Source code for librosa.display

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
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

Deprecated
----------
.. autosummary::
    :toctree: generated/

    waveplot

"""

import warnings

import numpy as np
from matplotlib.cm import get_cmap
from matplotlib.axes import Axes
from matplotlib.ticker import Formatter, ScalarFormatter
from matplotlib.ticker import LogLocator, FixedLocator, MaxNLocator
from matplotlib.ticker import SymmetricalLogLocator
import matplotlib
from packaging.version import parse as version_parse


from . import core
from . import util
from .util.exceptions import ParameterError

__all__ = [
    "specshow",
    "waveshow",
    "waveplot",
    "cmap",
    "TimeFormatter",
    "NoteFormatter",
    "LogHzFormatter",
    "ChromaFormatter",
    "TonnetzFormatter",
    "AdaptiveWaveplot",
]


[docs]class TimeFormatter(Formatter): """A 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') """
[docs] def __init__(self, lag=False, unit=None): if unit not in ["s", "ms", None]: raise ParameterError("Unknown time unit: {}".format(unit)) self.unit = unit self.lag = lag
def __call__(self, x, pos=None): """Return the time format as pos""" _, dmax = self.axis.get_data_interval() vmin, vmax = self.axis.get_view_interval() # In lag-time axes, anything greater than dmax / 2 is negative time if self.lag and x >= dmax * 0.5: # In lag mode, don't tick past the limits of the data if x > dmax: return "" value = np.abs(x - dmax) # Do we need to tweak vmin/vmax here? sign = "-" else: value = x sign = "" if self.unit == "s": s = "{:.3g}".format(value) elif self.unit == "ms": s = "{:.3g}".format(value * 1000) else: if vmax - vmin > 3600: # Hours viz s = "{:d}:{:02d}:{:02d}".format( int(value / 3600.0), int(np.mod(value / 60.0, 60)), int(np.mod(value, 60)), ) elif vmax - vmin > 60: # Minutes viz s = "{:d}:{:02d}".format(int(value / 60.0), int(np.mod(value, 60))) elif vmax - vmin >= 1: # Seconds viz s = "{:.2g}".format(value) else: # Milliseconds viz s = "{:.3f}".format(value) return "{:s}{:s}".format(sign, s)
[docs]class NoteFormatter(Formatter): """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 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') """
[docs] def __init__(self, octave=True, major=True, key="C:maj"): self.octave = octave self.major = major self.key = key
def __call__(self, x, pos=None): if x <= 0: return "" # Only use cent precision if our vspan is less than an octave vmin, vmax = self.axis.get_view_interval() if not self.major and vmax > 4 * max(1, vmin): return "" cents = vmax <= 2 * max(1, vmin) return core.hz_to_note(x, octave=self.octave, cents=cents, key=self.key)
[docs]class SvaraFormatter(Formatter): """Ticker 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`` 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') """
[docs] def __init__(self, Sa, octave=True, major=True, abbr=False, mela=None): if Sa is None: raise ParameterError( "Sa frequency is required for svara display formatting" ) self.Sa = Sa self.octave = octave self.major = major self.abbr = abbr self.mela = mela
def __call__(self, x, pos=None): if x <= 0: return "" # Only use cent precision if our vspan is less than an octave vmin, vmax = self.axis.get_view_interval() if not self.major and vmax > 4 * max(1, vmin): return "" if self.mela is None: return core.hz_to_svara_h(x, self.Sa, octave=self.octave, abbr=self.abbr) else: return core.hz_to_svara_c( x, self.Sa, self.mela, octave=self.octave, abbr=self.abbr )
