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In fact, if you donot import InteractiveUtils, you still can use the subtype and supertype function
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| language | contributors | filename | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Julia |
|
learnjulia.jl |
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 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
# 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
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. Only if they contain only ASCII characters can
# they be safely indexed.
ascii("This is a string")[1]
# => 'T': ASCII/Unicode U+0054 (category Lu: Letter, uppercase)
# Julia indexes from 1
# Otherwise, iterating over strings is recommended (map, for loops, etc).
# $ can be used for string interpolation:
"2 + 2 = $(2 + 2)" # => "2 + 2 = 4"
# You can put any Julia expression inside the parentheses.
# Another way to format strings is the printf macro from the stdlib Printf.
using Printf
@printf "%d is less than %f\n" 4.5 5.3 # => 5 is less than 5.300000
# Printing is easy
println("I'm Julia. Nice to meet you!") # => I'm Julia. Nice to meet you!
# String can be compared lexicographically
"good" > "bye" # => true
"good" == "good" # => true
"1 + 2 = 3" == "1 + 2 = $(1 + 2)" # => true
####################################################
## 2. Variables and Collections
####################################################
# You don't declare variables before assigning to them.
some_var = 5 # => 5
some_var # => 5
# Accessing a previously unassigned variable is an error
try
some_other_var # => ERROR: UndefVarError: some_other_var 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
☃ = 8 # => 8
# These are especially handy for mathematical notation
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!
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
empty_dict = Dict() # => Dict{Any,Any} with 0 entries
# You can create a dictionary using a literal
filled_dict = Dict("one" => 1, "two" => 2, "three" => 3)
# => Dict{String,Int64} with 3 entries:
# => "two" => 2, "one" => 1, "three" => 3
# Look up values with []
filled_dict["one"] # => 1
# Get all keys
keys(filled_dict)
# => 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(filled_dict)
# => 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), filled_dict) # => true
in(("two" => 3), filled_dict) # => false
haskey(filled_dict, "one") # => true
haskey(filled_dict, 1) # => false
# Trying to look up a non-existent key will raise an error
try
filled_dict["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, default_value)
get(filled_dict, "one", 4) # => 1
get(filled_dict, "four", 4) # => 4
# Use Sets to represent collections of unordered, unique values
empty_set = Set() # => Set(Any[])
# Initialize a set with values
filled_set = Set([1, 2, 2, 3, 4]) # => Set([4, 2, 3, 1])
# Add more values to a set
push!(filled_set, 5) # => Set([4, 2, 3, 5, 1])
# Check if the values are in the set
in(2, filled_set) # => true
in(10, filled_set) # => false
# There are functions for set intersection, union, and difference.
other_set = Set([3, 4, 5, 6]) # => Set([4, 3, 5, 6])
intersect(filled_set, other_set) # => Set([4, 3, 5])
union(filled_set, other_set) # => 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
some_var = 5
# Here is an if statement. Indentation is not meaningful in Julia.
if some_var > 10
println("some_var is totally bigger than 10.")
elseif some_var < 10 # This elseif clause is optional.
println("some_var is smaller than 10.")
else # The else clause is optional too.
println("some_var 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
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 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(normal_arg, optional_positional_arg=2; keyword_arg="foo")
println("normal arg: $normal_arg")
println("optional arg: $optional_positional_arg")
println("keyword arg: $keyword_arg")
end
# => all_the_args (generic function with 2 methods)
all_the_args(1, 3, keyword_arg=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
mane_color
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
eye_color
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.mane_color)-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]
# ;}
Further Reading
You can get a lot more detail from the Julia Documentation
The best place to get help with Julia is the (very friendly) Discourse forum.