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
class_docstr_tok: ["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","\u017dalik",",","B.","Sweep\u2010line","algorithm","for","constrained","Delaunay","triangulation"]
code: def _distances_from_line(self, edge, points):
      # Distance of a set of points from a given line
      e1 = self.pts[edge[0]]
      e2 = self.pts[edge[1]]
      distances = []
      for i in points:
          p = self.pts[i]
          proj = self._projection(e1, p, e2)
          distances.append(((p - proj) ** 2).sum() ** 0.5)
      assert distances[0] == 0 and distances[-1] == 0
      return distances
  
imports: from __future__ import division, print_function
  from itertools import permutations
  import numpy
  from collections import OrderedDict
label_desc: Use image node_id 19 for calling the Triangulation obj's underlying member method code with example usage: obj._distances_from_line(edge, points) and returns: distances