Cython A Guide For Python Programmers Pdf
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Pythonic Geodynamics
This book addresses students and young researchers who want to learn to use numerical modeling to solve problems in geodynamics. Intended as an easy-to-use and self-learning guide, readers only need a basic background in calculus to approach most of the material. The book difficulty increases very gradually, through four distinct parts. The first is an introduction to the Python techniques necessary to visualize and run vectorial calculations. The second is an overview with several examples on classical Mechanics with examples taken from standard introductory physics books. The third part is a detailed description of how to write Lagrangian, Eulerian and Particles in Cell codes for solving linear and non-linear continuum mechanics problems. Finally the last one address advanced techniques like tree-codes, Boundary Elements, and illustrates several applications to Geodynamics. The entire book is organized around numerous examples in Python, aiming at encouraging the reader to le arn by experimenting and experiencing, not by theory.
Cython
Author: Kurt W. Smith
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2015-01-21
Build software that combines Python’s expressivity with the performance and control of C (and C++). It’s possible with Cython, the compiler and hybrid programming language used by foundational packages such as NumPy, and prominent in projects including Pandas, h5py, and scikits-learn. In this practical guide, you’ll learn how to use Cython to improve Python’s performance—up to 3000x— and to wrap C and C++ libraries in Python with ease. Author Kurt Smith takes you through Cython’s capabilities, with sample code and in-depth practice exercises. If you’re just starting with Cython, or want to go deeper, you’ll learn how this language is an essential part of any performance-oriented Python programmer’s arsenal. Use Cython’s static typing to speed up Python code Gain hands-on experience using Cython features to boost your numeric-heavy Python Create new types with Cython—and see how fast object-oriented programming in Python can be Effectively organize Cython code into separate modules and packages without sacrificing performance Use Cython to give Pythonic interfaces to C and C++ libraries Optimize code with Cython’s runtime and compile-time profiling tools Use Cython’s prange function to parallelize loops transparently with OpenMP
Cython
Build software that combines Python{u2019}s expressivity with the performance and control of C (and C++). It{u2019}s possible with Cython, the compiler and hybrid programming language used by foundational packages such as NumPy, and prominent in projects including Pandas, h5py, and scikits-learn. In this practical guide, you{u2019}ll learn how to use Cython to improve Python{u2019}s performance{u2014}up to 3000x{u2014} and to wrap C and C++ libraries in Python with ease. Author Kurt Smith takes you through Cython{u2019}s capabilities, with sample code and in-depth practice exercises. If you{u2019}re just starting with Cython, or want to go deeper, you{u2019}ll learn how this language is an essential part of any performance-oriented Python programmer{u2019}s arsenal. Use Cython{u2019}s static typing to speed up Python code Gain hands-on experience using Cython features to boost your numeric-heavy Python Create new types with Cython{u2014}and see how fast object-oriented programming in Python can be Effectively organize Cython code into separate modules and packages without sacrificing performance Use Cython to give Pythonic interfaces to C and C++ libraries Optimize code with Cython{u2019}s runtime and compile-time profiling tools Use Cython{u2019}s prange function to parallelize loops transparently with OpenMP.