--- language: julia contributors: - ["Leah Hanson", "http://leahhanson.us"] filename: 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 the current development version of Julia, as of October 18th, 2013. ```ruby # Single line comments start with a hash. #################################################### ## 1. Primitive Datatypes and Operators #################################################### # Everything in Julia is a expression. # There are several basic types of numbers. 3 #=> 3 (Int64) 3.2 #=> 3.2 (Float64) 2 + 1im #=> 2 + 1im (Complex{Int64}) 2//3 #=> 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 5 / 2 #=> 2.5 # dividing an Int by an Int always results in a Float 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 2 $ 4 #=> 6 # bitwise xor 2 >>> 1 #=> 1 # logical shift right 2 >> 1 #=> 1 # arithmetic shift right 2 << 1 #=> 4 # logical/arithmetic shift left # You can use the bits function to see the binary representation of a number. bits(12345) #=> "0000000000000000000000000000000000000000000000000011000000111001" bits(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' # A string can be indexed like an array of characters "This is a string"[1] #=> 'T' # Julia indexes from 1 # However, this is will not work well for UTF8 strings, # so 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 parenthesis. # Another way to format strings is the printf macro. @printf "%d is less than %f" 4.5 5.3 # 5 is less than 5.300000 #################################################### ## 2. Variables and Collections #################################################### # Printing is easy println("I'm Julia. Nice to meet you!") # 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: some_other_var not defined catch e println(e) end # Variable names start with a letter. # After that, you can use letters, digits, underscores, and exclamation points. SomeOtherVar123! = 6 #=> 6 # You can also use unicode characters ☃ = 8 #=> 8 # These are especially handy for mathematical notation 2 * π #=> 6.283185307179586 # A note on naming conventions in Julia: # # * Names of variables are in lower case, with word separation indicated by # underscores ('\_'). # # * 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 Int64 Array # 1-dimensional array literals can be written with comma-separated values. b = [4, 5, 6] #=> 3-element Int64 Array: [4, 5, 6] b[1] #=> 4 b[end] #=> 6 # 2-dimentional arrays use space-separated values and semicolon-separated rows. matrix = [1 2; 3 4] #=> 2x2 Int64 Array: [1 2; 3 4] # 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 and b is now [4,5] # Let's put it back push!(b,6) # b is now [4,5,6] again. 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 shift and unshift shift!(a) #=> 1 and a is now [2,4,3,4,5,6] unshift!(a,7) #=> [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 Int64 Array: [5,4,6] sort(arr) #=> [4,5,6]; arr is still [5,4,6] sort!(arr) #=> [4,5,6]; arr is now [4,5,6] # Looking out of bounds is a BoundsError try a[0] #=> ERROR: BoundsError() in getindex at array.jl:270 a[end+1] #=> ERROR: BoundsError() in getindex at array.jl:270 catch e println(e) end # Errors list the line and file they came from, even if it's in the standard # library. If you built Julia from source, you can look in the folder base # inside the julia folder to find these files. # You can initialize arrays from ranges a = [1:5] #=> 5-element Int64 Array: [1,2,3,4,5] # You can look at ranges with slice syntax. a[1:3] #=> [1, 2, 3] a[2:] #=> [2, 3, 4, 5] 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 is now [3,5] # Concatenate lists with append! b = [1,2,3] append!(a,b) # Now a is [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) # an (Int64,Int64,Int64) tuple. tup[1] #=> 1 try: tup[1] = 3 #=> ERROR: no method setindex!