code: def in1d_events(ar1, ar2):
      
      ar1 = np.ascontiguousarray(ar1)  # change memory alignement for c++ library
      ar2 = np.ascontiguousarray(ar2)  # change memory alignement for c++ library
      tmp = np.empty_like(ar1, dtype=np.uint8)  # temporary result array filled by c++ library, bool type is not supported with cython/numpy
      return analysis_functions.get_in1d_sorted(ar1, ar2, tmp)
docs: Does the same than np.in1d but uses the fact that ar1 and ar2 are sorted and the c++ library. Is therefore much much faster.