Cython A Guide For Python Programmers

Page 1 CythonBuild software program that combines Python’s expressivity with the performance Cythonhasprovenitselfand manage of C (and also C++). It’s feasible through Cython, the compiler and also “inmanyfoundationalhybrid programming language supplied by foundational packeras such as NumPy, and also influential in projects consisting of Pandas, h5py, and also scikits- Pythonprojectswherelearn. In this valuable overview, you’ll learn how to use Cython to enhance performanceiscrucial.Python’s performance—as much as 3000x—and to wrap C and also C++ libraries in Inthisbook,KurtSmithPython through ease.walksyouthroughAuthor Kurt Smith takes you via Cython’s capabilities, through sample everythingyouneedtocode and in-depth exercise exercises. If you’re just founding through Cython, or want to go deeper, you’ll learn exactly how this language is a critical component of any type of knowtotapintoCython'sperformance-oriented Python programmer’s arsenal. powerforyourownCythonprojects.■ Use Cython’s static inputting to speed up Python code ” —Robert Bradshaw ■ Gain hands-on suffer making use of Cython functions to increase your Lead Cython Developernumeric-hefty PythonA GUIDE FOR PYTHON PROGRAMMERS■ Create new forms with Cython—and also check out how quick object-oriented programming in Python can be■ Efectively organize Cython code into separate modules and packperiods without sacrifcing performance■ Use Cython to offer Pythonic interfaces to C and C++ libraries■ Optimize code via Cython’s runtime and compile-time profling tools■ Use Cython’s parray attribute to parallelize loops transparently with OpenMPKurt W. Smith uncovered Cython’s power throughout his graduate research in com-putational plasma physics. Today, he offers Cython extensively in his consulting job-related at Enbelieved, training scientists, engineers, and also researchers in Python, NumPy, Cython, and parallel and also high-performance computer.PROGR AMMING/PY THON Twitter:

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CythonBuild software application that combines Python’s expressivity through the performance Cythonhasprovenitselfand also manage of C (and also C++). It’s feasible through Cython, the compiler and also “inmanyfoundationalhybrid programming language supplied by foundational packperiods such as NumPy, and also influential in projects consisting of Pandas, h5py, and scikits- Pythonprojectswherelearn. In this practical overview, you’ll learn just how to use Cython to enhance performanceisinstrumental.Python’s performance—up to 3000x—and also to wrap C and C++ libraries in Inthisbook,KurtSmithPython through ease.walksyouthroughAuthor Kurt Smith takes you via Cython’s capabilities, with sample everythingyouneedtocode and also detailed exercise exercises. If you’re simply starting through Cython, or want to go deeper, you’ll learn exactly how this language is an important part of any kind of knowtotapintoCython'sperformance-oriented Python programmer’s arsenal. powerforyourownCythontasks.■ Use Cython’s static inputting to speed up Python code ” —Robert Bradshaw ■ Gain hands-on experience making use of Cython functions to increase your Lead Cython Developernumeric-hefty PythonA GUIDE FOR PYTHON PROGRAMMERS■ Create brand-new types through Cython—and also check out how quick object-oriented programming in Python have the right to be■ Efectively organize Cython code right into separate modules and packeras without sacrifcing performance■ Use Cython to give Pythonic interencounters to C and also C++ libraries■ Optimize code via Cython’s runtime and compile-time profling tools■ Use Cython’s pselection function to parallelize loops transparently via OpenMPKurt W. Smith uncovered Cython’s power throughout his graduate research in com-putational plasma physics. Today, he offers Cython broadly in his consulting work at Enbelieved, training scientists, engineers, and researchers in Python, NumPy, Cython, and parallel and high-performance computing.PROGR AMMING/PY THON Twitter:

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For Ellen, Zélie, Leo, and Hugh—my muses.An inconvenience is just an adundertaking mistakenly considered; an adventureis an inconvenience rightly thought about.— GKCA dead thing can go with the stream, however just a living thing deserve to go versus it.— GKC.

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Table of ContentsPreconfront. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi1. Cython Essentials. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Comparing Python, C, and Cython 2Function Call Overhead 5Looping 6Math Operations 6Stack Versus Heap Allocation 6Tempering Our Enthusiasm 7Wrapping C Code with Cython 8Synopsis 102. Compiling and Running Cython Code. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11The Cython Compilation Pipeline 12Installing and Testing Our Setup 13The Standard Way: Using distutils via cythonize 14Our distutils Script 15Compiling via distutils on Mac OS X and also Linux 15Compiling with distutils on Windows 16Using Our Exstress Module 17Interenergetic Cython with IPython’s %%cython Magic 19Compiling On-the-Fly via pyximport 21Controlling pyximport and also Managing Dependencies 22pyximport Example through External Dependencies 23Rolling Our Own and also Compiling by Hand also 24Using Cython through Other Build Systems 26CMake and also Cython 26SCons and also Cython 26Make and Cython 26v.

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C-Level Initialization and also Finalization 85cdef and also cpdef Methods 86Inheritance and Subclassing 89Casting and also Subclasses 90Extension Type Objects and None 91Extension Type Properties in Cython 92Special Methods Are Even More Special 94Arithmetic Methods 94Rich Comparisons 96Iterator Support 98Rundown 996. Organizing Cython Code. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101Cython Implementation (.pyx) and also Declaration (.pxd) Files 102The cimport Statement 105Precharacterized Definition Files 107Include Files and the include Statement 109Organizing and Compiling Cython Modules Inside Python Packages 110Summary 1137. Wrapping C Libraries through Cython. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115Declaring External C Code in Cython 115Cython Does Not Automate Wrapping 117Declaring External C Functions and also typedefs 118Declaring and also Wrapping C structs, unions, and also enums 119Wrapping C Functions 121Wrapping C structs via Exstress and anxiety Types 122Constants, Other Modifiers, and Controlling What Cython Generates 125Error Checking and Raising Exceptions 128Callbacks 128Callbacks and Exception Propagation 133Rundown 1348. Wrapping C++ Libraries through Cython. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135Simple Example: MT_RNG Class 135The Wrapper Extension Type 137Compiling through C++ 138Using Our Wrapper from Python 139Overloaded Methods and also Functions 140Operator Overloading 142C++ Exceptions 144Stack and Heap Allocation of C++ Instances 145Table of Materials | vii.


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Working through C++ Class Hierarchies 146C++ Templates 147Templated Functions and also Cython’s Foffered Types 148Templated Classes 149Iterators and Nested Classes 150Included STL Container Class Declarations 151Memory Management and also Smart Pointers 154Rundown 1579. Cython Profiling Tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Cython Runtime Profiling 159Performance Profiling and Annotations 164Outline 17010. Cython, NumPy, and also Typed Memoryviews. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171The Power of the New Buffer Protocol 172The memorywatch Type 173Typed Memoryviews 176Typed Memorysee Example 176C-Level Access to Typed Memorycheck out File 177Trading Safety for Performance 178Declaring Typed Memoryviews 179Using Typed Memoryviews 183Beyond Buffers 187Wrapping C and C++ Arrays 189Correct (and Automatic) Memory Management with Cython and also C Arrays 189Rundown 19211. Cython in Practice: Spectral Norm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193Synopsis of the Spectral Norm Python Code 193Performance Profiling 196Cythonizing Our Code 197Adding Static Type Indevelopment 198Using Typed Memoryviews 198Comparing to the C Implementation 200Overview 20012. Parallel Programming via Cython. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201Thread-Based Parallelism and the Global Interpreter Lock 201The nogil Function Attribute 202The via nogil Context Manager 203Using pvariety to Parallelize Loops 204viii | Table of Materials.