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Julia in a Nutshell

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. Julia provides ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming. 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:

Although one sometimes speaks of dynamic languages as being "typeless", they are definitely not: every object, whether primitive or user-defined, has a type. The lack of type declarations in most dynamic languages, however, means that one cannot instruct the compiler about the types of values, and often cannot explicitly talk about types at all. In static languages, on the other hand, while one can – and usually must – annotate types for the compiler, types exist only at compile time and cannot be manipulated or expressed at run time. In Julia, types are themselves run-time objects, and can also be used to convey information to the compiler.

While the casual programmer need not explicitly use types or multiple dispatch, they are the core unifying features of Julia: functions are defined on different combinations of argument types, and applied by dispatching to the most specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" an operation as in traditional object-oriented dispatch. Operators are just functions with special notation – to extend addition to new user-defined data types, you define new methods for the + function. Existing code then seamlessly applies to the new data types.

Partly because of run-time type inference (augmented by optional type annotations), and partly because of a strong focus on performance from the inception of the project, Julia's computational efficiency exceeds that of other dynamic languages, and even rivals that of statically-compiled languages. For large scale numerical problems, speed always has been, continues to be, and probably always will be crucial: the amount of data being processed has easily kept pace with Moore's Law over the past decades.

Reading

FFI

Notes

From the official docs

Syntax

Semicolons optional. In the REPL, semicolon prevents display of the expression preceding it.

To evalue expressions in a source file: include("file.jl"). To run code from a file non-interactively, use julia script.jl arg1 arg2 arg3. To evaluate a literal string at the command-line, use -e:

$ julia -e 'println(PROGRAM_FILE); for x in ARGS; println(x); end' foo bar

foo
bar

Local variables are mutable by default and do not require declaration before use. Names are case-sensitive. Unicode source.

All variables can be rebound, including predefined values (pi, sqrt, etc). However, if the variable is "in use elsewhere", it prevents the redefinition.

Operators (like (+)) are valid identifiers and can be re-bound.

Variable types

(U)Int8/16/32/64/128, Bool, Float16/32/64.

Supports complex and rational numbers in a manner that looks relatively native. im is the suffix for complex number literals. Rationals are constructed via the // operator. Rational numbers support the usual mathematical operations: 2//3 == 6//9 is true. 3//4 > 2//3 is also true. 2//4 + 1//6 yields 2//3.

Strings are finite sequences of characters (Char: "32-bit primitive type with a special literal representation and appropriate arithmetic behaviors, and which can be converted to a numeric value representing a Unicode code point"). String literals denoted by double quotes or triple quotes: str = "Hello world" or """Contains "quote" characters""". Strings concatenate using the * operator: greet = "Hello"; whom = "world"; greet * ", " * whom * ".\n" yields Hello, world.. Strings also support $-prefixed interpolation (using rounded brackets to group into a single expression if necessary or desired).

REPL

Local value ans is always bound to the result of the most-recent expression. This is not supported when not running in the interactive interpreter/REPL.

Stylistic Conventions

While Julia imposes few restrictions on valid names, it has become useful to adopt the following conventions:

Learn X in Y minutes Quick Intro

Julia is a new homoiconic functional language focused on technical computing. While having the full power of homoiconic macros, first-class functions, and low-level control, Julia is as easy to learn and use as Python.

This is based on Julia version 1.0.0.

