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