class_docstr: Region-aware form of the sampling path.
  
  Uses region points to guess a likelihood contour gradient.
class_docstr_tok: ["Region-aware","form","of","the","sampling","path",".","Uses","region","points","to","guess","a","likelihood","contour","gradient","."]
docstr: Advance beyond the current path, extrapolate from the end point.
  
  Parameters
  -----------
  i: int
      index on path.
  
  returns
  --------
  coords: vector
      coordinates of the new point.
docstr_tok: ["Advance","beyond","the","current","path",",","extrapolate","from","the","end","point",".","Parameters","--","--","--","--","--","-","i",":","int","index","on","path",".","returns","--","--","--","--","coords",":","vector","coordinates","of","the","new","point","."]
code: def extrapolate(self, i):
      """Advance beyond the current path, extrapolate from the end point.
  
      Parameters
      -----------
      i: int
          index on path.
  
      returns
      --------
      coords: vector
          coordinates of the new point.
  
      """
      if i >= 0:
          j, xj, vj, Lj = max(self.samplingpath.points)
          deltai = i - j
          assert deltai > 0, ("should be extrapolating", i, j)
      else:
          j, xj, vj, Lj = min(self.samplingpath.points)
          deltai = i - j
          assert deltai < 0, ("should be extrapolating", i, j)
  
      newpoint = extrapolate_ahead(deltai, xj, vj, contourpath=self)
      return newpoint
  
imports: import numpy
  from numpy.linalg import norm
  import matplotlib.pyplot
label_desc: Use image node_id 4 for calling the ContourSamplingPath obj's underlying member method code with example usage: obj.extrapolate(i) and returns: newpoint