def background_skyimage_2fhl(counts):
log.info('Computing background map.')
images = GammaImages(counts.data, header=counts.wcs.to_header())
source_kernel = Tophat2DKernel(5)
source_kernel.normalize('peak')
background_kernel = Ring2DKernel(20, 20)
background_kernel.normalize('peak')
ikbe = IKBE(
images=images,
source_kernel=source_kernel.array,
background_kernel=background_kernel.array,
significance_threshold=5,
mask_dilation_radius=3,
)
mask_data, background_data = ikbe.run()
mask = SkyMap.empty_like(counts)
mask.data = mask_data
background = SkyMap.empty_like(counts)
background.data = background_data
return mask, background
def background_skyimage_2fhl(counts):
log.info('Computing background map.')
images = GammaImages(counts.data, header=counts.wcs.to_header())
source_kernel = Tophat2DKernel(5)
source_kernel.normalize('peak')
background_kernel = Ring2DKernel(20, 20)
background_kernel.normalize('peak')
ikbe = IKBE(
images=images,
source_kernel=source_kernel.array,
background_kernel=background_kernel.array,
significance_threshold=5,
mask_dilation_radius=3,
)
mask_data, background_data = ikbe.run()
mask = SkyMap.empty_like(counts)
mask.data = mask_data
background = SkyMap.empty_like(counts)
background.data = background_data
return mask, background
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 `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)
# 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()
flux_background_data = new_background.data / exposure_spec_cube.data[0]
flux_background = fits.ImageHDU(data=flux_background_data.value,
header=flux_hdu.header)
filebase = raw_input('Output file base: ')
flux_background.writeto('{0}_background.fits'.format(filebase), clobber=True)
mask.writeto('{0}_mask.fits'.format(filebase), clobber=True)
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()
flux_background_data = new_background.data / exposure_spec_cube.data[0]
flux_background = fits.ImageHDU(data=flux_background_data.value, header=flux_hdu.header)
filebase = raw_input("Output file base: ")
flux_background.writeto("{0}_background.fits".format(filebase), clobber=True)
mask.writeto("{0}_mask.fits".format(filebase), clobber=True)
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
)
ibe.run(filebase='TestOutput')
#n_iterations = 3
"""
# *** RUN & PLOT ***
plt.figure(figsize=(4,6))
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.subplot(n_iterations+3, 1, 1)
background_hdu = counts
data = counts.data[:, 700:1400]
# *** 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 ***
plt.figure(figsize=(8, 4))
for iteration in range(n_iterations):
ibe.run_iteration()
mask_hdu = ibe.mask_image_hdu
mask = mask_hdu.data[:, 1400:2000]
plt.subplot(n_iterations, 2, 2 * iteration + 1)
background_hdu = ibe.background_image_hdu
