While maintaining the main structure of the first edition, this revised edition of Learning SciPy for Numerical and Scientific Computing includes a set of companion IPython Notebooks. This will help students, researchers, and practitioners modify and incorporate in their own work, the set of tested code snippets that are presented in the book, as the pedagogical strategy. This will also show and illustrate the computing power that SciPy brings to the fingertips of anyone interested in performing numerical computation via the unique flexibility offered by the Python computer language.
We should mention, however, that the IPython Notebooks will make sense to anyone starting in the field only if they are read alongside the corresponding section in the book, helping you to develop skills in the use of SciPy to solve large scale numerical problems while gaining understanding of the conditions and limitations associated with the modules contained in SciPy. Certainly, the already knowledgeable reader will find pleasure as they encounter material they already know, but will be challenged to devise better ways to accomplish with the same level of clarity presented in the book with the many computational tasks used to illustrate the functionality of SciPy.
SciPy has been an integral part of the computational environment of choice for many scientists for years. One of our challenges today is to bring together professionals with different backgrounds, technologies, and expertise in software (from the pure mathematician, to the hardcore engineer) to contribute independent of their working environments.
SciPy in Python is a perfect platform to coordinate projects in a smooth, reliable, and coherent environment. It allows performing most tasks with ease; reason being that many dedicated software tools easily integrate with the core features of SciPy, therefore, interfacing with non-Python-based software packages and tools is becoming increasingly simple.
In summary, this book presents the most robust programming environment to date. We will show you how to use this system from basic manipulation of data, to a very detailed exposition through examples in different branches of science and engineering.
Chapter 1, Introduction to SciPy, shows the benefits of using the combination of Python, NumPy, SciPy, and matplotlib as a programming environment for scientific purposes. You will learn how to install, test, and explore the environments, use them for quick computations, and figure out a few good ways to search for help. A brief introduction on how to open the companion IPython Notebooks that comes with this book is also presented.
Chapter 2, Working with the NumPy Array As a First Step to SciPy, explores in depth the creation and basic manipulation of the object array used by SciPy, as an overview of the NumPy libraries.
Chapter 3, SciPy for Linear Algebra, covers applications of SciPy to applications with large matrices, including solving systems or computation of eigenvalues and eigenvectors.
Chapter 4, SciPy for Numerical Analysis, is without a doubt one of the most interesting chapters in this book. It covers with great detail the definition and manipulation of functions (one or several variables), the extraction of their roots, extreme values (optimization), computation of derivatives, integration, interpolation, regression, and applications to the solution of ordinary differential equations.
Chapter 5, SciPy for Signal Processing, explores construction, acquisition, quality improvement, compression, and feature extraction of signals (in any dimension). It is covered with beautiful and interesting examples from the field of image processing.
Chapter 6, SciPy for Data Mining, covers applications of SciPy for collection, organization, analysis, and interpretation of data, with examples taken from statistics and clustering.
Chapter 7, SciPy for Computational Geometry, explores the construction of triangulation of points, convex hulls, Voronoi diagrams, and applications, including the solving of the two dimensional Laplace Equation via the Finite Element Method in a rectangular grid. At this point in the book, it will be possible to combine techniques from all the previous chapters to show state-of-the-art research performed with ease with SciPy, and we will explore a few good examples from Material Science and Experimental Physics.
Chapter 8, Interaction with Other Languages, introduces one of the main strengths of SciPy—the ability to interact with other languages such as C/C++, Fortran, R, and MATLAB/Octave.
To work with the examples and try out the code in this book, all you need is a recent build of Python (2.7 or higher) with the libraries NumPy, SciPy, and matplotlib. Recipes to install all these are provided throughout the book.
This book is for scientists, engineers, programmers, or analysts with knowledge of Python. For some of the sections, a decent command over linear algebra, calculus, and some statistics is needed to understand some of the concepts, but otherwise this book is mostly self-contained.
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We can include other contexts through the use of the include directive."
A block of code is set as follows:
solve(A, b, sym_pos=False, lower=False, overwrite_a=False, overwrite_b=False, debug=False) spsolve(A, b[, permc_spec, use_umfpack])
The reader with the required background should recognize the Python prompt >>> followed by a space and then the code field. Any command-line input or output is written as follows:
>>> from scipy import stats >>> result=scipy.stats.bayes_mvs(scores)
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "clicking the Next button moves you to the next screen".
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