# Define the grouping
nbins=10
zenith_bins=[0,10,20,30,40,50,90]
#zenith_bins=[min(zen_ang), 10.0, 20.0, 30.0, 45.0, 60.0, max(zen_ang)]
zenith_bins=zenith_bins*u.deg
axes = [ObservationGroupAxis('ZEN_PNT', zenith_bins, fmt='edges')]
# Create the ObservationGroups object
obs_groups = ObservationGroups(axes)
# write it to file
filename = str(outdir + "/group-def.fits")
obs_groups.obs_groups_table.write(filename, overwrite=True)
obs_table_with_group_id = obs_groups.apply(datastore.obs_table.select_obs_id(agnid))
#gammacat exclusion mask
fil_gammacat="/Users/asinha/Gammapy-dev/gammapy-extra/datasets/catalogs/gammacat/gammacat.fits.gz"
cat = SourceCatalogGammaCat(filename=fil_gammacat)
exclusion_table = cat.table.copy()
exclusion_table.rename_column('ra', 'RA')
exclusion_table.rename_column('dec', 'DEC')
radius = exclusion_table['morph_sigma'].data
radius[np.isnan(radius)] = 0.3
exclusion_table['Radius'] = radius * u.deg
exclusion_table = Table(exclusion_table)
#now run the bgmaker
bgmaker = OffDataBackgroundMaker(
data_store=datastore,
from gammapy.utils.nddata import sqrt_space
from gammapy.data import DataStore, ObservationGroupAxis, ObservationGroups
from gammapy.background import EnergyOffsetBackgroundModel
from gammapy.background import OffDataBackgroundMaker
#create a directory
os.mkdir("background")
#observation list
name = "PKS 2155-304"
name = "Crab"
datastore = DataStore.from_dir("$HESS_DATA")
src = SkyCoord.from_name(name)
sep = SkyCoord.separation(src, datastore.obs_table.pointing_radec)
srcruns = (datastore.obs_table[sep < 2.0 * u.deg])
obsid = srcruns['OBS_ID'].data
mylist = datastore.obs_list(obsid[:30])
# Define the grouping
zenith_bins = np.linspace(0, 90, 6)
axes = [ObservationGroupAxis('ZEN_PNT', zenith_bins, fmt='edges')]
# Create the ObservationGroups object
obs_groups = ObservationGroups(axes)
# write it to file
filename = str(scratch_dir / 'group-def.fits')
obs_groups.obs_groups_table.write(filename, overwrite=True)
obs_table_with_group_id = obs_groups.apply(srcruns[0:30])
# Define the grouping you want to use to group the obervations to make the acceptance curves
# Here we use 2 Zenith angle bins only, you can also add efficiency bins for example etc...
axes = [ObservationGroupAxis('ZEN_PNT', [0, 49, 90], fmt='edges')]
# Create the ObservationGroups object
obs_groups = ObservationGroups(axes)
# write it to file
filename = str(scratch_dir / 'group-def.fits')
obs_groups.obs_groups_table.write(filename, overwrite=True)
# Create a new ObservationTable with the column group_id
# You give the runs list you want to use to produce the background model that are in your obs table.
# Here very simple only the 4 Crab runs...
list_ids = [23523, 23526, 23559, 23592]
obs_table_with_group_id = obs_groups.apply(
data_store.obs_table.select_obs_id(list_ids))
# ### Make table of known gamma-ray sources to exclude
#
# We need a mask to remove known sources from the observation. We use TeVcat and exclude a circular region of at least 0.3° radius. Here since we use Crab runs, we will remove the Crab events from the FOV to select only the OFF events to build the acceptance curves. Of cource normally you use thousand of AGN runs to build coherent acceptance curves.
