new_contents: import operator
  import collections
  from itertools import groupby
  
  
  class AnalyzerError(Exception):
      pass
  
  
  def decide_optimal_candidate(candidates):
      """Take a dictionary of candidate alphabets and their associated
      accumulative lengths and decide which candidate alphabet is the best
      (minimum) with what confidence.
  
      Returns a pair with the decision. The first element of the pair is which
      candidate alphabet is best; the second element is the confidence level for
      the decision.
      """
  
      # sort sampleset groups by length
      sorted_candidate_lengths = sorted(
          candidates.items(),
          key=operator.itemgetter(1)
      )
  
      # Extract candidate with minimum length and the next best competitor
      # candidate. In case of binary search, these will be the only two
      # candidates.
      min_candidate = sorted_candidate_lengths[0]
      next_best_candidate = sorted_candidate_lengths[1]
  
      min_candidate_length = min_candidate[1]
      min_candidate_alphabet = min_candidate[0]
  
      next_best_length = next_best_candidate[1]
  
      # extract a confidence value for our decision based on the second-best candidate
      confidence = float(next_best_length - min_candidate_length) / next_best_length
  
      return min_candidate_alphabet, confidence
  
  
  def decide_next_world_state(samplesets):
      """Take a list of samplesets and extract a decision for a state transition
      with some confidence.
  
      Argument:
      samplesets -- a list of samplesets.
  
      This list must must contain at least two elements so that we have some basis
      for comparison. Each of the list's elements must share the same world state
      (knownsecret and knownalphabet) so that we are comparing on the same basis.
      The samplesets must contain at least two different candidate alphabets so
      that a decision can be made. It can contain multiple samplesets collected
      over the same candidate alphabet.
  
      Returns a pair with the decision. The first element of the pair is the new
      state of the world; the second element of the pair is the confidence with
      which the analyzer is suggesting the state transition.
      """
      # Ensure we have enough sample sets to compare.
      assert(len(samplesets) > 1)
  
      # Ensure all samplesets are extending the same known state
      knownsecret = samplesets[0].knownsecret
      knownalphabet = samplesets[0].knownalphabet
      victim = samplesets[0].victim
      target = victim.target
      for sampleset in samplesets:
          if sampleset.knownsecret != knownsecret:
              raise AnalyzerError('samplesets do not share the same world state')
          if sampleset.knownalphabet != knownalphabet:
              raise AnalyzerError('samplesets do not share the same world state')
          if sampleset.victim != victim:
              raise AnalyzerError('samplesets do not share the same victim')
  
      # Split samplesets based on alphabetvector under consideration
      # and accumulate data lengths for each candidate.
      candidate_lengths = collections.defaultdict(lambda: 0)
      for sampleset in samplesets:
          candidate_lengths[sampleset.candidatealphabet] += len(sampleset.data)
  
      # Ensure we have a decision to make
      assert(len(candidate_lengths) > 1)
  
      min_vector, confidence = decide_optimal_candidate(candidate_lengths)
  
      # use minimum group's alphabet vector
      decision_knownalphabet = min_vector
      # known secret remains the same as in all current samplesets
      decision_knownsecret = knownsecret
  
      if len(decision_knownalphabet) == 1:
          # decision vector was one character, so we can extend the known secret
          decision_knownsecret += decision_knownalphabet
          decision_knownalphabet = target.alphabet
  
      state = {
          "knownsecret": decision_knownsecret,
          "knownalphabet": decision_knownalphabet
      }
  
      return state, confidence
  
subject: Add first version of analyzer