ó ˽÷Xc@s¿dZddlZddlmZdgZidgd6dgd6dgd6dgd 6d gd 6d gd 6d d dgd 6d gd 6d gd6d gd6dddgd6ddd gd6dddgd6dgd6dddgd6dgd6dgd6dddgd6dgd6dgd6dgd6dgd6dddgd6dgd6dddgd6dddgd6dgd 6dgd!6d!d d gd"6d gd#6d#d"dgd$6Zdddd%„ZdS(&sTools for MLS generationiÿÿÿÿNi(t_max_len_seq_innert max_len_seqiiiiiiii i i i i iiiiiiiiiiiiiiiiiii c Cs%|dkrt|tkrXtjttjƒƒƒ}td|jƒ|jƒfƒ‚ntjt|tj ƒ}n‚tj tj|tj ƒƒddd…}tj |dkƒsØtj ||kƒsØ|j dkrçtdƒ‚ntj |ƒ}d|d}|dkr|}n't|ƒ}|dkr@tdƒ‚n|dkrmtj|d tjd d ƒ}n'tj|d td d ƒjtjƒ}|jdks²|j |krÁtd ƒ‚ntj|dkƒråtd ƒ‚ntj|d tjd d ƒ}t|||||ƒ}||fS(sD Maximum length sequence (MLS) generator. Parameters ---------- nbits : int Number of bits to use. Length of the resulting sequence will be ``(2**nbits) - 1``. Note that generating long sequences (e.g., greater than ``nbits == 16``) can take a long time. state : array_like, optional If array, must be of length ``nbits``, and will be cast to binary (bool) representation. If None, a seed of ones will be used, producing a repeatable representation. If ``state`` is all zeros, an error is raised as this is invalid. Default: None. length : int, optional Number of samples to compute. If None, the entire length ``(2**nbits) - 1`` is computed. taps : array_like, optional Polynomial taps to use (e.g., ``[7, 6, 1]`` for an 8-bit sequence). If None, taps will be automatically selected (for up to ``nbits == 32``). Returns ------- seq : array Resulting MLS sequence of 0's and 1's. state : array The final state of the shift register. Notes ----- The algorithm for MLS generation is generically described in: https://en.wikipedia.org/wiki/Maximum_length_sequence The default values for taps are specifically taken from the first option listed for each value of ``nbits`` in: http://www.newwaveinstruments.com/resources/articles/ m_sequence_linear_feedback_shift_register_lfsr.htm .. versionadded:: 0.15.0 Examples -------- MLS uses binary convention: >>> from scipy.signal import max_len_seq >>> max_len_seq(4)[0] array([1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0], dtype=int8) MLS has a white spectrum (except for DC): >>> import matplotlib.pyplot as plt >>> from numpy.fft import fft, ifft, fftshift, fftfreq >>> seq = max_len_seq(6)[0]*2-1 # +1 and -1 >>> spec = fft(seq) >>> N = len(seq) >>> plt.plot(fftshift(fftfreq(N)), fftshift(np.abs(spec)), '.-') >>> plt.margins(0.1, 0.1) >>> plt.grid(True) >>> plt.show() Circular autocorrelation of MLS is an impulse: >>> acorrcirc = ifft(spec * np.conj(spec)).real >>> plt.figure() >>> plt.plot(np.arange(-N/2+1, N/2+1), fftshift(acorrcirc), '.-') >>> plt.margins(0.1, 0.1) >>> plt.grid(True) >>> plt.show() Linear autocorrelation of MLS is approximately an impulse: >>> acorr = np.correlate(seq, seq, 'full') >>> plt.figure() >>> plt.plot(np.arange(-N+1, N), acorr, '.-') >>> plt.margins(0.1, 0.1) >>> plt.grid(True) >>> plt.show() s/nbits must be between %s and %s if taps is NoneNiÿÿÿÿiisEtaps must be non-empty with values between zero and nbits (inclusive)is)length must be greater than or equal to 0tdtypetordertcs1state must be a 1-dimensional array of size nbitssstate must not be all zeros(tNonet _mls_tapstnptarraytlisttkeyst ValueErrortmintmaxtintptuniquetanytsizetascontiguousarraytinttonestint8tbooltastypetndimtalltemptyR(tnbitststatetlengthttapst known_tapstn_maxtseq((s8/tmp/pip-build-7oUkmx/scipy/scipy/signal/_max_len_seq.pyRs4S  +9     !'(t__doc__tnumpyRRt__all__RRR(((s8/tmp/pip-build-7oUkmx/scipy/scipy/signal/_max_len_seq.pyts  O>4844