}Kg dZddlmZddlmZddlZddlZddlm Z ddl m Z ddlmZddlmZddlmZddlmZdd lmZmZdd lmZdd lmZmZmZm Z m!Z!m"Z"m#Z#dd l$m%Z%er$ddlZdd l&m'Z'm(Z(ddl)m*Z*ddl+m,Z-ddl.m/Z/ddl0m1Z1gdZ2GddejfZ4GddejfZ5GddejfZ6GddejfZ7GddejfZ8GddejfZ9Gdd ejfZ:Gd!d"ejfZ;Gd#d$ejfZ<Gd%d&Z=d'd(d)d*d+ dSd,Z>d-Z?d.Z@d/ZAd0ZBd1ZCd2ZDd3ZEeFeEDcgc]}||fc}ee@e@Dcgc]}|c}zeeAeADcgc]}|c}zeeBeBDcgc]}|c}zeeCeCDcgc]}|c}zeeDeDDcgc]}|c}zZGddddd4d5ddddd6d7d8d9d:dddd'd;d'dddd< dTd=ZHd>ZId?ZJdUd@ZKdAZL dVdBZM dW dXdCZN dY dZdDZO d[ d\dEZP d] d^dFZQd_d`dGZR da dbdHZS dc dddIZTdedJZU da dbdKZVdLZWd4dMdNd8dOdPdd;dedQ dfdRZXycc}wcc}wcc}wcc}wcc}wcc}w)ga Display ======= Data visualization ------------------ .. autosummary:: :toctree: generated/ specshow waveshow Axis formatting --------------- .. autosummary:: :toctree: generated/ TimeFormatter NoteFormatter SvaraFormatter FJSFormatter LogHzFormatter ChromaFormatter ChromaSvaraFormatter ChromaFJSFormatter TonnetzFormatter Miscellaneous ------------- .. autosummary:: :toctree: generated/ cmap AdaptiveWaveplot ) annotations)productN) colormaps)core)util) rename_kw Deprecated)ParameterError) TYPE_CHECKINGAny CollectionOptionalUnionCallableDict) _FloatLike_co)QuadMeshPolyCollection)Line2D)Path) MarkerStyle)Colormap) specshowwaveshowcmap TimeFormatter NoteFormatter FJSFormatterLogHzFormatterChromaFormatterChromaSvaraFormatterChromaFJSFormatterTonnetzFormatterAdaptiveWaveplotc$eZdZdZdddZdddZy) ruqA tick formatter for time axes. Automatically switches between seconds, minutes:seconds, or hours:minutes:seconds. Parameters ---------- lag : bool If ``True``, then the time axis is interpreted in lag coordinates. Anything past the midpoint will be converted to negative time. unit : str or None Abbreviation of the string representation for axis labels and ticks. List of supported units: * `"h"`: hour-based format (`H:MM:SS`) * `"m"`: minute-based format (`M:SS`) * `"s"`: second-based format (`S.sss` in scientific notation) * `"ms"`: millisecond-based format (`s.µµµ` in scientific notation) * `None`: adaptive to the duration of the underlying time range: similar to `"h"` above 3600 seconds; to `"m"` between 60 and 3600 seconds; to `"s"` between 1 and 60 seconds; and to `"ms"` below 1 second. See Also -------- matplotlib.ticker.Formatter Examples -------- For normal time >>> import matplotlib.pyplot as plt >>> times = np.arange(30) >>> values = np.random.randn(len(times)) >>> fig, ax = plt.subplots() >>> ax.plot(times, values) >>> ax.xaxis.set_major_formatter(librosa.display.TimeFormatter()) >>> ax.set(xlabel='Time') Manually set the physical time unit of the x-axis to milliseconds >>> times = np.arange(100) >>> values = np.random.randn(len(times)) >>> fig, ax = plt.subplots() >>> ax.plot(times, values) >>> ax.xaxis.set_major_formatter(librosa.display.TimeFormatter(unit='ms')) >>> ax.set(xlabel='Time (ms)') For lag plots >>> times = np.arange(60) >>> values = np.random.randn(len(times)) >>> fig, ax = plt.subplots() >>> ax.plot(times, values) >>> ax.xaxis.set_major_formatter(librosa.display.TimeFormatter(lag=True)) >>> ax.set(xlabel='Lag') NcD|dvrtd|||_||_y)N)hmsmsNzUnknown time unit: )r unitlag)selfr-r,s S/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/librosa/display.py__init__zTimeFormatter.__init__s, 2 2 #6tf!=> > c |jj\}}|jj\}}|jr)||dzk\r!||kDryt j ||z }d}n|}d}|j dk(s|j e||z dkDr]djt|dz tt j|d z d tt j|d } n|j d k(s|j D||z d kDr>> import matplotlib.pyplot as plt >>> values = librosa.midi_to_hz(np.arange(48, 72)) >>> fig, ax = plt.subplots(nrows=2) >>> ax[0].bar(np.arange(len(values)), values) >>> ax[0].set(ylabel='Hz') >>> ax[1].bar(np.arange(len(values)), values) >>> ax[1].yaxis.set_major_formatter(librosa.display.NoteFormatter()) >>> ax[1].set(ylabel='Note') c<||_||_||_||_yrJ)octavemajorkeyunicode)r.rXrYrZr[s r/r0zNoteFormatter.