dense-basis 0.1.4

SED fitting with non-parametric star formation histories

# Dense Basis SED fitting

An implementation of the Dense Basis method tailored to SED fitting, in particular, to the task of recovering accurate star formation history (SFH) information from galaxy spectral energy distributions (SEDs). The current code is being adapted from its original use-case (simultaneously fitting specific large catalogs of galaxies) to being a general purpose SED fitting code and acting as a module to compress and decompress SFHs.

As such, it is currently in an beta phase, where existing modules are being improved upon and crash-tested and thorough documentation is being written. If you are interested in using, testing or extending the repository, please shoot me an email.

### Installation and usage:

To use the package, clone the repository and run python setup.py install within the dense_basis folder. More detailed intstructions can be found at [dense-basis.readthedocs.io](https://dense-basis.readthedocs.io).

Documentation on usage and basic tutorials can also be found at [dense-basis.readthedocs.io](https://dense-basis.readthedocs.io).

A good place to get started is [here](https://github.com/kartheikiyer/dense_basis/blob/master/docs/tutorials/getting_started.ipynb).

References: - [Iyer & Gawiser (2017)](https://iopscience.iop.org/article/10.3847/1538-4357/aa63f0/meta) - [Iyer et al. (2019)](https://iopscience.iop.org/article/10.3847/1538-4357/ab2052/meta)

Contact: - [e-mail]

### Changelog

v.0.1.4 - The FSPS/python-FSPS requirement is no longer necessary, if a user requires only the GP-SFH module. - added more options to SFR sampling - flat in SFR, sSFR or lognormal in sSFR. removed the separate sample_sSFR_prior option - added option for tx_alpha sampling from IllustrisTNG (0<z<6, Nparam<10) - removed the squeeze_tx option - this can be effectively implemented with a larger value for the concentration parameter - implemented rough mass-metallicity prior - implemented flat and exponential dust priors for the Calzetti law, and a rough implementaion of the CF00 law using priors from Pacifici+16 - removed sample_all_params_safesSFR, and the safedraw=True in make_N_prior_draws - removed the min SFR in the sample_sfh_tuple function - updated the GP tuple_to_sfh module to decouple SFR if necessary. - overhauled the generate_atlas() and load_atlas() functions, - shifted storage of precomputed pregrids/atlas(es) from scipy.io to hickle