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Getting started with Julia Programming Language
Getting started with Julia Programming Language

Getting started with Julia Programming Language: Enter the exciting world of Julia, a high-performance language for technical computing

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Getting started with Julia Programming Language

Chapter 1. Installing the Julia Platform

This chapter guides you through the download and installation of all the necessary components of Julia. The topics covered in this chapter are as follows:

  • Installing Julia
  • Working with Julia's shell
  • Start-up options and Julia scripts
  • Packages
  • Installing and working with Julia Studio
  • Installing and working with IJulia
  • Installing Sublime-IJulia
  • Installing Juno
  • Other editors and IDEs
  • Working of Julia

By the end of this chapter, you will have a running Julia platform. Moreover, you will be able to work with Julia's shell as well as with editors or integrated development environments with a lot of built-in features to make development more comfortable.

Installing Julia

The Julia platform in binary (that is, executable) form can be downloaded from http://julialang.org/downloads/. It exists for three major platforms (Windows, Linux, and OS X) in 32- and 64-bit format, and is delivered as a package or in an archive format. You should use the current official stable release when doing serious professional work with Julia (at the time of writing, this is Version 0.3). If you would like to investigate the latest developments, install the upcoming version (which is now Version 0.4). The previous link contains detailed and platform-specific instructions for the installation. We will not repeat these instructions here completely, but we will summarize some important points.

Windows version – usable from Windows XP SP2 onwards

You need to keep the following things in mind if you are using the Windows OS:

  1. As a prerequisite, you need the 7zip extractor program, so first download and install http://www.7-zip.org/download.html.
  2. Now, download the...

Working with Julia's shell

We started with Julia's shell in the previous section (refer to the preceding two screenshots) to verify the correctness of the installation, by issuing the julia command in a terminal session. The shell or REPL is Julia's working environment, where you can interact with the Just in Time (JIT) compiler to test out pieces of code. When satisfied, you can copy and paste this code into a file with a .jl extension, such as program.jl. Alternatively, you can continue the work on this code from within a text editor or an IDE, such as the ones we will point out later in this chapter. After the banner with Julia's logo has appeared, you get a julia> prompt for the input. To end this session, and get to the OS Command Prompt, type CTRL + D or quit(), and hit ENTER. To evaluate an expression, type it and press ENTER to show the result, as shown in the following screenshot:

Working with Julia's shell

Working with the REPL (1)

If, for some reason, you don't need to see the...

Startup options and Julia scripts

Without any options, the julia command starts up the REPL environment. A useful option to check your environment is julia –v. This shows Julia's version, for example, julia-version 0.3.2+2. (The versioninfo()function in REPL is more detailed, the VERSION constant gives you only the version number: v"0.3.2+2"). An option that lets you evaluate expressions on the command line itself is –e, for example:

  julia -e 'a = 6 * 7;
  println(a)'

The preceding commands print out 42 (on Windows, use " instead of the ' character).

Some other options useful for parallel processing will be discussed in Chapter 9, Running External Programs. Type julia –h for a list of all options.

A script.jl file with Julia source code can be started from the command line with the following command:

julia script.jl arg1 arg2 arg3

Here arg1, arg2, and arg3 are optional arguments to be used in the script's code. They are available...

Packages

Most of the standard library in Julia (can be found in /share/julia/base relative to where Julia was installed) is written in Julia itself. The rest of Julia's code ecosystem is contained in packages that are simply Git repositories. They are most often authored by external contributors, and already provide functionality for such diverse disciplines such as bioinformatics, chemistry, cosmology, finance, linguistics, machine learning, mathematics, statistics, and high-performance computing. A searchable package list can be found at http://pkg.julialang.org/. Official Julia packages are registered in the METADATA.jl file in the Julia Git repository, available on GitHub at https://github.com/JuliaLang/METADATA.jl.

Julia's installation contains a built-in package manager Pkg for installing additional Julia packages written in Julia. The downloaded packages are stored in a cache ready to be used by Julia given by Pkg.dir(), which are located at c:\users\username\.julia\vn.m...

Installing and working with Julia Studio

Julia Studio is a free desktop app for working with Julia that runs on Linux, Windows, and OS X (http://forio.com/labs/julia-studio/). It works with the 0.3 release on Windows (Version 0.2.1 for Linux and OS X, at this time, if you want Julia Studio to work with Julia v0.3 on Linux and OS X, you have to do the compilation of the source code of the Studio yourself). It contains a sophisticated editor and integrated REPL, version control with Git, and a very handy side pane with access to the command history, filesystem, packages, and the list of edited documents. It is created by Forio, a company that makes software for simulations, data explorations, interactive learning, and predictive analytics. In the following screenshot, you can see some of Julia Studio's features, such as the Console section and the green Run button (or F5) in the upper-right corner. The simple program fizzbuzz.jl prints for the first 100 integers for "fizz"...

Installing and working with IJulia

IJulia (https://github.com/JuliaLang/IJulia.jl) is a combination of the IPython web frontend interactive environment (http://ipython.org/) with a Julia-language backend. It allows you to work with IPython's powerful graphical notebook (which combines code, formatted text, math, and multimedia in a single document) with qtconsole and regular REPL. Detailed instructions for installation are found at the GitHub page for IJulia (https://github.com/JuliaLang/IJulia.jl) and in the Julia at MIT notes (https://github.com/stevengj/julia-mit/blob/master/README.md). Here is a summary of the steps:

  1. Install Version 1.0 or later of IPython via easy_install or pip (on OS X and Windows, this is included in the Anaconda Python installation). On Linux, use apt-get install ipython. (For more information, refer to the IPython home page).
  2. Install PyQt4 or PySide for qtconsole.
  3. Install the IJulia package from the REPL with Pkg.add("IJulia").
  4. Install the PyPlot package...

Installing Sublime-IJulia

The popular Sublime Text editor (http://www.sublimetext.com/3) now has a plugin based on IJulia (https://github.com/quinnj/Sublime-IJulia) authored by Jacob Quinn. It gives you syntax highlighting, autocompletion, and an in-editor REPL, which you basically just open like any other text file, but it runs Julia code for you. You can also select some code from a code file and send it to the REPL with the shortcut CTRL + B, or send the entire file there. Sublime-IJulia provides a frontend to the IJulia backend kernel, so that you can start an IJulia frontend in a Sublime view and interact with the kernel. Here is a summary of the installation, for details you can refer to the preceding URL:

  1. From within the Julia REPL, install the ZMQ and IJulia packages.
  2. From within Sublime Text, install the Package Control package (https://sublime.wbond.net/installation).
  3. From within Sublime Text, install the IJulia package from the Sublime command palette.
  4. Ctrl + Shift + P opens up a...

Installing Juno

Another promising IDE for Julia and a work in progress by Mike Innes and Keno Fisher is Juno, which is based on the Light Table environment. The docs at http://junolab.org/docs/installing.html provides detailed instructions for installing and configuring Juno. Here is a summary of the steps:

  1. Get LightTable from http://lighttable.com.
  2. Start LightTable, install the Juno plugin through its plugin manager, and restart LightTable.

Light Table works extensively with a command palette that you can open by typing Ctrl + SPACE, entering a command, and then selecting it. Juno provides an integrated console, and you can evaluate single expressions in the code editor directly by typing Ctrl + Enter at the end of the line. A complete script is evaluated by typing Ctrl + Shift + Enter.

Other editors and IDEs

For terminal users, the available editors are as follows:

On Linux, gedit is very good. The Julia plugin works well and provides autocompletion. Notepad++ also has Julia support from the contrib directory mentioned earlier.

The SageMath project (https://cloud.sagemath.com/) runs Julia in the cloud within a terminal and lets you work with IPython notebooks. You can also work and teach with Julia in the cloud using the JuliaBox platform (https://juliabox.org/).

How Julia works

(You can safely skip this section on a first reading.)

Julia works with an LLVM JIT compiler framework that is used for just-in-time generation of machine code. The first time you run a Julia function, it is parsed and the types are inferred. Then, LLVM code is generated by the JIT (just-in-time) compiler, which is then optimized and compiled down to native code. The second time you run a Julia function, the native code already generated is called. This is the reason why, the second time you call a function with arguments of a specific type, it takes much less time to run than the first time (keep this in mind when doing benchmarks of Julia code). This generated code can be inspected. Suppose, for example, we have defined a f(x) = 2x + 5 function in a REPL session. Julia responds with the message, f (generic function with 1 method); the code is dynamic because we didn't have to specify the type of x or f. Functions are by default generic because they are ready to work...

Summary

By now, you should have been able to install Julia in a working environment you prefer. You should also have some experience with working in the REPL. We will put this to good use starting in the next chapter, where we will meet the basic data types in Julia, by testing out everything in the REPL.

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Description

This book is for you if you are a data scientist or working on any technical or scientific computation projects. The book assumes you have a basic working knowledge of high-level dynamic languages such as MATLAB, R, Python, or Ruby.

Who is this book for?

This book is for you if you are a data scientist or working on any technical or scientific computation projects. The book assumes you have a basic working knowledge of high-level dynamic languages such as MATLAB, R, Python, or Ruby.

What you will learn

  • Set up your Julia environment to achieve the highest productivity
  • Solve your tasks in a highlevel dynamic language and use types for your data only when needed
  • Create your own types to extend the builtin type system
  • Visualize your data in IJulia with plotting packages
  • Explore the use of builtin macros for testing, debugging, benchmarking, and more
  • Apply Julia to tackle problems concurrently and in a distributed environment
  • Integrate with other languages such as C, Python, and MATLAB
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Table of Contents

