"inj_dict": inj_kwargs,
"n_trials": 1, # 10,
# "n_steps": 15,
}
mh = MinimisationHandler.create(mh_dict)
scale_factor = 3 * mh.guess_scale(
) / 3 / 7 / scale_factor_per_decade[i]
print("Scale Factor: ", scale_factor_per_decade[i],
scale_factor)
# # # # # How to run on the cluster for sources < 3162
mh_dict["n_steps"] = 15
mh_dict["scale"] = scale_factor
# # analyse(mh_dict, cluster=False, n_cpu=35, n_jobs=10)
analyse(mh_dict, cluster=False, n_cpu=32, n_jobs=150)
# How to run on the cluster for sources > 3162
# _n_jobs = 50
# scale_loop = np.linspace(0, scale_factor, 15)
# print(scale_loop)
# for scale in scale_loop[:4]:
# print('Running ' + str(mh_dict["n_trials"]) + ' trials with scale ' + str(scale))
# mh_dict["fixed_scale"] = scale
# # # analyse(mh_dict, cluster=False, n_cpu=35, n_jobs=10)
# if scale == 0.:
# n_jobs = _n_jobs*10
# else:
# n_jobs = _n_jobs
# print("Submitting " + str(n_jobs) + " jobs")
# analyse(mh_dict, cluster=True, n_cpu=1, n_jobs=n_jobs)
"llh_dict":
llh_dict,
"scale":
scale,
"n_trials":
ntrials / cluster if cluster else ntrials,
"n_steps":
10,
}
job_id = None
if args.analyse:
job_id = analyse(
mh_dict,
cluster=True if cluster else False,
n_cpu=1 if cluster else 25,
n_jobs=cluster,
h_cpu="00:59:59",
)
job_ids.append(job_id)
full_res[str(n)] = mh_dict
sin_res[str(sindec)] = full_res
gamma_res[gamma] = sin_res
res[smoothing] = gamma_res
if cluster and np.any(job_ids):
logging.info(f"waiting for jobs {job_ids}")
"llh_sig_time_pdf": llh_time,
"llh_bkg_time_pdf": {
"time_pdf_name": "steady"
},
}
catalogue = np.load(updated_sn_catalogue_name("IIn"))
catalogue["distance_mpc"] = np.array([1] * len(catalogue))
dir_path = os.path.dirname(os.path.realpath(__file__))
temp_save_catalogue_to = f"{dir_path}/temp_check_stack_bias_equal_dist_cat.npy"
np.save(temp_save_catalogue_to, catalogue)
mh_dict = {
"name": "examples/crosscheck_stacking_equal_dist/",
"mh_name": "large_catalogue",
"dataset": ps_v002_p01.get_seasons(),
"catalogue": temp_save_catalogue_to,
# "catalogue": ps_stack_catalogue_name(0.1, 0.3),
# "catalogue": tde_catalogue_name("jetted"),
"inj_dict": inj_dict,
"llh_dict": llh_dict,
"scale": 10.0,
"n_trials": 30,
"n_steps": 10,
}
analyse(mh_dict, cluster=False, n_cpu=32)
rh = ResultsHandler(mh_dict)
os.remove(temp_save_catalogue_to)
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":
analyse(mh_dict, n_cpu=24, cluster=False)
# raw_input("prompt")
res[flare_length] = mh_dict
src_res[label] = res
wait_for_cluster() # for cluster
sens = [[] for _ in src_res]
fracs = [[] for _ in src_res]
disc_pots = [[] for _ in src_res]
sens_e = [[] for _ in src_res]
disc_e = [[] for _ in src_res]
"n_trials": 9,
}
mh = MinimisationHandler.create(mh_dict)
# Set the scale for the injection
scale_factor = 3 * mh.guess_scale() / 3
'''
# UNCOMMENT THIS IF:
# 1. It is the first time you are running this code
# 2. You want to run locally
# 3. If you are running on the cluster with < 1000 sources
# '''
mh_dict["n_steps"] = 15
mh_dict["scale"] = scale_factor
analyse(mh_dict, cluster=False, n_cpu=8, n_jobs=100)
'''
UNCOMMENT THIS IF:
1. If you are running on the cluster with > 1000 sources
'''
