Python + matplotlib implement the example code of the Box Bar Chart, pythonmatplotlib

Python + matplotlib implement the example code of the Box Bar Chart, pythonmatplotlib

Demo result:

Complete code:

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()

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