Tree Rings pattern using python

 import numpy as np

import matplotlib.pyplot as plt

def generate_tree_rings(num_rings=20, max_radius=10, noise_factor=0.2):

    fig, ax = plt.subplots(figsize=(6, 6))

    ax.set_xlim(-max_radius, max_radius)

    ax.set_ylim(-max_radius, max_radius)

    ax.set_aspect('equal')

    ax.axis('off')

    for i in range(1, num_rings + 1):

        radius = (i / num_rings) * max_radius

        theta = np.linspace(0, 2 * np.pi, 500)

        r_noise = radius + noise_factor * np.sin(5 * theta) * np.random.uniform(0.5, 1.5)

        x = r_noise * np.cos(theta)

        y = r_noise * np.sin(theta)

        ax.plot(x, y, color='saddlebrown', lw=1)

    plt.title("Tree Rings Pattern", fontsize=14, fontweight='bold')

    plt.show()

generate_tree_rings()

#source code --> clcoding.com

Code Explanation:

Step 1: Importing Required Libraries

import numpy as np

import matplotlib.pyplot as plt

NumPy (np): Used for mathematical operations, especially creating a grid of points.

Matplotlib (plt): Used for visualization, specifically to create a contour plot of the ripple pattern.

Step 2: Creating a Grid of Points

x = np.linspace(-5, 5, 400)  # X-axis range

y = np.linspace(-5, 5, 400)  # Y-axis range

X, Y = np.meshgrid(x, y)  # Create a coordinate grid

np.linspace(-5, 5, 400): Creates 400 evenly spaced points between -5 and 5 for both the X and Y axes.

np.meshgrid(x, y): Converts these 1D arrays into 2D coordinate matrices, so we can compute values at every point in a 2D space.

Step 3: Defining the Ripple Effect

R = np.sqrt(X**2 + Y**2)  # Compute radial distance from the center

Z = np.sin(5 * R) / (1 + R)  # Apply a damped sine wave function

R = np.sqrt(X**2 + Y**2):

Computes the radial distance of each point from the origin (0,0).

This ensures that waves radiate outward from the center.

Z = np.sin(5 * R) / (1 + R):

sin(5 * R): Generates oscillations that form the ripple pattern.

/(1 + R): Damps the waves so they fade out as they move away from the center.

Step 4: Plotting the Ripple Effect

plt.figure(figsize=(6, 6))  # Set figure size

plt.contourf(X, Y, Z, cmap='Blues')  # Create filled contour plot

plt.colorbar(label='Wave Intensity')  # Add color legend

plt.axis('off')  # Remove axes for a clean look

plt.title("Water Ripple Pattern")  # Set title

plt.show()  # Display the plot

plt.contourf(X, Y, Z, cmap='Blues'):

Creates smooth color-filled contours based on Z values.

cmap='Blues': Uses blue shades to resemble water ripples.

plt.colorbar(label='Wave Intensity'):

Adds a color legend indicating the intensity of the waves.

plt.axis('off'):

Removes axes to give a clean water effect.

plt.show():

Displays the final ripple effect.