code: def multiifo_noise_coinc_rate(rates, slop):
      
      ifos = numpy.array(sorted(rates.keys()))
      rates_raw = list(rates[ifo] for ifo in ifos)
      expected_coinc_rates = {}
  
      # Calculate coincidence for all-ifo combination
      # multiply product of trigger rates by the overlap time
      allowed_area = multiifo_noise_coincident_area(ifos, slop)
      rateprod = [numpy.prod(rs) for rs in zip(*rates_raw)]
      ifostring = ' '.join(ifos)
      expected_coinc_rates[ifostring] = allowed_area * numpy.array(rateprod)
  
      # if more than one possible coincidence type exists,
      # calculate coincidence for subsets through recursion
      if len(ifos) > 2:
          # Calculate rate for each 'miss-one-out' detector combination
          subsets = itertools.combinations(ifos, len(ifos) - 1)
          for subset in subsets:
              rates_subset = {}
              for ifo in subset:
                  rates_subset[ifo] = rates[ifo]
              sub_coinc_rates = multiifo_noise_coinc_rate(rates_subset, slop)
              # add these sub-coincidences to the overall dictionary
              for sub_coinc in sub_coinc_rates:
                  expected_coinc_rates[sub_coinc] = sub_coinc_rates[sub_coinc]
  
      return expected_coinc_rates
docs: Calculate the expected rate of noise coincidences for multiple detectors
  
      Parameters
      ----------
      rates: dict
          Dictionary keyed on ifo string
          Value is a sequence of single-detector trigger rates, units assumed
          to be Hz
      slop: float
          time added to maximum time-of-flight between detectors to account
          for timing error
  
      Returns
      -------
      expected_coinc_rates: dict
          Dictionary keyed on the ifo combination string
          Value is expected coincidence rate in the combination, units Hz