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  • Book Overview & Buying Learning SciPy for Numerical and Scientific Computing Second Edition
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Learning SciPy for Numerical and Scientific Computing Second Edition

Learning SciPy for Numerical and Scientific Computing Second Edition - Second Edition

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Learning SciPy for Numerical and Scientific Computing Second Edition

Learning SciPy for Numerical and Scientific Computing Second Edition

4.3 (10)
Buy this Book

Overview of this book

This book targets programmers and scientists who have basic Python knowledge and who are keen to perform scientific and numerical computations with SciPy.
Table of Contents (10 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introduction to SciPy
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1. Introduction to SciPy
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What is SciPy?
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Installing SciPy
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SciPy organization
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How to find documentation
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Scientific visualization
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How to open IPython Notebooks
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Summary
2
2. Working with the NumPy Array As a First Step to SciPy
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2. Working with the NumPy Array As a First Step to SciPy
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Object essentials
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Using datatypes
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Indexing and slicing arrays
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The array object
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Array routines
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Summary
3
3. SciPy for Linear Algebra
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3. SciPy for Linear Algebra
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Vector creation
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Vector operations
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Creating a matrix
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Matrix methods
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Summary
4
4. SciPy for Numerical Analysis
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4. SciPy for Numerical Analysis
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The evaluation of special functions
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Convenience and test functions
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Univariate polynomials
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The gamma function
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The Riemann zeta function
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Airy and Bairy functions
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The Bessel and Struve functions
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Other special functions
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Interpolation
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Regression
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Optimization
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Integration
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Ordinary differential equations
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Lorenz attractors
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Summary
5
5. SciPy for Signal Processing
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5. SciPy for Signal Processing
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Discrete Fourier Transforms
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Signal construction
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Filters
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Summary
6
6. SciPy for Data Mining
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6. SciPy for Data Mining
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Descriptive statistics
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Distributions
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Interval estimation, correlation measures, and statistical tests
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Distribution fitting
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Distances
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Clustering
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Summary
7
7. SciPy for Computational Geometry
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7. SciPy for Computational Geometry
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The structural model of oxides
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A finite element solver for Laplace's equation
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Summary
8
8. Interaction with Other Languages
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8. Interaction with Other Languages
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Interaction with Fortran
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Interaction with C/C++
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Interaction with MATLAB/Octave
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Summary
9
Index
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Index

Convenience and test functions

All the convenience functions are designed to facilitate a computational environment where the user does not need to worry about relative errors. The functions seem to be pointless at first sight, but behind their codes, there are state-of-the-art ideas that offer faster and more reliable results.

We have convenience functions beyond the ones defined in the NumPy libraries to find the solutions of trigonometric functions in degrees (cosdg, sindg, tandg, and cotdg); to compute angles in radians from their expressions in degrees, minutes, and seconds (radian); common powers (exp2 for 2**x, and exp10 for 10**x); and common functions for small values of the variable (log1p for log(1 + x), expm1 for exp(x) - 1, and cosm1 for cos(x) - 1).

For instance, in the following code snippet, the log1p function computes the natural logarithm of 1 + x. Why not simply add 1 to the value of x and then take the logarithm instead? Let's compare:

>>> import numpy
&gt...
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Programming languages
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Learning SciPy for Numerical and Scientific Computing Second Edition
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