# Learning SciPy for Numerical and Scientific Computing

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- Perform complex operations with large matrices, including eigenvalue problems, matrix decompositions, or solution to large systems of equations
- Step-by-step examples to easily implement statistical analysis and data mining that rivals in performance any of the costly specialized software suites
- Plenty of examples of state-of-the-art research problems from all disciplines of science, that prove how simple, yet effective, is to provide solutions based on SciPy

### Book Details

**Language :**English

**Paperback :**150 pages [ 235mm x 191mm ]

**Release Date :**February 2013

**ISBN :**1782161627

**ISBN 13 :**9781782161622

**Author(s) :**Francisco J. Blanco-Silva

**Topics and Technologies :**All Books, Big Data and Business Intelligence, Data, Open Source, Python

## Table of Contents

PrefaceChapter 1: Introduction to SciPy

Chapter 2: Top-level SciPy

Chapter 3: SciPy for Linear Algebra

Chapter 4: SciPy for Numerical Analysis

Chapter 5: SciPy for Signal Processing

Chapter 6: SciPy for Data Mining

Chapter 7: SciPy for Computational Geometry

Chapter 8: Interaction with Other Languages

Index

- Chapter 1: Introduction to SciPy
- What is SciPy?
- How to install SciPy
- SciPy organization
- How to find documentation
- Scientific visualization
- Summary

- Chapter 2: Top-level SciPy
- Object essentials
- Datatype
- Indexing
- The array object
- Array routines
- Routines for array creation
- Routines for the combination of two or more arrays
- Routines for array manipulation
- Routines to extract information from arrays
- Summary

- Chapter 3: SciPy for Linear Algebra
- Matrix creation
- Matrix methods
- Operations between matrices
- Functions on matrices
- Eigenvalue problems and matrix decompositions
- Image compression via the singular value decomposition
- Solvers
- Summary

- Chapter 4: SciPy for Numerical Analysis
- Evaluation of special functions
- Convenience and test functions
- Univariate polynomials
- The gamma function
- The Riemann zeta function
- Airy (and Bairy) functions
- Bessel and Struve functions
- Other special functions
- Interpolation and regression
- Optimization
- Minimization
- Roots
- Integration
- Exponential/logarithm integrals
- Trigonometric and hyperbolic trigonometric integrals
- Elliptic integrals
- Gamma and beta integrals
- Numerical integration
- Ordinary differential equations
- Lorenz Attractors
- Summary

- Chapter 5: SciPy for Signal Processing
- Discrete Fourier Transforms
- Signal construction
- Filters
- LTI system theory
- Filter design
- Window functions
- Image interpolation
- Morphology
- Summary

- Chapter 6: SciPy for Data Mining
- Descriptive statistics
- Distributions
- Interval estimation, correlation measures, and statistical tests
- Distribution fitting
- Distances
- Clustering
- Vector quantization and k-means
- Hierarchical clustering
- Summary

- Chapter 7: SciPy for Computational Geometry
- Structural model of oxides
- A finite element solver for Poisson's equation

- Summary

- Chapter 8: Interaction with Other Languages
- Fortran
- C/C++
- Matlab/Octave
- Summary

### Francisco J. Blanco-Silva

### Submit Errata

Please let us know if you have found any errors not listed on this list by completing our errata submission form. Our editors will check them and add them to this list. Thank you.

### Errata

- 1 submitted: last submission 05 Jun 2013**Errata type: Code | Page number: 16**

In the first code example, the third line of code snippet requires the following change:

import numpy

import matplotlib.pyplot**x = numpy.linspace(0,2*numpy.pi,32)**

fig=matplotlib.pyplot.figure() fig.plot(x,numpy.sin(x))

fig.savefig('sine.png')

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- Learn to store and manipulate large arrays of data in any dimension
- Accurately evaluate any mathematical function in any given dimension, as well as its integration, and solve systems of ordinary differential equations with ease
- Learn to deal with sparse data to perform any known interpolation, extrapolation, or regression scheme on it
- Perform statistical analysis, hypothesis test design and resolution, or data mining at high level, including clustering (hierarchical or through vector quantization), and learn to apply them to real-life problems
- Get to grips with signal processing — filtering audio, images, or video to extract information, features, or removing components
- Effectively learn about window functions, filters, spectral theory, LTY systems theory, morphological operations, and image interpolation
- Acquaint yourself with the power of distances, Delaunay triangulations, and Voronoi diagrams for computational geometry, and apply them to various engineering problems
- Wrap code in other languages directly into your SciPy-based workflow, as well as incorporating data written in proprietary format (audio or image, for example), or from other software suites like Matlab/Octave

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.

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.

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.