julia 可跟 C, Python 的程式互動。另外 juila 支援 meta programming 的形式。
Interacting with OS
在 REPL 可用 ; 進入 shell，執行 shell commands，如果在 julia，則是直接用 pwd()
shell> pwd
/Users/user
julia> pwd()
"/Users/user"filesystem operations
存取檔案的 functions
homedir(), joinpath()
為支援 linux, windows，使用 joinpath() 產生檔案的路徑
julia> homedir()
"/Users/user"
julia> joinpath(homedir(), "Documents")
"/Users/user/Documents"pwd(), cd()
取得目前的目錄，以及切換目錄
cd("../")readdir()
取得目錄內的檔案列表，包含目錄及檔案
readdir(".")mkdir()
d = joinpath(homedir(), "LearningJulia") mkdir(d)stat
stat("somefile")會取得StatStruct(mode=0o100644, size=28676)，而 StatStruct 裡面有這些欄位
ref: https://github.com/JuliaLang/julia/blob/master/base/stat.jl
ex:
stat("somefile").size
Name Description size The size (in bytes) of the file device ID of the device that contains the file inode The inode number of the file mode The protection mode of the file nlink The number of hard links to the file uid The user id of the owner of the file gid The group id of the file owner rdev If this file refers to a device, the ID of the device it refers to blksize The file-system preferred block size for the file blocks The number of such blocks allocated mtime Unix timestamp of when the file was last modified ctime Unix timestamp of when the file was created
- cp, mv
julia> cp("test", "test1") "test1" julia> isfile("test1") true julia> mv("test1", "test2") "test2"
isdir, homedir, basename, dirname, and splitdir
julia> homedir() "/Users/user" julia> joinpath(homedir(), "Documents") "/Users/user/Documents" julia> isdir(joinpath(homedir(), "Documents")) true julia> basename(homedir()) "user" julia> dirname(homedir()) "/Users" julia> splitdir(homedir()) ("/Users", "user")mkpath, ispath, abspath, and joinpath
julia> ispath(homedir()) true julia> abspath(".") "/Users/user/Downloads/" julia> joinpath(homedir(), "Documents") "/Users/user/Documents" julia> mkpath(joinpath("Users","adm")) "Users/adm" julia> for (root, dirs, files) in walkdir("Users") println("Directories in $root") end Directories in Users Directories in Users/adm
I/O Operations
STDOUT, STDERR, STDIN 是三個 global variables，分別為 standard output, error, input stram
open(), close(), write, read
julia> file = open("sample.txt")
IOStream(<file sample.txt>)
julia> file
IOStream(<file sample.txt>)
julia> lines = readlines(file)
2-element Array{String,1}:
"Hi there!"
"Learning Julia is so much fun."
julia> write("sample.txt", "hi how are you doing?")
21
julia> read("sample.txt")
21-element Array{UInt8,1}:
julia> readline("sample.txt")
"hi how are you doing?"
julia> close(file)讀取某個文字檔，計算裡面有幾個 "Julia" 這個 word
# Arguments
# ARGS[0] is the command itself
in_file = ARGS[1]
out_file = ARGS[2]
# Keeping track using a counter
counter = 0
for line in eachline(in_file)
for word in split(line)
if word == "Julia"
global counter += 1
end
end
end
# Write the contents to the o/p file
write(out_file, "The count for the word julia is $counter")
# Finally, read the contents to inform the user
for line in readlines(out_file)
println(line)
endshell> julia sample.jl sample.txt out.txt
The count for the word julia is 4Calling C and Python
Calling C from Julia
compile (ex: LLVM) 需要知道什麼，才能由 Julia 呼叫 C
- Name of the library
- Name of the function
- Number and types of the arguments (也稱為 Arity)
- return type of the function
- values of the arguments passed
julia 使用 ccall((:name,"lib"), return_type, (arg1_type, arg2_type...), arg1, arg2) 處理
呼叫 C 標準函式庫的 clock function
julia> ccall((:clock, :libc), Int64, ())
3206897
julia> ccall((:clock, :libc), Cint, ())
3213482
julia> Int32 == Cint
trueCint 是 C 的資料型別，等於 signed int c-type，另外還有 Cint, Cuint, Clong, Culong, and Cchar
呼叫 getenv，取得 SHELL value，Ptr{Cchar} 是參數的資料型別，後面 return value 資料型別也是 Ptr{Cchar}
julia> syspath = ccall( (:getenv, :libc), Ptr{Cchar}, (Ptr{Cchar},), "SHELL")
Ptr{Int8} @0x00007ffee37b857a
julia> unsafe_string(syspath)
"/bin/bash"C 的 struct 可以替換為 julia 的 composite types
Calling Python form Julia
使用 PyCall
julia> using Pkg
julia> Pkg.add("PyCall")
julia> using PyCall
julia> py"len('julia')"
5
julia> py"str(5)"
"5"
julia> py"""
print('hello world')
"""
hello world如果要使用 python 的 built-in data type (ex: dict)，可使用 pybuiltin
julia> pybuiltin(:dict)(a=1,b=2)
Dict{Any,Any} with 2 entries:
"b" => 2
"a" => 1
julia> d = pycall(pybuiltin("dict"), Any, a=1, b=2)
PyObject {'b': 2, 'a': 1}
julia> typeof(d)
PyObject
# 利用 PyDict 轉換為 julia dict object
julia> julia_dictionary = PyDict{Symbol, Int64}(pycall(pybuiltin("dict"), Any, a=1, b=2))
PyDict{Symbol,Int64,true} with 2 entries:
:b => 2
:a => 1
julia> typeof(julia_dictionary)
PyDict{Symbol,Int64,true}
julia> julia_dictionary[:a]
1
# 產生一個 kw 參數的 function
julia> f(; a=0, b=0) = [10a, b]
f (generic function with 1 method)
# 以 dict 呼叫 function
julia> f(;julia_dictionary...)