__init__s    r1Nc|dkry|jj\}}|js|dtd|zkDry|dtd|zk}t j ||j ||j|jS)Apply the formatter to positionrr4rrXcentsrZr[) r7r9rYmaxr hz_to_noterXrZr[)r.r?r@rCrDras r/rGzNoteFormatter.__call__s} 6YY002 dzzdQQ%55C4L(( dkkDHHdll  r1)TTC:majT)rXrHrYrHrZrOr[rHrJrKrPrUr1r/rrsF%R       r1rcDeZdZdZ d ddZdddZy) SvaraFormatteraTicker formatter for Svara Parameters ---------- octave : bool If ``True``, display the octave number along with the note name. Otherwise, only show the note name (and cent deviation) major : bool If ``True``, ticks are always labeled. If ``False``, ticks are only labeled if the span is less than 2 octaves Sa : number > 0 Frequency (in Hz) of Sa mela : str or int For Carnatic svara, the index or name of the melakarta raga in question To use Hindustani svara, set ``mela=None`` unicode : bool If ``True``, use unicode symbols for accidentals. If ``False``, use ASCII symbols for accidentals. See Also -------- NoteFormatter matplotlib.ticker.Formatter librosa.hz_to_svara_c librosa.hz_to_svara_h Examples -------- >>> import matplotlib.pyplot as plt >>> values = librosa.midi_to_hz(np.arange(48, 72)) >>> fig, ax = plt.subplots(nrows=2) >>> ax[0].bar(np.arange(len(values)), values) >>> ax[0].set(ylabel='Hz') >>> ax[1].bar(np.arange(len(values)), values) >>> ax[1].yaxis.set_major_formatter(librosa.display.SvaraFormatter(261)) >>> ax[1].set(ylabel='Note') Ncr| td||_||_||_||_||_||_y)Nz5Sa frequency is required for svara display formatting)r SarXrYabbrmelar[)r.rhrXrYrirjr[s r/r0zSvaraFormatter.__init__1sE : G      r1c|dkry|jj\}}|js|dtd|zkDry|jBt j ||j|j|j|jSt j||j|j|j|j|jS)Nrr4r^rrhrXrir[rhrjrXrir[) r7r9rYrbrjr hz_to_svara_hrhrXrir[ hz_to_svara_cr.r?r@rCrDs r/rGzSvaraFormatter.__call__Fs 6YY002 dzzdQQ%55 99 %%dggdkk 4<< %%77YY{{YY  r1)TTFNT) rhrLrXrHrYrHrirHrjOptional[Union[str, int]]r[rHrJrKrPrUr1r/rfrfs]-d*.     ( *r1rfcFeZdZdZdddd ddZdd dZy) raTicker formatter for Functional Just System (FJS) notation Parameters ---------- fmin : float The unison frequency for this axis intervals : str or array of float in [1, 2) The interval specification for the frequency axis. See `core.interval_frequencies` for supported values. major : bool If ``True``, ticks are always labeled. If ``False``, ticks are only labeled if the span is less than 2 octaves unison : str The unison note name. If not provided, it will be inferred from fmin. unicode : bool If ``True``, use unicode symbols for accidentals. If ``False``, use ASCII symbols for accidentals. See Also -------- NoteFormatter hz_to_fjs matplotlib.ticker.Formatter Examples -------- >>> import matplotlib.pyplot as plt >>> values = librosa.midi_to_hz(np.arange(48, 72)) >>> fig, ax = plt.subplots(nrows=2) >>> ax[0].bar(np.arange(len(values)), values) >>> ax[0].set(ylabel='Hz') >>> ax[1].bar(np.arange(len(values)), values) >>> ax[1].yaxis.set_major_formatter(librosa.display.NoteFormatter()) >>> ax[1].set(ylabel='Note') TN)rYunisonr[c||_||_||_||_||_||_||_tj|||||_ y)Nfmin intervalsbins_per_octave) rvrYrsr[rwn_binsrxrinterval_frequencies frequencies_)r.rvryrxrwrYrsr[s r/r0zFJSFormatter.__init__sR    " . 55 O r1c~|dkry|jj\}}|js|dtd|zkDryt j t j||jd}tj|j||j|j|j}|S)r]rr4r^r)rvrsr[)r7r9rYrbr match_eventsr: atleast_1dr{r hz_to_fjsrvrsr[)r.r?r@rCrDidxlabels r/rGzFJSFormatter.__call__s 6YY002 dzzdQQ%55 a 0$2C2CDQG^^   c ";;LL    r1)rvr=ryr=rxr=rwUnion[str, Collection[float]]rYrHrsrIr[rHrJrKrPrUr1r/rr_sc)d $      1     ,r1rc$eZdZdZdddZdddZy) r aTicker formatter for logarithmic frequency Parameters ---------- major : bool If ``True``, ticks are always labeled. If ``False``, ticks are only labeled if the span is less than 2 octaves See Also -------- NoteFormatter matplotlib.ticker.Formatter Examples -------- >>> import matplotlib.pyplot as plt >>> values = librosa.midi_to_hz(np.arange(48, 72)) >>> fig, ax = plt.subplots(nrows=2) >>> ax[0].bar(np.arange(len(values)), values) >>> ax[0].yaxis.set_major_formatter(librosa.display.LogHzFormatter()) >>> ax[0].set(ylabel='Hz') >>> ax[1].bar(np.arange(len(values)), values) >>> ax[1].yaxis.set_major_formatter(librosa.display.NoteFormatter()) >>> ax[1].set(ylabel='Note') c||_yrJrY)r.rYs r/r0zLogHzFormatter.