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Learning Cython Programming (Second Edition)

Learning Cython Programming (Second Edition) - Second Edition

By : Philip Herron
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Learning Cython Programming (Second Edition)

Learning Cython Programming (Second Edition)

1 (1)
By: Philip Herron
Buy this Book

Overview of this book

Cython is a hybrid programming language used to write C extensions for Python language. Combining the practicality of Python and speed and ease of the C language it’s an exciting language worth learning if you want to build fast applications with ease. This new edition of Learning Cython Programming shows you how to get started, taking you through the fundamentals so you can begin to experience its unique powers. You’ll find out how to get set up, before exploring the relationship between Python and Cython. You’ll also look at debugging Cython, before moving on to C++ constructs, Caveat on C++ usage, Python threading and GIL in Cython. Finally, you’ll learn object initialization and compile time, and gain a deeper insight into Python 3, which will help you not only become a confident Cython developer, but a much more fluent Python developer too.
Table of Contents (8 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Cython Won't Bite
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1. Cython Won't Bite
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Installing Cython
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Getting started – Hello World
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Summary
2
2. Understanding Cython
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2. Understanding Cython
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Linking models
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Cython keyword – cdef
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Typedef and function pointers
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The public keyword
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Keyword cpdef
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Logging from C/C++ into Python
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Using Python ConfigParser from C/C++
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Callbacks from Python to C/C++
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Cython PXD
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Integration with build systems
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Summary
3
3. Extending Applications
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3. Extending Applications
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Cython pure Python code
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Compiling pure Python code
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Python garbage collector
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Extending Tmux
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Embedding Python
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Cythonizing struct cmd_entry
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Implementing a Tmux command
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Hooking everything together
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Summary
4
4. Debugging Cython
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4. Debugging Cython
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Using GDB with Cython
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Cython caveats
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Summary
5
5. Advanced Cython
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5. Advanced Cython
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Cython and C++
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C++ new and del keyword
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Overloading
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Templates
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Static class member attribute
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Calling C++ functions – Caveat
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Python distutils
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Python threading and GIL
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Cython keywords
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Messaging server
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Caveat on GIL
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Unit testing the native code
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Preventing subclassing
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Parsing large amounts of data
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Summary
6
6. Further Reading
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6. Further Reading
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Overview
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OpenMP support
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Compile time preprocessor
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Python 3
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PyPy
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AutoPXD
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Pyrex and Cython
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SWIG and Cython
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Cython and NumPy
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Numba versus Cython
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Parakeet and Numba
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Relevant Links
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Summary
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Index
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Index

Numba versus Cython

Numba is another way to get your Python code to become almost native to your host system by outputting the code to be run on LLVM seamlessly. Numba makes use of decorators such as the following:

@autojit
def myFunction (): ...

Numba also integrates with NumPy. On the whole, it sounds great. Unlike Cython, you only apply decorators to pure Python code, and it does everything for you, but you may find that the optimizations will be fewer and not as powerful.

Numba does not integrate with C/C++ to the extent that Cython does. If you want it to integrate, you need to use Foreign Function Interfaces (FFI) to wrap calls. You also need to define structs and work with C types in Python code in a very abstract sense to a point where you don't really have much control as compared with Cython.

Numba is mostly comprised of decorators, such as @locals, from Cython. But in the end, all this creates is just-in-time-compiled functions with a proper native function signature. Since you can...

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Learning Cython Programming (Second Edition)
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