演示结果：

完整代码：

import matplotlib.pyplot as pltimport numpy as npfrom matplotlib.image import BboxImagefrom matplotlib._png import read_pngimport matplotlib.colorsfrom matplotlib.cbook import get_sample_dataclass 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_imageclass 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()

总结

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