Hands-on signal analysis with Python : an introduction / Thomas Haslwanter.
2021
TK5102.9 .H35 2021
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Details
Title
Hands-on signal analysis with Python : an introduction / Thomas Haslwanter.
ISBN
9783030579036 (electronic book)
3030579034 (electronic book)
9783030579043 (print)
3030579042
9783030579050 (print)
3030579050
3030579026
9783030579029
3030579034 (electronic book)
9783030579043 (print)
3030579042
9783030579050 (print)
3030579050
3030579026
9783030579029
Published
Cham, Switzerland : Springer, [2021]
Language
English
Description
1 online resource (xvi, 267 pages) : illustrations (some color)
Item Number
10.1007/978-3-030-57903-6 doi
Call Number
TK5102.9 .H35 2021
Dewey Decimal Classification
621.382/20285
Summary
This book provides the tools for analyzing data in Python: different types of filters are introduced and explained, such as FIR-, IIR- and morphological filters, as well as their application to one- and two-dimensional data. The required mathematics are kept to a minimum, and numerous examples and working Python programs are included for a quick start. The goal of the book is to enable also novice users to choose appropriate methods and to complete real-world tasks such as differentiation, integration, and smoothing of time series, or simple edge detection in images. An introductory section provides help and tips for getting Python installed and configured on your computer. More advanced chapters provide a practical introduction to the Fourier transform and its applications such as sound processing, as well as to the solution of equations of motion with the Laplace transform. A brief excursion into machine learning shows the powerful tools that are available with Python. This book also provides tips for an efficient programming work flow: from the use of a debugger for finding mistakes, code-versioning with git to avoid the loss of working programs, to the construction of graphical user interfaces (GUIs) for the visualization of data. Working, well-documented Python solutions are included for all exercises, and IPython/Jupyter notebooks provide additional help to get people started and outlooks for the interested reader.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from digital title page (viewed on June 22, 2021).
Available in Other Form
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Table of Contents
Introduction
Python
Data Input
Data Display
Data Filtering
Event- and Feature-Finding
Statistics
Parameter Fitting
Spectral Signal Analysis
Solving Equations of Motion
Machine Learning
Useful Programming Tools.
Python
Data Input
Data Display
Data Filtering
Event- and Feature-Finding
Statistics
Parameter Fitting
Spectral Signal Analysis
Solving Equations of Motion
Machine Learning
Useful Programming Tools.