使用 Matplotlib 在Python中绘制四边形网格

Matplotlib是一个基于 NumPy 数组的多平台数据可视化库，旨在与更广泛的 SciPy 堆栈一起使用。 Matplotlib 也能够很好地与许多操作系统和图形后端配合使用。 matplotlib.pyplot也可用于类似 MATLAB 的绘图框架。

绘制四边形网格

使用 pyplot 模块的pcolormesh()函数，它类似于pcolor()函数，但 pcolor 返回PolyCollection而 pcolormesh 返回matplotlib.collections.QuadMesh 。 pcolormesh 更快，因此可以处理更大的数组。

Syntax : pcolormesh(cmap = [None | Colormap], alpha = [0<=scalar<=1 | None], edgecolors = [None | color | 'face'], shading = ['gouraud' | 'flat'], norm = [None | Normalize], vimax = [scalar | None], vimin = [scalar | None])

Parameters:

1. cmap : It can be None or matplotlib has a number of built-in colormaps accessible via matplotlib.cm.get_cmap
2. alpha : It can be None or alpha value between 0 to 1.
3. edgecolors :
   • If its None, edges will not be visible.
   • ‘face’ represents the same color as faces.
   • color sequence will set a color.
4. shading : It can be either ‘flat’ or ‘gouraud’
5. norm : If its None defaults to normalize().
6. vimax : It can be either None or the scalar value.
7. vimin : It can be either None or the scalar value. ( vimax and vimin are used in conjunction with normalize data)

示例 1：

import matplotlib.pyplot as plt
import numpy as np
  
  
x1, y1 = 0.1, 0.05
  
# generate 2-D grids for the
# x & y bounds
y, x = np.mgrid[slice(-3, 3 + y1, y1), slice(-3, 4 + x1, x1)]
z = (1 - x / 2. + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)
  
# Remove the last value from the
# z array as z must be inside x
# and y bounds.
z = z[:-1, :-1]
z_min, z_max = -np.abs(z).max(), np.abs(z).max()
  
plt.subplot()
  
plt.pcolormesh(x, y, z, 
               cmap ='YlGn', 
               vmin = z_min, 
               vmax = z_max,
               edgecolors = 'face',
               shading ='flat')
  
plt.title('pcolormesh_example')
  
# set the limits of the plot
# to the limits of the data
plt.axis([x.min(), x.max(), y.min(), y.max()])
  
plt.colorbar()
plt.show()

输出 ：

示例 2：

import matplotlib.pyplot as plt
import numpy as np
  
  
x = np.array([[0, 1, 2, 3], 
              [0, 1, 2, 3],
              [0, 1, 2, 3],
              [0, 1, 2, 3]]) 
  
y = np.array([[0.0, 0.0, 0.0, 0],
              [1.0, 1.0, 1.0, 1], 
              [2.0, 2.0, 2.0, 2],
              [3, 3, 3, 3]]) 
  
values = np.array([[0, 0.5, 1], 
                   [1, 1.5, 2],
                   [2, 2.5, 3]])
  
fig, ax = plt.subplots()
  
ax.pcolormesh(x, y, values)
ax.set_aspect('equal')
ax.set_title("pcolormesh_example2")

输出 ：