====== 学习Julia：一个Demo涵盖绝大部分语法 ======
Source: https://learnxinyminutes.com/
===== 英文版 =====
# 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]
# ;}
===== 中文版 =====
# 单行注释只需要一个井号「#」
#= 多行注释
只需要以「#=」开始「=#」结束
还可以嵌套.
=#
####################################################
## 1. 原始类型与操作符
####################################################
# Julia 中一切皆为表达式
# 这是一些基本数字类型
typeof(3) # => Int64
typeof(3.2) # => Float64
typeof(2 + 1im) # => Complex{Int64}
typeof(2 // 3) # => Rational{Int64}
# 支持所有的普通中缀操作符
1 + 1 # => 2
8 - 1 # => 7
10 * 2 # => 20
35 / 5 # => 7.0
10 / 2 # => 5.0 # 整数除法总是返回浮点数
div(5, 2) # => 2 # 使用 div 可以获得整除的结果
5 \ 35 # => 7.0
2^2 # => 4 # 幂运算，不是异或 (xor)
12 % 10 # => 2
# 用括号提高优先级
(1 + 3) * 2 # => 8
# 位操作符
~2 # => -3 # 按位非 (not)
3 & 5 # => 1 # 按位与 (and)
2 | 4 # => 6 # 按位或 (or)
xor(2, 4) # => 6 # 按位异或 (xor)
2 >>> 1 # => 1 # 逻辑右移
2 >> 1 # => 1 # 算术右移
2 << 1 # => 4 # 逻辑/算术左移
# 可以用函数 bitstring 查看二进制数。
bitstring(12345)
# => "0000000000000000000000000000000000000000000000000011000000111001"
bitstring(12345.0)
# => "0100000011001000000111001000000000000000000000000000000000000000"
# 布尔值是原始类型
true
false
# 布尔操作符
!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
# 链式比较
1 < 2 < 3 # => true
2 < 3 < 2 # => false
# 字符串可以由「"」创建
"This is a string."
# 字符字面量可用「'」创建
'a'
# 字符串使用 UTF-8 编码
# 可以像取数组取值一样用 index 取出对应字符
ascii("This is a string")[1]
# => 'T': ASCII/Unicode U+0054 (category Lu: Letter, uppercase)
# Julia 的 index 从 1 开始 :(
# 但只有在字符串仅由 ASCII 字符构成时，字符串才能够被安全的引索
# 因此建议使用遍历器 (map, for loops, 等)
# $ 可用于字符插值:
"2 + 2 = $(2 + 2)" # => "2 + 2 = 4"
# 可以将任何 Julia 表达式放入括号。
# 另一种输出格式化字符串的方法是使用标准库 Printf 中的 Printf 宏
using Printf
@printf "%d is less than %f\n" 4.5 5.3 # => 5 is less than 5.300000
# 打印字符串很容易
println("I'm Julia. Nice to meet you!") # => I'm Julia. Nice to meet you!
# 字符串可以按字典序进行比较
"good" > "bye" # => true
"good" == "good" # => true
"1 + 2 = 3" == "1 + 2 = $(1 + 2)" # => true
####################################################
## 2. 变量与集合
####################################################
# 给变量赋值就是声明变量
some_var = 5 # => 5
some_var # => 5
# 访问未声明变量会抛出异常
try
some_other_var # => ERROR: UndefVarError: some_other_var not defined
catch e
println(e)
