Python Scripting for Computational Science (Texts in Computational Science and Engineering Book 3)

Numerous readers of the second edition have noti?ed me about misprints and possible improvements of the text and the associated computer codes. The resulting modi?cations 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 di?erent 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 exempli?ed to make the reader aware of new tools. Chapter 4.4.4 is new and gives a taste of symbolic mathematics in Python. 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.

The goal of this book is to teach computational scientists how to develop tailored, flexible, and human-efficient working environments built from small programs (scripts) written in the easy-to-learn, high-level language Python. The focus is on examples and applications of relevance to computational scientists: gluing existing applications and tools, e.g. for automating simulation, data analysis, and visualization; steering simulations and computational experiments; equipping old programs with graphical user interfaces; making computational Web applications; and creating interactive interfaces with a Maple/Matlab-like syntax to numerical applications in C/C++ or Fortran. In short, scripting with Python makes you much more productive, increases the reliability of your scientific work and lets you have more fun - on Unix, Windows and Macintosh. All the tools and examples in this book are open source codes. The third edition is compatible with the new NumPy implementation and features updated information, correction of errors, and improved associated software tools.

The goal of this book is to teach computational scientists how to develop tailored, flexible, and human-efficient working environments built from small programs (scripts) written in the easy-to-learn, high-level language Python. The focus is on examples and applications of relevance to computational scientists: gluing existing applications and tools, e.g. for automating simulation, data analysis, and visualization; steering simulations and computational experiments; equipping old programs with graphical user interfaces; making computational Web applications; and creating interactive interfaces with a Maple/Matlab-like syntax to numerical applications in C/C++ or Fortran. In short, scripting with Python makes you much more productive, increases the reliability of your scientific work and lets you have more fun - on Unix, Windows and Macintosh. All the tools and examples in this book are open source codes. The second edition features new material, reorganization of text, improved examples and tools, updated information, and correction of errors. In Risk Analysis of Complex and Uncertain Systems acknowledged risk authority Tony Cox shows all risk practitioners how Quantitative Risk Assessment (QRA) can be used to improve risk management decisions and policies. It develops and illustrates QRA methods for complex and uncertain biological, engineering, and social systems – systems that have behaviors that are just too complex to be modeled accurately in detail with high confidence – and shows how they can be applied to applications including assessing and managing risks from chemical carcinogens, antibiotic resistance, mad cow disease, terrorist attacks, and accidental or deliberate failures in telecommunications network infrastructure. This book was written for a broad range of practitioners, including decision risk analysts, operations researchers and management scientists, quantitative policy analysts, economists, health and safety risk assessors, engineers, and modelers. "The goal of this book is to teach computational scientists how to develop tailored, flexible, and human-efficient working environments built from small programs (scripts) written in the easy-to-learn, high-level language Python. The focus is on examples and applications of relevance to computational scientists: gluing existing applications and tools, e.g. for automating simulation, data analysis, and visualization; steering simulations and computational experiments; equipping old programs with graphical user interfaces; making computational Web applications; and creating interactive interfaces with a Maple/Matlab-like syntax to numerical applications in C/C++ or Fortran. All the tools and examples in this book are open source codes. The third edition is compatible with the new NumPy implementation and features updated information, correction of errors, and improved associated software tools."--Jacket

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Front Matter....Pages I-XXVI Introduction....Pages 1-25 Getting Started with Python Scripting....Pages 27-72 Basic Python....Pages 73-130 Numerical Computing in Python....Pages 131-188 Combining Python with Fortran, C, and C++....Pages 189-226 Introduction to GUI Programming....Pages 227-294 Web Interfaces and CGI Programming....Pages 295-318 Advanced Python....Pages 319-449 Fortran Programming with NumPy Arrays....Pages 451-482 C and C++ Programming with NumPy Arrays....Pages 483-528 More Advanced GUI Programming....Pages 529-603 Tools and Examples....Pages 605-676 Back Matter....Pages 677-756 The second edition of this text has been revised to reflect changes to the ASQ Body of Knowledge for the Certified Quality Engineer (CQE), covering all topics required by the quality engineer for day-to-day practices in planning, testing, finance and management Quality engineers, like other professionals, are expected to conduct themselves in a manner appropriate with their professional standing.