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A Primer on Scientfic Programming with Python, Third Edition .pdf：https://t00y.com/file/21552682-413157943

书籍信息 作 者：

Timothy J. Barth

Michael Griebel

David E. Keyes

Risto M. Nieminen

Dirk Roose

Tamar Schlick

出版社：- 出版年：2009 ISBN 978-3-642-30292-3

版 本：第 1 版 页 数：832 页 

书籍介绍 

The aim of this book is to teach computer programming using exam-

ples from mathematics and the natural sciences. We have chosen to use

the Python programming language because it combines remarkable ex-

pressive power with very clean, simple, and compact syntax. Python is

easy to learn and very well suited for an introduction to computer pro-

gramming. Python is also quite similar to Matlab and a good language

for doing mathematical computing. It is easy to combine Python with

compiled languages, like Fortran, C, and C++, which are widely used

languages for scientific computations. A seamless integration of Python

with Java is offered by a special version of Python called Jython.

The examples in this book integrate programming with applications

to mathematics, physics, biology, and finance. The reader is expected to

have knowledge of basic one-variable calculus as taught in mathematics-

intensive programs in high schools. It is certainly an advantage to take

a university calculus course in parallel, preferably containing both clas-

sical and numerical aspects of calculus. Although not strictly required,

a background in high school physics makes many of the examples more

meaningful.

Many introductory programming books are quite compact and focus

on listing functionality of a programming language. However, learning

to program is learning how to think as a programmer. This book has its

main focus on the thinking process, or equivalently: programming as a

problem solving technique. That is why most of the pages are devoted

to case studies in programming, where we define a problem and explain

how to create the corresponding program. New constructions and pro-

gramming styles (what we could call theory) is also usually introduced

via examples. Special attention is paid to verification of programs and

to finding errors. These topics are very demanding for mathematical

software, because the unavoidable numerical approximation errors are

possibly mixed with programming mistakes.