text: import matplotlib.pyplot as plt
  
  def plotTSP(paths, points, num_iters=1):
  
      """
      path: List of lists with the different orders in which the nodes are visited
      points: coordinates for the different nodes
      num_iters: number of paths that are in the path list
  
      """
  
      # Unpack the primary TSP path and transform it into a list of ordered
      # coordinates
  
      x = []; y = []
      for i in paths[0]:
          x.append(points[i][0])
          y.append(points[i][1])
  
      plt.plot(x, y, 'co')
  
      # Set a scale for the arrow heads (there should be a reasonable default for this, WTF?)
      a_scale = float(max(x))/float(100)
  
      # Draw the older paths, if provided
      if num_iters > 1:
  
          for i in range(1, num_iters):
  
              # Transform the old paths into a list of coordinates
              xi = []; yi = [];
              for j in paths[i]:
                  xi.append(points[j][0])
                  yi.append(points[j][1])
  
              plt.arrow(xi[-1], yi[-1], (xi[0] - xi[-1]), (yi[0] - yi[-1]),
                      head_width = a_scale, color = 'r',
                      length_includes_head = True, ls = 'dashed',
                      width = 0.001/float(num_iters))
              for i in range(0, len(x) - 1):
                  plt.arrow(xi[i], yi[i], (xi[i+1] - xi[i]), (yi[i+1] - yi[i]),
                          head_width = a_scale, color = 'r', length_includes_head = True,
                          ls = 'dashed', width = 0.001/float(num_iters))
  
      # Draw the primary path for the TSP problem
      plt.arrow(x[-1], y[-1], (x[0] - x[-1]), (y[0] - y[-1]), head_width = a_scale,
              color ='g', length_includes_head=True)
      for i in range(0,len(x)-1):
          plt.arrow(x[i], y[i], (x[i+1] - x[i]), (y[i+1] - y[i]), head_width = a_scale,
                  color = 'g', length_includes_head = True)
  
      #Set axis too slitghtly larger than the set of x and y
      plt.xlim(min(x)*1.1, max(x)*1.1)
      plt.ylim(min(y)*1.1, max(y)*1.1)
      plt.show()
  