((Int64,Int64,Int64),Int64,Int64) catch e println(e) end # Many list 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 is now 1, b is now 2 and c is now 3 # Tuples are created even if you leave out the parentheses d, e, f = 4, 5, 6 #=> (4,5,6) # 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 is now 5 and e is now 4 # Dictionaries store mappings empty_dict = Dict() #=> Dict{Any,Any}() # You can create a dictionary using a literal filled_dict = ["one"=> 1, "two"=> 2, "three"=> 3] # => Dict{ASCIIString,Int64} # Look up values with [] filled_dict["one"] #=> 1 # Get all keys keys(filled_dict) #=> KeyIterator{Dict{ASCIIString,Int64}}(["three"=>3,"one"=>1,"two"=>2]) # Note - dictionary keys are not sorted or in the order you inserted them. # Get all values values(filled_dict) #=> ValueIterator{Dict{ASCIIString,Int64}}(["three"=>3,"one"=>1,"two"=>2]) # 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-existant key will raise an error try filled_dict["four"] #=> ERROR: key not found: four in getindex at dict.jl:489 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{Int64}(1,2,3,4) # Add more values to a set push!(filled_set,5) #=> Set{Int64}(5,4,2,3,1) # 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{Int64}(6,4,5,3) intersect(filled_set, other_set) #=> Set{Int64}(3,4,5) union(filled_set, other_set) #=> Set{Int64}(1,2,3,4,5,6) setdiff(Set(1,2,3,4),Set(2,3,5)) #=> Set{Int64}(1,4) #################################################### ## 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 String. for animal=["dog", "cat", "mouse"] println("$animal is a mammal") # You can use $ to interpolate variables or expression into strings end # prints: # 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("$animal is a mammal") end # prints: # dog is a mammal # cat is a mammal # mouse is a mammal for a in ["dog"=>"mammal","cat"=>"mammal","mouse"=>"mammal"] println("$(a[1]) is a $(a[2])") end # prints: # dog is a mammal # cat is a mammal # mouse is a mammal for (k,v) in ["dog"=>"mammal","cat"=>"mammal","mouse"=>"mammal"] println("$k is a $v") end # prints: # dog is a mammal # cat is a mammal # mouse is a mammal # While loops loop while a condition is true x = 0 while x < 4 println(x) x += 1 # Shorthand for x = x + 1 end # prints: # 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 $y") # Functions return the value of their last statement x + y end add(5, 6) #=> 11 after printing out "x is 5 and y is 6" # You can define functions that take a variable number of # positional arguments function varargs(args...) return args # use the keyword return to return anywhere in the function end #=> varargs (generic function with 1 method) varargs(1,2,3) #=> (1,2,3) # The ... is called a splat. # We just used it in a function definition. # It can also be used in a fuction call, # where it will splat an Array or Tuple's contents into the argument list. Set([1,2,3]) #=> Set{Array{Int64,1}}([1,2,3]) # produces a Set of Arrays Set([1,2,3]...) #=> Set{Int64}(1,2,3) # this is equivalent to Set(1,2,3) x = (1,2,3) #=> (1,2,3) Set(x) #=> Set{(Int64,Int64,Int64)}((1,2,3)) # a Set of Tuples Set(x...) #=> Set{Int64}(2,3,1) # You can define functions with optional positional arguments function defaults(a,b,x=5,y=6) return "$a $b and $x $y" end 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: no method defaults(Char,) defaults() #=> ERROR: no methods defaults() catch e println(e) end # You can define functions that take keyword arguments function keyword_args(;k1=4,name2="hello") # note the ; return ["k1"=>k1,"name2"=>name2] end keyword_args(name2="ness") #=> ["name2"=>"ness","k1"=>4] keyword_args(k1="mine") #=> ["k1"=>"mine","name2"=>"hello"] 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(1, 3, keyword_arg=4) # prints: # 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 # 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 # You can also name the internal function, if you want function create_adder(x) function adder(y) x + y end adder end add_10 = create_adder(10) 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 for nicer maps [add_10(i) for i=[1, 2, 3]] #=> [11, 12, 13] [add_10(i) for i in [1, 2, 3]] #=> [11, 12, 13] #################################################### ## 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 used the `type` keyword. # type Name # field::OptionalType # ... # end type 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 Cat # 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) #=> 6-element Array{Any,1}: # Complex{Float16} # Complex{Float32} # Complex{Float64} # Complex{T<:Real} # ImaginaryUnit # Real