Ebook Free Python Scripting for Computational Science (Texts in Computational Science and Engineering) (v. 3)
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Python Scripting for Computational Science (Texts in Computational Science and Engineering) (v. 3)
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Product details
Series: Texts in Computational Science and Engineering (Book 3)
Hardcover: 726 pages
Publisher: Springer; 1 edition (September 20, 2004)
Language: English
ISBN-10: 3540435085
ISBN-13: 978-3540435082
Product Dimensions:
6.2 x 1.2 x 9.2 inches
Shipping Weight: 2.6 pounds
Average Customer Review:
4.6 out of 5 stars
14 customer reviews
Amazon Best Sellers Rank:
#1,826,140 in Books (See Top 100 in Books)
This book is fantastic. The first third is dedicated to basic Numpy and "daily" operations that engineers and scientists encounter when working with Python, so it resembles a lot to any Numpy/Python book. Nothing "new".The other third, however, is dedicated to GUI programming and integration with Scientific Software. It is full of very useful examples that are not difficult to replicate/modify for your needs.It also addresses more advanced GUI programming using Canvas, C/C++ integration, efficiency, and other subjects I haven't read yet. If you ask me, it has everything I need. And man, when you find yourself without internet connection and *need* to make something work, books can really save you. True story.5 stars for this one.
I bought this book, just for a couple of the chapters, but i found myself using more of this book then i expected, and reading all the chapters(even the fortran stuff). I found this book better then all my other "scientific python" books, in that my other books really built toyish apps. This book is meant for people doing production computational science work in python. It doesn't have much btw on super computer's programming and python, aside from a lot on how to integrate c/c++/fortan libraries(i.e. anyone doing major work in python probably is integrating to things like Tesla/Hadoop/mpi ... etc ... and the book didn't go to that level).
Great reference and well written with excellent examples.
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.
Exactly what i needed and help me out.
The book is great
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
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!
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