code: def mtie(data, rate=1.0, data_type="phase", taus=None):
      
      phase = input_to_phase(data, rate, data_type)
      (phase, m, taus_used) = tau_generator(phase, rate, taus)
      devs = np.zeros_like(taus_used)
      deverrs = np.zeros_like(taus_used)
      ns = np.zeros_like(taus_used)
  
      for idx, mj in enumerate(m):
          rw = mtie_rolling_window(phase, int(mj + 1))
          win_max = np.max(rw, axis=1)
          win_min = np.min(rw, axis=1)
          tie = win_max - win_min
          dev = np.max(tie)
          ncount = phase.shape[0] - mj
          devs[idx] = dev
          deverrs[idx] = dev / np.sqrt(ncount)
          ns[idx] = ncount
  
      return remove_small_ns(taus_used, devs, deverrs, ns)
docs: Maximum Time Interval Error.
  
      Parameters
      ----------
      data: np.array
          Input data. Provide either phase or frequency (fractional,
          adimensional).
      rate: float
          The sampling rate for data, in Hz. Defaults to 1.0
      data_type: {'phase', 'freq'}
          Data type, i.e. phase or frequency. Defaults to "phase".
      taus: np.array
          Array of tau values, in seconds, for which to compute statistic.
          Optionally set taus=["all"|"octave"|"decade"] for automatic
          tau-list generation.
  
      Notes
      -----
      this seems to correspond to Stable32 setting "Fast(u)"
      Stable32 also has "Decade" and "Octave" modes where the
      dataset is extended somehow?