Abstract types

Julia does not allow abstract types to be instantiated. They can only be used to create a logical hierarchy of types. The following figure shows this hierarchy for numeric types introduced in the first lecture.

All types depicted in blue are abstract types, and all green types are concrete types. For example, Int8, Int16, Int32, Int64 and Int128 are signed integer types, UInt8, UInt16, UInt32, UInt64 and UInt128 are unsigned integer types, while Float16, Float32 and Float64 are floating-point types. In many cases, the inputs must be of a specific type. An algorithm to find the greatest common denominator should work any integer types, but it should not work for any floating-point inputs. Abstract types specify these cases and provide a context into which concrete types can fit.

Abstract types are defined by abstract type followed by the type name. It is possible to specify a type to be a subtype of another abstract type. The definition of abstract numeric types would be:

abstract type Number end
abstract type Real <: Number end
abstract type AbstractFloat <: Real end
abstract type AbstractIrrational <: Real end
abstract type Integer <: Real end
abstract type Signed <: Integer end
abstract type Unsigned <: Integer end

When no supertype is specified, such as for Number, the default supertype is Any. The Any type is sometimes called the top type since all types are its subtypes. The bottom type is Union{}, and all types are supertypes of Union{}.

The <: operator can be used to check if the left operand is a subtype of the right operand.

julia> Signed <: Integer
true

julia> Signed <: Number
true

julia> Signed <: AbstractFloat
false

Julia also provides the isa function, which checks if a variable is an instance of a type.

julia> isa(1, Int64) # equivalent to typeof(1) <: Int64
true

julia> isa(1, Integer) # equivalent to typeof(1) <: Integer
true

julia> isa(1, AbstractFloat) # equivalent to typeof(1) <: AbstractFloat
false

Other handy functions are isabstracttype and isconcretetype that check whether a type is abstract and concrete, respectively.

julia> isabstracttype(Real)
true

julia> isabstracttype(Float64)
false

julia> isconcretetype(Real)
false

julia> isconcretetype(Float64)
true

Composite types

A composite type is a collection of key-value pairs. In many languages, composite types are the only kind of user-definable type. Even though Julia allows defining other types, composite types are used the most. Their main goal is to collect all information about one object within one structure. We will soon define the Rectangle type containing information about the size and the bottom-left point position of a rectangle. Collecting this information into one structure makes it simple to pass all information about the rectangle as arguments and use it for further computation. Moreover, it is possible to use composite types in combination with multiple-dispatch and define specialized functions for custom types.

The struct keyword defines composite types. It is followed by the composite type name and field names, where the latter may be annotated with types.

struct Rectangle
    bottomleft::Vector{Float64}
    width
    height
end

If the type annotation is omitted, Any is used, and such a field may contain any value. A Julia convention suggests making the first letter in custom type names uppercase. We can create a new instance of the above type by calling Rectangle as a function. Its input arguments represent the fields of the Rectangle type.

julia> r = Rectangle([1,2], 3, 4)
Rectangle([1.0, 2.0], 3, 4)

julia> isa(r, Rectangle)
true

A constructor is calling a type as a function. Two constructors are automatically generated when a type is created. One accepts any arguments and converts them to the field types, and the other accepts arguments that match the field types exactly. If all fields are Any, only one constructor is generated. Julia creates these two constructors to make it easier to add new definitions without replacing the default constructor. We can list all constructors by the methods function.

julia> methods(Rectangle)
# 2 methods for type constructor:
 [1] Rectangle(bottomleft::Vector{Float64}, width, height)
     @ none:2
 [2] Rectangle(bottomleft, width, height)
     @ none:2

The fields of composite types can be accessed via the dot notation similarly to named tuples or via the getproperty function.

julia> r.width
3

julia> getproperty(r, :width)
3

The fields can be then accessed anywhere, for example, within a function.

julia> area(r::Rectangle) = r.width * r.height
area (generic function with 1 method)

julia> function vertices(r::Rectangle)
           x, y = r.bottomleft
           w, h = r.width, r.height
           return [[x, y], [x + w, y], [x + w, y + h], [x, y + h]]
       end
vertices (generic function with 1 method)

julia> area(r)
12

julia> vertices(r)
4-element Vector{Vector{Float64}}:
 [1.0, 2.0]
 [4.0, 2.0]
 [4.0, 6.0]
 [1.0, 6.0]

