"time_pdf_name": "FixedRefBox",
"fixed_ref_time_mjd": t_start,
"pre_window": 0,
"post_window": flare_length,
"time_smear_bool": True,
"min_offset": 0.0,
"max_offset": search_window - flare_length,
}
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":
injection_time = llh_time
# Loop over spectral indices
for gamma in gammas:
full_name = time_name + str(gamma) + "/"
length = float(time_key)
scale = (
0.1
* (
flux_to_k(
reference_sensitivity(
np.sin(closest_src["dec_rad"]), gamma=gamma
)
* 40
* math.sqrt(float(len(catalogue)))
)
* 200.0
)
/ length
)
if cat == "IIn":
scale *= 1.5
injection_energy = dict(llh_energy)
injection_energy["gamma"] = gamma
"Name": "Box",
"Pre-Window": 0,
"Post-Window": flare_length,
"Time Smear?": True,
"Min Offset": 0.,
"Max Offset": max_window - flare_length
}
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
}
cat_path = ps_catalogue_name(sindec)
decname = name + "sindec=" + '{0:.2f}'.format(sindec) + "/"
full_name = decname + label + "/"
injection_time = dict(llh_time)
injection_time["Post-Window"] = length
inj_kwargs = {
"Injection Energy PDF": injection_energy,
"Injection Time PDF": injection_time,
"Poisson Smear?": True,
}
scale = flux_to_k(reference_sensitivity(sindec)) * 40
mh_dict = {
"name": full_name,
"datasets": [ps_v002_p01[-1]],
"catalogue": cat_path,
"inj kwargs": inj_kwargs,
"llh kwargs": llh_kwargs,
"scale": scale,
"n_trials": 5,
"n_steps": 10
}
analysis_path = analysis_dir + full_name
try:
source_res = dict()
cat_path = ps_catalogue_name(sindec)
sindec_key = "sindec=" + '{0:.2f}'.format(sindec)
name = name_root + sindec_key + "/"
src_res = dict()
for length in lengths:
full_name = name + str(length) + "/"
scale = flux_to_k(reference_sensitivity(sindec) * 20) * max(
1, window / abs(length)
)
# Standard Time Integration
llh_time = {
"Name": "FixedRefBox",
"Fixed Ref Time (MJD)": ref_time,
"Pre-Window": 0,
"Post-Window": length
}
llh_kwargs = {
"LLH Energy PDF": energy_pdf,
"LLH Time PDF": llh_time,
config_mh = []
for sin_dec in sin_decs:
name = seed_name + "sindec=" + "{0:.2f}".format(sin_dec) + "/"
llh_dict = {
"name": "standard_overlapping",
"LLH Energy PDF": injection_energy,
"LLH Time PDF": injection_time,
"pull_name": pull_corrector,
"floor_name": floor,
}
scale = flux_to_k(reference_sensitivity(sin_dec, gamma)) * 5
mh_dict = {
"name": name,
"mh_name": "fixed_weights",
"datasets": [IC86_1_dict],
"catalogue": ps_catalogue_name(sin_dec),
"llh_dict": llh_dict,
"inj kwargs": inj_dict,
"n_trials": 20,
"n_steps": 15,
"scale": scale,
}
pkl_file = make_analysis_pickle(mh_dict)
full_name = name + str(gamma) + "/"
injection_time = llh_time = {"Name": "FixedEndBox"}
injection_energy = dict(llh_energy)
injection_energy["E Min"] = e_min
injection_energy["Gamma"] = gamma
inj_kwargs = {
"Injection Energy PDF": injection_energy,
"Injection Time PDF": injection_time,
"Poisson Smear?": True,
}
scale = flux_to_k(
reference_sensitivity(np.sin(catalogue["dec"]),
gamma=gamma)) * 60 * (e_min / 100.)**0.2
mh_dict = {
"name":
full_name,
"datasets":
custom_dataset(txs_sample_v1, catalogue,
llh_kwargs["LLH Time PDF"]),
"catalogue":
txs_cat_path,
"inj kwargs":
inj_kwargs,
"llh kwargs":
llh_kwargs,
"scale":
scale,
"fixed_ref_time_mjd": t_start,
"pre_window": 0.,
"post_window": flare_length,
"time_smear_bool": True,
"min_offset": 0.,
"max_offset": max_window - flare_length
}
inj_dict = {
"injection_energy_pdf": injection_energy,
"injection_sig_time_pdf": injection_time,
}
# Sets a default flux scale for signal injection
scale = flux_to_k(reference_sensitivity(np.sin(catalogue["dec_rad"]))
* (50 * max_window / flare_length))
if cat != "AT2018cow":
