演示丝带盒

演示丝带盒示例

  1. import numpy as np
  3. from matplotlib import cbook, colors as mcolors
  4. from matplotlib.image import BboxImage
  5. import matplotlib.pyplot as plt
  8. class RibbonBox:
  10.     original_image = plt.imread(
  11.         cbook.get_sample_data("Minduka_Present_Blue_Pack.png"))
  12.     cut_location = 70
  13.     b_and_h = original_image[:, :, 2:3]
  14.     color = original_image[:, :, 2:3] - original_image[:, :, 0:1]
  15.     alpha = original_image[:, :, 3:4]
  16.     nx = original_image.shape[1]
  18.     def __init__(self, color):
  19.         rgb = mcolors.to_rgba(color)[:3]
  20.         self.im = np.dstack(
  21.             [self.b_and_h - self.color * (1 - np.array(rgb)), self.alpha])
  23.     def get_stretched_image(self, stretch_factor):
  24.         stretch_factor = max(stretch_factor, 1)
  25.         ny, nx, nch = self.im.shape
  26.         ny2 = int(ny*stretch_factor)
  27.         return np.vstack(
  28.             [self.im[:self.cut_location],
  29.              np.broadcast_to(
  30.                  self.im[self.cut_location], (ny2 - ny, nx, nch)),
  31.              self.im[self.cut_location:]])
  34. class RibbonBoxImage(BboxImage):
  35.     zorder = 1
  37.     def __init__(self, bbox, color, **kwargs):
  38.         super().__init__(bbox, **kwargs)
  39.         self._ribbonbox = RibbonBox(color)
  41.     def draw(self, renderer, *args, **kwargs):
  42.         bbox = self.get_window_extent(renderer)
  43.         stretch_factor = bbox.height / bbox.width
  45.         ny = int(stretch_factor*self._ribbonbox.nx)
  46.         if self.get_array() is None or self.get_array().shape[0] != ny:
  47.             arr = self._ribbonbox.get_stretched_image(stretch_factor)
  48.             self.set_array(arr)
  50.         super().draw(renderer, *args, **kwargs)
  53. if True:
  54.     from matplotlib.transforms import Bbox, TransformedBbox
  55.     from matplotlib.ticker import ScalarFormatter
  57.     # Fixing random state for reproducibility
  58.     np.random.seed(19680801)
  60.     fig, ax = plt.subplots()
  62.     years = np.arange(2004, 2009)
  63.     box_colors = [(0.8, 0.2, 0.2),
  64.                   (0.2, 0.8, 0.2),
  65.                   (0.2, 0.2, 0.8),
  66.                   (0.7, 0.5, 0.8),
  67.                   (0.3, 0.8, 0.7),
  68.                   ]
  69.     heights = np.random.random(years.shape) * 7000 + 3000
  71.     fmt = ScalarFormatter(useOffset=False)
  72.     ax.xaxis.set_major_formatter(fmt)
  74.     for year, h, bc in zip(years, heights, box_colors):
  75.         bbox0 = Bbox.from_extents(year - 0.4, 0., year + 0.4, h)
  76.         bbox = TransformedBbox(bbox0, ax.transData)
  77.         rb_patch = RibbonBoxImage(bbox, bc, interpolation="bicubic")
  79.         ax.add_artist(rb_patch)
  81.         ax.annotate(r"%d" % (int(h/100.)*100),
  82.                     (year, h), va="bottom", ha="center")
  84.     patch_gradient = BboxImage(ax.bbox, interpolation="bicubic", zorder=0.1)
  85.     gradient = np.zeros((2, 2, 4))
  86.     gradient[:, :, :3] = [1, 1, 0.]
  87.     gradient[:, :, 3] = [[0.1, 0.3], [0.3, 0.5]]  # alpha channel
  88.     patch_gradient.set_array(gradient)
  89.     ax.add_artist(patch_gradient)
  91.     ax.set_xlim(years[0] - 0.5, years[-1] + 0.5)
  92.     ax.set_ylim(0, 10000)
  94.     plt.show()

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