def make_tev_catalog_data():
click.secho('Making TeV catalog data (from gamma-cat)...', fg='green')
out_dir = DATA_DIR / 'cat/tev'
out_dir.mkdir(parents=True, exist_ok=True)
cat = SourceCatalogGammaCat(filename='input_data/gammacat.fits.gz')
cols = ['source_id', 'common_name', 'ra', 'dec', 'glon', 'glat',
'other_names', 'classes']
cat.table = cat.table[cols]
data = cat._data_python_list
filename = out_dir / 'cat.json'
click.secho('Writing tev {}'.format(filename), fg='green')
dump_to_json(data, filename)
def __init__(self, row_idxs=None):
folder = os.environ['GAMMA_CAT']
filename = os.path.join(folder, 'output/gammacat.fits.gz')
log.info('Reading {}'.format(filename))
self.cat = SourceCatalogGammaCat(filename)
all_row_idxs = list(range(len(self.cat.table)))
if row_idxs:
row_idxs = sorted(set(row_idxs) & set(all_row_idxs))
else:
row_idxs = all_row_idxs
self.row_idxs = row_idxs
def prepare_the_pwn_table():
gammacat = SourceCatalogGammaCat()
cat_table = gammacat.table
mask_pwn = cat_table['classes'] == 'pwn'
cat_pwn = cat_table[mask_pwn]
cat_pwn.pprint()
mask_nan = np.isfinite(cat_pwn['spec_flux_above_1TeV'])
cat_pwn = cat_pwn[mask_nan]
hgpscat = SourceCatalogHGPS(os.environ['HGPS'])
hgpscat_table = hgpscat.table
mask_pwn_hgps = hgpscat_table['Source_Class'] == 'PWN'
hgpscat_pwn = hgpscat_table[mask_pwn_hgps]
# hgpscat_pwn.pprint()
print('----------------- PWN -----------------------')
print(hgpscat_pwn['Source_Name', 'Flux_Spec_Int_1TeV'])
num_pwn = len(hgpscat_pwn)
print('num_pwn = ', num_pwn)
mask_composite_hgps = hgpscat_table['Source_Class'] == 'Composite'
hgpscat_composite = hgpscat_table[mask_composite_hgps]
# print(hgpscat_composite['Source_Name','Flux_Spec_Int_1TeV'])
idxx1 = np.where(hgpscat_composite['Source_Name'] == 'HESS J1714-385')[0]
# print(len(idxx1), idxx1[0])
hgpscat_composite.remove_row(int(idxx1[0]))
print('----------------- composite -----------------------')
print(hgpscat_composite['Source_Name', 'Flux_Spec_Int_1TeV'])
num_composite = len(hgpscat_composite)
print('num composite = ', num_composite)
mask_unid_hgps = hgpscat_table['Source_Class'] == 'Unid'
hgpscat_unid = hgpscat_table[mask_unid_hgps]
# hgpscat_unid.pprint()
# print(hgpscat_unid['Source_Name','Flux_Spec_Int_1TeV'])
idx1 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1852-000')[0]
hgpscat_unid.remove_row(int(idx1[0]))
idx2 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1457-593')[0]
hgpscat_unid.remove_row(int(idx2[0]))
idx3 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1503-582')[0]
hgpscat_unid.remove_row(int(idx3[0]))
idx4 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1646-458')[0]
hgpscat_unid.remove_row(int(idx4[0]))
idx5 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1641-463')[0]
hgpscat_unid.remove_row(int(idx5[0]))
idx6 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1729-345')[0]
hgpscat_unid.remove_row(int(idx6[0]))
idx7 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1747-248')[0]
hgpscat_unid.remove_row(int(idx7[0]))
idx8 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1800-240')[0]
hgpscat_unid.remove_row(int(idx8[0]))
idx9 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1808-204')[0]
hgpscat_unid.remove_row(int(idx9[0]))
idx10 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1832-085')[0]
hgpscat_unid.remove_row(int(idx10[0]))
idx11 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1832-093')[0]
hgpscat_unid.remove_row(int(idx11[0]))
idx12 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1848-018')[0]
hgpscat_unid.remove_row(int(idx12[0]))
idx13 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1923+141')[0]
hgpscat_unid.remove_row(int(idx13[0]))
idx14 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1943+213')[0]
hgpscat_unid.remove_row(int(idx14[0]))
idx15 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1702-420')[0]
hgpscat_unid.remove_row(int(idx15[0]))
idx16 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1844-030')[0]
hgpscat_unid.remove_row(int(idx16[0]))
idx17 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1746-285')[0]
