NAME

       mAdd - Re-project and mosaic your images, with background rectification

SYNOPSIS

       mAdd  [-p  imgdir]  [-n(o-areas)]  [-a  mean|median|count]  [-e(xact-size)] [-d level] [-s
       statusfile] images.tbl template.hdr out.fits

DESCRIPTION

       Coadd the reprojected images in an input list to form an output mosaic  with  FITS  header
       keywords  specified in a header file. Creates two output files, one containing the coadded
       pixel values, and the other containing coadded pixel area values. The  pixel  area  values
       can  be  used  as  a  weighting  function  if the output pixel values are themselves to be
       coadded with other projected images, and may also be used in validating  the  fidelity  of
       the output pixel values.

OPTIONS

       -p imgdir
              Specifies path to directory containing reprojected images.  If the -p switch is not
              included, mAdd will look for the input images in the current working directory.

       -n     Co-addition ignores weighting by pixel areas and performs coaddition based only  on
              pixel  postions.   This  flag  refers to input images; the area file for the output
              image will still be created.

       -a type
              Select type of averaging to be done on accumulated pixel  values  (either  mean  or
              median).   To  generate  a  map  showing  counts  of  how many times each pixel was
              overlapped by the input images, use count.

       -e     Enables exact size mode. The output image will match the header  template  exactly,
              instead of shrinking the output to fit the data.

       -d level
              Turns on debugging to the specified level (1-3).

       -s statusfile
              mAdd output and errors will be written to statusfile instead of stdout.

ARGUMENTS

       images.tbl
              ASCII  table  (generated  by  mImgtbl)  containing  metadata  for  all images to be
              coadded.

       template.hdr
              FITS header template to be used in generation of output FITS

       out.fits
              Name of output FITS image.

RESULT

       If successful, mAdd creates a FITS file out.fits that is a coadd of all the FITS files  in
       the   table   images.tbl,  according  to  the  header  template  given.   A  corresponding
       out_area.fits is also created.

MESSAGES

       OK     [struct stat = "OK", time=time]

       ERROR  MPI initialization failed

       ERROR  Invalid argument for -a flag

       ERROR  Cannot open status file: statusfile

       ERROR  Invalid image metadata file: filename

       ERROR  Need columns: cntr,fname, crpix1, crpix2, cdelt1, cdelt2, naxis1,  naxis2,  crval1,
              crval2 in image list

       ERROR  Memory allocation failed

       ERROR  Too many open files

       ERROR  Input wcsinit() failed.

       ERROR  Failed to allocate enough memory for output arrays

       ERROR  Bad WCS in header template.

       ERROR  Images are not in same pixel space.

       ERROR  Template file not found.

       ERROR  FITS library error

       ERROR  general error message

EXAMPLES

       The  following example runs mAdd on 4 FITS images, generating the output file mosaic.fits.
       Related files are images.tbl and template.hdr.

       $ mAdd -p proj images.tbl template.hdr output/mosaic.fits
              [struct stat="OK", time=1]

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.

       Although the memory limitation for output images has been overcome  in  versions  2.x  and
       above  of  Montage,  it  is  still  possible  (though unlikely) to create an out-of-memory
       situation due to the size and number of input images. mAdd builds the output image one row
       at  a  time,  and  stores every pixel from any input image that contributes to that row in
       memory.

       If you have a large enough mosaic, it is almost always more efficient (and often easier on
       the  user)  to  tile  it. There are tools in Montage to help with this and these have been
       brought together under mAddExec. In fact, even if you want a single output image,  it  may
       be  faster to do it in two steps: mAddExec to create a set of tiles, and then mAdd to make
       a final mosaic from these tiles. There is absolutely no loss of information in doing this.

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.