9b95e0f07b60b6a144893dcc506dfaf90db61c95,librosa/feature/utils.py,,stack_memory,#,119

Before Change

    history = np.vstack([np.roll(data, -i * delay, axis=1) for i in range(n_steps)[::-1]])

    // Trim to original width
    if delay > 0:
        history = history[:, :t]
    else:
        history = history[:, -t:]

    // Make contiguous
    return np.asfortranarray(history)

After Change

    data = np.pad(data, [(0, 0), padding], **kwargs)

    // Construct the shape of the target array
    shape = list(data.shape)
    shape[0] = shape[0] * n_steps
    shape[1] = t
    shape = tuple(shape)

    // Construct the output array to match layout and dtype of input

Instances