code: def lpc(blk, order=None):
    
    if order < 100:
      return lpc.nautocor(blk, order)
    try:
      return lpc.kautocor(blk, order)
    except ParCorError:
      return lpc.nautocor(blk, order)
docs: Find the Linear Predictive Coding (LPC) coefficients as a ZFilter object,
    the analysis whitening filter. This implementation uses the autocorrelation
    method, using the Levinson-Durbin algorithm or Numpy pseudo-inverse for
    linear system solving, when needed.
  
    Parameters
    ----------
    blk :
      An iterable with well-defined length. Don't use this function with Stream
      objects!
    order :
      The order of the resulting ZFilter object. Defaults to ``len(blk) - 1``.
  
    Returns
    -------
    A FIR filter, as a ZFilter object. The mean squared error over the given
    block is in its "error" attribute.
  
    Hint
    ----
    See ``lpc.kautocor`` example, which should apply equally for this strategy.
  
    See Also
    --------
    levinson_durbin :
      Levinson-Durbin algorithm for solving Yule-Walker equations (Toeplitz
      matrix linear system).
    lpc.nautocor:
      LPC coefficients from linear system solved with Numpy pseudo-inverse.
    lpc.kautocor:
      LPC coefficients obtained with Levinson-Durbin algorithm.