In this chapter, we will focus on two aspects of testing for scientific programming. The first aspect is the often difficult topic of what to test in scientific computing. The second aspect covers the question of how to test. We will distinguish between manual and automated testing. Manual testing is what is done by every programmer to quickly check that an implementation is working or not. Automated testing is the refined, automated variant of that idea. We will introduce some tools available for automatic testing in general, with a view on the particular case of scientific computing.
Scientific Computing with Python 3
By :
Scientific Computing with Python 3
By:
Overview of this book
Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more.
Table of Contents (23 chapters)
Scientific Computing with Python 3
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Acknowledgement
Preface
Free Chapter
Getting Started
Variables and Basic Types
Container Types
Linear Algebra – Arrays
Advanced Array Concepts
Plotting
Functions
Classes
Iterating
Error Handling
Namespaces, Scopes, and Modules
Input and Output
Testing
Comprehensive Examples
Symbolic Computations - SymPy
References
Customer Reviews