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Julia Cookbook
Julia Cookbook

Julia Cookbook: Over 40 recipes to get you up and running with programming using Julia

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Julia Cookbook

Chapter 2. Metaprogramming

In this chapter, we will cover the following recipes:

  • Representation of a Julia program
  • Programs for metaprogramming
  • Expressions and functions for metaprogramming
  • Macros
  • Advanced concepts in macros
  • Function and code generation

Introduction

Metaprogramming is a concept where by a language can express its own code as a data structure of itself. For example, Lisp expresses code in the form of Lisp arrays, which are data structures in Lisp itself. Similarly, even Julia can express its code as data structures.

This makes it possible for Julia to generate and transform code through a Julia program. Julia has really nice reflection properties. So, the property of metaprogramming makes it easy to handle repetitive programming and function execution in data science and, especially, while handling big data in the Map Reduce framework.

Representation of a Julia program

In this section, you will study the life of a Julia program and how it is actually represented and interpreted by Julia. You will also learn what is meant by "a language expressing its own code as a data structure of itself."

This section will act as a foundation for learning about the concept of metaprogramming and how Julia uses it for generating code.

Getting ready

To get started with this section, you must simply have your Julia REPL up-and-running.

How to do it...

Firstly, it is very important to know that every Julia program starts out as a string. Let's consider a short program for adding two variables as our Julia code and use it to learn how Julia interprets programs:

code = "a + b"

It would look like this:

How to do it...

Now, if you parse the preceding string code, it would return an object of type Expression. Let's check it by actually parsing an example Julia program and checking for its type:

check = parse(code)

The output would...

Symbols and expressions

In this section, you will learn about symbols and expressions in detail. They have a syntactic importance in the metaprogramming concepts of Julia. So, this section would explain them in detail, so as to appreciate the concepts covered so far and those to follow.

Symbols

Symbols are the basic blocks of expressions. They are used for concatenating two strings together. They are also used as interned strings while building expressions.

Getting ready

There aren't any major requirements for this chapter. The only requirement is that your Julia REPL should be up and running.

How to do it...

Symbols can take in some arguments and then return the concatenated string of the string representations of those arguments. This is an example of how you can do it in the REPL:

  • symbol("FirstName", "LastName")

    The output of the preceding command would look like this:

    How to do it...

  • symbol("FirstName", 45)

    The output of the preceding command would look like this:

    How to do it...

  • symbol("Foo", :Bar, 86)

    The output of the preceding command would look like:

    How to do it...

Symbols create interned strings that are used for building expressions. An interned string is an immutable string that is used during string processing for optimizing time and space. The character : is used...

Quoting

The usage of a semicolon to represent expressions is known as quoting. The characters inside the parentheses after the semicolon constitute an Expression object.

How to do it...

To check this behavior, let's check for the type of a similar statement that has an object inside the parentheses after a semicolon. This can be done in the REPL as follows:

typeof(:((a + b) * c) / 6))

The preceding command gives the following output:

How to do it...

Multiple expressions can be represented as a block by quoting them. The syntax would be as follows:

exp = quote
              some code
              some more code
              more code
              a little more
              ...
           end

An example with some code inside the code block would look like this:

How to do it...

Now, let's verify the type of the exp variable with the typeof() function.

How to do it...

So, the the code block enclosed inside quote and end is indeed an expression.

How it works...

Quoting is the concept of creating expression objects...

Interpolation

Sometimes, construction on Expression objects is difficult, especially when you have multiple objects and/or variables. This is used for easy and readable expression construction.

So, interpolation is a way to deal with this. Such objects can be interpolated into the expression construction through a $ prefix. This process is also called splicing expressions, variables, or literals into quoted expressions.

How to do it...

Suppose there is a literal p, which has to be interpolated for constructing an expression with other literals; this is how it would be done:

p = 6;
exp = :(20 + $p)

This is how it would look:

How to do it...