dataset = custom_dataset(txs_sample_v2, catalogue,
llh_dict["llh_sig_time_pdf"])
else:
dataset = gfu_v002_p04
mh_dict = {
"name": full_name,
"mh_name": mh_name,
"dataset": dataset,
"catalogue": cat_path,
"inj_dict": inj_dict,
"llh_dict": llh_dict,
if llh_name == "fixed_energy":
llh_dict["LLH Energy PDF"]["Gamma"] = gamma
inj_dict = {
"Injection Time PDF": {
"Name": "Steady"
},
"Injection Energy PDF": {
"Name": "Power Law",
"Gamma": gamma,
},
"fixed_n": 30
}
mh_dict = {
"name": name,
"mh_name": "fixed_weights",
"datasets": [IC86_1_dict],
# "catalogue": ps_catalogue_name(sin_dec),
"catalogue": tde_catalogue_name("jetted"),
"llh_dict": llh_dict,
"inj kwargs": inj_dict
}
scale = flux_to_k(reference_sensitivity(
sin_dec, gamma)) * 125 * ([4.0, 1.0, 0.3, 10.0][j])
mh = MinimisationHandler.create(mh_dict)
mh.iterate_run(scale=scale, n_steps=2, n_trials=100)
rh = ResultsHandler(mh_dict)
if not mh_name == "large_catalogue" else "standard_matrix",
"llh_energy_pdf": llh_energy,
"llh_sig_time_pdf": llh_time,
"llh_bkg_time_pdf": {
"time_pdf_name": "steady"
},
"gamma_precision": args.smoothing,
"smoothing_order": args.smoothing,
}
full_name = f"{hsphere_name}/{gamma:.2f}"
length = 365 * 7
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()}")
"pre_window": 0.0,
"post_window": flare_length,
"time_smear_bool": True,
"min_offset": 0.0,
"max_offset": max_window - flare_length,
}
inj_dict = {
"injection_energy_pdf": injection_energy,
"injection_sig_time_pdf": injection_time,
}
# Sets a default flux scale for signal injection
scale = flux_to_k(
reference_sensitivity(np.sin(catalogue["dec_rad"])) *
(50 * max_window / flare_length))
if cat != "AT2018cow":
dataset = custom_dataset(txs_sample_v2, catalogue,
llh_dict["llh_sig_time_pdf"])
else:
dataset = gfu_v002_p04
mh_dict = {
"name": full_name,
"mh_name": mh_name,
"dataset": dataset,
"catalogue": cat_path,
"inj_dict": inj_dict,
"llh_dict": llh_dict,
llh_dict = {
"llh_name": "standard",
"llh_energy_pdf": llh_energy,
"llh_sig_time_pdf": llh_time,
"llh_bkg_time_pdf": {
"time_pdf_name": "steady"
},
}
full_name = f"{name}/{gamma:.2f}"
length = 365 * 7
scale = (0.5 * (flux_to_k(
reference_sensitivity(np.sin(closest_src["dec_rad"]),
gamma=gamma) * 40 *
math.sqrt(float(len(catalogue)))) * 200.0) / length)
if ps:
scale *= 2
if cat == "IIn":
scale *= 1.5
if cat == "IIn":
scale *= 0.3
if cat == "IIP":
scale *= 1
if not northern and weighted:
if cat == "IIn":
scale *= 1.5
catalogue["Relative Injection Weight"] = np.exp(
np.random.normal(0., 2., int(n)))
catalogue["Distance (Mpc)"] = np.ones(int(n))
catalogue["Ref Time (MJD)"] = np.random.uniform(55710., 56010, int(n))
cat_path = catalogue_dir + "random/" + str(n) + "_cat.npy"
try:
os.makedirs(os.path.dirname(cat_path))
except OSError:
pass
np.save(cat_path, catalogue)
# cat_path = catalogue_dir + "TDEs/TDE_silver_catalogue.npy"
# catalogue = np.load(cat_path)
scale = flux_to_k(reference_sensitivity(np.sin(0.0))) * 40 * math.sqrt(
float(n))
mh_dict = {
"name": name,
"datasets": ps_v002_p01,
"catalogue": cat_path,
"inj kwargs": inj_kwargs,
"llh kwargs": llh_kwargs,
"scale": scale,
"n_trials": 5,
"n_steps": 15
}
analysis_path = analysis_dir + name
"Time-Integrated",
"10 Day Flare",
"2 Day Flare",
][i]
f_name = ["negative_n_s", "positive_n_s", "flare_winter", "flare_murase"][i]
flare_name = name + f_name + "/"
res = dict()
for gamma in gammas:
full_name = flare_name + str(gamma) + "/"
scale = flux_to_k(
reference_sensitivity(np.sin(catalogue["dec"]), gamma=gamma) * 50
)
if i > 1:
scale *= 10 ** (i - 1)
inj = dict(inj_kwargs)
inj["Injection Energy PDF"] = dict(inj["Injection Energy PDF"])
inj["Injection Energy PDF"]["Gamma"] = gamma
