Python_Scripting_for_Computational_Science_Third_Edition.pdf

Hans Petter Langtangen

Python Scripting

for Computational

Science

Third Edition

With 62 Figures

123

Hans Petter Langtangen

Simula Research Laboratory

Martin Linges vei 17, Fornebu

P. O. B o x 1 3 4

1325 Lysaker, Norway

hpl@simula.no

On leave from:

Department of Informatics

University of Oslo

P. O. B o x 1 0 8 0 B l i n d e r n

0316 Oslo, Norway

http://folk.uio.no/hpl

The author of this book has received ﬁnancial support from the NFF – Norsk faglitterær

forfatter- og oversetterforening.

ISBN 978-3-540-73915-9

e-ISBN 978-3-540-73916-6

DOI 10.1007/978-3-540-73916-6

Texts in Computational Science and Engineering ISSN 1611-0994

Library of Congress Control Number: 2007940499

Mathematics Subject Classiﬁcation (2000): 65Y99, 68N01, 68N15, 68N19, 68N30, 97U50, 97U70

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Preface to the Third Edition

Numerous readers of the second edition have notiﬁed me about misprints and

possible improvements of the text and the associated computer codes. The

resulting modiﬁcations have been incorporated in this new edition and its

accompanying software.

The major change between the second and third editions, however, is

caused by the new implementation of Numerical Python, now called numpy .

The new numpy package encourages a slightly diﬀerent syntax compared to

the old Numeric implementation, which was used in the previous editions.

Since Numerical Python functionality appears in a lot of places in the book,

there are hence a huge number of updates to the new suggested numpy syntax,

especially in Chapters 4, 9, and 10.

The second edition was based on Python version 2.3, while the third

edition contains updates for version 2.5. Recent Python features, such as

generator expressions (Chapter 8.9.4), Ctypes for interfacing shared libraries

in C (Chapter 5.2.2), the with statement (Chapter 3.1.4), and the subprocess

module for running external processes (Chapter 3.1.3) have been exempliﬁed

to make the reader aware of new tools. Regarding Chapter 3.1.3, os.system

is not used in the book anymore, instead we recommend the commands or

subprocess modules.

Chapter 4.4.4 is new and gives a taste of symbolic mathematics in Python.

Chapters 5 and 10 have been extended with new material. For example,

F2PY and the Instant tool are very convenient for interfacing C code, and

this topic is treated in detail in Chapters 5.2.2, 10.1.1, and 10.1.2 in the

new edition. Installation of Python itself and the many add-on modules have

become increasingly simpler over the years with setup.py scripts, which has

made it natural to simplify the descriptions in Appendix A.

The py4cs package with software tools associated with this book has un-

dergone a major revision and extension, and the package is now maintained

under the name scitools and distributed separately. The name py4cs is still

oﬀered as a nickname for scitools to make old scripts work. The new scitools

package is backward compatible with py4cs from the second edition.

Several people has helped me with preparing the new edition. In par-

ticular, the substantial eﬀorts of Pearu Peterson, Ilmar Wilbers, Johannes

H. Ring, and Rolv E. Bredesen are highly appreciated.

The Springer staﬀ has, as always, been a great pleasure to work with.

Special thanks go to Martin Peters, Thanh-Ha Le Thi, and Andrea Kohler

for their extensive help with this and other book projects.

Oslo, September 2007

Hans Petter Langtangen

Preface to the Second Edition

The second edition features new material, reorganization of text, improved

examples and software tools, updated information, and correction of errors.

This is mainly the result of numerous eager readers around the world who

have detected misprints, tested program examples, and suggested alternative

ways of doing things. I am greatful to everyone who has sent emails and

contributed with improvements. The most important changes in the second

edition are brieﬂy listed below.

Already in the introductory examples in Chapter 2 the reader now gets a

glimpse of Numerical Python arrays, interactive computing with the IPython

shell, debugging scripts with the aid of IPython and Pdb, and turning “ﬂat”

scripts into reusable modules (Chapters 2.2.5, 2.2.6, and 2.5.3 are added).