__init__s  r1Nc|dkry|jj\}}|js|dtd|zkDry|dS)r]rr4r^rg)r7r9rYrbrps r/rGzLogHzFormatter.__call__sG 6YY002 dzzdQQ%55Ar1)T)rYrHrJrKrPrUr1r/r r s6 r1r c$eZdZdZdddZdddZy) r!acA formatter for chroma axes See Also -------- matplotlib.ticker.Formatter Examples -------- >>> import matplotlib.pyplot as plt >>> values = np.arange(12) >>> fig, ax = plt.subplots() >>> ax.plot(values) >>> ax.yaxis.set_major_formatter(librosa.display.ChromaFormatter()) >>> ax.set(ylabel='Pitch class') c ||_||_yrJrZr[)r.rZr[s r/r0zChromaFormatter.__init__s r1Ncptjt|dd|j|jS)Format for chroma positionsFr`)r midi_to_noter=rZr[r.r?r@s r/rGzChromaFormatter.__call__s,  F5488T\\  r1)rdT)rZrOr[rHrJrKrPrUr1r/r!r!s  r1r!c:eZdZdZ d ddZdddZy) r"a!A formatter for chroma axes with svara instead of notes. If mela is given, Carnatic svara names will be used. Otherwise, Hindustani svara names will be used. If `Sa` is not given, it will default to 0 (equivalent to `C`). See Also -------- ChromaFormatter NcD|d}||_||_||_||_y)Nr)rhrjrir[)r.rhrjrir[s r/r0zChromaSvaraFormatter.__init__s* :B   r1c4|jLtjt||j|jd|j |j Stjt||jd|j |j S)rFrmrl)rjrmidi_to_svara_cr=rhrir[midi_to_svara_hrs r/rGzChromaSvaraFormatter.__call__su 99 ''A77YYYY  ''A4775tyy$,, r1)NNTT)rhOptional[float]rjzOptional[Union[int, str]]rirHr[rHrJrKrPrUr1r/r"r"sE  #*.   (    r1r"c:eZdZdZdddd ddZd d dZy) r#aA formatter for chroma axes with functional just notation See Also -------- matplotlib.ticker.Formatter Examples -------- >>> import matplotlib.pyplot as plt >>> values = np.arange(12) >>> fig, ax = plt.subplots() >>> ax.plot(values) >>> ax.yaxis.set_major_formatter(librosa.display.ChromaFJSFormatter(intervals="ji5", bins_per_octave=12)) >>> ax.set(ylabel='Pitch class') CTN)rsr[rxcX||_||_||_ t|ts t |}t|t std|d||_tj|jd||j|_ y#t$r}td|d|d}~wwxYw)Nzbins_per_octave=z must be integer-valuedrruz intervals=z? must be of type str or a collection of numbers between 1 and 2) rsr[rw isinstancerOlenr=r rxrrz intervals_ TypeError)r.rwrsr[rxexcs r/r0zChromaFJSFormatter.__init__?s  " i-"%i.os3$&&77NO)8D "77$$# $ 4 4 DO   YK'fg  sA3B B)B$$B)ctj|jt||jz|j |j }|S)r)rsr[)rinterval_to_fjsrr=rxrsr[)r.r?r@labs r/rGzChromaFJSFormatter.__call__^sB'' OOCFT%9%99 :;;LL   r1)rwrrsrOr[rHrxrMrJrKrPrUr1r/r#r#.sD()- 1    ' >r1r#ceZdZdZdddZy)r$a`A formatter for tonnetz axes See Also -------- matplotlib.ticker.Formatter Examples -------- >>> import matplotlib.pyplot as plt >>> values = np.arange(6) >>> fig, ax = plt.subplots() >>> ax.plot(values) >>> ax.yaxis.set_major_formatter(librosa.display.TonnetzFormatter()) >>> ax.set(ylabel='Tonnetz') Nc"gdt|S)zFormat for tonnetz positions)z5$_x$z5$_y$zm3$_x$zm3$_y$zM3$_x$zM3$_y$)r=rs r/rGzTonnetzFormatter.__call__ysOPSTUPVWWr1rJrK)rQrRrSrTrGrUr1r/r$r$hs  Xr1r$cpeZdZdZ d d dZddZdd ddZdddd Zdd Zy )r%ayA helper class for managing adaptive wave visualizations. This object is used to dynamically switch between sample-based and envelope-based visualizations of waveforms. When the display is zoomed in such that no more than `max_samples` would be visible, the sample-based display is used. When displaying the raw samples would require more than `max_samples`, an envelope-based plot is used instead. You should never need to instantiate this object directly, as it is constructed automatically by `waveshow`. Parameters ---------- times : np.ndarray An array