def initMESE3yr(mode='box', **kwargs):
arr_exp = cache.load(filename_pickle + "MESE/exp3yr.pickle")
arr_mc = cache.load(filename_pickle + "MESE/mc.pickle")
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.92, 3 + 1),
np.linspace(-0.92, hem, 10 + 1),
]))
energy_bins = [
np.linspace(2., 8.5, 40 + 1),
np.linspace(-1., hem, 4 + 1),
]
llh_model = EnergyLLH(energy_bins,
sinDec_bins=dec_bins,
allow_empty=True,
kernel=1,
seed=2)
llh = PointSourceLLH(arr_exp,
arr_mc,
livetime=livetimeMESE3yr,
llh_model=llh_model,
mode=mode,
**kwargs)
return (llh, arr_exp, arr_mc)
def init79(decorr=False, mode='box', **kwargs):
if decorr:
arr_exp = cache.load(filename_pickle + "IC79/noMESE/exp.pickle")
arr_mc = cache.load(filename_pickle + "IC79/noMESE/mc.pickle")
else:
arr_exp = np.load(filename_pickle + "standard/IC79b_exp.npy")
arr_mc = np.load(filename_pickle + "standard/IC79b_corrected_MC.npy")
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.75, 10 + 1),
np.linspace(-0.75, 0.0, 15 + 1),
np.linspace(0.0, 1., 20 + 1),
]))
energy_bins = [np.linspace(2., 9., 40 + 1), dec_bins]
llh_model = EnergyLLH(energy_bins,
sinDec_bins=dec_bins,
allow_empty=True,
kernel=1,
seed=2)
llh = PointSourceLLH(arr_exp,
arr_mc,
livetime=livetime_IC79,
llh_model=llh_model,
mode=mode,
**kwargs)
return (llh, arr_exp, arr_mc)
def init3yr(decorr=False, mode='box', **kwargs):
if decorr:
arr_exp = cache.load(filename_pickle + "epinat_3yr/noMESE/exp.pickle")
arr_mc = cache.load(filename_pickle + "epinat_3yr/noMESE/mc.pickle")
else:
arr_exp = cache.load(filename_pickle + "epinat_3yr/exp.pickle")
arr_mc = cache.load(filename_pickle + "epinat_3yr/mc.pickle")
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.93, 4 + 1),
np.linspace(-0.93, -0.3, 10 + 1),
np.linspace(-0.3, 0.05, 9 + 1),
np.linspace(0.05, 1., 18 + 1),
]))
energy_bins = [np.linspace(1., 9.5, 50 + 1), dec_bins]
llh_model = EnergyLLH(energy_bins,
sinDec_bins=dec_bins,
allow_empty=True,
kernel=1,
seed=2)
llh = PointSourceLLH(arr_exp,
arr_mc,
livetime=livetime3yr,
llh_model=llh_model,
mode=mode,
**kwargs)
return (llh, arr_exp, arr_mc)
def initMESE3yr(energy=True, mode='all', **kwargs):
arr_exp = cache.load(filename_pickle + "MESE/exp3yr.pickle")
arr_mc = cache.load(filename_pickle + "MESE/mc.pickle")
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.92, 3 + 1),
np.linspace(-0.92, hem, 10 + 1),
]))
energy_bins = [
np.linspace(2., 8.5, 40 + 1),
np.linspace(-1., hem, 4 + 1),
]
if energy:
llh_model = EnergyLLH(energy_bins, sinDec_bins=dec_bins)
else:
llh_model = ClassicLLH(sinDec_bins=dec_bins, sinDec_range=[-1., 1.])
llh = PointSourceLLH(arr_exp,
arr_mc,
livetime=livetimeMESE3yr,
llh_model=llh_model,
mode=mode,
**kwargs)
return (llh, arr_exp, arr_mc)
def initMESEfollowup(energy=True, mode='all', **kwargs):
arr_exp = cache.load(filename_pickle + "MESE/expfollowup.pickle")
arr_mc = cache.load(filename_pickle + "MESE/mc.pickle")
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.93, 4 + 1),
np.linspace(-0.93, hem, 12 + 1),
]))
energy_bins = [np.linspace(2., 8.5, 67 + 1), np.linspace(-1., hem, 4 + 1)]
arr_mc = arr_mc[arr_mc["logE"] > 1.]
