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Home > Programming > Programming Language > Julia 1.0 Programming
Julia 1.0 Programming
Julia 1.0 Programming

Julia 1.0 Programming: Dynamic and high-performance programming to build fast scientific applications , Second Edition

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Julia 1.0 Programming

Variables, Types, and Operations

Julia is an optionally typed language, which means that the user can choose to specify the type of arguments passed to a function and the type of variables used inside a function. Julia's type system is the key for its performance; understanding it well is important, and it can pay off to use type annotations, not only for documentation or tooling, but also for execution speed. This chapter discusses the realm of elementary built-in types in Julia, the operations that can be performed on them, as well as the important concepts of types and scope.

The following topics are covered in this chapter:

  • Variables, naming conventions, and comments
  • Types
  • Integers
  • Floating point numbers
  • Elementary mathematical functions and operations
  • Rational and complex numbers
  • Characters
  • Strings
  • Regular expressions
  • Ranges and arrays
  • Dates and times
  • Scope and constants...

Variables, naming conventions, and comments

Data is stored in values such as 1, 3.14, and "Julia", and every other value has a type, for example, the type of 3.14 is Float64. Some other examples of elementary values and their data types are 42 of the Int64 type, true and false of the Bool type, and 'X' of the Char type.

Julia, unlike many modern programming languages, differentiates between single characters and strings. Strings can contain any number of characters, and are specified using double quotes—single quotes are only used for a character literal. Variables are the names that are bound to values by assignments, such as x = 42. They have the type of the value they contain (or reference); this type is given by the typeof function. For example, typeof(x) returns Int64.

The type of a variable can change, because putting x = "I am Julia...

Types

Julia's type system is unique. Julia behaves as a dynamically typed language (such as Python, for instance) most of the time. This means that a variable bound to an integer at one point might later be bound to a string. For example, consider the following:

julia> x = 10 
10 
julia> x = "hello" 
"hello"

However, one can, optionally, add type information to a variable. This causes the variable to only accept values that match that specific type. This is done through a type of annotation. For instance, declaring x::String implies that only strings can be bound to x; in general, it looks like var::TypeName. These are used the most often to qualify the arguments a function can take. The extra type information is useful for documenting the code, and often allows the JIT compiler to generate better-optimized native code. It also allows the development...

Integers

Julia offers support for integer numbers ranging from types Int8 to Int128, with 8 to 128 representing the number of bits used, and with unsigned variants with a U prefix, such as UInt8. The default type (which can also be used as Int) is Int32 or Int64, depending on the target machine architecture. The bit width is given by the Sys.WORD_SIZE variable. The number of bits used by the integer affects the maximum and minimum value this integer can have. The minimum and maximum values are given by the typemin() and typemax() functions, respectively; for example, typemax(Int16) returns 32767.

If you try to store a number larger than that allowed by typemax, overflow occurs. For example, note the following:

julia> typemax(Int) 
9223372036854775807 # might be different on 32 bit platform 
julia> ans + 1 
-9223372036854775808

Overflow checking is not automatic, so an explicit...

Floating point numbers

Floating point numbers follow the IEEE 754 standard and represent numbers with a decimal point, such as 3.14, or an exponent notation, such as 4e-14, and come in the types Float16 up to Float64, the last one being used for double precision.

Single precision is achieved through the use of the Float32 type. Single precision float literals must be written in scientific notation, such as 3.14f0, but with f, where one normally uses e. That is, 2.5f2 indicates 2.5*10^2 with single precision, while 2.5e2 indicates 2.5*10^2 in double precision. Julia also has a BigFloat type for arbitrary-precision floating numbers computations.

A built-in type promotion system takes care of all the numeric types that can work together seamlessly, so that there is no explicit conversion needed. Special values exist: Inf and -Inf are used for infinity, and NaN is used...

Elementary mathematical functions and operations

You can view the binary representation of any number (integer or float) with the bitstring function, for example, bitstring(3) returns "0000000000000000000000000000000000000000000000000000000000000011".

To round a number, use the round() function which returns a floating point number. All standard mathematical functions are provided, such as sqrt(), cbrt(), exp(), log(), sin(), cos(), tan(), erf() (the error function), and many more (refer to the URL mentioned at the end of this section). To generate a random number, use rand().