The convenient function fieldnames returns a tuple with names of all structure fields represented as symbols.

julia> fieldnames(Rectangle)
(:bottomleft, :width, :height)

julia> fieldnames(typeof(r))
(:bottomleft, :width, :height)

class Rectangle:
    def __init__(self, bottomleft, width, height):
        self.bottomleft = bottomleft
        self.width = width
        self.height = height

    def area(self):
        return self.width * self.height

    def vertices(self):
        x, y = self.bottomleft
        w, h = self.width, self.height
        return [[x, y], [x + w, y], [x + w, y + h], [x, y + h]]

In [2]: r = Rectangle([1.0, 2.0], 3, 4)

In [3]: r.area()
Out[3]: 12

In [4]: r.vertices()
Out[4]: [[1.0, 2.0], [4.0, 2.0], [4.0, 6.0], [1.0, 6.0]]

Mutable composite types

Composite types declared with struct keyword are immutable and cannot be modified after being constructed.

julia> r.bottomleft = [2;2]
ERROR: setfield!: immutable struct of type Rectangle cannot be changed

However, immutability is not recursive. If an immutable object contains a mutable object, such as an array, elements of this mutable object can be modified. Even though Rectangle is an immutable type, its bottomleft field is a mutable array and can be changed.

julia> r.bottomleft[1] = 5
5

julia> r.bottomleft
2-element Vector{Float64}:
 5.0
 2.0

julia> area(r)
12

julia> vertices(r)
4-element Vector{Vector{Float64}}:
 [5.0, 2.0]
 [8.0, 2.0]
 [8.0, 6.0]
 [5.0, 6.0]

To allow changing their fields, we need to define composite types as mutable by adding the mutable keyword.

mutable struct MutableRectangle
    bottomleft::Vector{Float64}
    width
    height
end

We can work with mutable and immutable types in the same way.

julia> mr = MutableRectangle([1,2], 3, 4)
MutableRectangle([1.0, 2.0], 3, 4)

julia> isa(mr, MutableRectangle)
true

Similarly to accessing field values, we can change them by the dot notation or the setproperty! function.

julia> mr.width = 1.5
1.5

julia> setproperty!(mr, :height, 2.5)
2.5

julia> mr
MutableRectangle([1.0, 2.0], 1.5, 2.5)

julia> const AbstractRectangle = Union{Rectangle, MutableRectangle}
Union{MutableRectangle, Rectangle}

julia> Rectangle <: AbstractRectangle
true

julia> MutableRectangle <: AbstractRectangle
true

julia> perimeter(r::AbstractRectangle) = 2*(r.width + r.height)
perimeter (generic function with 1 method)

julia> perimeter(r)
14

julia> perimeter(mr)
8.0

Parametric types

An important and powerful feature of the Julia type system is that it is parametric. Types can take parameters, and type declarations introduce a whole family of new types (one for each possible combination of parameter values). Parametric (abstract) types can be defined as follows:

abstract type AbstractPoint{T} end

struct Point{T <: Real} <: AbstractPoint{T}
    x::T
    y::T
end

The example above defines a parametric abstract type AbstractPoint and its parametric subtype Point. The declaration of the concrete type Point{T <: Real} has two fields of type T, where T can be any subtype of Real. This definition ensures that both fields are always of the same type. Note that Point{Float64} is a concrete type equivalent to replacing T in the definition of Point by Float64.

julia> isconcretetype(Point{Float64})
true

This single declaration declares a concrete type for each type T that is a subtype of Real. The Point type itself is also a valid type object, containing all instances Point{Float64}, Point{Int64}, etc., as subtypes.

julia> Point{Float64} <: Point <: AbstractPoint
true

julia> Point{Int64} <: Point <: AbstractPoint
true

Concrete Point types with different T values are never subtypes of each other. Even though Float64 is a subtype of Real, Point{Float64} is not a subtype of Point{Real}.

julia> Point{Float64} <: Point{Real}
false

julia> Point{Float64} <: AbstractPoint{Float64}
true

julia> Point{Float64} <: AbstractPoint{Real}
false

This behaviour has important consequences: while any instance of Point{Float64} may be represented as an instance of Point{Real}, these two types have different representations in memory:

• An instance of Point{Float64} can be efficiently represented as a pair of 64-bit values;
• An instance of Point{Real} must be able to hold any pair of Real values. Since instances of Real can have arbitrary size and structure, an instance of Point{Real} must be represented as a pair of pointers to individually allocated Real objects.

