Book Image

Learning Jupyter 5 - Second Edition

Book Image

Learning Jupyter 5 - Second Edition

Overview of this book

The Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples. The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode. By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you’ve explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization.
Table of Contents (18 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Julia unit testing


As a full language, Julia has unit-testing abilities to make sure that your code is performing as expected. The unit tests usually reside in the tests folder.

Two of the standard functions available for unit testing in Julia are FactCheck and Base.Test. They both do the same thing, but react differently to failed tests. FactCheck will generate an error message that will not stop processing on a failure. If you provide an error handler, that error handler will take control of the test.

Base.Test will throw an exception and stop processing on the first test failure. In that regard, it is probably not useful as a unit-testing function so much as a runtime test that you may put in place to make sure parameters are within reason or otherwise. Just stop processing before something bad happens.

 

Both packages are built in to the standard Julia distributions.

As an example, we can create a unit tests Notebook that does the same tests and see the resulting, different responses for...