end
# 变量名必须以下划线或字母开头
# 之后任何字母，数字，下划线，叹号都是合法的。
SomeOtherVar123! = 6 # => 6
# 甚至可以用 unicode 字符
☃ = 8 # => 8
# 用数学符号非常方便
2 * π # => 6.283185307179586
# 注意 Julia 的命名规约:
#
# * 名称可以用下划线「_」分割。
# 不过一般不推荐使用下划线，除非不用变量名就会变得难于理解
#
# * 类型名以大写字母开头，单词以 CamelCase 方式连接，无下划线。
#
# * 函数与宏的名字小写，无下划线。
#
# * 会改变输入的函数名末位为「!」。
# 这类函数有时被称为 mutating functions 或 in-place functions.
# 数组存储一列值，index 从 1 开始
a = Int64[] # => 0-element Array{Int64,1}
# 一维数组可以以逗号分隔值的方式声明
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
# 二维数组以分号分隔维度
matrix = [1 2; 3 4] # => 2×2 Array{Int64,2}: [1 2; 3 4]
# 指定数组的类型
b = Int8[4, 5, 6] # => 3-element Array{Int8,1}: [4, 5, 6]
# 使用 push! 和 append! 往数组末尾添加元素
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]
# 用 pop 弹出尾部的元素
pop!(b) # => 6
b # => [4,5]
# 再放回去
push!(b, 6) # => [4,5,6]
b # => [4,5,6]
a[1] # => 1 # 永远记住 Julia 的引索从 1 开始！而不是 0！
# 用 end 可以直接取到最后索引。它可以用在任何索引表达式中
a[end] # => 6
# 数组还支持 popfirst! 和 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]
# 以叹号结尾的函数名表示它会改变参数的值
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]
# 数组越界会抛出 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:203
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:211
catch e
println(e)
end
# 报错时错误会指出出错的文件位置以及行号，标准库也一样
# 你可以在 Julia 安装目录下的 share/julia 文件夹里找到这些标准库
# 可以用 range 初始化数组
a = [1:5;] # => 5-element Array{Int64,1}: [1,2,3,4,5]
# 注意！分号不可省略
a2 = [1:5] # => 1-element Array{UnitRange{Int64},1}: [1:5]
# 可以切割数组
a[1:3] # => [1, 2, 3]
a[2:end] # => [2, 3, 4, 5]
# 用 splice! 切割原数组
arr = [3,4,5]
splice!(arr, 2) # => 4
arr # => [3,5]
# 用 append! 连接数组
b = [1,2,3]
append!(a, b) # => [1, 2, 3, 4, 5, 1, 2, 3]
a # => [1, 2, 3, 4, 5, 1, 2, 3]
# 检查元素是否在数组中
in(1, a) # => true
# 用 length 获得数组长度
length(a) # => 8
# 元组(Tuples)是不可变的
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
# 大多数组的函数同样支持元组
length(tup) # => 3
tup[1:2] # => (1,2)
in(2, tup) # => true
# 可以将元组的元素解包赋给变量
a, b, c = (1, 2, 3) # => (1,2,3)
a # => 1
b # => 2
c # => 3
# 不用括号也可以
d, e, f = 4, 5, 6 # => (4,5,6)
d # => 4
e # => 5
f # => 6
# 单元素 tuple 不等于其元素值
(1,) == 1 # => false
(1) == 1 # => true
# 交换值
e, d = d, e # => (5,4)
d # => 5
e # => 4
# 字典用于储存映射(mappings)（键值对）
empty_dict = Dict() # => Dict{Any,Any} with 0 entries
# 也可以用字面量创建字典
filled_dict = Dict("one" => 1, "two" => 2, "three" => 3)
# => Dict{String,Int64} with 3 entries:
# => "two" => 2, "one" => 1, "three" => 3
# 用 [] 获得键值
filled_dict["one"] # => 1
# 获得所有键
keys(filled_dict)
# => Base.KeySet for a Dict{String,Int64} with 3 entries. Keys:
# => "two", "one", "three"
# 注意，键的顺序不是插入时的顺序
# 获得所有值
values(filled_dict)
# => Base.ValueIterator for a Dict{String,Int64} with 3 entries. Values:
# => 2, 1, 3
# 注意，值的顺序也一样
# 用 in 检查键值是否已存在，用 haskey 检查键是否存在
in(("one" => 1), filled_dict) # => true
in(("two" => 3), filled_dict) # => false
haskey(filled_dict, "one") # => true
haskey(filled_dict, 1) # => false
# 获取不存在的键的值会抛出异常
try
filled_dict["four"] # => ERROR: KeyError: key "four" not found
catch e
println(e)
end
# 使用 get 可以提供默认值来避免异常
# get(dictionary,key,default_value)
get(filled_dict, "one", 4) # => 1
get(filled_dict, "four", 4) # => 4
# Set 表示无序不可重复的值的集合
empty_set = Set() # => Set(Any[])
# 初始化一个带初值的 Set
filled_set = Set([1, 2, 2, 3, 4]) # => Set([4, 2, 3, 1])
# 新增值
push!(filled_set, 5) # => Set([4, 2, 3, 5, 1])
# 检查 Set 中是否存在某值
in(2, filled_set) # => true
in(10, filled_set) # => false
# 交集，并集，差集
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. 控制语句
####################################################
# 声明一个变量
some_var = 5
# 这是一个 if 语句块，其中的缩进不是必须的
if some_var > 10
println("some_var is totally bigger than 10.")
elseif some_var < 10 # elseif 是可选的
println("some_var is smaller than 10.")
else # else 也是可选的
println("some_var is indeed 10.")