This efficiency gain is magnified for arrays: Array{Float64} can be stored as a contiguous memory block of 64-bit floating-point values, whereas Array{Real} is an array of pointers to Real objects.

Since Point{Float64} is not a subtype of Point{Real}, the following method cannot be applied to arguments of type Point{Float64}.

julia> coordinates(p::Point{Real}) = (p.x, p.y)

julia> coordinates(Point(1,2))
ERROR: MethodError: no method matching coordinates(::Point{Int64})
[...]

julia> coordinates(Point(1.0,2.0))
ERROR: MethodError: no method matching coordinates(::Point{Float64})
[...]

The correct way to define a method that accepts all arguments of type Point{T} where T is a subtype of Real is as follows:

julia> coordinates(p::Point{<:Real}) = (p.x, p.y)
coordinates (generic function with 1 method)

julia> coordinates(Point(1,2))
(1, 2)

julia> coordinates(Point(1.0,2.0))
(1.0, 2.0)

It is also possible to define a function for all subtypes of some abstract type.

julia> Base.show(io::IO, p::AbstractPoint) = print(io, coordinates(p))

julia> Point(4, 2)
(4, 2)

julia> Point(0.2, 1.3)
(0.2, 1.3)

There are two ways how to instantiate the Point type. The first one does not specify the T parameter and lets Julia automatically decide the appropriate type. The second one specifies the T parameter manually.

julia> Point(1, 2)
(1, 2)

julia> Point{Float32}(1, 2)
(1.0f0, 2.0f0)

The first way works only if the arguments have the same type.

julia> Point(1, 2.0)
ERROR: MethodError: no method matching Point(::Int64, ::Float64)

Closest candidates are:
  Point(::T, !Matched::T) where T<:Real
   @ Main none:3
[...]

This situation can be handled by defining custom constructors, as we will discuss in the next section.

struct Point3D{T <: Real} <: AbstractPoint{T}
    x::T
    y::T
    z::T
end

coordinates(p::Point3D) = (p.x, p.y, p.z)

julia> Point3D(1, 2, 3)
(1, 2, 3)

julia> Point3D{Float32}(1, 2, 3)
(1.0f0, 2.0f0, 3.0f0)

Constructors

Constructors are functions that create new instances of composite types. When a user defines a new composite type, Julia creates the default constructors. Sometimes it is helpful to add additional constructors. In the example from the previous section, we may want to create an instance of Point from two numbers with different types. This can be achieved by defining the following constructor.

Point(x::Real, y::Real) = Point(promote(x, y)...)

The promote function converts its arguments to the supertype that can represent both inputs. For example, promote(1, 2.3) results in the tuple (1.0, 2.3) because it is possible to represent Int64 by Float64, but not the other way round. We can test the new constructor on the example from the end of the previous section. As expected, the result has the type Point{Float64}.

julia> Point(1, 2.0)
(1.0, 2.0)

julia> typeof(Point(1, 2.0))
Point{Float64}

The constructor defined above is the outer constructor because it is defined outside of the type definition. A constructor behaves like any other function in Julia and may have multiple methods. We can define new methods to add additional functionality to a constructor. On the other hand, outer constructors cannot construct self-referential objects or instances with some special properties. In such a case, we have to use inner constructors, which differ from outer constructors in two aspects:

1. They are declared inside the composite type declaration rather than outside of it.
2. They have access to the local function new that creates new instances of the composite type.

For example, one may want to create a type with two real numbers, where the first number cannot be greater than the second one. The inner constructor can ensure this.

struct OrderedPair{T <: Real}
    x::T
    y::T

    function OrderedPair(x::Real, y::Real)
        x > y && error("the first argument must be less than or equal to the second one")
        xp, yp = promote(x, y)
        return new{typeof(xp)}(xp, yp)
    end
end

If an inner constructor method is provided, no default constructor method is constructed. The example above ensures that any instance of the OrderedPair satisfies x <= y.

julia> OrderedPair(1,2)
OrderedPair{Int64}(1, 2)

julia> OrderedPair(2,1)
ERROR: the first argument must be less than or equal to the second one
[...]