# Single line comments start with a hash (pound) symbol.
#= Multiline comments can be written
   by putting '#=' before the text  and '=#'
   after the text. They can also be nested.
=#

####################################################
## 1. Primitive Datatypes and Operators
####################################################

# Everything in Julia is an expression.

# There are several basic types of numbers.
typeof(3)       # => Int64
typeof(3.2)     # => Float64
typeof(2 + 1im) # => Complex{Int64}
typeof(2 // 3)  # => Rational{Int64}

# All of the normal infix operators are available.
1 + 1      # => 2
8 - 1      # => 7
10 * 2     # => 20
35 / 5     # => 7.0
10 / 2     # => 5.0  # dividing integers always results in a Float64
div(5, 2)  # => 2    # for a truncated result, use div
5 \ 35     # => 7.0
2^2        # => 4    # power, not bitwise xor
12 % 10    # => 2

# Enforce precedence with parentheses
(1 + 3) * 2  # => 8

# Julia (unlike Python for instance) has integer under/overflow
10^19      # => -8446744073709551616
# use bigint or floating point to avoid this
big(10)^19 # => 10000000000000000000
1e19       # => 1.0e19
10.0^19    # => 1.0e19

# Bitwise Operators
~2         # => -3 # bitwise not
3 & 5      # => 1  # bitwise and
2 | 4      # => 6  # bitwise or
xor(2, 4)  # => 6  # bitwise xor
2 >>> 1    # => 1  # logical shift right
2 >> 1     # => 1  # arithmetic shift right
2 << 1     # => 4  # logical/arithmetic shift left

# Use the bitstring function to see the binary representation of a number.
bitstring(12345)
# => "0000000000000000000000000000000000000000000000000011000000111001"
bitstring(12345.0)
# => "0100000011001000000111001000000000000000000000000000000000000000"

# Boolean values are primitives
true
false

# Boolean operators
!true   # => false
!false  # => true
1 == 1  # => true
2 == 1  # => false
1 != 1  # => false
2 != 1  # => true
1 < 10  # => true
1 > 10  # => false
2 <= 2  # => true
2 >= 2  # => true
# Comparisons can be chained, like in Python but unlike many other languages
1 < 2 < 3  # => true
2 < 3 < 2  # => false

# Strings are created with "
"This is a string."

# Character literals are written with '
'a'

# Strings are UTF8 encoded, so strings like "π" or "☃" are not directly equivalent
# to an array of single characters.
# Only if they contain only ASCII characters can they be safely indexed.
ascii("This is a string")[1]    # => 'T'
# => 'T': ASCII/Unicode U+0054 (category Lu: Letter, uppercase)
# Beware, Julia indexes everything from 1 (like MATLAB), not 0 (like most languages).
# Otherwise, iterating over strings is recommended (map, for loops, etc).

# String can be compared lexicographically, in dictionnary order:
"good" > "bye"   # => true
"good" == "good" # => true
"1 + 2 = 3" == "1 + 2 = $(1 + 2)" # => true

# $(..) can be used for string interpolation:
"2 + 2 = $(2 + 2)" # => "2 + 2 = 4"
# You can put any Julia expression inside the parentheses.

# Printing is easy
println("I'm Julia. Nice to meet you!")  # => I'm Julia. Nice to meet you!

# Another way to format strings is the printf macro from the stdlib Printf.
using Printf   # this is how you load (or import) a module
@printf "%d is less than %f\n" 4.5 5.3   # => 5 is less than 5.300000


####################################################
## 2. Variables and Collections
####################################################

# You don't declare variables before assigning to them.
someVar = 5  # => 5
someVar      # => 5

# Accessing a previously unassigned variable is an error
try
    someOtherVar  # => ERROR: UndefVarError: someOtherVar not defined
catch e
    println(e)
end

# Variable names start with a letter or underscore.
# After that, you can use letters, digits, underscores, and exclamation points.
SomeOtherVar123! = 6  # => 6

# You can also use certain unicode characters
# here ☃ is a Unicode 'snowman' characters, see http://emojipedia.org/%E2%98%83%EF%B8%8F if it displays wrongly here
☃ = 8  # => 8
# These are especially handy for mathematical notation, like the constant π
2 * π  # => 6.283185307179586

# A note on naming conventions in Julia:
#
# * Word separation can be indicated by underscores ('_'), but use of
#   underscores is discouraged unless the name would be hard to read
#   otherwise.
#
# * Names of Types begin with a capital letter and word separation is shown
#   with CamelCase instead of underscores.
#
# * Names of functions and macros are in lower case, without underscores.
#
# * Functions that modify their inputs have names that end in !. These
#   functions are sometimes called mutating functions or in-place functions.

