AdaptiveRingBackgroundEstimator¶
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class
gammapy.background.
AdaptiveRingBackgroundEstimator
(r_in, r_out_max, width, stepsize=<Quantity 0.02 deg>, threshold_alpha=0.1, theta=<Quantity 0.22 deg>, method='fixed_width')[source]¶ Bases:
object
Adaptive ring background algorithm.
This algorithm extends the standard
RingBackground
method by adapting the size of the ring to achieve a minimum on / off exposure ratio (alpha) in regions where the area to estimate the background from is limited.Parameters: r_in :
Quantity
Inner radius of the ring.
r_out_max :
Quantity
Maximal outer radius of the ring.
width :
Quantity
Width of the ring.
stepsize :
Quantity
Stepsize used for increasing the radius.
threshold_alpha : float
Threshold on alpha above which the adaptive ring takes action.
theta :
Quantity
Integration radius used for alpha computation.
method : {‘fixed_width’, ‘fixed_r_in’}
Adaptive ring method.
Examples
Here’s an example how to use the
AdaptiveRingBackgroundEstimator
:from astropy import units as u from gammapy.background import AdaptiveRingBackgroundEstimator from gammapy.image import SkyImageList filename = '$GAMMAPY_EXTRA/test_datasets/unbundled/poisson_stats_image/input_all.fits.gz' images = SkyImageList.read(filename) images['exposure'].name = 'exposure_on' adaptive_ring_bkg = RingBackgroundEstimator(r_in=0.22 * u.deg, r_out_max=0.8 * u.deg, width=0.1 * u.deg) results = adaptive_ring_bkg.run(images) results['background'].show()
Methods Summary
kernels
(image)Ring kernels according to the specified method. run
(images)Run adaptive ring background algorithm. Methods Documentation
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kernels
(image)[source]¶ Ring kernels according to the specified method.
Parameters: image :
SkyImage
Sky image specifying the WCS information.
Returns: kernels : list
List of
Ring2DKernel
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