Inner constructors have an additional advantage. Since outer constructors create the object by calling an appropriate inner constructor, even if we define any number of outer constructors, the resulting instances of the OrderedPair type will always satisfy x <= y.

julia> NTuple{2, Int64} <: Tuple{Int64, Int64}
true

struct PointND{N, T <: Real} <: AbstractPoint{T}
    x::NTuple{N, T}

    function PointND(args::Real...)
        vals = promote(args...)
        return new{length(args), eltype(vals)}(vals)
    end
end

coordinates(p::PointND) = p.x
dim(p::PointND{N}) where N = N

julia> p = PointND(1, 2)
(1, 2)

julia> dim(p)
2

julia> p = PointND(1, 2.2, 3, 4.5)
(1.0, 2.2, 3.0, 4.5)

julia> dim(p)
4

Default field values

It may be beneficial to define custom types with default field values. Since a constructor is a function, one way to achieve this is to use optional or keyword arguments in its declaration. Another option is to use the @kwdef macro from Base that automatically defines keyword-based constructors.

Base.@kwdef struct MyType
    a::Int # required keyword
    b::Float64 = 2.3
    c::String = "hello"
end

The methods function shows that Julia created three constructors. The @kwdef macro creates the first constructor; the other two constructors are the default constructors.

julia> methods(MyType)
# 3 methods for type constructor:
[1] MyType(; a, b, c) in Main at util.jl:478
[2] MyType(a::Int64, b::Float64, c::String) in Main at none:2
[3] MyType(a, b, c) in Main at none:2

A MyType instance can be created by the default constructors.

julia> MyType(1, 2.3, "aaa")
MyType(1, 2.3, "aaa")

The other way is to use the constructor with predefined field values. Then all values have to be passed as keyword arguments. The fields without default values are mandatory keyword arguments: we have to specify them.

julia> MyType(; a = 3)
MyType(3, 2.3, "hello")

julia> MyType(; a = 5, b = 4.5)
MyType(5, 4.5, "hello")

julia> (m::MyType)() = (m.a, m.b, m.c)

julia> m = MyType(; a = 5, b = 4.5)
MyType(5, 4.5, "hello")

julia> m()
(5, 4.5, "hello")

(m::MyType)(x::Real) = m.a*x + m.b
(m::MyType)(x::String) = "$(m.c), $(x)"

julia> m(1)
9.5

julia> m("world")
"hello, world"

Base.@kwdef struct Gauss{T<:Real}
    μ::T = 0
    σ::T = 1

    function Gauss(μ::Real, σ::Real)
        σ^2 > 0 || error("the variance `σ^2` must be positive")
        pars = promote(μ, σ)
        return new{eltype(pars)}(pars...)
    end
end

(d::Gauss)(x::Real) = exp(-1/2 * ((x - d.μ)/d.σ)^2)/(d.σ * sqrt(2*π))

julia> gauss = Gauss()
Gauss{Int64}(0, 1)

julia> gauss(0)
0.3989422804014327

julia> step = 0.01
0.01

julia> x = -100:step:100;

julia> sum(Gauss(), x) * step
1.0000000000000002

julia> sum(Gauss(0.1, 2.3), x) * step
1.0

plot(x, Gauss())

using RecipesBase

@recipe function f(d::Gauss, x = (d.μ - 4d.σ):0.1:(d.μ + 4d.σ))
    seriestype  :=  :path
    label --> "Gauss(μ = $(d.μ), σ = $(d.σ))"
    xguide --> "x"
    yguide --> "f(x)"
    linewidth --> 2
    return x, d.(x)
end

d = Gauss()
plot(x, d.(x);
    seriestype := :path,
    label = "Gauss(μ = $(d.μ), σ = $(d.σ))",
    xguide = "x",
    yguide = "f(x)",
    linewidth = 2
)

using Plots

plot(Gauss())
plot!(Gauss(4, 2); linewidth = 4, color = :red)
plot!(Gauss(-3, 2); label = "new label", linestyle = :dash)