[docs]class LogHzFormatter(Formatter): """Ticker 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') """
[docs] def __init__(self, major=True): self.major = major
def __call__(self, x, pos=None): if x <= 0: return "" vmin, vmax = self.axis.get_view_interval() if not self.major and vmax > 4 * max(1, vmin): return "" return "{:g}".format(x)
[docs]class ChromaFormatter(Formatter): """A 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') """
[docs] def __init__(self, key="C:maj"): self.key = key
def __call__(self, x, pos=None): """Format for chroma positions""" return core.midi_to_note(int(x), octave=False, cents=False, key=self.key)
[docs]class ChromaSvaraFormatter(Formatter): """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 """
[docs] def __init__(self, Sa=None, mela=None, abbr=True): if Sa is None: Sa = 0 self.Sa = Sa self.mela = mela self.abbr = abbr
def __call__(self, x, pos=None): """Format for chroma positions""" if self.mela is not None: return core.midi_to_svara_c( int(x), Sa=self.Sa, mela=self.mela, octave=False, abbr=self.abbr ) else: return core.midi_to_svara_h( int(x), Sa=self.Sa, octave=False, abbr=self.abbr )
[docs]class TonnetzFormatter(Formatter): """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') """ def __call__(self, x, pos=None): """Format for tonnetz positions""" return [r"5$_x$", r"5$_y$", r"m3$_x$", r"m3$_y$", r"M3$_x$", r"M3$_y$"][int(x)]
[docs]class AdaptiveWaveplot: """A 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 """
[docs] def __init__(self, times, y, steps, envelope, sr=22050, max_samples=11025): self.times = times self.samples = y self.steps = steps self.envelope = envelope self.sr = sr self.max_samples = max_samples
def update(self, ax): """Update 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 """ lims = ax.viewLim # Does our width cover fewer than max_samples? # If so, then use the sample-based plot if lims.width * self.sr <= self.max_samples: self.envelope.set_visible(False) self.steps.set_visible(True) # Now check that our viewport xdata = self.steps.get_xdata() if lims.x0 <= xdata[0] or lims.x1 >= xdata[-1]: # Viewport expands beyond current data in steps; update # we want to cover a window of self.max_samples centered on the current viewport midpoint_time = (lims.x1 + lims.x0) / 2 idx_start = np.searchsorted( self.times, midpoint_time - 0.5 * self.max_samples / self.sr ) self.steps.set_data( self.times[idx_start : idx_start + self.max_samples], self.samples[idx_start : idx_start + self.max_samples], ) else: # Otherwise, use the envelope plot self.envelope.set_visible(True) self.steps.set_visible(False) ax.figure.canvas.draw_idle()
[docs]def cmap(data, robust=True, cmap_seq="magma", cmap_bool="gray_r", cmap_div="coolwarm"): """Get 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 """ data = np.atleast_1d(data) if data.dtype == "bool": return get_cmap(cmap_bool, lut=2) data = data[np.isfinite(data)] if robust: min_p, max_p = 2, 98 else: min_p, max_p = 0, 100 min_val, max_val = np.percentile(data, [min_p, max_p]) if min_val >= 0 or max_val <= 0: return get_cmap(cmap_seq) return get_cmap(cmap_div)
def __envelope(x, hop): """Compute 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). """ x_frame = np.abs(util.frame(x, frame_length=hop, hop_length=hop)) return x_frame.max(axis=1)
