import matplotlib.pyplot as plt from BH_mass_function import gene_BHBH, dl, solve_z import pickle import glob import random import scipy.optimize as op import time a, mbh_max, mbh_min = 2.35, 80., 5. filename = 'sim_a_{0}_max_{1}_min_{2}'.format(round(a, 2), round(mbh_max, 1), round(mbh_min, 1)) if_file = glob.glob(filename) if if_file == []: event_rate, zs_detected, masses, rhos_detected = test.mc_year_rate( a=a, mbh_max=mbh_max, mbh_min=mbh_min) dl_zs = dl(zs_detected) sim_data = [event_rate, zs_detected, masses, rhos_detected, dl_zs] pickle.dump(sim_data, open(filename, 'wb')) else: event_rate, zs_detected, masses, rhos_detected, dl_zs = pickle.load( open(filename, 'rb')) #print "The yearly rate using MC simulating:", event_rate # ##averaged Chirp mass #print 'averaged Chirp mass, m1, m2: ', np.average(masses[:,0]), np.average(masses[:,1]), np.average(masses[:,2]) zs, chirp_mass, m1, m2, lumi_dis = zs_detected, masses[:, 0], masses[:, 1], masses[:, 2], dl_zs #plt.hist(masses[:,:2])
round(mbh_min, 1)) if_file = glob.glob(filename) if if_file == []: event_rate, zs_detected, masses, rhos_detected = test.mc_year_rate( a=a, mbh_max=mbh_max, mbh_min=mbh_min) sim_data = [event_rate, zs_detected, masses, rhos_detected] pickle.dump(sim_data, open(filename, 'wb')) else: event_rate, zs_detected, masses, rhos_detected = pickle.load( open(filename, 'rb')) print "The yearly rate using MC simulating:", event_rate #averaged Chirp mass print 'averaged Chirp mass, m1, m2: ', np.average(masses[:, 0]), np.average( masses[:, 1]), np.average(masses[:, 2]) lum_dis = dl(zs_detected) zs, chirp_mass, m1, m2, lumi_dis = zs_detected, masses[:, 0], masses[:, 1], masses[:, 2], lum_dis #plt.hist(masses[:,:2]) #plt.hist(zs_detected) #plt.hist(m1) #plt.hist(rhos_detected) #plt.plot(m1,m2,'.') ##plt.yscale('log', nonposy='clip') #plt.show() #import corner #fig = corner.corner(np.column_stack((m1,m2)), labels=["$m1$", "$m2$"],title_kwargs={"fontsize": 12}) ## quantiles=[0.16, 0.84],show_titles=True,