RingBackgroundEstimator

class gammapy.background.RingBackgroundEstimator(r_in, width, use_fft_convolution=False)

Bases: object

Ring background method for cartesian coordinates.

• Step 1: apply exclusion mask
• Step 2: ring-correlate
• Step 3: apply psi cut

TODO: add method to apply the psi cut

Parameters:

r_in : Quantity

Inner ring radius

width : Quantity

Ring width.

use_fft_convolution : bool

Use FFT convolution?

See also

gammapy.detect.KernelBackgroundEstimator, AdaptiveRingBackgroundEstimator

Examples

Example using RingBackgroundEstimator:

from gammapy.maps import Map
from gammapy.background import RingBackgroundEstimator

filename = '$GAMMAPY_DATA/tests/unbundled/poisson_stats_image/input_all.fits.gz'
images = {
    'counts': Map.read(filename, hdu='counts'),
    'exposure_on': Map.read(filename, hdu='exposure'),
    'exclusion': Map.read(filename, hdu='exclusion'),
}

ring_bkg = RingBackgroundEstimator(r_in='0.35 deg', width='0.3 deg')
results = ring_bkg.run(images)
results['background'].plot()

Attributes Summary

parameters

dict of parameters

Methods Summary

kernel(image)	Ring kernel.
run(images)	Run ring background algorithm.

Attributes Documentation

parameters

dict of parameters

Methods Documentation

kernel(image)

Ring kernel.

Parameters:

image : WcsNDMap

Input Map

Returns:

ring : Ring2DKernel

Ring kernel.

run(images)

Run ring background algorithm.

Required Maps: {required}

Parameters:

images : dict of WcsNDMap

Input sky maps.

Returns:

result : dict of WcsNDMap

Result sky maps