def test_ring_background_estimator(images):
ring = RingBackgroundEstimator(0.35 * u.deg, 0.3 * u.deg)
result = ring.run(images)
in_fov = images["background"].data > 0
assert_allclose(result["background_ring"].data[in_fov], 2.0)
assert_allclose(result["alpha"].data[in_fov].mean(), 0.003488538457592745)
assert_allclose(result["exposure_off"].data[in_fov].mean(),
305.1268970794541)
assert_allclose(result["off"].data[in_fov].mean(), 610.2537941589082)
assert_allclose(result["off"].data[~in_fov], 0.0)
assert_allclose(result["exposure_off"].data[~in_fov], 0.0)
assert_allclose(result["alpha"].data[~in_fov], 0.0)
#
# We'll run a spectral analysis using the classical reflected regions background estimation method,
# and using the on-off (often called WSTAT) likelihood function.
#
# ### Extraction
#
# The first step is to "extract" the spectrum, i.e. 1-dimensional counts and exposure and background vectors, as well as an energy dispersion matrix from the data and IRFs.
# In[24]:
bkg_estimator = ReflectedRegionsBackgroundEstimator(
obs_list=obs_list,
on_region=on_region,
exclusion_mask=exclusion_mask,
)
bkg_estimator.run()
bkg_estimate = bkg_estimator.result
bkg_estimator.plot()
# In[25]:
extract = SpectrumExtraction(
obs_list=obs_list,
bkg_estimate=bkg_estimate,
)
extract.run()
# ### Model fit
#
# The next step is to fit a spectral model, using all data (i.e. a "global" fit, using all energies).