def tominimize(p):
""" Log of posterior probability function """
lnp = lnpriorfn(p)
if not np.isfinite(lnp):
return (-np.inf)
# compute the model brightness profile
f = ModelJ1615(p)
ModelVal = sampleProfile(f, Rmin, dR, nxy, dxy, u, v)
chi2 = chi2compute(ModelVal, Re, Im, w)
return (chi2)
def lnpostfnbis(p):
""" Log of posterior probability function using less galario (faster)"""
lnp = lnpriorfn(p)
if not np.isfinite(lnp):
return (-np.inf)
# compute the model brightness profile
f = ModelJ1615(p)
ModelVal = sampleProfile(f, Rmin, dR, nxy, dxy, u, v)
chi2 = chi2compute(ModelVal, Re, Im, w)
return (-0.5 * chi2)
def tominimize(p):
""" Only for the classical optimization process """
print(p)
lnp = lnpriorfn(p)
if not np.isfinite(lnp):
return (-np.inf)
# compute the model brightness profile
f = ModelJ1615(p)
ModelVal = sampleProfile(f, Rmin, dR, nxy, dxy, u, v)
chi2 = chi2compute(ModelVal, Re, Im, w)
return (chi2 / 1000000)
def lnpostfn(p, p_ranges, rmin, dr, nr, nxy, dxy, u, v, re, im, w):
""" Log of posterior probability function """
### apply prior
lnprior = lnpriorfn(p, p_ranges)
if not np.isfinite(lnprior):
return -np.inf
### unpack the parameters
f0_a, sigma_a, inc_a, pa_a, dra_a, ddec_a, f0_b, sigma_b, inc_b, pa_b, dra_b, ddec_b = p
### convert to correct units
f0_a = 10.**f0_a
f0_b = 10.**f0_b
sigma_a *= arcsec
sigma_b *= arcsec
inc_a *= deg
inc_b *= deg
pa_a *= deg
pa_b *= deg
dra_a *= arcsec
dra_b *= arcsec
ddec_a *= arcsec
ddec_b *= arcsec
### get gaussian profile
f_a = GaussianProfile(f0_a, sigma_a, rmin, dr, nr)
f_b = GaussianProfile(f0_b, sigma_b, rmin, dr, nr)
### calculate the visibilities of the two gaussians
Vmod_a = g_double.sampleProfile(f_a, rmin, dr, nxy, dxy, u, v, inc=inc_a, PA=pa_a, dRA=dra_a, dDec=ddec_a)
Vmod_b = g_double.sampleProfile(f_b, rmin, dr, nxy, dxy, u, v, inc=inc_b, PA=pa_b, dRA=dra_b, dDec=ddec_b)
### calculate the chi2 using the sum of the visibilities
chi2 = g_double.reduce_chi2(re, im, w, Vmod_a + Vmod_b)
### return likelihood
return -0.5 * chi2 + lnprior