imaka

INSTALL (from git)

Dependencies:

• python (preferably via Anaconda, as it includes necessary pacakges like astropy).
• astropy
• photutils
• ccdproc
• flystar (https://github.com/jluastro/imaka)

Summary of Basic reduction and Analysis

Part 1: Make sky and flat images and use them to reduce science images

Skies and Darks: These are from calibration data taken at the beginning and sometimes end of the night.

• make_flat(): takes in Darks and creates a flat from them
  • Requires: input of flat numbers and locations
  • Outputs:
    • sta/reduce/calib/flats.fits
    • sta/reduce/calib/flats.list
  • Will scan flat files if needed (in flats original location)
  • Creates a mask from this flat to cut noisy edges: sta/reduce/calib/mask.fits
• make_sky(): Takes in sky files and combines in a master sky
  • Requires: input of sky numbers and locations
  • Outputs:
    • sta/reduce/sky/fld2_sky.fits
    • sta/reduce/sky/fld2_sky.list
  • scans sky fits if needed (in fits original location)

Reducing: Reduction takes into account both skies and darks for the final science image

• reduce_fld2()
  • Requires: flats and sky fits
  • Will treat overscan regions for science images: sta/Fld2/ *_scan.fits
  • Will clean scanned files: sta/reduce/Fld2/*_scan_clean.fits

Part 2: Find stars in clean images and calculate stats

Star Finding: Uses the DAO Starfinder

• find_stars_fld2()
  • Requires: mask, cleaned image files
  • Outputs:
    • sta/reduce/Fld2/*_scan_clean_stars.txt : a list of all sources centroids, peak brightness, and fwhm, etc.
    • sta/reduce/Fld2/*_scan_clean_pfs_mod.fits : average model psf
    • sta/reduce/Fld2/*_scan_clean_pfs_obs.fits : average observed psf

Star Stats: This is run from the reduce file on a list of clean files. Each starlist found will give

• calc_star_stats()
  • Requires: stars.txt files, cleaned images
  • Calculates: From starlists
    • emperical FWHM
    • encircled energy (EE) at 25, 50 and 80
    • NEA (? encircled energy and plate scale radius…)
  • Outputs:
    • sta/reduce/Fld2/*_stars_stats.fits : saved files of parameters
    • sta/reduce/Fld2/stats/stats<KEY>.FITS : summary of all files passed in
    • sta/reduce/Fld2/ee/ee* : encircled energy profile saved
• fit_moffat() : A PSF fit that is more extended than a gaussian.
  • Requires: start lists, star stats
  • Outputs:
    • sta/reduce/Fld2/*_stars_stats_mdp.fits : Save and updated list of stars with all their moffat fits.
    • sta/reduce/Fld2/*_psf_mof_oversamp#.fits: Save the average PSF (flux-weighted). oversampling set at 2

Part 3: Make a stack of images for each mode and analyze stacks

• append_massdimm()
  • Requires: stats files in /stats/ folder
  • Pulls MASS DIMM data based on time
  • Outputs:
    • reduce/stats/stats*_mdp.fits
• stack()
  • Requires: stars.txt, cleaned images
  • Loop through all the starlists to get transformations, then shift images to those starlists
  • Outputs:
    • reduce/stacks/fld2_stack_<key>.fits : an image formed through stacking medians
• analyze_stacks()
  • Requires: stacked images, mask
  • Runs starfinding, star stats, and moffat fitting on stacked images
  • Outputs:
    • reduce/stacks/fld2_stack_<key>_<file>.fits : general psf_mod, psf_obs, psf_mof_oversample, stars.txt, star_stats, star_stats_mdp

Summary of Four Filter Reduction and Analysis

General goal: Know filter rotation for each file, split individual starlists by filter, combine STATS on starlists

• split_filters()

  • Requires: stars.txt
  • split star lists by color, saving each in own txt, noting orientation
  • iters by rotation key
  • Outputs
    • reduce/Fld/*<filt>_<order>_stars.txt

• calc_fourfilt_stats()

  • Requires: stats, clean images, starlists
  • for each suffix, for each color, collect color’s star stats
  • Outputs
    • reduce/stats/stats_<key>_<color>.fits