NAME

       mViewer - Render multi-dimensional images and large-scale images

DESCRIPTION

       mViewer generates a JPEG image file from a FITS file (or a set of three FITS files in full
       color).  A data range for each image can be defined, and the data can be stretched by  any
       power  of  the  log() function (including zero: linear) or using custom gaussian histogram
       equalization algorithms.  Pseudo-color color tables can be applied in single-image mode.

       mViewer can also generate overlays on the image of coordinate grids, source catalogs (with
       scaled symbols), image outlings from metadata tables, plus various markers and labels.

       Along  with  a  few  other  Montage modules, mViewer can be wrapped to support interactive
       image analysis from Python or through AJAX web interfaces.

       The functionality of mViewer goes beyond what is reasonable to capture in a man page.  The
       user is therefore directed to the mViewer documentation suite.

       Examples:

       To create a grayscale image from a FITS file:

       To create a full color image from three co-registered FITS files:

       A complex example with a catalog overlay (symbol size, shape and color controlled by table
       columns), image metadata, a coordinate grid and some custom labeling:

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.