llh_model = EnergyLLH(energy_bins,
sinDec_bins=dec_bins,
allow_empty=True,
kernel=1,
seed=2)
llh = PointSourceLLH(arr_exp,
arr_mc,
livetime=livetimeMESEfollowup,
llh_model=llh_model,
mode=mode,
**kwargs)
return (llh, arr_exp, arr_mc)
def init86I(energy=True, decorr=False, nopull=False, mode='all', **kwargs):
if decorr:
arr_exp = cache.load(filename_pickle + "IC86I/noMESE/exp.pickle")
arr_mc = cache.load(filename_pickle + "IC86I/noMESE/mc.pickle")
elif nopull:
arr_exp = cache.load(filename_pickle + "nopull/IC86I/exp.pickle")
arr_mc = cache.load(filename_pickle + "nopull/IC86I/mc.pickle")
else:
arr_exp = cache.load(filename_pickle + "IC86I/exp.pickle")
arr_mc = cache.load(filename_pickle + "IC86I/mc.pickle")
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.2, 10 + 1),
np.linspace(-0.2, hem, 4 + 1),
np.linspace(hem, 0.2, 5 + 1),
np.linspace(0.2, 1., 10),
]))
#energy_bins = [np.linspace(1., 10., 67 + 1), dec_bins]
energy_bins = [np.linspace(1.5, 9.5, 64 + 1), dec_bins]
if energy:
llh_model = EnergyLLH(energy_bins, sinDec_bins=dec_bins)
else:
llh_model = ClassicLLH(sinDec_bins=dec_bins, sinDec_range=[-1., 1.])
llh = PointSourceLLH(arr_exp,
arr_mc,
livetime=livetime_IC86I,
llh_model=llh_model,
mode=mode,
**kwargs)
return (llh, arr_exp, arr_mc)
def init79(energy=True, decorr=False, mode='all', **kwargs):
if decorr:
arr_exp = cache.load(filename_pickle + "IC79/noMESE/exp.pickle")
arr_mc = cache.load(filename_pickle + "IC79/noMESE/mc.pickle")
else:
arr_exp = cache.load(filename_pickle + "IC79/exp.pickle")
arr_mc = cache.load(filename_pickle + "IC79/mc.pickle")
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.75, 10 + 1),
np.linspace(-0.75, 0.0, 15 + 1),
np.linspace(0.0, 1., 20 + 1),
]))
#energy_bins = [np.linspace(2., 9., 67 + 1), dec_bins]
energy_bins = [np.linspace(2.5, 8.5, 64 + 1), dec_bins]
if energy:
llh_model = EnergyLLH(energy_bins, sinDec_bins=dec_bins)
else:
llh_model = ClassicLLH(sinDec_bins=dec_bins, sinDec_range=[-1., 1.])
llh = PointSourceLLH(arr_exp,
arr_mc,
livetime=livetime_IC79,
llh_model=llh_model,
mode=mode,
**kwargs)
return (llh, arr_exp, arr_mc)
def init86I(decorr=False, nopull=False, mode='box', **kwargs):
if decorr:
arr_exp = cache.load(filename_pickle + "IC86I/noMESE/exp.pickle")
arr_mc = cache.load(filename_pickle + "IC86I/noMESE/mc.pickle")
elif nopull:
arr_exp = np.load(filename_pickle + "nopull/IC86_exp.npy")
arr_mc = np.load(filename_pickle + "nopull/IC86_corrected_MC.npy")
else:
arr_exp = np.load(filename_pickle + "standard/IC86_exp.npy")
arr_mc = np.load(filename_pickle + "standard/IC86_corrected_MC.npy")
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.2, 10 + 1),
np.linspace(-0.2, hem, 4 + 1),
np.linspace(hem, 0.2, 5 + 1),
np.linspace(0.2, 1., 10),
]))
energy_bins = [np.linspace(1., 10., 40 + 1), dec_bins]
llh_model = EnergyLLH(energy_bins,
sinDec_bins=dec_bins,
allow_empty=True,
kernel=1,
seed=2)
llh = PointSourceLLH(arr_exp,
arr_mc,
livetime=livetime_IC86I,
llh_model=llh_model,
mode=mode,
**kwargs)
return (llh, arr_exp, arr_mc)
def initialize_llh(self, skipped=None, scramble=False):
'''
Grab data and format it all into a skylab llh object
'''
if self.exp is None:
self.get_data()
if self._verbose:
print("Initializing Point Source LLH in Skylab")
assert self._fix_index != self._float_index,\
'Must choose to either float or fix the index'
if self._fix_index:
llh_model = EnergyLLH(
twodim_bins=[self.energy_bins, self.sinDec_bins],
allow_empty=True,
spectrum=PowerLaw(A=1, gamma=self.index, E0=1000.))
