# ##############################################################################
result = {
"ts": [],
"ns": [],
"gamma": [],
"pval": [],
"sigma": [],
"n_unblindings": n_unblindings,
"scrambled": scramble,
}
# Get the unblinded result(s)
for i in tqdm(range(n_unblindings)):
bf_ts, bf_params = multillh.fit_source(src_ra=ra,
src_dec=dec,
src_w=w,
scramble=scramble)
# Calculate the p-value and significance from the BG only TS distribution
pval = bg_ts_dist.sf(bf_ts)
sigma = prob2sigma(1. - pval)
result["ts"].append(bf_ts)
result["ns"].append(bf_params["nsources"])
result["gamma"].append(bf_params["gamma"])
result["pval"].append(pval[0])
result["sigma"].append(sigma[0])
# ##############################################################################
# Print result
ns = np.zeros(ntrials, dtype=float)
gamma = np.zeros(ntrials, dtype=float)
nsig_injected = np.zeros(ntrials, dtype=int)
tenth = ntrials // 10
for i in range(ntrials):
# Sample signal events and insert into the trial method
nsig = rndgen.poisson(lam=mu, size=1)
signal_sam = multiinj.sample(nsig)
# Convert to a form skylab understands and does not reject
signal_sam = convert_signal_sample(signal_sam, multillh)
res = multillh.fit_source(src_ra=src_ra,
src_dec=src_dec,
src_w=src_w,
scramble=True,
inject=signal_sam)
bf_params = res[1]
# Store results
nsig_injected[i] = nsig
ts[i] = res[0]
ns[i] = bf_params["nsources"]
gamma[i] = bf_params["gamma"]
if (i + 1) % tenth == 0:
print("{:.0%}".format((i + 1) / ntrials))
print(":: Done. {} ::".format(sec2str(time() - _t0)))