分段色彩图的自定义色彩图边界

我有下图，其中包含具有某些量化特征的多个向量的可用数据：

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.colors as colors

fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot()
category = [0,0,0,0.1,0.4,0.9,1.5]
r = np.random.uniform(size=[len(category)*100]).reshape(len(category),100)

norm = matplotlib.colors.Normalize(vmin=min(category), vmax=max(category))
bounds = np.array([0, 0.3, 0.5, 1.5])
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=3)
cmap = matplotlib.cm.ScalarMappable(norm=norm, cmap=colors.ListedColormap(['green', 'blue', 'red']))
cmap.set_array([])
for no, cat in enumerate(category):
    ax.plot(r[no][r[no]>0.1],no*np.ones(100)[r[no]>0.1],'o',color=cmap.to_rgba(category[no]))
cbar = fig.colorbar(cmap, ax=ax, pad=0.01)

我想知道是否有任何方法可以移动颜色图特征边界以对应于图片中向量之间的边界？ （如黑色箭头所示）

我以为spacing='proportional'会对我有帮助，但是，这取决于特征，并且我想让它取决于具有某些特征范围的向量的数量。

Additionally, is it possible to use these custom boundaries for gradient (not segmented) colormap?

我找到了做我所要求的事情的方法。 这个想法是创建一个新变量，该变量取决于每组中向量的数量，并使用该变量spacing='proportional'。这是 MWE：

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.colors as colors

fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot()
category = np.array([0,0,0,0.1,0.4,0.9,1.5])
r = np.random.uniform(size=[len(category)*100]).reshape(len(category),100)

norm = matplotlib.colors.Normalize(vmin=min(category), vmax=max(category))

lev1 = 0.3
lev2 = 0.5

gr0 = (category<=lev1).sum()
gr2 = (category>lev2).sum()
gr1 = len(category) - gr0 - gr2
frac = np.array([gr0,gr1,gr2])/len(category)
bounds = np.array([0, frac[0], frac[1]+frac[0], 1])
gr_color = np.where(category<lev1,0,np.where(category>=lev2,1,frac[0]+frac[1]/2))

norm = colors.BoundaryNorm(boundaries=bounds, ncolors=3)
cmap = matplotlib.cm.ScalarMappable(norm=norm, cmap=colors.ListedColormap(['green', 'blue', 'red']))
cmap.set_array([])
for no, cat in enumerate(category):
    ax.plot(r[no][r[no]>0.1],no*np.ones(100)[r[no]>0.1],'o',color=cmap.to_rgba(gr_color[no]))
cbar = fig.colorbar(cmap, ax=ax, pad=0.01,spacing='proportional')
dic = {bounds[0] : 0 ,bounds[1] : lev1, bounds[2] : lev2,bounds[3] : "1.5"}
labels = [bounds[i] if t not in dic.keys() else dic[t] for i,t in enumerate(bounds)]
cbar.ax.set_yticklabels(labels)