----------------------------------------------------------------------
This pipeline takes raw data from LCO NRES archives and creates a timeseries of the S-Index for each star it is run on. The S-index is a proxy for magnetic activity in stars. Please see the Mount Wilson Observatory HK project for more information.

Authors: Adam Goga - adfgoga@gmail.com
         Ricky Egeland - egeland@ucar.edu
         Travis Metcalfe - travis@spsci.org
         Tim Brown (original pipeline based on Tim Brown's IDL pipeline)


Notes:
Working.ipynb is the main Jupyter notebook which contains the wrapper for the pipeline.

The following is the required folder structure to get the pipeline to run on your data:
nreshk > python files.py
       > data\
            > 22049\ #Star folders must start with a number
            > 17051\ #another example of star folder
            > flats\ #must contain only flat files, the pipeline *should* be able to read flat files in any sub directory
            > LabSpectra\ #the reference spectra folder which will be loaded in for cross correlation
       >output\ #created by the pipeline to show time series information on each star and nightly observation's debug info
      
Star directories should contain sub-directories for each observation, maintaining the directory name contained within the .tar.gz files obtained from the LCO archive.

All spectra and flat files should be in .fits format; not the compressed .fits.fz format that comes from the LCO archive.  Use `funpack` to decompress the files.

At this time, stellar effective temperatures are hard-coded into a dictionary named `tEffLookup` in the helpers.py module.  If you are running the pipeline on a star not listed in that dictionary, it must be added first.  (A better configuration method will be implemented in a future version.)



Additional information and references:
Physics today overview - https://physicstoday.scitation.org/doi/10.1063/PT.3.3956
Metcalfe et. al (2016) - https://ui.adsabs.harvard.edu/abs/2016ApJ...826L...2M/abstract
Metcalfe & van Saders - https://ui.adsabs.harvard.edu/abs/2017SoPh..292..126M/abstract