Use parentheses ( ) around expressions to enforce precedence. Chained assignments, such as a = b = c = d = 1, are allowed. The assignments are evaluated right-to-left. Assignments for different variables can be combined, as shown in the following example:

  a = 1; b = 2; c = 3; d =...

Rational and complex numbers

Julia supports these types out of the box. The global constant im represents the square root of -1, so that 3.2 + 7.1im is a complex number with floating point coefficients, so it is of the type Complex{Float64}.

This is the first example of a parametric type in Julia. For this example, we can write this as Complex{T}, where type T can take a number of different type values, such as Int32, Int64, or Float64.

All operations and elementary functions, such as exp(), sqrt(), sinh(), real(), imag(), abs(), and so on, are also defined on complex numbers; for example, abs(3.2 + 7.1im) = 7.787810988975015.

If a and b are two variables that contain a number, use complex(a,b) to form a complex number with them. Rational numbers are useful when you want to work with exact ratios of integers, for example, 3//4, which is of type Rational{Int64}.

Again, comparisons...

Characters

Like C or Java, but unlike Python, Julia implements a type for a single character, the Char type. A character literal is written as 'A', where typeof('A') returns Char. A Char value is a Unicode code point, and it ranges from '\0' to '\Uffffffff'. Convert this to its code point with Int(): Int('A') returns 65, and Int('α') returns 945, so this takes two bytes.

The reverse also works: Char(65) returns 'A', Char(945) returns '\u3b1', which is the code point for α (3b1 is hexadecimal for 945).

Unicode characters can be entered by a \u in single quotes, followed by four hexadecimal digits (ranging from 0-9 or A-F), or \U followed by eight hexadecimal digits. The isvalid(Char, value) function can test whether a number returns an existing Unicode character: isvalid(Char...

Variables, naming conventions, and comments

Data is stored in values such as1, 3.14, and "Julia", and every other value has a type, for example, the type of 3.14 isFloat64. Some other examples of elementary values and their data types are 42 of the Int64 type, true and false of the Bool type, and 'X' of the Char type.

Julia, unlike many modern programming languages, differentiates between single characters and strings. Strings can contain any number of characters, and are specified using double quotes—single quotes are only used for a character literal. Variables are the names that are bound to values by assignments, such asx = 42. They have the type of the value they contain (or reference); this type is given by the typeof function. For example, typeof(x) returns Int64.

The type of a variable can change, because putting x = "I am Julia" now results in typeof(x) returning String. In Julia, we don't have to declare a variable (that indicates its type) such as in C or Java, for instance, but...

Types

Julia's type system is unique. Julia behaves as a dynamically typed language (such as Python, for instance) most of the time. This means that a variable bound to an integer at one point might later be bound to a string. For example, consider the following:

julia> x = 1010julia> x = "hello""hello"

However, one can, optionally, add type information to a variable. This causes the variable to only accept values that match that specific type. This is done through a type of annotation. For instance, declaring x::String implies that only strings can be bound to x; in general, it looks like var::TypeName. These are used the most often to qualify the arguments a function can take. The extra type information is useful for documenting the code, and often allows the JIT compiler to generate better-optimized native code. It also allows the development environments to give more support, and code tools such as a linter that can check your code for possible wrong type use.

Here is an example: a...

Integers

Julia offers support for integer numbers ranging from types Int8 to Int128, with 8 to 128 representing the number of bits used, and with unsigned variants with a U prefix, such as UInt8. The default type (which can also be used as Int) is Int32 or Int64, depending on the target machine architecture. The bit width is given by the Sys.WORD_SIZE variable. The number of bits used by the integer affects the maximum and minimum value this integer can have. The minimum and maximum values are given by the typemin() and typemax() functions, respectively; for example, typemax(Int16) returns 32767.

If you try to store a number larger than that allowed by typemax, overflow occurs. For example, note the following:

julia> typemax(Int)9223372036854775807 # might be different on 32 bit platformjulia> ans + 1-9223372036854775808

Overflow checking is not automatic, so an explicit check (for example, the result has the wrong sign) is needed when this can occur. Integers can also be written in binary...

Floating point numbers

Floating point numbers follow the IEEE 754 standard and represent numbers with a decimal point, such as 3.14, or an exponent notation, such as 4e-14, and come in the types Float16 up to Float64, the last one being used for double precision.