# In[7]:
cat = SourceCatalogGammaCat()
exclusion_table = cat.table.copy()
exclusion_table.rename_column('ra', 'RA')
exclusion_table.rename_column('dec', 'DEC')
radius = exclusion_table['morph_sigma'].data
radius[np.isnan(radius)] = 0.3
exclusion_table['Radius'] = radius * u.deg
exclusion_table = Table(exclusion_table)
def make_cubes(ereco, etrue, use_etrue, center):
tmpdir = os.path.expandvars('$GAMMAPY_EXTRA') + "/test_datasets/cube/data"
outdir = tmpdir
outdir2 = os.path.expandvars(
'$GAMMAPY_EXTRA') + '/test_datasets/cube/background'
if os.path.isdir("data"):
shutil.rmtree("data")
if os.path.isdir("background"):
shutil.rmtree("background")
Path(outdir2).mkdir()
ds = DataStore.from_dir("$GAMMAPY_EXTRA/datasets/hess-crab4-hd-hap-prod2")
ds.copy_obs(ds.obs_table, tmpdir)
data_store = DataStore.from_dir(tmpdir)
# Create a background model from the 4 crab run for the counts ouside the exclusion region. it's just for test, normaly you take 8000 thousands AGN runs to build this kind of model
axes = [ObservationGroupAxis('ZEN_PNT', [0, 49, 90], fmt='edges')]
obs_groups = ObservationGroups(axes)
obs_table_with_group_id = obs_groups.apply(data_store.obs_table)
obs_groups.obs_groups_table.write(outdir2 + "/group-def.fits",
overwrite=True)
# Exclusion sources table
cat = SourceCatalogGammaCat()
exclusion_table = cat.table
exclusion_table.rename_column('ra', 'RA')
exclusion_table.rename_column('dec', 'DEC')
radius = exclusion_table['morph_sigma']
radius.value[np.isnan(radius)] = 0.3
exclusion_table['Radius'] = radius
exclusion_table = Table(exclusion_table)
bgmaker = OffDataBackgroundMaker(data_store,
outdir2,
run_list=None,
obs_table=obs_table_with_group_id,
ntot_group=obs_groups.n_groups,
excluded_sources=exclusion_table)
bgmaker.make_model("2D")
bgmaker.smooth_models("2D")
bgmaker.save_models("2D")
bgmaker.save_models(modeltype="2D", smooth=True)
shutil.move(str(outdir2), str(outdir))
fn = outdir + '/background/group-def.fits'
hdu_index_table = bgmaker.make_total_index_table(
data_store=data_store,
modeltype='2D',
out_dir_background_model="background",
filename_obs_group_table=fn,
smooth=True)
fn = outdir + '/hdu-index.fits.gz'
hdu_index_table.write(fn, overwrite=True)
offset_band = Angle([0, 2.49], 'deg')
ref_cube_images = make_empty_cube(image_size=50,
energy=ereco,
center=center)
ref_cube_exposure = make_empty_cube(image_size=50,
energy=etrue,
center=center,
data_unit="m2 s")
data_store = DataStore.from_dir(tmpdir)
refheader = ref_cube_images.sky_image_ref.to_image_hdu().header
exclusion_mask = SkyMask.read(
'$GAMMAPY_EXTRA/datasets/exclusion_masks/tevcat_exclusion.fits')
exclusion_mask = exclusion_mask.reproject(reference=refheader)
# Pb with the load psftable for one of the run that is not implemented yet...
data_store.hdu_table.remove_row(14)
cube_maker = StackedObsCubeMaker(empty_cube_images=ref_cube_images,
empty_exposure_cube=ref_cube_exposure,
offset_band=offset_band,
data_store=data_store,
obs_table=data_store.obs_table,
exclusion_mask=exclusion_mask,
save_bkg_scale=True)
cube_maker.make_cubes(make_background_image=True, radius=10.)
obslist = [data_store.obs(id) for id in data_store.obs_table["OBS_ID"]]
ObsList = ObservationList(obslist)
mean_psf_cube = make_mean_psf_cube(image_size=50,
energy_cube=etrue,
center_maps=center,
center=center,
ObsList=ObsList,
spectral_index=2.3)
if use_etrue:
mean_rmf = make_mean_rmf(energy_true=etrue,
energy_reco=ereco,
center=center,
ObsList=ObsList)
filename_mask = 'exclusion_mask.fits'
filename_counts = 'counts_cube.fits'
filename_bkg = 'bkg_cube.fits'
filename_significance = 'significance_cube.fits'
filename_excess = 'excess_cube.fits'
if use_etrue:
filename_exposure = 'exposure_cube_etrue.fits'
filename_psf = 'psf_cube_etrue.fits'
filename_rmf = 'rmf.fits'
mean_rmf.write(filename_rmf, clobber=True)
else:
filename_exposure = 'exposure_cube.fits'
filename_psf = 'psf_cube.fits'
exclusion_mask.write(filename_mask, clobber=True)
cube_maker.counts_cube.write(filename_counts,
format="fermi-counts",
clobber=True)
cube_maker.bkg_cube.write(filename_bkg,
format="fermi-counts",
clobber=True)
cube_maker.significance_cube.write(filename_significance,
format="fermi-counts",
clobber=True)
cube_maker.excess_cube.write(filename_excess,
format="fermi-counts",
clobber=True)
cube_maker.exposure_cube.write(filename_exposure,
format="fermi-counts",
clobber=True)
mean_psf_cube.write(filename_psf, format="fermi-counts", clobber=True)
crab_pos = SkyCoord.from_name('crab')
crab_pos
datastore
datastore.table
datastore.Table
datastore.obs_table
datastore.obs_table["ZEN_PNT"]<20.0
from gammapy.data import ObservationGroups, ObservationGroupAxis
zenith = Angle([0, 30, 40, 50], 'deg')
ntels = [3, 4]
obs_groups = ObservationGroups([
ObservationGroupAxis('ZENITH', zenith, fmt='edges'),
ObservationGroupAxis('N_TELS', ntels, fmt='values'),
])
print(obs_groups.info)
obs1=obs_groups.apply(datastore.obs_table)
zenith = Angle([0, 30, 40, 50], 'deg')
ntels = [3, 4]
obs_groups = ObservationGroups([
ObservationGroupAxis('ZEN_PNT', zenith, fmt='edges'),
ObservationGroupAxis('N_TELS', ntels, fmt='values'),
])
obs_table
zenith = Angle([0, 30, 40, 50], 'deg')
ntels = [3, 4]
obs_groups = ObservationGroups([
ObservationGroupAxis('ZEN_PNT', zenith, fmt='edges'),
ObservationGroupAxis('N_TELS', ntels, fmt='values'),
])
obs1=obs_groups.apply(datastore.obs_table)
