| Python Scripting for Computational Science (Texts in Computational Science and Engineering) |  | Author: Hans Petter Langtangen
Publisher: Springer
List Price: $69.95
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Rating:  10 reviews
Media: Hardcover
Edition: 3rd
Pages: 758
Number Of Items: 1
Shipping Weight (lbs): 2.4
Dimensions (in): 9.4 x 6.4 x 1.2
ISBN: 3540739157
Dewey Decimal Number: 511
EAN: 9783540739159
ASIN: 3540739157
Publication Date: December 11, 2007
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Product Description
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.
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| Customer Reviews:
Showing reviews 1-5 of 10
 Excellent Text and Reference! June 12, 2010
Alex
As an intermediate Python programmer, this excellent book has become my go to reference for useful intermediate and advanced techniques that I can locate and learn quickly. The writing is clear and not overly verbose. In addition to a wide array of numerical and scientific examples, the book is helpful for a wide range of programming issues, such as gluing together disparate legacy applications, interfacing to C++, regression testing numerical code, building GUI's, web programming, etc.
Outstanding introduction to Python and Numpy May 15, 2010
Rich (San Francisco, CA)
1 out of 1 found this review helpful
I've bought what seems to (my wife) be every Python book out there and I can't tell you how sick I am of spam, spam, spam code! (trivial and obfuscated Python code examples with a common theme focused around one Monty Python skit or another...) Spam code seems to prevail in other Python books.
Here finally is a book with code examples that are very clear, are immediately useful to the serious programmer and filled with real life discourse on relative performance differences between Python and other languages that have a reputation for speed. There are clear examples of 'number crunching', producing images and even video animations, hooks into other scientific packages such as MathLab, etc.
If you are interested in really learning Python, want to come away from an hour or twos worth of coding experience with a module or two that you can use tomorrow and are not interested in code examples extolling Monty Python silliness, then this is the book for you.
While this book is about twice as expensive as many of my other Python books, I wish I had purchased this one first. Even though I've been using Python, seemingly every day, for two years, I kept finding nuggets in this book with what seemed to be every turn of the page. My focus right now is processing extremely large data sets of binary data but I'll soon be looking at image processing and I know I'll be reaching for this book over and over again. Don't hesitate! Just buy the book!
*The* reference for folks who work with Matlab July 26, 2009
G. Jaouen (Florida)
5 out of 5 found this review helpful
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 4.1.4), so that you can decide for yourself if NumPy is useful for your application.
I will not comment on the parts of the book that deal with C and FORTRAN integration because I leave that to more able programmers. I also will not comment on the extensive GUI building chapters because I do not build GUIs. I will point out, though, that I have derived full value out of this book simply by reading, and re-reading chapters 2, 3, 4 and 8. Some will argue that there is too much "basic Python" in these chapters for the whole to be considered advanced computational science -- my opinion is that even when the author describes "basic Python", his examples and intuition make it so that even one who has read a couple of reference books cover-to-cover will learn something about using "basic Python" to perform numerical analysis in a more efficient way. In fact, the book is a testament to doing really convoluted things in a really compact and elegant manner!
Absolutly Outstanding May 2, 2008
Stanely S. Forrester (Batavia, Illinois United States)
9 out of 9 found this review helpful
Python Scripting for Computational Science is both an introduction to the Python language and an excellent reference for the intermediate developer. The approach taken by the author is to present the language in the form of tasks to be solved accompanied by example code. As expected for a book on scientific computing the modules covered in the examples emphasize numerical packages but this in no way detracts from the applicability to general Python enthusiast.
What really makes this book more than just another Python introduction is that the author bridges the gap between complied and interpreted code. He demonstrates how the speed of execution of compiled code can be tied to the rapid pace at which scripts can be developed. Examples are provided for interfacing C, C++ and FORTRAN code with Python. Calls to precompiled applications are also covered and the examples were easily adapted to my favorite computational tools. One of the risks with doing numerical work in a scripting language is the possibility of straying into computationally intensive tasks to which interpreted code is not well suited . Latter chapters discuss how to identify these portions of your code and how to migrating these tasks to a compiled language.
good book April 5, 2008
M. Jamróz (Poland)
3 out of 4 found this review helpful
If you want to learn Python, you should get it. Author do not build some "big" application (like "internet store software" or "bookstore software") from beginning to end, but rather give you a lot of practical examples of using python.
It is not like in others book that examples include only learned functions/methods, but use topics from the rest of book (you have example on page 25 and note that explanation of this and that function you found on page 543). By that you have interesting examples to use in real-world problems, not only examples to explain freshly learned topic.
In other books interesting examples of use python you found on page 3234, because only when author introduce all useful functions. In this book nice examples is even on first pages.
You learn how to use numerical packages (numpy) in python, using some useful tricks on lists and arrays, introduce to using graphical interface in Tk.
Showing reviews 1-5 of 10
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