Function plot_input

bindsnet/analysis/plotting.py:21–70

Plots a two-dimensional image and its corresponding spike-train representation. :param image: A 2D array of floats depicting an input image. :param inpt: A 2D array of floats depicting an image's spike-train encoding. :param label: Class label of the input data. :param axes: Us

(
    image: torch.Tensor,
    inpt: torch.Tensor,
    label: Optional[int] = None,
    axes: List[Axes] = None,
    ims: List[AxesImage] = None,
    figsize: Tuple[int, int] = (8, 4),
)

def plot_input(
    image: torch.Tensor,
    inpt: torch.Tensor,
    label: Optional[int] = None,
    axes: List[Axes] = None,
    ims: List[AxesImage] = None,
    figsize: Tuple[int, int] = (8, 4),
) -> Tuple[List[Axes], List[AxesImage]]:
    # language=rst
    """
    Plots a two-dimensional image and its corresponding spike-train representation.

    :param image: A 2D array of floats depicting an input image.
    :param inpt: A 2D array of floats depicting an image's spike-train encoding.
    :param label: Class label of the input data.
    :param axes: Used for re-drawing the input plots.
    :param ims: Used for re-drawing the input plots.
    :param figsize: Horizontal, vertical figure size in inches.
    :return: Tuple of ``(axes, ims)`` used for re-drawing the input plots.
    """
    local_image = image.detach().clone().cpu().numpy()
    local_inpy = inpt.detach().clone().cpu().numpy()

    if axes is None:
        fig, axes = plt.subplots(1, 2, figsize=figsize)
        ims = (
            axes[0].imshow(local_image, cmap="binary", aspect="auto"),
            axes[1].imshow(local_inpy, cmap="binary", aspect="auto"),
        )

        if label is None:
            axes[0].set_title("Current image")
        else:
            axes[0].set_title("Current image (label = %d)" % label)

        axes[1].set_title("Reconstruction")

        for ax in axes:
            ax.set_xticks(())
            ax.set_yticks(())

        fig.tight_layout()
    else:
        if label is not None:
            axes[0].set_title("Current image (label = %d)" % label)

        ims[0].set_data(local_image)
        ims[1].set_data(local_inpy)

    return axes, ims


def plot_spikes(