U 5d@s0dZddlZddlZddlmZddlmZddlm Z m Z ddlm Z m Z m Z ddlmZddlZddlmZd d lmZd d lmZd d lmZd d lmZdddddddddg ZGddde ZGddde ZGddde ZGddde ZGddde ZGddde Z Gddde Z!Gd ddZ"ed!d"d#d$d%d&dZ#d'd(Z$eddddd)d*ddddd+d,d-dddd!d.d!dd/d0dZ%d1d2Z&d3d4Z'd5d6Z(d7d8Z)dSd9d:Z*dTd;d<Z+dUd=d>Z,dVd?d@Z-dWdAdBZ.dXdCdDZ/dYdEdFZ0dGdHZ1dZdIdJZ2dKdLZ3ed)dMdNd+dOdPdddQdRdZ4dS)[a Display ======= Data visualization ------------------ .. autosummary:: :toctree: generated/ specshow waveshow Axis formatting --------------- .. autosummary:: :toctree: generated/ TimeFormatter NoteFormatter SvaraFormatter LogHzFormatter ChromaFormatter ChromaSvaraFormatter TonnetzFormatter Miscellaneous ------------- .. autosummary:: :toctree: generated/ cmap AdaptiveWaveplot N)get_cmap)Axes) FormatterScalarFormatter) LogLocator FixedLocator MaxNLocator)SymmetricalLogLocator)parse)core)util)ParameterError)deprecate_positional_argsspecshowwaveshowcmap TimeFormatter NoteFormatterLogHzFormatterChromaFormatterTonnetzFormatterAdaptiveWaveplotc@s$eZdZdZdddZd ddZdS) raA tick formatter for time axes. Automatically switches between seconds, minutes:seconds, or hours:minutes:seconds. Parameters ---------- lag : bool If ``True``, then the time axis is interpreted in lag coordinates. Anything past the midpoint will be converted to negative time. unit : str or None Abbreviation of the physical unit for axis labels and ticks. Either equal to `s` (seconds) or `ms` (milliseconds) or None (default). If set to None, the resulting TimeFormatter object adapts its string representation to the duration of the underlying time range: `hh:mm:ss` above 3600 seconds; `mm:ss` between 60 and 3600 seconds; and `ss` below 60 seconds. See also -------- matplotlib.ticker.Formatter Examples -------- For normal time >>> import matplotlib.pyplot as plt >>> times = np.arange(30) >>> values = np.random.randn(len(times)) >>> fig, ax = plt.subplots() >>> ax.plot(times, values) >>> ax.xaxis.set_major_formatter(librosa.display.TimeFormatter()) >>> ax.set(xlabel='Time') Manually set the physical time unit of the x-axis to milliseconds >>> times = np.arange(100) >>> values = np.random.randn(len(times)) >>> fig, ax = plt.subplots() >>> ax.plot(times, values) >>> ax.xaxis.set_major_formatter(librosa.display.TimeFormatter(unit='ms')) >>> ax.set(xlabel='Time (ms)') For lag plots >>> times = np.arange(60) >>> values = np.random.randn(len(times)) >>> fig, ax = plt.subplots() >>> ax.plot(times, values) >>> ax.xaxis.set_major_formatter(librosa.display.TimeFormatter(lag=True)) >>> ax.set(xlabel='Lag') FNcCs&|dkrtd|||_||_dS)N)smsNzUnknown time unit: {})rformatunitlag)selfrrr3/tmp/pip-unpacked-wheel-8l90aumz/librosa/display.py__init__~szTimeFormatter.__init__c Cs$|j\}}|j\}}|jrN||dkrN||kr:dSt||}d}n|}d}|jdkrld|} n|jdkrd|d} n||dkrd t|d tt |d d tt |d } nR||d krd t|d tt |d } n$||dkrd|} n d|} d|| S)zReturn the time format as pos?-rz{:.3g}riiz{:d}:{:02d}:{:02d}g @gN@<z {:d}:{:02d}r z{:.2g}z{:.3f}z{:s}{:s}) axisZget_data_intervalget_view_intervalrnpabsrrintmod) rxpos_Zdmaxvminvmaxvaluesignrrrr __call__s2      "  zTimeFormatter.__call__)FN)N__name__ __module__ __qualname____doc__r!r3rrrr rDs9 c@s$eZdZdZd ddZd ddZdS) ra Ticker formatter for Notes 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 key : str Key for determining pitch spelling. unicode : bool If ``True``, use unicode symbols for accidentals. If ``False``, use ASCII symbols for accidentals. See also -------- LogHzFormatter 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') TC:majcCs||_||_||_||_dSN)octavemajorkeyunicode)rr;r<r=r>rrr r!szNoteFormatter.