3D Atmospheric Cloud Simulation using Python

 

import matplotlib.pyplot as plt

import numpy as np

from mpl_toolkits.mplot3d import Axes3D

np.random.seed(42)

cloud_volume=np.random.rand(30,30,30)

x,y,z=np.indices(cloud_volume.shape)

threshold=0.7

mask=cloud_volume>threshold

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

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

ax.scatter(x[mask],y[mask],z[mask],c='white',alpha=0.5,s=10)

ax.set_facecolor('skyblue')

ax.set_title('3D Atmospheric Cloud Simulation')

ax.set_xlabel('X axis')

ax.set_ylabel('Y axis')

ax.set_xlabel('Z axis')

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

plt.tight_layout()

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Import Required Libraries

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

numpy: For numerical operations and array manipulations.

matplotlib.pyplot: For creating plots and visualizations.

Axes3D: Enables 3D plotting functionality in Matplotlib.

2. Create a 3D Volume (Simulated Cloud Data)

np.random.seed(42)

cloud_volume = np.random.rand(30, 30, 30)

np.random.seed(42): Sets a fixed seed so the random values are reproducible.

cloud_volume = np.random.rand(30, 30, 30): Generates a 3D array (30×30×30) of random values between 0 and 1, representing cloud density.

3. Create Grid Indices for the Volume

x, y, z = np.indices(cloud_volume.shape)

np.indices(): Creates coordinate grid arrays corresponding to each voxel in the 3D space. Now you have x, y, and z arrays of shape (30, 30, 30) for mapping points.

4. Apply a Density Threshold

threshold = 0.7

mask = cloud_volume > threshold

threshold = 0.7: Defines a cutoff for what’s considered "dense enough" to visualize.

mask = cloud_volume > threshold: Creates a boolean mask where only voxels with density greater than 0.7 are selected as cloud points.

5. Plot the Cloud Points

fig = plt.figure(figsize=(10, 8))

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

plt.figure(figsize=(10, 8)): Sets up the figure window with a specific size.

projection='3d': Enables 3D plotting inside the subplot.

6. Scatter Plot the Cloud Voxels

ax.scatter(x[mask], y[mask], z[mask], c='white', alpha=0.5, s=10)

ax.scatter(): Plots each voxel as a white semi-transparent dot.

x[mask], y[mask], z[mask]: Only plots the voxels that passed the threshold.

alpha=0.5: Controls transparency (semi-transparent clouds).

s=10: Dot size.

7. Style and Label the Plot

ax.set_facecolor('skyblue')

ax.set_title("3D Atmospheric Cloud Simulation")

ax.set_xlabel("X axis")

ax.set_ylabel("Y axis")

ax.set_zlabel("Z axis")

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

ax.set_facecolor('skyblue'): Gives the background a sky-blue color to resemble the atmosphere.

set_title, set_xlabel, set_ylabel, set_zlabel: Adds plot and axis labels.

set_box_aspect([1,1,1]): Ensures equal scaling across all axes for a proportional 3D view.

8. Finalize and Display the Plot

plt.tight_layout()

plt.show()

plt.tight_layout(): Adjusts layout so nothing overlaps.

plt.show(): Displays the plot window.