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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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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

Descriptive statistics

We often require the analysis of data in which certain features are grouped in different regions, each with different sizes, values, shapes, and so on. The scipy.ndimage.measurements submodule has the right tools for this task, and the best way to illustrate the capabilities of the module is by means of exhaustive examples. For example, for binary images of zeros and ones, it is possible to label each blob (areas of contiguous pixels with value one) and obtain the number of these with the label command. If we desire to obtain the center of mass of the blobs, we may do so with the center_of_mass command. We may see these operations in action once again in the application to obtain the structural model of oxides in Chapter 7, SciPy for Computational Geometry.

For nonbinary data, the scipy.ndimage.measurements submodule provides the usual basic statistical measurements (value and location of extreme values, mean, standard deviation, sum, variance, histogram, and so on...

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Learning SciPy for Numerical and Scientific Computing Second Edition
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