elif self._float_index:
llh_model = EnergyLLH(
twodim_bins=[self.energy_bins, self.sinDec_bins],
allow_empty=True,
bounds=self._index_range,
seed = self.index)
box = TemporalModel(
grl=self.grl,
poisson_llh=True,
days=self._nb_days,
signal=BoxProfile(self.start, self.stop))
if skipped is not None:
self.exp = self.remove_event(self.exp, self.dset, skipped)
if self.extension is not None:
src_extension = float(self.extension)
self.save_items['extension'] = src_extension
else:
src_extension = None
llh = PointSourceLLH(
self.exp, # array with data events
self.mc, # array with Monte Carlo events
self.livetime, # total livetime of the data events
ncpu=5, # use 10 CPUs when computing trials
scramble=scramble, # set to False for unblinding
timescramble=True, # not just RA scrambling
llh_model=llh_model, # likelihood model
temporal_model=box, # use box for temporal model
nsource_bounds=(0., 1e3), # bounds on fitted ns
src_extension = src_extension, # Model symmetric extended source
nsource=1., # seed for nsignal fit
seed=self.llh_seed)
return llh
def init59(energy=True, mode='all', nopull=False, **kwargs):
if nopull:
arr_exp = cache.load(filename_pickle + "nopull/IC59/exp.pickle")
arr_mc = cache.load(filename_pickle + "nopull/IC59/mc.pickle")
else:
arr_exp = cache.load(filename_pickle + "IC59/exp.pickle")
arr_mc = cache.load(filename_pickle + "IC59/mc.pickle")
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.95, 2 + 1),
np.linspace(-0.95, -0.25, 25 + 1),
np.linspace(-0.25, 0.05, 15 + 1),
np.linspace(0.05, 1., 10 + 1),
]))
dec_bins_logE = np.unique(
np.concatenate([
np.linspace(-1., -0.05, 20 + 1),
np.linspace(0.05, 1., 10 + 1),
]))
#energy_bins = [np.linspace(2., 9.5, 67 + 1), dec_bins_logE]
energy_bins = [np.linspace(2.5, 8.5, 64 + 1), dec_bins_logE]
if energy:
llh_model = EnergyLLH(energy_bins, sinDec_bins=dec_bins)
else:
llh_model = ClassicLLH(sinDec_bins=dec_bins, sinDec_range=[-1., 1.])
llh = PointSourceLLH(arr_exp,
arr_mc,
livetime=livetime_IC59,
llh_model=llh_model,
mode=mode,
**kwargs)
return (llh, arr_exp, arr_mc)
def init40(energy=True, mode='all', nopull=False, **kwargs):
if nopull:
arr_exp = cache.load(filename_pickle + "nopull/IC40/exp.pickle")
arr_mc = cache.load(filename_pickle + "nopull/IC40/mc.pickle")
else:
arr_exp = cache.load(filename_pickle + "IC40/exp.pickle")
arr_mc = cache.load(filename_pickle + "IC40/mc.pickle")
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.25, 10 + 1),
np.linspace(-0.25, 0.0, 5 + 1),
np.linspace(0.0, 1., 10 + 1),
]))
dec_bins_logE = np.unique(
np.concatenate([
np.linspace(-1., -0.25, 10 + 1),
np.linspace(-0.25, 0.0, 10 + 1),
np.linspace(0.0, 1., 10 + 1),
]))
#These binnings are done, year specifically, in load.py from stefan.
#energy_bins = [np.linspace(2., 9., 75 + 1), dec_bins_logE]
energy_bins = [np.linspace(2.6, 8.4, 64 + 1), dec_bins_logE]
if energy:
llh_model = EnergyLLH(energy_bins, sinDec_bins=dec_bins)
else:
llh_model = ClassicLLH(sinDec_bins=dec_bins, sinDec_range=[-1., 1.])
llh = PointSourceLLH(arr_exp,
arr_mc,
livetime=livetime_IC40,
llh_model=llh_model,
mode=mode,
**kwargs)
return (llh, arr_exp, arr_mc)
def init59(mode='box', nopull=False, **kwargs):
if nopull:
arr_exp = np.load(filename_pickle + "nopull/IC59_exp.npy")
arr_mc = np.load(filename_pickle + "nopull/IC59_corrected_MC.npy")
else:
arr_exp = np.load(filename_pickle + "standard/IC59_exp.npy")
arr_mc = np.load(filename_pickle + "standard/IC59_corrected_MC.npy")
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.95, 2 + 1),
np.linspace(-0.95, -0.25, 25 + 1),
np.linspace(-0.25, 0.05, 15 + 1),
np.linspace(0.05, 1., 10 + 1),
]))
dec_bins_logE = np.unique(
np.concatenate([
np.linspace(-1., -0.05, 20 + 1),
np.linspace(0.05, 1., 10 + 1),
]))
energy_bins = [np.linspace(2., 9.5, 50 + 1), dec_bins_logE]
llh_model = EnergyLLH(energy_bins,
sinDec_bins=dec_bins,
allow_empty=True,
kernel=1,
seed=2)
llh = PointSourceLLH(arr_exp,
arr_mc,
livetime=livetime_IC59,
llh_model=llh_model,
mode=mode,
**kwargs)
return (llh, arr_exp, arr_mc)
def init40(mode='box', nopull=False, **kwargs):
if nopull:
arr_exp = np.load(filename_pickle + "nopull/IC40_exp.npy")
arr_mc = np.load(filename_pickle + "nopull/IC40_corrected_MC.npy")
else:
arr_exp = np.load(filename_pickle + "standard/IC40_exp.npy")
arr_mc = np.load(filename_pickle + "standard/IC40_corrected_MC.npy")
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.25, 10 + 1),
np.linspace(-0.25, 0.0, 5 + 1),
np.linspace(0.0, 1., 10 + 1),
]))
dec_bins_logE = np.unique(
np.concatenate([
np.linspace(-1., -0.25, 10 + 1),
np.linspace(-0.25, 0.0, 10 + 1),
np.linspace(0.0, 1., 10 + 1),
]))
#These binnings are done, year specifically, in load.py from stefan.
energy_bins = [np.linspace(1., 10., 35 + 1), dec_bins_logE]
llh_model = EnergyLLH(energy_bins,
sinDec_bins=dec_bins,
allow_empty=True,
kernel=1,
seed=2)
llh = PointSourceLLH(arr_exp,
arr_mc,
livetime=livetime_IC40,
llh_model=llh_model,
mode=mode,
**kwargs)
return (llh, arr_exp, arr_mc)
llh = datascript.load4yr(mode='box', sirin=True, nopull=True)
elif catalog == 'Milagro17':
llh = datascript.init86I(mode='box')
else:
if years == 3:
if catalog == '2LAC':
llh = datascript.load3yr_no40(mode='all')
else:
llh = datascript.load3yr(mode='all')
elif years == 4:
llh = datascript.load4yr(mode='all')
if years == 1:
bckg_trials = PointSourceLLH.do_trials(
llh[0],
n_iter=batchsize,
src_ra=src_ra,
src_dec=src_dec,
src_w=modelweights['{}'.format(llhweight)])
else:
bckg_trials = FastMultiPointSourceLLH.do_trials(
llh[0],
n_iter=batchsize,
src_ra=src_ra,
src_dec=src_dec,
src_w=modelweights['{}'.format(llhweight)])
#choose an output dir, and make sure it exists
this_dir = os.path.dirname(os.path.abspath(__file__))
if catalog == 'WHSP_Blazars':
out_dir = misc.ensure_dir(
def config(seasons, seed=1, scramble=True, verbose=True, dec=0.):
r""" Configure multi season point source likelihood and injector.
Parameters
----------
seasons : list
List of season names
seed : int
Seed for random number generator
Returns
-------
multillh : MultiPointSourceLLH
Multi year point source likelihood object
inj : PointSourceInjector
Point source injector object
"""
# store individual llh as lists to prevent pointer over-writing
llh = []
multillh = FastMultiPointSourceLLH(seed=seed, ncpu=25)
# setup likelihoods
if verbose: print("\n seasons:")
for season in np.atleast_1d(seasons):
#sample = 'PointSourceTracks7yr_galactic_plane'
#sample = 'PointSourceTracks7yr'
#sample = 'PointSourceTracks_v002p01b'
sample = 'PointSourceTracks'
exp, mc, livetime = Datasets[sample].season(season)
sinDec_bins = Datasets[sample].sinDec_bins(season)
energy_bins = Datasets[sample].energy_bins(season)
mc = mc[mc["logE"] > 1.]
if verbose:
print(" - %-15s (livetime %7.2f days, %6d events)" %
(season, livetime, exp.size))
llh_model = EnergyLLH(twodim_bins=[energy_bins, sinDec_bins],
allow_empty=True,
bounds=[1., 4.],
seed=2.,
kernel=1)
llh.append(
PointSourceLLH(exp,
mc,
livetime,
mode="box",
seed=seed,
scramble=scramble,
llh_model=llh_model,
nsource_bounds=(0., 1e3),
delta_ang=np.deg2rad(1.),
nsource=15.))
multillh.add_sample(season, llh[-1])
# save a little RAM by removing items copied into LLHs
del exp, mc
# END for (season)
#######
# LLH #
#######
#############################################################################
############
# INJECTOR #
############
'''
inj = PointSourceInjector(gamma = 2., E0 = 1*TeV, seed = seed)
inj.fill(dec, multillh.exp, multillh.mc, multillh.livetime)
'''
inj = None
############
# INJECTOR #
############
#############################################################################
'''
if verbose:
print("\n fitted spectrum:")
vals = (inj.E0/TeV)
print(" - dN/dE = A (E / %.1f TeV)^-index TeV^-1cm^-2s^-1" % vals)
print(" - index is *fit*")
print("\n injected spectrum:")
vals = (inj.E0/TeV, inj.gamma)
print(" - dN/dE = A (E / %.1f TeV)^-%.2f TeV^-1cm^-2s^-1" % vals)
'''
return (multillh, inj)
calctype = "disc"
else:
print("Calculating Sensitivity...")
alpha = 0.5
beta = 0.9
TSval = TSval_median
calctype = "sens"
if catalog:
if years == 1:
sensitivity = PointSourceLLH.weighted_sensitivity(
llhmodel,
src_ra=src_ra,
src_dec=src_dec,
alpha=alpha,
beta=beta,
inj=inj,
TSval=TSval,
src_w=modelweights['{}'.format(injweight)],
eps=0.04,
n_iter=100)
else:
sensitivity = MultiPointSourceLLH.weighted_sensitivity(
llhmodel,
src_ra=src_ra,
src_dec=src_dec,
alpha=alpha,
beta=beta,
inj=inj,
TSval=TSval,
src_w=modelweights['{}'.format(injweight)],
def config(alert_ind,
seed=1,
scramble=True,
e_range=(0, np.inf),
g_range=[1., 5.],
gamma=2.0,
E0=1 * TeV,
remove=False,
ncpu=20,
nside=256,
poisson=False,
injector=True,
verbose=True,
smear=True):
r""" Configure point source likelihood and injector.
