AIPS NRAO AIPS HELP file for NNLSQ in 31DEC21



As of Sun May 16 11:44:40 2021


NNLSQ: Non-Negative-Least-Squares decomposition of spectrum

INPUTS

INNAME                             Input image name (name)
INCLASS                            Input image name (class)
INSEQ             0.0     9999.0   Input image name (seq. #)
INDISK            0.0        9.0   Input image disk unit #
OUTNAME                            Output image name (name)
OUTCLASS                           Output image name (class)
OUTSEQ           -1.0     9999.0   Output image name (seq. #)
OUTDISK           0.0        9.0   Output image disk unit #.
BLC                                Bottom left corner of input
TRC                                Top right corner of input
CPARM                              [1]=Sigma {default 1.0}

HELP SECTION

NNLSQ
Task: This program solves for the set of positive (non-negative)
      components which when convolved with a specified Gaussian
      make a least squares fit to the dirty spectrum. For best
      results (minimum broadening of the profiles combined with
      best S/N) the sigma of the Gaussian should be similar to
      that of the instrumental profile (1-1.5 is probably good).
Adverbs:
  INNAME.....Input image name (name).       Standard defaults.
  INCLASS....Input image name (class).      Standard defaults.
  INSEQ......Input image name (seq. #).     0 => highest.
  INDISK.....Disk drive # of input image.   0 => any.
  OUTNAME....Output image name (name).      Standard defaults.
  OUTCLASS...Output image name (class).     Standard defaults.
  OUTSEQ.....Output image name (seq. #).    0 => highest unique.
  OUTDISK....Disk drive # of output image.  0 => highest number
             with sufficient space.
  BLC........Bottom right corner in input image of desired
             subimage.  Default is entire image.
  TRC........Top right corner in input image of desired
             subimage.  Default is entire image.
  CPARM......CPARM(1) = Sigma of Gaussians (default 1.0)

EXPLAIN SECTION


AIPS