = Map.read(*args, **kwargs)
return cls(psf_kernel_map)
[docs] @classmethod
def from_table_psf(cls, table_psf, geom, max_radius=None, factor=4):
"""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 : `~gammapy.irf.EnergyDependentTablePSF`
Input table PSF
geom : `~gammapy.maps.WcsGeom`
Target geometry. The PSF kernel will be centered on the central pixel.
The geometry axes should contain an axis with name "energy"
max_radius : `~astropy.coordinates.Angle`
Maximum radius of the PSF kernel.
factor : int
Oversample factor to compute the PSF
Returns
-------
kernel : `~gammapy.irf.PSFKernel`
the kernel Map with reduced geometry according to the max_radius
"""
# TODO : use PSF containment radius if max_radius is None
if max_radius is not None:
geom = _make_kernel_geom(geom, max_radius)
geom_upsampled = geom.upsample(factor=factor)
rad = geom_upsampled.separation(geom.center_skydir)
if isinstance(table_psf, EnergyDependentTablePSF):
energy_axis = geom.axes["energy_true"]
energy = energy_axis.center[:, np.newaxis, np.newaxis]
data = table_psf.evaluate(energy=energy, rad=rad).value
else:
try:
nbin = geom.axes[0].nbin
except IndexError:
nbin = 1
data = table_psf.evaluate(rad=rad).value
data = data * np.ones(nbin).reshape((-1, 1, 1))
kernel_map = Map.from_geom(geom=geom_upsampled, data=data)
kernel_map = kernel_map.downsample(factor, preserve_counts=True)
return cls(kernel_map, normalize=True)
[docs] @classmethod
def from_gauss(
cls, geom, sigma, max_radius=None, containment_fraction=0.99, factor=4
):
"""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 : `~gammapy.maps.WcsGeom`
Map geometry
sigma : `~astropy.coordinates.Angle`
Gaussian width.
max_radius : `~astropy.coordinates.Angle`
Desired kernel map size.
factor : int
Oversample factor to compute the PSF
Returns
-------
kernel : `~gammapy.irf.PSFKernel`
the kernel Map with reduced geometry according to the max_radius
"""
sigma = Angle(sigma)
if max_radius is None:
max_radius = (
Gauss2DPDF(sigma.deg).containment_radius(
containment_fraction=containment_fraction
)
* u.deg
)
max_radius = Angle(max_radius)
# Create a new geom according to given input
geom = _make_kernel_geom(geom, max_radius)
rad = Angle(np.linspace(0.0, max_radius.deg, 200), "deg")
table_psf = TablePSF.from_shape(shape="gauss", width=sigma, rad=rad)
return cls.from_table_psf(table_psf, geom=geom, factor=factor)
[docs] def write(self, *args, **kwargs):
"""Write the Map object which contains the PSF kernel to file."""
self.psf_kernel_map.write(*args, **kwargs)
[docs] def to_image(self, spectrum=None, exposure=None, keepdims=True):
"""Transform 3D PSFKernel into a 2D PSFKernel.
Parameters
----------
spectrum : `~gammapy.modeling.models.SpectralModel`
Spectral model to compute the weights.
Default is power-law with spectral index of 2.
exposure : `~astropy.units.Quantity` or `~numpy.ndarray`
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.
keepdims : bool
If true, the resulting PSFKernel wil keep an energy axis with one bin.
Default is True.
Returns
-------
weighted_kernel : `~gammapy.irf.PSFKernel`
the weighted kernel summed over energy
"""
map = self.psf_kernel_map
if spectrum is None:
spectrum = PowerLawSpectralModel(index=2.0)
if exposure is None:
exposure = np.ones(map.geom.axes[0].center.shape)
exposure = u.Quantity(exposure)
if exposure.shape != map.geom.axes[0].center.shape:
raise ValueError("Incorrect exposure_array shape")
# Compute weights vector
energy_edges = map.geom.axes["energy_true"].edges
weights = spectrum.integral(
energy_min=energy_edges[:-1], energy_max=energy_edges[1:], intervals=True
)
weights *= exposure
weights /= weights.sum()
spectrum_weighted_kernel = map.copy()
spectrum_weighted_kernel.quantity *= weights[:, np.newaxis, np.newaxis]
return self.__class__(spectrum_weighted_kernel.sum_over_axes(keepdims=keepdims))
[docs] def slice_by_idx(self, slices):
"""Slice by idx"""
kernel = self.psf_kernel_map.slice_by_idx(slices=slices)
return self.__class__(psf_kernel_map=kernel)