a ùcã@sÄdZddlZddlmZddlmZmZmZm Z Gdd„deƒZ ddd„Z e d krÀdd l mZddlmZejed d ƒded d ƒdfZe ddd¡Ze eedZe ¡ejdejjddS)z# Linear Time Frequency Processing. éN)ÚBaseTFRepresentation)Ú nearest_oddÚdividerÚmoduloÚizakcs<eZdZdZdZd ‡fdd„ Zdd„Zd‡fd d „ Z‡ZS)ÚShortTimeFourierTransformzShort time Fourier transform.ZstftNcstt|ƒj||||ddS)a}Create a ShortTimeFourierTransform object. :param signal: Signal to be analyzed. :param timestamps: Time instants of the signal (default: ``np.arange(len(signal))``) :param n_fbins: Number of frequency bins (default: ``len(signal)``) :param fwindow: Frequency smoothing window (default: Hamming window of length ``len(signal) / 4``) :type signal: array-like :type timestamps: array-like :type n_fbins: int :type fwindow: array-like :return: ShortTimeFourierTransform object :Example: >>> from tftb.generators import fmconst >>> sig = np.r_[fmconst(128, 0.2)[0], fmconst(128, 0.4)[0]] >>> stft = ShortTimeFourierTransform(sig) >>> tfr, t, f = stft.run() >>> stft.plot() #doctest: +SKIP .. plot:: docstring_plots/processing/stft.py )ÚsignalÚn_fbinsÚ timestampsÚfwindowN)ÚsuperrÚ__init__)Úselfrr r r ©Ú __class__©úc/nfs/NAS7/SABIOD/METHODE/ermites/ermites_venv/lib/python3.9/site-packages/tftb/processing/linear.pyr s  ýz"ShortTimeFourierTransform.__init__c Csd|jjddd}tt|jdƒ|gƒ}tjtj|t |jj¡t  |jjd¡dfdd  t ¡ }tjtj|t |jj¡|j jdt  |jjd¡fdd  t ¡}t  |j¡}t|jjdƒD]r}||}||}t  ||d¡  t ¡} t |j| |j¡} |j || d  t ¡|||   t ¡|j| |f<qÌtjj|jdd|_|j|j|jfS)zCompute the STFT according to: .. math:: X[m, w] = \sum_{n=-\infty}^{\infty}x[n]w[n - m]e^{-j\omega n} Where :math:`w` is a Hamming window.rééç@©Zaxis)r ÚshapeÚminÚroundr ÚnpZc_ZonesÚtsÚarangeÚastypeÚintrZconjÚrangeÚtfrÚ remainderÚfftÚfreqs) rZlhZrangeminZstartsZendsZ conj_fwindowZicolÚstartÚendÚtauÚindexrrrÚrun4s6ÿþþÿþþ ÿzShortTimeFourierTransform.runÚcmapTçš™™™™™©?c s”|jdt|jdƒ…dd…f|_|jdt|jdƒ…|_|rRt |j¡d|_t |j¡|}d|j|j|k<tt|ƒj f|||dœ|¤ŽdS)aÏDisplay the spectrogram of an STFT. :param ax: axes object to draw the plot on. If None(default), one will be created. :param kind: Choice of visualization type, either "cmap"(default) or "contour". :param sqmod: Whether to take squared modulus of TFR before plotting. (Default: True) :param threshold: Percentage of the maximum value of the TFR, below which all values are set to zero before plotting. :param **kwargs: parameters passed to the plotting function. :type ax: matplotlib.axes.Axes object :type kind: str :type sqmod: bool :type threshold: float :return: None :rtype: None Nrrg)ÚaxÚkindÚ threshold) r rr r#rÚabsZamaxr rÚplot)rr+r,Zsqmodr-ÚkwargsZ _thresholdrrrr/Ms"ÿþzShortTimeFourierTransform.plot)NNN)Nr)Tr*) Ú__name__Ú __module__Ú __qualname__Ú__doc__Únamer r(r/Ú __classcell__rrrrrs rc Cs |durt|jdƒ\}}|dur.t|ƒ\}}|durlt t d¡t ddt|ƒ¡d¡}|tj |¡}t ||jdt |ƒƒ}t |jdt |ƒƒ}t |jdt |ƒƒ}|jd}|ddkrÐt dƒ‚t  d|jdt |ƒd|d|¡} t  |jdf¡} t  |jd|dd¡| } t  |jd|dd¡| } || t | d| ¡ t ¡<| jt |ƒt |ƒdd } tjj| jdd t |¡}t  ||f¡}tjd|dt d }t|ƒD]D}t|||||ƒ t ¡}|t ||ddd…f¡d7}qÆd||t d¡k<||}t t|ƒ¡|jd}tj |jd|ftd }t d|jdd¡}t|ƒD]X}t|||||jdƒ t ¡d}tjj|tj ||¡dd |dd…|f<qt|tj|jd|d  t ¡dd…f}t |¡d}|||fS) aÒCompute the Gabor representation of a signal. :param signal: Singal to be analyzed. :param n_coeff: number of Gabor coefficients in time. :param q_oversample: Degree of oversampling :param window: Synthesis window :type signal: array-like :type n_coeff: integer :type q_oversample: int :type window: array-like :return: Tuple of Gabor coefficients, biorthogonal window associated with the synthesis window. :rtype: tuple Nrg{®Gázt?éÿÿÿÿrrz.The window function should have an odd length.ÚF)Úorderr)Zdtype)Ústep)rrrÚexpÚlogÚlinspacerZlinalgZnormrÚfloatÚ ValueErrorrÚzerosrrZreshaper"ÚTÚsqrtrrr.ÚspacingÚrealrÚcomplexZfftshift)rZn_coeffZ q_oversampleZwindowÚ_ÚmZmbÚnbZnhÚalphaZhn1r$r%ZmsigZdzthZmzhÚxÚlÚmodZdztgamZgamZdgrn1ÚkÚnr'Zdgrr rrrÚgaborjsH &  ,   & &0&rOÚ__main__)Úfmconsté€gš™™™™™É?gš™™™™™Ù?ré)r T)Zshow_tfr))NNN)r4ÚnumpyrZtftb.processing.baserZ tftb.utilsrrrrrrOr1Ztftb.generatorsrQZmatplotlib.pyplotZpyplotZpltZr_Úsigr=rr r(r/ÚcmZviridisrrrrÚ s X <  "