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Python Scripting for Computational Science (Texts in Computational Science and Engineering) Hardcover

ISBN-13: 978-3540739159 ISBN-10: 3540739157 Edition: 3rd

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Product Details

  • Series: Texts in Computational Science and Engineering (Book 3)
  • Hardcover: 756 pages
  • Publisher: Springer; 3rd edition (January 9, 2009)
  • Language: English
  • ISBN-10: 3540739157
  • ISBN-13: 978-3540739159
  • Product Dimensions: 9.4 x 6.4 x 1.3 inches
  • Shipping Weight: 2.8 pounds (View shipping rates and policies)
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (11 customer reviews)
  • Amazon Best Sellers Rank: #88,542 in Books (See Top 100 in Books)

Editorial Reviews

Review

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)


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Customer Reviews

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Getting Started With Python Scripting--38pp Interesting examples.
C. Dunn
The downloadable base of utilities is a great set of examples that also proves useful in everyday life.
marcel
I'm giving this book five stars because it was basically written for me.
G. Jaouen

Most Helpful Customer Reviews

163 of 168 people found the following review helpful By C. Dunn on October 14, 2004
Format: Hardcover
The author has 2 main goals:

1) To improve the productivity of scientists familiar with specific software systems (especially Matlab, Maple, and Mathematica) by teaching them to "glue" applications together.

2) To advocate Python as the preferred "glue" language. In his own words, "I hope to convince computational scientists having experience with Perl that Python is a preferable alternative, especially for large long-term projects."

He has certainly done a creditable job. As an expert in computational differential equations, he neglects neither efficiency nor correctness, while stressing both simplicity and reliability. In this sense, he has done a great service to the Python community.

The question is: What justifies the purchase of his book?

The answer is: Chapters 4, 9, and 10.

Contents:

1. Introduction--26pp

Very convincing arguments.

2. Getting Started With Python Scripting--38pp

Interesting examples.

3. Basic Python--56pp

A too-quick tutorial. Go to python dot org instead.

4. Numerical Computing in Python--48pp

Stellar explanations of vectorized array operations.

5. Combining Python with Fortran, C, and C++--36pp

Details use of Fortran2Py and SWIG. Mentions many alternatives.

6. Introduction to GUI Programming--70pp

Useful examples of Tkinter/pmw widgets.

7. Web Interfaces and CGI Programming--24pp

Good source of ideas.

8. Advanced Python--132pp

Deep and extensive. Includes: option parsing, regular expressions, data persistence and compression, object-oriented programming, exceptions, generic programming, efficiency.

9.
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11 of 11 people found the following review helpful By G. Jaouen on July 26, 2009
Format: Hardcover
I'm giving this book five stars because it was basically written for me. I don't mean that literally, of course. I say that because the usual methods of googling for answers and reading the manual do not work when you are trying to push the limits of what a tool is capable of doing. I do numerical computations for a variety of things -- finding patterns in large data sets, automating data collection and analysis, converting raw serial output into convenient CSV, plotting multidimensional datasets etc. Over the years, I have collected a large number of productivity habits with Matlab, which allows me to do ridiculously convoluted things in a short period of time. You just have to read the introduction of any Python manual to understand why I am switching from Matlab to Python. The problem is -- what will replace all these productivity habits? They need to be replaced with "Pythonic" habits, something that can take years of practice.

The beauty about Langtangen's book is that it runs through every one of those techniques. Instead of giving a basic example (what your google search would have provided) or a complete list of, ahem, useless techniques (what the manual would have provided), you get exactly what a seasoned data analyst needs to know to get moving with state-of-the-art commands. The author also discusses optimizations and alternatives in each chapter.

The book is also the best source for explaining *why* NumPy should be used by people working with large datasets. Folks love to create toolkits for Python, but some of these are a list of non-intuitive shortcuts that don't provide a substantial improvement over basic Python. Langtangen goes through the pain of explaining the benefits of the package (chapter 4.1.
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16 of 18 people found the following review helpful By W Boudville HALL OF FAMETOP 1000 REVIEWERVINE VOICE on November 12, 2006
Format: Hardcover
Langtangen's emphasis here is on a reader who comes from a strong background in engineering or science, and is familiar with common computational ideas and has done some programming, but not necessarily in Python. The typical book on Python is aimed at a general programming reader, and the examples in such a book usually are quite elementary, from a computational viewpoint.

The merit of Langtangen's book is that he gets into a lot of computational ideas. This is not a trivial book. Aspects like parsing data in files, connecting to local and remote hosts, and interacting with programs written in other languages are covered. For the latter, the important cases of Fortran and C programs are explained. The choices of these languages is deliberate. In science and engineering, they are the dominant languages for raw computation. And you are likely to have legacy code written in these, that you cannot abandon while using Python.
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58 of 76 people found the following review helpful By Braddock Gaskill on June 3, 2005
Format: Hardcover Verified Purchase
I bought this book as an experienced programmer and Unix user expecting more of a "Numerical Recepies in Python" emphasis on the efficient implementation of algorithms which happen to be in Python. I should have paid more attention to the description.

This book is really more of a "Grad Student's Guide to Everyday Python Usage". I imagine it would be very valuable to a mathematics Grad student without too much programming or shell experience, looking for an alternative to Matlab. However, there is very little "Computational Science" in this book. Do NOT expect a cookbook of high performance algorithm implementations.

The book is a very verbose 700+ pages, all in an unexciting academic LaTeX format. The author works through idiom after idiom for accomplishing different tasks in fairly stand-alone sub-sections without much of a feeling of conceptual "flow" between them. It sort of feels like reading through the author's personal lab notes that he took everytime he learned a new language feature or trick.

If you are an experienced programmer, you will quickly get impatient with the verbose presentation that emphasizes idioms and examples instead of fundamental concepts and syntax reference tables. But, if you are an experienced programmer, you are not the target audience for this book.

Braddock Gaskill
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