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)
def run_benchmark():
# Set up data store and select N_OBS times the observation OBS_ID
data_store = DataStore.from_dir('$GAMMAPY_EXTRA/test_datasets/cta_1dc/')
obs_ids = OBS_ID * np.ones(N_OBS)
obs_list = data_store.obs_list(obs_id=obs_ids)
target_position = SkyCoord(0, 0, unit='deg', frame='galactic')
on_radius = 0.2 * u.deg
on_region = CircleSkyRegion(center=target_position, radius=on_radius)
bkg_estimator = RingBackgroundEstimator(
r_in=0.5 * u.deg,
width=0.2 * u.deg,
)
# Define reference image centered on the target
xref = target_position.galactic.l.value
yref = target_position.galactic.b.value
ref_image = SkyImage.empty(
nxpix=800,
nypix=600,
binsz=0.02,
xref=xref,
yref=yref,
proj='TAN',
coordsys='GAL',
)
exclusion_mask = ref_image.region_mask(on_region)
exclusion_mask.data = 1 - exclusion_mask.data
image_estimator = IACTBasicImageEstimator(
reference=ref_image,
emin=100 * u.GeV,
emax=100 * u.TeV,
offset_max=3 * u.deg,
background_estimator=bkg_estimator,
exclusion_mask=exclusion_mask,
)
result = image_estimator.run(obs_list)
def test_map_maker_ring(observations):
ring_bkg = RingBackgroundEstimator(r_in="0.5 deg", width="0.4 deg")
geomd = geom(ebounds=[0.1, 1, 10])
mask = Map.from_geom(geomd)
regions = CircleSkyRegion(SkyCoord(0, 0, unit="deg", frame="galactic"),
radius=0.5 * u.deg)
mask.data = mask.geom.region_mask([regions], inside=False)
maker = MapMakerRing(geomd, 2.0 * u.deg, mask, ring_bkg)
maps = maker.run(observations)
assert_allclose(np.nansum(maps["on"].data), 21981, rtol=1e-2)
assert_allclose(np.nansum(maps["exposure_off"].data), 8310.035, rtol=1e-2)
assert maps["on"].geom.is_image is False
images = maker.run_images(observations)
assert_allclose(np.nansum(images["on"].data), 21981, rtol=1e-2)
assert_allclose(np.nansum(images["exposure_off"].data),
109751.45,
rtol=1e-2)
assert images["on"].geom.is_image is True
print(ref_image)
# ### Compute images
#
# We use the ring background estimation method, and an exclusion mask that excludes the bright source at the Galactic center.
# In[11]:
exclusion_mask = ref_image.region_mask(on_region)
exclusion_mask.data = 1 - exclusion_mask.data
exclusion_mask.plot()
# In[12]:
bkg_estimator = RingBackgroundEstimator(
r_in=0.5 * u.deg,
width=0.2 * u.deg,
)
image_estimator = IACTBasicImageEstimator(
reference=ref_image,
emin=100 * u.GeV,
emax=100 * u.TeV,
offset_max=3 * u.deg,
background_estimator=bkg_estimator,
exclusion_mask=exclusion_mask,
)
images = image_estimator.run(obs_list)
images.names
# ### Show images
#
# Let's define a little helper function and then show all the resulting images that were computed.
# ### Choose an exclusion mask.
# We choose an exclusion mask on the position of MSH 1552.
# In[ ]:
regions = CircleSkyRegion(center=pos_msh1552, radius=0.3 * u.deg)
mask = Map.from_geom(geom)
mask.data = mask.geom.region_mask([regions], inside=False)
mask.get_image_by_idx([0]).plot()
# Now, we instantiate the ring background
# In[ ]:
ring_bkg = RingBackgroundEstimator(r_in="0.5 deg", width="0.3 deg")
# To facilitate classical image analysis, we have a special class called `MapMakerRing`. Here, we do the analysis over the integrated energy range. To do an analysis for each image slice of the map (eg: to investigate the significance of the source detection with energy, just call `run` instead of `run_images`
# In[ ]:
get_ipython().run_cell_magic(
'time', '',
'im = MapMakerRing(\n geom=geom,\n offset_max=2.0 * u.deg,\n exclusion_mask=mask,\n background_estimator=ring_bkg,\n)\nimages = im.run_images(observations)'
)
# We will use `compute_lima_on_off_image` in `gammapy.detect` to compute significance and excess maps. A common debug plot during Ring Analysis is to make histograms of the `off` count rates, so we will plot that as well.
#
# For this we will first create a Tophat2DKernel convolution kernel for the significance maps. To convert from angles to pixel scales, we use `geom.pixel_scales`:
# In[ ]:
)
# 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,
)
image_estimator = IACTBasicImageEstimator(
reference=ref_image,
emin=100 * u.GeV,
emax=100 * u.TeV,
#offset_max=3 * u.deg,
background_estimator=bkg_estimator,
exclusion_mask=exclusion_mask,
)
images_new = image_estimator.run(obs_list)
print("\n\n\n")
print(images_new.names)
print(result2.names)
print("\n\n\n")
# Let's have a look at the background image and the exclusion mask