def run_benchmark():
# Set up data store and select N_OBS times the observation OBS_ID
data_store = DataStore.from_dir('$GAMMAPY_EXTRA/test_datasets/cta_1dc/')
obs_ids = OBS_ID * np.ones(N_OBS)
obs_list = data_store.obs_list(obs_id=obs_ids)
target_position = SkyCoord(0, 0, unit='deg', frame='galactic')
on_radius = 0.2 * u.deg
on_region = CircleSkyRegion(center=target_position, radius=on_radius)
bkg_estimator = RingBackgroundEstimator(
r_in=0.5 * u.deg,
width=0.2 * u.deg,
)
# Define reference image centered on the target
xref = target_position.galactic.l.value
yref = target_position.galactic.b.value
ref_image = SkyImage.empty(
nxpix=800,
nypix=600,
binsz=0.02,
xref=xref,
yref=yref,
proj='TAN',
coordsys='GAL',
)
exclusion_mask = ref_image.region_mask(on_region)
exclusion_mask.data = 1 - exclusion_mask.data
image_estimator = IACTBasicImageEstimator(
reference=ref_image,
emin=100 * u.GeV,
emax=100 * u.TeV,
offset_max=3 * u.deg,
background_estimator=bkg_estimator,
exclusion_mask=exclusion_mask,
)
result = image_estimator.run(obs_list)
def run_benchmark():
# Set up data store and select N_OBS times the observation OBS_ID
data_store = DataStore.from_dir('$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)
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
# This doesn't work yet ... need to do SkyImage.plot fixes:
# fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 3))
# background_image.plot(ax=axes[0])
# exclusion_image.plot(ax=axes[1])
# significance_image.plot(ax=axes[2])
print("bkg metodo 1")
background_image = result2['background']
background_image2 = images_new['background']
norm = simple_norm(background_image.data, stretch='sqrt', min_cut=0, max_cut=0.5)
# 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.
# In[13]:
def show_image(image, radius=3, vmin=0, vmax=3):
"""Little helper function to show the images for this application here."""
image.smooth(radius=radius).show(vmin=vmin, vmax=vmax, add_cbar=True)
image.cutout(
position=SkyCoord(0.5, 0, unit='deg', frame='galactic'),
size=(2 * u.deg, 3 * u.deg),
exclusion_mask.plot(add_cbar=True)
bkg_estimator = FoVBackgroundEstimator()
emin = 0.5 * u.TeV
emax = 20 * u.TeV
offset_max = 2.3 * u.deg
image_estimator = IACTBasicImageEstimator(reference=ref_image,
emin=emin,
emax=emax,
offset_max=offset_max,
background_estimator=bkg_estimator,
exclusion_mask=exclusion_mask)
comp_image = image_estimator.run(mylist)
comp_image.names
images = comp_image
print("Total number of counts", images[0].data.sum())
print("Total number of excess events", images[3].data.sum())
fermi = SkyImage.read("FDA16.fits")
fermi_rep = fermi.reproject(ref_image)
#Make sure that the exclusion mask properly excludes all sources
masked_excess = SkyImage.empty_like(images['excess'])
masked_excess.data = comp_image['excess'].data * exclusion_mask.data
masked_excess_cutout = masked_excess.cutout(position=SkyCoord(