Book Image

Mastering Julia - Second Edition

By : Malcolm Sherrington
Book Image

Mastering Julia - Second Edition

By: Malcolm Sherrington

Overview of this book

Julia is a well-constructed programming language which was designed for fast execution speed by using just-in-time LLVM compilation techniques, thus eliminating the classic problem of performing analysis in one language and translating it for performance in a second. This book is a primer on Julia’s approach to a wide variety of topics such as scientific computing, statistics, machine learning, simulation, graphics, and distributed computing. Starting off with a refresher on installing and running Julia on different platforms, you’ll quickly get to grips with the core concepts and delve into a discussion on how to use Julia with various code editors and interactive development environments (IDEs). As you progress, you’ll see how data works through simple statistics and analytics and discover Julia's speed, its real strength, which makes it particularly useful in highly intensive computing tasks. You’ll also and observe how Julia can cooperate with external processes to enhance graphics and data visualization. Finally, you will explore metaprogramming and learn how it adds great power to the language and establish networking and distributed computing with Julia. By the end of this book, you’ll be confident in using Julia as part of your existing skill set.
Table of Contents (14 chapters)

More about functions

We have met functions in previous chapters defined as a function() … end block and shown that there is a convenient one-line syntax for the simplest of cases:

# rsq(x) = 1/(x*x) is exactly equivalent to:
function rsq(x)
  y = 1/(x*x)
  return y
end

The y variable is not needed (of course). It is local to the rsq() function and has no existence outside the function call, and the last statement could be written as return 1/(x*x) or even just as 1/(x*x), since functions in Julia return their last value.

The do syntax

In the previous chapter, we looked at ways of performing operations using broadcasting as an alternative to conventional for … end loops and/or list comprehensions. In order to work more compactly, it is often useful to use a different construct using a do … end block, which we will introduce here:

julia> map([1,2,3,4]) do x
         rsq(x)
  ...