def _res(x):
z = x[0]
v = x[1]
lnA = x[2]
line_flux = np.exp(lnA)
residuals = []
for cam in cams:
wave = spectra.wave[cam]
res = Resolution(spectra.resolution_data[cam][fiber, :])
flux = spectra.flux[cam][fiber, :]
ivar = spectra.ivar[cam][fiber, :]
mask = spectra.mask[cam][fiber, :]
tw = twave(wave.min(), wave.max())
rflux = doublet_obs(z,
tw,
wave,
res,
continuum=0.0,
sigmav=v,
r=0.0,
linea=0.0,
lineb=lineb)
rflux *= line_flux
res = np.sqrt(ivar[mask]) * np.abs(flux[mask] - rflux[mask])
residuals += res.tolist()
residuals = np.array(residuals)
print(z, v, lnA, residuals.sum())
return residuals
def _res(x):
z = x[0]
v = x[1]
result = 0.0
residuals = []
prior = gaussian_prior(z, rrz, rrzerr)
prior *= gaussian_prior(v, 90, 50.)
for i, singlet in enumerate(singlets):
lineidb = singlet['INDEX']
lineb = singlet['WAVELENGTH']
if singlet['MASKED'] == 1:
continue
postage = postages[lineidb]
cams = list(postage.keys())
lnA = x[i + 2]
line_flux = np.exp(lnA)
for cam in cams:
wave = postage[cam].wave
res = Resolution(postage[cam].R)
flux = postage[cam].flux
ivar = postage[cam].ivar
mask = postage[cam].mask == 0
tw = twave(wave.min(), wave.max())
# lineida irrelevant when line ratio, r, is zero.
rflux = doublet_obs(z, tw, wave, res, continuum=0.0, sigmav=v, r=0.0, linea=0.0, lineb=lineb)
rflux *= line_flux
res = np.sqrt(ivar[mask]) * (flux[mask] - rflux[mask])
residuals += res.tolist()
for i in np.arange(ndoublet):
doublet = doublets[doublets['DOUBLET'] == i]
if doublet['MASKED'][0] == 1:
continue
linea = doublet['WAVELENGTH'][0]
lineb = doublet['WAVELENGTH'][1]
lineidb = doublet['INDEX'][1]
postage = postages[lineidb]
cams = list(postage.keys())
lnA = x[2 * i + 2 + nsinglet]
line_flux = np.exp(lnA)
r = x[2 * i + 3 + nsinglet]
prior *= gaussian_prior(r, 0.7, 0.2)
for cam in cams:
wave = postage[cam].wave
res = Resolution(postage[cam].R)
flux = postage[cam].flux
ivar = postage[cam].ivar
mask = postage[cam].mask == 0
tw = twave(wave.min(), wave.max())
# print(lineidb, cam, z, v, r, line_flux, X2)
rflux = doublet_obs(z, tw, wave, res, continuum=0.0, sigmav=v, r=r, linea=linea, lineb=lineb)
rflux *= line_flux
res = np.sqrt(ivar[mask]) * (flux[mask] - rflux[mask])
residuals += res.tolist()
# Include a parameter prior.
nelement = len(residuals)
residuals += [np.sqrt(2. * mlogprior(prior) / (1. + nelement))]
residuals = np.array(residuals)
return residuals