Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Learning SciPy for Numerical and Scientific Computing Second Edition
  • Table Of Contents Toc
Learning SciPy for Numerical and Scientific Computing Second Edition

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

4.3 (10)
Buy this Book
close
close
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)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Lock Free Chapter
1
1. Introduction to SciPy
Icon
1. Introduction to SciPy
Icon
What is SciPy?
Icon
Installing SciPy
Icon
SciPy organization
Icon
How to find documentation
Icon
Scientific visualization
Icon
How to open IPython Notebooks
Icon
Summary
2
2. Working with the NumPy Array As a First Step to SciPy
Icon
2. Working with the NumPy Array As a First Step to SciPy
Icon
Object essentials
Icon
Using datatypes
Icon
Indexing and slicing arrays
Icon
The array object
Icon
Array routines
Icon
Summary
3
3. SciPy for Linear Algebra
Icon
3. SciPy for Linear Algebra
Icon
Vector creation
Icon
Vector operations
Icon
Creating a matrix
Icon
Matrix methods
Icon
Summary
4
4. SciPy for Numerical Analysis
Icon
4. SciPy for Numerical Analysis
Icon
The evaluation of special functions
Icon
Convenience and test functions
Icon
Univariate polynomials
Icon
The gamma function
Icon
The Riemann zeta function
Icon
Airy and Bairy functions
Icon
The Bessel and Struve functions
Icon
Other special functions
Icon
Interpolation
Icon
Regression
Icon
Optimization
Icon
Integration
Icon
Ordinary differential equations
Icon
Lorenz attractors
Icon
Summary
5
5. SciPy for Signal Processing
chevron up
Icon
5. SciPy for Signal Processing
Icon
Discrete Fourier Transforms
Icon
Signal construction
Icon
Filters
Icon
Summary
6
6. SciPy for Data Mining
Icon
6. SciPy for Data Mining
Icon
Descriptive statistics
Icon
Distributions
Icon
Interval estimation, correlation measures, and statistical tests
Icon
Distribution fitting
Icon
Distances
Icon
Clustering
Icon
Summary
7
7. SciPy for Computational Geometry
Icon
7. SciPy for Computational Geometry
Icon
The structural model of oxides
Icon
A finite element solver for Laplace's equation
Icon
Summary
8
8. Interaction with Other Languages
Icon
8. Interaction with Other Languages
Icon
Interaction with Fortran
Icon
Interaction with C/C++
Icon
Interaction with MATLAB/Octave
Icon
Summary
9
Index
Icon
Index

Discrete Fourier Transforms

Discrete Fourier Transform (DFT) transforms any signal from its time/space domain into a related signal in frequency domain. This allows us not only to analyze the different frequencies of the data, but also enables faster filtering operations, when used properly. It is possible to turn a signal in frequency domain back to its time/spatial domain, thanks to the Inverse Fourier Transform (IFT). We will not go into details of the mathematics behind these operators, since we assume familiarity at some level with this theory. We will focus on syntax and applications instead.

The basic routines in the scipy.fftpack module compute the DFT and its inverse, for discrete signals in any dimension – fft, ifft (one dimension); fft2, ifft2 (two dimensions); fftn, ifftn (any number of dimensions). All of these routines assume that the data is complex valued. If we know beforehand that a particular dataset is actually real valued, and should offer real-valued frequencies...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Learning SciPy for Numerical and Scientific Computing Second Edition
End of Section 5
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon