def run_stv()

in monitoring/stv_tool.py [0:0]

• 20 lines of code
• 9 McCabe index (conditional complexity)

def run_stv(names: list, ordered_votes: list, num_seats: int):

  # Note: ORDERED_VOTES is a list of lists. The interior lists are a list
  # of candidate (human) NAMES, as ordered by the voter. The outer list is
  # simply all the votes that were recorded.

  # NOTE: files use labels such as "a" or "f", but this STV algorithm
  # works with the human names. Cuz why not.

  # NOTE: NAMES must be a list for repeatability purposes. It does not
  # need to obey any particular ordering rules, but when re-running
  # tallies, NAMES must be presented with the same ordering.

  assert len(set(names)) == len(names), "duplicates present!"
  assert num_seats > 0

  candidates = CandidateList(names)

  # name -> Candidate
  remap = dict((c.name, c) for c in candidates.l)

  # We can test that ordering of voters has no bearing. Perform runs
  # and alter the .seed() value.
  #ordered_votes = list(ordered_votes); random.seed(1); random.shuffle(ordered_votes); print('VOTE:', ordered_votes[0])

  # VOTES is a list of ordered-lists of Candidate objects
  votes = [[remap[n] for n in choices] for choices in ordered_votes]

  if candidates.count(ELECTED + HOPEFUL) <= num_seats:
    dbg('All candidates elected')
    candidates.change_state(HOPEFUL, ELECTED)
    return candidates

  quota = None  # not used on first pass
  iteration = 1
  while candidates.count(ELECTED) < num_seats:
    dbg('Iteration %d', iteration)
    iteration += 1
    quota = iterate_one(quota, votes, candidates, num_seats)
    candidates.reverse_random()

  dbg('All seats full')
  candidates.change_state(HOPEFUL, ELIMINATED)
  return candidates