0105 Custom Types

Some stuff to set-up the environment..

julia> cd(@__DIR__)
julia> using Pkg
julia> Pkg.activate(".")
       # If using a Julia version different than 1.10 please uncomment and run the following line (reproductibility guarantee will however be lost)
       # Pkg.resolve()
       # Pkg.instantiate() # run this if you didn't in Segment 01.01  Activating project at `~/work/SPMLJ/SPMLJ/buildedDoc/01_-_JULIA1_-_Basic_Julia_programming`
julia> using Random
julia> Random.seed!(123)Random.TaskLocalRNG()
julia> using InteractiveUtils # loaded automatically when working... interactively

"Type" of types

In Julia primitive, composite and abstract types can all be defined by the user

julia> primitive type APrimitiveType 819200 end # name and size in bit - multiple of 8 and below 8388608 (1MB)
julia> primitive type APrimitiveType2 819200 end
julia> 819200/(8*1024)100.0
julia> struct ACompositeType end                # fields, constructors.. we'll see this later in details
julia> abstract type AnAbstractType end         # no objects, no instantialisation of objects

Composite types

julia> mutable struct Foo # starting with a capital letter
           field1
           field2::String
           field3::ACompositeType
       end

mutable struct Foo # error! can't change a struct after I defined it end

julia> fieldnames(Foo)(:field1, :field2, :field3)
julia> o = Foo(123,"aaa",ACompositeType()) # call the default constructor (available automatically) - order matters!Main.var"Main".Foo(123, "aaa", Main.var"Main".ACompositeType())
julia> typeof(o)Main.var"Main".Foo

Outer constructor

julia> function Foo(f2,f3=ACompositeType()) # "normal" functions just happens it has the name of the object to create
           if startswith(f2,"Booo")         # put whatever logic you wish
               return nothing
           end
           return Foo(123,f2,f3)            # call the default constructor
       endMain.var"Main".Foo
julia> o = Foo(123,"aaa", ACompositeType()) # call the default constructorMain.var"Main".Foo(123, "aaa", Main.var"Main".ACompositeType())
julia> o = Foo("blaaaaa")                   # call the outer constructor we definedMain.var"Main".Foo(123, "blaaaaa", Main.var"Main".ACompositeType())
julia> o.field1       # access fields123
julia> o.field1 = 321 # modify field (because type defined as "mutable" !!!)321
julia> oMain.var"Main".Foo(321, "blaaaaa", Main.var"Main".ACompositeType())
julia> function Base.show(io::IO, x::Foo)             # function(o) rather than o.function()
           println(io,"My custom representation for Foo objects")
           println(io,"Field1: $(o.field1)")
           println(io,"Field2: $(o.field2)")
       end
julia> oMy custom representation for Foo objects
Field1: 321
Field2: blaaaaa

Inner constructor

julia> mutable struct Foo2
           field1::Int64
           field2::String
           function Foo2(f1,f2,f3)
               # ... logic
               return new(f1+f2,f3)
           end
       end

julia> Foo2(1,2,"aaa")
       # Foo2(1,"aaa") # Error, no default constructor !Main.var"Main".Foo2(3, "aaa")

You can also use a macro (requires Julia 1.1) to automatically define an (outer) keyword_based constructor with support for optional arguments:

julia> Base.@kwdef struct Kfoo
          x::Int64 = 1
          y = 2
          z
       endMain.var"Main".Kfoo
julia> Kfoo(z=3)Main.var"Main".Kfoo(1, 2, 3)

Note that, at time of writing, @kwdef is not exported by Julia base, meaning that while widely used, it is considered still "experimental" and its usage may change in future Julia minor versions.

Custom pretty-printing

We can customise the way our custom type is rendered by overriding the Base.show function for our specific type.

