code: def systematic_resample(weights):
      
      N = len(weights)
  
      # make N subdivisions, and choose positions with a consistent random offset
      positions = (random() + np.arange(N)) / N
  
      indexes = np.zeros(N, 'i')
      cumulative_sum = np.cumsum(weights)
      i, j = 0, 0
      while i < N:
          if positions[i] < cumulative_sum[j]:
              indexes[i] = j
              i += 1
          else:
              j += 1
      return indexes
docs: Performs the systemic resampling algorithm used by particle filters.
  
      This algorithm separates the sample space into N divisions. A single random
      offset is used to to choose where to sample from for all divisions. This
      guarantees that every sample is exactly 1/N apart.
  
      Parameters
      ----------
      weights : list-like of float
          list of weights as floats
  
      Returns
      -------
  
      indexes : ndarray of ints
          array of indexes into the weights defining the resample. i.e. the
          index of the zeroth resample is indexes[0], etc.