ftj dZddlZddlmZdgZiddgddgddgd dgd d gd d gd gd dd gdd gddgdgddgddgdddgdgdddgddggddgdgdgdggdd ggd!gd"d#gd$ggd%d&ggd'd(Zd*d)ZdS)+zTools for MLS generationN)_max_len_seq_inner max_len_seq)r r r )rrr )rrr )rrr)rrr)rrr)rr))rrr)r r!)r)rrrrrrrrrr!r r#r" ctjjdkr tjn tj}||t vrrtjtt }td| d| dtjt ||}ntj tj||ddd}tj |dks#tj ||ks |jdkrtd tj|}d |zdz }||}n$t|}|dkrtd |"tj|tjd }n9tj|t$d tj}|jdks |j|krtdtj|dkrtdtj|tjd }t/|||||}||fS)a} 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: https://web.archive.org/web/20181001062252/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 numpy as np >>> 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() rNznbits must be between z and z if taps is NonerrzEtaps must be non-empty with values between zero and nbits (inclusive)rz)length must be greater than or equal to 0c)dtypeorderz'state must be a 1-D array of size nbitszstate must not be all zeros)npintpitemsizeint32int64 _mls_tapsarraylistkeys ValueErrorminmaxuniqueanysizeintonesint8boolastypendimallemptyr)nbitsstatelengthtaps taps_dtype known_tapsn_maxseqs W/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/scipy/signal/_max_len_seq.pyrrsFf WYY/144"(J |  ! !$y~~'7'7"8"899JCjnn6F6FCC * 0 0CCCDD Dx %(*55y$ 3344TTrT: 6$(   ;rvdUl33 ;ty1}}:;; ;x~~ XNE ~V A::HII I }RWC888d#666==bgFF zQ%*--BCCC veqj86777 (6 4 4 4C tUE63 ? ?E :)NNN)__doc__numpyr*r__all__r/rrJrIrOs222222 / )Q )Q )Q )Q )Q )Q )Q  )  )! ) 1# )');;; )8:KKK )  ) 2$ )(*KKK )9;bT )" )(<