def make_background_image():
"""Estimate background image.
See the `IterativeKernelBackgroundEstimator` tutorial and
documentation how it works, or the SciNeGHe 2014 proceeding
by Ellis Owen et al.
"""
radius = Angle(0.2, 'deg')
r_in = Angle(0.3, 'deg')
r_out = Angle(0.7, 'deg')
significance_threshold = 5
mask_dilation_radius = Angle(0.1, 'deg')
max_iterations = 3
hdu = fits.open(COUNTS_IMAGE)['COUNTS']
binsz = hdu.header['CDELT2']
images = GammaImages(counts=hdu.data, header=hdu.header)
# TODO: we should have utility functions to initialise
# kernels with angles so that we don't have to convert to pix here.
source_kernel = binary_disk(radius=radius.deg / binsz)
background_kernel = binary_ring(r_in=r_in.deg / binsz,
r_out=r_out.deg / binsz)
estimator = IterativeKernelBackgroundEstimator(
images=images,
source_kernel=source_kernel,
background_kernel=background_kernel,
significance_threshold=significance_threshold,
mask_dilation_radius=mask_dilation_radius.deg / binsz,
)
print('Running background estimation ...')
estimator.run(max_iterations=max_iterations)
print('Writing {}'.format(MASK_IMAGE))
estimator.mask_image_hdu.writeto(MASK_IMAGE, clobber=True)
print('Writing {}'.format(BACKGROUND_IMAGE))
estimator.background_image_hdu.writeto(BACKGROUND_IMAGE, clobber=True)
def make_background_image():
"""Estimate background image.
See the `KernelBackgroundEstimator` tutorial and
documentation how it works, or the SciNeGHe 2014 proceeding
by Ellis Owen et al.
"""
radius = Angle(0.2, 'deg')
r_in = Angle(0.3, 'deg')
r_out = Angle(0.7, 'deg')
significance_threshold = 5
mask_dilation_radius = Angle(0.1, 'deg')
max_iterations = 3
hdu = fits.open(COUNTS_IMAGE)['COUNTS']
binsz = hdu.header['CDELT2']
images = KernelBackgroundEstimatorData(counts=hdu.data, header=hdu.header)
# TODO: we should have utility functions to initialise
# kernels with angles so that we don't have to convert to pix here.
source_kernel = binary_disk(radius=radius.deg/binsz)
background_kernel = binary_ring(r_in=r_in.deg/binsz,
r_out=r_out.deg/binsz)
estimator = KernelBackgroundEstimator(
images=images, source_kernel=source_kernel, background_kernel=background_kernel,
significance_threshold=significance_threshold,
mask_dilation_radius=mask_dilation_radius.deg/binsz,
)
print('Running background estimation ...')
estimator.run(max_iterations=max_iterations)
print('Writing {}'.format(MASK_IMAGE))
estimator.mask_image_hdu.writeto(MASK_IMAGE, clobber=True)
print('Writing {}'.format(BACKGROUND_IMAGE))
estimator.background_image_hdu.writeto(BACKGROUND_IMAGE, clobber=True)
energy_exp = Quantity([10000, 500000], 'MeV')
exposure_data = Quantity(exposure_hdu.data, '')
exposure_spec_cube = SpectralCube(data=exposure_data,
wcs=exposure_wcs,
energy=energy_exp)
exposure_spec_cube = exposure_spec_cube.reproject_to(flux_spec_cube)
counts_data = flux_spec_cube.data * exposure_spec_cube.data
counts = fits.ImageHDU(data=counts_data[0], header=flux_hdu.header)
# Start with flat background estimate
# Background must be provided as an ImageHDU
background_data = np.ones_like(counts_data, dtype=float)
background = fits.ImageHDU(data=background_data[0], header=flux_hdu.header)
images = GammaImages(counts=counts, background=background)
source_kernel = binary_disk(CORRELATION_RADIUS).astype(float)
source_kernel /= source_kernel.sum()
background_kernel = np.ones((5, 100))
background_kernel /= background_kernel.sum()
# *** ITERATOR ***
ibe = IterativeKernelBackgroundEstimator(
images=images,
source_kernel=source_kernel,
background_kernel=background_kernel,
significance_threshold=SIGNIFICANCE_THRESHOLD,
mask_dilation_radius=MASK_DILATION_RADIUS)
mask, new_background = ibe.run()
exposure_hdu = fits.open(exposure_file)[0]
