DOCSLIB.ORG

Julia Language Documentation Release Development

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Julia Language Documentation Release Development Julia Language Documentation Release development Jeff Bezanson, Stefan Karpinski, Viral Shah, Alan Edelman, et al. September 08, 2014 Contents 1 The Julia Manual 1 1.1 Introduction...............................................1 1.2 Getting Started..............................................2 1.3 Integers and Floating-Point Numbers..................................6 1.4 Mathematical Operations......................................... 12 1.5 Complex and Rational Numbers..................................... 17 1.6 Strings.................................................. 21 1.7 Functions................................................. 32 1.8 Control Flow............................................... 39 1.9 Variables and Scoping.......................................... 48 1.10 Types................................................... 52 1.11 Methods................................................. 66 1.12 Constructors............................................... 72 1.13 Conversion and Promotion........................................ 79 1.14 Modules................................................. 84 1.15 Metaprogramming............................................ 86 1.16 Arrays.................................................. 93 1.17 Sparse Matrices............................................. 99 1.18 Parallel Computing............................................ 100 1.19 Running External Programs....................................... 105 1.20 Calling C and Fortran Code....................................... 111 1.21 Performance Tips............................................. 115 2 The Julia Standard Library 119 2.1 Built-ins................................................. 119 2.2 Built-in Modules............................................. 160 2.3 Extras................................................... 162 2.4 Math & Numerical............................................ 187 3 Available Packages 205 3.1 ArgParse................................................. 205 3.2 Benchmark................................................ 205 3.3 BinDeps................................................. 206 3.4 BloomFilters............................................... 206 3.5 Cairo................................................... 206 3.6 Calculus................................................. 207 3.7 Calendar................................................. 207 3.8 Clp.................................................... 207 i 3.9 Clustering................................................ 208 3.10 CoinMP.................................................. 208 3.11 Color................................................... 208 3.12 Compose................................................. 209 3.13 ContinuedFractions............................................ 209 3.14 DataFrames................................................ 209 3.15 Debug................................................... 210 3.16 DecisionTree............................................... 210 3.17 Devectorize................................................ 210 3.18 DimensionalityReduction........................................ 211 3.19 Distance................................................. 211 3.20 Distributions............................................... 211 3.21 Example................................................. 212 3.22 FITSIO.................................................. 212 3.23 FastaRead................................................ 212 3.24 FileFind.................................................. 213 3.25 GLM................................................... 213 3.26 GLPK................................................... 213 3.27 GLUT.................................................. 214 3.28 Gadfly.................................................. 214 3.29 GetC................................................... 214 3.30 Graphs.................................................. 215 3.31 Grid.................................................... 215 3.32 Gtk.................................................... 215 3.33 HDF5................................................... 216 3.34 HTTP................................................... 216 3.35 Hadamard................................................ 216 3.36 HypothesisTests............................................. 217 3.37 ICU.................................................... 217 3.38 IniFile.................................................. 217 3.39 Iterators.................................................. 218 3.40 Ito..................................................... 218 3.41 JSON................................................... 218 3.42 Jyacas................................................... 219 3.43 KLDivergence.............................................. 219 3.44 LM.................................................... 219 3.45 Languages................................................ 220 3.46 LinProgGLPK.............................................. 220 3.47 Loss.................................................... 220 3.48 MAT................................................... 221 3.49 MCMC.................................................. 221 3.50 MLBase................................................. 221 3.51 MarketTechnicals............................................. 222 3.52 MathProg................................................. 222 3.53 Mongrel2................................................. 222 3.54 Mustache................................................. 223 3.55 NHST................................................... 223 3.56 Named.................................................. 