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# Demo Ribbon Box

    import matplotlib.pyplot as plt
    import numpy as np
    from matplotlib.image import BboxImage

    from matplotlib._png import read_png
    import matplotlib.colors
    from matplotlib.cbook import get_sample_data

    class RibbonBox(object):

        original_image = read_png(get_sample_data("Minduka_Present_Blue_Pack.png",
                                                  asfileobj=False))
        cut_location = 70
        b_and_h = original_image[:, :, 2]
        color = original_image[:, :, 2] - original_image[:, :, 0]
        alpha = original_image[:, :, 3]
        nx = original_image.shape[1]

        def __init__(self, color):
            rgb = matplotlib.colors.to_rgba(color)[:3]

            im = np.empty(self.original_image.shape,
                          self.original_image.dtype)

            im[:, :, :3] = self.b_and_h[:, :, np.newaxis]
            im[:, :, :3] -= self.color[:, :, np.newaxis]*(1. - np.array(rgb))
            im[:, :, 3] = self.alpha

            self.im = im

        def get_stretched_image(self, stretch_factor):
            stretch_factor = max(stretch_factor, 1)
            ny, nx, nch = self.im.shape
            ny2 = int(ny*stretch_factor)

            stretched_image = np.empty((ny2, nx, nch),
                                       self.im.dtype)
            cut = self.im[self.cut_location, :, :]
            stretched_image[:, :, :] = cut
            stretched_image[:self.cut_location, :, :] = \
                self.im[:self.cut_location, :, :]
            stretched_image[-(ny - self.cut_location):, :, :] = \
                self.im[-(ny - self.cut_location):, :, :]

            self._cached_im = stretched_image
            return stretched_image

    class RibbonBoxImage(BboxImage):
        zorder = 1

        def __init__(self, bbox, color,
                     cmap=None,
                     norm=None,
                     interpolation=None,
                     origin=None,
                     filternorm=1,
                     filterrad=4.0,
                     resample=False,
                     **kwargs
                     ):

            BboxImage.__init__(self, bbox,
                               cmap=cmap,
                               norm=norm,
                               interpolation=interpolation,
                               origin=origin,
                               filternorm=filternorm,
                               filterrad=filterrad,
                               resample=resample,
                               **kwargs
                               )

            self._ribbonbox = RibbonBox(color)
            self._cached_ny = None

        def draw(self, renderer, *args, **kwargs):

            bbox = self.get_window_extent(renderer)
            stretch_factor = bbox.height / bbox.width

            ny = int(stretch_factor*self._ribbonbox.nx)
            if self._cached_ny != ny:
                arr = self._ribbonbox.get_stretched_image(stretch_factor)
                self.set_array(arr)
                self._cached_ny = ny

            BboxImage.draw(self, renderer, *args, **kwargs)

    if 1:
        from matplotlib.transforms import Bbox, TransformedBbox
        from matplotlib.ticker import ScalarFormatter

        # Fixing random state for reproducibility
        np.random.seed(19680801)

        fig, ax = plt.subplots()

        years = np.arange(2004, 2009)
        box_colors = [(0.8, 0.2, 0.2),
                      (0.2, 0.8, 0.2),
                      (0.2, 0.2, 0.8),
                      (0.7, 0.5, 0.8),
                      (0.3, 0.8, 0.7),
                      ]
        heights = np.random.random(years.shape) * 7000 + 3000

        fmt = ScalarFormatter(useOffset=False)
        ax.xaxis.set_major_formatter(fmt)

        for year, h, bc in zip(years, heights, box_colors):
            bbox0 = Bbox.from_extents(year - 0.4, 0., year + 0.4, h)
            bbox = TransformedBbox(bbox0, ax.transData)
            rb_patch = RibbonBoxImage(bbox, bc, interpolation="bicubic")

            ax.add_artist(rb_patch)

            ax.annotate(r"%d" % (int(h/100.)*100),
                        (year, h), va="bottom", ha="center")

        patch_gradient = BboxImage(ax.bbox,
                                   interpolation="bicubic",
                                   zorder=0.1,
                                   )
        gradient = np.zeros((2, 2, 4), dtype=float)
        gradient[:, :, :3] = [1, 1, 0.]
        gradient[:, :, 3] = [[0.1, 0.3], [0.3, 0.5]]  # alpha channel
        patch_gradient.set_array(gradient)
        ax.add_artist(patch_gradient)

        ax.set_xlim(years[0] - 0.5, years[-1] + 0.5)
        ax.set_ylim(0, 10000)

        fig.savefig('ribbon_box.png')
        plt.show()

Total running time of the script: ( 0 minutes 0.234 seconds)

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