# Arrays store a sequence of values indexed by integers 1 through n:
a = Int64[] # => 0-element Array{Int64,1}

# 1-dimensional array literals can be written with comma-separated values.
b = [4, 5, 6] # => 3-element Array{Int64,1}: [4, 5, 6]
b = [4; 5; 6] # => 3-element Array{Int64,1}: [4, 5, 6]
b[1]    # => 4
b[end]  # => 6

# 2-dimensional arrays use space-separated values and semicolon-separated rows.
matrix = [1 2; 3 4] # => 2×2 Array{Int64,2}: [1 2; 3 4]

# Arrays of a particular type
b = Int8[4, 5, 6] # => 3-element Array{Int8,1}: [4, 5, 6]

# Add stuff to the end of a list with push! and append!
# By convention, the exclamation mark '!' is appended to names of functions
# that modify their arguments
push!(a, 1)    # => [1]
push!(a, 2)    # => [1,2]
push!(a, 4)    # => [1,2,4]
push!(a, 3)    # => [1,2,4,3]
append!(a, b)  # => [1,2,4,3,4,5,6]

# Remove from the end with pop
pop!(b)  # => 6
b # => [4,5]

# Let's put it back
push!(b, 6)  # => [4,5,6]
b # => [4,5,6]

a[1]  # => 1  # remember that Julia indexes from 1, not 0!

# end is a shorthand for the last index. It can be used in any
# indexing expression
a[end]  # => 6

# we also have popfirst! and pushfirst!
popfirst!(a)  # => 1 
a # => [2,4,3,4,5,6]
pushfirst!(a, 7)  # => [7,2,4,3,4,5,6]
a # => [7,2,4,3,4,5,6]

# Function names that end in exclamations points indicate that they modify
# their argument.
arr = [5,4,6]  # => 3-element Array{Int64,1}: [5,4,6]
sort(arr)      # => [4,5,6]
arr            # => [5,4,6]
sort!(arr)     # => [4,5,6]
arr            # => [4,5,6]

# Looking out of bounds is a BoundsError
try
    a[0] 
    # => ERROR: BoundsError: attempt to access 7-element Array{Int64,1} at 
    # index [0]
    # => Stacktrace:
    # =>  [1] getindex(::Array{Int64,1}, ::Int64) at .\array.jl:731
    # =>  [2] top-level scope at none:0
    # =>  [3] ...
    # => in expression starting at ...\LearnJulia.jl:180
    a[end + 1] 
    # => ERROR: BoundsError: attempt to access 7-element Array{Int64,1} at 
    # index [8]
    # => Stacktrace:
    # =>  [1] getindex(::Array{Int64,1}, ::Int64) at .\array.jl:731
    # =>  [2] top-level scope at none:0
    # =>  [3] ...
    # => in expression starting at ...\LearnJulia.jl:188
catch e
    println(e)
end

# Errors list the line and file they came from, even if it's in the standard
# library. You can look in the folder share/julia inside the julia folder to
# find these files.

# You can initialize arrays from ranges
a = [1:5;]  # => 5-element Array{Int64,1}: [1,2,3,4,5]
a2 = [1:5]  # => 1-element Array{UnitRange{Int64},1}: [1:5]