For nested quoting, each symbol must be quoted separately, along with splicing the overall parentheses of the nested expression:

p = 6;
q = 7;
:(:p in $(  :(:p * :q ) ) )

This is how it would look in the REPL:

How to do it...

Even data structures can be spliced into an expression construction. Now, let's consider the tuple data structure for splicing into an expression builder...

Introduction


Metaprogramming is a concept where by a language can express its own code as a data structure of itself. For example, Lisp expresses code in the form of Lisp arrays, which are data structures in Lisp itself. Similarly, even Julia can express its code as data structures.

This makes it possible for Julia to generate and transform code through a Julia program. Julia has really nice reflection properties. So, the property of metaprogramming makes it easy to handle repetitive programming and function execution in data science and, especially, while handling big data in the Map Reduce framework.

Representation of a Julia program


In this section, you will study the life of a Julia program and how it is actually represented and interpreted by Julia. You will also learn what is meant by "a language expressing its own code as a data structure of itself."

This section will act as a foundation for learning about the concept of metaprogramming and how Julia uses it for generating code.

Getting ready

To get started with this section, you must simply have your Julia REPL up-and-running.

How to do it...

Firstly, it is very important to know that every Julia program starts out as a string. Let's consider a short program for adding two variables as our Julia code and use it to learn how Julia interprets programs:

code = "a + b"

It would look like this:

Now, if you parse the preceding string code, it would return an object of type Expression. Let's check it by actually parsing an example Julia program and checking for its type:

check = parse(code)

The output would look like this:

You will learn...

Symbols and expressions


In this section, you will learn about symbols and expressions in detail. They have a syntactic importance in the metaprogramming concepts of Julia. So, this section would explain them in detail, so as to appreciate the concepts covered so far and those to follow.

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Key benefits

  • Follow a practical approach to learn Julia programming the easy way
  • Get an extensive coverage of Julia’s packages for statistical analysis
  • This recipe-based approach will help you get familiar with the key concepts in Julia

Description

Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation. Later on, you’ll see how to optimize data science programs with parallel computing and memory allocation. You’ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform. This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.

Who is this book for?

This book is for data scientists and data analysts who are familiar with the basics of the Julia language. Prior experience of working with high-level languages such as MATLAB, Python, R, or Ruby is expected.

What you will learn

  • Extract and handle your data with Julia
  • Uncover the concepts of metaprogramming in Julia
  • Conduct statistical analysis with StatsBase.jl and Distributions.jl
  • Build your data science models
  • Find out how to visualize your data with Gadfly
  • Explore big data concepts in Julia
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Table of Contents

6 Chapters
1. Extracting and Handling Data Chevron down icon Chevron up icon
2. Metaprogramming Chevron down icon Chevron up icon
3. Statistics with Julia Chevron down icon Chevron up icon
4. Building Data Science Models Chevron down icon Chevron up icon
5. Working with Visualizations Chevron down icon Chevron up icon
6. Parallel Computing Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.3
(3 Ratings)
5 star 0%
4 star 0%
3 star 66.7%
2 star 0%
1 star 33.3%
Kindle Customer Mar 02, 2017
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
If you don't know the language and/or have minimal programming experience, this could be a good resource. If you're looking for the definitive 'Advanced Julia Developer's Guide', look elsewhere (and please let me know if you find it)...
Amazon Verified review Amazon
MSE fanatic Feb 10, 2017
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I bought this book because I wanted to extend my Julia knowledge and capabilities. The book is fairly good, it covers a lot of data sciences related Julia packages and examples on how to do trivial tasks like reading files or performing an analysis of data. However, I was hoping since the book is titled as being a cookbook, there would be more examples of best methods for programming algorithms in Julia. I was anticipating a book more like a short numerical recipes book in Julia. Also, if the price for the printed version was around $15-20, I would have given this 4-stars.
Amazon Verified review Amazon
KarEl BrightShooster Feb 05, 2017
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Im Grunde kann man die Inhalte des Buches in den help-pages von julia-lang nachlesen, das ist zu wenig Mehrwert in diesem Buch
Amazon Verified review Amazon
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