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.data -= ref_image.region_mask(off)
exclusion_mask.data[exclusion_mask.data<0]=0 #overlapping pixels go to negative values. Fixing them to zero
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)
def indiv():
images=image_estimator.run_indiv(mylist)
backnorm=[]
for im in images:
backnorm.append(im["background"].meta['NORM'])
def summed():
exclusion_mask = ref_image.region_mask(off_regions[0])
exclusion_mask.data = 1 - exclusion_mask.data
for off in off_regions[1:]:
exclusion_mask.data -= ref_image.region_mask(off)
exclusion_mask.data[exclusion_mask.data < 0] = 0
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)
images = image_estimator.run_indiv(mylist)
fermi = "FDA16.fits"
diffuse = SkyImage.read(fermi)
diffuse_rep = diffuse.reproject(ref_image)
#make exclusion and cutouts
exc_list = []
diffuse_list = []
for i in range(len(mylist)):
# 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.
# In[13]:
def show_image(image, radius=3, vmin=0, vmax=3):
"""Little helper function to show the images for this application here."""