[docs]@util.decorators.deprecated("0.8.1", "0.9.0") def waveplot( y, sr=22050, max_points=5e4, x_axis="time", offset=0.0, max_sr=1000, ax=None, **kwargs, ): """Plot the amplitude envelope of a waveform. If ``y`` is monophonic, a filled curve is drawn between ``[-abs(y), abs(y)]``. If ``y`` is stereo, the curve is drawn between ``[-abs(y[1]), abs(y[0])]``, so that the left and right channels are drawn above and below the axis, respectively. Long signals (``duration >= max_points``) are down-sampled to at most ``max_sr`` before plotting. .. warning:: This function is deprecated in librosa 0.8.1 and will be removed in 0.9.0. Its functionality is replaced and extended by `waveshow`. Parameters ---------- y : np.ndarray [shape=(n,) or (2,n)] audio time series (mono or stereo) sr : number > 0 [scalar] sampling rate of ``y`` max_points : postive number or None Maximum number of time-points to plot: if ``max_points`` exceeds the duration of ``y``, then ``y`` is downsampled. If `None`, no downsampling is performed. 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 max_sr : number > 0 [scalar] Maximum sampling rate for the visualization kwargs Additional keyword arguments to `matplotlib.pyplot.fill_between` Returns ------- pc : matplotlib.collections.PolyCollection The PolyCollection created by `fill_between`. See also -------- waveshow librosa.resample matplotlib.pyplot.fill_between Examples -------- Plot a monophonic waveform >>> import matplotlib.pyplot as plt >>> y, sr = librosa.load(librosa.ex('choice'), duration=10) >>> fig, ax = plt.subplots(nrows=3, sharex=True, sharey=True) >>> librosa.display.waveplot(y, sr=sr, ax=ax[0]) >>> ax[0].set(title='Monophonic') >>> ax[0].label_outer() Or a stereo waveform >>> y, sr = librosa.load(librosa.ex('choice', hq=True), mono=False, duration=10) >>> librosa.display.waveplot(y, sr=sr, ax=ax[1]) >>> ax[1].set(title='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.waveplot(y_harm, sr=sr, alpha=0.25, ax=ax[2]) >>> librosa.display.waveplot(y_perc, sr=sr, color='r', alpha=0.5, ax=ax[2]) >>> ax[2].set(title='Harmonic + Percussive') """ util.valid_audio(y, mono=False) if not (isinstance(max_sr, (int, np.integer)) and max_sr > 0): raise ParameterError("max_sr must be a non-negative integer") target_sr = sr hop_length = 1 # Pad an extra channel dimension, if necessary if y.ndim == 1: y = y[np.newaxis, :] if max_points is not None: if max_points <= 0: raise ParameterError("max_points must be strictly positive") if max_points < y.shape[-1]: target_sr = min(max_sr, (sr * y.shape[-1]) // max_points) hop_length = sr // target_sr # Reduce by envelope calculation y = __envelope(y, hop_length) y_top = y[0] y_bottom = -y[-1] axes = __check_axes(ax) kwargs.setdefault("color", next(axes._get_lines.prop_cycler)["color"]) locs = offset + core.times_like(y_top, sr=sr, hop_length=hop_length) out = axes.fill_between(locs, y_bottom, y_top, **kwargs) axes.set_xlim([locs.min(), locs.max()]) # Construct tickers and locators __decorate_axis(axes.xaxis, x_axis) return out
