inj_kwargs = {
"injection_energy_pdf": injection_energy,
"injection_time_pdf": injection_time,
}
scale = flux_to_k(
reference_sensitivity(np.sin(dec)) *
(50 * search_window / flare_length))
mh_dict = {
"name":
full_name,
"mh_name":
mh_name,
"datasets":
custom_dataset(txs_sample_v1, catalogue,
llh_kwargs["llh_time_pdf"]),
"catalogue":
cat_path,
"inj_dict":
inj_kwargs,
"llh_dict":
llh_kwargs,
"scale":
scale,
"n_trials":
100,
"n_steps":
15, # number of flux values
}
# if mh_name == "flare":
"None/" + str(n) + "sources")
catalogue = np.load(fs_sources(n, sindec))
closest_src = np.sort(catalogue, order="distance_mpc")[0]
scale = (flux_to_k(
reference_sensitivity(np.sin(closest_src["dec_rad"]),
gamma=gamma) * 40 *
(math.log(float(len(catalogue)), 4) + 1)) * 200.0) / length
mh_dict = {
"name":
name,
"mh_name":
mh_name,
"dataset":
custom_dataset(ps_v002_p01, catalogue,
llh_dict["llh_sig_time_pdf"]),
"catalogue":
fs_sources(n, sindec),
"inj_dict":
inj_dict,
"llh_dict":
llh_dict,
"scale":
scale,
"n_trials":
ntrials / cluster if cluster else ntrials,
"n_steps":
10,
}
job_id = None
"Fixed Ref Time (MJD)": ref_time,
"Pre-Window": 0,
"Post-Window": length
}
llh_kwargs = {
"LLH Energy PDF": energy_pdf,
"LLH Time PDF": llh_time,
"Fit Gamma?": True,
"Fit Negative n_s?": True,
"Fit Weights?": False
}
mh_dict = {
"name": full_name,
"datasets": custom_dataset(ps_v002_p01, np.load(cat_path),
llh_kwargs["LLH Time PDF"]),
"catalogue": cat_path,
"inj kwargs": inj,
"llh kwargs": llh_kwargs,
"scale": scale,
"n_trials": 100,
"n_steps": 10
}
analysis_path = analysis_dir + full_name
try:
os.makedirs(analysis_path)
except OSError:
pass
inj_kwargs = {
"Injection Energy PDF": injection_energy,
"Injection Time PDF": injection_time,
"Poisson Smear?": True,
}
scale = old_div(100 * math.sqrt(float(len(catalogue))) * flux_to_k(
reference_sensitivity(np.sin(closest_src["dec"]), gamma=2)
) * max_window, flare_length)
# print scale
mh_dict = {
"name": full_name,
"datasets": custom_dataset(txs_sample_v2, catalogue,
llh_kwargs["LLH Time PDF"]),
"catalogue": cat_path,
"inj kwargs": inj_kwargs,
"llh kwargs": llh_kwargs,
"scale": scale,
"n_trials": 1,
"n_steps": 15
}
pkl_file = make_analysis_pickle(mh_dict)
# rd.submit_to_cluster(pkl_file, n_jobs=500)
res[flare_length] = mh_dict
src_res[label] = res
for pdf_type in sn_times[cat]:
llh_times = sn_time_pdfs(cat, pdf_type=pdf_type)
name = f"{name_root}/{pdf_type}/{cat}"
bkg_ts = f"{bkg_ts_root}/{pdf_type}/{cat}"
for llh_time in llh_times:
time = (llh_time["decay_time"] if "decay" in pdf_type else
llh_time["pre_window"] + llh_time["post_window"])
unblind_llh = {
"llh_name": "standard",
"llh_energy_pdf": llh_energy,
"llh_sig_time_pdf": llh_time,
}
unblind_dict = {
"name": name,
"ts_type": "Fit Weights",
"mh_name": "fit_weights",
"dataset": custom_dataset(ps_v002_p01, catalogue, llh_time),
"catalogue": cat_path,
"llh_dict": unblind_llh,
"background_ts": f"{bkg_ts}/{time}/",
}
ub = create_unblinder(unblind_dict, mock_unblind=True)
name = name_root + cat.replace(" ", "") + "/"
logging.info(f"{name}")
bkg_ts = bkg_ts_root + cat.replace(" ", "") + "/Fit Weights/"
cat_path = tde_catalogue_name(cat)
catalogue = load_catalogue(cat_path)
unblind_dict = {
"name":
name,
"mh_name":
"fit_weights",
"dataset":
custom_dataset(txs_sample_v1, catalogue, llh_dict["llh_sig_time_pdf"]),
"catalogue":
cat_path,
"llh_dict":
llh_dict,
"background_ts":
bkg_ts
}
# ub = create_unblinder(unblind_dict, mock_unblind=False)
ub = create_unblinder(unblind_dict,
mock_unblind=False,
disable_warning=True)
r = ub.res_dict
print(r)
nu_flux = f_nu_to_cr * cr_flux
energy_pdf = {
"Name": "Power Law",
"Gamma": 2.0,
"Energy Flux": nu_flux,
"E Min": 10**2,
}
inj_kwargs = {
"Injection Energy PDF": energy_pdf,
"Injection Time PDF": time_pdf,
"Poisson Smear?": True,
}
seasons = custom_dataset(txs_sample_v1, [source], time_pdf)
n_inj = 0
for season in seasons:
n_inj += calculate_neutrinos(source, season, inj_kwargs)
n_injs.append("{0:.2f}".format(n_inj))
res_dict[source["Name"]] = n_injs
print("\n")
print("N_base \t N_scale \t Source")
for (source, n_inj) in res_dict.items():
scale = (flux_to_k(
reference_sensitivity(np.sin(0.5), gamma=gamma) * 40 *
math.sqrt(float(len(catalogue)))) * 200.0) / length
if hsphere == "southern":
scale *= 5
if seasons == ["IC40"]:
scale *= 4
data = ps_v002_p01
if seasons:
data = data.get_seasons(*seasons)
dataset = custom_dataset(data, catalogue,
llh_dict["llh_sig_time_pdf"])
logger.debug(f"{dataset.keys()}")
mh_dict = {
"name": full_name,
"mh_name": mh_name,
"dataset": dataset,
"catalogue": cat_path,
"inj_dict": inj_dict,
"llh_dict": llh_dict,
"fixed_scale": 0,
"n_trials": ntrials / cluster if cluster else ntrials,
"n_steps": 10,
"allow_extrapolated_sensitivity": False,
}
# "time_pdf_name": "fixed_ref_box",
# "fixed_ref_time_mjd": 56927.86,
# "pre_window": 0.,
# "post_window": 57116.76 - 56927.86,
# }
llh_energy = {
"energy_pdf_name": "power_law",
}
unblind_llh = {
"llh_name": "standard",
"llh_energy_pdf": llh_energy,
"llh_sig_time_pdf": llh_time,
}
name = "analyses/benchmarks/TXS_0506+056/"
unblind_dict = {
"name": name,
"mh_name": "fixed_weights",
"dataset": custom_dataset(
ps_v003_p02, txs_catalogue, unblind_llh["llh_sig_time_pdf"]
),
# "dataset": txs_sample_v1.get_seasons(""),
"catalogue": txs_cat_path,
"llh_dict": unblind_llh,
}
ub = create_unblinder(unblind_dict, mock_unblind=False)
