Releases: bd-j/prospector
Releases · bd-j/prospector
v1.4.0
v1.3.0
- Adds the prospector-beta SFH priors and documentation courtesy @wangbingjie
- Bugfixes in emission line masking, polynomial regularization, sfr_ratio
clipping (h/t mjastro, wangbingjie, davidjsetton) - Documentation updates
v1.2.0
- Added
LineSpecModel
for fitting integrated emission line fluxes (h/t @kgarofali). - Add
AGNSpecModel
with a scalable, empirical AGN emission line template. - Fix floating point issue with Dirichlet SFH transforms (h/t TakumiSTanaka)
- Implement
nested_target_n_effective
as dynesty stopping criterion. - Fixes to the dynesty interface for dynesty >= 2.0 (h/t mjastro)
- Fix sign error in Powell minimization (h/t blanton144)
- Fix bugs in parameter template for emission line fitting.
- numerous documentation updates including nebular emission treatments.
v1.1
- Improved treatment of emission lines in
SpecModel
, including ability to ignore selected lines entirely. - New
NoiseModelKDE
andKernel
classes to accommodate non-Gaussian and correlated uncertainties, courtesy of @wpb-astro - New flexible SFH parameterization courtesy @wrensuess
- Support for
sedpy.observate.FilterSet
objects and computing rest-frame absolute magnitudes. - Documentation updates, including a dedicated SFH page and a quickstart.
- Several bugfixes including fixes to the
"logm_sfh"
parameter template, a fix for the nested sampling argument parsing, and bestfit spectrum saving.
v1.0
v0.4.0
- New
models.SpecModel
class that handles much of the conversion from FSPS spectra to observed frame spectra (redshifting, smoothing, dimming, spectroscopic calibration, filter projections) internally instead of relying onsource
classes. - The
SpecModel
class enables analytic marginalization of emission line amplitudes, with or without FSPS based priors - A new mixture model option in the likelihood to handle outlier points
- A noise model kernel for photometric calibration offsets.
- Rename
mean_model
topredict
(old method kept for backwards compatibility) - Some fixes to priors and optimization
- Python3 compatibility improvements (now developed and tested with Python3)
v0.3.0
New UI, based on argparse command line options and a high level fit_model
function
-
new
prospector_parse
module that generates a default argument parser -
importable default probability function as
fitting.lnprobfn
-
new high level
fit_model
method that can use emcee, dynesty, or optimization algorithms -
documentation and new notebook reflect UI changes
-
model_setup
methods raise aFutureWarning
-
better usage of warnings
-
non-object prior methods removed
v0.2.1
-
Bugfixes, including the polynomial optimization in PolySedModel
-
New prior objects (
SkewNorm
,StudentT
) -
New parameter templates (IGM, continuity SFH, spectral smoothing)
-
Restart emcee chains
-
Python3 compatibility improvements
-
Sweet new animated figure for the documentation.