 

 

Single precision is achieved through the use of the Float32 type. Single precision float literals must be written in scientific notation, such as 3.14f0, but with f, where one normally uses e. That is, 2.5f2 indicates 2.5*10^2 with single precision, while 2.5e2 indicates 2.5*10^2 in double precision. Julia also has a BigFloat type for arbitrary-precision floating numbers computations.

A built-in type promotion system takes care of all the numeric types that can work together seamlessly, so that there is no explicit conversion needed. Special values exist: Inf and -Inf are used for infinity, and NaN is used for "not a number" values such as the result of 0/0 or Inf - Inf.

Floating point arithmetic in all programming languages is often...

Elementary mathematical functions and operations

You can view the binary representation of any number (integer or float) with the bitstring function, for example, bitstring(3) returns "0000000000000000000000000000000000000000000000000000000000000011".

 

 

To round a number, use the round() function which returns a floating point number. All standard mathematical functions are provided, such as sqrt(), cbrt(), exp(), log(), sin(), cos(), tan(), erf() (the error function), and many more (refer to the URL mentioned at the end of this section). To generate a random number, use rand().

Use parentheses ( ) around expressions to enforce precedence. Chained assignments, such as a = b = c = d = 1, are allowed. The assignments are evaluated right-to-left. Assignments for different variables can be combined, as shown in the following example:

  a = 1; b = 2; c = 3; d = 4  a, b = c, d

Now, a has a value of 3 and b has a value of 4. In particular, this makes an easy swap possible:

  a, b = b, a   # now a is...

Rational and complex numbers

Julia supports these types out of the box. The global constantim represents the square root of -1, so that 3.2 + 7.1im is a complex number with floating point coefficients, so it is of the type Complex{Float64}.

This is the first example of a parametric type in Julia. For this example, we can write this asComplex{T}, where type T can take a number of different type values, such as Int32, Int64, or Float64.

All operations and elementary functions, such as exp(), sqrt(), sinh(), real(), imag(), abs(), and so on, are also defined on complex numbers; for example, abs(3.2 + 7.1im)= 7.787810988975015.

If a and b are two variables that contain a number, use complex(a,b) to form a complex number with them. Rational numbers are useful when you want to work with exact ratios of integers, for example, 3//4, which is of type Rational{Int64}.

Again, comparisons and standard operations are defined: float() converts to a floating point number, and num() and den() gives the numerator...

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Key benefits

  • Leverage Julia's high speed and efficiency for your applications
  • Work with Julia in a multi-core, distributed, and networked environment
  • Apply Julia to tackle problems concurrently and in a distributed environment

Description

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.

Who is this book for?

Julia 1.0 Programming is for you if you are a statistician or data scientist who wants a crash course in the Julia programming language while building big data applications. A basic knowledge of mathematics is needed to understand the various methods that are used or created during the course of the book to exploit the capabilities that Julia is designed with.

What you will learn

  • Set up your Julia environment to achieve high productivity
  • Create your own types to extend the built-in type system
  • Visualize your data in Julia with plotting packages
  • Explore the use of built-in macros for testing and debugging, among other uses
  • Apply Julia to tackle problems concurrently
  • Integrate Julia with other languages such as C, Python, and MATLAB
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Publication date : Sep 24, 2018
Length: 196 pages
Edition : 2nd
Language : English
ISBN-13 : 9781788999090
Category :
Programming
Languages :
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Concepts :
Programming Language
Tools :
MATLAB

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Publication date : Sep 24, 2018
Length: 196 pages
Edition : 2nd
Language : English
ISBN-13 : 9781788999090
Category :
Programming
Languages :
Julia
Concepts :
Programming Language
Tools :
MATLAB

