Plot Single 3D Point on Top of plot_surface in Python – Matplotlib

Matplotlib is a widely-used plotting library in Python that offers various tools and functionalities for creating 2D and 3D visualizations. In this article, we will explore how to use Matplotlib to plot a single 3D point on top of a 3d surface plot.

Plot Single 3D Point on Top of Surface

Let us understand the Plotting single 3D point on top of the plot surface in Python using Matplotlib. To plot a single 3D point on top of a surface plot using Matplotlib in Python, you’ll need to ensure the following prerequisites are met.

Python: Make sure you have Python installed on your system. You can download the latest version of Python from the official Python website (https://www.python.org) and follow the installation instructions based on your operating system.

Matplotlib: Install the Matplotlib library, which provides plotting functionalities in Python. You can install it using the following command.

!pip install matplotlib

NumPy: Install the NumPy library, which is used for numerical computations in Python. It provides efficient array operations and mathematical functions that are useful for generating data. You can install it using the following command.

!pip install numpy

Once you have Python installed along with the required libraries, you should be ready to plot a single 3D point on top of a surface plot using Matplotlib in Python.

Step by Step process

Step 1: Import the necessary libraries

Let’s get started by importing the necessary libraries. We need NumPy for some math operations, Matplotlib for plotting, and mpl_toolkits.mplot3d for creating 3D plots.

Python

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

Step 2: Generate data for the plot_surface plot

Now, imagine we are creating a beautiful mountain landscape. We’ll generate data to create a 3D surface plot. We define the x and y coordinates using np.linspace() and create a grid of points using np.meshgrid(). Then, we calculate the z coordinate using a fancy math formula (in this case, np.sin(np.sqrt(X**2 + Y**2))). This will give us the heights of our mountain landscape.

Python

# Generate data for the plot_surface plot
X = np.linspace(-5, 5, 100)
Y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(X, Y)
Z = np.sin(np.sqrt(X**2 + Y**2))

Step 3: 3D surface plot

To visualize our landscape, we need to follow below steps.

• We create a 3D figure and axes to work with using plt.figure() and add_subplot().
• Then We'll plot our mountain landscape using plot_surface(). This will create a beautiful 3D plot of our landscape with peaks and valleys.
• Set labels for the x, y, and z axes using ax.set_xlabel(), ax.set_ylabel(), and ax.set_zlabel(), respectively.
• Set a title for the plot using ax.set_title().
• display the plot using plt.show()

Python

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

ax.set_title('3D Surface plot')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()

Output:

Step 4: Find the x,y & z values for 3d points

Here, We are considering Single 3D points in the middle of the XY surface. So, we will consider.

point_x = Mean of X
point_y = Mean of Y

And for the z points we are considering 1.5 times of Max value of Z i.e

point_z = 1.5 * Max value of Z

Python3

# X-coordinate of the point
point_x = X.mean()
# Y-coordinate of the point
point_y = Y.mean()
# Z-coordinate of the point
point_z = 1.5*Z.max()

print('3D point(x,y,z):',point_x,point_y,point_z)

Output:

3D point(x,y,z): -6.679101716144942e-17 -2.7284841053187845e-16 1.4999964535592252

Step 5: Plot the 3d points

using ax.scatter(point_x, point_y, point_z, color='red', s=50). Specify the coordinates of the point (point_x, point_y, point_z) and customize the appearance (color, size)

Python

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

ax.scatter(point_x, point_y, point_z, color='red', s=50)

# Customize the plot (add labels, titles, etc.)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Single Point in 3D')

# Adjust the viewing perspective if needed
ax.view_init(elev=20, azim=45)

# Show the plot
plt.show()

Output:

Single 3D Point on Top of Surface Full code

To plot the Single 3D Point on Top of the Surface, we will merge both the above graph using the bellow code

• Generate data for the plot_surface plot
• Create a 3D figure and axes
• PLot 3D surface using ax.plot_surface()
• Compute 3D point (x,y,z) coordinate
• Plot the single 3d point on the same axis

Python3

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

# Generate data for the plot_surface plot
X = np.linspace(-5, 5, 100)
Y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(X, Y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# create a 3D figure and axes
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# 3D surface plot
ax.plot_surface(X, Y, Z, cmap='viridis')

# 3D point (x,y,z) coordinate
# X-coordinate of the point
point_x = X.mean()
# Y-coordinate of the point
point_y = Y.mean()
# Z-coordinate of the point
point_z = 1.5*Z.max()
# Print 3d point
print('3D point(x,y,z):',point_x,point_y,point_z)

# Plot the single 3d point on the same axis
ax.scatter(point_x, point_y, point_z, color='red', s=50)

# Set x,y & z label
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
# Set Tile
ax.set_title('3D Point on 3D surface')

plt.show()

Output:

3D point(x,y,z): -6.679101716144942e-17 -2.7284841053187845e-16 1.4999964535592252