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  This class creates a smoothed sky map from an irregularly spaced
  set of measurements. The default smoothing kernel is a
  gaussian. The class also implements sigma clipping.
  The smoothed map is evaluated according to
  m(x,y) = sum( w_i v_i) / sum(w_i)
  where v_i is the ith sampled data point and w_i is a smoothing
  function. In this class, w_i is a gaussian centered on v_i, with a
  fwhm specified by the user.
  The variance map is given by
  v(x,y) = sum(w_i^2 dv_i^2) / sum(w_i)^2
  where dv_i^2 is the variance on measurement i


  weight: Implementation of the weighting kernel.
  getMap: return the smoothed map
  getIncluded: return a 1/0 bit vectors, listing which data points
              were included/clipped.
  writeFits: output the map to a fits file
  makeMap: calculate the smoothed map
  makeMapClip: Iteratively calculate map with sigma clipping
  init: Create the object
  cleanup: Destroy the object




  nicest: Overloads the weight method to implement Marco
          Lombardi's NICEST algorithm.

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