def tab_prior(u):
m = (0.03 * u[0] + 0.001) / 0.019
a = (agelim - 1)* u[1] + 1
tsamp = np.array([u[2],u[3],u[4],u[5],u[6],u[7],u[8],u[9], u[10], u[11]])
taus = stats.t.ppf( q = tsamp, loc = 0, scale = 0.3, df =2.)
m1, m2, m3, m4, m5, m6, m7, m8, m9, m10 = logsfr_ratios_to_masses(logmass = 0, logsfr_ratios = taus, agebins = agebins) * 1E9
z = stats.norm.ppf(u[12],loc = specz, scale = 0.003)
d = u[13]
return [m, a, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, z, d]
def tab_prior(u):
m = (0.03 * u[0] + 0.001) / 0.019
a = (agelim - LBT[0])* u[1] + LBT[0]
tsamp = np.array([u[2],u[3],u[4],u[5],u[6],u[7],u[8],u[9], u[10], u[11]])
taus = stats.t.ppf( q = tsamp, loc = 0, scale = 0.3, df =2.)
masses = logsfr_ratios_to_masses(logmass = 0, logsfr_ratios = taus, agebins = agebins) * 1E9
t1, t2, t3, t4, t5, t6, t7, t8, t9, t10 = np.array(masses / time_per_bin)[::-1]
z = specz + 0.002*(2*u[12] - 1)
d = 1*u[13]
lm = 11.0 + 1.25*(2*u[14] - 1)
return [m, a, t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, z, d, lm]
def galfit_prior(u):
m = (0.03 * u[0] + 0.001) / 0.019
a = (agelim - 1) * u[1] + 1
tsamp = np.array(
[u[2], u[3], u[4], u[5], u[6], u[7], u[8], u[9], u[10], u[11]])
taus = stats.t.ppf(q=tsamp, loc=0, scale=0.3, df=2.)
m1, m2, m3, m4, m5, m6, m7, m8, m9, m10 = logsfr_ratios_to_masses(
logmass=0, logsfr_ratios=taus, agebins=get_agebins(a)) * 1E9
z = stats.norm.ppf(u[12], loc=specz, scale=0.005)
d = u[13]
bp1 = Gaussian_prior(u[14], [-0.5, 0.5], 0, 0.25)
rp1 = Gaussian_prior(u[15], [-0.5, 0.5], 0, 0.25)
ba = log_10(u[16], [0.1, 10])
bb = log_10(u[17], [0.0001, 1])
bl = log_10(u[18], [0.01, 1])
ra = log_10(u[19], [0.1, 10])
rb = log_10(u[20], [0.0001, 1])
rl = log_10(u[21], [0.01, 1])
lwa = get_lwa([m, a, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10],
get_agebins(a))
return [
m, a, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, z, d, bp1, rp1, ba, bb,
bl, ra, rb, rl, lwa
]
def tab_prior(u):
msamp = np.array([u[0],u[1],u[2],u[3],u[4],u[5],u[6],u[7],u[8],u[9]])
logZ = stats.t.ppf( q = msamp, loc = 0, scale = 0.1, df =3.)
m1, m2, m3, m4, m5, m6, m7, m8, m9, m10 = logZ_to_zratio(logZ,agebins)[::-1]
a = (agelim - LBT[0])* u[10] + LBT[0]
tsamp = np.array([u[11],u[12],u[13],u[14],u[15],u[16],u[17],u[18], u[19], u[20]])
taus = stats.t.ppf( q = tsamp, loc = 0, scale = 0.3, df =2.)
masses = logsfr_ratios_to_masses(logmass = 0, logsfr_ratios = taus, agebins = agebins) * 1E9
t1, t2, t3, t4, t5, t6, t7, t8, t9, t10 = np.array(masses / time_per_bin)[::-1]
z = stats.norm.ppf(u[21],loc = specz, scale = 0.003)
d = u[22]
lm = stats.norm.ppf(u[23],loc = 10.75, scale = 0.5)
return [m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, a, t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, z, d, lm]