Signal Interference Mesh Pattern using Python

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

x = np.linspace(-10, 10, 200)

y = np.linspace(-10, 10, 200)

X, Y = np.meshgrid(x, y)

sources = [

    {'center': (-3, -3), 'freq': 2.5},

    {'center': (3, 3), 'freq': 3.0},

    {'center': (-3, 3), 'freq': 1.8},

]

Z = np.zeros_like(X)

for src in sources:

    dx = X - src['center'][0]

    dy = Y - src['center'][1]

    r = np.sqrt(dx**2 + dy**2) + 1e-6  

    Z += np.sin(src['freq'] * r) / r  

fig = plt.figure(figsize=(6, 8))

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

ax.plot_wireframe(X, Y, Z, rstride=3, cstride=3, color='mediumblue', alpha=0.8, linewidth=0.5)

ax.set_title("Signal Interference Mesh", fontsize=16)

ax.set_xlabel("X")

ax.set_ylabel("Y")

ax.set_zlabel("Amplitude")

ax.set_box_aspect([1,1,0.5])

plt.tight_layout()

plt.show()

#source code --> clcoding.com

Code Explanation:

1. Importing Required Libraries

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

Explanation:

numpy (as np) is used for efficient array manipulation and mathematical operations.

matplotlib.pyplot (as plt) is used for plotting 2D/3D graphs.

mpl_toolkits.mplot3d.Axes3D enables 3D plotting with Matplotlib.

2. Creating the Grid

x = np.linspace(-10, 10, 200)

y = np.linspace(-10, 10, 200)

X, Y = np.meshgrid(x, y)

Explanation:

np.linspace(-10, 10, 200) creates 200 evenly spaced points between -10 and 10 for both x and y.

np.meshgrid(x, y) creates 2D grid coordinates X and Y, representing the Cartesian plane over which signals will be calculated.

3. Defining Signal Sources

sources = [

    {'center': (-3, -3), 'freq': 2.5},

    {'center': (3, 3), 'freq': 3.0},

    {'center': (-3, 3), 'freq': 1.8},

]

Explanation:

This list defines 3 point sources, each with:

A center coordinate in 2D space (x, y)

A freq (frequency) value that affects the signal's oscillation

4. Calculating the Resulting Signal

Z = np.zeros_like(X)

for src in sources:

    dx = X - src['center'][0]

    dy = Y - src['center'][1]

    r = np.sqrt(dx**2 + dy**2) + 1e-6

    Z += np.sin(src['freq'] * r) / r

Explanation:

Z = np.zeros_like(X) initializes a 2D grid to accumulate the total signal amplitude at each point.

For each source:

dx, dy: distance in X and Y from the source center.

r: radial distance from the source to each grid point (with a small epsilon added to avoid division by zero).

np.sin(freq * r) / r: simulates a wave signal from a point source that decays with distance.

These signals are added together to simulate interference.

5. Plotting the 3D Wireframe

fig = plt.figure(figsize=(6, 8))

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

Explanation:

A figure object is created with a specified size (6x8 inches).

add_subplot(111, projection='3d') creates a 3D axis for plotting.

6. Rendering the Mesh Plot

ax.plot_wireframe(X, Y, Z, rstride=3, cstride=3, color='mediumblue', alpha=0.8, linewidth=0.5)

Explanation:

plot_wireframe creates a mesh-style 3D plot showing how the signal amplitude varies.

rstride and cstride control mesh resolution.

color, alpha, and linewidth adjust aesthetics.

7. Setting Titles and Labels

ax.set_title("Signal Interference Mesh", fontsize=16)

ax.set_xlabel("X")

ax.set_ylabel("Y")

ax.set_zlabel("Amplitude")

ax.set_box_aspect([1,1,0.5])

Explanation:

Adds a title and axis labels to explain what the axes represent.

set_box_aspect controls the 3D plot's aspect ratio for better visual balance.

8. Finalizing and Displaying the Plot

plt.tight_layout()

plt.show()

Explanation:

tight_layout() adjusts spacing to prevent clipping.

show() renders the final interactive 3D plot window.