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

Before Change

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

    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

    if n_steps < 1:
        raise ParameterError("n_steps must be a positive integer")

    if data.ndim > 2:
        raise ParameterError("Input must be at most 2-dimensional. "
                             "Given data.shape={}".format(data.shape))

    if delay == 0:
        raise ParameterError("delay must be a non-zero integer")

    data = np.atleast_2d(data)
    t = data.shape[-1]
    
    if t < 1:
        raise ParameterError("Cannot stack memory when input data has "
                             "no columns. Given data.shape={}".format(data.shape))
    kwargs.setdefault("mode", "constant")

    if kwargs["mode"] == "constant":
        kwargs.setdefault("constant_values", [0])

    // Pad the end with zeros, which will roll to the front below
    if delay > 0:
        padding = (int((n_steps - 1) * delay), 0)
    else:
        padding = (0, int((n_steps - 1) * -delay))

    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
    history = np.empty_like(data, shape=shape)

    // Populate the output array
    __stack(history, data, n_steps, delay)

    return history


@jit(nopython=True, cache=True)
def __stack(history, data, n_steps, delay):

Instances