如何在python中3D绘制2个变量的函数? [英] How to 3D plot function of 2 variables in python?

问题描述

我正在尝试以 3D 方式绘制多种类型阻尼的振动放大系数.为了让那些不知道它是什么的人简化它，基本上，你有 3 个变量:

I am trying to 3D plot the magnification factor in vibrations for multiple types of damping. To simplify it for those who have no idea what it is, basically, you have 3 variables:

• beta，在 0 和无限之间变化，但我想以 0.2 的间隔将其可视化为从 0 到 3.
• 阻尼比 d 在 0 到无穷大之间变化，但我想以 0.1 的间隔从 0 到 1 绘制它.
• 最后是nu，这是一个根据之前的两个变量而变化的函数.

我的直觉说我应该用 (X,Y,Z) = (beta, d, nu) 来绘制它，但我刚刚开始使用这个库，我对 python 有点陌生，我只是使用它当我需要在课堂上可视化或计算问题时.我尝试为 beta 和 d 创建 2 个数组，但我不知道我应该为 nu 创建数组，因为它取决于两者.

My intuition says that I should plot this with (X,Y,Z) = (beta, d, nu), but I am just starting to use this library and I am kind of new to python, I just use it when I need to visualize or calculate problems in class. I tried creating 2 arrays for beta and d, but I don't know I should create the array for nu, since it depends on both.

这是我到现在为止的一段代码:

This is the piece of code I have until now:

    import math
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D


nu = []
b = [0.1 + i / 100 for i in range(0, 510)]
damp = [0.1 + i/10 for i in range(0,510)]

for d in damp:
    nu_new = []
    nu.append(nu_new)
    for beta in b:
        nu_new.append( math.sqrt(1+(2*d*beta)**2)/ math.sqrt((1-beta**2)**2+(2*d*beta)**2))

fig = plt.figure()
ax = Axes3D(fig)
ax.plot(b, d, nu)
plt.show()

我在试图绘制这个时有点卡住了，所以如果你有任何建议，我会很高兴.

I am kind of stuck trying to plot this, so if you have any suggestion I would be glad.

推荐答案

这应该有效:我不是 Python 专家，尤其是这两个 for 循环可能非常非 Pythonic，但它可以完成工作.

This should work: I'm not a Python expert and especially the two for loops might be very unpythonic, but it gets the job done.

import math
import matplotlib.pyplot as plt
import numpy as np

b = np.arange(0.2, 3.2, 0.2)
d = np.arange(0.1, 1.0, 0.1)
nu = np.zeros( (b.size, d.size) )
counter_y = 0

for deta in d:
    counter_x = 0
    for beta in b:
        nu[counter_x, counter_y] = math.sqrt( 1 + (2*deta*beta)**2 ) / math.sqrt( (1-beta**2)**2 + (2*deta*beta)**2)
        counter_x += 1
    counter_y += 1

X, Y = np.meshgrid(d, b)

fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')
ax.plot_surface(X, Y, nu)

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