Parameters
----------
alert_ind: int
index of IceCube alert event
seed : int
Seed for random number generator
Returns
-------
llh : PointSourceLLH
Point source likelihood object
inj : PriorInjector
Point source injector object
"""
seasons = [("GFUOnline_v001p02", "IC86, 2011-2018"),
("GFUOnline_v001p02", "IC86, 2019")]
#skymaps_path = '/data/user/steinrob/millipede_scan_archive/fits_v3_prob_map/'
#files = glob(skymaps_path + '*.fits')
#skymap_fits = fits.open(files[alert_ind])[0].data
#Turn this into a function read_alert_event()
#skymap_files = glob('/data/ana/realtime/alert_catalog_v2/2yr_prelim/fits_files/Run13*.fits.gz')
skymap_files = glob(
'/data/ana/realtime/alert_catalog_v2/fits_files/Run1*.fits.gz')
#skymap_f = fits.open(skymap_files[alert_ind])
#skymap_fits = skymap_f[1].data
#skymap_header = skymap_f[1].header
skymap_fits, skymap_header = hp.read_map(skymap_files[alert_ind],
h=True,
verbose=False)
skymap_header = {name: val for name, val in skymap_header}
run_id, ev_id = skymap_header['RUNID'], skymap_header['EVENTID']
ev_mjd = skymap_header['EVENTMJD']
ev_iso = skymap_header['START']
signalness = skymap_header['SIGNAL']
ev_en = skymap_header['ENERGY']
ev_ra, ev_dec = np.radians(skymap_header['RA']), np.radians(
skymap_header['DEC'])
ev_stream = skymap_header['I3TYPE']
skymap_llh = skymap_fits.copy()
skymap_fits = np.exp(-1. * skymap_fits /
2.) #Convert from 2LLH to unnormalized probability
skymap_fits = np.where(skymap_fits > 1e-12, skymap_fits, 0.0)
skymap_fits = skymap_fits / np.sum(skymap_fits)
if smear:
ninety_msk = skymap_llh < 64.2
init_nside = hp.get_nside(skymap_llh)
cdf = np.cumsum(np.sort(skymap_fits[ninety_msk][::-1]))
pixs_above_ninety = np.count_nonzero(cdf > 0.1)
original_ninety_area = hp.nside2pixarea(init_nside) * pixs_above_ninety
new_ninety_area = hp.nside2pixarea(init_nside) * np.count_nonzero(
skymap_fits[ninety_msk])
original_ninety_radius = np.sqrt(original_ninety_area / np.pi)
new_ninety_radius = np.sqrt(new_ninety_area / np.pi)
scaled_probs = scale_2d_gauss(skymap_fits, original_ninety_radius,
new_ninety_radius)
skymap_fits = scaled_probs
if hp.pixelfunc.get_nside(skymap_fits) != nside:
skymap_fits = hp.pixelfunc.ud_grade(skymap_fits, nside)
skymap_fits = skymap_fits / skymap_fits.sum()
#print(hp.pixelfunc.get_nside(skymap_fits))
spatial_prior = SpatialPrior(skymap_fits, containment=0.99)
llh = [] # store individual llh as lists to prevent pointer over-writing
multillh = MultiPointSourceLLH(ncpu=1)
if verbose:
print("\n seasons:")
for season in np.atleast_1d(seasons):
sample = season[0]
name = season[1]
exp, mc, livetime = Datasets[sample].season(name,
floor=np.radians(0.2))
sinDec_bins = Datasets[sample].sinDec_bins(name)
energy_bins = Datasets[sample].energy_bins(name)
msg = " - % 15s (" % season
msg += "livetime %7.2f days, %6d events" % (livetime, exp.size)
msg += ", mjd0 %.2f" % min(exp['time'])
msg += ", mjd1 %.2f)" % max(exp['time'])
if verbose:
print(msg)
llh_model = EnergyLLH(twodim_bins=[energy_bins, sinDec_bins],
allow_empty=True,
bounds=g_range,
seed=gamma,
kernel=1,
ncpu=ncpu)
llh.append(
PointSourceLLH(exp,
mc,
livetime,
mode="box",
scramble=scramble,
llh_model=llh_model,
nsource_bounds=(0., 1e3),
nsource=1.))