__init__NcCsb|dkr dS|j\}}|js6|dtd|kr6dS|dtd|k}tj||j||j|jdS)Nrr#r r;centsr=r>) r&r'r<maxr Z hz_to_noter;r=r>)rr,r-r/r0rBrrr r3szNoteFormatter.__call__)TTr9T)Nr4rrrr rs' c@s$eZdZdZd ddZd ddZdS) SvaraFormatteraTicker formatter for Svara Parameters ---------- octave : bool If ``True``, display the octave number along with the note name. Otherwise, only show the note name (and cent deviation) major : bool If ``True``, ticks are always labeled. If ``False``, ticks are only labeled if the span is less than 2 octaves Sa : number > 0 Frequency (in Hz) of Sa mela : str or int For Carnatic svara, the index or name of the melakarta raga in question To use Hindustani svara, set ``mela=None`` unicode : bool If ``True``, use unicode symbols for accidentals. If ``False``, use ASCII symbols for accidentals. See also -------- NoteFormatter matplotlib.ticker.Formatter librosa.hz_to_svara_c librosa.hz_to_svara_h Examples -------- >>> import matplotlib.pyplot as plt >>> values = librosa.midi_to_hz(np.arange(48, 72)) >>> fig, ax = plt.subplots(nrows=2) >>> ax[0].bar(np.arange(len(values)), values) >>> ax[0].set(ylabel='Hz') >>> ax[1].bar(np.arange(len(values)), values) >>> ax[1].yaxis.set_major_formatter(librosa.display.SvaraFormatter(261)) >>> ax[1].set(ylabel='Note') TFNcCs8|dkrtd||_||_||_||_||_||_dS)Nz5Sa frequency is required for svara display formatting)rSar;r<abbrmelar>)rrEr;r<rFrGr>rrr r!"szSvaraFormatter.__init__cCs|dkr dS|j\}}|js6|dtd|kr6dS|jdkr\tj||j|j|j |j dStj ||j|j|j|j |j dSdS)Nrr#r?r rEr;rFr>rErGr;rFr>) r&r'r<rCrGr Z hz_to_svara_hrEr;rFr>Z hz_to_svara_crr,r-r/r0rrr r32s* zSvaraFormatter.__call__)TTFNT)Nr4rrrr rDs0 rDc@s$eZdZdZdddZd ddZdS) raTicker 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') TcCs ||_dSr:r<)rr<rrr r!hszLogHzFormatter.__init__NcCs@|dkr dS|j\}}|js6|dtd|kr6dSd|S)Nrr#r?r z{:g})r&r'r<rCrrJrrr r3ls zLogHzFormatter.__call__)T)Nr4rrrr rLs c@s$eZdZdZd ddZd ddZdS) racA formatter for chroma axes See also -------- matplotlib.ticker.Formatter Examples -------- >>> import matplotlib.pyplot as plt >>> values = np.arange(12) >>> fig, ax = plt.subplots() >>> ax.plot(values) >>> ax.yaxis.set_major_formatter(librosa.display.ChromaFormatter()) >>> ax.set(ylabel='Pitch class') r9TcCs||_||_dSr:r=r>)rr=r>rrr r!szChromaFormatter.__init__NcCstjt|dd|j|jdS)Format for chroma positionsFrA)r Z midi_to_noter*r=r>rr,r-rrr r3szChromaFormatter.__call__)r9T)Nr4rrrr rys c@s$eZdZdZdddZd ddZdS) ChromaSvaraFormattera!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 NTcCs(|dkr d}||_||_||_||_dS)Nr)rErGrFr>)rrErGrFr>rrr r!s zChromaSvaraFormatter.__init__cCsN|jdk r,tjt||j|jd|j|jdStjt||jd|j|jdSdS)rMNFrIrH)rGr Zmidi_to_svara_cr*rErFr>Zmidi_to_svara_hrNrrr r3s   zChromaSvaraFormatter.__call__)NNTT)Nr4rrrr rOs rOc@seZdZdZdddZdS)ra`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') NcCsddddddgt|S)zFormat for tonnetz positionsz5$_x$z5$_y$zm3$_x$zm3$_y$zM3$_x$zM3$_y$)r*rNrrr r3szTonnetzFormatter.__call__)N)r5r6r7r8r3rrrr rsc@s"eZdZdZd ddZddZdS) raA 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.Lines2D 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. See Also -------- waveshow "V+cCs(||_||_||_||_||_||_dSr:)timessamplesstepsenvelopesr max_samples)rrRyrTrUrVrWrrr r!s zAdaptiveWaveplot.