Preface

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:

1. 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.
2. The second part covers the basics of Python, Scientific Python with applications to spectral analysis and digital filtering. This book will not teach you how to program in Python, but the focus is on examples in signal processing. There are several good resources to learn Python that can be used together with this book in course.

The e-book is available for sale at http://leanpub.com/pyageng and the HTML version is and will be available for free at http://pyageng.mpastell.com/book .

Why write another book about measurements and Python?

I started to write this text as short lecture notes around 2010 to cover the essential things for my course. The motivation was to give students one relatively short source of information instead trying to find suitable chapters from several sources. I have revised the text during several iterations of the course and decided to collect in book hoping that it will be useful to future Agricultural Engineers and everyone else interested in the subject.

Why Python for Agricultural Engineers?

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.

Matti Pastell

5th February 2016, Helsinki, Finland