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)
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)
from gammapy.detect import KernelBackgroundEstimator
# Parameters
CORRELATION_RADIUS = 10 # Pixels
SIGNIFICANCE_THRESHOLD = 5 # Sigma
MASK_DILATION_RADIUS = 0.5 * u.deg
# Load example images.
filename = ('$GAMMAPY_EXTRA/datasets/source_diffuse_separation/'
'galactic_simulations/fermi_counts.fits')
counts = SkyImage.read(filename)
center = SkyCoord(0, 0, frame='galactic', unit='deg')
images = SkyImageList()
images['counts'] = counts.cutout(center, (10 * u.deg, 80 * u.deg))
kernel_src = Tophat2DKernel(CORRELATION_RADIUS).array
kernel_bkg = np.ones((10, 150))
kbe = KernelBackgroundEstimator(
kernel_src=kernel_src,
kernel_bkg=kernel_bkg,
significance_threshold=SIGNIFICANCE_THRESHOLD,
mask_dilation_radius=MASK_DILATION_RADIUS,
)
result = kbe.run(images)
kbe.images_stack_show()
plt.show()
# Start with flat background estimate
background = np.ones_like(counts, dtype=float)
images = KernelBackgroundEstimatorData(counts=counts, background=background)
# CORRELATION_RADIUS
source_kernel = np.ones((5, 5))
background_kernel = np.ones((100, 10))
kbe = KernelBackgroundEstimator(images=images,
source_kernel=source_kernel,
background_kernel=background_kernel,
significance_threshold=SIGNIFICANCE_THRESHOLD,
mask_dilation_radius=MASK_DILATION_RADIUS)
kbe.run(n_iterations=3)
# In[ ]:
kbe.run_iteration()
# In[ ]:
kbe._data[1].print_info()
# ## Plot results
#
# TODO
# In[ ]:
import numpy as np
from gammapy.maps import Map
from astropy.convolution import Ring2DKernel, Tophat2DKernel
from gammapy.detect import KernelBackgroundEstimator
counts = Map.create(npix=100, binsz=1)
counts.data += 42
counts.data[50][50] = 1000
source_kernel = Tophat2DKernel(3)
bkg_kernel = Ring2DKernel(radius_in=4, width=2)
kbe = KernelBackgroundEstimator(kernel_src=source_kernel.array,
kernel_bkg=bkg_kernel.array)
result = kbe.run({'counts': counts})
result['exclusion'].plot()
# To use the `KernelBackgroundEstimator` you first have to set
# up a source and background kernel and put the counts image input
# into a container `SkyImageList` class.
images = SkyImageList()
images['counts'] = counts_image2
print("3")
kbe = KBE(
kernel_src=source_kernel,
kernel_bkg=background_kernel,
significance_threshold=5,
mask_dilation_radius=0.06 * u.deg,
)
# This takes about 10 seconds on my machine
result2 = kbe.run(images)
print("results2")
print(result2.names)
####altro modo stima bkg
obs_list = data_store.obs_list(obs_ids)
target_position = SkyCoord(0, 0, unit='deg', frame='galactic')
on_radius = 0.3 * u.deg
on_region = CircleSkyRegion(center=target_position, radius=on_radius)
exclusion_mask = ref_image.region_mask(on_region)
exclusion_mask.data = 1 - exclusion_mask.data
bkg_estimator = RingBackgroundEstimator(
r_in=0.5 * u.deg,
width=0.2 * u.deg,
)
