# create MST over a created graph
  q = []
  for i in range(len(points)):
      for j in range(i+1, len(points)):
          dis = abs(points[i][0]-points[j][0]) + abs(points[i][1] - points[j][1])
          q.append((dis, i, j))
  
  # MST search algorithm Kruskal
  def find(x):
    """ find connected parent """
    if (x != parent[x]):
        parent[x] = find(parent[x])
    return parent[x]

  def union(x, y):
    """ merge two sets to the bigger one"""
    if size[x] > size[y]:
        size[x] += size[y]
        parent[y] = x
    else:
        size[y] += size[x]
        parent[x] = y
          
  n = len(points)
  parent = [i for i in range(n+1)]  # [0, n]
  size = [1 for _ in range(n+1)]    # [0, n]
  q.sort()  # sort edges
  res = 0
  count = 0
  for w, u, v in q:
      rA, rB = find(u), find(v)
      if rA == rB:
          continue
      union(rA, rB)
      res += w
      # Optimize so that we don't traverse all edges
      count += 1
      if count == n:
          return res
  return res 

	# MST search algorithm Prim
	G = collections.defaultdict(list)
	for i in range(len(points)):
		for j in range(i+1, len(points)):
			dis = abs(points[i][0]-points[j][0]) + abs(points[i][1] - points[j][1])
			G[i].append((dis, j))
			G[j].append((dis, i))

	visited = {0}
	pq = G[0]
	heapq.heapify(pq)
	res = 0
	while len(visited) < len(points) and pq:
		w, v = heapq.heappop(pq)
		if v not in visited:
			res += w
			visited.add(v)
			for w, nei in G[v]:
				if nei not in visited:
					heapq.heappush(pq, (w,nei))
	return res