我试图将多个Contourf图合并为一个，我设法使用alpha=0.5做到了这一点，但是fill元素意味着并非所有图都可见。
我的代码是：
fig,ax = plt.subplots(figsize = (20,16))
b=ax.contourf(dfE,4,cmap='Greens', alpha=0.5, linewidths=(3,))
cbax2 = fig.add_axes([0.91, 0.41, 0.02, 0.2])
cb2 = plt.colorbar(b, cax=cbax2)
d = ax.contourf(dfH,4,cmap='Reds', linewidths=(3,), alpha=0.5)
cbax4 = fig.add_axes([0.91, 0.19, 0.02, 0.2])
cb4 = plt.colorbar(d, cax=cbax4)
f = ax.contourf(dfS,3,cmap='Wistia', linewidths=(3,), alpha=0.5)
cbax6 = fig.add_axes([0.97, 0.41, 0.02, 0.2])
cb6 = plt.colorbar(f, cax=cbax6)
g = ax.contourf(dfT,4,cmap='Purples', linewidths=(2,), alpha=0.5)
cbax7 = fig.add_axes([0.97, 0.63, 0.02, 0.2])
cb7 = plt.colorbar(g, cax=cbax7)
h = ax.contourf(dfC,4,cmap='Blues', linewidths=(3,), alpha=0.5)
cbax8 = fig.add_axes([0.91, 0.63, 0.02, 0.2])
cb8 = plt.colorbar(h, cax=cbax8)
ax.set_ylim([0, 16])
ax.set_xlim([0, 16])
ax.set_xlabel('Principal Component 1', size = 25)
ax.set_ylabel('Principal Component 2', size = 25)
cb4.set_label('Helix (H)',size = 15)
cb2.set_label('Sheet (E)',size = 15)
cb8.set_label('Other (C)',size = 15)
cb7.set_label('H-Bonded Turn (T)',size = 15)
cb6.set_label('Bend (S)',size = 15)
ax.set_title('8-State PCA Analysis: 108 Dimensions', size = 30)
plt.show()
我的情节是：
最佳答案
如果要将它们全部放在同一张图上，则应尝试设置轮廓线级别而不显示较小的值。您还可以降低不太重要的数据的Alpha值。
这是一个示例，其中我设置了轮廓线级别并使用extend='max'，以便不显示最低轮廓线级别以下的值，而将上方的值阴影化为最大值：
import numpy as np
from matplotlib import pyplot as plt
#Create the grid
x = np.arange(-20,21,1)
y=x
X,Y = np.meshgrid(x,y)
#Create the functions to plot
Z1 = 1000-np.abs(X**2+(Y+4)**3)
Z2 = 1000-np.abs((X+4)**3+Y**3)
Z3 = 1000-np.abs((Y+2)**3+(X-3)**3)
Z4 = 1000-np.abs(X**2+Y**3-1)
fig = plt.figure(figsize=(8,8))
ax = plt.subplot(111)
#Plot using contourf making sure you set your contour levels and don't show the lowest levels
b=ax.contourf(X,Y,Z1/np.nanmax(Z1),[.25,.5,.75],alpha=.5,cmap='Greens',linewidths=3,extend='max')
d=ax.contourf(X,Y,Z2/np.nanmax(Z2),[.25,.5,.75],alpha=.5,cmap='Reds',linewidths=3,extend='max')
f=ax.contourf(X,Y,Z3/np.nanmax(Z3),[.25,.5,.75],alpha=.5,cmap='Blues',linewidths=3,extend='max')
g=ax.contourf(X,Y,Z4/np.nanmax(Z4),[.25,.5,.75],alpha=.5,cmap='Purples',linewidths=3,extend='max')
plt.show()
考虑使用轮廓图
正如我在评论中提到的那样，您应该考虑使用等高线图来表示更改线色的数据。您还可以更改
linestyles和linewidths以突出显示要在情节中传达的消息。您还可以结合使用contour()和contourf()绘图来更好地突出显示数据。import numpy as np
from matplotlib import pyplot as plt
#Create the grid
x = np.arange(-20,21,1)
y=x
X,Y = np.meshgrid(x,y)
#Create the functions to plot
Z1 = 1000-np.abs(X**2+(Y+4)**3)
Z2 = 1000-np.abs((X+4)**3+Y**3)
Z3 = 1000-np.abs((Y+2)**3+(X-3)**3)
Z4 = 1000-np.abs(X**2+Y**3-1)
fig = plt.figure(figsize=(8,8))
ax = plt.subplot(111)
#Plot using contour instead of contourf and change the colors
b=ax.contour(X,Y,Z1/np.nanmax(Z1),[.25,.5,.75],alpha=.8,colors='Green',linewidths=3)
d=ax.contour(X,Y,Z2/np.nanmax(Z2),[.25,.5,.75],alpha=.8,colors='Red',linewidths=3)
f=ax.contour(X,Y,Z3/np.nanmax(Z3),[.25,.5,.75],alpha=.8,colors='Blue',linewidths=3)
g=ax.contour(X,Y,Z4/np.nanmax(Z4),[.25,.5,.75],alpha=.8,colors='Purple',linewidths=3,linestyles='dashed')
plt.show()
关于python - 如何合并多个等高线图？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/57525830/