2-element Array{Int64,1}:
10
2Expressions
這是 metaprogramming 的功能，這是參考 LISP 的功能
首先說明 julia 如何 interprets a code
julia> code = "println(\"hello world \")"
"println(\"hello world \")"
julia> expression = Meta.parse(code)
:(println("hello world "))
julia> typeof(expression)
Exprjulia> expression.args
2-element Array{Any,1}:
:println
"hello world "
julia> expression.head
:call
julia> dump(expression)
Expr
head: Symbol call
args: Array{Any}((2,))
1: Symbol println
2: String "hello world "產生 expression，用 eval 運算， :call 是 symbol，表示 head of the expression
julia> sample_expr = Expr(:call, +, 10, 20)
:((+)(10, 20))
julia> eval(sample_expr)
30
julia> sample_expr.args
3-element Array{Any,1}:
+
10
20將參數替換為 :x, :y 變數，也可以直接寫 x, y
julia> x = 10; y =10
10
julia> sample_expr = Expr(:call, +, :x, :y)
:((+)(x, y))
julia> sample_expr = Expr(:call, +, x, y)
:((+)(10, 10))
julia> eval(sample_expr)
20也可以使用 $
julia> x = 10; y=10
10
julia> e = :($x + $y)
:(10 + 10)
julia> eval(e)
20所以有 : 跟 $ 兩種方式可產生 Expr object
- Using quotes(:) at runtime
- Using dollar($) ar parse time
另外還有一個方式，是使用 quote 這個 keyword。quote 跟 : 的差別，是用 quote 可讓程式碼排列更好，比較容易實作
julia> quote
30 * 100
end
quote
#= REPL[88]:2 =#
30 * 100
end
julia> eval(ans)
3000
julia> :(30 * 100)
:(30 * 100)
julia> eval(ans)
3000Macros
類似 function，但 function 使用 variables 為參數，而 macros 使用 expressions，並回傳 modified expressions，呼叫 macro 是用 @
macro NAME
# some custom code
# return modified expression
end因為 REPL 建立的 macro 是在 Main module 裡面
julia> macro HELLO(name)
:( println("Hello! ", $name))
end
@HELLO (macro with 1 method)
julia> @HELLO("world")
Hello! world
julia> macroexpand(Main, :(@HELLO("world")))
:((Main.println)("Hello! ", "world"))why metaprogramming?
metaprogramming 可省略很多重複的程式碼
for sym in [:foo, :bar, :baz]
@eval function $(Symbol(sym))(n::Int64)
for i in 1:n
println( $(string(sym)) )
end
end
end可這樣使用
foo(1)
bar(2)
baz(3)for header in [:h1, :h2, :h3, :h4, :h5, :h6]
@eval function $(Symbol(header))(text::String)
println("<" * $(string(header))* ">" * " $text " * "</" * $(string(header))* ">")
end
end
h1("Hello world!")
# <h1> Hello world! </h1>
h3("Hello world!")