# You can look at ranges with slice syntax.
a[1:3]    # => [1, 2, 3]
a[2:end]  # => [2, 3, 4, 5]

# Remove elements from an array by index with splice!
arr = [3,4,5]
splice!(arr, 2) # => 4 
arr # => [3,5]

# Concatenate lists with append!
b = [1,2,3]
append!(a, b) # => [1, 2, 3, 4, 5, 1, 2, 3]
a # => [1, 2, 3, 4, 5, 1, 2, 3]

# Check for existence in a list with in
in(1, a)  # => true

# Examine the length with length
length(a)  # => 8

# Tuples are immutable.
tup = (1, 2, 3)  # => (1,2,3)
typeof(tup) # => Tuple{Int64,Int64,Int64}
tup[1] # => 1
try
    tup[1] = 3  
    # => ERROR: MethodError: no method matching 
    # setindex!(::Tuple{Int64,Int64,Int64}, ::Int64, ::Int64)
catch e
    println(e)
end

# Many array functions also work on tuples
length(tup) # => 3
tup[1:2]    # => (1,2)
in(2, tup)  # => true

# You can unpack tuples into variables
a, b, c = (1, 2, 3)  # => (1,2,3)  
a  # => 1
b  # => 2
c  # => 3

# Tuples are created even if you leave out the parentheses
d, e, f = 4, 5, 6  # => (4,5,6)
d  # => 4
e  # => 5
f  # => 6

# A 1-element tuple is distinct from the value it contains
(1,) == 1 # => false
(1) == 1  # => true

# Look how easy it is to swap two values
e, d = d, e  # => (5,4) 
d  # => 5
e  # => 4

# Dictionaries store mappings
emptyDict = Dict()  # => Dict{Any,Any} with 0 entries

# You can create a dictionary using a literal
filledDict = Dict("one" => 1, "two" => 2, "three" => 3)
# => Dict{String,Int64} with 3 entries:
# =>  "two" => 2, "one" => 1, "three" => 3

# Look up values with []
filledDict["one"]  # => 1

# Get all keys
keys(filledDict)
# => Base.KeySet for a Dict{String,Int64} with 3 entries. Keys:
# =>  "two", "one", "three"
# Note - dictionary keys are not sorted or in the order you inserted them.

# Get all values
values(filledDict)
# => Base.ValueIterator for a Dict{String,Int64} with 3 entries. Values: 
# =>  2, 1, 3
# Note - Same as above regarding key ordering.

# Check for existence of keys in a dictionary with in, haskey
in(("one" => 1), filledDict)  # => true
in(("two" => 3), filledDict)  # => false
haskey(filledDict, "one")     # => true
haskey(filledDict, 1)         # => false

# Trying to look up a non-existent key will raise an error
try
    filledDict["four"]  # => ERROR: KeyError: key "four" not found
catch e
    println(e)
end

# Use the get method to avoid that error by providing a default value
# get(dictionary, key, defaultValue)
get(filledDict, "one", 4)   # => 1
get(filledDict, "four", 4)  # => 4

# Use Sets to represent collections of unordered, unique values
emptySet = Set()  # => Set(Any[])
# Initialize a set with values
filledSet = Set([1, 2, 2, 3, 4])  # => Set([4, 2, 3, 1])

# Add more values to a set
push!(filledSet, 5)  # => Set([4, 2, 3, 5, 1])

# Check if the values are in the set
in(2, filledSet)   # => true
in(10, filledSet)  # => false

# There are functions for set intersection, union, and difference.
otherSet = Set([3, 4, 5, 6])         # => Set([4, 3, 5, 6])
intersect(filledSet, otherSet)      # => Set([4, 3, 5])
union(filledSet, otherSet)          # => Set([4, 2, 3, 5, 6, 1])
setdiff(Set([1,2,3,4]), Set([2,3,5])) # => Set([4, 1])

####################################################
## 3. Control Flow
####################################################

# Let's make a variable
someVar = 5

# Here is an if statement. Indentation is not meaningful in Julia.
if someVar > 10
    println("someVar is totally bigger than 10.")
elseif someVar < 10    # This elseif clause is optional.
    println("someVar is smaller than 10.")
else                    # The else clause is optional too.
    println("someVar is indeed 10.")
end
# => prints "some var is smaller than 10"

