Testing Matplotlib with complex 3D surfaces

In this project, we are evaluating Matplotlib and Python for their capabilities in rendering complex 3D surfaces, aiming to identify the most straightforward methods for visualization.
The initial impetus for this research stemmed from the development of advanced beamforming algorithms for sensor arrays using Python, alongside sophisticated noise reduction techniques employing Machine Learning or Deep Learning.
Designing optimal sensor arrays necessitates the visualization of 3D beampatterns to assess and refine the performance of these beamforming algorithms. Additionally, incorporating animations enhances the understanding of dynamic changes in these patterns.
Our exploration extended to determining which Matplotlib modules could serve as effective substitutes for the simple Matlab functions used in plotting 3D implicit surfaces and polyhedrons.

^{Stéphane Dedieu
December 2022}

Part I: 3D curves and surfaces defined with Parametric systems.

Jupyter Notebook: 3D Parametric systems

Part II: 3D surfaces defined with implicit equations: F(x,y,z)=0.

Jupyter Notebook: 3D Implicit surfaces

Part III: Polyhedrons and star-shaped polyhedrons.

Jupyter Notebook: Polyhedrons

Part IV: 3D surfaces - Animations.

Jupyter Notebook: 3D surfaces - Animations

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