Book Image

Julia 1.0 Programming - Second Edition

By : Ivo Balbaert
Book Image

Julia 1.0 Programming - Second Edition

By: Ivo Balbaert

Overview of this book

The release of Julia 1.0 is now ready to change the technical world by combining the high productivity and ease of use of Python and R with the lightning-fast speed of C++. Julia 1.0 programming gives you a head start in tackling your numerical and data problems. You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. With the help of practical examples, this book walks you through two important collection types: arrays and matrices. In addition to this, you will be taken through how type conversions and promotions work. In the course of the book, you will be introduced to the homo-iconicity and metaprogramming concepts in Julia. You will understand how Julia provides different ways to interact with an operating system, as well as other languages, and then you'll discover what macros are. Once you have grasped the basics, you’ll study what makes Julia suitable for numerical and scientific computing, and learn about the features provided by Julia. By the end of this book, you will also have learned how to run external programs. This book covers all you need to know about Julia in order to leverage its high speed and efficiency for your applications.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Generic functions and multiple dispatch


We have already seen that functions are inherently defined as generic, that is, they can be used for different types of their arguments. The compiler will generate a separate version of the function each time it is called with arguments of a new type. In Julia, a concrete version of a function for a specific combination of argument types is called a method. To define a new method for a function (also called overloading), just use the same function name but a different signature, that is, with different argument types. A list of all the methods is stored in a virtual method table (vtable) on the function itself; methods do not belong to a particular type. When a function is called, Julia will lookup in vtableat runtime to find which concrete method it should call, based on the types of all its arguments; this is Julia's multiple dispatch mechanism, which Python, C++, or Fortran do not implement this. It allows open extensions where normal object-oriented...