SciPy has been an integral part of the computational environment of choice of many scientists for years. One of the challenges of our trade is to bring to a single workstation the production of professionals with different visions, techniques, tools, and software (from the pure mathematician, to the hardcore engineer).
We are required to produce scripts in which, for example, there are combinations of experiments written and performed in SciPy itself, C/C++, Fortran, R, or MATLAB®. We often receive extremely large amounts of raw data from some signal acquisition device. From all this heterogeneous material, we employ SciPy to retrieve this data, manipulate it, experiment it, analyze it, and once finished with the analysis, produce high-quality documentation with professional-looking diagrams and visualizations aids.
SciPy is the perfect way to coordinate everything in a smooth, reliable, and coherent way. It allows performing all these tasks with ease. This is partly because many dedicated software tools easily extend the core features of SciPy, and interfacing with non-Python-based packages and software is extremely easy.
In summary this book presents the most robust programming environment to date. We will show you how to use this system from basic training of manipulation of data, to a very detailed exposition through examples of state-of-the-art research 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. We will learn how to install it, explore the environment, use it for some quick computations, and figure out a few good ways to search for help.
Chapter 2, Top-level 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 many applications. 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 Sciences 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 are shown as follows: "Within a terminal session, change directories to the folder where the NumPy libraries are stored, that contains the setup.py file."
A block of code is set as follows:
import numpy
import matplotlib.pyplot
x=numpy.linspace(0,numpy.pi,32)
fig=matplotlib.pyplot.figure()
fig.plot(x, numpy.sin(x))
fig.savefig('sine.png')Any command-line input or output is written as follows:
% python setup.py build –fcompiler=<compiler>
New terms and important words are shown in bold.
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