README.md

k2phot

Extract photometry from K2 images

Current Version: v1.0

Dependencies

Tested with Python v2.7.3, 2.7.6, 2.7.8

• NumPy (tested with 1.6.2, 1.8.1)
• SciPy (tested with 0.7.0, 0.10.1, 0.14.0)
• AstroPy (for io.fits; tested with 0.4, 0.4.1)
• pyfits (needed to run k2sc).
• Pandas (0.14.1)
• skimage (0.10.0)
• matplotlib / pylab (tested with 1.1.0, 1.3.1)
• photutils
• emcee
• george (requires eigen) complicated install, consult evernote

Example

Running the pipeline on C5 data

1. Download pixel data

2. Collect the fits file meta data

   $ cd $K2_ARCHIVE/pixel/
   $ scrape_fits_headers $(find C5 -name "*.fits") C5_headers.db

3. Select a output channel to use. Channels 4 and 33 are a good bet.

4. Make the transformation files

   $ cd C5 # Must be in this directory to run code
   $ make_channel_transform.py --help
   $ make_channel_transform.py 4 C5 ../C5_headers.db ${K2PHOTFILES}/pixeltrans_C5_ch04.h5
   ```
   
   

5. Inspect the transformation files

   from k2phot import channel_transform as ct
   trans,pnts =  ct.read_channel_transform(args.transfn)
   ct.plot_trans(trans,pnts)
   
   phot = k2phot.phot.read_fits(args.fitsfn,'optimum')
   k2phot.plotting.phot.lightcurve_segments(phot.lc)   

   

6. If statisfied, run the photometric pipeline with. There are two decorrelation algorithms to use, we recommend k2sc

   import 
   k2phot.pipeline_k2sc
   k2phot.pipeline_k2sc(
       pixfile,lcfile,transfile,splits, debug=debug, tlimits=tlimits, tex=tex,
       plot_backend=plot_backend, aper_custom=aper_custom, xy=xy,
       transitParams=transitParams, transitArgs=transitArgs
   )