Cython A Guide for Python Programmers 1st Edition by Kurt W Smith ISBN 1491901551 9781491901557

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Product details:

ISBN 10: 1491901551
ISBN 13: 9781491901557
Author: Kurt W Smith

Cython A Guide for Python Programmers 1st Edition Table of contents:

1. Cython Essentials

Comparing Python, C, and Cython

Function Call Overhead

Looping

Math Operations

Stack Versus Heap Allocation

Tempering Our Enthusiasm

Wrapping C Code with Cython

Summary

2. Compiling and Running Cython Code

The Cython Compilation Pipeline

Installing and Testing Our Setup

C and C++ compilers

Installing Cython

The Standard Way: Using distutils with cythonize

Our distutils Script

Compiling with distutils on Mac OS X and Linux

Compiling with distutils on Windows

Using Our Extension Module

Interactive Cython with IPython’s %%cython Magic

Compiling On-the-Fly with pyximport

Controlling pyximport and Managing Dependencies

pyximport Example with External Dependencies

Rolling Our Own and Compiling by Hand

Using Cython with Other Build Systems

CMake and Cython

SCons and Cython

Make and Cython

Compiler Directives

Summary

3. Cython in Depth

Interpreted Versus Compiled Execution

Dynamic Versus Static Typing

Static Type Declaration with cdef

Automatic Type Inference in Cython

C Pointers in Cython

Mixing Statically and Dynamically Typed Variables

The bint type

Integral type conversions and overflow

Floating-point type conversions

Complex types

bytes type

str and unicode types

Statically Declaring Variables with a Python Type

Static Typing for Speed

Reference Counting and Static String Types

Cython’s Three Kinds of Functions

Python Functions in Cython with the def Keyword

C Functions in Cython with the cdef Keyword

Combining def and cdef Functions with cpdef

Functions and Exception Handling

Functions and the embedsignature Compiler Directive

Type Coercion and Casting

Declaring and Using structs, unions, and enums

Type Aliasing with ctypedef

Cython for Loops and while Loops

Guidelines for Efficient Loops

Loop Example

The Cython Preprocessor

Bridging the Python 2 and Python 3 Divide

str, unicode, bytes, and All That

Summary

4. Cython in Practice: N-Body Simulation

Overview of the N-Body Python Code

Converting to Cython

Python Data Structures and Organization

Converting Data Structures to structs

Running the Cythonized Version

Summary

5. Cython and Extension Types

Comparing Python Classes and Extension Types

Extension Types in Cython

Type Attributes and Access Control

C-Level Initialization and Finalization

cdef and cpdef Methods

Inheritance and Subclassing

Casting and Subclasses

Extension Type Objects and None

Extension Type Properties in Cython

Special Methods Are Even More Special

Arithmetic Methods

Rich Comparisons

Iterator Support

Summary

6. Organizing Cython Code

Cython Implementation (.pyx) and Declaration (.pxd) Files

The cimport Statement

Predefined Definition Files

Using cimport with a module in a package

Using cimport with an object from a dotted module name

Multiple named cimports

Using cimport with an alias

Using cimport with C++ STL template classes

Include Files and the include Statement

Organizing and Compiling Cython Modules Inside Python Packages

Summary

7. Wrapping C Libraries with Cython

Declaring External C Code in Cython

Cython Does Not Automate Wrapping

Declaring External C Functions and typedefs

Declaring and Wrapping C structs, unions, and enums

Wrapping C Functions

Wrapping C structs with Extension Types

Constants, Other Modifiers, and Controlling What Cython Generates

Error Checking and Raising Exceptions

Callbacks

Callbacks and Exception Propagation

Summary

8. Wrapping C++ Libraries with Cython

Simple Example: MT_RNG Class

The Wrapper Extension Type

Compiling with C++

Using Our Wrapper from Python

Overloaded Methods and Functions

Operator Overloading

C++ Exceptions

Stack and Heap Allocation of C++ Instances

Working with C++ Class Hierarchies

C++ Templates

Templated Functions and Cython’s Fused Types

Templated Classes

Iterators and Nested Classes

Included STL Container Class Declarations

Memory Management and Smart Pointers

Summary

9. Cython Profiling Tools

Cython Runtime Profiling

Performance Profiling and Annotations

Summary

10. Cython, NumPy, and Typed Memoryviews

The Power of the New Buffer Protocol

The memoryview Type

Typed Memoryviews

Typed Memoryview Example

C-Level Access to Typed Memoryview Data

Trading Safety for Performance

Declaring Typed Memoryviews

Using Typed Memoryviews

Beyond Buffers

Wrapping C and C++ Arrays

Correct (and Automatic) Memory Management with Cython and C Arrays

Summary

11. Cython in Practice: Spectral Norm

Overview of the Spectral Norm Python Code

Performance Profiling

Cythonizing Our Code

Adding Static Type Information

Using Typed Memoryviews

Comparing to the C Implementation

Summary

12. Parallel Programming with Cython

Thread-Based Parallelism and the Global Interpreter Lock

The nogil Function Attribute

The with nogil Context Manager

Using prange to Parallelize Loops

Using prange

prange Options

Using prange for Reductions

Parallel Programming Pointers and Pitfalls

Summary

13. Cython in Context

Cython Versus Project X

Other Ahead-of-Time Compilers for Python

Python Wrapper Projects

Just-in-Time Compilers for Python

Summary

Index

Colophon

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Tags: Kurt W Smith, Cython, Python Programmers