import numpy as np
import astropy.time as astrotime
from _paths import PATHS
from _loader import runlist_loader
src_path = os.path.join(PATHS.local, "hese_scan_maps_truncated")
runlist_path = os.path.join(PATHS.local, "runlists")
outpath = os.path.join(PATHS.local, "source_list")
if not os.path.isdir(outpath):
os.makedirs(outpath)
# Load runlists
runlists = runlist_loader("all")
# Load sources up to HESE 6yr, list from:
# https://wiki.icecube.wisc.edu/index.php/Analysis_of_pre-public_alert_HESE/EHE_events#HESE
# Last Run ID is 127853 from late 86V (2015) run, next from 7yr is 128290
src_files = sorted(glob(os.path.join(src_path, "*.json.gz")))
sources = []
for src_file in src_files:
with gzip.open(src_file) as _f:
src_dict = json.load(_f)
# Build a compact version with all relevant infos
src_i = {}
for key in ["run_id", "event_id", "mjd"]:
src_i[key] = src_dict[key]
# Store best fit from direct local trafo and map maximum
hor = np.sin(np.deg2rad(30))
sd_lo, sd_hi = -1., 1.
sindec_bins = np.unique(
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 = {
# Load files and build the models one after another to save memory
bg_injs = {}
sig_injs = {}
llhs = {}
# Load files and build the models one after another to save memory
sample_names = _loader.source_list_loader()
for key in sample_names:
print("\n" + 80 * "#")
print("# :: Setup for sample {} ::".format(key))
opts = _loader.settings_loader(key)[key].copy()
exp_off = _loader.off_data_loader(key)[key]
mc = _loader.mc_loader(key)[key]
srcs = _loader.source_list_loader(key)[key]
runlist = _loader.runlist_loader(key)[key]
# Process to tdepps format
srcs_rec = make_src_records(srcs, dt0=dt0, dt1=dt1)
# Setup BG injector
bg_inj_i = TimeDecDependentBGDataInjector(inj_opts=opts["bg_inj_opts"],
random_state=rndgen)
bg_inj_i.fit(X=exp_off, srcs=srcs_rec, run_list=runlist)
bg_injs[key] = bg_inj_i
# Setup LLH model and LLH
fmod = opts["model_energy_opts"].pop("flux_model")
flux_model = flux_model_factory(fmod["model"], **fmod["args"])
opts["model_energy_opts"]["flux_model"] = flux_model
llhmod = GRBModel(X=exp_off,
MC=mc,