[docs]def specshow( data, x_coords=None, y_coords=None, x_axis=None, y_axis=None, sr=22050, hop_length=512, fmin=None, fmax=None, tuning=0.0, bins_per_octave=12, key="C:maj", Sa=None, mela=None, thaat=None, auto_aspect=True, htk=False, ax=None, **kwargs, ): """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 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]+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. 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='flat'`` - ``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") """ if np.issubdtype(data.dtype, np.complexfloating): warnings.warn( "Trying to display complex-valued input. " "Showing magnitude instead." ) data = np.abs(data) kwargs.setdefault("cmap", cmap(data)) kwargs.setdefault("rasterized", True) kwargs.setdefault("edgecolors", "None") kwargs.setdefault("shading", "flat") all_params = dict( kwargs=kwargs, sr=sr, fmin=fmin, fmax=fmax, tuning=tuning, bins_per_octave=bins_per_octave, hop_length=hop_length, key=key, htk=htk, ) # Get the x and y coordinates y_coords = __mesh_coords(y_axis, y_coords, data.shape[0], **all_params) x_coords = __mesh_coords(x_axis, x_coords, data.shape[1], **all_params) axes = __check_axes(ax) out = axes.pcolormesh(x_coords, y_coords, data, **kwargs) __set_current_image(ax, out) axes.set_xlim(x_coords.min(), x_coords.max()) axes.set_ylim(y_coords.min(), y_coords.max()) # Set up axis scaling __scale_axes(axes, x_axis, "x") __scale_axes(axes, y_axis, "y") # Construct tickers and locators __decorate_axis(axes.xaxis, x_axis, key=key, Sa=Sa, mela=mela, thaat=thaat) __decorate_axis(axes.yaxis, y_axis, key=key, Sa=Sa, mela=mela, thaat=thaat) # If the plot is a self-similarity/covariance etc. plot, square it if __same_axes(x_axis, y_axis, axes.get_xlim(), axes.get_ylim()) and auto_aspect: axes.set_aspect("equal") return out
def __set_current_image(ax, img): """Helper 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. """ if ax is None: import matplotlib.pyplot as plt plt.sci(img) def __mesh_coords(ax_type, coords, n, **kwargs): """Compute axis coordinates""" if coords is not None: if len(coords) < n: raise ParameterError( "Coordinate shape mismatch: " "{}<{}".format(len(coords), n) ) return coords coord_map = { "linear": __coord_fft_hz, "fft": __coord_fft_hz, "fft_note": __coord_fft_hz, "fft_svara": __coord_fft_hz, "hz": __coord_fft_hz, "log": __coord_fft_hz, "mel": __coord_mel_hz, "cqt": __coord_cqt_hz, "cqt_hz": __coord_cqt_hz, "cqt_note": __coord_cqt_hz, "cqt_svara": __coord_cqt_hz, "chroma": __coord_chroma, "chroma_c": __coord_chroma, "chroma_h": __coord_chroma, "time": __coord_time, "s": __coord_time, "ms": __coord_time, "lag": __coord_time, "lag_s": __coord_time, "lag_ms": __coord_time, "tonnetz": __coord_n, "off": __coord_n, "tempo": __coord_tempo, "fourier_tempo": __coord_fourier_tempo, "frames": __coord_n, None: __coord_n, } if ax_type not in coord_map: raise ParameterError("Unknown axis type: {}".format(ax_type)) return coord_map[ax_type](n, **kwargs) def __check_axes(axes): """Check if "axes" is an instance of an axis object. If not, use `gca`.""" if axes is None: import matplotlib.pyplot as plt axes = plt.gca() elif not isinstance(axes, Axes): raise ParameterError( "`axes` must be an instance of matplotlib.axes.Axes. " "Found type(axes)={}".format(type(axes)) ) return axes def __scale_axes(axes, ax_type, which): """Set the axis scaling""" kwargs = dict() if which == "x": if version_parse(matplotlib.__version__) < version_parse("3.3.0"): thresh = "linthreshx" base = "basex" scale = "linscalex" else: thresh = "linthresh" base = "base" scale = "linscale" scaler = axes.set_xscale limit = axes.set_xlim else: if version_parse(matplotlib.