# <h3> Hello world! </h3>Built-in macros
以下是所有內建的 macros
julia> @
@MIME_str @boundscheck @edit @html_str @nospecialize @text_str
@__DIR__ @cfunction @elapsed @inbounds @polly @threadcall
@__FILE__ @cmd @enum @info @r_str @time
@__LINE__ @code_llvm @error @inline @raw_str @timed
@__MODULE__ @code_lowered @eval @int128_str @s_str @timev
@__dot__ @code_native @evalpoly @isdefined @show @uint128_str
@allocated @code_typed @fastmath @label @simd @v_str
@assert @code_warntype @functionloc @less @specialize @view
@async @debug @generated @macroexpand @static @views
@b_str @deprecate @gensym @macroexpand1 @sync @warn
@big_str @doc @goto @noinline @task @which@time
可取得執行某一段程式的耗費時間
julia> function recursive_sum(n) if n == 0 return 0 else return n + recursive_sum(n-1) end end recursive_sum (generic function with 1 method) julia> @time recursive_sum(10000) 0.005359 seconds (3.22 k allocations: 183.158 KiB) 50005000@elapsed
類似 @time，但只有回傳時間 in Float64
julia> @elapsed recursive_sum(10000) 3.0984e-5 julia> @elapsed recursive_sum(10000) 2.4597e-5@show
會回傳 expression
julia> @show(println("hello world")) hello world println("hello world") = nothing julia> @show(:(println("hello world"))) $(Expr(:quote, :(println("hello world")))) = :(println("hello world")) :(println("hello world")) julia> @show(:(3*2)) $(Expr(:quote, :(3 * 2))) = :(3 * 2) :(3 * 2) julia> @show(3*2) 3 * 2 = 6 6 julia> @show(Int64) Int64 = Int64 Int64@which
如果有某個 function 有多個 methods，也就是 multiple dispatch 的功能，想知道會呼叫哪一個 method
julia> function tripple(n::Int64) 3n end tripple (generic function with 1 method) julia> function tripple(n::Float64) 3n end tripple (generic function with 2 methods) julia> methods(tripple) # 2 methods for generic function "tripple": [1] tripple(n::Float64) in Main at REPL[11]:2 [2] tripple(n::Int64) in Main at REPL[10]:2 julia> @which tripple(10) tripple(n::Int64) in Main at REPL[10]:2 julia> @which tripple(10.0) tripple(n::Float64) in Main at REPL[11]:2@task
類似 coroutine，用來產生一個 task，而不是直接執行
julia> say_hello() = println("hello world") say_hello (generic function with 1 method) julia> say_hello_task = @task say_hello() Task (runnable) @0x0000000113ed9210 julia> istaskstarted(say_hello_task) false julia> schedule(say_hello_task) hello world Task (queued) @0x0000000113ed9210 julia> yield() julia> istaskdone(say_hello_task) true@codellvm, @codelowered, @codetyped,@codenative, and @code_warntype
瞭解 code 在 julia 中的所有形式
function fibonacci(n::Int64) if n < 2 n else fibonacci(n-1) + fibonacci(n-2) end end fibonacci(n::Int64) = n < 2 ? n : fibonacci(n-1) + fibonacci(n-2) # 轉換為 single static assignment，每個變數只會被 assigned 一次，在使用變數前都會先定義 @code_lowered fibonacci(10) # @code_typed fibonacci(10) @code_warntype fibonacci(10) # 使用 LLVM C++ API 產生 LLVM intermediate representation @code_llvm fibonacci(10) # binary code in memory @code_native fibonacci(10)
Type introspection
首先定義新的 type: Student，產生兩個物件
struct Student
name::String
age::Int64
end
alpha = Student("alpha",24)
beta = Student("beta",25)可檢查物件的資料型別，或是使用 isa 判斷是否為某個 function 產生的 type
julia> typeof(alpha)
Student
julia> isa(alpha, Student)
true
julia> alpha isa Student
truereflection
可在 runtime 查詢物件的 attributes。在 julia 產生 function 後，就可以查詢 function 有幾個參數，有哪些 methods。
function calculate_quad(a::Int64,b::Int64,c::Int64,x::Int64)
return a*x^2 + b*x + c
end
calculate_quad(1,2,3,4)
function calculate_quad(a::Int64,b::Int64,c::Int64,x::Float64)
return a*x^2 + b*x + c
end
calculate_quad(1,2,3,4.75)
julia> methods(calculate_quad)
# 2 methods for generic function "calculate_quad":
[1] calculate_quad(a::Int64, b::Int64, c::Int64, x::Float64) in Main at REPL[38]:2
[2] calculate_quad(a::Int64, b::Int64, c::Int64, x::Int64) in Main at REPL[36]:2julia> fieldnames(Student)
(:name, :age)
julia> Student.types
svec(String, Int64)
julia> typeof(Student.types)
Core.SimpleVector
沒有留言:
張貼留言