223 3.57 ODBC.................................................. 224 3.58 OpenGL................................................. 224 3.59 Optim................................................... 224 3.60 Options.................................................. 225 3.61 PLX.................................................... 225 3.62 PatternDispatch.............................................. 225 ii 3.63 Profile.................................................. 226 3.64 ProjectTemplate............................................. 226 3.65 RDatasets................................................. 226 3.66 Resampling................................................ 227 3.67 Rif.................................................... 227 3.68 SDL.................................................... 227 3.69 Seq.................................................... 228 3.70 Sims................................................... 228 3.71 Stats................................................... 228 3.72 SymbolicLP............................................... 229 3.73 TextAnalysis............................................... 229 3.74 TextWrap................................................. 229 3.75 TimeSeries................................................ 230 3.76 Tk..................................................... 230 3.77 Trie.................................................... 230 3.78 UTF16.................................................. 231 3.79 WAV................................................... 231 3.80 Winston.................................................. 231 3.81 ZMQ................................................... 232 3.82 kNN................................................... 232 4 Indices and tables 233 Python Module Index 235 Python Module Index 237 iii iv CHAPTER 1 The Julia Manual Release devel Date September 08, 2014 1.1 Introduction Scientific computing has traditionally required the highest performance, yet domain experts have largely moved to slower dynamic languages for daily work. We believe there are many good reasons to prefer dynamic languages for these applications, and we do not expect their use to diminish. Fortunately, modern language design and compiler techniques make it possible to mostly eliminate the performance trade-off and provide a single environment productive enough for prototyping and efficient enough for deploying performance-intensive applications. The Julia programming language fills this role: it is a flexible dynamic language, appropriate for scientific and numerical computing, with performance comparable to traditional statically-typed languages. Julia features optional typing, multiple dispatch, and good performance, achieved using type inference and just-in-time (JIT) compilation, implemented using LLVM. It is multi-paradigm, combining features of imperative, functional, and object-oriented programming. The syntax of Julia is similar to MATLAB® and consequently MATLAB programmers should feel immediately comfortable with Julia. While MATLAB is quite effective for prototyping and exploring numerical linear algebra, it has limitations for programming tasks outside of this relatively narrow scope. Julia keeps MATLAB’s ease and expressiveness for high-level numerical computing, but transcends its general programming limitations. To achieve this, Julia builds upon the lineage of mathematical programming languages, but also borrows much from popular dynamic languages, including Lisp, Perl, Python, Lua, and Ruby. The most significant departures of Julia from typical dynamic languages are: • The core language imposes very little; the standard library is written in Julia itself, including primitive operations like integer arithmetic • A rich language of types for constructing and describing objects, that can also optionally be used to make type declarations • The ability to define function behavior across many combinations
Load more

Recommended publications
  • A Note on Random Number Generation
    A note on random number generation Christophe Dutang and Diethelm Wuertz September 2009 1 1 INTRODUCTION 2 \Nothing in Nature is random. number generation. By \random numbers", we a thing appears random only through mean random variates of the uniform U(0; 1) the incompleteness of our knowledge." distribution. More complex distributions can Spinoza, Ethics I1. be generated with uniform variates and rejection or inversion methods. Pseudo random number generation aims to seem random whereas quasi random number generation aims to be determin- istic but well equidistributed. 1 Introduction Those familiars with algorithms such as linear congruential generation, Mersenne-Twister type algorithms, and low discrepancy sequences should Random simulation has long been a very popular go directly to the next section. and well studied field of mathematics. There exists a wide range of applications in biology, finance, insurance, physics and many others. So 2.1 Pseudo random generation simulations of random numbers are crucial. In this note, we describe the most random number algorithms At the beginning of the nineties, there was no state-of-the-art algorithms to generate pseudo Let us recall the only things, that are truly ran- random numbers. And the article of Park & dom, are the measurement of physical phenomena Miller (1988) entitled Random generators: good such as thermal noises of semiconductor chips or ones are hard to find is a clear proof. radioactive sources2. Despite this fact, most users thought the rand The only way to simulate some randomness function they used was good, because of a short on computers are carried out by deterministic period and a term to term dependence.
