Download PDF Effective Computation in Physics: Field Guide to Research with Python
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Effective Computation in Physics: Field Guide to Research with Python
Download PDF Effective Computation in Physics: Field Guide to Research with Python
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About the Author
Anthony Scopatz is a computational physicist and long time Python developer, Anthony holds his BS in Physics from UC, Santa Barbara and a Ph.D. in Mechanical / Nuclear Engineering from UT Austin. A former Enthought employee, he spent his post-doctoral studies at the FLASH Center at the University of Chicago in the Astrophysics Department. He is currently a Staff Scientist at the University of Wisconsin-Maidson in Engineering Physics. Anthony’s research interests revolve around essential physics modeling of the nuclear fuel cycle, and information theory & entropy. Anthony is proudly a fellow of the Python Software Foundation and has published and spoken at numerous conferences on a variety of science & software development topics.Kathryn Huff is a Fellow with the Berkeley Institute for Data Science and a postdoctoral scholar with the Nuclear Science and Security Consortium at the University of California Berkeley. In 2013, she received her Ph.D. in Nuclear Engineering from the University of Wisconsin Madison. She also holds a bachelor's degree in Physics from the University of Chicago. She has participated in varied research including experimental cosmological astrophysics, experimental non-equilibrium granular material phase dynamics, computational nuclear fuel cycle analysis, and computational reactor accident neutronics. At Wisconsin, she was a founder of The Hacker Within scientific computing group and has been an instructor for Software Carpentry since 2011. Among other professional service, she is currently an division officer in the American Nuclear Society and has served two consecutive years as the Technical Program Co-Chair of the Scientific Computing with Python (SciPy) conference.
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Product details
Paperback: 552 pages
Publisher: O'Reilly Media; 1 edition (July 5, 2015)
Language: English
ISBN-10: 1491901535
ISBN-13: 978-1491901533
Product Dimensions:
7 x 1.2 x 9.2 inches
Shipping Weight: 2 pounds (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
6 customer reviews
Amazon Best Sellers Rank:
#88,864 in Books (See Top 100 in Books)
This book should be required (and enjoyable) reading for any student/researcher/scientist/engineer new to scientific computing. The book presents a modern account of the Python scientific computing environment from the command line to publication and everything good in between e.g. NumPy to parallel computing to version control and much more (see http://physics.codes for more). The book also helps you get quickly going with Python on your computer via the Anaconda environment which is very nice. I bought the Kindle version and plan to read it on my iPad while I try the programs (freely available for download at the above website) on my Macbook (ideal combination in my opinion) - it's never been more fun to be in scientific computing that it is these days and this book will help you get started or, like me, maintain and update your overall computational skill set. A must buy and a must read! Enjoy!
This book has been teaching me more than effective computation! I've learned about the community, the myths and the jokes so that I can laugh as well. The digital version contains color-coded python code, really awesome
Given the comments I had seen by others I had higher expectations for this book. There is a brief section early in the book titled "Who this book is not for" that states "If you have already been working as a software developer for several years, this book will help you only minimally". If I had seen that before purchasing the book I probably wouldn't have purchased it. While I wouldn't describe myself as a software developer, coding has always been an essential part of my professional toolkit. I was hoping for something that would help take my game to the next level but this wasn't it.
I’ve taught myself to code, not counting a crappy freshman C++ upteen years ago. I use MATLAB and I’m learning to dabble in Python. The reason I like this book is not for the Python parts, per se, but becuase it fills in holes in my formal training, like details on the shell as a programming tool, and how to more fully take advantage of Git for version control. As the book’s forward says, this book is for physicists (well, I’m a materials scientist) who want to learn grown-up software development, not for people who are already expert software developers.I haven’t read the whole book, so I’m sure there are things I could criticize, but having skipped around to read the parts that I know the least about, it’s spectacular so far. If you’re unsure read the Kindle preview; it overviews the book well.———Edit: now, having read the whole thing, I can say this book is almost perfect for people like me: good at math and your domain science, but lacking in computer science training.
I picked this book up because I thought the title was interesting and figured I could always improve upon my repertoire of tools and skills. Although most of the topics covered were review for me, it was a delightful text to read. I strongly believe this should be required reading for any first year graduate student in science and engineering who intendeds to rely heavily on computation for both simulation and/or analysis. The authors nicely walk you through the basics of scripting, coding, and data management. The authors then provide an overview of best practices for project management and scientific software. An easy and insightful read, strongly recommend.
I got this book from the local library and am glad that I didn't shell out the money, to be honest. Judging by the introduction, I should be the key audience for this book, a scientist interested and somewhat familiar with Python; I'm mainly using Matlab at work. Initially, I had the hope that advanced concepts would be explained by example using real-world problems as hinted at in the introduction. That's not the case at all, though. The authors lightly touch upon a multitude of random topics. There's breadth here, but only very shallow depth. To someone considering this book, try to get it from a library and judge for yourself. If you want to learn more about Python, go with 'Fluent Python', if you like to use Python for data analysis in science, I like 'Data Analysis with Open Source Tools'. For more advanced Python programming, have a look at 'Effective Python', though that one is not for the faint of heart;-)
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