Book Image

NumPy Essentials

By : Leo (Liang-Huan) Chin, Tanmay Dutta, Shane Holloway
Book Image

NumPy Essentials

By: Leo (Liang-Huan) Chin, Tanmay Dutta, Shane Holloway

Overview of this book

In today’s world of science and technology, it’s all about speed and flexibility. When it comes to scientific computing, NumPy tops the list. NumPy gives you both the speed and high productivity you need. This book will walk you through NumPy using clear, step-by-step examples and just the right amount of theory. We will guide you through wider applications of NumPy in scientific computing and will then focus on the fundamentals of NumPy, including array objects, functions, and matrices, each of them explained with practical examples. You will then learn about different NumPy modules while performing mathematical operations such as calculating the Fourier Transform; solving linear systems of equations, interpolation, extrapolation, regression, and curve fitting; and evaluating integrals and derivatives. We will also introduce you to using Cython with NumPy arrays and writing extension modules for NumPy code using the C API. This book will give you exposure to the vast NumPy library and help you build efficient, high-speed programs using a wide range of mathematical features.
Table of Contents (16 chapters)
NumPy Essentials
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface

Chapter 9. Introduction to the NumPy C-API

NumPy is a general-purpose library, designed to address most of the needs of a developer of scientific applications. However, as the code base and coverage of an application increase, so does the computation, and sometimes users demand more specific operations and optimized code segments. We have shown how NumPy and Python have tools, such as f2py and Cython, to address these demands. These tools may be an excellent choice for rewriting your functions to a natively compiled code in order to provide extra speed. But there may be some cases (leveraging a C library, such as NAG, to write some analytics) where you may want to do something more radical such as create a new data structure specifically for your own library. This would require you to have access to low-level controls in the Python interpreter. In this chapter, we will be looking into how to do this using the C-API provided by Python and its extension, the NumPy C-API. The C-API itself is...