Python-matplotlib 多子图共用colorbar

#将颜色映射到 vmin~vmax 之间
norm = matplotlib.colors.Normalize(vmin=0, vmax=60)

import pandas as pd
import numpy as np
import matplotlib
import matplotlib.cm as cm
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = ['Arial']

test_data = pd.read_excel('GBRT_SVR_DNN_esti_save_angle_process.xlsx')

x = test_data['true_data'].values.ravel() #真实值
y = test_data['model01_estimated'].values.ravel()#预测值
y2 = test_data['SVR_estimated'].values.ravel()#预测值2
y3 = test_data['DNN_model01_estimated'].values.ravel()#预测值3

nbins = 150
#模型一结果
H, xedges, yedges = np.histogram2d(x, y, bins=nbins)
H = np.rot90(H)
H = np.flipud(H)
Hmasked = np.ma.masked_where(H==0,H)

#模型二结果
H2, xedges2, yedges2 = np.histogram2d(x, y2, bins=nbins)
H2 = np.rot90(H2)
H2 = np.flipud(H2)
Hmasked2 = np.ma.masked_where(H2==0,H2)

#模型三结果
H3, xedges3, yedges3 = np.histogram2d(x, y3, bins=nbins)
H3 = np.rot90(H3)
H3 = np.flipud(H3)
Hmasked3 = np.ma.masked_where(H3==0,H3)

#可视化绘制
fig, (ax1, ax2,ax3) = plt.subplots(nrows=1,ncols=3,figsize=(12,3),dpi=200)

#将颜色映射到 vmin~vmax 之间
norm = matplotlib.colors.Normalize(vmin=0, vmax=60)

#模型一结果绘图
im1 = ax1.pcolormesh(xedges, yedges, Hmasked, cmap=cm.get_cmap('jet'),norm=norm)
#fig.colorbar(im1, ax=ax1)

#模型二结果绘图
im2 = ax2.pcolormesh(xedges2, yedges2, Hmasked2, cmap=cm.get_cmap('jet'), norm=norm)
#fig.colorbar(im2, ax=ax2)

#模型二结果绘图
im3 = ax3.pcolormesh(xedges3, yedges3, Hmasked3, cmap=cm.get_cmap('jet'), norm=norm,)
#fig.colorbar(im3, ax=ax3)

#前面三个子图的总宽度 为 全部宽度的 0.9；剩下的0.1用来放置colorbar
fig.subplots_adjust(right=0.9)
position = fig.add_axes([0.92, 0.12, 0.015, .78 ])#位置[左,下,右,上]
cb = fig.colorbar(im3, cax=position)

#设置colorbar标签字体等
colorbarfontdict = {"size":15,"color":"k",'family':'Times New Roman'}
cb.ax.set_title('Counts',fontdict=colorbarfontdict,pad=8)
cb.ax.tick_params(labelsize=11,direction='in')
cb.ax.set_yticklabels(['0','10','20','30','40','50','>60'],family='Times New Roman')
#suptitle（）中x,y 属性的设置为调整title与子图(subplots)之间的距离
fig.suptitle('One Colorbar for Multiple Plot ',size=20,family='Times New Roman',x=.5,y=1.05)
plt.savefig(r'E:\Data_resourses\DataCharm 公众号\Python\学术图表绘制\scatter_One_Colorbar.png',
            width=7,height=5,dpi=900,bbox_inches='tight')
plt.show()

#设置colorbar标签字体等
colorbarfontdict = {"size":15,"color":"k",'family':'Times New Roman'}
cb.ax.set_title('Counts',fontdict=colorbarfontdict,pad=8)
cb.ax.tick_params(labelsize=11,direction='in')
cb.ax.set_yticklabels(['0','10','20','30','40','50','>60'],family='Times New Roman')

fig.subplots_adjust(right=0.9)
position = fig.add_axes([0.92, 0.12, 0.015, .78 ])#位置[左,下,右,上]
cb = fig.colorbar(im3, cax=position)

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde

x = test_data['true_data'].values.ravel() #真实值
y = test_data['model01_estimated'].values.ravel()#预测值
#计算点密度
#np.vstack:按垂直方向（行顺序）堆叠数组构成一个新的数组
xy = np.vstack([x,y])
z = gaussian_kde(xy)(xy)
idx = z.argsort()
x, y, z = x[idx], y[idx], z[idx]
fig, ax = plt.subplots(figsize=(7,5),dpi=200)
ax.scatter(x, y, c=z, s=3, edgecolor='')
plt.savefig(r'E:\Data_resourses\DataCharm 公众号\Python\学术图表绘制\scatter_gaussian_kde.png',
            width=7,height=5,dpi=900,bbox_inches='tight')
plt.show()

idx = z.argsort()
x, y, z = x[idx], y[idx], z[idx]