    [Show full text]
  • Red Hat Enterprise Linux 8 Security Hardening
    Red Hat Enterprise Linux 8 Security hardening Securing Red Hat Enterprise Linux 8 Last Updated: 2021-09-06 Red Hat Enterprise Linux 8 Security hardening Securing Red Hat Enterprise Linux 8 Legal Notice Copyright © 2021 Red Hat, Inc. The text of and illustrations in this document are licensed by Red Hat under a Creative Commons Attribution–Share Alike 3.0 Unported license ("CC-BY-SA"). An explanation of CC-BY-SA is available at http://creativecommons.org/licenses/by-sa/3.0/ . In accordance with CC-BY-SA, if you distribute this document or an adaptation of it, you must provide the URL for the original version. Red Hat, as the licensor of this document, waives the right to enforce, and agrees not to assert, Section 4d of CC-BY-SA to the fullest extent permitted by applicable law. Red Hat, Red Hat Enterprise Linux, the Shadowman logo, the Red Hat logo, JBoss, OpenShift, Fedora, the Infinity logo, and RHCE are trademarks of Red Hat, Inc., registered in the United States and other countries. Linux ® is the registered trademark of Linus Torvalds in the United States and other countries. Java ® is a registered trademark of Oracle and/or its affiliates. XFS ® is a trademark of Silicon Graphics International Corp. or its subsidiaries in the United States and/or other countries. MySQL ® is a registered trademark of MySQL AB in the United States, the European Union and other countries. Node.js ® is an official trademark of Joyent. Red Hat is not formally related to or endorsed by the official Joyent Node.js open source or commercial project.
    [Show full text]
  • Package 'Randtoolbox'
    Package ‘randtoolbox’ January 31, 2020 Type Package Title Toolbox for Pseudo and Quasi Random Number Generation and Random Generator Tests Version 1.30.1 Author R port by Yohan Chalabi, Christophe Dutang, Petr Savicky and Di- ethelm Wuertz with some underlying C codes of (i) the SFMT algorithm from M. Mat- sumoto and M. Saito, (ii) the Knuth-TAOCP RNG from D. Knuth. Maintainer Christophe Dutang <[email protected]> Description Provides (1) pseudo random generators - general linear congruential generators, multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister algorithm and WELL generators); (2) quasi random generators - the Torus algorithm, the Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some generator tests - the gap test, the serial test, the poker test. See e.g. Gentle (2003) <doi:10.1007/b97336>. The package can be provided without the rngWELL dependency on demand. Take a look at the Distribution task view of types and tests of random number generators. Version in Memoriam of Diethelm and Barbara Wuertz. Depends rngWELL (>= 0.10-1) License BSD_3_clause + file LICENSE NeedsCompilation yes Repository CRAN Date/Publication 2020-01-31 10:17:00 UTC R topics documented: randtoolbox-package . .2 auxiliary . .3 coll.test . .4 coll.test.sparse . .6 freq.test . .8 gap.test . .9 get.primes . 11 1 2 randtoolbox-package getWELLState . 12 order.test . 12 poker.test . 14 pseudoRNG . 16 quasiRNG . 22 rngWELLScriptR . 26 runifInterface . 27 serial.test . 29 soboltestfunctions . 31 Index 33 randtoolbox-package General remarks on toolbox for pseudo and quasi random number generation Description The randtoolbox-package started in 2007 during an ISFA (France) working group.