We first need to import Base.show

julia> import Base.show
julia> mutable struct FooPoint
           x::Int64
           y::Int64
       end
julia> function show(io::IO, ::MIME"text/plain", x::FooPoint)
           # if get(io, :compact, true) ... # we can query the characteristics of the output
           print(io,"A FooPoint struct")
       endshow (generic function with 932 methods)
julia> function show(io::IO, x::FooPoint) # overridden by print
           print(io, "FooPoint x is $(x.x) and y is $(x.y) \nThat's all!")
       endshow (generic function with 933 methods)
julia> foo_obj=FooPoint(1,2)A FooPoint struct
julia> display(foo_obj)
julia> show(foo_obj)FooPoint x is 1 and y is 2 
That's all!
julia> print(foo_obj)FooPoint x is 1 and y is 2 
That's all!
julia> println(foo_obj)FooPoint x is 1 and y is 2 
That's all!

Parametric types

julia> struct Point{T<:Number} # T must be a child of type "Number"
          x::T
          y::T
       end
julia> o = Point(1,2)Main.var"Main".Point{Int64}(1, 2)
julia> Point(1.0,2.)
       # Point(1,2.0) # error !Main.var"Main".Point{Float64}(1.0, 2.0)
julia> function Point(x::T, y::T=zero(T)) where {T}
           return Point(x,y)
       endMain.var"Main".Point
julia> Point(2)Main.var"Main".Point{Int64}(2, 0)
julia> Point(1.5)Main.var"Main".Point{Float64}(1.5, 0.0)
julia> abstract type Figure{T<:Number} end
julia> a = Array{Int64,2}(undef,2,2) # Array is nothing else than a parametric type with 2 parameters2×2 Matrix{Int64}:
          112688  140147903425776
 140147903425488  140147903426064
julia> typeof(a)Matrix{Int64} (alias for Array{Int64, 2})
julia> eltype(a)Int64

As we see for arrays, parameters doesn't need to be types, but can be any value of a bits type (in practice an integer value) :

julia> struct MyType{T,N}
         data::Array{T,N}
       end
julia> intMatrixInside = MyType([1 2 3; 4 5 6])Main.var"Main".MyType{Int64, 2}([1 2 3; 4 5 6])
julia> floatVectorInside = MyType([1 2 3])Main.var"Main".MyType{Int64, 2}([1 2 3])
julia> function getPlane(o::MyType{T,N},dim,pos) where {T,N}
       sizes = size(o.data)
       if length(sizes) > N
         error("Dim over the dimensions of the data")
       elseif sizes[dim] < pos
         error("Non enought elements in dimension $dim to cut at $pos")
       end
       return selectdim(o.data,dim,pos)
       endgetPlane (generic function with 1 method)
julia> getPlane(intMatrixInside,1,2)3-element view(::Matrix{Int64}, 2, :) with eltype Int64:
 4
 5
 6

A package where non-type parameters are emploied to boost speed is StaticArray.jl where one parameter is the size of the array that hence become known at compile time

Inheritance

julia> abstract type MyOwnGenericAbstractType end                       # the highest-level
julia> abstract type MyOwnAbstractType1 <: MyOwnGenericAbstractType end # child of MyOwnGenericAbstractType
julia> abstract type MyOwnAbstractType2 <: MyOwnGenericAbstractType end # also child of MyOwnGenericAbstractType
julia> mutable struct AConcreteTypeA <: MyOwnAbstractType1
         f1::Int64
         f2::Int64
       end
julia> mutable struct AConcreteTypeB <: MyOwnAbstractType1
         f1::Float64
       end
julia> mutable struct AConcreteTypeZ <: MyOwnAbstractType2
         f1::String
       end
julia> oA = AConcreteTypeA(2,10)Main.var"Main".AConcreteTypeA(2, 10)
julia> oB = AConcreteTypeB(1.5)Main.var"Main".AConcreteTypeB(1.5)
julia> oZ = AConcreteTypeZ("aa")Main.var"Main".AConcreteTypeZ("aa")
julia> supertype(AConcreteTypeA)Main.var"Main".MyOwnAbstractType1
julia> subtypes(MyOwnAbstractType1)Any[]