__version__) < version_parse("3.3.0"): thresh = "linthreshy" base = "basey" scale = "linscaley" else: thresh = "linthresh" base = "base" scale = "linscale" scaler = axes.set_yscale limit = axes.set_ylim # Map ticker scales if ax_type == "mel": mode = "symlog" kwargs[thresh] = 1000.0 kwargs[base] = 2 elif ax_type in ["cqt", "cqt_hz", "cqt_note", "cqt_svara"]: mode = "log" kwargs[base] = 2 elif ax_type in ["log", "fft_note", "fft_svara"]: mode = "symlog" kwargs[base] = 2 kwargs[thresh] = core.note_to_hz("C2") kwargs[scale] = 0.5 elif ax_type in ["tempo", "fourier_tempo"]: mode = "log" kwargs[base] = 2 limit(16, 480) else: return scaler(mode, **kwargs) def __decorate_axis(axis, ax_type, key="C:maj", Sa=None, mela=None, thaat=None): """Configure axis tickers, locators, and labels""" if ax_type == "tonnetz": axis.set_major_formatter(TonnetzFormatter()) axis.set_major_locator(FixedLocator(0.5 + np.arange(6))) axis.set_label_text("Tonnetz") elif ax_type == "chroma": axis.set_major_formatter(ChromaFormatter(key=key)) degrees = core.key_to_degrees(key) axis.set_major_locator( FixedLocator(0.5 + np.add.outer(12 * np.arange(10), degrees).ravel()) ) axis.set_label_text("Pitch class") elif ax_type == "chroma_h": if Sa is None: Sa = 0 axis.set_major_formatter(ChromaSvaraFormatter(Sa=Sa)) if thaat is None: # If no thaat is given, show all svara degrees = np.arange(12) else: degrees = core.thaat_to_degrees(thaat) # Rotate degrees relative to Sa degrees = np.mod(degrees + Sa, 12) axis.set_major_locator( FixedLocator(0.5 + np.add.outer(12 * np.arange(10), degrees).ravel()) ) axis.set_label_text("Svara") elif ax_type == "chroma_c": if Sa is None: Sa = 0 axis.set_major_formatter(ChromaSvaraFormatter(Sa=Sa, mela=mela)) degrees = core.mela_to_degrees(mela) # Rotate degrees relative to Sa degrees = np.mod(degrees + Sa, 12) axis.set_major_locator( FixedLocator(0.5 + np.add.outer(12 * np.arange(10), degrees).ravel()) ) axis.set_label_text("Svara") elif ax_type in ["tempo", "fourier_tempo"]: axis.set_major_formatter(ScalarFormatter()) axis.set_major_locator(LogLocator(base=2.0)) axis.set_label_text("BPM") elif ax_type == "time": axis.set_major_formatter(TimeFormatter(unit=None, lag=False)) axis.set_major_locator(MaxNLocator(prune=None, steps=[1, 1.5, 5, 6, 10])) axis.set_label_text("Time") elif ax_type == "s": axis.set_major_formatter(TimeFormatter(unit="s", lag=False)) axis.set_major_locator(MaxNLocator(prune=None, steps=[1, 1.5, 5, 6, 10])) axis.set_label_text("Time (s)") elif ax_type == "ms": axis.set_major_formatter(TimeFormatter(unit="ms", lag=False)) axis.set_major_locator(MaxNLocator(prune=None, steps=[1, 1.5, 5, 6, 10])) axis.set_label_text("Time (ms)") elif ax_type == "lag": axis.set_major_formatter(TimeFormatter(unit=None, lag=True)) axis.set_major_locator(MaxNLocator(prune=None, steps=[1, 1.5, 5, 6, 10])) axis.set_label_text("Lag") elif ax_type == "lag_s": axis.set_major_formatter(TimeFormatter(unit="s", lag=True)) axis.set_major_locator(MaxNLocator(prune=None, steps=[1, 1.5, 5, 6, 10])) axis.set_label_text("Lag (s)") elif ax_type == "lag_ms": axis.set_major_formatter(TimeFormatter(unit="ms", lag=True)) axis.set_major_locator(MaxNLocator(prune=None, steps=[1, 1.5, 5, 6, 10])) axis.set_label_text("Lag (ms)") elif ax_type == "cqt_note": axis.set_major_formatter(NoteFormatter(key=key)) # Where is C1 relative to 2**k hz? log_C1 = np.log2(core.note_to_hz("C1")) C_offset = 2.0 ** (log_C1 - np.floor(log_C1)) axis.set_major_locator(LogLocator(base=2.0, subs=(C_offset,))) axis.set_minor_formatter(NoteFormatter(key=key, major=False)) axis.set_minor_locator( LogLocator(base=2.0, subs=C_offset * 2.0 ** (np.arange(1, 12) / 12.0)) ) axis.set_label_text("Note") elif ax_type == "cqt_svara": axis.set_major_formatter(SvaraFormatter(Sa=Sa, mela=mela)) # Find the offset of Sa relative to 2**k Hz sa_offset = 2.0 ** (np.log2(Sa) - np.floor(np.log2(Sa))) axis.set_major_locator(LogLocator(base=2.0, subs=(sa_offset,))) axis.set_minor_formatter(SvaraFormatter(Sa=Sa, mela=mela, major=False)) axis.set_minor_locator( LogLocator(base=2.0, subs=sa_offset * 2.0 ** (np.arange(1, 12) / 12.0)) ) axis.set_label_text("Svara") elif ax_type in ["cqt_hz"]: axis.set_major_formatter(LogHzFormatter()) log_C1 = np.log2(core.note_to_hz("C1")) C_offset = 2.0 ** (log_C1 - np.floor(log_C1)) axis.set_major_locator(LogLocator(base=2.0, subs=(C_offset,))) axis.set_major_locator(LogLocator(base=2.0)) axis.set_minor_formatter(LogHzFormatter(major=False)) axis.set_minor_locator( LogLocator(base=2.0, subs=C_offset * 2.0 ** (np.arange(1, 12) / 12.0)) ) axis.set_label_text("Hz") elif ax_type == "fft_note": axis.set_major_formatter(NoteFormatter(key=key)) # Where is C1 relative to 2**k hz? log_C1 = np.log2(core.note_to_hz("C1")) C_offset = 2.0 ** (log_C1 - np.floor(log_C1)) axis.set_major_locator(SymmetricalLogLocator(axis.get_transform())) axis.set_minor_formatter(NoteFormatter(key=key, major=False)) axis.set_minor_locator( LogLocator(base=2.0, subs=2.0 ** (np.arange(1, 12) / 12.0)) ) axis.set_label_text("Note") elif ax_type == "fft_svara": axis.set_major_formatter(SvaraFormatter(Sa=Sa, mela=mela)) # Find the offset of Sa relative to 2**k Hz log_Sa = np.log2(Sa) sa_offset = 2.0 ** (log_Sa - np.floor(log_Sa)) axis.set_major_locator( SymmetricalLogLocator( axis.get_transform(), base=2.0, subs=[sa_offset] ) ) axis.set_minor_formatter(SvaraFormatter(Sa=Sa, mela=mela, major=False)) axis.set_minor_locator( LogLocator(base=2.0, subs=sa_offset * 2.0 ** (np.arange(1, 12) / 12.0)) ) axis.set_label_text("Svara") elif ax_type in ["mel", "log"]: axis.set_major_formatter(ScalarFormatter()) axis.set_major_locator(SymmetricalLogLocator(axis.get_transform())) axis.set_label_text("Hz") elif ax_type in ["linear", "hz", "fft"]: axis.set_major_formatter(ScalarFormatter()) axis.set_label_text("Hz") elif ax_type in ["frames"]: axis.set_label_text("Frames") elif ax_type in ["off", "none", None]: axis.set_label_text("") axis.set_ticks([]) else: raise ParameterError("Unsupported axis type: {}".format(ax_type)) def __coord_fft_hz(n, sr=22050, **_kwargs): """Get the frequencies for FFT bins""" n_fft = 2 * (n - 1) # The following code centers the FFT bins at their frequencies # and clips to the non-negative frequency range [0, nyquist] basis = core.fft_frequencies(sr=sr, n_fft=n_fft) fmax = basis[-1] basis -= 0.5 * (basis[1] - basis[0]) basis = np.append(np.maximum(0, basis), [fmax]) return basis def __coord_mel_hz(n, fmin=0, fmax=None, sr=22050, htk=False, **_kwargs): """Get the frequencies for Mel bins""" if fmin is None: fmin = 0 if fmax is None: fmax = 0.5 * sr basis = core.mel_frequencies(n, fmin=fmin, fmax=fmax, htk=htk) basis[1:] -= 0.5 * np.diff(basis) basis = np.append(np.maximum(0, basis), [fmax]) return basis def __coord_cqt_hz(n, fmin=None, bins_per_octave=12, sr=22050, **_kwargs): """Get CQT bin