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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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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

Signal construction

To aid the construction of signals with predetermined properties, the scipy.signal module has a nice collection of the most frequent one-dimensional waveforms in the literature – chirp and sweep_poly (for the frequency-swept cosine generator), gausspulse (a Gaussian modulated sinusoid), sawtooth and square (for the waveforms with those names). They all take as their main parameter a one-dimensional ndarray representing the times at which the signal is to be evaluated. Other parameters control the design of the signal according to frequency or time constraints. Let's take a look into the following code snippet which illustrates the use of these one dimensional waveforms that we just discussed:

>>> import numpy
>>> from scipy.signal import chirp, sawtooth, square, gausspulse
>>> import matplotlib.pyplot as plt
>>> t=numpy.linspace(-1,1,1000)
>>> plt.subplot(221); plt.ylim([-2,2])
>>> plt.plot(t,chirp(t,f0...
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Learning SciPy for Numerical and Scientific Computing Second Edition
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