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Table of Contents

11 Chapters
Installing the Julia Platform Chevron down icon Chevron up icon
Installing the Julia Platform
Installing Julia
Working with Julia's REPL
Startup options and Julia scripts
Packages
Installing and working with IJulia
Installing Juno
Installing julia-vscode
Installing Sublime-IJulia
Other editors and IDEs
How Julia works
Summary
Variables, Types, and Operations Chevron down icon Chevron up icon
Variables, Types, and Operations
Variables, naming conventions, and comments
Types
Integers
Floating point numbers
Elementary mathematical functions and operations
Rational and complex numbers
Characters
Strings
Regular expressions
Ranges and arrays
Dates and times
Scope and constants
Summary
Functions Chevron down icon Chevron up icon
Functions
Defining functions
Optional and keyword arguments
Anonymous functions
First-class functions and closures
functions
Broadcasting
Map, filter, and list comprehensions
Generic functions and multiple dispatch
Summary
Control Flow Chevron down icon Chevron up icon
Control Flow
Conditional evaluation
Repeated evaluation
Exception handling
Scope revisited
Tasks
Summary
Collection Types Chevron down icon Chevron up icon
Collection Types
Matrices
Tuples
Dictionaries
Sets
An example project – word frequency
Summary
More on Types, Methods, and Modules Chevron down icon Chevron up icon
More on Types, Methods, and Modules
Type annotations
The type hierarchy – subtypes and supertypes
Concrete and abstract types
User-defined and composite types
Types and collections – inner constructors
Type unions
Parametric types and methods
Standard modules and paths
Summary
Metaprogramming in Julia Chevron down icon Chevron up icon
Metaprogramming in Julia
Expressions and symbols
Evaluation and interpolation
Defining macros
Built-in macros
Reflection capabilities
Summary
I/O, Networking, and Parallel Computing Chevron down icon Chevron up icon
I/O, Networking, and Parallel Computing
Basic input and output
Working with files
Using DataFrames
Working with TCP sockets and servers
Interacting with databases
Parallel operations and computing
Summary
Running External Programs Chevron down icon Chevron up icon
Running External Programs
Running shell commands
Calling C and Fortran
Calling Python
Performance tips
Summary
The Standard Library and Packages Chevron down icon Chevron up icon
The Standard Library and Packages
Digging deeper into the standard library
Julia's package manager
Graphics in Julia
Using Plots on data
Summary
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
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Gianluigi Piva Jul 09, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Dopo Python, ritengo he per la parte matematica sia veramente notevole
Amazon Verified review Amazon
SaSy Mar 09, 2019
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Ein gutes Buch für den Einsteiger in die Julia-Programmierung. Ich würde aber definitiv mit dem Kauf bis zur nächsten Auflage warten, da noch einige Inkonsistenzen im Buch zu finden sind (ich habe die 2. Auflage). Offensichtlich ist das Buch ein Update für die Beschreibung der Version 0.6 (oder 0.7). Mit dem Wechsel auf 1.0 haben sich aber einige Befehle dauerhaft geändert (type heißt jetzt struct oder @parallel ist jetzt @distributed). Die Code-Beispiele berücksichtigen diese Änderungen. Der Text erwähnt allerdings noch die alten Befehle. Dies kann zu Verwirrungen führen. Ich benutze das Buch, wenn ich schnell eine Kleinigkeit nachschlagen will. Dafür ist es ganz gut. Bei Julia ist jedoch auch die Online-Dokumentation sehr gut verfasst. Das Buch ist natürlich demgegenüber übersichtlicher gestaltet, deckt aber auch nur einen Bruchteil ab.
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Template Metaprogramming with C++
Template Metaprogramming with C++
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Aug 2022 480 pages
Full star icon 4.6 (14)
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Domain-Driven Design with Golang
Domain-Driven Design with Golang
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Dec 2022 204 pages
Full star icon 4.4 (19)
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About the author

Profile icon Ivo Balbaert
Ivo Balbaert
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Ivo Balbaert is a lecturer in web programming and databases at CVO Antwerpen, a community college in Belgium. He received a PhD in applied physics from the University of Antwerp in 1986. He worked for 20 years in the software industry as a developer and consultant in several companies, and for 10 years as project manager at the University Hospital of Antwerp. From 2000 onwards, he switched to partly teaching and partly developing software (at KHM Mechelen, CVO Antwerpen). He also wrote an introductory book in Dutch about developing in Ruby and Rails, Programmeren met Ruby en Rails, published by Van Duuren Media. In 2012, he authored a book on the Go programming language, The Way to Go, published by iUniverse. He has written a number of introductory books for new programming languages, notably Dart, Julia, Rust, and Red, all published by Packt.
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Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

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On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

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What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela