这三个函数在本质上是相同的，我们先来研究np.ogrid()函数，代码如下：
# -*- coding: utf-8 -*-
"""
np.ogrid(), np.mgrid(), np.meshgrid()
"""
import numpy as np
import matplotlib.pyplot as plt
class Debug:
def __init__(self):
self.x = []
self.y = []
def mainProgram(self):
self.y, self.x = np.ogrid[0:5, 0:5]
print("The value of x is: ")
print(self.x)
print("The value of y is: ")
print(self.y)
print("The result of np.ogrid[0:5, 0:5] is: ")
print(np.ogrid[0:5, 0:5])
# create a 2D intensity value
intensity = np.random.random_sample(size=(5, 5))
fig = plt.figure(1)
ax = fig.add_subplot(1, 1, 1, projection="3d")
ax.plot_surface(self.x, self.y, intensity)
plt.show()
if __name__ == '__main__':
main = Debug()
main.mainProgram()
"""
The value of x is:
[[0 1 2 3 4]]
The value of y is:
[[0]
[1]
[2]
[3]
[4]]
The result of np.ogrid[0:5, 0:5] is:
[array([[0],
[1],
[2],
[3],
[4]]), array([[0, 1, 2, 3, 4]])]
"""
我们可以看到，这里的np.ogrid()会返回一个列表代表的稀疏网格，第一个元素沿着y轴，第二个元素沿着x轴。这与我们之前研究的np.repeat()函数的坐标轴表示是一致的。
接下来我们看一下np.mgrid()函数。代码如下：
# -*- coding: utf-8 -*-
"""
np.ogrid(), np.mgrid(), np.meshgrid()
"""
import numpy as np
import matplotlib.pyplot as plt
class Debug:
def __init__(self):
self.x = []
self.y = []
def mainProgram(self):
self.y, self.x = np.mgrid[0:5, 0:5]
print("The value of x is: ")
print(self.x)
print("The value of y is: ")
print(self.y)
print("The result of np.mgrid[0:5, 0:5] is: ")
print(np.mgrid[0:5, 0:5])
# create a 2D intensity value
intensity = np.random.random_sample(size=(5, 5))
fig = plt.figure(1)
ax = fig.add_subplot(1, 1, 1, projection="3d")
ax.plot_surface(self.x, self.y, intensity)
plt.show()
if __name__ == '__main__':
main = Debug()
main.mainProgram()
"""
The value of x is:
[[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]]
The value of y is:
[[0 0 0 0 0]
[1 1 1 1 1]
[2 2 2 2 2]
[3 3 3 3 3]
[4 4 4 4 4]]
The result of np.mgrid[0:5, 0:5] is:
[[[0 0 0 0 0]
[1 1 1 1 1]
[2 2 2 2 2]
[3 3 3 3 3]
[4 4 4 4 4]]
[[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]]]
"""
对比于np.ogrid()函数，这里的np.mgrid()函数给出的网格数组为一个完全填充的数组。网格中每个点的坐标x，y值均被给出了。
最后我们研究一下np.meshgrid()。代码如下：
# -*- coding: utf-8 -*-
"""
np.ogrid(), np.mgrid(), np.meshgrid()
"""
import numpy as np
import matplotlib.pyplot as plt
class Debug:
def __init__(self):
self.x = []
self.y = []
def mainProgram(self):
x = np.arange(5)
y = np.arange(5)
self.x, self.y = np.meshgrid(x, y)
print("The value of x is: ")
print(self.x)
print("The value of y is: ")
print(self.y)
print("The result of np.meshgrid() is: ")
print(np.meshgrid(x, y))
# create a 2D intensity value
intensity = np.random.random_sample(size=(5, 5))
fig = plt.figure(1)
ax = fig.add_subplot(1, 1, 1, projection="3d")
ax.plot_surface(self.x, self.y, intensity)
plt.show()
if __name__ == '__main__':
main = Debug()
main.mainProgram()
"""
The value of x is:
[[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]]
The value of y is:
[[0 0 0 0 0]
[1 1 1 1 1]
[2 2 2 2 2]
[3 3 3 3 3]
[4 4 4 4 4]]
The result of np.meshgrid() is:
[array([[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]]), array([[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]])]
"""
我们运行后可以发现，三者均可以画出三维曲面图，说明三者获得的网格形式是等价的。并且对比输出结果，我们可以看到。它们只是在网格坐标表示次序上存在差别，在本质上并无差别，都是一样的。
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