13 Chapters
The Rationale for Julia Chevron down icon Chevron up icon
1. Installing the Julia Platform Chevron down icon Chevron up icon
2. Variables, Types, and Operations Chevron down icon Chevron up icon
3. Functions Chevron down icon Chevron up icon
4. Control Flow Chevron down icon Chevron up icon
5. Collection Types Chevron down icon Chevron up icon
6. More on Types, Methods, and Modules Chevron down icon Chevron up icon
7. Metaprogramming in Julia Chevron down icon Chevron up icon
8. I/O, Networking, and Parallel Computing Chevron down icon Chevron up icon
9. Running External Programs Chevron down icon Chevron up icon
10. The Standard Library and Packages Chevron down icon Chevron up icon
A. List of Macros and Packages Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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adnan baloch Mar 26, 2015
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Scientists and students dealing with technical computing problems have a real conundrum at their hands when it comes to solving their problems using programming. If they use a specialized language, they have to give up speed. If they turn to C or C++ for their programming needs, they have to look at a significant time investment most of which might be spent not solving their problem but wrestling with the quirky language design decisions of C/C++. The best of both these worlds was conceived at MIT in the form of the Julia programming language. Striving to keep the performance at most twice as slow as C/C++, the author shows how easy it is to use Julia as a one-stop solution for all our technical computing needs. Installation, Julia shell, Julia studio and Ijulia are introduced in the first chapter thus making the readers confident that they can get to work as soon as possible no matter their preference of development environment. The next chapter introduces the variables and types in Julia. The author dedicates an entire chapter to functions in Julia and shows why Julia deserves to be called a functional language. Control flow, exception handling and tasks (coroutines) form the subject of the next chapter. The author moves onto complex types consisting of many elements which are dubbed collection types in Julia. The author uses the example of a word frequency program in just a little over a dozen lines of code to demonstrate the power of Julia. Multiple dispatch and metaprogramming are strengths that set Julia apart and the author makes sure that the readers understand them thoroughly. Working with CSV files and the concept of dataframes is explained clearly. The fact that a dataframe is kind of an array with different types in its columns provides the user a lot of versatility in attacking common data problems. Connecting with databases, networking and parallel computation using Julia are also given ample explanation for the benefit of the readers. Interfacing with the command line, C, Fortran and Python are touched upon in a chapter that ends with valuable performance tips to get the most out of Julia. The final chapter leaves the readers with the opportunity to explore Julia's rich standard library and shows a plotting example using Gadfly. Julia's excellent package manager and almost 400 packages mean that the readers will find themselves busy with Julia long after they have finished this comprehensive introduction to one of the rising stars of the technical computing scene.
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Dirk Harms-Merbtiz Nov 25, 2016
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Quick read and to the point. Took a couple hours to go through. Amazon, don't require so many words in reviews.
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Jordi C May 10, 2015
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Great introduction to Julia. In the past I tried to learn Julia several times, using online documentation and tutorials, but I never found the right help that caught my attention, so I never learnt. On the other hand, this book guided me smoothly and easily through the Julia language, so I really recommend it.The book is well structured, from the syntax basics to useful features as error handling, tasks, running shell commands and python libraries, parallel computing and metaprogramming, features that also seem easy to use with Julia.I found the level of the book adequate to start with the language (I am already fluent in Python and Fortran, which I use for scientific computing).
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ruben Apr 11, 2015
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I would like to thank the author because I have seen this book as a great opportunity area where we have to work.I want to say that I am teacher in engineering like mechatronics, electromechanic, industrial and we have been working with this new technologies to solve practical problems in the industry I could say that the problems that we have to teach we apply the content of the book to solve themThanks.
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Oleg Okun Apr 19, 2015
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What is Julia? Julia is a fairly new programming language (borne in 2012) that aims at reaching two objectives: 1) quick solution prototyping like in MATLAB, R or Python and 2) fast computational time and efficient memory management like in FORTRAN, C or C++. Thus, one could say that Julia tries to bridge the gap between programming languages of two types - high-level but slow and fast but low-level.As such, Julia is a primal competitor to MATLAB, R, and Python. However, competition does not completely exclude collaboration; this is the case for Julia that has packages for calling code written in these and some other languages like Java. Needless to say, Julia is open source and free with a liberal MIT license, which is important for wide adoption. To date, there are 549 external packages that can be found at http://pkg.julialang.org/ .Chapter 1 explains how to install Julia for Windows, Ubuntu and OS X, shows example of work in Julia shell, and lists IDEs like Juno that can be used. Chapter 2 describes Julia's types of variables (numbers, characters, strings, ranges, arrays) that are the key for its performance, while Chapter 3 discusses functions (their definition and types). Control flow operators (if, for, while, break, continue) and exception handling are introduced in Chapter 3. As the extensions of Chapter 2, Chapter 5 talks about matrices (multidimensional arrays), tuples, sets and dictionaries, while Chapter 6 goes into detail of type annotation and conversion, type hierarchy, abstract, parametric, and user-defined types. The concept of metaprogramming (which can even generate code) is explained in Chapter 7. Chapter 8 explores I/O (file reading from and writing to several formats such as CSV, JSON, XML, HDF5, DataFrames for tabular data representation), networking, interacting with databases, and parallel computing. How to call from within Julia the code written in another programming language like C, FORTRAN and Python as well as performance recommendations are explained in Chapter 9. Chapter 10 looks at the standard library of functions and packages and provides guidelines how to use a package manager and visualize data.One may say that everything abovementioned can be found in Julia's documentation, but to have these topics in one place such as this book is certainly a must for new converts and seasonable experts alike.
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  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
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