odi-tools

A suite of tools written in Pyraf, Astropy, Scipy, and Numpy to process individual QuickReduced images into single stacked images using a set of "best practices" for ODI data.

We are currently in the process of writing full documentation for odi-tools and its individual components. This documentation can be found at this link.

---

---

Installation

simply fork this repository and clone onto your local machine, e.g.: git clone https://github.com/bjanesh/odi-tools.git

optionally add this folder to your $PATH

to run the scripts you'll need to install a number of dependencies:

pip install numpy scipy astropy astroquery pyraf matplotlib pandas photutils tqdm

It is possible to install these packages without root access by using the --user option:

pip install --user package-name

As noted on the astropy website, it might also be beneficial to use the --no-deps option when installing astropy to stop pip from automatically upgrading any of your previously installed packages, such as numpy.

pip install --no-deps astropy

Basic Usage

All you need to do to get started is download your QR-ed data from the ODI-PPA using the wget download command, then follow these steps (if you aren't sure what to do, see below for more details).

1. rename the images folder created by the wget command to avoid confusion
2. unpack the compressed fits files using funpack link
3. copy example_config.yaml to your data directory as config.yaml and edit the file to match your preferences/data. You do not need to rename your images or use any particular numbering sequence, though using dither sequence ID numbers is recommended for clarity. You may use more than 9 images, but be sure to give each image a unique ID! For any additional dither sequences we recommend using, e.g., 11-19, 21-29, etc. NOTE: the current version of the example_config.yaml file contains the current recommended set of options for odi-tools.
4. run odi_process.py in the folder containing the unpacked fits images. This will (optionally) illumination correct the images, reproject them to a common pixel scale, and perform background subtraction on them.
5. this will take a while, so make sure nothing bad happened
6. run odi_scalestack_process.py in the folder containing the unpacked fits images. This will detect bright stellar sources in the images and use them to calculate a scaling factor relative to the image in the sequence with the lowest airmass, then apply the scale, stack the images, then add in a common background value. Finally, the images are flipped and optionally, aligned using integer pixel shifts.
7. Finished! Check your images to make sure everything went okay.

Processing details

(current as of 2 May 2016)

1. Add the images you wish to reduce to a collection in the ODI-PPA and from the collection action menu, choose QuickReduce.
2. Run QuickReduce with the following options:
   ☑️ WCS
   ☑️ Photometry
   ☑️ Fringe (i- and z-band only)
   ☑️ Persistency
   ☑️ Nonlinearity
   ☑️ Pupil Ghost (calibrations)
   ❌ Pupil Ghost (science)
   ☑️ Use Bad Pixel Masks
   ☑️ Cosmic Ray Removal, 3 iteration(s)
   Make sure you name the job and check the "Email me when finished" box. Wait.
3. Download the QuickReduce data to your hard drive using the "Download Results" button on the QuickReduce job page. You don't need to select any of the optional boxes, just name the job and click submit. Eventually a wget command will pop up. Copy it to your clipboard, navigate to the folder you want the data to go into, then paste the wget command in your command line.
4. Move all individual .fz files into the top level folder: mv calibrated/**/*.fz .
5. Unpack the compressed fits files using funpack link
6. You need to rename your files to match the appropriate dither pointing identification. for example, QR files are named by the pattern OBSID_OBJECT_FILTER.JOBID.fits. The final digit of the OBSID e.g. 20151008T195949.1 needs to match the number sequence of the dithers 1-9. Your data may not match this pattern due to restarted observations, multiple night observations, etc. Support will be added in the future for FITS header keywords providing this functionality without user interaction.
7. Once your files are uncompressed and renamed, run odi_process.py in the folder with the images. This script will do the following (most of these steps are applied on an OTA by OTA basis, not the entire image at once):
8. create catalogs of SDSS and 2MASS stars from the QuickReduce header tables. These will be used later for a bunch of things.
9. create lists of all object images
10. open each object image and create a mask of bad pixels and a mask of all objects in the image, write the name of these masks to the header.
11. take the object images and the bad pixel/object masks and do a masked median combine to create a dark sky flat (one per filter).
12. illumination correction -- divide each object image by its filter's dark sky flat.
13. use the SDSS coordinates from before to select bright stars and use them to correct the WCS in each image using iraf.mscred.msccmatch (this works poorly in crowded fields at the moment). Do three iterations of fix_wcs.
14. now that the WCS is good in each image, reproject each image of the object in all filters to a common system, using the target RA and Dec as the reference point. (using iraf.mscred.mscimage)
15. measure the mean stellar fwhm in each OTA using iraf.imexam
16. measure and subtract the median background in each image. Put down 100 random boxes around the image, find the median value and subtract. This takes advantage of the object masks created earlier to know where objects are.
17. write out all the measured properties into a table so we know what happened and can see if anything went wrong.
18. Run odi_scalestack_process.py in the top-level folder. This will determine scaling factors, scale the image, and stack them into single images per filter.
19. find sources in the image using daofind
20. build a matched catalog of stars, making sure all the stars are visible on all the other images and not in a cell gap/etc.
21. measure the fwhm to find an aperture size, then iraf.apphot.phot the sources in every image to get their total flux.
22. choose a reference image (LOWEST AIRMASS)
23. use the measured fluxes to compute a flux ratio between the reference image and all other images -- then divide each image by this ratio to scale each image to a common flux.
24. stack the images using iraf.imcombine(wcs='yes'). This will take a long time since it is combining each individual OTA from each image. (with ODI5x6 data, around 270 images!) This produces a fits file named object_filter.fits.
25. Your images are now stacked. If you want to use our typical photometric calibration procedure, run odi_phot_process.py in the top-level folder. This will also (eventually) set some header keywords that will be useful to you in the future.
26. Fix the WCS for the full image (one iteration). If things are good, this will only be a few arcseconds of shift.
27. set the effective airmass for the stacked image to be that of the scaling reference image.
28. get photometry of all SDSS catalog stars from the stacked images using a large aperture (4.5x fwhm).
29. do matched pair photometric calibration using the photometry of the SDSS stars and the catalog values. See full_calibrate.py:388 for full details.
30. determine an aperture correction for each filter using the brightest SDSS stars.
31. find all sources in the images using daofind, iraf.apphot.phot them, and determine their RA and Dec.
32. based on RA and Dec, match the sources between the images, and calibrate the photometry using predetermined coefficients. Print calibrated magnitudes out to a file, and make a CMD of the final sources.