How To 3d Plot Function Of 2 Variables In Python?

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:

Solution 1:

If you're using numpy, then don't use the math module. Numpy as all of the math functions built in but they work on numpy arrays far better. We can calculate nu at all of our b, d values with the aid of a meshgrid.

A meshgrid can take 2 1D arrays, and return 2 2D arrays such that every index in the arrays corresponds to a unique pair of elements from the original 1D arrays.

import matplotlib.pyplotas plt
from mpl_toolkits.mplot3dimportAxes3D
import numpy as np

b = np.arange(0.2, 3.2, 0.2)
d = np.arange(0.1, 1.0, 0.1)

B, D = np.meshgrid(b, d)
nu = np.sqrt( 1 + (2*D*B)**2 ) / np.sqrt( (1-B**2)**2 + (2*D*B)**2)

fig = plt.figure()
ax = Axes3D(fig)
ax.plot_surface(B, D, nu)
plt.xlabel('b')
plt.ylabel('d')
plt.show()

This produces:

Additionally, 3D plots tend to block seeing all of the data (because a spike hides things behind it). I would recommend a pcolormesh or a contourf plot. In the later case the last 6 lines become:

plt.contourf(B, D, nu)
plt.colorbar()
plt.xlabel('b')
plt.ylabel('d')
plt.show()

which produces:

Solution 2:

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.pyplotas 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 = 0for deta ind:
    counter_x = 0for beta inb:
        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)

Solution 3:

what you need is to first create a matplotlib figure.

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

Further, all your variable should be of same size. So, your variable d should be an array of same length as the others.

If you turn your variable d into array of 0.1 of length 510 as others, you get following.

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


nu = []
b = [0.1 + i / 100for i inrange(0, 510)]
d = 0.1for beta in b:
    nu.append( math.sqrt(1+(2*d*beta)**2)/ math.sqrt((1-beta**2)**2+(2*d*beta)**2))

#turned d into array of length 510 with 0.1 for each value
d = np.ones(510)*0.1


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

you get: