class_docstr: Constrained delaunay triangulation
  
  Implementation based on [1]_.
  
  Parameters
  ----------
  pts : array
      Nx2 array of points.
  edges : array
      Nx2 array of edges (dtype=int).
  
  Notes
  -----
  * Delaunay legalization is not yet implemented. This produces a proper
    triangulation, but adding legalisation would produce fewer thin
    triangles.
  * The pts and edges arrays may be modified.
  
  References
  ----------
  .. [1] Domiter, V. and Žalik, B. Sweep‐line algorithm for constrained
     Delaunay triangulation
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docstr: Returns +1 if edge[0]->point is clockwise from edge[0]->edge[1],
  -1 if counterclockwise, and 0 if parallel.
docstr_tok: ["Returns","+1","if","edge","[","0","]","-",">","point","is","clockwise","from","edge","[","0","]","-",">","edge","[","1","]",",","-1","if","counterclockwise",",","and","0","if","parallel","."]
code: def _orientation(self, edge, point):
      """Returns +1 if edge[0]->point is clockwise from edge[0]->edge[1],
      -1 if counterclockwise, and 0 if parallel.
      """
      v1 = self.pts[point] - self.pts[edge[0]]
      v2 = self.pts[edge[1]] - self.pts[edge[0]]
      c = np.cross(v1, v2)  # positive if v1 is CW from v2
      return 1 if c > 0 else (-1 if c < 0 else 0)
  
imports: from __future__ import division, print_function
  from itertools import permutations
  import numpy
  from collections import OrderedDict
label_desc: Use image node_id 25 for calling the Triangulation obj's underlying member method code with example usage: obj._orientation(edge, point) and returns: unknown