containing the time index (in seconds) for each sample. y : np.ndarray An array containing the (monophonic) wave samples. steps : matplotlib.lines.Line2D The matplotlib artist used for the sample-based visualization. This is constructed by `matplotlib.pyplot.step`. envelope : matplotlib.collections.PolyCollection The matplotlib artist used for the envelope-based visualization. This is constructed by `matplotlib.pyplot.fill_between`. sr : number > 0 The sampling rate of the audio max_samples : int > 0 The maximum number of samples to use for sample-based display. transpose : bool If `True`, display the wave vertically instead of horizontally. See Also -------- waveshow Fc||_||_||_||_||_||_||_d|_d|_yrJ) timessamplesstepsenvelopesr max_samples transposecidax)r.ryrrrrrs r/r0zAdaptiveWaveplot.__init__sD     &""&*.r1c(|jdy)z%Disconnect callback methods on deleteTstrictN) disconnect)r.s r/__del__zAdaptiveWaveplot.__del__s t$r1 xlim_changedsignalc|j||_|jj||j|_y)aConnect the adaptor to a signal on an axes object. Note that if the adaptor has already been connected to an axes object, that connect is first broken and then replaced by a new callback. Parameters ---------- ax : matplotlib.axes.Axes The axes to connect with this adaptor's `update` signal : string, {"xlim_changed", "ylim_changed"} The signal to connect See Also -------- disconnect N)rr callbacksconnectupdater)r.rrs r/rzAdaptiveWaveplot.connects3. <<'' <r1rc|jrA|jjj|jd|_|rd|_yyy)aDisconnect the adaptor's update callback. Parameters ---------- strict : bool If `True`, remove references to the connected axes. If `False` (default), only disconnect the callback. This functionality is intended primarily for internal use, and should have no observable effects for users. See Also -------- connect N)rrrr)r.rs r/rzAdaptiveWaveplot.disconnectsB 77 GG   ( ( 2DH r1c$|j}|jrd|j|jz}|j|j }}|j |j}}|jj}nc|j|jz}|j|j}}|j|j }}|jj}||jkr|jj!d|jj!d||dks||dk\r||zdz } t#j$|j| d|jz|jz z } |jj'|| | |jz|| | |jzn6|jj!d|jj!d|j(j*j-y)aUpdate the matplotlib display according to the current viewport limits. This is a callback function, and should not be used directly. Parameters ---------- ax : matplotlib.axes.Axes The axes object to update FTrr_r3N)viewLimrheightry0y1rrr get_ydatawidthx0x1 get_xdatarr set_visibler: searchsortedset_datafigurecanvas draw_idle) r.rlimsdimstartendxdataydatadata midpoint_time idx_starts r/rzAdaptiveWaveplot.updateszz >>++'C$''3E<<5E::'')D**tww&C$''3E::t||5E::'')D $"" " MM % %e , JJ " "4 (Q3$r(?"' 1 OOJJ d6F6F0F0P P  ##)i$2B2B&BC)i$2B2B&BC MM % %d + JJ " "5 ) ""$r1N)"V+F)r np.ndarrayrrrrrrrrLrr=rrH)rNNone)r mplaxes.AxesrrOrNr)rrHrNr)rrrNr) rQrRrSrTr0rrrrrUr1r/r%r%~s(` // / / ! /  ///(%% = = =  =:,1,-%r1r%Tmagmagray_rcoolwarm)robustcmap_seq cmap_boolcmap_divctj|}|jdk(r t|S|tj|}|rd\}}nd\}}tj |||g\}}|dk\s|dkr t|St|S)aGet a default colormap from the given data. If the data is boolean, use a black and white colormap. If the data has both positive and negative values, use a diverging colormap. Otherwise, use a sequential colormap. Parameters ---------- data : np.ndarray Input data robust : bool If True, discard the top and bottom 2% of data when calculating range. cmap_seq : str The sequential colormap name cmap_bool : str The boolean colormap name cmap_div : str The diverging colormap name Returns ------- cmap : matplotlib.colors.Colormap The colormap to use for ``data`` See Also -------- matplotlib.pyplot.colormaps rH)r_b)rdr)r:r~dtypemcmisfinite percentile) rrrrrmin_pmax_pmin_valmax_vals r/rr$sP == D zzV9~  D! "D  u u}}TE5>:GW!|w!