end
# => some_var is smaller than 10.
# For 循环遍历
# 可迭代的类型包括：Range, Array, Set, Dict 和 AbstractString
for animal = ["dog", "cat", "mouse"]
println("$animal is a mammal")
# 你可以用 $ 将变量或表达式插入字符串中
end
# => dog is a mammal
# => cat is a mammal
# => mouse is a mammal
# 你也可以不用「=」而使用「in」
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 $to")
end
# => mouse is a mammal
# => cat is a mammal
# => dog is a mammal
# 注意！这里的输出顺序和上面的不同
for (k, v) in Dict("dog" => "mammal", "cat" => "mammal", "mouse" => "mammal")
println("$k is a $v")
end
# => mouse is a mammal
# => cat is a mammal
# => dog is a mammal
# While 循环
let x = 0
while x < 4
println(x)
x += 1 # x = x + 1 的缩写
end
end
# => 0
# => 1
# => 2
# => 3
# 用 try/catch 处理异常
try
error("help")
catch e
println("caught it $e")
end
# => caught it ErrorException("help")
####################################################
## 4. 函数
####################################################
# 关键字 function 用于定义函数
# function name(arglist)
# body...
# end
function add(x, y)
println("x is $x and y is $y")
# 函数会返回最后一行的值
x + y
end
add(5, 6)
# => x is 5 and y is 6
# => 11
# 更紧凑的定义函数
f_add(x, y) = x + y # => f_add (generic function with 1 method)
f_add(3, 4) # => 7
# 函数可以将多个值作为元组返回
fn(x, y) = x + y, x - y # => fn (generic function with 1 method)
fn(3, 4) # => (7, -1)
# 还可以定义接收可变长参数的函数
function varargs(args...)
return args
# 使用 return 可以在函数内的任何地方返回
end
# => varargs (generic function with 1 method)
varargs(1,2,3) # => (1,2,3)
# 省略号「...」称为 splat
# 刚刚用在了函数定义中
# 在调用函数时也可以使用它，此时它会把数组或元组解包为参数列表
add([5,6]...) # 等价于 add(5,6)
x = (5, 6) # => (5,6)
add(x...) # 等价于 add(5,6)
# 可定义带可选参数的函数
function defaults(a, b, x=5, y=6)
return "$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
# 还可以定义带关键字参数的函数
function keyword_args(;k1=4, name2="hello") # 注意分号 ';'
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]
# 可以在一个函数中组合各种类型的参数
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 有一等函数
function create_adder(x)
adder = function (y)
return x + y
end
return adder
end
# => create_adder (generic function with 1 method)
# 这是用 "stabby lambda syntax" 创建的匿名函数
(x -> x > 2)(3) # => true
# 这个函数和上面的 create_adder 是等价的
function create_adder(x)
y -> x + y
end
# => create_adder (generic function with 1 method)
# 你也可以给内部函数起个名字
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
# 内置的高阶函数有
map(add_10, [1,2,3]) # => [11, 12, 13]
filter(x -> x > 5, [3, 4, 5, 6, 7]) # => [6, 7]
# 还可以使用 list comprehensions 让 map 更美观
[add_10(i) for i = [1, 2, 3]] # => [11, 12, 13]
[add_10(i) for i in [1, 2, 3]] # => [11, 12, 13]
