Book: A Primer on Scientific Programming with Python (Texts in Computational Science and Engineering)

ISBN: 9783642302923

Publisher: Springer

Year: 2012

The aim of this book is to teach computer programming using examples

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

the Python programming language because it combines remarkable expressive

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

easy to learn and very well suited for an introduction to computer programming.

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 mathematicsintensive

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

a university calculus course in parallel, preferably containing both classical

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 programming

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.

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