|8} x=r1c|tjtj|||}|j dS)zCompute the max-envelope of non-overlapping frames of x at length hop x is assumed to be multi-channel, of shape (n_channels, n_samples). ) frame_length hop_lengthr)r7)r:r;rframerb)r?hopx_frames r/ __enveloper`s0 ffTZZDEG ;;A; r1)chromachroma_hchroma_c chroma_fjs)cqt_hzcqt_note cqt_svara)linearffthzfft_note fft_svara)timer(r)r*r+)r-lag_hlag_mlag_slag_ms)tempo fourier_tempomellogtonnetzframesri rdF)x_coordsy_coordsx_axisy_axisrrn_fft win_lengthrvfmax tempo_min tempo_maxtuningrxrZrhrjthaat auto_aspecthtkr[rwrsrc tj|jtjr,t j ddtj |}|jdt||jdd|jdd|jd d t||| | | ||||||||| }t|||jd fi|}t|||jd fi|}t|}|j|||fi|}t||t||d| | t||d| | t!|j"||||||| |||t%| t!|j&||||||| |||t%| t)|||j+|j-r|r|j/d|S)a"Display a spectrogram/chromagram/cqt/etc. For a detailed overview of this function, see :ref:`sphx_glr_auto_examples_plot_display.py` Parameters ---------- data : np.ndarray [shape=(d, n)] Matrix to display (e.g., spectrogram) sr : number > 0 [scalar] Sample rate used to determine time scale in x-axis. hop_length : int > 0 [scalar] Hop length, also used to determine time scale in x-axis n_fft : int > 0 or None Number of samples per frame in STFT/spectrogram displays. By default, this will be inferred from the shape of ``data`` as ``2 * (d - 1)``. If ``data`` was generated using an odd frame length, the correct value can be specified here. win_length : int > 0 or None The number of samples per window. By default, this will be inferred to match ``n_fft``. This is primarily useful for specifying odd window lengths in Fourier tempogram displays. x_axis, y_axis : None or str Range for the x- and y-axes. Valid types are: - None, 'none', or 'off' : no axis decoration is displayed. Frequency types: - 'linear', 'fft', 'hz' : frequency range is determined by the FFT window and sampling rate. - 'log' : the spectrum is displayed on a log scale. - 'fft_note': the spectrum is displayed on a log scale with pitches marked. - 'fft_svara': the spectrum is displayed on a log scale with svara marked. - 'mel' : frequencies are determined by the mel scale. - 'cqt_hz' : frequencies are determined by the CQT scale. - 'cqt_note' : pitches are determined by the CQT scale. - 'cqt_svara' : like `cqt_note` but using Hindustani or Carnatic svara - 'vqt_fjs' : like `cqt_note` but using Functional Just System (FJS) notation. This requires a just intonation-based variable-Q transform representation. All frequency types are plotted in units of Hz. Any spectrogram parameters (hop_length, sr, bins_per_octave, etc.) used to generate the input data should also be provided when calling `specshow`. Categorical types: - 'chroma' : pitches are determined by the chroma filters. Pitch classes are arranged at integer locations (0-11) according to a given key. - `chroma_h`, `chroma_c`: pitches are determined by chroma filters, and labeled as svara in the Hindustani (`chroma_h`) or Carnatic (`chroma_c`) according to a given thaat (Hindustani) or melakarta raga (Carnatic). - 'chroma_fjs': pitches are determined by chroma filters using just intonation. All pitch classes are annotated. - 'tonnetz' : axes are labeled by Tonnetz dimensions (0-5) - 'frames' : markers are shown as frame counts. Time types: - 'time' : markers are shown as milliseconds, seconds, minutes, or hours. Values are plotted in units of seconds. - 'h' : markers are shown as hours, minutes, and seconds. - 'm' : markers are shown as minutes and seconds. - 's' : markers are shown as seconds. - 'ms' : markers are shown as milliseconds. - 'lag' : like time, but past the halfway point counts as negative values. - 'lag_h' : same as lag, but in hours, minutes and seconds. - 'lag_m' : same as lag, but in minutes and seconds. - 'lag_s' : same as lag, but in seconds. - 'lag_ms' : same as lag, but in milliseconds. Rhythm: - 'tempo' : markers are shown as beats-per-minute (BPM) using a logarithmic scale. This is useful for visualizing the outputs of `feature.tempogram`. - 