    [Show full text]
  • Scribble As Preprocessor
    Scribble as Preprocessor Version 8.2.0.8 Matthew Flatt and Eli Barzilay September 25, 2021 The scribble/text and scribble/html languages act as “preprocessor” languages for generating text or HTML. These preprocessor languages use the same @ syntax as the main Scribble tool (see Scribble: The Racket Documentation Tool), but instead of working in terms of a document abstraction that can be rendered to text and HTML (and other formats), the preprocessor languages work in a way that is more specific to the target formats. 1 Contents 1 Text Generation 3 1.1 Writing Text Files . .3 1.2 Defining Functions and More . .7 1.3 Using Printouts . .9 1.4 Indentation in Preprocessed output . 11 1.5 Using External Files . 16 1.6 Text Generation Functions . 19 2 HTML Generation 23 2.1 Generating HTML Strings . 23 2.1.1 Other HTML elements . 30 2.2 Generating XML Strings . 32 2.3 HTML Resources . 36 Index 39 Index 39 2 1 Text Generation #lang scribble/text package: scribble-text-lib The scribble/text language provides everything from racket/base, racket/promise, racket/list, and racket/string, but with additions and a changed treatment of the module top level to make it suitable as for text generation or a preprocessor language: • The language uses read-syntax-inside to read the body of the module, similar to §6.7 “Document Reader”. This means that by default, all text is read in as Racket strings; and @-forms can be used to use Racket functions and expression escapes. • Values of expressions are printed with a custom output function.
    [Show full text]
  • C Constants and Literals Integer Literals Floating-Point Literals
    C Constants and Literals The constants refer to fixed values that the program may not alter during its execution. These fixed values are also called literals. Constants can be of any of the basic data types like an integer constant, a floating constant, a character constant, or a string literal. There are also enumeration constants as well. The constants are treated just like regular variables except that their values cannot be modified after their definition. Integer literals An integer literal can be a decimal, octal, or hexadecimal constant. A prefix specifies the base or radix: 0x or 0X for hexadecimal, 0 for octal, and nothing for decimal. An integer literal can also have a suffix that is a combination of U and L, for unsigned and long, respectively. The suffix can be uppercase or lowercase and can be in any order. Here are some examples of integer literals: Floating-point literals A floating-point literal has an integer part, a decimal point, a fractional part, and an exponent part. You can represent floating point literals either in decimal form or exponential form. While representing using decimal form, you must include the decimal point, the exponent, or both and while representing using exponential form, you must include the integer part, the fractional part, or both. The signed exponent is introduced by e or E. Here are some examples of floating-point literals: 1 | P a g e Character constants Character literals are enclosed in single quotes, e.g., 'x' and can be stored in a simple variable of char type. A character literal can be a plain character (e.g., 'x'), an escape sequence (e.g., '\t'), or a universal character (e.g., '\u02C0').
    [Show full text]
  • Section “Common Predefined Macros” in the C Preprocessor
    The C Preprocessor For gcc version 12.0.0 (pre-release) (GCC) Richard M. Stallman, Zachary Weinberg Copyright c 1987-2021 Free Software Foundation, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation. A copy of the license is included in the section entitled \GNU Free Documentation License". This manual contains no Invariant Sections. The Front-Cover Texts are (a) (see below), and the Back-Cover Texts are (b) (see below). (a) The FSF's Front-Cover Text is: A GNU Manual (b) The FSF's Back-Cover Text is: You have freedom to copy and modify this GNU Manual, like GNU software. Copies published by the Free Software Foundation raise funds for GNU development. i Table of Contents 1 Overview :::::::::::::::::::::::::::::::::::::::: 1 1.1 Character sets:::::::::::::::::::::::::::::::::::::::::::::::::: 1 1.2 Initial processing ::::::::::::::::::::::::::::::::::::::::::::::: 2 1.3 Tokenization ::::::::::::::::::::::::::::::::::::::::::::::::::: 4 1.4 The preprocessing language :::::::::::::::::::::::::::::::::::: 6 2 Header Files::::::::::::::::::::::::::::::::::::: 7 2.1 Include Syntax ::::::::::::::::::::::::::::::::::::::::::::::::: 7 2.2 Include Operation :::::::::::::::::::::::::::::::::::::::::::::: 8 2.3 Search Path :::::::::::::::::::::::::::::::::::::::::::::::::::: 9 2.4 Once-Only Headers::::::::::::::::::::::::::::::::::::::::::::: 9 2.5 Alternatives to Wrapper #ifndef ::::::::::::::::::::::::::::::
    [Show full text]