julia> function foo(a :: MyOwnGenericAbstractType)                      # good for everyone
         println("Default implementation: $(a.f1)")
       endfoo (generic function with 1 method)
julia> foo(oA) # Default implementation: 2Default implementation: 2
julia> foo(oB) # Default implementation: 1.5Default implementation: 1.5
julia> foo(oZ) # Default implementation: aaDefault implementation: aa
julia> function foo(a :: MyOwnAbstractType1)                            # specialisation for MyOwnAbstractType1
         println("A more specialised implementation: $(a.f1*4)")
       endfoo (generic function with 2 methods)
julia> foo(oA) # A more specialised implementation: 8A more specialised implementation: 8
julia> foo(oB) # A more specialised implementation: 6.0A more specialised implementation: 6.0
julia> foo(oZ) # Default implementation: aa                             # doesn't match the specialisation, default to foo(a :: MyOwnGenericAbstractType)Default implementation: aa
julia> function foo(a :: AConcreteTypeA)
            println("A even more specialised implementation: $(a.f1 + a.f2)")
       endfoo (generic function with 3 methods)
julia> foo(oA) # A even more specialised implementation: 12A even more specialised implementation: 12
julia> foo(oB) # A more specialised implementation: 6.0A more specialised implementation: 6.0
julia> foo(oZ) # Default implementation: aaDefault implementation: aa

julia> Vector{Int64} <: AbstractVector{Int64}true
julia> Int64 <: Numbertrue
julia> Vector{Int64} <: Vector{Number}false
julia> AbstractVector{Int64} <: AbstractVector{Number}false

Object-oriented model

OO model based on composition

julia> struct Shoes
          shoesType::String
          colour::String
       end
julia> struct Person
         myname::String
         age::Int64
       end
julia> struct Student
          p::Person        # by referencing a `Person`` object, we do not need to repeat its fields
          school::String
          shoes::Shoes     # same for `shoes`
       end
julia> struct Employee
          p::Person
          monthlyIncomes::Float64
          company::String
          shoes::Shoes
       end
julia> gymShoes = Shoes("gym","white")Main.var"Main".Shoes("gym", "white")
julia> proShoes = Shoes("classical","brown")Main.var"Main".Shoes("classical", "brown")
julia> Marc     = Student(Person("Marc",15),"Divine School",gymShoes)Main.var"Main".Student(Main.var"Main".Person("Marc", 15), "Divine School", Main.var"Main".Shoes("gym", "white"))
julia> MrBrown  = Employee(Person("Brown",45),3200.0,"ABC Corporation Inc.", proShoes)Main.var"Main".Employee(Main.var"Main".Person("Brown", 45), 3200.0, "ABC Corporation Inc.", Main.var"Main".Shoes("classical", "brown"))
julia> function printMyActivity(self::Student)
          println("Hi! I am $(self.p.myname), I study at $(self.school) school, and I wear $(self.shoes.colour) shoes") # I can use the dot operator chained...
       endprintMyActivity (generic function with 1 method)
julia> function printMyActivity(self::Employee)
         println("Good day. My name is $(self.p.myname), I work at $(self.company) company and I wear $(self.shoes.colour) shoes")
       endprintMyActivity (generic function with 2 methods)
julia> printMyActivity(Marc)     # Hi! I am Marc, ...Hi! I am Marc, I study at Divine School school, and I wear white shoes
julia> printMyActivity(MrBrown)  # Good day. My name is MrBrown, ...Good day. My name is Brown, I work at ABC Corporation Inc. company and I wear brown shoes

OO models based on Specialisation (Person → Student) or Weack Relation (Person → Shoes) instead of Composition (Person → Arm) can be implemented using third party packages, like e.g. SimpleTraits.jl or OOPMacro.jl

View this file on Github.

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