3D Contour Plotting in Python using Matplotlib

Matplotlib was introduced keeping in mind, only two-dimensional plotting. But at the time when the release of 1.0 occurred, the 3d utilities were developed upon the 2d and thus, we have 3d implementation of data available today! The 3d plots are enabled by importing the mplot3d toolkit. Let’s look at a 3d contour diagram of a 3d cosine function. The code is attached for reference.

Function Arguments	Description
meshgrid	Function of numpy used to create a rectangular grid out of two given one-dimensional arrays representing the Cartesian indexing or Matrix indexing
plt.axes()	To create object of the axis
ax.contour3D	To form the contour
ax.set_xlabel	To label the X-axis
ax.set_title	To give a title to the plot

Example 1:

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

x = [i for i in range(0, 200, 100)]
y = [i for i in range(0, 200, 100)]

X, Y = np.meshgrid(x, y)
Z = []
for i in x:
    t = []
    for j in y:
        t.append(math.cos(math.sqrt(i*2+j*2)))
    Z.append(t)

fig = plt.figure()
ax = plt.axes(projection='3d')
ax.contour3D(X, Y, Z, 50, cmap=cm.cool)
ax.set_xlabel('a')
ax.set_ylabel('b')
ax.set_zlabel('c')
ax.set_title('3D contour for cosine')
plt.show()

Output:

Example 2: Let’s look at another 3d diagram for better understanding of the concept. This time, of a 3d tan function.

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

x = [i for i in range(0, 200, 100)]
y = [i for i in range(0, 200, 100)]

X, Y = np.meshgrid(x, y)
Z = []
for i in x:
    t = []
    for j in y:
        t.append(math.tan(math.sqrt(i*2+j*2)))
    Z.append(t)

fig = plt.figure()
ax = plt.axes(projection='3d')
ax.contour3D(X, Y, Z, 50, cmap=cm.cool)
ax.set_xlabel('a')
ax.set_ylabel('b')
ax.set_zlabel('c')
ax.set_title('3D contour for tan')
plt.show()

Output: