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  • Book Overview & Buying Learning Cython Programming (Second Edition)
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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

Cython and NumPy

NumPy is a scientific library designed to provide functionality similar to or on par with MATLAB, which is a paid proprietary mathematics package. NumPy has a lot of popularity with Cython users since you can seek out more performance from your highly computational code using C types. In Cython, you can import this library as follows:

import numpy as np
cimport numpy as np

np.import_array()

You can access full Python APIs as follows:

np.PyArray_ITER_NOTDONE

So, you can integrate with iterators at a very native area of the API. This allows NumPy users to get a lot of speed when working with native types via something as follows:

cdef double * val = (<double*>np.PyArray_MultiIter_DATA(it, 0))[0]

We can cast the data from the array to double, and it's a cdef type in Cython to work with now. For more information and NumPy tutorials, visit https://github.com/cython/cython/wiki/tutorials-numpy.

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Programming languages
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