subtypes(Cat) #=> 0-element Array{Any,1} # Every type has a super type; use the `super` function to get it. typeof(5) #=> Int64 super(Int64) #=> Signed super(Signed) #=> Real super(Real) #=> Number super(Number) #=> Any super(super(Signed)) #=> Number super(Any) #=> Any # All of these type, except for Int64, are abstract. # <: is the subtyping operator type Lion <: Cat # Lion is a subtype of Cat mane_color roar::String 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::String) = Lion("green",roar) # This is an outer constructor because it's outside the type definition type 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) #=> "rawwr" meow(Lion("brown","ROAAR")) #=> "ROAAR" meow(Panther()) #=> "grrr" # Review the local type hierarchy issubtype(Tiger,Cat) #=> false issubtype(Lion,Cat) #=> true issubtype(Panther,Cat) #=> true # Defining a function that takes Cats function pet_cat(cat::Cat) println("The cat says $(meow(cat))") end pet_cat(Lion("42")) #=> prints "The cat says 42" try pet_cat(tigger) #=> ERROR: no method 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()) #=> prints The orange tiger wins! fight(tigger,Lion("ROAR")) #=> prints 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()) #=> prints The orange tiger wins! fight(tigger,Lion("ROAR")) #=> prints 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()) #=> prints The victorious cat says grrr try fight(Panther(),Lion("RAWR")) #=> ERROR: no method fight(Panther,Lion) catch end # Also let the cat go first fight(c::Cat,l::Lion) = println("The cat beats the Lion") #=> Warning: New definition # fight(Cat,Lion) at none:1 # is ambiguous with # fight(Lion,Cat) at none:2. # Make sure # fight(Lion,Lion) # is defined first. #fight (generic function with 4 methods) # This warning is because it's unclear which fight will be called in: fight(Lion("RAR"),Lion("brown","rarrr")) #=> prints The victorious cat says rarrr # The result may be different in other versions of Julia fight(l::Lion,l2::Lion) = println("The lions come to a tie") fight(Lion("RAR"),Lion("brown","rarrr")) #=> prints The lions come to a tie # Under the hood # You can take a look at the llvm intermediate code and the assembly code generated. square_area(l) = l * l # square_area (generic function with 1 method) square_area(5) #25 code_native(square_area, (Int32,)) # What happens when we feed square_area an integer? # .section __TEXT,__text,regular,pure_instructions # Filename: none # Source line: 1 # Prologue # push RBP # mov RBP, RSP # Source line: 1 # movsxd RAX, EDI # Fetch l from memory? # imul RAX, RAX # 32bit square of l and store the result in RAX # pop RBP # Restore old base pointer # ret # Result will still be in RAX code_native(square_area, (Float32,)) # .section __TEXT,__text,regular,pure_instructions # Filename: none # Source line: 1 # push RBP # mov RBP, RSP # Source line: 1 # vmulss XMM0, XMM0, XMM0 # Scalar single precision multiplication (AVX) (in this case square the number) # pop RBP # ret code_native(square_area, (Float64,)) # .section __TEXT,__text,regular,pure_instructions # Filename: none # Source line: 1 # push RBP # mov RBP, RSP # Source line: 1 # vmulsd XMM0, XMM0, XMM0 # Scalar double precision multiplacation (AVX) # pop RBP # ret # # Note that julia will use floating point instructions if any of the arguements 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,)) # .section __TEXT,__text,regular,pure_instructions # Filename: none # Source line: 1 # push RBP # mov RBP, RSP # Source line: 1 # vcvtsi2sd XMM0, XMM0, EDI # Load integer (r) from memory # movabs RAX, 4593140240 # Load pi # vmulsd XMM1, XMM0, QWORD PTR [RAX] # pi * r # vmulsd XMM0, XMM0, XMM1 # (pi * r) * r # pop RBP # ret # code_native(circle_area, (Float64,)) # .section __TEXT,__text,regular,pure_instructions # Filename: none # Source line: 1 # push RBP # mov RBP, RSP # movabs RAX, 4593140496 # Source line: 1 # vmulsd XMM1, XMM0, QWORD PTR [RAX] # vmulsd XMM0, XMM1, XMM0 # pop RBP # ret # ``` ## Further Reading You can get a lot more detail from [The Julia Manual](http://docs.julialang.org/en/latest/manual/) The best place to get help with Julia is the (very friendly) [mailing list](https://groups.google.com/forum/#!forum/julia-users).