Python中的 Matplotlib.pyplot.contourf()

Matplotlib是Python中的一个库，它是 NumPy 库的数值数学扩展。 Pyplot是Matplotlib模块的基于状态的接口，它提供了一个类似 MATLAB 的接口。

matplotlib.pyplot.contourf()函数

matplotlib 库的 pyplot 模块中的contourf()函数用于绘制等高线。但是contourf绘制填充轮廓，而contourf绘制轮廓线。

Syntax: matplotlib.pyplot.contourf(\*args, data=None, \*\*kwargs)

Parameters: This method accept the following parameters that are described below:

• X, Y: These parameter are the coordinates of the values in Z.
• Z : This parameter is the height values over which the contour is drawn.
• levels : This parameter is used to determine the numbers and positions of the contour lines / regions.

Returns: This returns the following:

• c :This returns the QuadContourSet.

下面的示例说明了 matplotlib.pyplot 中的 matplotlib.pyplot.contourf()函数：

示例 #1：

# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt
from numpy import ma
from matplotlib import ticker, cm
   
N = 1000
x = np.linspace(-6.0, 6.0, N)
y = np.linspace(-7.0, 7.0, N)
X, Y = np.meshgrid(x, y)
   
Z1 = np.exp(X * Y)
z = 50 * Z1
z[:5, :5] = -1
z = ma.masked_where(z <= 0, z)
   
cs = plt.contourf(X, Y, z,
                  locator = ticker.LogLocator(),
                  cmap ="bone")
  
cbar = plt.colorbar(cs)
  
plt.title('matplotlib.pyplot.contourf() Example')
plt.show()

输出：

示例 #2：

# Implementation of matplotlib function
import matplotlib.pyplot as plt
import numpy as np
   
# invent some numbers, turning 
# the x and y arrays into simple
# 2d arrays, which make combining 
# them together easier.
x = np.linspace(-3, 15, 50).reshape(1, -1)
y = np.linspace(-3, 15, 20).reshape(-1, 1)
z = np.cos(x)*2 - np.sin(y)*2
  
# we no longer need x and y to
# be 2 dimensional, so flatten them.
x, y = x.flatten(), y.flatten()
   
cs = plt.contourf(x, y, z, 
                  hatches =['-', '/',
                            '\\', '//'],
                  cmap ='Greens',
                  extend ='both',
                  alpha = 1)
  
plt.colorbar(cs)
  
plt.title('matplotlib.pyplot.contourf() Example')
plt.show()

输出：