Skip to content

Latest commit

 

History

History
64 lines (52 loc) · 2.65 KB

CHANGELOG.rst

File metadata and controls

64 lines (52 loc) · 2.65 KB
Raw

v1.2.0 (2022-12-31)

  • Document, improvements, and bugfixes in LineSpecModel (h/t @kgarofali)
  • Add AGNSpecModel with a scalable, empirical AGN emission line template.
  • Fix floating point issue with Dirichlet SFH transforms.
  • 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.
  • numeropus documentation updates including nebular emission details.

v1.1.0 (2022-02-20)

  • Improved treatment of emission lines in SpecModel, including ability to ignore selected lines entirely.
  • New NoiseModelKDE and Kernel 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 (2020-12-02)

Release to accompany submitted paper. Includes

  • New plotting module
  • Demonstrations of MPI usage with dynesty
  • Numerous small bugfixes.

v0.4 (2020-07-08)

  • 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 on source 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 (for diagonal covariance matrices)
  • A noise model kernel for photometric calibration offsets.
  • Rename mean_model() to predict() (old method kept for backwards compatibility)
  • Some fixes to priors and optimization
  • Python3 compatibility improvements (now developed and tested with Python3)

v0.3 (2019-04-23)

  • New UI, based on argparse command line options and a high level ``fit_model()` function that can use emcee, dynesty, or optimization algorithms
  • New prospector_parse module that generates a default argument parser.
  • Importable default probability function as fitting.lnprobfn()
  • Non-object prior methods removed
  • Documentation and new notebook reflect UI changes
  • model_setup methods are deprecated, better usage of warnings