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]
labels = []
for i, (f_type, res) in enumerate(sorted(src_res.items())):
for (length, rh_dict) in sorted(res.items()):
rh = ResultsHandler(rh_dict)
inj_time = length * (60 * 60 * 24)
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}")
wait_for_cluster(job_ids)
for smoothing, gamma_res in res.items():
for gamma, sin_res in gamma_res.items():
for sindec in same_sindecs:
full_res = sin_res[str(sindec)]
sens = [[], [], []]
for n in full_res:
logging.debug(f"n = {n}, type={type(n)}")
# 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")
# analyse(mh_dict, cluster=True, n_cpu=1, n_jobs=n_jobs)
gamma_dict[gamma_index] = mh_dict
res[nr_srcs] = gamma_dict
all_res[unique_key] = res
wait_for_cluster()
logging.getLogger().setLevel("INFO")
# Create plots and save data in file data.out
for (cat_key, res_dict) in all_res.items():
agn_type = cat_key.split("_")[0]
xray_cat = cat_key.split(str(agn_type) + '_')[-1]
full_cat = load_catalogue(agn_catalogue_name(agn_type, xray_cat))
full_flux = np.sum(full_cat["base_weight"])
# neutrino flux (using joint paper) divided by AGN flux calculated with luminosity function