'fourier_tempo' : same as `'tempo'`, but used when tempograms are calculated in the Frequency domain using `feature.fourier_tempogram`. x_coords, y_coords : np.ndarray [shape=data.shape[0 or 1]] Optional positioning coordinates of the input data. These can be use to explicitly set the location of each element ``data[i, j]``, e.g., for displaying beat-synchronous features in natural time coordinates. If not provided, they are inferred from ``x_axis`` and ``y_axis``. fmin : float > 0 [scalar] or None Frequency of the lowest spectrogram bin. Used for Mel, CQT, and VQT scales. If ``y_axis`` is `cqt_hz` or `cqt_note` and ``fmin`` is not given, it is set by default to ``note_to_hz('C1')``. fmax : float > 0 [scalar] or None Used for setting the Mel frequency scales tempo_min : float > 0 [scalar] Lowest tempo (in beats per minute) for tempogram display. tempo_max : float > 0 [scalar] Highest tempo (in beats per minute) for tempogram display. tuning : float Tuning deviation from A440, in fractions of a bin. This is used for CQT frequency scales, so that ``fmin`` is adjusted to ``fmin * 2**(tuning / bins_per_octave)``. bins_per_octave : int > 0 [scalar] Number of bins per octave. Used for CQT frequency scale. key : str The reference key to use when using note axes (`cqt_note`, `chroma`). Sa : float or int If using Hindustani or Carnatic svara axis decorations, specify Sa. For `cqt_svara`, ``Sa`` should be specified as a frequency in Hz. For `chroma_c` or `chroma_h`, ``Sa`` should correspond to the position of Sa within the chromagram. If not provided, Sa will default to 0 (equivalent to `C`) mela : str or int, optional If using `chroma_c` or `cqt_svara` display mode, specify the melakarta raga. thaat : str, optional If using `chroma_h` display mode, specify the parent thaat. intervals : str or array of floats in [1, 2), optional If using an FJS notation (`chroma_fjs`, `vqt_fjs`), the interval specification. See `core.interval_frequencies` for a description of supported values. unison : str, optional If using an FJS notation (`chroma_fjs`, `vqt_fjs`), the pitch name of the unison interval. If not provided, it will be inferred from `fmin` (for VQT display) or assumed as `'C'` (for chroma display). auto_aspect : bool Axes will have 'equal' aspect if the horizontal and vertical dimensions cover the same extent and their types match. To override, set to `False`. htk : bool If plotting on a mel frequency axis, specify which version of the mel scale to use. - `False`: use Slaney formula (default) - `True`: use HTK formula See `core.mel_frequencies` for more information. unicode : bool If using note or svara decorations, setting `unicode=True` will use unicode glyphs for accidentals and octave encoding. Setting `unicode=False` will use ASCII glyphs. This can be helpful if your font does not support musical notation symbols. ax : matplotlib.axes.Axes or None Axes to plot on instead of the default `plt.gca()`. **kwargs : additional keyword arguments Arguments passed through to `matplotlib.pyplot.pcolormesh`. By default, the following options are set: - ``rasterized=True`` - ``shading='auto'`` - ``edgecolors='None'`` The ``cmap`` option if not provided, is inferred from data automatically. Set ``cmap=None`` to use matplotlib's default colormap. Returns ------- colormesh : `matplotlib.collections.QuadMesh` The color mesh object produced by `matplotlib.pyplot.pcolormesh` See Also -------- cmap : Automatic colormap detection matplotlib.pyplot.pcolormesh Examples -------- Visualize an STFT power spectrum using default parameters >>> import matplotlib.pyplot as plt >>> y, sr = librosa.load(librosa.ex('choice'), duration=15) >>> fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True) >>> D = librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max) >>> img = librosa.display.specshow(D, y_axis='linear', x_axis='time', ... sr=sr, ax=ax[0]) >>> ax[0].set(title='Linear-frequency power spectrogram') >>> ax[0].label_outer() Or on a logarithmic scale, and using a larger hop >>> hop_length = 1024 >>> D = librosa.amplitude_to_db(np.abs(librosa.stft(y, hop_length=hop_length)), ... ref=np.max) >>> librosa.display.specshow(D, y_axis='log', sr=sr, hop_length=hop_length, ... x_axis='time', ax=ax[1]) >>> ax[1].set(title='Log-frequency power spectrogram') >>> ax[1].label_outer() >>> fig.colorbar(img, ax=ax, format="%+2.f dB") zBTrying to display complex-valued input. 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This function constructs a plot which adaptively switches between a raw samples-based view of the signal (`matplotlib.pyplot.step`) and an amplitude-envelope view of the signal (`matplotlib.pyplot.fill_between`) depending on the time extent of the plot's viewport. More specifically, when the plot spans a time interval of less than ``max_points / sr`` (by default, 1/2 second), the samples-based view is used, and otherwise a downsampled amplitude envelope is used. This is done to limit the complexity of the visual elements to guarantee an efficient, visually interpretable plot. When using interactive rendering (e.g., in a Jupyter notebook or IPython console), the plot will automatically update as the view-port is changed, either through widget controls or programmatic updates. .. note:: When visualizing stereo waveforms, the amplitude envelope will be generated so that the upper limits derive from the left channel, and the lower limits derive from the right channel, which can produce a vertically asymmetric plot. When zoomed in to the sample view, only the first channel will be shown. If you want to visualize both channels at the sample level, it is recommended to plot each signal independently. Parameters ---------- y : np.ndarray [shape=(n,) or (2,n)] audio time series (mono or stereo) sr : number > 0 [scalar] sampling rate of ``y`` (samples per second) max_points : positive integer Maximum number of samples to draw. When the plot covers a time extent smaller than ``max_points / sr`` (default: 1/2 second), samples are drawn. If drawing raw samples would exceed `max_points`, then a downsampled amplitude envelope extracted from non-overlapping windows of `y` is visualized instead. The parameters of the amplitude envelope are defined so that the resulting plot cannot produce more than `max_points` frames. axis : str or None Display style of the axis ticks and tick markers. Accepted values are: - 'time' : markers are shown as milliseconds, seconds, minutes, or hours. Values are plotted in units of seconds. - 'h' : markers are shown as hours, minutes, and seconds. - 'm' : markers are shown as minutes and seconds. - 's' : markers are shown as seconds. - 'ms' : markers are shown as milliseconds. - 'lag' : like time, but past the halfway point counts as negative values. - 'lag_h' : same as lag, but in hours. - 'lag_m' : same as lag, but in minutes. - 'lag_s' : same as lag, but in seconds. - 'lag_ms' : same as lag, but in milliseconds. - `None`, 'none', or 'off': ticks and tick markers are hidden. x_axis : Deprecated Equivalent to `axis` parameter, included for backward compatibility. .. warning:: This parameter is deprecated as of 0.10.0 and will be removed in 1.0. Use `axis=` instead going forward. ax : matplotlib.axes.Axes or None Axes to plot on instead of the default `plt.gca()`. offset : float Horizontal offset (in seconds) to start the waveform plot marker : string Marker symbol to use for sample values. (default: no markers) See Also: `matplotlib.markers`. where : string, {'pre', 'mid', 'post'} This setting determines how both waveform and envelope plots interpolate between observations. See `matplotlib.pyplot.step` for details. Default: 'post' label : string [optional] The label string applied to this plot. Note that the label transpose : bool If `True`, display the wave vertically instead of horizontally. **kwargs Additional keyword arguments to `matplotlib.pyplot.fill_between` and `matplotlib.pyplot.step`. Note that only those arguments which are common to both functions will be supported. Returns ------- librosa.display.AdaptiveWaveplot An object of type `librosa.display.AdaptiveWaveplot` See Also -------- AdaptiveWaveplot matplotlib.pyplot.step matplotlib.pyplot.fill_between matplotlib.pyplot.fill_betweenx matplotlib.markers Examples -------- Plot a monophonic waveform with an envelope view >>> import matplotlib.pyplot as plt >>> y, sr = librosa.load(librosa.ex('choice'), duration=10) >>> fig, ax = plt.subplots(nrows=3, sharex=True) >>> librosa.display.waveshow(y, sr=sr, ax=ax[0]) >>> ax[0].set(title='Envelope view, mono') >>> ax[0].label_outer() Or a stereo waveform >>> y, sr = librosa.load(librosa.ex('choice', hq=True), mono=False, duration=10) >>> librosa.display.waveshow(y, sr=sr, ax=ax[1]) >>> ax[1].set(title='Envelope view, stereo') >>> ax[1].label_outer() Or harmonic and percussive components with transparency >>> y, sr = librosa.load(librosa.ex('choice'), duration=10) >>> y_harm, y_perc = librosa.effects.hpss(y) >>> librosa.display.waveshow(y_harm, sr=sr, alpha=0.5, ax=ax[2], label='Harmonic') >>> librosa.display.waveshow(y_perc, sr=sr, color='r', alpha=0.5, ax=ax[2], label='Percussive') >>> ax[2].set(title='Multiple waveforms') >>> ax[2].legend() Zooming in on a plot to show raw sample values >>> fig, (ax, ax2) = plt.subplots(nrows=2, sharex=True) >>> ax.set(xlim=[6.0, 6.01], title='Sample view', ylim=[-0.2, 0.2]) >>> librosa.display.waveshow(y, sr=sr, ax=ax, marker='.', label='Full signal') >>> librosa.display.waveshow(y_harm, sr=sr, alpha=0.5, ax=ax2, label='Harmonic') >>> librosa.display.waveshow(y_perc, sr=sr, color='r', alpha=0.5, ax=ax2, label='Percussive') >>> ax.label_outer() >>> ax.legend() >>> ax2.legend() Plotting a transposed wave along with a self-similarity matrix >>> fig, ax = plt.subplot_mosaic("hSSS;hSSS;hSSS;.vvv") >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> chroma = librosa.feature.chroma_cqt(y=y, sr=sr) >>> sim = librosa.segment.recurrence_matrix(chroma, mode='affinity') >>> librosa.display.specshow(sim, ax=ax['S'], sr=sr, ... x_axis='time', y_axis='time', ... auto_aspect=False) >>> ax['S'].label_outer() >>> ax['S'].sharex(ax['v']) >>> ax['S'].sharey(ax['h']) >>> ax['S'].set(title='Self-similarity') >>> librosa.display.waveshow(y, ax=ax['v']) >>> ax['v'].label_outer() >>> ax['v'].set(title='transpose=False') >>> librosa.display.waveshow(y, ax=ax['h'], transpose=True) >>> ax['h'].label_outer() >>> ax['h'].set(title='transpose=True') F)monorNrz max_points=z must be strictly positiverr7z0.10.0z1.0)old_name old_valuenew_name new_valueversion_deprecatedversion_removedrrr ylim_changed)rrcolor)stepr)rrrr)r valid_audiondimr:newaxisr r,r rbr+rr times_like fill_betweenr1 fill_betweenxr2rr( get_colorrr%rrr0)rrrr7rrrrrrrr"r8ry_envy_bottomy_toprrrfillerrdec_axisrradaptors r/rr*sB QU# vv{ bjj!m Q{:,6PQRR  D #  DQ z12J q* %ERyj%(eH T__Q2!< rs#H#'!%3+RRR"?'/.* $e I''e PC I''C L[Y(([|V9&&Vr)Y(()X i)) 8+9..+\7,,7tXy**X,c%c%R 9 9 9 9  9  99x   #$^aV^$*,<=>=Qq=>?-787Qq789..9:9Qq9:;..9:9Qq9: ; -787Qq78 9 &*%)   $  !#!$&*&*26 !%5N N#N# N  N  N NN NN N NNN N !N" #N$ $%N& $'N( )N*+N, -N./N001N2 3N4 5N67N89Nb 0+f -f     sBn 7;    &3 GJ              (%)   !      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