# For loops iterate over iterables.
# Iterable types include Range, Array, Set, Dict, and AbstractString.
for animal = ["dog", "cat", "mouse"]
    println("$animal is a mammal")
    # You can use $ to interpolate variables or expression into strings.
    # In this special case, no need for parenthesis: $animal and $(animal) give the same
end
# => dog is a mammal
# => cat is a mammal
# => mouse is a mammal

# You can use 'in' instead of '='.
for animal in ["dog", "cat", "mouse"]
    println("$animal is a mammal")
end
# => dog is a mammal
# => cat is a mammal
# => mouse is a mammal

for pair in Dict("dog" => "mammal", "cat" => "mammal", "mouse" => "mammal")
    from, to = pair
    println("$from is a $to")
end
# => mouse is a mammal
# => cat is a mammal
# => dog is a mammal

for (k, v) in Dict("dog" => "mammal", "cat" => "mammal", "mouse" => "mammal")
    println("$k is a $v")
end
# => mouse is a mammal
# => cat is a mammal
# => dog is a mammal

# While loops loop while a condition is true
let x = 0
    while x < 4
        println(x)
        x += 1  # Shorthand for in place increment: x = x + 1
    end
end
# => 0
# => 1
# => 2
# => 3

# Handle exceptions with a try/catch block
try
    error("help")
catch e
    println("caught it $e")
end
# => caught it ErrorException("help")

####################################################
## 4. Functions
####################################################

# The keyword 'function' creates new functions
# function name(arglist)
#   body...
# end
function add(x, y)
    println("x is $x and y is $y")

    # Functions return the value of their last statement
    x + y
end

add(5, 6)
# => x is 5 and y is 6
# => 11

# Compact assignment of functions
f_add(x, y) = x + y  # => f_add (generic function with 1 method)
f_add(3, 4)  # => 7

# Function can also return multiple values as tuple
fn(x, y) = x + y, x - y # => fn (generic function with 1 method)
fn(3, 4)  # => (7, -1)

# You can define functions that take a variable number of
# positional arguments
function varargs(args...)
    return args
    # use the keyword return to return anywhere in the function
end
# => varargs (generic function with 1 method)

varargs(1, 2, 3)  # => (1,2,3)

# The ... is called a splat.
# We just used it in a function definition.
# It can also be used in a function call,
# where it will splat an Array or Tuple's contents into the argument list.
add([5,6]...)  # this is equivalent to add(5,6)

x = (5, 6)  # => (5,6)
add(x...)  # this is equivalent to add(5,6)


# You can define functions with optional positional arguments
function defaults(a, b, x=5, y=6)
    return "$a $b and $x $y"
end
# => defaults (generic function with 3 methods)

defaults('h', 'g')  # => "h g and 5 6"
defaults('h', 'g', 'j')  # => "h g and j 6"
defaults('h', 'g', 'j', 'k')  # => "h g and j k"
try
    defaults('h')  # => ERROR: MethodError: no method matching defaults(::Char)
    defaults()  # => ERROR: MethodError: no method matching defaults()
catch e
    println(e)
end

# You can define functions that take keyword arguments
function keyword_args(;k1=4, name2="hello")  # note the ;
    return Dict("k1" => k1, "name2" => name2)
end
# => keyword_args (generic function with 1 method)

keyword_args(name2="ness")  # => ["name2"=>"ness", "k1"=>4]
keyword_args(k1="mine")     # => ["name2"=>"hello", "k1"=>"mine"]
keyword_args()              # => ["name2"=>"hello", "k1"=>4]

# You can combine all kinds of arguments in the same function
function all_the_args(normalArg, optionalPositionalArg=2; keywordArg="foo")
    println("normal arg: $normalArg")
    println("optional arg: $optionalPositionalArg")
    println("keyword arg: $keywordArg")
end
# => all_the_args (generic function with 2 methods)

all_the_args(1, 3, keywordArg=4)
# => normal arg: 1
# => optional arg: 3
# => keyword arg: 4