  • Julia: a Fresh Approach to Numerical Computing
    RSDG @ UCL Julia: A Fresh Approach to Numerical Computing Mosè Giordano @giordano [email protected] Knowledge Quarter Codes Tech Social October 16, 2019 Julia’s Facts v1.0.0 released in 2018 at UCL • Development started in 2009 at MIT, first • public release in 2012 Julia co-creators won the 2019 James H. • Wilkinson Prize for Numerical Software Julia adoption is growing rapidly in • numerical optimisation, differential equations, machine learning, differentiable programming It is used and taught in several universities • (https://julialang.org/teaching/) Mosè Giordano (RSDG @ UCL) Julia: A Fresh Approach to Numerical Computing October 16, 2019 2 / 29 Julia on Nature Nature 572, 141-142 (2019). doi: 10.1038/d41586-019-02310-3 Mosè Giordano (RSDG @ UCL) Julia: A Fresh Approach to Numerical Computing October 16, 2019 3 / 29 Solving the Two-Language Problem: Julia Multiple dispatch • Dynamic type system • Good performance, approaching that of statically-compiled languages • JIT-compiled scripts • User-defined types are as fast and compact as built-ins • Lisp-like macros and other metaprogramming facilities • No need to vectorise: for loops are fast • Garbage collection: no manual memory management • Interactive shell (REPL) for exploratory work • Call C and Fortran functions directly: no wrappers or special APIs • Call Python functions: use the PyCall package • Designed for parallelism and distributed computation • Mosè Giordano (RSDG @ UCL) Julia: A Fresh Approach to Numerical Computing October 16, 2019 4 / 29 Multiple Dispatch using DifferentialEquations
    [Show full text]
  • CIS Ubuntu Linux 18.04 LTS Benchmark
    CIS Ubuntu Linux 18.04 LTS Benchmark v1.0.0 - 08-13-2018 Terms of Use Please see the below link for our current terms of use: https://www.cisecurity.org/cis-securesuite/cis-securesuite-membership-terms-of-use/ 1 | P a g e Table of Contents Terms of Use ........................................................................................................................................................... 1 Overview ............................................................................................................................................................... 12 Intended Audience ........................................................................................................................................ 12 Consensus Guidance ..................................................................................................................................... 13 Typographical Conventions ...................................................................................................................... 14 Scoring Information ..................................................................................................................................... 14 Profile Definitions ......................................................................................................................................... 15 Acknowledgements ...................................................................................................................................... 17 Recommendations ............................................................................................................................................
    [Show full text]
  • Gretl User's Guide
    Gretl User’s Guide Gnu Regression, Econometrics and Time-series Allin Cottrell Department of Economics Wake Forest university Riccardo “Jack” Lucchetti Dipartimento di Economia Università Politecnica delle Marche December, 2008 Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation (see http://www.gnu.org/licenses/fdl.html). Contents 1 Introduction 1 1.1 Features at a glance ......................................... 1 1.2 Acknowledgements ......................................... 1 1.3 Installing the programs ....................................... 2 I Running the program 4 2 Getting started 5 2.1 Let’s run a regression ........................................ 5 2.2 Estimation output .......................................... 7 2.3 The main window menus ...................................... 8 2.4 Keyboard shortcuts ......................................... 11 2.5 The gretl toolbar ........................................... 11 3 Modes of working 13 3.1 Command scripts ........................................... 13 3.2 Saving script objects ......................................... 15 3.3 The gretl console ........................................... 15 3.4 The Session concept ......................................... 16 4 Data files 19 4.1 Native format ............................................. 19 4.2 Other data file formats ....................................... 19 4.3 Binary databases ..........................................
    [Show full text]
  • PYTHON NOTES What Is Python?