__init__cCs|j}|j|j|jkr|jd|jd|j}|j|dksV|j |dkr|j |jd}t |j |d|j|j}|j |j |||j|j|||jn|jd|jd|jjdS)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 object The axes object to update FTrr@r"N)ZviewLimwidthrVrWrUZ set_visiblerTZ get_xdataZx0x1r(Z searchsortedrRset_datarSfigureZcanvasZ draw_idle)raxZlimsZxdataZ midpoint_timeZ idx_startrrr updates$      zAdaptiveWaveplot.updateN)rPrQ)r5r6r7r8r!r_rrrr rs' TZmagmaZgray_rZcoolwarm)robustcmap_seq cmap_boolcmap_divc Csxt|}|jdkr t|ddS|t|}|rr^cKs@t|jtjr(tjdddt|}|dt||dd|dd|d d t ||| | | | |||| ||d }t |||j d f|}t |||j d f|}t |}|j |||f|}t||t||dt||dt|j|| ||||dt|j|| ||||dt||||r<|r<|d|S)acDisplay 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 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). - '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. - '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_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 and CQT 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 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. 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'`` 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. Showing magnitude instead.r@ stacklevelrZ rasterizedTZ edgecolorsNoneZshadingauto) kwargsrVrvrwrxryrjrtrur=r|r>rr r,rX)r=rErGrzr>equal)r(Z issubdtypergZcomplexfloatingwarningswarnr) setdefaultrdict __mesh_coordsshape __check_axesZ pcolormesh__set_current_image __scale_axes__decorate_axisxaxisZyaxis __same_axesZget_xlimZget_ylimZ set_aspect)rirprqrrrsrVrjrtrurvrwrxryr=rErGrzr{r|r>r^r all_paramsaxesoutrrr rlsjc        cCs"|dkrddlm}||dS)zHelper to set the current image in pyplot mode. If the provided ``ax`` is not `None`, then we assume that the user is using the object API. In this case, the pyplot current image is not set. Nr)matplotlib.pyplotpyplotZsci)r^imgpltrrr rs rcKs|dk r@t|||dfkr|dkrddlm}|}nt|ts:tdt||S)zDCheck if "axes" is an instance of an axis object. If not, use `gca`.NrzG`axes` must be an instance of matplotlib.axes.Axes. 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Did you remember to set all spectrogram parameters in specshow?r?r})r rgetZcqt_frequenciesr(anyrr)rrvryrVrZfreqsrrr rs rcKstjdd|||ddS)zGet chroma bin numbersrrF)numZendpoint)r(Zlinspace)rryrrrr rsrcKs tj|d||ddd}|S)zTempo coordinatesr rVrjN)r Ztempo_frequencies)rrVrjrrrrr rsrcKs(|dkrd|d}tj|||d}|S)zFourier tempogram coordinatesNr@r )rVrjru)r Zfourier_tempo_frequencies)rrVrjrurrrrr rs rcKs t|S)zGet bare positions)r(r)rrrrr rsrcKstjt|||dS)z Get time coordinates from framesr)r Zframes_to_timer(r)rrVrjrrrr rsrcCs ||ko|dk }||k}|o|S)z?Check if two axes are the same, used to determine squared plotsNr)rrrsZxlimZylimZaxes_same_and_not_noneZ axes_same_limrrr rsrrQrr#post)rV max_pointsrroffsetmarkerwherelabelr^cKsNtj|dd|jdkr*|tjddf}|dkr@td|t|} d| krh| dt | j j dt d|j d|} t|| } | d | d} }|tj||dd }| j|d||dd|ff||d | \}| j|dt|| | | |f||d | }t||d||||d }| jd |j|| t| j||S)aVisualize a waveform in the time domain. 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. x_axis : str or None Display of the x-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. - '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_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. 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 **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.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() F)Zmonor Nrz'max_points={} must be strictly positivecolorrYr)rr)stepr)rVrWZ xlim_changed)r Z valid_audiondimr(ZnewaxisrrrrnextZ _get_linesZ prop_cyclerrCrrlr Z times_likerZ fill_betweenrr callbacksconnectr_rr)rXrVrrrrrrrr^rrrjZy_envZy_bottomZy_toprRrTrUZadaptorrrr rs\     )r9NNNT)rPN)rNrPF)NrorP)ro)rPrm)rPrmN)rPrm)5r8rZnumpyr(Z matplotlib.cmrZmatplotlib.axesrZmatplotlib.tickerrrrrrr rZpackaging.versionr rr#r r Zutil.exceptionsrZutil.decoratorsr__all__rrrDrrrOrrrrlrrrrrrrrrrrrrrrrrrrr s#         mAZ-(W8  ,8 +