LabelledArrays.jl is a package which provides arrays with labels, i.e. they are
arrays which `map`

, `broadcast`

, and all of that good stuff, but their components
are labelled. Thus for instance you can set that the second component is named
`:second`

and retrieve it with `A.second`

.

The `SLArray`

and `SLVector`

macros are for creating static LabelledArrays.
First you create the type and then you can use that constructor to generate
instances of the labelled array.

```
ABC = @SLVector (:a,:b,:c)
A = ABC(1,2,3)
A.a == 1
ABCD = @SLArray (2,2) (:a,:b,:c,:d)
B = ABCD(1,2,3,4)
B.c == 3
B[2,2] == B.d
```

Here we have that `A == [1,2,3]`

and for example `A.b == 2`

. We can create a
typed `SLArray`

via:

```
SVType = @SLVector Float64 (:a,:b,:c)
```

Alternatively, you can also construct a static labelled array using the
`SLVector`

constructor by writing out the entries as keyword arguments:

```
julia> SLVector(a=1, b=2, c=3)
3-element SLArray{Tuple{3},1,(:a, :b, :c),Int64}:
1
2
3
```

For general N-dimensional labelled arrays, you need to specify the size
(`Tuple{dim1,dim2,...}`

) as the type parameter to the `SLArray`

constructor:

```
julia> SLArray{Tuple{2,2}}(a=1, b=2, c=3, d=4)
2×2 SLArray{Tuple{2,2},2,(:a, :b, :c, :d),Int64}:
1 3
2 4
```

Constructing copies with some items changed is supported by a keyword constructor whose first argument is the source and additonal keyword arguments change several entries.

```
julia> v1 = SLVector(a=1.1, b=2.2, c=3.3);
julia> v2 = SLVector(v1; b=20.20, c=30.30 )
3-element SLArray{Tuple{3},Float64,1,3,(:a, :b, :c)}:
1.1
20.2
30.3
```

```
julia> ABCD = @SLArray (2,2) (:a,:b,:c,:d);
julia> B = ABCD(1,2,3,4);
julia> B2 = SLArray(B; c=30 )
2×2 SLArray{Tuple{2,2},Int64,2,4,(:a, :b, :c, :d)}:
1 30
2 4
```

One can also specify the indices directly.

```
julia> EFG = @SLArray (2,2) (e=1:3, f=4, g=2:4);
julia> y = EFG(1.0,2.5,3.0,5.0)
2×2 SLArray{Tuple{2,2},Float64,2,4,(e = 1:3, f = 4, g = 2:4)}:
1.0 3.0
2.5 5.0
julia> y.g
3-element view(reshape(::StaticArrays.SArray{Tuple{2,2},Float64,2,4}, 4), 2:4) with eltype Float64:
2.5
3.0
5.0
julia> Arr = @SLArray (2, 2) (a = (2, :), b = 3);
julia> z = Arr(1, 2, 3, 4);
julia> z.a
2-element view(::StaticArrays.SArray{Tuple{2,2},Int64,2,4}, 2, :) with eltype Int64:
2
4
```

The `LArrays`

s are fully mutable arrays with labels. There is no performance
loss by using the labelled instead of indexing. Using the macro with values
and labels generates the labelled array with the given values:

```
A = @LArray [1,2,3] (:a,:b,:c)
A.a == 1
```

One can generate a labelled array with undefined values by instead giving the dimensions:

```
A = @LArray Float64 (2,2) (:a,:b,:c,:d)
W = rand(2,2)
A .= W
A.d == W[2,2]
```

or using an `@LVector`

shorthand:

```
A = @LVector Float64 (:a,:b,:c,:d)
A .= rand(4)
```

As with `SLArray`

, alternative constructors exist that use the keyword argument
form:

```
julia> LVector(a=1, b=2, c=3)
3-element LArray{Int64,1,(:a, :b, :c)}:
1
2
3
julia> LArray((2,2); a=1, b=2, c=3, d=4) # need to specify size as first argument
2×2 LArray{Int64,2,(:a, :b, :c, :d)}:
1 3
2 4
```