####################################################
## 5. 类型
####################################################
# Julia 有类型系统
# 所有的值都有类型；但变量本身没有类型
# 你可以用 `typeof` 函数获得值的类型
typeof(5) # => Int64
# 类型是一等值
typeof(Int64) # => DataType
typeof(DataType) # => DataType
# DataType 是代表类型的类型，也代表他自己的类型
# 类型可用于文档化代码、执行优化以及多重派分(dispatch)
# Julia 并不只是静态的检查类型
# 用户还可以自定义类型
# 就跟其它语言的 record 或 struct 一样
# 用 `struct` 关键字定义新的类型
# struct Name
# field::OptionalType
# ...
# end
struct Tiger
taillength::Float64
coatcolor # 不带类型标注相当于 `::Any`
end
# 默认构造函数的参数是类型的属性，按类型定义中的顺序排列
tigger = Tiger(3.5, "orange") # => Tiger(3.5, "orange")
# 用新类型作为构造函数还会创建一个类型
sherekhan = typeof(tigger)(5.6, "fire") # => Tiger(5.6, "fire")
# 类似 struct 的类型被称为具体类型
# 它们可被实例化，但不能有子类型
# 另一种类型是抽象类型
# 抽象类型名
abstract type Cat end # 仅仅是指向类型结构层次的一个名称
# 抽象类型不能被实例化，但可以有子类型
# 例如，Number 就是抽象类型
subtypes(Number) # => 2-element Array{Any,1}:
# => Complex
# => Real
subtypes(Cat) # => 0-element Array{Any,1}
# AbstractString，类如其名，也是一个抽象类型
subtypes(AbstractString) # => 4-element Array{Any,1}:
# => String
# => SubString
# => SubstitutionString
# => Test.GenericString
# 所有的类型都有父类型。可以用函数 `supertype` 得到父类型
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
# 除了 Int64 外，其余的类型都是抽象类型
typeof("fire") # => String
supertype(String) # => AbstractString
supertype(AbstractString) # => Any
supertype(SubString) # => AbstractString
# <: 是子类型化操作符
struct Lion <: Cat # Lion 是 Cat 的子类型
mane_color
roar::AbstractString
end
# 可以继续为你的类型定义构造函数
# 只需要定义一个与类型同名的函数，并调用已有的构造函数得到正确的类型
Lion(roar::AbstractString) = Lion("green", roar) # => Lion
# 这是一个外部构造函数，因为它在类型定义之外
struct Panther <: Cat # Panther 也是 Cat 的子类型
eye_color
Panther() = new("green")
# Panthers 只有这个构造函数，没有默认构造函数
end
# 像 Panther 一样使用内置构造函数，让你可以控制如何构建类型的值
# 应该尽量使用外部构造函数，而不是内部构造函数
####################################################
## 6. 多分派
####################################################
# Julia 中所有的函数都是通用函数，或者叫做泛型函数(generic functions)
# 也就是说这些函数都是由许多小方法组合而成的
# Lion 的每一种构造函数都是通用函数 Lion 的一个方法
# 我们来看一个非构造函数的例子
# 首先，让我们定义一个函数 meow
# Lion, Panther, Tiger 的 meow 定义分别为
function meow(animal::Lion)
animal.roar # 使用点记号「.」访问属性
end
# => meow (generic function with 1 method)
function meow(animal::Panther)
"grrr"
end
# => meow (generic function with 2 methods)
function meow(animal::Tiger)
"rawwwr"
end
# => meow (generic function with 3 methods)
# 试试 meow 函数
meow(tigger) # => "rawwwr"
meow(Lion("brown", "ROAAR")) # => "ROAAR"
meow(Panther()) # => "grrr"
# 回顾类型的层次结构
Tiger <: Cat # => false
Lion <: Cat # => true
Panther <: Cat # => true
# 定义一个接收 Cat 类型的函数
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
# 在面向对象语言中，通常都是单分派
# 这意味着使用的方法取决于第一个参数的类型
# 而 Julia 中选择方法时会考虑到所有参数的类型
# 让我们定义一个有更多参数的函数，这样我们就能看出区别
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")) # => fight(tigger, Lion("ROAR"))
# 让我们修改一下传入 Lion 类型时的行为
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!
# 我们不需要一只老虎参与战斗
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
# 试试把 Cat 放在前面
fight(c::Cat, l::Lion) = println("The cat beats the Lion")
# => fight (generic function with 4 methods)
# 由于无法判断该使用哪个 fight 方法，而产生了错误
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
# 在不同版本的 Julia 中错误信息可能有所不同
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
# 深入编译器之后
# 你还可以看看 llvm 以及它生成的汇编代码
square_area(l) = l * l # => square_area (generic function with 1 method)
square_area(5) # => 25
# 当我们喂给 square_area 一个整数时会发生什么？
code_native(square_area, (Int32,), syntax = :intel)
# .text
# ; Function square_area {
# ; Location: REPL[116]:1 # 函数序言 (Prologue)
# push rbp
# mov rbp, rsp
# ; Function *; {
# ; Location: int.jl:54
# imul ecx, ecx # 求 l 的平方，并把结果放在 ECX 中
# ;}
# mov eax, ecx
# pop rbp # 还原旧的基址指针(base pointer)
# ret # 返回值放在 EAX 中
# nop dword ptr [rax + rax]
# ;}
# 使用 syntax 参数指定输出语法。默认为 AT&T 格式，这里指定为 Intel 格式
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 # 标量双精度乘法 (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 # 标量双精度乘法 (AVX)
# ;}
# pop rbp
# ret
# nop word ptr [rax + rax]
# ;}
# 注意！只要参数中有浮点数，Julia 就会使用浮点指令
# 让我们计算一下圆的面积
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 # 从内存中读取整数 r
# movabs rax, 497710928 # 读取 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]
# ;}