sedkit

Introduction

sedkit is a collection of pure Python modules for simple SED construction and analysis. Users can create individual SEDs or SED catalogs from spectra and/or photometry and calculate fundamental parameters (f_bol, M_bol, L_bol, T_eff, mass, log(g)) using the methods presented in Filippazzo et al. (2015).

Installation

Install via PyPI with

pip install sedkit

or via conda with

git clone https://github.com/hover2pi/sedkit.git
cd sedkit
conda env create -f env/environment-3.11.yml --force
conda activate sedkit
python setup.py install

Demo

An SED can be constructed by importing and initializing an SED object.

from sedkit import SED
trap1 = SED(name='Trappist-1')

The name argument triggers a lookup in the Simbad database for meta, astrometric, and spectral type data. Interstellar reddening is calculated when possible.

Photometry can be added manually...

trap1.add_photometry('Johnson.V', 18.798, 0.082, '2006AJ....132.1234C')
trap1.add_photometry('Cousins.R', 16.466, 0.065, '2006AJ....132.1234C')
trap1.add_photometry('Cousins.I', 14.024, 0.115, '2006AJ....132.1234C')

...and/or retrieved from Vizier catalogs with built-in methods.

trap1.find_2MASS()
trap1.find_WISE()

Spectrum arrays or ASCII/FITS files can also be added to the SED data.

from pkg_resources import resource_filename
spec_file = resource_filename('sedkit', 'data/Trappist-1_NIR.fits')
import astropy.units as u
trap1.add_spectrum_file(spec_file, wave_units=u.um, flux_units=u.erg/u.s/q.cm**2/u.AA)

Other data which may affect the calculated and inferred fundamantal parameters can be set at any time.

trap1.spectral_type = 'M8', '2006AJ....132.1234C'
trap1.age = 7.6*u.Gyr, 2.2*u.Gyr, '2017ApJ...845..110B'
trap1.radius = 0.121*u.R_sun, 0.003*u.R_sun, '2021PSJ.....2....1A'

Results can be calculated at any time by checking the results property.

trap1.results

A variety of evolutionary model grids can be used to infer fundamental parameters,

trap1.evo_model = 'DUSTY00'
trap1.infer_mass()

A variety of atmospheric model grids can be fit to the data with or without MCMC analysis,

from sedkit import BTSettl
trap1.fit_modelgrid(BTSettl(), mcmc=True)

And any arbitrary atlas of models can be applied as well.

from sedkit import SpexPrismLibrary
trap1.fit_modelgrid(SpexPrismLibrary())

Inspect the SED at any time with the interactive plotting method.

trap1.plot(integral=True, best_fit=True)

References for all data can be accessed via the refs attribute.

Entire catalogs of SED objects can also be created and their properties can be arbitrarily compared and analyzed with the sedkit.catalog.Catalog() object.

Please read the full documentation for details on this functionality and much more.

Documentation

Full documentation for the latest build can be found on ReadTheDocs.

The package also contains detailed Jupyter notebooks highlighting the core functionality of its primary classes, including

• sedkit.spectrum.Spectrum
• sedkit.sed.SED
• sedkit.catalog.Catalog

If you use or reference this software, please cite Filippazzo et al. (submitted to PASP)

Licensed

This project is Copyright (c) Joe Filippazzo and licensed under the terms of the BSD 3-Clause license. See the licenses folder for more information.