    PYTHON NOTES What is Python? Python is a popular programming language. It was created in 1991 by Guido van Rossum. It is used for: web development (server-side), software development, mathematics, system scripting. What can Python do? Python can be used on a server to create web applications. Python can connect to database systems. It can also read and modify files. Python can be used to handle big data and perform complex mathematics. Python can be used for production-ready software development. Why Python? Python works on different platforms (Windows, Mac, Linux, Raspberry Pi, etc). Python has a simple syntax similar to the English language. Python has syntax that allows developers to write programs with fewer lines than some other programming languages. Python runs on an interpreter system, meaning that code can be executed as soon as it is written. This means that prototyping can be very quick. Good to know The most recent major version of Python is Python 3, which we shall be using in this tutorial. However, Python 2, although not being updated with anything other than security updates, is still quite popular. Python Syntax compared to other programming languages Python was designed to for readability, and has some similarities to the English language with influence from mathematics. Python uses new lines to complete a command, as opposed to other programming languages which often use semicolons or parentheses. Python relies on indentation, using whitespace, to define scope; such as the scope of loops, functions and classes. Other programming languages often use curly-brackets for this purpose. Python Install Many PCs and Macs will have python already installed.
    [Show full text]
  • Variables and Calculations
    ¡ ¢ £ ¤ ¥ ¢ ¤ ¦ § ¨ © © § ¦ © § © ¦ £ £ © § ! 3 VARIABLES AND CALCULATIONS Now you’re ready to learn your first ele- ments of Python and start learning how to solve programming problems. Although programming languages have myriad features, the core parts of any programming language are the instructions that perform numerical calculations. In this chapter, we’ll explore how math is performed in Python programs and learn how to solve some prob- lems using only mathematical operations. ¡ ¢ £ ¤ ¥ ¢ ¤ ¦ § ¨ © © § ¦ © § © ¦ £ £ © § ! Sample Program Let’s start by looking at a very simple problem and its Python solution. PROBLEM: THE TOTAL COST OF TOOTHPASTE A store sells toothpaste at $1.76 per tube. Sales tax is 8 percent. For a user-specified number of tubes, display the cost of the toothpaste, showing the subtotal, sales tax, and total, including tax. First I’ll show you a program that solves this problem: toothpaste.py tube_count = int(input("How many tubes to buy: ")) toothpaste_cost = 1.76 subtotal = toothpaste_cost * tube_count sales_tax_rate = 0.08 sales_tax = subtotal * sales_tax_rate total = subtotal + sales_tax print("Toothpaste subtotal: $", subtotal, sep = "") print("Tax: $", sales_tax, sep = "") print("Total is $", total, " including tax.", sep = ") Parts of this program may make intuitive sense to you already; you know how you would answer the question using a calculator and a scratch pad, so you know that the program must be doing something similar. Over the next few pages, you’ll learn exactly what’s going on in these lines of code. For now, enter this program into your Python editor exactly as shown and save it with the required .py extension. Run the program several times with different responses to the question to verify that the program works.
    [Show full text]
  • An Introduction to Programming in Fortran 90
    Guide 138 Version 3.0 An introduction to programming in Fortran 90 This guide provides an introduction to computer programming in the Fortran 90 programming language. The elements of programming are introduced in the context of Fortran 90 and a series of examples and exercises is used to illustrate their use. The aim of the course is to provide sufficient knowledge of programming and Fortran 90 to write straightforward programs. The course is designed for those with little or no previous programming experience, but you will need to be able to work in Linux and use a Linux text editor. Document code: Guide 138 Title: An introduction to programming in Fortran 90 Version: 3.0 Date: 03/10/2012 Produced by: University of Durham Computing and Information Services This document was based on a Fortran 77 course written in the Department of Physics, University of Durham. Copyright © 2012 University of Durham Computing and Information Services Conventions: In this document, the following conventions are used: A bold typewriter font is used to represent the actual characters you type at the keyboard. A slanted typewriter font is used for items such as filenames which you should replace with particular instances. A typewriter font is used for what you see on the screen. A bold font is used to indicate named keys on the keyboard, for example, Esc and Enter, represent the keys marked Esc and Enter, respectively. Where two keys are separated by a forward slash (as in Ctrl/B, for example), press and hold down the first key (Ctrl), tap the second (B), and then release the first key.
    [Show full text]