inj = PointSourceInjector(Gamma, sinDec_bandwidth=.05, src_dec=src_dec, seed=0)
results = PointSourceLLH.weighted_sensitivity(llh_single,
src_ra=src_ra,
src_dec=src_dec,
alpha=.5,
beta=.9,
inj=inj,
trials={
'n_inj': [],
'TS': [],
'nsources': [],
'gamma': []
},
bckg_trials=bckg_trials,
eps=0.01,
n_iter=250,
maxtrial=1000)
#Currently have n_iter down from 1000 to reduce estimation time. Also lowering maxtrial from 1000 to 500.
print results
#choose an output dir, and make sure it exists
this_dir = os.path.dirname(os.path.abspath(__file__))
out_dir = misc.ensure_dir(datafolder + 'sensitivity/')
# save the output
outfile = out_dir + 'single_sensitivity_dec{0:+010.5}.array'.format(dec_deg)
print 'Saving', outfile, '...'
cache.save(results, outfile)
llhmodel,
src_ra=src_ra,
src_dec=src_dec,
alpha=.5,
beta=.9,
inj=inj,
mc=MC,
TSval=TSval,
w_theoMC=modelweights['{}'.format(llhweight)],
w_theo=modelweights['{}'.format(injweight)],
w_theo_fit=modelweights['{}'.format(injweight)],
eps=0.05,
n_iter=100)
print sensitivity
#discovery = PointSourceLLH.weighted_sensitivity(llhmodel,src_ra=src_ra,src_dec=src_dec,alpha=2.867e-7,beta=.5,inj=inj,trials={'n_inj':[],'TS':[],'nsources':[],'gamma':[]},bckg_trials=bckg_trials,eps=0.01,n_iter=250)
#print discovery
#choose an output dir, and make sure it exists
this_dir = os.path.dirname(os.path.abspath(__file__))
sens_dir = misc.ensure_dir(
'/data/user/brelethford/Output/stacking_sensitivity/4yr_Starburst/{0}yr/{1}_mhuber_git/{2}_inj/sensitivity/'
.format(str(n), llhweight, injweight))
# save the output
outfile_sens = sens_dir + 'gamma{}.array'.format(Gamma)
print 'Saving', outfile_sens, '...'
cache.save(sensitivity, outfile_sens)
#cache.save(discovery, outfile_disc)
###################################
# BACKGROUND ONLY TS DISTRIBUTION #
###################################
print("\nRunning background only trials ...")
print("Grabbing background trials from {}...".format(bg_dir))
batches = [
cache.load(bg_dir + file) for file in os.listdir(bg_dir)
if file.endswith('.array')
]
trials = np.concatenate(batches, axis=1)
#save trials and fit params
outfile = sens_dir + 'trials.array'
cache.save(trials, outfile)
ts_parameters = fit_background(trials,
dir_name + "background_trials_%s.png" % tag)
print("Finished background fit.")
outfile = sens_dir + 'bg_fit.array'
cache.save(ts_parameters, outfile)
#These get used below for fitting the disc. pot.
median_ts, eta, ndf, scale = ts_parameters
print("\nBackground only TS > 0 described by:")
print(" Median TS: %6.4f" % median_ts)
print(" PDF(TS>0): %6.4f * chi2(ts / %.4f, ndf = %.4f)" % (eta, scale, ndf))
'--batchsize',
dest='batchsize',
type=int,
default=1000,
metavar='BATCHSIZE',
help='Assigns how many background trials are used in each batch.')
opts, args = parser.parse_args()
batch = opts.batch
batchsize = opts.batchsize
##For this check we'll use the gamma weighting scheme for llh method and injection:
llhmodel = data_double.init(energy=True, weighting=flux)
bckg_trials = PointSourceLLH.background_scrambles(llhmodel,
src_ra,
src_dec,
alpha=0.5,
maxiter=batchsize)
#choose an output dir, and make sure it exists
this_dir = os.path.dirname(os.path.abspath(__file__))
out_dir = misc.ensure_dir(
'/data/user/brelethford/Output/stacking_sensitivity/2LAC/flux/background_trials/'
)
# save the output
outfile = out_dir + 'background_batch_{}.array'.format(batch)
print 'Saving', outfile, '...'
cache.save(bckg_trials, outfile)
src_ra=src_ra,
src_dec=src_dec,
alpha=2.867e-7,
beta=.5,
inj=inj,
trials={
'n_inj': [],
'TS': [],
'nsources': [],
'gamma': []
},
bckg_trials=bckg_trials,
eps=0.01,
n_iter=250)
print discovery
#choose an output dir, and make sure it exists
this_dir = os.path.dirname(os.path.abspath(__file__))
#sens_dir = misc.ensure_dir ('/data/user/brelethford/Output/stacking_sensitivity/SwiftBAT70m/{0}/{1}_inj/sensitivity/'.format(llhweight, injweight))
disc_dir = misc.ensure_dir(
'/data/user/brelethford/Output/stacking_sensitivity/SwiftBAT70m/{0}/{1}_inj/disc/'
.format(llhweight, injweight))
# save the output
#outfile_sens = sens_dir + 'gamma{}.array'.format(Gamma)
outfile_disc = disc_dir + 'gamma{}.array'.format(Gamma)
print 'Saving', outfile_disc, '...'
#cache.save(sensitivity, outfile_sens)
cache.save(discovery, outfile_disc)
#!/usr/bin/env python
import numpy as np
from icecube.umdtools import cache
#make sources
N = 10
dec_limits = np.radians([-85.0, 85.0])
ra_limits = np.radians([0.0, 360.0])
decs = np.linspace(dec_limits[0], dec_limits[1], N)
ras = np.linspace(ra_limits[0], 2 * ra_limits[1], N) % (2 * np.pi)
ns = np.arange(1, N + 1)
params = {'n': ns, 'dec': decs, 'ra': ras}
cache.save(params, 'params.pickle')
#Also save it as pickle and a txtfile for the svn site
outfolder = "/data/i3home/brelethford/csky/stacktest/teststack/"
outfile = open(outfolder + "params.txt", "w")
outfile.write('N Dec (radians) RA (radians)\n')
for n, dec, ra in zip(ns, decs, ras):
outfile.write('{0} {1:f} {2:f}\n'.format(n, dec, ra))
#!/usr/bin/env python
import numpy as np
import icecube.astro as astro
from icecube.umdtools import cache
rawparams = np.genfromtxt('blackhole233.csv', skip_header=True,
delimiter=',').T
l, b, z, mass, flux2micron, d1Mpc, M_by_R2 = rawparams[1:]
ra, dec = astro.gal_to_equa(l, b)
params = {
'ra': list(ra),
'dec': list(dec),
'z': list(z),
'mass': list(mass),
'flux2m': list(flux2micron),
'distMpc': list(d1Mpc),
'M_by_R2': list(M_by_R2)
}
cache.save(params,
'/data/user/brelethford/Data/blackhole/pickle/params.pickle')
#
#def get_flux_bckg_trials():
# return PointSourceLLH.background_scrambles(llh_flux,src_ra,src_dec,alpha=0.5)
#
#bckg_trials_flux = cache.get (datafolder + 'SwiftBAT70m/pickle/bckg_trials_flux.pickle', get_flux_bckg_trials)
#
#
#def get_redshift_bckg_trials():
# return PointSourceLLH.background_scrambles(llh_redshift,src_ra,src_dec,alpha=0.5)
#
#bckg_trials_redshift = cache.get (datafolder + 'SwiftBAT70m/pickle/bckg_trials_redshift.pickle', get_redshift_bckg_trials)
#
##This one's gonna work a little differently than the single source sensitivity. First off, we need to calculate the background scrambles ahead of time, with the definition provided in psLLH_stack.py. I'll try to do this all within the same function:##
## Background Trials have the following keys:##
##['beta', 'TS_beta', 'beta_err', 'n_inj', 'nsources', 'TS', 'gamma']##
## Let's use a uniform weight (none) first to yield our bckg trials. ##
#choose an output dir, and make sure it exists
this_dir = os.path.dirname(os.path.abspath(__file__))
out_dir = misc.ensure_dir(
'/data/user/brelethford/Output/stacking_sensitivity/SwiftBAT70m/uniform/background_trials/'
)
# save the output
outfile = out_dir + 'background_batch_{}.array'.format(batch)
print 'Saving', outfile, '...'
cache.save(bckg_trials_uniform, outfile)
# 'r' is the signal PDF divided by background PDF for an event
# setting values less than 1e-10 to zero
analysis.set_config(min_r=1.0e-10,
sigma_t_truncate=0,
sigma_t_min=1.0e-3,
sigma_t_max=1.0e7,
max_delang=np.pi)
# perform background trials
analysis.set_bg_thrower()
tsd_bg_tw = grbllh.do_trials(N, [analysis],
llh_type=grbllh.LlhType.max_source,
mu=0.,
seed=options.randomseed)
# Store the TestStatDist object in a pickle file
if try_one_source:
cache.save(
tsd_bg_tw,
"/data/user/sfahey/FRB/L2_Analysis/tsd/bg/tsd_oneSrc_seed%s_dT%.2f" %
(str(options.randomseed).zfill(2), dT))
else:
cache.save(
[tsd_bg_tw],
"/data/user/sfahey/FRB/L2_Analysis/tsd/bg/tsd_maxburst_seed%s_dT%.3f" %
(str(options.randomseed).zfill(3), dT))
print "Time: ", time.time() - time0