hgpscat_unid.remove_row(int(idx17[0]))
idx18 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1745-303')[0]
hgpscat_unid.remove_row(int(idx18[0]))
idx19 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1741-302')[0]
hgpscat_unid.remove_row(int(idx19[0]))
idx20 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1745-290')[0]
hgpscat_unid.remove_row(int(idx20[0]))
idx21 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1626-490')[0]
hgpscat_unid.remove_row(int(idx21[0]))
# idx2 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1708-410')[0]
# hgpscat_unid.remove_row(int(idx2[0]))
# idx1 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1018-589B')[0]
# hgpscat_unid.remove_row(int(idx1[0]))
# idx15 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1843-033')[0]
# hgpscat_unid.remove_row(int(idx15[0]))
# idx5 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1614-518')[0]
# hgpscat_unid.remove_row(int(idx5[0]))
# idx21 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1507-622')[0]
# hgpscat_unid.remove_row(int(idx21[0]))
# idx27 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1841-055')[0]
# hgpscat_unid.remove_row(int(idx27[0]))
# idx20 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1713-381')[0]
# hgpscat_unid.remove_row(int(idx20[0]))
# idx6 = np.where(hgpscat_unid['Source_Name'] == 'HESS J1634-472')[0]
# hgpscat_unid.remove_row(int(idx6[0]))
# print(hgpscat_unid['Source_Name','Flux_Spec_Int_1TeV'])
num_unid = len(hgpscat_unid)
print('num_unid = ', num_unid)
hgpscat_pwn_extended = vstack(
[hgpscat_pwn, hgpscat_unid, hgpscat_composite])
print('----------------- extended -----------------------')
print(hgpscat_pwn_extended['Source_Name', 'Flux_Spec_Int_1TeV'])
num_pwn_extended = len(hgpscat_pwn_extended)
print('num_tot = ', num_pwn_extended)
crab_flux_above_1TeV = define_flux_crab_above_energy()
print('crab_flux_above_1TeV ', crab_flux_above_1TeV)
flux_above_1TeV_cu = []
sigma = []
for id in range(len(hgpscat_pwn_extended)):
# print((hgpscat_pwn_extended[id]['Source_Name']),(hgpscat_pwn_extended[id]['Flux_Spec_Int_1TeV']))
ff = hgpscat_pwn_extended[id]['Flux_Spec_Int_1TeV'] * u.Unit(
'cm-2 s-1')
flux_cu = (ff / crab_flux_above_1TeV).to('%')
flux_above_1TeV_cu.append(flux_cu)
#print(hgpscat_pwn_extended[id]['Flux_Spec_Int_1TeV'], flux_cu)
sigma.append(hgpscat_pwn_extended[id]['Size'] / 2.0)
flux_above_1TeV_cuu = u.Quantity(flux_above_1TeV_cu)
sigma_d = u.Quantity(sigma)
hgpscat_pwn_extended['flux_above_1TeV_cu'] = Column(
flux_above_1TeV_cuu,
description='Integral flux above 1 TeV in crab units')
hgpscat_pwn_extended['sigma'] = Column(
sigma_d, description='radius of angular extension')
# print(hgpscat_pwn_extended['flux_crab'])
how_many_above_10, how_many_8to10, how_many_6to8, how_many_4to6, how_many_2to4, how_many_1to2, how_many_below1 = 0, 0, 0, 0, 0, 0, 0
for row in range(len(hgpscat_pwn_extended)):
if (hgpscat_pwn_extended[row]['flux_above_1TeV_cu'] > 10):
#print('CC crab>10: ', row, hgpscat_pwn_extended[row]['flux_above_1TeV_cu'], hgpscat_pwn_extended[row]['Source_Name'])
how_many_above_10 += 1
if (hgpscat_pwn_extended[row]['flux_above_1TeV_cu'] > 8
and hgpscat_pwn_extended[row]['flux_above_1TeV_cu'] < 10):
#print('CC 8-10: ', row, hgpscat_pwn_extended[row]['flux_above_1TeV_cu'], hgpscat_pwn_extended[row]['Source_Name'])
how_many_8to10 += 1
if (hgpscat_pwn_extended[row]['flux_above_1TeV_cu'] > 6
and hgpscat_pwn_extended[row]['flux_above_1TeV_cu'] < 8):
#print('CC 6-8: ', row, hgpscat_pwn_extended[row]['flux_above_1TeV_cu'], hgpscat_pwn_extended[row]['Source_Name'])
how_many_6to8 += 1
if (hgpscat_pwn_extended[row]['flux_above_1TeV_cu'] > 4
and hgpscat_pwn_extended[row]['flux_above_1TeV_cu'] < 6):
#print('CC 4-6: ', row, hgpscat_pwn_extended[row]['flux_above_1TeV_cu'], hgpscat_pwn_extended[row]['Source_Name'])
how_many_6to8 += 1
if (hgpscat_pwn_extended[row]['flux_above_1TeV_cu'] > 2
and hgpscat_pwn_extended[row]['flux_above_1TeV_cu'] < 4):
#print('CC 2-4: ', row, hgpscat_pwn_extended[row]['flux_above_1TeV_cu'], hgpscat_pwn_extended[row]['Source_Name'])
