答案 0 :(得分:3)
我认为这样做会更好,但是基于@ImportanceOfBeingErnest的评论,我想这实际上是可行的方法:
from matplotlib.path import Path
from matplotlib.patches import PathPatch
x = [np.random.normal(loc=i, scale=1, size=(100,)) for i in range(5)]
fig, ax = plt.subplots()
violins = ax.violinplot(x)
ymin, ymax = ax.get_ylim()
xmin, xmax = ax.get_xlim()
# create a numpy image to use as a gradient
Nx,Ny=1,1000
imgArr = np.tile(np.linspace(0,1,Ny), (Nx,1)).T
cmap = 'hsv'
for violin in violins['bodies']:
path = Path(violin.get_paths()[0].vertices)
patch = PathPatch(path, facecolor='none', edgecolor='none')
ax.add_patch(patch)
img = ax.imshow(imgArr, origin="lower", extent=[xmin,xmax,ymin,ymax], aspect="auto",
cmap=cmap,
clip_path=patch)