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Learning SciPy for Numerical and Scientific Computing von [Silva, Francisco Javier Blanco]

Learning SciPy for Numerical and Scientific Computing Kindle Edition


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Länge: 150 Seiten Sprache: Englisch

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Produktbeschreibungen

Kurzbeschreibung

In Detail

It's essential to incorporate workflow data and code from various sources in order to create fast and effective algorithms to solve complex problems in science and engineering. Data is coming at us faster, dirtier, and at an ever increasing rate. There is no need to employ difficult-to-maintain code, or expensive mathematical engines to solve your numerical computations anymore. SciPy guarantees fast, accurate, and easy-to-code solutions to your numerical and scientific computing applications.

"Learning SciPy for Numerical and Scientific Computing" unveils secrets to some of the most critical mathematical and scientific computing problems and will play an instrumental role in supporting your research. The book will teach you how to quickly and efficiently use different modules and routines from the SciPy library to cover the vast scope of numerical mathematics with its simplistic practical approach that's easy to follow.

The book starts with a brief description of the SciPy libraries, showing practical demonstrations for acquiring and installing them on your system. This is followed by the second chapter which is a fun and fast-paced primer to array creation, manipulation, and problem-solving based on these techniques.

The rest of the chapters describe the use of all different modules and routines from the SciPy libraries, through the scope of different branches of numerical mathematics. Each big field is represented: numerical analysis, linear algebra, statistics, signal processing, and computational geometry. And for each of these fields all possibilities are illustrated with clear syntax, and plenty of examples. The book then presents combinations of all these techniques to the solution of research problems in real-life scenarios for different sciences or engineering — from image compression, biological classification of species, control theory, design of wings, to structural analysis of oxides.

Approach

A step-by-step practical tutorial with plenty of examples on research-based problems from various areas of science, that prove how simple, yet effective, it is to provide solutions based on SciPy.

Who this book is for

This book is targeted at anyone with basic knowledge of Python, a somewhat advanced command of mathematics/physics, and an interest in engineering or scientific applications---this is broadly what we refer to as scientific computing.

This book will be of critical importance to programmers and scientists who have basic Python knowledge and would like to be able to do scientific and numerical computations with SciPy.

Über den Autor und weitere Mitwirkende

Francisco J. Blanco-Silva

The owner of a scientific consulting company—Tizona Scientific Solutions—and adjunct faculty in the Department of Mathematics of the University of South Carolina, Dr. Blanco-Silva obtained his formal training as an applied mathematician at Purdue University. He enjoys problem solving, learning, and teaching. An avid programmer and blogger, when it comes to writing he relishes finding that common denominator among his passions and skills, and making it available to everyone.

He coauthored Chapter 5 of the book Modeling Nanoscale Imaging in Electron Microscopy, Springer by Peter Binev, Wolfgang Dahmen, and Thomas Vogt.


Produktinformation

  • Format: Kindle Edition
  • Dateigröße: 3686 KB
  • Seitenzahl der Print-Ausgabe: 150 Seiten
  • Verlag: Packt Publishing (22. Februar 2013)
  • Verkauf durch: Amazon Media EU S.à r.l.
  • Sprache: Englisch
  • ISBN-10: 1782161635
  • ISBN-13: 978-1782161639
  • ASIN: B00BAOC2KG
  • Text-to-Speech (Vorlesemodus): Aktiviert
  • X-Ray:
  • Word Wise: Nicht aktiviert
  • Verbesserter Schriftsatz: Nicht aktiviert
  • Durchschnittliche Kundenbewertung: Schreiben Sie die erste Bewertung
  • Amazon Bestseller-Rang: #329.118 Bezahlt in Kindle-Shop (Siehe Top 100 Bezahlt in Kindle-Shop)

  •  Ist der Verkauf dieses Produkts für Sie nicht akzeptabel?

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Amazon.com: HASH(0xa3153ab0) von 5 Sternen 11 Rezensionen
11 von 11 Kunden fanden die folgende Rezension hilfreich
HASH(0xa30383f0) von 5 Sternen Great for scientists, engineers, programmers and data analysts 27. April 2013
Von Parsa - Veröffentlicht auf Amazon.com
Format: Taschenbuch
This is a fantastic book for scientists, engineers, applied mathematicians, statisticians, programmers, and data analysts who have computation problems in mind and are looking to use an open-source programming language with plenty of modules to solve them. Python is my favorite high-level language because it's intuitive, very easy to install (if you own a Mac then you already have it!) and it has so many useful functions in the various module libraries.

My favorite thing about it this book is that when a module is introduced, the author gives a list of many relevant functions when appropriate. For example, when he introduces the linear algebra module (scipy.linalg) in Chapter 3, he goes through many of the matrix creation and operations functions that I didn't even know existed, and I'm an intermediate-level Python/NumPy user. He discusses solving large linear systems, eigenvalue problems, and FIVE different matrix decompositions as well as the corresponding module functions for each type of problem. This book is worth the price for Chapter 3 alone.

