true_ns = []
ra = []
dec = []
gammaList = []
mean_ninj = []
flux_list = []
true_ras = []
true_decs = []
seed_counter = 0
for gamma in gammas:
f = FastResponseAnalysis(cascade_healpy_file,
start,
stop,
save=False,
alert_event=True,
smear=False,
alert_type='cascade')
inj = f.initialize_injector(gamma=gamma)
scale_arr = []
step_size = 10
for i in range(1, 20 * step_size + 1, step_size):
scale_arr.append([])
for j in range(5):
scale_arr[-1].append(inj.sample(i, poisson=False)[0][0])
scale_arr = np.median(scale_arr, axis=1)
try:
scale_factor = np.min(np.argwhere(scale_arr > 0)) * step_size + 1.
except:
print("Scale factor thing for prior injector didn't work")
h=True,
verbose=False)
skymap_header = {name: val for name, val in skymap_header}
ev_mjd = skymap_header['EVENTMJD']
ev_run, ev_id = skymap_header['RUNID'], skymap_header['EVENTID']
source = {"Skipped Events": [(ev_run, ev_id)]}
source['Name'] = "RUN {} EVENT {} time window {:.2e}".format(
str(skymap_header['RUNID']), str(skymap_header['EVENTID']), args.deltaT)
source['alert_type'] = 'track'
deltaT = args.deltaT / 86400.
event_mjd = ev_mjd
start_mjd = event_mjd - (deltaT / 2.)
stop_mjd = event_mjd + (deltaT / 2.)
start = Time(start_mjd, format='mjd').iso
stop = Time(stop_mjd, format='mjd').iso
f = FastResponseAnalysis(skymap_files[args.index],
start,
stop,
save=False,
alert_event=True,
smear=args.smear,
**source)
inj = f.initialize_injector(
gamma=2.5) #just put this here to initialize f.spatial_prior
ts = f.unblind_TS()
smear_str = 'smeared/' if args.smear else 'norm_prob/'
res = f.save_results(alt_path=output_paths + smear_str)
true_ns = []
ra = []
dec = []
gammaList = []
mean_ninj = []
flux_list = []
true_ras = []
true_decs = []
seed_counter = 0
for gamma in gammas:
f = FastResponseAnalysis(skymap_files[args.index],
start,
stop,
save=False,
alert_event=True,
smear=args.smear,
alert_type='track')
inj = f.initialize_injector(gamma=gamma)
scale_arr = []
step_size = 10
for i in range(1, 20 * step_size + 1, step_size):
scale_arr.append([])
for j in range(5):
scale_arr[-1].append(inj.sample(i, poisson=False)[0][0])
scale_arr = np.median(scale_arr, axis=1)
try:
scale_factor = np.min(np.argwhere(scale_arr > 0)) * step_size + 1.
except:
print("Scale factor thing for prior injector didn't work")
+ 'fast-response/fast_response/cascades_results/skymaps/IceCube-Cascade' \
+ '_{}_{}.hp'.format(int(run_id), int(event_id))
trials_per_sig = args.ntrials
tsList_prior = []
nsList_prior = []
ra = []
dec = []
seed_counter = 0
f = FastResponseAnalysis(cascade_healpy_file,
start,
stop,
save=False,
alert_event=True,
smear=False,
alert_type='cascade')
inj = f.initialize_injector(
gamma=2.5) #just put this here to initialize f.spatial_prior
for jj in range(trials_per_sig):
seed_counter += 1
try:
val = f.llh.scan(0.0,
0.0,
scramble=True,
seed=seed_counter,
spatial_prior=f.spatial_prior,
time_mask=[deltaT / 2., event_mjd],
final_args[name] = new_arg
else:
final_args[name] = argument
print('')
import logging as log
from astropy.time import Time
from astropy.coordinates import Angle
import astropy.units as u
from fast_response.FastResponseAnalysis import FastResponseAnalysis
log.basicConfig(level=log.ERROR)
source = final_args
source['alert_event'] = args.alert_event
####################### INITIALIZE FAST RESPONSE OBJECT #######################
f = FastResponseAnalysis(source['Location'], source['Start Time'], source['Stop Time'], **source)
# Point source, gw, etc. handling done in analysis object instantiation
f.unblind_TS()
f.plot_ontime()
f.ns_scan()
f.calc_pvalue(ntrials = args.ntrials)
if not args.nodiag:
f.make_dNdE()
f.plot_tsd()
f.upper_limit()
results = f.save_results()
f.generate_report()
if args.document:
subprocess.call(['cp','-r',results['analysispath'],
trials_per_sig = args.ntrials
tsList_prior = []
tsList = []
nsList = []
nsList_prior = []
true_ns = []
ra = []
dec = []
seed_counter = 0
f = FastResponseAnalysis(skymap_files[args.index],
start,
stop,
save=False,
alert_event=True,
smear=args.smear,
alert_type='track')
inj = f.initialize_injector(
gamma=2.5) #just put this here to initialize f.spatial_prior
for jj in range(trials_per_sig):
seed_counter += 1
try:
val = f.llh.scan(0.0,
0.0,
scramble=True,
seed=seed_counter,
spatial_prior=f.spatial_prior,
time_mask=[deltaT / 2., event_mjd],
pixel_scan=[f.nside, 4.0],
skymap_files = glob(
'/data/ana/realtime/alert_catalog_v2/2yr_prelim/fits_files/Run13*.fits.gz')
start_mjd = 58484.0
stop_mjd = start_mjd + (args.deltaT / 86400.)
start = Time(start_mjd, format='mjd').iso
stop = Time(stop_mjd, format='mjd').iso
deltaT = args.deltaT / 86400.
trials_per_sig = args.ntrials
seed_counter = args.seed
f = FastResponseAnalysis(skymap_files[0],
start,
stop,
save=False,
alert_event=True)
f.llh.nbackground = args.bkg * args.deltaT / 1000.
#inj = f.initialize_injector(gamma=2.5) #just put this here to initialize f.spatial_prior
#print f.llh.nbackground
#results_array = []
npix = hp.nside2npix(f.nside)
shape = (args.ntrials, npix)
maps = sparse.lil_matrix(shape, dtype=float)
for jj in range(trials_per_sig):
seed_counter += 1
val = f.llh.scan(
0.0,