当然有可能：

x = arange(1,5,0.01)
yarr = vstack((x,))
y = sin(x)

imshow(yarr, extent=(min(x),max(x), min(y),max(y)), cmap=cm.hot)
plot(x, y, color='cornflowerblue',lw=4)

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extent关键字将图像的限制与打印数据相匹配。
这将给你给予：

也可以在所需位置（例如x=5）Resulting Figure中包含您自己的发散色图，其中发散点

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

## Create colormap for J background
low = (np.array([255,244,132])/255).tolist() # 
high = (np.array([84,236,231])/255).tolist() # 

color1 = [low, (1,1,1)] #from low to white
color2= [(1,1,1), high ] # from white to high 

cm1 = LinearSegmentedColormap.from_list("Custom", color1, N=100)
cm2 = LinearSegmentedColormap.from_list("Custom", color2, N=100)

## Create a figure and axis for the main plot
fig, ax1 = plt.subplots()
x_data = np.linspace(0, 10, 100)
y_data = np.sin(x_data)
plt.plot(x_data, y_data)

## poit for 'white'
div_point_x =  5

## scale the imshow for the plot area
xp = ax1.get_xlim()
yp = ax1.get_ylim()

# color scale for both parts of the imshow
x1 = np.linspace(xp[0], div_point_x, 256)
x2 = np.linspace(div_point_x, xp[1], 256)
y1 = np.ones(256)*yp[1]

X1, Y1 = np.meshgrid(x1, y1)
X2, Y1 = np.meshgrid(x2, y1)

gradient1 = (X1*Y1)
gradient2 = (X2*Y1)

## plot the blackgroun with diverging colormap
plt.imshow(gradient1, cmap=cm1, extent=(xp[0], div_point_x, yp[0], yp[1]),aspect='auto', origin='lower',alpha=0.7)
plt.imshow(gradient2, cmap=cm2, extent=(div_point_x, xp[1], yp[0], yp[1]),aspect='auto', origin='lower',alpha=0.7)

plt.xlim(xp)
plt.ylim(yp)

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