 Название: Measurements and Data Analysis for Agricultural Engineers Using Python
Автор: Matti Pastell
Издательство: Leanpub
Год: March 1, 2016
Страниц: 131
Язык: английский
Формат: pdf (true), epub
Размер: 10.2 MB
This book is based on my lecture notes from the course “Measurement technology” at my previous position in the Department of Agricultural Sciences at the University of Helsinki.
The book has two parts:
The beginning covers the basics of the theory needed to understand proper sampling and planning of measurement systems. It also covers basics of data loggers and sensor connections. The book doesn’t cover sensor, this information is difficult to keep up to date and there are several books focusing on sensors already.
The second part covers the basics of Python, Scientific Python with applications to spectral analysis and digital filtering. The focus is on examples in signal processing.
The reason I chose Python for my teaching was that I wanted to have one language that can be used to write software for data collection, interface hardware and analyze data. I also wanted to have a language that is easy to learn, free for students to use for homework and on their own projects and works on multiple operating systems.
In addition to Python being popular general purpose A lot of dataloggers have drivers for Python, the SciPy stack is a powerful option for scientific computing and OpenCV Python interface allows the implementation of popular computer vision applications.
Contents:
Chapter 1: Measurement basics
• 1.4.1 Analog signals
• 1.4.2 Digital signals
Chapter 2: Datalogger hardware and sensor connections
• 2.1.1 Microcontrollers
• 2.3 Connecting sensors to loggers
Chapter 3: Connecting loggers to computers
• 3.1 Serial port
• 3.2 USB (universal serial bus)
• 3.3 Ethernet
• 3.4 Wireless connections
Chapter 4: Introduction to Programming and Python
Chapter 5: Scientific Python
• 5.1 NumPy arrays
• 5.2 Pandas dataframes
Chapter 6: Plotting
Chapter 7: Frequency Analysis
• 7.1 Time and frequency domain
• 7.2 Discrete Fourier Transform
• 7.3 Practical DFT using FFT in Python
Chapter 8: Digital Signal Processing
• 8.4 Filter properties using SciPy
• 8.10.1 FIR filters with SciPy
• 8.11.1 IIR filters with SciPy
Chapter 9: References
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