Add Documenter docs for LabelledArrays

.github/workflows/Documentation.yml

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+name: Documentation
+
+on:
+  push:
+    branches:
+      - master
+      - 'release-'
+    tags: '*'
+  pull_request:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: '1'
+      - name: Install dependencies
+        run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
+      - name: Build and deploy
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
+          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # For authentication with SSH deploy key
+        run: julia --project=docs/ docs/make.jl

.gitignore

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 *.jl.*.cov
 *.jl.mem
 Manifest.toml
+.vscode/

docs/.gitignore

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+build/
+site/

docs/Project.toml

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+[deps]
+Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+LabelledArrays = "2ee39098-c373-598a-b85f-a56591580800"
+
+[compat]
+Documenter = "0.27"

docs/make.jl

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+using Documenter, LabelledArrays
+
+include("pages.jl")
+
+makedocs(
+    sitename="LabelledArrays.jl",
+    authors="Chris Rackauckas",
+    modules=[LabelledArrays],
+    clean=true,doctest=false,
+    format = Documenter.HTML(analytics = "UA-90474609-3",
+                             assets = ["assets/favicon.ico"],
+                             canonical="https://labelledarrays.sciml.ai/stable/"),
+    pages=pages
+)
+
+deploydocs(
+   repo = "github.com/SciML/LabelledArrays.jl.git";
+   push_preview = true
+)

docs/pages.jl

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+# Put in a separate page so it can be used by SciMLDocs.jl
+
+pages=[
+    "Home" => "index.md",
+    "SLArrays" => "SLArrays.md",
+    "LArrays" => "LArrays.md",
+    "Example: Nice DiffEq Syntax Without A DSL" => "Example_dsl.md",
+    "Relation to NamedTuples" => "NamedTuples_relation.md",
+    "Note_labelled_slices.md"
+]

docs/src/Example_dsl.md

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+# Example: Nice DiffEq Syntax Without A DSL
+
+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:
+
+```julia
+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`:
+
+```julia
+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())
+```

docs/src/LArrays.md

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+# LArrays
+
+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:
+
+```julia
+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:
+
+```julia
+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:
+
+```julia
+A = @LVector Float64 (:a,:b,:c,:d)
+A .= rand(4)
+```
+
+As with `SLArray`, alternative constructors exist that use the keyword argument
+form:
+
+```julia
+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
+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.

docs/src/NamedTuples_relation.md

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+# Relation to NamedTuples
+
+Julia's Base has NamedTuples in v0.7+. They are constructed as:
+
+```julia
+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
+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.

docs/src/Note_labelled_slices.md

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+# Note: Labelled slices
+
+This functionality has been removed from LabelledArrays.jl, but can 
+replicated with the same compile-time performance and indexing syntax 
+using [DimensionalData.jl](https://github.com/rafaqz/DimensionalData.jl).

docs/src/SLArrays.md

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+# SLArrays
+
+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.
+
+```julia
+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:
+
+```julia
+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
+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
+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
+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
+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
+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
+```

docs/src/index.md

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+# LabelledArrays.jl: Arrays with Label Goodness
+
+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`.
+
+## Installation
+
+To install LabelledArrays.jl, use the Julia package manager:
+
+```julia
+using Pkg
+Pkg.add("LabelledArrays")
+```
+
+## Contributing
+
+- Please refer to the
+  [SciML ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://github.com/SciML/ColPrac/blob/master/README.md)
+  for guidance on PRs, issues, and other matters relating to contributing to SciML.
+- There are a few community forums:
+    - the #diffeq-bridged channel in the [Julia Slack](https://julialang.org/slack/)
+    - [JuliaDiffEq](https://gitter.im/JuliaDiffEq/Lobby) on Gitter
+    - on the [Julia Discourse forums](https://discourse.julialang.org)
+    - see also [SciML Community page](https://sciml.ai/community/)