Book Image

Julia 1.0 Programming Complete Reference Guide

By : Ivo Balbaert, Adrian Salceanu
Book Image

Julia 1.0 Programming Complete Reference Guide

By: Ivo Balbaert, Adrian Salceanu

Overview of this book

Julia offers the high productivity and ease of use of Python and R with the lightning-fast speed of C++. There’s never been a better time to learn this language, thanks to its large-scale adoption across a wide range of domains, including fintech, biotech and artificial intelligence (AI). You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. This Learning Path walks you through two important collection types: arrays and matrices. You’ll be taken through how type conversions and promotions work, and in further chapters you'll study how Julia interacts with operating systems and other languages. You’ll also learn about the use of macros, what makes Julia suitable for numerical and scientific computing, and how to run external programs. Once you have grasped the basics, this Learning Path goes on to how to analyze the Iris dataset using DataFrames. While building a web scraper and a web app, you’ll explore the use of functions, methods, and multiple dispatches. In the final chapters, you'll delve into machine learning, where you'll build a book recommender system. By the end of this Learning Path, you’ll be well versed with Julia and have the skills you need to leverage its high speed and efficiency for your applications. This Learning Path includes content from the following Packt products: • Julia 1.0 Programming - Second Edition by Ivo Balbaert • Julia Programming Projects by Adrian Salceanu
Table of Contents (18 chapters)

Calling C and Fortran

While Julia can rightfully claim to obviate the need to write some C or Fortran code, it is possible that you will need to interact with the existing C or Fortran shared libraries. Functions in such a library can be called directly by Julia, with no glue code, boilerplate code, or compilation needed. Because Julia's LLVM compiler generates native code, calling a C function from Julia has exactly the same overhead as calling the same function from C code itself. However, first, we need to know a few more things:

  • For calling out to C, we need to work with pointer types; a native pointer Ptr{T} is nothing more than the memory address for a variable of type T. You can use Cstring if the value is null-terminated.
  • At this lower level, the term primitive is also used. primitive is a concrete type whose data consists of bits, such as...