Normal view

## Python for Signal Processing : Featuring IPython Notebooks

Material type: TextSeries: eBooks on DemandPublisher: Dordrecht : Springer, 2013Description: 1 online resource (133 p.)ISBN: 9783319013428Genre/Form: Electronic books.Additional physical formats: Print version:: Python for Signal Processing : Featuring IPython NotebooksDDC classification: 005.133 | 620 LOC classification: QA76.73 .P98Online resources: Click here to view this ebook.
Contents:
Preface; Contents; Chapter 1: Introduction; 1.1 Introduction; 1.2 Installation and Setup; 1.3 Numpy; 1.3.1 Numpy Arrays and Memory; 1.3.2 Numpy Matrices; 1.3.3 Numpy Broadcasting; 1.4 Matplotlib; 1.5 Alternatives to Matplotlib; 1.6 IPython; 1.6.1 IPython Notebook; 1.7 Scipy; 1.8 Computer Algebra; 1.9 Interfacing with Compiled Libraries; 1.10 Other Resources; Appendix; Chapter 2: Sampling Theorem; 2.1 Sampling Theorem; 2.2 Reconstruction; 2.3 The Story So Far; 2.4 Approximately Time-Limited-Functions; 2.5 Summary; Appendix; Chapter 3: Discrete-Time Fourier Transform
3.1 Fourier Transform Matrix3.2 Computing the DFT; 3.3 Understanding Zero-Padding; 3.4 Summary; Appendix; Chapter 4: Introducing Spectral Analysis; 4.1 Seeking Better Frequency Resolution with Longer DFT; 4.2 The Uncertainty Principle Strikes Back!; 4.3 Circular Convolution; 4.4 Spectral Analysis Using Windows; 4.5 Window Metrics; 4.5.1 Processing Gain; 4.5.2 Equivalent Noise Bandwidth; 4.5.3 Peak Sidelobe Level; 4.5.4 3-dB Bandwidth; 4.5.5 Scalloping Loss; 4.6 Summary; Appendix; Chapter 5: Finite Impulse Response Filters; 5.1 FIR Filters as Moving Averages
5.2 Continuous-Frequency Filter Transfer Function5.3 Z-Transform; 5.4 Causality; 5.5 Symmetry and Anti-symmetry; 5.6 Extracting the Real Part of the Filter Transfer Function; 5.7 The Story So Far; 5.8 Filter Design Using the Window Method; 5.8.1 Using Windows for FIR Filter Design; 5.9 The Story So Far; 5.10 Filter Design Using the Parks-McClellan Method; 5.11 Summary; Appendix; References; Symbols; Index
Summary: This book covers the fundamental concepts in signal processing illustrated with Python code and made available via IPython Notebooks, which are live, interactive, browser-based documents that allow one to change parameters, redraw plots, and tinker with the ideas presented in the text. Everything in the text is computable in this format and thereby invites readers to ""experiment and learn"" as they read. The book focuses on the core, fundamental principles of signal processing. The code corresponding to this book uses the core functionality of the scientific Python toolchain that should remai
Tags from this library: No tags from this library for this title.
Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
Online
QA76.73 .P98 (Browse shelf) http://uttyler.eblib.com/patron/FullRecord.aspx?p=1466319 Available EBL1466319

Preface; Contents; Chapter 1: Introduction; 1.1 Introduction; 1.2 Installation and Setup; 1.3 Numpy; 1.3.1 Numpy Arrays and Memory; 1.3.2 Numpy Matrices; 1.3.3 Numpy Broadcasting; 1.4 Matplotlib; 1.5 Alternatives to Matplotlib; 1.6 IPython; 1.6.1 IPython Notebook; 1.7 Scipy; 1.8 Computer Algebra; 1.9 Interfacing with Compiled Libraries; 1.10 Other Resources; Appendix; Chapter 2: Sampling Theorem; 2.1 Sampling Theorem; 2.2 Reconstruction; 2.3 The Story So Far; 2.4 Approximately Time-Limited-Functions; 2.5 Summary; Appendix; Chapter 3: Discrete-Time Fourier Transform

3.1 Fourier Transform Matrix3.2 Computing the DFT; 3.3 Understanding Zero-Padding; 3.4 Summary; Appendix; Chapter 4: Introducing Spectral Analysis; 4.1 Seeking Better Frequency Resolution with Longer DFT; 4.2 The Uncertainty Principle Strikes Back!; 4.3 Circular Convolution; 4.4 Spectral Analysis Using Windows; 4.5 Window Metrics; 4.5.1 Processing Gain; 4.5.2 Equivalent Noise Bandwidth; 4.5.3 Peak Sidelobe Level; 4.5.4 3-dB Bandwidth; 4.5.5 Scalloping Loss; 4.6 Summary; Appendix; Chapter 5: Finite Impulse Response Filters; 5.1 FIR Filters as Moving Averages

5.2 Continuous-Frequency Filter Transfer Function5.3 Z-Transform; 5.4 Causality; 5.5 Symmetry and Anti-symmetry; 5.6 Extracting the Real Part of the Filter Transfer Function; 5.7 The Story So Far; 5.8 Filter Design Using the Window Method; 5.8.1 Using Windows for FIR Filter Design; 5.9 The Story So Far; 5.10 Filter Design Using the Parks-McClellan Method; 5.11 Summary; Appendix; References; Symbols; Index

This book covers the fundamental concepts in signal processing illustrated with Python code and made available via IPython Notebooks, which are live, interactive, browser-based documents that allow one to change parameters, redraw plots, and tinker with the ideas presented in the text. Everything in the text is computable in this format and thereby invites readers to ""experiment and learn"" as they read. The book focuses on the core, fundamental principles of signal processing. The code corresponding to this book uses the core functionality of the scientific Python toolchain that should remai

Description based upon print version of record.

### Author notes provided by Syndetics

Dr. José Unpingco is an onsite technical director for large-scale signal and image processing for the (USA) Department of Defense (DoD). He is also the lead scientific Python instructor for DoD labs nationwide. He has worked in industry as an engineer, analyst, consultant, and instructor for his entire career, with deep experience in a wide array of signal and data processing technologies.

There are no comments on this title.