Mastering Julia

Malcolm Sherrington

• Editorial: Packt Publishing
• Año: 2015
• ISBN:
  9781783553310
• Edición: 1
• Categoría: Computer Science
• Categoría: Systems Architecture

This hands-on guide is aimed at practitioners of data science. The book assumes some previous skills with Julia and skills in coding in a scripting language such as Python or R, or a compiled language such as C or Java.

Mastering Julia

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Table of Contents

Mastering Julia

Credits

About the Author

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. The Julia Environment

Introduction

Philosophy

Role in data science and big data

Comparison with other languages

Features

Getting started

Julia sources

Building from source

Installing on CentOS

Mac OS X and Windows

Exploring the source stack

Juno

IJulia

A quick look at some Julia

Julia via the console

Installing some packages

A bit of graphics creating more realistic graphics with Winston

My benchmarks

Package management

Listing, adding, and removing

Choosing and exploring packages

Statistics and mathematics

Data visualization

Web and networking

Database and specialist packages

How to uninstall Julia

Adding an unregistered package

What makes Julia special

Parallel processing

Multiple dispatch

Homoiconic macros

Interlanguage cooperation

Summary

2. Developing in Julia

Integers, bits, bytes, and bools

Integers

Logical and arithmetic operators

Booleans

Arrays

Operations on matrices

Elemental operations

A simple Markov chain – cat and mouse

Char and strings

Characters

Strings

Unicode support

Regular expressions

Byte array literals

Version literals

An example

Real, complex, and rational numbers

Reals

Operators and built-in functions

Special values

BigFloats

Rationals

Complex numbers

Juliasets

Composite types

More about matrices

Vectorized and devectorized code

Multidimensional arrays

Broadcasting

Sparse matrices

Data arrays and data frames

Dictionaries, sets, and others

Dictionaries

Sets

Other data structures

Summary

3. Types and Dispatch

Functions

First-class objects

Passing arguments

Default and optional arguments

Variable argument list

Named parameters

Scope

The Queen's problem

Julia's type system

A look at the rational type

A vehicle datatype

Typealias and unions

Enumerations (revisited)

Multiple dispatch

Parametric types

Conversion and promotion

Conversion

Promotion

A fixed vector module

Summary

4. Interoperability

Interfacing with other programming environments

Calling C and Fortran

Mapping C types

Array conversions

Type correspondences

Calling a Fortran routine

Calling curl to retrieve a web page

Python

Some others to watch

The Julia API

Calling API from C

Metaprogramming

Symbols

Macros

Testing

Error handling

The enum macro

Tasks

Parallel operations

Distributed arrays

A simple MapReduce

Executing commands

Running commands

Working with the filesystem

Redirection and pipes

Perl one-liners

Summary

5. Working with Data

Basic I/O

Terminal I/O

Disk files

Text processing

Binary files

Structured datasets

CSV and DLM files

HDF5

XML files

DataFrames and RDatasets

The DataFrames package

DataFrames

RDatasets

Subsetting, sorting, and joining data

Statistics

Simple statistics

Samples and estimations

Pandas

Selected topics

Time series

Distributions

Kernel density

Hypothesis testing

GLM

Summary

6. Scientific Programming

Linear algebra

Simultaneous equations

Decompositions

Eigenvalues and eigenvectors

Special matrices

A symmetric eigenproblem

Signal processing

Frequency analysis

Filtering and smoothing

Digital signal filters

Image processing

Differential equations

The solution of ordinary differential equations

Non-linear ordinary differential equations

Partial differential equations

Optimization problems

JuMP

Optim

NLopt

Using with the MathProgBase interface

Stochastic problems

Stochastic simulations

SimJulia

Bank teller example

Bayesian methods and Markov processes

Monte Carlo Markov Chains

MCMC frameworks

Summary

7. Graphics

Basic graphics in Julia

Text plotting

Cairo

Winston

Data visualization

Gadfly

Compose

Graphic engines

PyPlot

Gaston

PGF plots

Using the Web

Bokeh

Plotly

Raster graphics

Cairo (revisited)

Winston (revisited)

Images and ImageView

Summary

8. Databases

A basic view of databases

The red pill or the blue pill?

Interfacing to databases

Other considerations

Relational databases

Building and loading

Native interfaces

ODBC

Other interfacing techniques

DBI

SQLite

MySQL

PostgreSQL

PyCall

JDBC

NoSQL datastores

Key-value systems

Document datastores

RESTful interfacing

JSON

Web-based databases

Graphic systems

Summary

9. Networking

Sockets and servers

Well-known ports

UDP and TCP sockets in Julia

A "Looking-Glass World" echo server

Named pipes

Working with the Web

A TCP web service

The JuliaWeb group

The "quotes" server

WebSockets

Messaging...