Book Image

Time Series Indexing

By : Mihalis Tsoukalos
Book Image

Time Series Indexing

By: Mihalis Tsoukalos

Overview of this book

Time series are everywhere, ranging from financial data and system metrics to weather stations and medical records. Being able to access, search, and compare time series data quickly is essential, and this comprehensive guide enables you to do just that by helping you explore SAX representation and the most effective time series index, iSAX. The book begins by teaching you about the implementation of SAX representation in Python as well as the iSAX index, along with the required theory sourced from academic research papers. The chapters are filled with figures and plots to help you follow the presented topics and understand key concepts easily. But what makes this book really great is that it contains the right amount of knowledge about time series indexing using the right amount of theory and practice so that you can work with time series and develop time series indexes successfully. Additionally, the presented code can be easily ported to any other modern programming language, such as Swift, Java, C, C++, Ruby, Kotlin, Go, Rust, and JavaScript. By the end of this book, you'll have learned how to harness the power of iSAX and SAX representation to efficiently index and analyze time series data and will be equipped to develop your own time series indexes and effectively work with time series data.
Table of Contents (11 chapters)

Writing Python tests

In this last section of this chapter, we are going to learn about Python testing and write three tests for our code with the help of the pytest package.

As the pytest package is not installed by default, the first task you should carry out is installing it using your favorite method. Part of the pytest package is the pytest command-line utility, which is used for running the tests.

Unit testing

In this section, we are writing unit tests, which are usually functions that we write to make sure that our code works as expected. The result of a unit test is either PASS or FAIL. The more extensive the unit testing is, the more useful it is.

After a successful installation, if you execute the pytest command on a directory that does not contain any valid tests, you are going to get information about your system and your Python installation. On a macOS machine, the output is the following:

$ pytest
========================= test session starts ==========...