def acorr_lpc(x, axis=-<NUM_LIT:1>):
if not np.isrealobj(x):<EOL><INDENT>raise ValueError("<STR_LIT>")<EOL><DEDENT>maxlag = x.shape[axis]<EOL>nfft = int(<NUM_LIT:2> ** nextpow2(<NUM_LIT:2> * maxlag - <NUM_LIT:1>))<EOL>if axis != -<NUM_LIT:1>:<EOL><INDENT>x = np.swapaxes(x, -<NUM_LIT:1>, axis)<EOL><DEDENT>a = _acorr_last_axis(x, nfft, maxlag)<EOL>if axis != -<NUM_LIT:1>:<EOL><INDENT>a = np.swapaxes(a, -<NUM_LIT:1>, axis)<EOL><DEDENT>return a<EOL>
Compute autocorrelation of x along the given axis.

    This compute the biased autocorrelation estimator (divided by the size of
    input signal)

    Notes
    -----
        The reason why we do not use acorr directly is for speed issue.