src_ra = np.radians(src_ra)
## NOTE: ADD WEIGHTS HERE FOR THE INJECTED EVENTS!! (Naturally only for flux, redshift. (tried with theo_weight, haven't tested it yet)
inj = PointSourceInjector(Gamma,
sinDec_bandwidth=.05,
src_dec=src_dec,
theo_weight=flux,
seed=0)
results = PointSourceLLH.weighted_sensitivity(llh_flux,
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 results
#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/flux/disc/'
)
##I need to have this
src_dec=[np.radians(dec_deg)]
src_ra=[0.0]
## NOTE: ADD WEIGHTS HERE FOR THE INJECTED EVENTS!! (Naturally only for flux, redshift.
##For now the weighted sensitivity function only works if there are at least two sources. to push it through for a single source, I'll copy the source location.##
### Mike - This is the step that allows the function weighted_sensitivity to process! ###
#src_dec=[src_dec[0],src_dec[0]]
#src_ra=[src_ra[0],src_ra[0]]
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, miniter=2500)# 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 (outfolder+'sensitivity/')
# save the output
outfile = out_dir + 'dec{0:+010.5}.array'.format(dec_deg)
print 'Saving', outfile, '...'
cache.save(results, outfile)
llhmodel = data_multi.init79(energy=True, mode='box')
elif year == '86':
llhmodel = data_multi.init86I(energy=True, mode='box')
elif year == '59':
llhmodel = data_multi.init59(energy=True, mode='box')
#If I change the injection range I'll redefine the _e_range variable in ps_injector_stack.py.
sensitivity = PointSourceLLH.weighted_sensitivity(llhmodel,
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=0.02,
n_iter=250)
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__))
