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Python-driven 3D Data Generation for WebGL Visualization

importnumpy as np importmatplotlib.pyplot as plt # Generate 3D data for WebGL visualization def generate_3d_data(num_points): # Generate random data points in 3D space x = np.random.rand(num_points) y = np.random.rand(num_points) z = np.random.rand(num_points) return x, y, z # Example usage num_points = int(input("Enter the number of data points to generate: ")) x_data, y_data, z_data = generate_3d_data(num_points) # Visualize the data using Matplotlib (optional) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(x_data, y_data, z_data, c='b', marker='o') ax.set_xlabel('X Label') ax.set_ylabel('Y Label') ax.set_zlabel('Z Label') plt.show()

In this code:

• The generate_3d_data function generates random data points in 3D space.
• These data points could represent, for example, coordinates of objects in a 3D scene.
• While not directly interacting with WebGL, this Python script generates data that could be visualized using WebGL for 3D graphics rendering in a web environment.

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