Several parts of Chapter 4 on numerical computing have been extended (es-

pecially Chapters 4.3.5, 4.3.6, 4.3.7, and 4.4). Many smaller changes have

been implemented in Chapter 8; the larger ones concern exemplifying Tar

archives instead of ZIP archives in Chapter 8.3.4, rewriting of the mate-

rial on generators in Chapter 8.9.4, and an example in Chapter 8.6.13 on

adding new methods to a class without touching the original source code

and without changing the class name. Revised and additional tips on opti-

mizing Python code have been included in Chapter 8.10.3, while the new

Chapter 8.10.4 contains a case study on the eciency of various implemen-

tations of a matrix-vector product. To optimize Python code, we now also

introduce the Psyco and Weave tools (see Chapters 8.10.4, 9.1, 10.1.3, and

10.4.1). To reduce complexity of the principal software example in Chapters 9

and 10, I have removed evaluation of string formulas. Instead, one can use

the revised StringFunction tool from Chapter 12.2.1 (the text and software

regarding this tool have been completely rewritten). Appendix B.5 has been

totally rewritten: now I introduce Subversion instead of CVS, which results

in simpler recipes and shorter text. Many new Python tools have emerged

since the ﬁrst printing and comments about some of these are inserted many

places in the text.

Numerous sections or paragraphs have been expanded, condensed, or re-

moved. The sequence of chapters is hardly changed, but a couple of sections

have been moved. The numbering of the exercises is altered as a result of

both adding and removing exerises.

Finally, I want to thank Martin Peters, Thanh-Ha Le Thi, and Andrea

Kohler in the Springer system for all their help with preparing a new edition.

Oslo, October 2005

Hans Petter Langtangen

Preface to the First Edition

The primary purpose of this book is to help scientists and engineers work-

ing intensively with computers to become more productive, have more fun,

and increase the reliability of their investigations. Scripting in the Python

programming language can be a key tool for reaching these goals [27,29].

The term scripting means diﬀerent things to diﬀerent people. By scripting

I mean developing programs of an administering nature, mostly to organize

your work, using languages where the abstraction level is higher and program-

ming is more convenient than in Fortran, C, C++, or Java. Perl, Python,

Ruby, Scheme, and Tcl are examples of languages supporting such high-level

programming or scripting. To some extent Matlab and similar scientiﬁc com-

puting environments also fall into this category, but these environments are

mainly used for computing and visualization with built-in tools, while script-

ing aims at gluing a range of diﬀerent tools for computing, visualization, data

analysis, ﬁle/directory management, user interfaces, and Internet communi-

cation. So, although Matlab is perhaps the scripting language of choice in

computational science today, my use of the term scripting goes beyond typi-

cal Matlab scripts. Python stands out as the language of choice for scripting

in computational science because of its very clean syntax, rich modulariza-

tion features, good support for numerical computing, and rapidly growing

popularity.

What Scripting is About. The simplest application of scripting is to write

short programs (scripts) that automate manual interaction with the com-

puter. That is, scripts often glue stand-alone applications and operating sys-

tem commands. A primary example is automating simulation and visual-

ization: from an eﬀective user interface the script extracts information and

generates input ﬁles for a simulation program, runs the program, archive data

ﬁles, prepares input for a visualization program, creates plots and animations,

and perhaps performs some data analysis.

More advanced use of scripting includes rapid construction of graphical

user interfaces (GUIs), searching and manipulating text (data) ﬁles, manag-

ing ﬁles and directories, tailoring visualization and image processing environ-

ments to your own needs, administering large sets of computer experiments,

and managing your existing Fortran, C, or C++ libraries and applications

directly from scripts.

Scripts are often considerably faster to develop than the corresponding

programs in a traditional language like Fortran, C, C++, or Java, and the

code is normally much shorter. In fact, the high-level programming style and

tools used in scripts open up new possibilities you would hardly consider as

a Fortran or C programmer. Furthermore, scripts are for the most part truly

cross-platform, so what you write on Windows runs without modiﬁcations

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