One can also specify the indices directly.

```
julia> z = @LArray [1.,2.,3.] (a = 1:2, b = 2:3);
julia> z.b
2-element view(::Array{Float64,1}, 2:3) with eltype Float64:
2.0
3.0
julia> z = @LArray [1 2; 3 4] (a = (2, :), b = 2:3);
julia> z.a
2-element view(::Array{Int64,2}, 2, :) with eltype Int64:
3
4
```

The labels of LArray and SLArray can be accessed
by function `symbols`

, which returns a tuple of symbols.

LabelledArrays.jl are a way to get DSL-like syntax without a macro. In this case, we can solve differential equations with labelled components by making use of labelled arrays, and always refer to the components by name instead of index.

Let's solve the Lorenz equation. Using `@LVector`

s, we can do:

```
using LabelledArrays, OrdinaryDiffEq
function lorenz_f(du,u,p,t)
du.x = p.σ*(u.y-u.x)
du.y = u.x*(p.ρ-u.z) - u.y
du.z = u.x*u.y - p.β*u.z
end
u0 = @LArray [1.0,0.0,0.0] (:x,:y,:z)
p = @LArray [10.0, 28.0, 8/3] (:σ,:ρ,:β)
tspan = (0.0,10.0)
prob = ODEProblem(lorenz_f,u0,tspan,p)
sol = solve(prob,Tsit5())
# Now the solution can be indexed as .x/y/z as well!
sol[10].x
```

We can also make use of `@SLVector`

:

```
LorenzVector = @SLVector (:x,:y,:z)
LorenzParameterVector = @SLVector (:σ,:ρ,:β)
function f(u,p,t)
x = p.σ*(u.y-u.x)
y = u.x*(p.ρ-u.z) - u.y
z = u.x*u.y - p.β*u.z
LorenzVector(x,y,z)
end
u0 = LorenzVector(1.0,0.0,0.0)
p = LorenzParameterVector(10.0,28.0,8/3)
tspan = (0.0,10.0)
prob = ODEProblem(f,u0,tspan,p)
sol = solve(prob,Tsit5())
```

Julia's Base has NamedTuples in v0.7+. They are constructed as:

```
p = (σ = 10.0,ρ = 28.0,β = 8/3)
```

and they support `p[1]`

and `p.σ`

as well. The `LVector`

, `SLVector`

, `LArray`

and `SLArray`

constructors also support named tuples as their arguments:

```
julia> LVector((a=1, b=2))
2-element LArray{Int64,1,(:a, :b)}:
1
2
julia> SLVector((a=1, b=2))
2-element SLArray{Tuple{2},1,(:a, :b),Int64}:
1
2
julia> LArray((2,2), (a=1, b=2, c=3, d=4))
2×2 LArray{Int64,2,(:a, :b, :c, :d)}:
1 3
2 4
julia> SLArray{Tuple{2,2}}((a=1, b=2, c=3, d=4))
2×2 SLArray{Tuple{2,2},2,(:a, :b, :c, :d),Int64}:
1 3
2 4
```

Converting to a named tuple from a labelled array x is available
using `convert(NamedTuple, x)`

. Furthermore, `pairs(x)`

creates an iterator that is functionally the same as
`pairs(convert(NamedTuple, x))`

, yielding `:label => x.label`

for each label of the array.

There are some crucial differences between a labelled array and
a named tuple. Labelled arrays can have any dimensions while
named tuples are always 1D. A named tuple can have different types
on each element, while an `SLArray`

can only have one element
type and furthermore it has the actions of a static vector.
As a result `SLArray`

has less element type information, which
improves compilation speed while giving more vector functionality
than a NamedTuple. `LArray`

also only has a single element type and,
unlike a named tuple, is mutable.

This functionality has been removed from LabelledArrays.jl, but can replicated with the same compile-time performance and indexing syntax using DimensionalData.jl.

10/20/2017

5 days ago

193 commits