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License: MIT License
stellar continuum fitting with a PCA basis set
License: MIT License
KDEUnivariate
in find_pcs.py
tries to import factorial from scipy.misc. This fails, so working around by hard-coding KDE plotting as false in find_pcs.PCA_Result.qty_hist
. This is a hot-fix for mostly-unused functionality, not a long-term solution.
allow results files to include most any attribute of the PCA_Results
object
Veldisp space isn't well-populated enough, doesn't make sense to realize only once per CSP
using the biweight midcov from astropy 2.0 may help with spectral covariance computation
generate all models with appropriate velocity dispersions
do we adjust bounds of random sampling, or take all models and sample them uniformly?
plot fitted veldisp against DAP veldisp results
find the average signal to noise of reobserved spectra, and use that to scale the signal to noise in the spectral covariance calculations.
replace main diagonal with ivar
steps:
These cause a problem with mass-weighted age calculations, and also are just generally messy. Could get around them with wrapping directly around fsps fortran code and specifying a piecewise SFH
Check in Conroy+ '09 and with Bob/Jay
Replace StellarMass_PCA.reconstruct_full()
and similar methods with a more flexible reconstruction object. Put it in reconstruct.py
or something. Transparently implement spaxel-wise and cube-wise reconstruction.
new ivar should have units compatible with 1 / f**2.
add property to dered object
Natively, fsps uses basel or miles spectral library, which is much lower-res than MaNGA data is. Want Castelli+Kurucz+Conroy library, which is unpublished and unavailable (in fact, throws errors when run)
M/L profile of typical Sc (or whatever else)
Doesn't account for bursts in FSPS pre-computation, just uses integrate.quad
on SFR (doesn't actually use masses at each age through FSPS).
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