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The DESK is an SED-fitting python scripts for fitting data from evolved stars

Project description

Dusty-Evolved-Star-Kit

The DESK is an SED-fitting python package for fitting data from evolved stars (photometry or spectra) with radiative transfer model grids. The package is currently in development and all contributions are welcomed. For current progress, see the ‘Issues’ tab on the Github page. The package is ideal for fitting small samples of dusty evolved stars. It will soon utilize a bayesian-fitting strategy with mass-loss rate and luminosity distributions as inputs (priors), and will provide a better fit to these broader sample properties.

Input: A csv file with the first column as wavelength in um and second column as flux in Jy. To fit multiple csv files, put them in a directory, and use the directory name as the input.

Output: A csv file including the best fit model and corresponding stellar parameters, as well as an optional figure of the fit SED.

Available model grids: Several grids are already available upon installation. A range of other model grids, including state-of-the-art dust-growth models by Nanni et al. (2019) , are downloaded automatically and used when selected. Descriptions of the model grids can be found in the Documentation. A module for creating your own DUSTY grid is under development, but for now, please email me (Dr. Steven Goldman) directly for potential grid requests or for help with the package.

Credits: This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

1.7.2 (2020-08-05)

Updates plots with now 17 source maximum for single SED figure. The sed and sed_indivscripts now use a common set of functions to minimize duplicated code, and ensure consistency.

1.7.1 (2020-08-05)

Minor bug fixes to the data retrieval script.

1.7.0 (2020-07-24)

First major release of least-squares fitting DESK. The package is stable with pytest testing through Github-actions (Ubuntu and OSX: Python 3.6, 3.7, 3.8), documentation with Sphinx on readthedocs, coverage with codecov, code quality checks with codacy, installation with pip, and hosted on Github. Model grids are downloaded using direct-download links on Box.

1.3.1 (2019-04-29)

  • First release on PyPI.

Project details


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