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()
