xenial (1) mAdd.1.gz

Provided by: montage_4.0+dfsg-3_amd64 bug

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.

       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.