mDAGTbls(1)


NAME

   mDAGTbls  -  Construct  tables  of  projected  and background corrected
   images

SYNOPSIS

     mDAGTbls images.tbl region.hdr rimages.tbl pimages.tbl cimages.tbl

DESCRIPTION

     mDAGTbls  is  primarily  a  utility  for  mDAG.   In  normal  Montage
   processing,
     one  can build support files as you go: retrieve a set of images from
   an
     archive, then make a list of  the  images  retrieved;  reproject  the
   images, then
     make a list of the successfully reprojected images; etc.

     To  support  a  workflow  manager which plans processing using an up-
   front
     Directed Acyclic Graph (DAG) you need to have pre-determined  all  of
   this,
     plus  which  image will overlap (for background matching).  You don't
   have
     to be completely right (some overlaps may end up empty or some images
   may
     have  no real data in the region of interest) but you need to build a
   complete
     superset.  mDAGTbls does part of this.  Based on a raw image list, it
     determines as best it can the image lists (raw images in  the  region
   of
     interest  from larger list of images we have, projected and corrected
   lists
     from that). mOverlaps can build the  difference  list  from  the  raw
   list.

     We  document  this  here  since  the  same analysis may be of use for
   planning
     other than in mDAG.

ARGUMENTS

   images.tbl

            The set of images we have.  For instance, a set  download  via
          mArchiveList/mArchiveGet
            and  often  a  superset  of  what  we  are interested in for a
          specific mosaic.

   region.hdr

            A FITS header template file.  This is used  to  determine  the
          region of interest
            for this run.

   rimages.tbl

            The "raw" (input) image we will use in this run.

   pimages.tbl

            The corresponding projected image list.

   cimages.tbl

            The corresponding background-corrected image list.

RESULT

     If successful, the three output image lists will be created. NOTE: It
   is assumed
     that the actual raw, reprojected and background-corrected images will
   be kept in
     separate   directories,  so  the  file  names  in  these  tables  are
   identical.

MESSAGES

   ERROR  Cannot open status file: filename

   ERROR  Invalid image metadata file: tblfile

   ERROR  Not enough information to  determine  coverages  (CDELTs  or  CD
          matrix)

   ERROR  Need  columns:  cntr  ctype1  ctype2  nl ns crval1 crval2 crpix1
          crpix2 cdelt1 cdelt2 crota2 fname (equinox optional)

   ERROR  Invalid output metadata file: tblfile

   ERROR  Bad WCS for image integer

   ERROR  Output wcsinit() failed.

   ERROR  general error message

EXAMPLES

   mDAGTbls images.tbl region.hdr rimages.tbl pimages.tbl cimages.tbl
          [struct stat="OK", count="48", total="48"]

BUGS

   The drizzle algorithm has been implemented but has not been  tested  in
   this release.

   If  a header template contains carriage returns (i.e., created/modified
   on a Windows machine), the cfitsio library will be unable  to  read  it
   properly,  resulting  in  the  error: [struct stat="ERROR", status=207,
   msg="illegal character in keyword"]

   It is best  for  the  background  correction  algorithms  if  the  area
   described  in  the header template completely encloses all of the input
   images in their entirety. If parts of input images are "chopped off" by
   the  header  template,  the  background correction will be affected. We
   recommend  you  use  an  expanded  header  for  the  reprojection   and
   background  modeling  steps, returning to the originally desired header
   size for the final coaddition. The default background matching  assumes
   that  there  are  no non-linear background variations in the individual
   images (and therefore in the overlap  differences).  If  there  is  any
   uncertainty  in  this  regard,  it is safer to turn on the "level only"
   background matching (the "-l" flag in mBgModel.

COPYRIGHT

   2001-2015 California Institute of Technology, Pasadena, California

   If  your  research  uses  Montage,   please   include   the   following
   acknowledgement:  "This  research  made use of Montage. It is funded by
   the National Science Foundation under Grant Number ACI-1440620, and was
   previously    funded    by   the   National   Aeronautics   and   Space
   Administration's   Earth   Science   Technology   Office,   Computation
   Technologies  Project,  under  Cooperative  Agreement  Number  NCC5-626
   between NASA and the California Institute of Technology."

   The Montage distribution includes an adaptation of the MOPEX  algorithm
   developed at the Spitzer Science Center.





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