outdir = "/Users/nab/Desktop/Projects/ACTPol_Cluster_Like/" #dir_name = "/Users/nab/Desktop/Projects/ACTPol_Cluster_Like/v9working_run/" #dir_name1 = "/Users/nab/Desktop/Projects/ACTPol_Cluster_Like/highbias_v10/" #dir_name2 = "/Users/nab/Desktop/Projects/ACTPol_Cluster_Like/lowbias_v10/" if args.test: dir_name1 = "/Users/nab/Desktop/Projects/ACTPol_Cluster_Like/ACT_chains/" chain1 = "sz_chain_test_sim_pars_v1_0.dat" burnins = 2000 names = ['As', 'omch2', 'ombh2', 's8'] labels = ['A_s', '\Omega_c h2', '\Omega_b h^2', '\sigma_8'] out1 = load_single_sample(dir_name1, chain1, burnins) samples1 = MCSamples(samples=out1, names=names, labels=labels) p = samples1.getParams() samples1.addDerived(old_div((p.omch2 + p.ombh2), 0.7**2), name='om', label='\Omega_M') plt.figure() g = plots.getSubplotPlotter() g.triangle_plot([samples1], params=['omch2', 'ombh2', 's8', 'om'], filled=True) plt.savefig(outdir + "simtest_parsTestv1.png") elif args.s8test: dir_name1 = "/Users/nab/Desktop/Projects/ACTPol_Cluster_Like/ACT_chains/" chain1 = "sz_chain_test_chains_v4_0.dat" #chain1 = "sz_likelival_test_s8_mock.dat"
] names4 = ['Om0', 'h', 'rdrag', 'P1', 'P2', 'Q1', 'Q2', 'sigma8'] labels4 = ['Om0', 'h', 'rd', 'P1', 'P2', 'Q1', 'Q2', 'sigma8'] comb = [r'lcdm', r'wcdm', r'cpl', r'pade'] comb1 = np.loadtxt('lcdm/lcdm_d+panth+Bao+fs8+Hz+5july.dat') comb2 = np.loadtxt('wcdm/wcdm_d+panth+Bao+Hz+fs8-5july.dat') comb3 = np.loadtxt('cpl/cpl_d+panth+Bao+Hz+fs8+5july.dat') comb4 = np.loadtxt('padesahintest/pade-d+Hz+panth+Bao+fs8+26june.dat') print('Creating MCSamples for data.......') c1 = MCSamples(samples=comb1, names=names1, labels=labels1) c2 = MCSamples(samples=comb2, names=names2, labels=labels2) c3 = MCSamples(samples=comb3, names=names3, labels=labels3) c4 = MCSamples(samples=comb4, names=names4, labels=labels4) param1 = c1.getParams() P = param1.sigma8 * (param1.Om0 / 0.3)**0.5 c1.addDerived(P, name=r'k') #, label=r'c/h_rd') c1.updateBaseStatistics() W = np.mean(P) print(W) o = c1.twoTailLimits(r'k', .68) print(o) print([P.mean()]) print([P.mean() - 1. * P.std(), P.mean() + 1. * P.std()]) """ param2 = c2.getParams() P= param2.sigma8*(param2.Om0/0.3)**0.5 c2.addDerived(P, name=r'k')#, label=r'c/h_rd') c2.updateBaseStatistics()