# Julia has first class functions
function create_adder(x)
    adder = function (y)
        return x + y
    end
    return adder
end
# => create_adder (generic function with 1 method)

# This is "stabby lambda syntax" for creating anonymous functions
(x -> x > 2)(3)  # => true

# This function is identical to create_adder implementation above.
function create_adder(x)
    y -> x + y
end
# => create_adder (generic function with 1 method)

# You can also name the internal function, if you want
function create_adder(x)
    function adder(y)
        x + y
    end
    adder
end
# => create_adder (generic function with 1 method)

add_10 = create_adder(10) # => (::getfield(Main, Symbol("#adder#11")){Int64}) 
                          # (generic function with 1 method)
add_10(3) # => 13


# There are built-in higher order functions
map(add_10, [1,2,3])  # => [11, 12, 13]
filter(x -> x > 5, [3, 4, 5, 6, 7])  # => [6, 7]

# We can use list comprehensions
[add_10(i) for i = [1, 2, 3]]   # => [11, 12, 13]
[add_10(i) for i in [1, 2, 3]]  # => [11, 12, 13]
[x for x in [3, 4, 5, 6, 7] if x > 5] # => [6, 7]

####################################################
## 5. Types
####################################################

# Julia has a type system.
# Every value has a type; variables do not have types themselves.
# You can use the `typeof` function to get the type of a value.
typeof(5)  # => Int64

# Types are first-class values
typeof(Int64)     # => DataType
typeof(DataType)  # => DataType
# DataType is the type that represents types, including itself.

# Types are used for documentation, optimizations, and dispatch.
# They are not statically checked.

# Users can define types
# They are like records or structs in other languages.
# New types are defined using the `struct` keyword.

# struct Name
#   field::OptionalType
#   ...
# end
struct Tiger
    taillength::Float64
    coatcolor  # not including a type annotation is the same as `::Any`
end

# The default constructor's arguments are the properties
# of the type, in the order they are listed in the definition
tigger = Tiger(3.5, "orange")  # => Tiger(3.5,"orange")

# The type doubles as the constructor function for values of that type
sherekhan = typeof(tigger)(5.6, "fire")  # => Tiger(5.6,"fire")

# These struct-style types are called concrete types
# They can be instantiated, but cannot have subtypes.
# The other kind of types is abstract types.

# abstract Name
abstract type Cat end  # just a name and point in the type hierarchy

# Abstract types cannot be instantiated, but can have subtypes.
# For example, Number is an abstract type
subtypes(Number)  # => 2-element Array{Any,1}:
                  # =>  Complex
                  # =>  Real
subtypes(Cat)  # => 0-element Array{Any,1}

# AbstractString, as the name implies, is also an abstract type
subtypes(AbstractString)  # => 4-element Array{Any,1}:
                          # =>  String
                          # =>  SubString
                          # =>  SubstitutionString
                          # =>  Test.GenericString

# Every type has a super type; use the `supertype` function to get it.
typeof(5) # => Int64
supertype(Int64)    # => Signed
supertype(Signed)   # => Integer
supertype(Integer)  # => Real
supertype(Real)     # => Number
supertype(Number)   # => Any
supertype(supertype(Signed))  # => Real
supertype(Any)      # => Any
# All of these type, except for Int64, are abstract.
typeof("fire")      # => String
supertype(String)   # => AbstractString
# Likewise here with String
supertype(SubString)  # => AbstractString

# <: is the subtyping operator
struct Lion <: Cat  # Lion is a subtype of Cat
    maneColor
    roar::AbstractString
end

# You can define more constructors for your type
# Just define a function of the same name as the type
# and call an existing constructor to get a value of the correct type
Lion(roar::AbstractString) = Lion("green", roar)
# This is an outer constructor because it's outside the type definition

struct Panther <: Cat  # Panther is also a subtype of Cat
    eyeColor
    Panther() = new("green")
    # Panthers will only have this constructor, and no default constructor.
end
# Using inner constructors, like Panther does, gives you control
# over how values of the type can be created.
# When possible, you should use outer constructors rather than inner ones.

