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.