def kernel(self, pixel_size, offset_max=None,
discretize_model_kwargs=dict(factor=10)):
"""Make a 2-dimensional kernel image.
The kernel image is evaluated on a cartesian
grid with ``pixel_size`` spacing, not on the sphere.
Calls `astropy.convolution.discretize_model`,
allowing for accurate discretization.
Parameters
----------
pixel_size : `~astropy.coordinates.Angle` or `~astropy.units.Quantity`
Kernel pixel size
discretize_model_kwargs : dict
Keyword arguments passed to
`astropy.convolution.discretize_model`
Returns
-------
kernel : `numpy.array`
Kernel 2D image
Notes
-----
* In the future, `astropy.modeling.Fittable2DModel` and
`astropy.convolution.Model2DKernel` could be used to construct
the kernel.
"""
if not isinstance(pixel_size, Quantity):
raise ValueError("pixel_size must be an Angle or Quantity object.")
if offset_max == None:
offset_max = self._offset.max()
def _model(x, y):
"""Model in the appropriate format for discretize_model."""
offset = np.sqrt(x * x + y * y) * pixel_size
return self.eval(offset)
npix = int(offset_max.radian / pixel_size.radian)
pix_range = (-npix, npix + 1)
# FIXME: Using `discretize_model` is currently very cumbersome due to these issue:
# https://github.com/astropy/astropy/issues/2274
# https://github.com/astropy/astropy/issues/1763#issuecomment-39552900
#from astropy.modeling import Fittable2DModel
#
#class TempModel(Fittable2DModel):
# @staticmethod
# def eval(x, y):
# return 42 temp_model_function(x, y)
#
#temp_model = TempModel()
#import IPython; IPython.embed()
array = discretize_oversample_2D(_model,
x_range=pix_range, y_range=pix_range,
**discretize_model_kwargs)
return array
def kernel(self, pixel_size, offset_max=None, normalize=True,
discretize_model_kwargs=dict(factor=1)):
"""Make a 2-dimensional kernel image.
The kernel image is evaluated on a cartesian
grid with ``pixel_size`` spacing, not on the sphere.
Calls `astropy.convolution.discretize_model`,
allowing for accurate discretization.
Parameters
----------
pixel_size : `~astropy.coordinates.Angle`
Kernel pixel size
discretize_model_kwargs : dict
Keyword arguments passed to
`astropy.convolution.discretize_model`
Returns
-------
kernel : `~astropy.units.Quantity`
Kernel 2D image of Quantities
Notes
-----
* In the future, `astropy.modeling.Fittable2DModel` and
`astropy.convolution.Model2DKernel` could be used to construct
the kernel.
"""
if offset_max is None:
offset_max = self._offset.max()
def _model(x, y):
"""Model in the appropriate format for discretize_model."""
offset = np.sqrt(x * x + y * y) * pixel_size
return self.evaluate(offset)
npix = int(offset_max.radian / pixel_size.radian)
pix_range = (-npix, npix + 1)
# FIXME: Using `discretize_model` is currently very cumbersome due to these issue:
# https://github.com/astropy/astropy/issues/2274
# https://github.com/astropy/astropy/issues/1763#issuecomment-39552900
# from astropy.modeling import Fittable2DModel
#
# class TempModel(Fittable2DModel):
# @staticmethod
# def evaluate(x, y):
# return 42 temp_model_function(x, y)
#
# temp_model = TempModel()
array = discretize_oversample_2D(_model,
x_range=pix_range, y_range=pix_range,
**discretize_model_kwargs)
if normalize:
return array / array.value.sum()
else:
return array
def kernel(self,
reference,
containment=0.99,
normalize=True,
discretize_model_kwargs=dict(factor=10)):
"""
Make a 2-dimensional kernel image.
The kernel image is evaluated on a cartesian grid defined by the
reference sky image.
Parameters
----------
reference : `~gammapy.image.SkyImage` or `~gammapy.cube.SkyCube`
Reference sky image or sky cube defining the spatial grid.
containment : float
Minimal containment fraction of the kernel image.
normalize : bool
Whether to normalize the kernel.
Returns
-------
kernel : `~astropy.units.Quantity`
Kernel 2D image of Quantities
"""
from ..cube import SkyCube
rad_max = self.containment_radius(containment)
if isinstance(reference, SkyCube):
reference = reference.sky_image_ref
pixel_size = reference.wcs_pixel_scale()[0]
def _model(x, y):
"""Model in the appropriate format for discretize_model."""
rad = np.sqrt(x * x + y * y) * pixel_size
return self.evaluate(rad)
npix = int(rad_max.radian / pixel_size.radian)
pix_range = (-npix, npix + 1)
kernel = discretize_oversample_2D(_model,
x_range=pix_range,
y_range=pix_range,
**discretize_model_kwargs)
if normalize:
kernel = kernel / kernel.sum()
return kernel
def kernel(self, reference, containment=0.99, normalize=True,
discretize_model_kwargs=dict(factor=10)):
"""
Make a 2-dimensional kernel image.
The kernel image is evaluated on a cartesian grid defined by the
reference sky image.
Parameters
----------
reference : `~gammapy.image.SkyImage` or `~gammapy.cube.SkyCube`
Reference sky image or sky cube defining the spatial grid.
containment : float
Minimal containment fraction of the kernel image.
normalize : bool
Whether to normalize the kernel.
Returns
-------
kernel : `~astropy.units.Quantity`
Kernel 2D image of Quantities
"""
from ..cube import SkyCube
offset_max = self.containment_radius(containment)
if isinstance(reference, SkyCube):
reference = reference.sky_image_ref
pixel_size = reference.wcs_pixel_scale()[0]
def _model(x, y):
"""Model in the appropriate format for discretize_model."""
offset = np.sqrt(x * x + y * y) * pixel_size
return self.evaluate(offset)
npix = int(offset_max.radian / pixel_size.radian)
pix_range = (-npix, npix + 1)
kernel = discretize_oversample_2D(_model, x_range=pix_range, y_range=pix_range,
**discretize_model_kwargs)
if normalize:
return kernel / kernel.sum()
else:
return kernel
