add docs build workflow (#787)

* add docs build workflow

* fix chop docs

* Add disk

Author: jishnub

.github/workflows/docs.yml

@@ -0,0 +1,41 @@
+name: Documentation
+
+on:
+  pull_request:
+  push:
+    branches:
+      - 'master'
+      - 'backport**'
+    tags: '*'
+  release:
+    types: [published]
+
+jobs:
+  build:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        julia-version: [1]
+        os: [ubuntu-latest]
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: ${{ matrix.julia-version }}
+      - name: Cache artifacts
+        uses: actions/cache@v1
+        env:
+          cache-name: cache-artifacts
+        with:
+          path: ~/.julia/artifacts
+          key: ${{ runner.os }}-test-${{ env.cache-name }}-${{ hashFiles('**/Project.toml') }}
+          restore-keys: |
+            ${{ runner.os }}-test-${{ env.cache-name }}-
+            ${{ runner.os }}-test-
+            ${{ runner.os }}-
+      - 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 }}
+        run: julia --project=docs/ docs/make.jl

Project.toml

@@ -35,8 +35,9 @@ SpecialFunctions = "1.1, 2"
 julia = "1.6"
 
 [extras]
+Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "Random"]
+test = ["Documenter", "Test", "Random"]

docs/src/index.md

@@ -4,7 +4,7 @@ using ApproxFun
 
 # ApproxFun.jl Documentation
 
-ApproxFun is a package for approximating and manipulating functions, and for solving differential and integral equations.  
+ApproxFun is a package for approximating and manipulating functions, and for solving differential and integral equations.
 
 ## Introduction
 
@@ -20,7 +20,7 @@ Some traditional examples of bases ``\mathop{ψ}_1(x), \mathop{ψ}_2(x), …`` a
 2. Fourier series (for periodic functions on `0..2π`): ``1, \sin{x}, \cos{x}, \sin{2x}, …``
 3. Chebyshev series (for non-periodic functions on `-1..1`): ``1, x, \cos(2\arccos{x}), \cos(3\arccos{x}), …``
 
-In ApproxFun, functions are represented by a `Fun` with two components: `space`, which dictates the basis and `coefficients` which is a finite vector of coefficients.  Note that each `Fun` can have a different length vector of coefficients, allowing for approximation of many different functions to high accuracy.  
+In ApproxFun, functions are represented by a `Fun` with two components: `space`, which dictates the basis and `coefficients` which is a finite vector of coefficients.  Note that each `Fun` can have a different length vector of coefficients, allowing for approximation of many different functions to high accuracy.
 
 The approximation by a `Fun` can be determined by a variety of methods:
 
@@ -39,7 +39,7 @@ Fun(exp)
 
 determines that `f` can be approximated to roughly machine precision using 14 coefficients.  See [Constructors](usage/constructors.md) for more information.
 
-(3) Manipulation of `Fun`s give new `Fun`s, where the number of coefficients is determined from the input.  The simplest example is addition, which for compatible bases is just padding the vectors to the same length and adding.  
+(3) Manipulation of `Fun`s give new `Fun`s, where the number of coefficients is determined from the input.  The simplest example is addition, which for compatible bases is just padding the vectors to the same length and adding.
 
 ```@repl using-pkgs
 a = Fun(cos,Chebyshev()); ncoefficients(a)
@@ -62,7 +62,7 @@ a(0.1)*b(0.1) - (a*b)(0.1)
 
 The example of multiplication highlights the importance of adaptivity: if with a fixed discretization size, operations like multiplication would lose accuracy when the true function is no longer resolved by the discretization.  More complicated examples are solving differential equations, where the coefficients of the solution can be determined adaptively, see [Equations](usage/equations.md).
 
-ApproxFun supports a number of different spaces, as described in [Spaces](usage/spaces.md).  A key component of ApproxFun is support for interaction between different spaces.  This is crucial for efficient solution of differential equations, where linear operators are described as acting between different spaces, see [Operators](usage/operators.md).  
+ApproxFun supports a number of different spaces, as described in [Spaces](usage/spaces.md).  A key component of ApproxFun is support for interaction between different spaces.  This is crucial for efficient solution of differential equations, where linear operators are described as acting between different spaces, see [Operators](usage/operators.md).
 
 ## Contents
 

docs/src/internals/blocks.md

@@ -3,7 +3,7 @@
 Every space is divided into blocks, which are used to indicate, for example,
 polynomial degree.  The prototypical examples have trivial blocks,
 for example `Taylor()` and `Chebyshev()` have blocks of length 1, as
-each coefficient corresponds to a higher polynomial degree.   
+each coefficient corresponds to a higher polynomial degree.
 
 Usually, non-trivial block lengths arise from modifications of the
 spaces with trivial blocks.  For example, `Chebyshev(0..1) ∪ Chebyshev(2..3)`
@@ -16,4 +16,4 @@ block has length 1, then 2, and so on.
 `blocklengths(::Space)` gives an iterator that encodes the lengths of the blocks.
 For trivial blocks, this will return `Ones{Int}(∞)`.  For
 `Chebyshev(0..1) ∪ Chebyshev(2..3)` it returns `Fill(2,∞)`.
-For `Chebyshev() ⊗ Chebyshev()` it returns `InfiniteArrays.OneToInf()`.  
+For `Chebyshev() ⊗ Chebyshev()` it returns `InfiniteArrays.OneToInf()`.

docs/src/library.md

@@ -30,7 +30,7 @@ Curve
 ```
 
 ```@docs
-Disk
+ApproxFunFourier.Disk
 ```
 
 ```@docs
@@ -84,7 +84,7 @@ transform
 ```
 
 ```@docs
-evaluate(::Space,::AbstractVector,::)
+evaluate
 ```
 
 ```@docs
@@ -201,7 +201,6 @@ ApproxFun.values
 stride(::Fun)
 ```
 
-
 ## Modify a Fun
 
 
@@ -213,8 +212,9 @@ reverseorientation
 ApproxFun.setdomain
 ```
 
+
 ```@docs
-chop
+chop(::Fun, ::Any)
 ```
 
 
@@ -237,7 +237,7 @@ rangespace
 ```
 
 ```@docs
-getindex(::Operator,::,::)
+getindex(::Operator,::Any,::Any)
 ```
 
 ```@docs
@@ -246,7 +246,7 @@ getindex(::Operator,::Fun)
 
 
 ```@docs
-\(::Operator,::)
+\(::Operator,::Any)
 ```
 
 ```@docs

src/docs.jl

@@ -171,7 +171,7 @@ stride(::Fun)
 ## Modifiers
 
 """
-   chop(f::Fun,tol) -> Fun
+    chop(f::Fun,tol) -> Fun
 
 reduces the number of coefficients by dropping the tail that is below the specified tolerance.
 """

test/runtests.jl

@@ -1,6 +1,13 @@
 using ApproxFun, Random, Test
 import ApproxFunBase: testbandedblockbandedoperator, blocklengths, ∞
 
+using Documenter
+DocMeta.setdocmeta!(ApproxFun, :DocTestSetup, :(using ApproxFun); recursive=true)
+
+@testset "doctests" begin
+    doctest(ApproxFun, manual = false)
+end
+
 @time include("ReadmeTest.jl")
 @time include("ExtrasTest.jl")
 @time include("NumberTypeTest.jl")