8e4250ba8221d956c282067d11e2df8a4c3299ab,librosa/display.py,,__axis_log,#Any#Any#Any#Any#Any#Any#,414

Before Change

    im_phantom.set_data(*args)

    axes_phantom.images[0] = im_phantom
    axes_phantom.set_xlim(0, data.shape[1])
    axes_phantom.set_ylim(0, data.shape[0])

    positions = np.linspace(0, n, n_ticks, endpoint=False, dtype=int)
    values = np.linspace(0, 0.5 * sr, n, endpoint=True, dtype=int)

After Change

               **_kwargs):
    """Plot a log-scaled image"""

    axes_phantom = plt.gca()

    if kwargs is None:
        kwargs = dict()

    aspect = kwargs.pop("aspect", None)

    n, ticker, labeler = __get_shape_artists(data, horiz)
    t_log, t_inv = __log_scale(n)

    if horiz:
        args = (t_log, np.linspace(0, data.shape[0], data.shape[0], dtype=int),
                data)
    else:
        args = (np.linspace(0, data.shape[1], data.shape[1], dtype=int),
                t_log, data)

    im_phantom = img.NonUniformImage(axes_phantom,
                                     extent=(args[0].min(), args[0].max(),
                                             args[1].min(), args[1].max()),
                                     **kwargs)
    im_phantom.set_data(*args)

    kwargs["aspect"] = aspect

    axes_phantom.images[0] = im_phantom
    axes_phantom.set_xlim(args[0].min(), args[0].max())
    axes_phantom.set_ylim(args[1].min(), args[1].max())

    positions = np.linspace(0, n, n_ticks, endpoint=False, dtype=int)
    values = np.linspace(0, 0.5 * sr, n, endpoint=True, dtype=int)

Instances