####################################################
## 6. Multiple-Dispatch
####################################################

# In Julia, all named functions are generic functions
# This means that they are built up from many small methods
# Each constructor for Lion is a method of the generic function Lion.

# For a non-constructor example, let's make a function meow:

# Definitions for Lion, Panther, Tiger
function meow(animal::Lion)
    animal.roar  # access type properties using dot notation
end

function meow(animal::Panther)
    "grrr"
end

function meow(animal::Tiger)
    "rawwwr"
end

# Testing the meow function
meow(tigger)  # => "rawwwr"
meow(Lion("brown", "ROAAR"))  # => "ROAAR"
meow(Panther()) # => "grrr"

# Review the local type hierarchy
Tiger   <: Cat  # => false
Lion    <: Cat  # => true
Panther <: Cat  # => true

# Defining a function that takes Cats
function pet_cat(cat::Cat)
    println("The cat says $(meow(cat))")
end
# => pet_cat (generic function with 1 method)

pet_cat(Lion("42")) # => The cat says 42
try
    pet_cat(tigger) # => ERROR: MethodError: no method matching pet_cat(::Tiger)
catch e
    println(e)
end

# In OO languages, single dispatch is common;
# this means that the method is picked based on the type of the first argument.
# In Julia, all of the argument types contribute to selecting the best method.

# Let's define a function with more arguments, so we can see the difference
function fight(t::Tiger, c::Cat)
    println("The $(t.coatcolor) tiger wins!")
end
# => fight (generic function with 1 method)

fight(tigger, Panther())  # => The orange tiger wins!
fight(tigger, Lion("ROAR")) # => The orange tiger wins!

# Let's change the behavior when the Cat is specifically a Lion
fight(t::Tiger, l::Lion) = println("The $(l.maneColor)-maned lion wins!")
# => fight (generic function with 2 methods)

fight(tigger, Panther())  # => The orange tiger wins!
fight(tigger, Lion("ROAR")) # => The green-maned lion wins!

# We don't need a Tiger in order to fight
fight(l::Lion, c::Cat) = println("The victorious cat says $(meow(c))")
# => fight (generic function with 3 methods)

fight(Lion("balooga!"), Panther())  # => The victorious cat says grrr
try
    fight(Panther(), Lion("RAWR"))  
    # => ERROR: MethodError: no method matching fight(::Panther, ::Lion)
    # => Closest candidates are:
    # =>   fight(::Tiger, ::Lion) at ...
    # =>   fight(::Tiger, ::Cat) at ...
    # =>   fight(::Lion, ::Cat) at ...
    # => ...
catch e
    println(e)
end

# Also let the cat go first
fight(c::Cat, l::Lion) = println("The cat beats the Lion")
# => fight (generic function with 4 methods)

# This warning is because it's unclear which fight will be called in:
try
    fight(Lion("RAR"), Lion("brown", "rarrr"))
    # => ERROR: MethodError: fight(::Lion, ::Lion) is ambiguous. Candidates:
    # =>   fight(c::Cat, l::Lion) in Main at ...
    # =>   fight(l::Lion, c::Cat) in Main at ...
    # => Possible fix, define
    # =>   fight(::Lion, ::Lion)
    # => ...
catch e
    println(e)
end
# The result may be different in other versions of Julia

fight(l::Lion, l2::Lion) = println("The lions come to a tie") 
# => fight (generic function with 5 methods)
fight(Lion("RAR"), Lion("brown", "rarrr"))  # => The lions come to a tie