if "E Min" in list(inj["Injection Energy PDF"].keys()):
scale *= 10
mh_dict = {
# sindecs = np.linspace(0.90, -0.90, 3)
sindecs = np.linspace(0.90, -0.90, 19)
# sindecs = np.linspace(0.5, -0.5, 3)
#
analyses = []
cluster = True
job_ids = []
for sindec in sindecs:
cat_path = ps_catalogue_name(sindec)
subname = name + "/sindec=" + "{0:.2f}".format(sindec) + "/"
scale = flux_to_k(reference_sensitivity(sindec)) * 3
mh_dict = {
"name": subname,
"mh_name": "fixed_weights",
"dataset": ps_v003_p02,
"catalogue": cat_path,
"inj_dict": inj_kwargs,
"llh_dict": llh_kwargs,
"scale": scale,
"n_trials": 5000,
"n_steps": 15,
}
job_id = analyse(
mh_dict,
}
# set up an injection dictionary which will be equal to the time pdf dictionary
injection_time = llh_time
# Loop over spectral indices
for gamma in gammas:
full_name = time_name + str(gamma) + "/"
length = float(time_key)
# try to estimate a good scale based on the sensitivity from the 7-yr PS sensitivity
# at the declination of the closest source
scale = 0.025 * (flux_to_k(reference_sensitivity(
np.sin(closest_src["dec_rad"]), gamma=gamma
) * 40 * math.sqrt(float(len(catalogue)))) * 200.) / length
# in some cases the sensitivity is outside the tested range
# to get a good sensitivity, adjust the scale in these cases
if (gamma == 2.5) and (cat == 'IIn'):
scale *= 1.8
if (gamma == 2.5) and (cat == 'IIP'):
scale *= 0.15
if (gamma == 2.0) and (cat == 'IIP'):
scale *= 0.5
if cat == 'IIn':
scale *= 5
# set up an injection dictionary and set the desired spectral index
injection_energy = dict(llh_energy)
full_res = dict()
for i, n in enumerate(np.array(nsources)):
logging.info(f"stacking {n} sources")
logging.info(f"cat path is {fs_sources(n, sindec)}")
name = (gamma_name + "{:.4f}/".format(sindec) + str(n) +
"sources" if sindec is not None else gamma_name +
"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":
"Name": "FixedRefBox",
"Fixed Ref Time (MJD)": t_start,
"Pre-Window": 0,
"Post-Window": flare_length,
"Time Smear?": True,
"Min Offset": 0.,
"Max Offset": max_window - flare_length
}
inj_kwargs = {
"Injection Energy PDF": injection_energy,
"Injection Time PDF": injection_time,
"Poisson Smear?": True,
}
scale = flux_to_k(reference_sensitivity(np.sin(dec))
* ((70 * max_window)/ flare_length))
mh_dict = {
"name": full_name,
"mh_name": mh_name,
"datasets": gfu_v002_p01,
"catalogue": cat_path,
"inj kwargs": inj_kwargs,
"llh_dict": llh_kwargs,
"scale": scale,
"n_trials": 5,
"n_steps": 15
}
pkl_file = make_analysis_pickle(mh_dict)
}
name = "analyses/benchmarks/ps_sens_7yr"
# sindecs = np.linspace(0.90, -0.90, 3)
sindecs = np.linspace(0.90, -0.90, 9)
# sindecs = np.linspace(0.5, -0.5, 3)
analyses = []
for sindec in sindecs:
cat_path = ps_catalogue_name(sindec)
subname = name + "/sindec=" + "{0:.2f}".format(sindec) + "/"
scale = flux_to_k(reference_sensitivity(sindec)) * 5
mh_dict = {
"name": subname,
"mh_name": "fixed_weights",
"dataset": ps_v002_p01,
"catalogue": cat_path,
"inj_dict": inj_kwargs,
"llh_dict": llh_kwargs,
"scale": scale,
"n_trials": 50,
"n_steps": 10,
}
analyse(mh_dict, cluster=True, n_jobs=100)
'injection_sig_time_pdf': injection_time
}
llh_dict = {
"llh_name": "standard",
"llh_energy_pdf": llh_energy,
"llh_sig_time_pdf": llh_time,
"llh_bkg_time_pdf": {"time_pdf_name": "steady"}
}
full_name = f'{name}/{gamma:.2f}'
length = 365 * 7
scale = 0.5 * (flux_to_k(reference_sensitivity(
np.sin(closest_src["dec_rad"]), gamma=gamma
) * 40 * math.sqrt(float(len(catalogue)))) * 200.) / length
if ps:
scale *= 2
if cat == 'IIn':
scale *= 1.5
if cat == 'IIn':
scale *= 0.3
if cat == 'IIP':
scale *= 1
if not northern and weighted:
if cat == 'IIn':
scale *= 1.5
