def test_bottom_bracket_logz():
"""Test for zinterp.bracket_logz()"""
ZZsol = -1.98
zmet1, zmet2 = bracket_logz(ZZsol)
assert zmet1 == 1 and zmet2 == 2
f1 = np.atleast_1d(2.)
f2 = np.atleast_1d(4.)
f = interp_logz(zmet1, zmet2, ZZsol, f1, f2)
assert f[0] == f1[0]
def test_high_bracket_logz():
"""Test for Z above grid."""
# Test too high Z
logZZsol = 0.2 + 0.01
zmet1, zmet2 = bracket_logz(logZZsol)
assert zmet1 == 21 and zmet2 == 22
f1 = np.atleast_1d(2.)
f2 = np.atleast_1d(4.)
f = interp_logz(zmet1, zmet2, logZZsol, f1, f2)
assert f[0] == f2[0]
def interp_z_likelihood(args):
"""Generic single-pixel likeihood function where a single metallicity
is linearly interpolated.
Note that the SED can contain values of NaN, those bands will be
automatically ignored from the likelihood calculation
"""
global SP
theta, theta_names, phi, phi_names, B, area, sed, err, sed_bands, \
compute_bands = args
meta1 = np.empty(5, dtype=float)
meta2 = np.empty(5, dtype=float)
band_indices = np.array([compute_bands.index(b) for b in sed_bands])
# Evaluate the ln-likelihood function by interpolating between
# metallicty bracket
for name, val in itertools.chain(zip(theta_names, theta),
zip(phi_names, phi)):
if name == 'logmass':
logm = val
elif name == 'logZZsol':
logZZsol = val
elif name == 'logtau':
SP.params['tau'] = 10. ** val
elif name in SP.params.all_params: # list of parameter names
SP.params[name] = val
zmet1, zmet2 = bracket_logz(logZZsol)
# Compute fluxes with low metallicity
SP.params['zmet'] = zmet1
f1 = abs_ab_mag_to_micro_jy(
SP.get_mags(tage=13.8, bands=compute_bands),
phi[0])
meta1[0] = SP.stellar_mass
meta1[1] = SP.dust_mass
meta1[2] = SP.log_lbol
meta1[3] = SP.sfr
meta1[4] = SP.log_age
# Compute fluxes with high metallicity
SP.params['zmet'] = zmet2
f2 = abs_ab_mag_to_micro_jy(
SP.get_mags(tage=13.8, bands=compute_bands),
phi[0])
meta2[0] = SP.stellar_mass
meta2[1] = SP.dust_mass
meta2[2] = SP.log_lbol
meta2[3] = SP.sfr
meta2[4] = SP.log_age
# Interpolate and scale the SED by mass
model_mjy = 10. ** logm * interp_logz(zmet1, zmet2, logZZsol, f1, f2)
# Interpolate metadata between the two metallicities
meta = interp_logz(zmet1, zmet2, logZZsol, meta1, meta2)
# Compute likelihood
# Automatically ignores pixels with no measurement (Nan)
L = -0.5 * np.nansum(
np.power((model_mjy[band_indices] + B * area - sed) / err, 2.))
# Scale statistics by the total mass
blob = {"logMstar": logm + np.log10(meta[0]), # log solar masses,
"logMdust": logm + np.log10(meta[1]), # log solar masses
"logLbol": logm * meta[2], # log solar luminosities
"logSFR": logm * meta[3], # star formation rate, M_sun / yr
"logAge": meta[4],
"model_sed": model_mjy} # note: background no included
return L, blob
def __call__(self, theta):
"""Posterior likelihood call."""
# Evaluate prior
lnpriors = np.array([self._priors[n](v)
for n, v in zip(self._param_names, theta)])
lnprior = np.sum(lnpriors)
if ~np.isfinite(lnprior):
model_mjy = np.empty(self.n_computed_bands, np.float)
model_mjy.fill(np.nan)
blob = {"lnpost": -np.inf,
"logMstar": np.nan,
"logMdust": np.nan,
"logLbol": np.nan,
"logSFR": np.nan,
"logAge": np.nan,
"model_sed": model_mjy,
"lnpriors": lnpriors}
return -np.inf, blob
meta1 = np.empty(5, dtype=float)
meta2 = np.empty(5, dtype=float)
for name, val in zip(self._param_names, theta):
if name == 'logmass':
logm = val
elif name == 'logZZsol':
logZZsol = val
elif name == 'logtau':
self.sp.params['tau'] = 10. ** val
elif name in self.sp.params.all_params: # list of parameter names
self.sp.params[name] = val
zmet1, zmet2 = bracket_logz(logZZsol)
# Compute fluxes with low metallicity
self.sp.params['zmet'] = zmet1
f1 = abs_ab_mag_to_micro_jy(
self.sp.get_mags(tage=13.8, bands=self._compute_bands),
self.d)
meta1[0] = self.sp.stellar_mass
meta1[1] = self.sp.dust_mass
meta1[2] = self.sp.log_lbol
meta1[3] = self.sp.sfr
meta1[4] = self.sp.log_age
# Compute fluxes with high metallicity
self.sp.params['zmet'] = zmet2
f2 = abs_ab_mag_to_micro_jy(
self.sp.get_mags(tage=13.8, bands=self._compute_bands),
self.d)
meta2[0] = self.sp.stellar_mass
meta2[1] = self.sp.dust_mass
meta2[2] = self.sp.log_lbol
meta2[3] = self.sp.sfr
meta2[4] = self.sp.log_age
# Interpolate and scale the SED by mass
model_mjy = 10. ** logm * interp_logz(zmet1, zmet2, logZZsol, f1, f2)
# Interpolate metadata between the two metallicities
meta = interp_logz(zmet1, zmet2, logZZsol, meta1, meta2)
# Compute likelihood
# Automatically ignores pixels with no measurement (Nan)
lnL = -0.5 * np.nansum(
np.power((model_mjy[self.band_indices]
- self._sed) / self._err, 2.))
lnpost = lnprior + lnL
if ~np.isfinite(lnpost):
lnpost = -np.inf
# Scale statistics by the total mass
blob = {"lnpost": lnpost,
"logMstar": logm + np.log10(meta[0]), # log solar masses,
"logMdust": logm + np.log10(meta[1]), # log solar masses
"logLbol": logm * meta[2], # log solar luminosities
"logSFR": logm * meta[3], # star formation rate, M_sun / yr
"logAge": meta[4],
"model_sed": model_mjy,
"lnpriors": lnpriors}
return lnpost, blob