But thankfully, it goes on to discuss solving various common ODEs, optimization, the Runge-Kutta method, and numerical integration. And that's just Chapter 4. Again, the important detail here is how the author links each topic and problem to the corresponding SciPy module and relevant functions that do the vast majority of the work for you. He also shows how to use matplotlib for graphical purposes when a problem calls for it. Chapter 5 is about signal processing, which I didn't really understand but I think the gist of it is how to extrapolate from incomplete data and how to separate the signal from the noise.

I'm currently working as a data miner, which is the topic of Chapter 6. This is a nice introduction to the data analysis modules for SciPy: scipy.stats, scipy.spatial, and scipy.cluster. The data analysis examples were good, and the breakdown of hierarchical clustering was excellent, but I wished the chapter was a little longer. It is a great complement to McKinney's book on using Python for data analysis, which I also own.

All in all, I strongly recommend this book to anyone who has a computational problem to solve.
12 von 14 Kunden fanden die folgende Rezension hilfreich
HASH(0xa3088ae0) von 5 Sternen Very bad quality! 5. Februar 2014
Von James Leibert - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
OK, this is in principle not a bad tour of some key SciPy functionality, but there are some serious problems with this book.

I'm writing this review after spending 3 hours with this book. I am so angry that I felt I needed to let other people know.

There are two major errors in the first two pieces of code in the book. If you are new to SciPy, as I was, that means wasting 2 hours ploughing through the SciPy online documentation to figure out the correct code (it is not easy!). Since the main reason for buying the book is that the online documentation makes absolutely no sense to newcomers, it rather defeats the purpose of the book.

So, being a good citizen, I did what was requested at the front of the book and attempted to submit an errata form with the correct code, or at least see what others had submitted, but the site has been abandoned by its owner.

I recommend you never buy a book from PACKT publishing, it is a complete rip off.

As to finding a good introduction to SciPy online or elsewhere, good luck, I'm still looking.
7 von 7 Kunden fanden die folgende Rezension hilfreich
HASH(0xa3091c9c) von 5 Sternen Great book for learning SciPy 21. Mai 2013
Von Scott MacLachlan - Veröffentlicht auf Amazon.com
Format: Taschenbuch
Learning SciPy for Numerical and Scientific Computing is a great reference book for mathematicians, scientists, engineers, and programmers looking to expand their computational toolbox. While matlab-based prototyping has, for many years, been the unchallenged standard in the development of computational algorithms, the development of the NumPy and SciPy packages in the last decade offers another option. This book focuses on introducing the syntax and capabilities of the combination of NumPy, SciPy, and matplotlib for standard problems in scientific computing. The book is built around numerous examples, with clearly explained source code and motivating discussions. While the material covered spans the range of a good numerical analysis textbook (linear algebra, interpolation, rootfinding, integration, ODEs, signal processing, data mining, computational geometry), the focus of this book is much more on the use of SciPy for these tasks than the development of the mathematics behind them or their use in large-scale simulations. Thus, the book is the perfect introduction to python's scientific computing abilities for a programmer already versed in numerical analysis and familiar with another programming language.
2 von 2 Kunden fanden die folgende Rezension hilfreich
HASH(0xa3091504) von 5 Sternen Good book for Scientific Developers 24. April 2013
Von Marcel - Veröffentlicht auf Amazon.com
Format: Taschenbuch
Overall the Learning Scipy for Numerical and Scientific Computing book is a good book on Scipy covering lots of mathematics with examples in Python. The book has a good size and it helps the scientists and scientific developers (by the way the non-developers will face some difficulties due to the heavy math that comes with the examples) to have a good overview on the library before exploring the reference material.

Further details at my website: [...]
HASH(0xa3094234) von 5 Sternen Good overview of SciPy for Scientific Programmers 2. Juli 2014
Von Sujit Pal - Veröffentlicht auf Amazon.com
Format: Taschenbuch
SciPy is Python's scientific library, commonly used with NumPy (the matrix library) and Matplotlib (the plotting library). The book opens with the obligatory chapter about installing the software and a refresher on NumPy. It then goes on to give a good overview of SciPy's capabilities in Linear Algebra (various decompositions, solvers, etc), Numerical methods (interpolation, regression, optimization, differentiation, integration, etc), Signal Processing (FFT, filters, etc), Image Manipulation (affines), Data Mining (curve fitting, clustering, spatial distance measures) and Computational Geometry. Each chapter roughly corresponds to functionality provided by one or two SciPy packages. The general approach is to describe a problem, demonstrate a solution using one of the approaches, then list a variety of other algorithms/methods that are also provided by SciPy. It closes with details of integrating SciPy with other languages (F77, C/C++, MATLAB/Octave). As expected, the examples also make use of NumPy and Matplotlib. Since each of these subject areas require significant domain expertise, the book makes the (very reasonable IMO) assumption that the reader has sufficient background in scientific computing and is only interested in what SciPy provides in their chosen area. I found Chapters 3 (Linear Algebra) and 6 (Data Mining) most useful, although Chapters 4 (Numerical Methods) and 5 (Signal Processing) provided me with subjects for further exploration. Surprisingly, the book does not come with downloadable code samples - it would have been more useful if it did.
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