# Under the hood
# You can take a look at the llvm  and the assembly code generated.

square_area(l) = l * l  # square_area (generic function with 1 method)

square_area(5)  # => 25

# What happens when we feed square_area an integer?
code_native(square_area, (Int32,), syntax = :intel)
    #         .text
    # ; Function square_area {
    # ; Location: REPL[116]:1       # Prologue
    #         push    rbp
    #         mov     rbp, rsp
    # ; Function *; {
    # ; Location: int.jl:54
    #         imul    ecx, ecx      # Square l and store the result in ECX
    # ;}
    #         mov     eax, ecx
    #         pop     rbp           # Restore old base pointer
    #         ret                   # Result will still be in EAX
    #         nop     dword ptr [rax + rax]
    # ;}

code_native(square_area, (Float32,), syntax = :intel)
    #         .text
    # ; Function square_area {
    # ; Location: REPL[116]:1
    #         push    rbp
    #         mov     rbp, rsp
    # ; Function *; {
    # ; Location: float.jl:398
    #         vmulss  xmm0, xmm0, xmm0  # Scalar single precision multiply (AVX)
    # ;}
    #         pop     rbp
    #         ret
    #         nop     word ptr [rax + rax]
    # ;}

code_native(square_area, (Float64,), syntax = :intel)
    #         .text
    # ; Function square_area {
    # ; Location: REPL[116]:1
    #         push    rbp
    #         mov     rbp, rsp
    # ; Function *; {
    # ; Location: float.jl:399
    #         vmulsd  xmm0, xmm0, xmm0  # Scalar double precision multiply (AVX)
    # ;}
    #         pop     rbp
    #         ret
    #         nop     word ptr [rax + rax]
    # ;}

# Note that julia will use floating point instructions if any of the
# arguments are floats.
# Let's calculate the area of a circle
circle_area(r) = pi * r * r     # circle_area (generic function with 1 method)
circle_area(5)  # 78.53981633974483

code_native(circle_area, (Int32,), syntax = :intel)
    #         .text
    # ; Function circle_area {
    # ; Location: REPL[121]:1
    #         push    rbp
    #         mov     rbp, rsp
    # ; Function *; {
    # ; Location: operators.jl:502
    # ; Function *; {
    # ; Location: promotion.jl:314
    # ; Function promote; {
    # ; Location: promotion.jl:284
    # ; Function _promote; {
    # ; Location: promotion.jl:261
    # ; Function convert; {
    # ; Location: number.jl:7
    # ; Function Type; {
    # ; Location: float.jl:60
    #         vcvtsi2sd       xmm0, xmm0, ecx     # Load integer (r) from memory
    #         movabs  rax, 497710928              # Load pi
    # ;}}}}}
    # ; Function *; {
    # ; Location: float.jl:399
    #         vmulsd  xmm1, xmm0, qword ptr [rax] # pi * r
    #         vmulsd  xmm0, xmm1, xmm0            # (pi * r) * r
    # ;}}
    #         pop     rbp
    #         ret
    #         nop     dword ptr [rax]
    # ;}

code_native(circle_area, (Float64,), syntax = :intel)
    #         .text
    # ; Function circle_area {
    # ; Location: REPL[121]:1
    #         push    rbp
    #         mov     rbp, rsp
    #         movabs  rax, 497711048
    # ; Function *; {
    # ; Location: operators.jl:502
    # ; Function *; {
    # ; Location: promotion.jl:314
    # ; Function *; {
    # ; Location: float.jl:399
    #         vmulsd  xmm1, xmm0, qword ptr [rax]
    # ;}}}
    # ; Function *; {
    # ; Location: float.jl:399
    #         vmulsd  xmm0, xmm1, xmm0
    # ;}
    #         pop     rbp
    #         ret
    #         nop     dword ptr [rax + rax]
    # ;}

Interesting libraries

https://github.com/JuliaLang/julia
Julia is a high-level, high-performance, dynamic programming language. While it is a general-purpose language and can be used to write any application, many of its features are well suited for numerical analysis and computational science.


Tags: language   functional   dynamic   native  

Last modified 26 May 2024