Learning SciPy for Numerical and Scientific Computing - Second Edition

Quick solutions to complex numerical problems in physics, applied mathematics, and science with SciPy
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Learning SciPy for Numerical and Scientific Computing - Second Edition

Sergio J. Rojas G., Erik A Christensen, Francisco J. Blanco-Silva

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Quick solutions to complex numerical problems in physics, applied mathematics, and science with SciPy

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Book Details

ISBN 139781783987702
Paperback188 pages

Book Description

SciPy is an open source Python library used to perform scientific computing. The SciPy (Scientific Python) package extends the functionality of NumPy with a substantial collection of useful algorithms.

The book starts with a brief description of the SciPy libraries, followed by a chapter that is a fun and fast-paced primer on array creation, manipulation, and problem-solving. You will also learn how to use SciPy in linear algebra, which includes topics such as computation of eigenvalues and eigenvectors. Furthermore, the book is based on interesting subjects such as definition and manipulation of functions, computation of derivatives, integration, interpolation, and regression. You will also learn how to use SciPy in signal processing and how applications of SciPy can be used to collect, organize, analyze, and interpret data.

By the end of the book, you will have fast, accurate, and easy-to-code solutions for numerical and scientific computing applications.

Table of Contents

Chapter 1: Introduction to SciPy
What is SciPy?
Installing SciPy
SciPy organization
How to find documentation
Scientific visualization
How to open IPython Notebooks
Summary
Chapter 2: Working with the NumPy Array As a First Step to SciPy
Object essentials
Using datatypes
Indexing and slicing arrays
The array object
Array routines
Summary
Chapter 3: SciPy for Linear Algebra
Vector creation
Vector operations
Creating a matrix
Matrix methods
Summary
Chapter 4: SciPy for Numerical Analysis
The evaluation of special functions
Convenience and test functions
Univariate polynomials
The gamma function
The Riemann zeta function
Airy and Bairy functions
The Bessel and Struve functions
Other special functions
Interpolation
Regression
Optimization
Integration
Ordinary differential equations
Lorenz attractors
Summary
Chapter 5: SciPy for Signal Processing
Discrete Fourier Transforms
Signal construction
Filters
Summary
Chapter 6: SciPy for Data Mining
Descriptive statistics
Distributions
Interval estimation, correlation measures, and statistical tests
Distribution fitting
Distances
Clustering
Summary
Chapter 7: SciPy for Computational Geometry
The structural model of oxides
A finite element solver for Laplace's equation
Summary
Chapter 8: Interaction with Other Languages
Interaction with Fortran
Interaction with C/C++
Interaction with MATLAB/Octave
Summary

What You Will Learn

  • Get to know the benefits of using the combination of Python, NumPy, SciPy, and matplotlib as a programming environment for scientific purposes
  • Create and manipulate an object array used by SciPy
  • Use SciPy with large matrices to compute eigenvalues and eigenvectors
  • Focus on construction, acquisition, quality improvement, compression, and feature extraction of signals
  • Make use of SciPy to collect, organize, analyze, and interpret data, with examples taken from statistics and clustering
  • Acquire the skill of constructing a triangulation of points, convex hulls, Voronoi diagrams, and many similar applications
  • Find out ways that SciPy can be used with other languages such as C/C++, Fortran, and MATLAB/Octave

Authors

Table of Contents

Chapter 1: Introduction to SciPy
What is SciPy?
Installing SciPy
SciPy organization
How to find documentation
Scientific visualization
How to open IPython Notebooks
Summary
Chapter 2: Working with the NumPy Array As a First Step to SciPy
Object essentials
Using datatypes
Indexing and slicing arrays
The array object
Array routines
Summary
Chapter 3: SciPy for Linear Algebra
Vector creation
Vector operations
Creating a matrix
Matrix methods
Summary
Chapter 4: SciPy for Numerical Analysis
The evaluation of special functions
Convenience and test functions
Univariate polynomials
The gamma function
The Riemann zeta function
Airy and Bairy functions
The Bessel and Struve functions
Other special functions
Interpolation
Regression
Optimization
Integration
Ordinary differential equations
Lorenz attractors
Summary
Chapter 5: SciPy for Signal Processing
Discrete Fourier Transforms
Signal construction
Filters
Summary
Chapter 6: SciPy for Data Mining
Descriptive statistics
Distributions
Interval estimation, correlation measures, and statistical tests
Distribution fitting
Distances
Clustering
Summary
Chapter 7: SciPy for Computational Geometry
The structural model of oxides
A finite element solver for Laplace's equation
Summary
Chapter 8: Interaction with Other Languages
Interaction with Fortran
Interaction with C/C++
Interaction with MATLAB/Octave
Summary

Book Details

ISBN 139781783987702
Paperback188 pages
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