how_many_2to4 += 1
if (hgpscat_pwn_extended[row]['flux_above_1TeV_cu'] < 2
and hgpscat_pwn_extended[row]['flux_above_1TeV_cu'] > 1):
#print('CC 1-2: ', row, hgpscat_pwn_extended[row]['flux_above_1TeV_cu'], hgpscat_pwn_extended[row]['Source_Name'])
how_many_1to2 += 1
if (hgpscat_pwn_extended[row]['flux_above_1TeV_cu'] < 1):
#print('CC below1: ', row, hgpscat_pwn_extended[row]['flux_above_1TeV_cu'], hgpscat_pwn_extended[row]['Source_Name'])
how_many_below1 += 1
print('how_many_above_10: ', how_many_above_10)
print('how_many_8to10: ', how_many_8to10)
print('how_many_6to8: ', how_many_6to8)
print('how_many_4to6: ', how_many_4to6)
print('how_many_2to4: ', how_many_2to4)
print('how_many_1to2: ', how_many_1to2)
print('how_many_below1: ', how_many_below1)
return hgpscat_pwn_extended
from gammapy.catalog import (
SourceCatalog3FGL,
SourceCatalogGammaCat,
SourceCatalog3FHL,
)
from gammapy.utils.fitting import Fit
# ## Load spectral points
#
# For this analysis we choose to work with the source 'HESS J1507-622' and the associated Fermi-LAT sources '3FGL J1506.6-6219' and '3FHL J1507.9-6228e'. We load the source catalogs, and then access source of interest by name:
# In[ ]:
fermi_3fgl = SourceCatalog3FGL()
fermi_3fhl = SourceCatalog3FHL()
gammacat = SourceCatalogGammaCat("$GAMMAPY_DATA/gamma-cat/gammacat.fits.gz")
# In[ ]:
source_gammacat = gammacat["HESS J1507-622"]
source_fermi_3fgl = fermi_3fgl["3FGL J1506.6-6219"]
source_fermi_3fhl = fermi_3fhl["3FHL J1507.9-6228e"]
# The corresponding flux points data can be accessed with `.flux_points` attribute:
# In[ ]:
flux_points_gammacat = source_gammacat.flux_points
flux_points_gammacat.table
# In the Fermi-LAT catalogs, integral flux points are given. Currently the flux point fitter only works with differential flux points, so we apply the conversion here.
def gammacat():
filename = "$GAMMAPY_DATA/catalogs/gammacat/gammacat.fits.gz"
return SourceCatalogGammaCat(filename=filename)
on_region = CircleSkyRegion(crab_pos, 0.15 * u.deg)
model = LogParabola(
alpha=2.3,
beta=0.01,
amplitude=1e-11 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.TeV,
)
flux_point_binning = EnergyBounds.equal_log_spacing(0.7, 30, 5, u.TeV)
# In[ ]:
exclusion_mask = Map.create(skydir=crab_pos, width=(10, 10), binsz=0.02)
gammacat = SourceCatalogGammaCat()
regions = []
for source in gammacat:
if not exclusion_mask.geom.contains(source.position):
continue
region = CircleSkyRegion(source.position, 0.15 * u.deg)
regions.append(region)
exclusion_mask.data = exclusion_mask.geom.region_mask(regions, inside=False)
exclusion_mask.plot()
# In[ ]:
config = dict(
outdir=".",
crab_01 = crab.integral(emin01, emax).value
crab_1 = crab.integral(emin1, emax).value
flux_int_cu = (ph_fl_01 / crab_01)
flux_int_cu01 = (ph_fl_1 / crab_1)
final['cr_fl_1'] = flux_int_cu
final['cr_fl_01'] = flux_int_cu01
final2 = final[(final.GLAT <= 2.) & (final.GLAT >= -2.) & (final.GLON <= 130.)
& (final.GLON >= -70)]
final3 = final[(final.GLAT <= 2.) & (final.GLAT >= -2.)]
gammacat_file = '../../known-sources/external-input/gammacat.fits.gz'
gammacat = SourceCatalogGammaCat(gammacat_file)
gammacat_pwn_glon, gammacat_pwn_glat = [], []
for source in gammacat:
if source.data.where.startswith('gal'):
if 'pwn' or 'unid' in source.data.classes:
try:
gammacat_pwn_glon.append(source.spatial_model().lon_0.value)
gammacat_pwn_glat.append(source.spatial_model().lat_0.value)
except:
None
gammacat_pwn_glat = np.array(gammacat_pwn_glat)
gammacat_pwn_glon = np.array(gammacat_pwn_glon)
gammacat_pwn_glon = np.concatenate([
gammacat_pwn_glon[gammacat_pwn_glon > 180] - 360,
#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,
outdir=outdir,
run_list=None,
obs_table=obs_table_with_group_id,
ntot_group=obs_groups.n_groups,
def catalog(self):
log.info('Reading catalog ...')
filename = CollectionConfig(
in_path=self.config.in_path,
out_path=self.config.out_path).gammacat_fits
return SourceCatalogGammaCat(filename=filename)
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)