下面是一个基于here和here的基本示例：

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

fig, ax = plt.subplots()

delta = 0.01

x = y = np.arange(-3.0, 3.01, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2

# get the mean of Z (say the density)
md = np.mean(Z)

# first create a masked version of Z to plot as a grey scale image
overdense = Z.copy()
overdense[Z < md] = np.ma.masked

ax.imshow(
    overdense,
    interpolation="bilinear",
    origin="lower",
    extent=[x[0], x[-1], y[0], y[-1]],
    cmap=colormaps["Greys"],
    norm=colors.Normalize(vmin=md, vmax=np.max(Z)),
)

# over plot the contours for the over dense and underdense regions
# (I'm defining the underdense contours to be 0.1 less than the mean density)
ax.contour(x, y, Z, levels=[md - 0.1, md], colors="k")

plt.show()

字符串
这给出：

的数据