def fac_s_eff_s(dl,mass_Chirp,thetas=None,r0 = 1527.):
'''
Purpose:
Use the MC result of Theta's to evalute the selection effect factor given the specific value
of dl and mass_Chrip.
Parameter
--------
dl: The luminosity distance
mass_Chirp: The Chirp mass
theta: The result of the Thetas distribution. If None, will MC by it self.
Return
--------
The possibility
Note
--------
The zs is calculated correspondingly from the dl, based on Om=0.3 and H0=70.
'''
if thetas is None:
thetas = random_Theta()
zs = solve_z(dl)
rhos = 8.*thetas * r0/dl * ((1+zs)*mass_Chirp/1.2)**(5/6.)
ratio = len(rhos[rhos>8])/float(len(rhos))
return ratio
0.17,
size=mass_Chirp.shape)
m1_mu = np.log(m1) # 0_med np.log(mu_star) = mu
m1_sigstar = np.exp(
m_noise_level
) #Not useful in the generate generation, but useful in understand the upper lower level.
m1_obs = np.random.lognormal(m1_mu, m_noise_level,
size=m1.shape) #Generating for the mu as med,
m1_sig_fake = m1_obs * m_noise_level #The fake "sigma", (m1_obs * m_noise_level) and (m1 * m_noise_level)
# prior = select_effect(m1_obs)
# prior_true = fac_s_eff_v(dl=dl, mass_Chirp=mass_Chirp, thetas=thetas)
# prior = prior_true
prior = fac_s_eff_v(dl=dl_noised, mass_Chirp=mass_Chirp_noised, thetas=thetas)
prior[prior == 0] = 0.001
sf_factor = 1 / prior
z_inf = solve_z(np.array(dl_noised))
sf_sigma = np.log(sf_factor) / 3
sf_factor = sf_factor / np.exp(sf_sigma**2 / 2)
print "m1_obs.min(), m1_obs.max():", m1_obs.min(), m1_obs.max()
#mini=fmin(posterior,para_ini,maxiter=1000, args=(m1_obs, m_noise_level, sf_factor,z_inf))
ndim, nwalkers = 4, 70
pos = [[a0, a1, mbh_max, mbh_min] + 1e-4 * np.random.randn(ndim)
for i in range(nwalkers)]
step_total = 500.
import emcee
sampler = emcee.EnsembleSampler(
nwalkers, ndim, posterior, args=(m1_obs, m_noise_level, sf_factor,
z_inf)) ###the input args() should change
sampler.run_mcmc(pos, step_total)
invprior_true, invprior_mean, invprior_median, m1_list, m1_obs_list, dl_list, massChirp_list = [
lines[:, i] for i in range(len(lines.T))
]
#plt.plot(np.log(np.array(m1_obs_list)), np.sqrt(2*np.log(np.array(invprior_mean)/np.array(invprior))),'.')
#plt.show()
#plt.plot(np.log(1/np.array(dl_list)), np.sqrt(2*np.log(np.array(invprior_mean)/np.array(invprior))),'.')
#plt.show()
#plt.plot(np.log(np.array(massChirp_list)**(5/6.)), np.sqrt(2*np.log(np.array(invprior_mean)/np.array(invprior))),'.')
#plt.show()
sigma = np.sqrt(2 *
np.log(np.array(invprior_mean) / np.array(invprior_median)))
#plt.plot(np.log10(invprior_true), sigma,'.')
#plt.show()
solve_z = np.vectorize(solve_z)
z = solve_z(np.array(dl_list))
plt.scatter(np.log(
np.array(dl_list) * (1 / np.array(massChirp_list)**(5 / 6.)) /
(1 + z)**(5 / 6.)),
sigma,
c=m1_obs_list)
plt.colorbar()
plt.show()
for loop in range(1):
idx = random.sample(index, 1000)
m1 = m1_all[idx]
dl = lumi_dis_all[idx]
dl_noised = np.random.lognormal(np.log(dl), 0.35, size=dl.shape)
mass_Chirp = chirp_mass_all[idx]
mass_Chirp_noised = np.random.lognormal(np.log(mass_Chirp),