/blendfitter

*blendfitter* is a collection of tools to analysze blended sources, including methods to extract information on RVs and bisecotrs directly from the CCFs of HARPS & Co

Primary LanguagePython

blendfitter

[beta version]

blendfitter is a collection of tools to analysze blended sources, including methods to extract information on RVs and bisectors directly from the CCFs of HARPS & Co. It makes use of the packages emcee (Markov Chain Monte Carlo sampling), celerite and george (Gaussian Process models).

The code is freely available at https://github.com/MNGuenther/blendfitter under the MIT License. Feedback and contributions are very welcome.

If you use blendfitter or parts of it in your work, please cite Günther et al., 2018 (link). Please also include the following acknowledgement: "This work makes use of the blendfitter package (Günther et al., 2018), is a collection of tools to analysze blended sources, including methods to extract information on RVs and bisecotrs directly from the CCFs of HARPS & Co. It makes use of the packages emcee (Foreman-Mackey 2013), celerite (Foreman-Mackey 2017) and george (\ref). This work makes further use of the python programming language (Rossum 1995) and the open-source python packages numpy (van der Walt, Colbert & Varoquaux 2011), scipy (Jones et al. 2001), matplotlib (Hunter 2007), tqdm (doi:10.5281/zenodo.1468033) and seaborn (https://seaborn.pydata.org/index.html)."

Table of contents

  1. How to use & examples References

1. How to use & examples

a) You have a directory full of HARPS CCF .fits files you want to re-analyze using a GP baseline to account for any systematics? Go no further than calling:

import blendfitter blendfitter.analyse_CCFs(indir)

b) You want to read out the relevant infos from the HARPS CCF .fits headers?

import blendfitter BJD, RV, RV_err, Contrast, FWHM = blendfitter.ccf_io.extract_HARPS_data_from_fits(fname)

References

  • Foreman-Mackey, D., Hogg, D. W., Lang, D. & Goodman, J. (2013), Publications of the Astronomical Society of the Pacific, 125, 306
  • Foreman-Mackey, D., Agol, E., Ambikasaran, S. & Angus, R. (2017), The Astronomical Journal, 154, 220
  • Hunter J. D., 2007, Comput. Sci. Eng., 9, 90
  • Jones E. et al., 2001, SciPy: Open Source Scientific tools for Python. Available at: http://www.scipy.org/
  • Rossum G., 1995, Technical Report, Python Reference Manual, Amsterdam, The Netherlands
  • van der Walt S., Colbert S. C., Varoquaux G., 2011, Comput. Sci. Eng., 13, 22