Python for Signal Processing : Featuring IPython Notebooks
By: Unpingco, José.Material type: TextSeries: eBooks on Demand.Publisher: Dordrecht : Springer, 2013Description: 1 online resource (133 p.).ISBN: 9783319013428.Subject(s): Data mining | Programming languages (Electronic computers) | Python (Computer program language) | Signal processingGenre/Form: Electronic books.Additional physical formats: Print version:: Python for Signal Processing : Featuring IPython NotebooksDDC classification: 005.133 | 620 Online resources: Click here to view this ebook.
|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.