Snapshots - How to create them

Kred contains several tools for creating snapshots/subimages of objects in situations where one knows in which image the object lies, or as in the case of the DECam images objects could be in one of many images, and one needs to identify which FITS file contains the object of interest.

For a single image such as MCELS one typically needs only to run a single program, namely XSnap.py.

XSnap is designed to work with a “master table” that contains, minimally, the RA and Dec for objects of interest. It produces a FITS file of the snapshot in the xdata/ directory and a figure in the ximage/ directory.

Like nearly all routines in the repository, the options can be displayed with:

XSnap.py -h

The documentation in XSnap is fairly complete.

Single-image workflow

A typical example of a command line for a single file would be:

XSnap.py -size 10 -type ha -min -1 -max 20 DECam_SWARP/LMC_c42_T01.ha.fits lmc_snr.txt

This will produce 10 arcmin sized snapshots of the LMC SNRs in this particular image, with plots scaled between -1 and 20. The output files will have the type “ha” in the filenames so that one can write files from different filters to the same output directory.

Multi-image workflow

The second situation for which XSnap is intended is when there are many images and one wants to select a specific image from which to create each snapshot.

In this case there are 3 steps:

  1. Summarize the images with ImageSum.py

  2. Select the best image for the snapshot of each object with ImageMatch2Source.py

  3. Make the snapshots with XSnap.py

Example:

ImageSum.py DECam_SUB2
ImageMatch2Source.py Image_Sum_DECam_SUB2.txt smc_snr_cotton24.txt s2_sub_r
XSnap.py -size 10 -type s2 XX_s2_sub_r.smc_snr_cotton24.txt smc_snr_cotton24.txt

These tools are largely designed for our kred reductions of the DECam data:

ImageSum.py

Searches all of the FITS files in the DECam_SUB2/ directory and any of its subfolders, and examines the files to find enough information to locate sources within them. It produces an output file, in this case Image_Sum_DECam_SUB2.txt, that contains this information.

ImageMatch2Source.py

Reads this file along with a master file containing all of the SNRs in the SMC, and locates the best image of type s2_sub_r from which to create a snapshot. This routine assumes that all files with s2_sub_r are effectively of the same group. It produces the file XX_s2_sub_r.smc_snr_cotton24.txt containing the results.

XSnap.py

Reads XX_s2_sub_r.smc_snr_cotton24.txt, which contains information about the best image for each source, and joins this with the table containing the object positions to produce the snapshots.