# _n_jobs = 100
# scale_loop = np.linspace(0, scale_factor, 15)
# print(scale_loop)
# for scale in scale_loop[:]:
# print('Running ' + str(mh_dict["n_trials"]) + ' trials with scale ' + str(scale))
# mh_dict["fixed_scale"] = scale
# if scale == 0.:
# n_jobs = _n_jobs*10
# else:
# n_jobs = _n_jobs
# print("Submitting " + str(n_jobs) + " jobs")
"mh_name": mh_name,
"dataset": custom_dataset(ps_v002_p01, catalogue,
llh_dict["llh_sig_time_pdf"]),
"catalogue": cat_path,
"inj_dict": inj_dict,
"llh_dict": llh_dict,
"scale": scale,
"n_trials": 500/cluster if cluster else 1000,
"n_steps": 10
}
# call the main analyse function
job_id = None
job_id = analyse(mh_dict,
cluster=True if cluster else False,
n_cpu=1 if cluster else 32,
n_jobs=cluster,
h_cpu='00:59:59')
job_ids.append(job_id)
time_res[gamma] = mh_dict
cat_res[time_key] = time_res
full_res[cat] = cat_res
# Wait for cluster. If there are no cluster jobs, this just runs
if cluster and np.any(job_ids):
logging.info(f'waiting for jobs {job_ids}')
wait_for_cluster(job_ids)
"datasets": ps_v002_p01.get_seasons("IC86_1"),
"catalogue": txs_cat_path,
"inj_dict": inj_kwargs,
"llh_dict": llh_kwargs,
"n_trials": 1,
"n_steps": 10
}
mh = MinimisationHandler.create(mh_dict)
scale = mh.guess_scale()
mh_dict["scale"] = scale
# Creates a Minimisation Handler using the dictionary, and runs the trials
analyse(mh_dict, n_cpu=2, n_jobs=1, cluster=False)
# wait_cluster()
# mh.iterate_run(scale, n_steps=mh_dict["n_steps"],
# n_trials=mh_dict["n_trials"])
# Creates a Results Handler to analyse the results, and calculate the
# sensitivity. This is the flux that needs to arrive at Earth, in order for
# IceCube to see an overfluctuation 90% of the time. Prints this information.
rh = ResultsHandler(mh_dict)
sens = rh.sensitivity
# Converts the flux at Earth to a required luminosity from the source,
# by scaling with distance and accounting for redshift
mh_dict = {
"name": full_name,
"mh_name": mh_name,
"dataset": dataset,
"catalogue": cat_path,
"inj_dict": inj_dict,
"llh_dict": llh_dict,
"scale": scale,
"n_trials": 10,
"n_steps": 15
}
# Run jobs on cluster
job_id = analyse(mh_dict,
cluster=cluster,
n_cpu=1 if cluster else 32,
h_cpu='00:59:59')
job_ids.append(job_id)
res[flare_length] = mh_dict
src_res[label] = res
cat_res[cat] = src_res
wait_cluster(job_ids)
# Wait for cluster jobs to finish
for (cat, src_res) in cat_res.items():
"n_trials": 1,
}
mh = MinimisationHandler.create(mh_dict)
# Set the scale for the injection
scale_factor = 3 * mh.guess_scale() / 3 / 7
'''
UNCOMMENT THIS IF:
1. It is the first time you are running this code
2. You want to run locally
3. If you are running on the cluster with < 1000 sources
'''
mh_dict["n_steps"] = 15
mh_dict["scale"] = scale_factor
analyse(mh_dict, cluster=True, n_cpu=8, n_jobs=100)
'''
UNCOMMENT THIS IF:
1. If you are running on the cluster with > 1000 sources
'''
# _n_jobs = 100
# scale_loop = np.linspace(0, scale_factor, 15)
# print(scale_loop)
# for scale in scale_loop[:]:
# print('Running ' + str(mh_dict["n_trials"]) + ' trials with scale ' + str(scale))
# mh_dict["fixed_scale"] = scale
# if scale == 0.:
# n_jobs = _n_jobs*10
# else:
# n_jobs = _n_jobs
# print("Submitting " + str(n_jobs) + " jobs")