exposure_wcs = WCS(exposure_hdu.header)
energy_exp = Quantity([10000, 500000], "MeV")
exposure_data = Quantity(exposure_hdu.data, "")
exposure_spec_cube = SpectralCube(data=exposure_data, wcs=exposure_wcs, energy=energy_exp)
exposure_spec_cube = exposure_spec_cube.reproject_to(flux_spec_cube)
counts_data = flux_spec_cube.data * exposure_spec_cube.data
counts = fits.ImageHDU(data=counts_data[0], header=flux_hdu.header)
# Start with flat background estimate
# Background must be provided as an ImageHDU
background_data = np.ones_like(counts_data, dtype=float)
background = fits.ImageHDU(data=background_data[0], header=flux_hdu.header)
images = GammaImages(counts=counts, background=background)
source_kernel = binary_disk(CORRELATION_RADIUS).astype(float)
source_kernel /= source_kernel.sum()
background_kernel = np.ones((5, 100))
background_kernel /= background_kernel.sum()
# *** ITERATOR ***
ibe = IterativeKernelBackgroundEstimator(
images=images,
source_kernel=source_kernel,
background_kernel=background_kernel,
significance_threshold=SIGNIFICANCE_THRESHOLD,
mask_dilation_radius=MASK_DILATION_RADIUS,
)
from astropy.utils.data import download_file
from gammapy.datasets import FermiGalacticCenter
from gammapy.background import IterativeKernelBackgroundEstimator, GammaImages
from gammapy.irf import EnergyDependentTablePSF
from gammapy.image import make_empty_image, catalog_image, binary_disk
from gammapy.image.utils import cube_to_image, solid_angle
counts = fits.open('fermi_counts_galactic.fits')[0]
# Start with flat background estimate
# Background must be provided as an ImageHDU
background_data=np.ones_like(counts.data, dtype=float)
background = fits.ImageHDU(data=background_data, header=counts.header)
images = GammaImages(counts=counts, background=background)
source_kernel = binary_disk(3).astype(float)
source_kernel /= source_kernel.sum()
background_kernel = np.ones((5, 50))
background_kernel /= background_kernel.sum()
# *** ITERATOR ***
ibe = IterativeKernelBackgroundEstimator(images=images,
source_kernel=source_kernel,
background_kernel=background_kernel,
significance_threshold=4,
mask_dilation_radius=3,
save_intermediate_results=True
)
MASK_DILATION_RADIUS = 10 # Pixels
psf_file = datasets.FermiGalacticCenter.psf()
# Load/create example model images.
filename = datasets.get_path('source_diffuse_separation/galactic_simulations/fermi_counts.fits',
location='remote')
# *** LOADING INPUT ***
# Counts must be provided as an ImageHDU
counts = fits.open(filename)[0].data
header = fits.open(filename)[0].header
images = GammaImages(counts=counts, header=header)
source_kernel = binary_disk(CORRELATION_RADIUS)
background_kernel = np.ones((10, 100))
# *** ITERATOR ***
ibe = IterativeKernelBackgroundEstimator(images=images,
source_kernel=source_kernel,
background_kernel=background_kernel,
significance_threshold=SIGNIFICANCE_THRESHOLD,
mask_dilation_radius=MASK_DILATION_RADIUS
)
n_iterations = 4
# *** RUN & PLOT ***
psf_file = datasets.FermiGalacticCenter.psf()
# Load/create example model images.
filename = datasets.get_path(
'source_diffuse_separation/galactic_simulations/fermi_counts.fits',
location='remote')
# *** LOADING INPUT ***
# Counts must be provided as an ImageHDU
counts = fits.open(filename)[0].data
header = fits.open(filename)[0].header
images = GammaImages(counts=counts, header=header)
source_kernel = binary_disk(CORRELATION_RADIUS)
background_kernel = np.ones((10, 100))
# *** ITERATOR ***
ibe = IterativeKernelBackgroundEstimator(
images=images,
source_kernel=source_kernel,
background_kernel=background_kernel,
significance_threshold=SIGNIFICANCE_THRESHOLD,
mask_dilation_radius=MASK_DILATION_RADIUS)
n_iterations = 4
# *** RUN & PLOT ***