U Kv fßã@sdZddlZddd„ZdS)aœCollection of alternative implementations for time series analysis >>> signal.fftconvolve(x,x[::-1])[len(x)-1:len(x)+10]/x.shape[0] array([ 2.12286549e+00, 1.27450889e+00, 7.86898619e-02, -5.80017553e-01, -5.74814915e-01, -2.28006995e-01, 9.39554926e-02, 2.00610244e-01, 1.32239575e-01, 1.24504352e-03, -8.81846018e-02]) >>> sm.tsa.stattools.acovf(X, fft=True)[:order+1] array([ 2.12286549e+00, 1.27450889e+00, 7.86898619e-02, -5.80017553e-01, -5.74814915e-01, -2.28006995e-01, 9.39554926e-02, 2.00610244e-01, 1.32239575e-01, 1.24504352e-03, -8.81846018e-02]) >>> import nitime.utils as ut >>> ut.autocov(s)[:order+1] array([ 2.12286549e+00, 1.27450889e+00, 7.86898619e-02, -5.80017553e-01, -5.74814915e-01, -2.28006995e-01, 9.39554926e-02, 2.00610244e-01, 1.32239575e-01, 1.24504352e-03, -8.81846018e-02]) éNTcCsbddlm}t |¡}|r&|| ¡}| ||ddd…¡t|ƒdt|ƒd…|jddS)a_autocovariance function with call to fftconvolve, biased Parameters ---------- x : array_like timeseries, signal demean : bool If true, then demean time series Returns ------- acovf : ndarray autocovariance for data, same length as x might work for nd in parallel with time along axis 0 r)ÚsignalNéÿÿÿÿéé )ZscipyrÚnpZasarrayZmeanZ fftconvolveÚlenÚshape)ÚxZdemeanr©r úC/tmp/pip-unpacked-wheel-2v6byqio/statsmodels/sandbox/archive/tsa.pyÚ acovf_ffts    r )T)Ú__doc__Znumpyrr r r r r Ús