multillh.add_sample(sample + " : " + name, llh[-1])
# save a little RAM by removing items copied into LLHs
del exp, mc
# END for (season)
######################### REMOVE EVENT
if injector is False:
return multillh, spatial_prior
else:
inj = PriorInjector(spatial_prior,
seed=seed,
gamma=gamma,
E0=1 * TeV,
bunchsize=10)
inj.fill(multillh.exp, multillh.mc, multillh.livetime)
if verbose:
print("\n injected spectrum:")
print(" - %s" % str(inj.spectrum))
return multillh, spatial_prior, inj
rnd_seed = 1
multillh = MultiPointSourceLLH(seed=rnd_seed, ncpu=40)
livetimes = _loader.livetime_loader()
for name, livetime in sorted(livetimes.items()):
print("\n# Setting up LLH for sample '{}'".format(name))
# Get exp data and MC
exp = _loader.exp_data_loader(name)[name]
mc = _loader.mc_loader(name)[name]
# Setup the energy LLH model with fixed index, only ns is fitted
settings = _loader.settings_loader(name, skylab_bins=use_skylab_bins)[name]
llh_model = EnergyLLH(**settings["llh_model_opts"])
llh = PointSourceLLH(exp,
mc,
livetime,
llh_model,
scramble=scramble,
**settings["llh_opts"])
multillh.add_sample(name, llh)
del exp
del mc
gc.collect()
# Load the BG only distribution
with gzip.open(pdf_path) as fp:
bg_ts_dist = ExpTailEmpiricalDist.from_json(fp)
print("Loaded BG only TS distribution from:\n {}".format(pdf_path))
# ##############################################################################
def config(seasons,
seed=1,
scramble=True,
e_range=(0, np.inf),
verbose=True,
gamma=2.0,
dec=0.,
remove=False,
src_w=None):
r""" Configure multi season point source likelihood and injector.
Parameters
----------
seasons : list
List of season names
seed : int
Seed for random number generator
Returns
-------
multillh : MultiPointSourceLLH
Multi year point source likelihood object
inj : PointSourceInjector
Point source injector object
"""
print("Scramble is %s" % str(scramble))
# store individual llh as lists to prevent pointer over-writing
llh = []
multillh = MultiPointSourceLLH(seed=seed, ncpu=25)
# setup likelihoods
if verbose: print("\n seasons:")
for season in np.atleast_1d(seasons):
sample = season[0]
name = season[1]
exp, mc, livetime = Datasets[sample].season(name)
sinDec_bins = Datasets[sample].sinDec_bins(name)
energy_bins = Datasets[sample].energy_bins(name)
if sample == "GFU" and remove:
exp = Datasets['GFU'].remove_ev(
exp, 58018.87118560489) # remove EHE 170922A
mc = mc[mc["logE"] > 1.]
if verbose:
print(" - % 15s : % 15s" % (sample, name))
vals = (livetime, exp.size, min(exp['time']), max(exp['time']))
print(
" (livetime %7.2f days, %6d events, mjd0 %.2f, mjd1 %.2f)"
% vals)
llh_model = EnergyLLH(twodim_bins=[energy_bins, sinDec_bins],
allow_empty=True,
bounds=[1., 4.],
seed=2.,
kernel=1)
llh.append(
PointSourceLLH(exp,
mc,
livetime,
mode="box",
scramble=scramble,
llh_model=llh_model,
nsource_bounds=(0., 1e3),
nsource=15.))
multillh.add_sample(sample + " : " + name, llh[-1])
# save a little RAM by removing items copied into LLHs
del exp, mc
# END for (season)
#######
# LLH #
#######
#############################################################################
############
# INJECTOR #
############
inj = PointSourceInjector(gamma=gamma,
E0=1 * TeV,
seed=seed,
e_range=e_range)
inj.fill(dec, multillh.exp, multillh.mc, multillh.livetime, src_w)
############
# INJECTOR #
############
#############################################################################
if verbose:
print("\n fitted spectrum:")
vals = (inj.E0 / TeV)
print(" - dN/dE = A (E / %.1f TeV)^-index TeV^-1cm^-2s^-1" % vals)
print(" - index is *fit*")
print("\n injected spectrum:")
vals = (inj.E0 / TeV, inj.gamma)
print(" - dN/dE = A (E / %.1f TeV)^-%.2f TeV^-1cm^-2s^-1" % vals)
return (multillh, inj)