frequencies""" if fmin is None: fmin = core.note_to_hz("C1") # Apply tuning correction fmin = fmin * 2.0 ** (_kwargs.get("tuning", 0.0) / bins_per_octave) # we drop by half a bin so that CQT bins are centered vertically freqs = core.cqt_frequencies( n + 1, fmin=fmin / 2.0 ** (0.5 / bins_per_octave), bins_per_octave=bins_per_octave, ) if np.any(freqs > 0.5 * sr): warnings.warn( "Frequency axis exceeds Nyquist. " "Did you remember to set all spectrogram parameters in specshow?" ) return freqs def __coord_chroma(n, bins_per_octave=12, **_kwargs): """Get chroma bin numbers""" return np.linspace(0, (12.0 * n) / bins_per_octave, num=n + 1, endpoint=True) def __coord_tempo(n, sr=22050, hop_length=512, **_kwargs): """Tempo coordinates""" basis = core.tempo_frequencies(n + 2, sr=sr, hop_length=hop_length)[1:] edges = np.arange(1, n + 2) return basis * (edges + 0.5) / edges def __coord_fourier_tempo(n, sr=22050, hop_length=512, **_kwargs): """Fourier tempogram coordinates""" n_fft = 2 * (n - 1) # The following code centers the FFT bins at their frequencies # and clips to the non-negative frequency range [0, nyquist] basis = core.fourier_tempo_frequencies( sr=sr, hop_length=hop_length, win_length=n_fft ) fmax = basis[-1] basis -= 0.5 * (basis[1] - basis[0]) basis = np.append(np.maximum(0, basis), [fmax]) return basis def __coord_n(n, **_kwargs): """Get bare positions""" return np.arange(n + 1) def __coord_time(n, sr=22050, hop_length=512, **_kwargs): """Get time coordinates from frames""" return core.frames_to_time(np.arange(n + 1), sr=sr, hop_length=hop_length) def __same_axes(x_axis, y_axis, xlim, ylim): """Check if two axes are the same, used to determine squared plots""" axes_same_and_not_none = (x_axis == y_axis) and (x_axis is not None) axes_same_lim = xlim == ylim return axes_same_and_not_none and axes_same_lim
[docs]def waveshow( y, sr=22050, max_points=11025, x_axis="time", offset=0.0, marker="", where="post", label=None, ax=None, **kwargs, ): """Visualize 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 : postive 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() """ util.valid_audio(y, mono=False) # Pad an extra channel dimension, if necessary if y.ndim == 1: y = y[np.newaxis, :] if max_points <= 0: raise ParameterError( "max_points={} must be strictly positive".format(max_points) ) # Create the adaptive drawing object axes = __check_axes(ax) if "color" not in kwargs: kwargs.setdefault("color", next(axes._get_lines.prop_cycler)["color"]) # Reduce by envelope calculation # this choice of hop ensures that the envelope has at most max_points values hop_length = max(1, y.shape[-1] // max_points) y_env = __envelope(y, hop_length) # Split the envelope into top and bottom y_bottom, y_top = -y_env[-1], y_env[0] times = offset + core.times_like(y, sr=sr, hop_length=1) # Only plot up to max_points worth of data here (steps,) = axes.step( times[:max_points], y[0, : max_points], marker=marker, where=where, **kwargs) envelope = axes.fill_between( times[: len(y_top) * hop_length : hop_length], y_bottom, y_top, step=where, label=label, **kwargs, ) adaptor = AdaptiveWaveplot( times, y[0], steps, envelope, sr=sr, max_samples=max_points ) axes.callbacks.connect("xlim_changed", adaptor.update) # Force an initial update to ensure the state is consistent adaptor.update(axes) # Construct tickers and locators __decorate_axis(axes.xaxis, x_axis) return adaptor