# llh86I= data_multi.init86I(energy=True,mode='box')
# llh59= data_multi.init59(energy=True,mode='box')
# llh40= data_multi.init40(energy=True,mode='box')
#
#else:
# llh40= data_multi.init40(energy=True, weighting = modelweights['{}'.format(llhweight)],mode='box')
# llh79 = data_multi.init79(energy=True, weighting = modelweights['{}'.format(llhweight)],mode='box')
# llh86I= data_multi.init86I(energy=True, weighting = modelweights['{}'.format(llhweight)],mode='box')
# llh59= data_multi.init59(energy=True, weighting = modelweights['{}'.format(llhweight)],mode='box')
import new_data_multi
## Like in the background trials, we have to define which llhmodel to use.
if llhweight == 'uniform':
llh79 = new_data_multi.init79(energy=True, mode='box')
llh86I = new_data_multi.init86I(energy=True, mode='box')
llh59 = new_data_multi.init59(energy=True, mode='box')
llh40 = new_data_multi.init40(energy=True, mode='box')
else:
llh40 = new_data_multi.init40(
energy=True,
weighting=modelweights['{}'.format(llhweight)],
mode='box')
llh79 = new_data_multi.init79(
energy=True,
weighting=modelweights['{}'.format(llhweight)],
mode='box')
llh86I = new_data_multi.init86I(
energy=True,
weighting=modelweights['{}'.format(llhweight)],
parser.add_option ('--years', dest = 'years', type = int,
default = 4, metavar = 'YEARS',
help = 'Number of years of data')
opts, args = parser.parse_args ()
batch = opts.batch
batchsize = opts.batchsize
llhweight = opts.llhweight
years = opts.years
## We'll assign the proper weighting scheme for the search, then use it to calculate and cache the associated bckg trials: ##
llh79 = new_data_multi.init79(energy=True,mode='box')
llh86I= new_data_multi.init86I(energy=True,mode='box')
llh59= new_data_multi.init59(energy=True,mode='box')
llh40= new_data_multi.init40(energy=True,mode='box')
#We've loaded in the appropriate llh samples, now let's put them both in the blender (not sure about weighting)
if years == 2:
samples = [llh79,llh86I]
elif years == 3:
samples = [llh59,llh79,llh86I]
elif years == 4:
samples = [llh40,llh59,llh79,llh86I]
llhmodel = new_data_multi.multi_init(samples,energy=True)
dest='years',
type=int,
default=4,
metavar='YEARS',
help='Number of years of data')
opts, args = parser.parse_args()
batch = opts.batch
batchsize = opts.batchsize
llhweight = opts.llhweight
years = opts.years
## We'll assign the proper weighting scheme for the search, then use it to calculate and cache the associated bckg trials: ##
llh79 = new_data_multi.init79(energy=True)
llh86I = new_data_multi.init86I(energy=True)
llh59 = new_data_multi.init59(energy=True)
llh40 = new_data_multi.init40(energy=True)
#We've loaded in the appropriate llh samples, now let's put them both in the blender (not sure about weighting)
if years == 2:
samples = [llh79, llh86I]
elif years == 3:
samples = [llh59, llh79, llh86I]
elif years == 4:
samples = [llh40, llh59, llh79, llh86I]
llhmodel = new_data_multi.multi_init(samples, energy=True)
bckg_trials = StackingMultiPointSourceLLH.do_trials(