Python Scripting for Computational Science

1. Front Matter

   Pages I-XXIV

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2. Introduction

   Pages 1-25

3. Getting Started with Python Scripting

   Pages 27-72

4. Basic Python

   Pages 73-129

5. Numerical Computing in Python

   Pages 131-181

6. Combining Python with Fortran, C, and C++

   Pages 183-218

7. Introduction to GUI Programming

   Pages 219-287

8. Web Interfaces and CGI Programming

   Pages 289-312

9. Advanced Python

   Pages 313-441

10. Fortran Programming with Numerical Python Arrays

    Pages 443-473

11. C and C++ Programming with Numerical Python Arrays

    Pages 475-514

12. More Advanced GUI Programming

    Pages 515-589

13. Tools and Examples

    Pages 591-662

14. Back Matter

    Pages 663-742

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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 brie?y 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 “?at” 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 (- pecially Chapters 4. 3. 5, 4. 3. 7, 4. 3. 8, 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 material on generators in Chapter 8. 9. 4, and an example in 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 op- mizing Python code have been included in Chapter 8. 10. 3, while the new Chapter 8. 10.

• C++ programming language
• FORTRAN
• Maple
• MatLab
• Open Source
• Windows
• computational science
• interfaces
• language
• scripting
• simulation
• software
• text processing
• user interface
• web applications

From the reviews of the second edition:

"This book addresses primarily a CSE (computational science and engineering) audience. … gives a clear and detailed account on the ways in which the surprisingly powerful Python language may aid the CSE community." (H. Muthsam, Monatshefte für Mathematik, Vol. 151 (4), 2007)

“This book is excellent for people in computational sciences wanting to learn Python, or for people new to numerical computations. Python is an exciting programming language (scripting language to be specific) allowing rapid application development. … the aim of the present book is … learning how to program in Python. With it’s 760 pages it offers many examples and tips. … meets its goals in a convincing way. It provides a very good start to do numerical computations with Python.” (Benny Malengier, Bulletin of the Belgian Mathematical Society, Vol. 15 (1), 2008)

• Simula Research Laboratory, Lysaker, Norway

  Hans Petter Langtangen

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