docs: def hist_1d_index(x, shape):
      """
      Fast 1d histogram of 1D indices with C++ inner loop optimization.
      Is more than 2 orders faster than np.histogram().
      The indices are given in coordinates and have to fit into a histogram of the dimensions shape.
      Parameters
      ----------
      x : array like
      shape : tuple
          tuple with x dimensions: (x,)
  
      Returns
      -------
      np.ndarray with given shape
  
      """
      if len(shape) != 1:
          raise InvalidInputError('The shape has to describe a 1-d histogram')
  
      # change memory alignment for c++ library
      x = np.ascontiguousarray(x.astype(np.int32))
      result = np.zeros(shape=shape, dtype=np.uint32)
      analysis_functions.hist_1d(x, shape[0], result)
      return result