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IPython Interactive Computing and Visualization Cookbook

You're reading from   IPython Interactive Computing and Visualization Cookbook Harness IPython for powerful scientific computing and Python data visualization with this collection of more than 100 practical data science recipes

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Product type Paperback
Published in Sep 2014
Publisher
ISBN-13 9781783284818
Length 512 pages
Edition 1st Edition
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Author (1):
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Cyrille Rossant Cyrille Rossant
Author Profile Icon Cyrille Rossant
Cyrille Rossant
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Table of Contents (17) Chapters Close

Preface 1. A Tour of Interactive Computing with IPython FREE CHAPTER 2. Best Practices in Interactive Computing 3. Mastering the Notebook 4. Profiling and Optimization 5. High-performance Computing 6. Advanced Visualization 7. Statistical Data Analysis 8. Machine Learning 9. Numerical Optimization 10. Signal Processing 11. Image and Audio Processing 12. Deterministic Dynamical Systems 13. Stochastic Dynamical Systems 14. Graphs, Geometry, and Geographic Information Systems 15. Symbolic and Numerical Mathematics Index

Optimizing Cython code by writing less Python and more C


In this recipe, we will consider a more complicated Cython example. Starting from a slow implementation in pure Python, we will use different Cython features to speed it up progressively.

We will implement a very simple ray tracing engine. Ray tracing consists of rendering a scene by simulating the physical properties of light propagation. This rendering method leads to photorealistic scenes, but it is computationally intensive.

Here, we will render a single sphere with diffuse and specular lighting. First we'll give the example's code in pure Python. Then, we will accelerate it incrementally with Cython.

Note

The code is long and contains many functions. We will first give the full code of the pure Python version. Then, we will just describe the changes required to accelerate the code with Cython. The entire scripts are available on the book's website.

How to do it…

  1. First, let's implement the pure Python version:

    In [1]: import numpy as...
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