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| Special Assistance in WebGL | Price (USD) | Time |
|---|---|---|
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| WebGL Coding Practice | $50 | 2 days |
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- WebGL Assignment Help
- Python-driven 3D Data Generation for WebGL Visualization
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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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