np.concatenate([
np.linspace(sd_lo, -hor, 3 + 1), # south
np.linspace(-hor, +hor, 14 + 1), # horizon
np.linspace(+hor, sd_hi, 3 + 1), # north
]))
# Make settings for each module per sample
sample_names = source_list_loader()
for key in sample_names:
print("Building settings file for sample '{}'".format(key))
# Load data that settings depend on
srcs = source_list_loader(key)[key]
runlist = runlist_loader(key)[key]
exp_off = off_data_loader(key)[key]
exp_on = on_data_loader(key)[key]
mc = mc_loader(key)[key]
# :: BG injector ::
# Rebinning for the rate model fits, use monthly bins
if key == "IC86_2012-2014":
n_rate_bins = 36
else:
n_rate_bins = 12
rate_rebins = np.linspace(np.amin(exp_off["time"]),
np.amax(exp_off["time"]), n_rate_bins)
# We want the parameter spline to stick a little bit to the points
spl_s = len(sindec_bins) / 2
bg_inj_opts = {
"sindec_bins": sindec_bins.tolist(), # JSON only saves lists
"rate_rebins": rate_rebins.tolist(),
"spl_s": spl_s,
nsrcs = len(sources)
ra = [src["ra"] for src in sources]
dec = [src["dec"] for src in sources]
# Theo weights need to be normalized manually in skylab?
w = np.ones(nsrcs, dtype=float) / float(nsrcs)
# Create the multi LLH
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
"IC86_2015": gfu,
}
key2skylab = {
"IC79": "IC79",
"IC86_2011": "IC86, 2011",
"IC86_2012-2014": "IC86, 2012-2014",
"IC86_2015": "IC86, 2015",
}
# Make settings for each module per sample
sample_names = sorted(_loader.livetime_loader().keys())
for key in sample_names:
print("Building settings file for sample '{}'".format(key))
# Load data that settings depend on
mc = _loader.mc_loader(key)[key]
# :: LLH model ::
# This is kind of arbitrary, but seems to produce robust PDFs
logE_bins = np.linspace(np.floor(np.amin(mc["logE"])),
np.ceil(np.amax(mc["logE"])), 30)
# Get the official binnings from skylab instead
if use_skylab_bins:
sindec_bins = key2set[key].sinDec_bins(key2skylab[key])
logE_bins = key2set[key].energy_bins(key2skylab[key])
llh_model_opts = {
# Watch the order of the bins in the list!
"twodim_bins": [logE_bins.tolist(),
sindec_bins.tolist()],