np.ogrid(),np.mgrid()和meshgrid()函数的关系
程序员文章站
2023-11-13 15:47:16
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 npimport matplotlib.pyplot as pltclass Debug: def __init__(self): self.x = [] self.y = [] def mainProgr...
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。
本文地址:https://blog.csdn.net/u011699626/article/details/109033156