Update documentation infrastructure and minor formatting

docs/Project.toml

@@ -3,4 +3,7 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
 
 [compat]
-Documenter = "0.27"
+Documenter = "1"
+
+[sources]
+FiniteDiff = {path = ".."}

docs/make.jl

@@ -23,7 +23,7 @@ open(joinpath(@__DIR__, "src", "index.md"), "w") do io
     for line in eachline(joinpath(dirname(@__DIR__), "README.md"))
         println(io, line)
     end
-    
+
     for line in eachline(joinpath(@__DIR__, "src", "reproducibility.md"))
         println(io, line)
     end
@@ -38,6 +38,7 @@ makedocs(sitename="FiniteDiff.jl",
     doctest=false,
     format=Documenter.HTML(assets=["assets/favicon.ico"],
         canonical="https://docs.sciml.ai/FiniteDiff/stable/"),
+    warnonly=[:missing_docs],
     pages=pages)
 
 deploydocs(repo="github.com/JuliaDiff/FiniteDiff.jl.git"; push_preview=true)

docs/src/assets/Project.toml

@@ -3,4 +3,7 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
 
 [compat]
-Documenter = "0.27"
+Documenter = "1"
+
+[sources]
+FiniteDiff = {path = ".."}

docs/src/reproducibility.md

@@ -35,20 +35,22 @@ You can also download the
 <a href="
 ```
 ```@eval
-using TOML
+using TOML, Markdown
 version = TOML.parse(read("../../Project.toml",String))["version"]
 name = TOML.parse(read("../../Project.toml",String))["name"]
 link = "https://github.com/SciML/"*name*".jl/tree/gh-pages/v"*version*"/assets/Manifest.toml"
+Markdown.MD(link)
 ```
 ```@raw html
 ">manifest</a> file and the
 <a href="
 ```
 ```@eval
-using TOML
+using TOML, Markdown
 version = TOML.parse(read("../../Project.toml",String))["version"]
 name = TOML.parse(read("../../Project.toml",String))["name"]
 link = "https://github.com/SciML/"*name*".jl/tree/gh-pages/v"*version*"/assets/Project.toml"
+Markdown.MD(link)
 ```
 ```@raw html
 ">project</a> file.

docs/src/tutorials.md

@@ -10,21 +10,21 @@ using FiniteDiff, StaticArrays
 
 fcalls = 0
 function f(dx,x) # in-place
-  global fcalls += 1
-  for i in 2:length(x)-1
-    dx[i] = x[i-1] - 2x[i] + x[i+1]
-  end
-  dx[1] = -2x[1] + x[2]
-  dx[end] = x[end-1] - 2x[end]
-  nothing
+    global fcalls += 1
+    for i in 2:length(x)-1
+        dx[i] = x[i-1] - 2x[i] + x[i+1]
+    end
+    dx[1] = -2x[1] + x[2]
+    dx[end] = x[end-1] - 2x[end]
+    nothing
 end
 
 const N = 10
 handleleft(x,i) = i==1 ? zero(eltype(x)) : x[i-1]
 handleright(x,i) = i==length(x) ? zero(eltype(x)) : x[i+1]
 function g(x) # out-of-place
-  global fcalls += 1
-  @SVector [handleleft(x,i) - 2x[i] + handleright(x,i) for i in 1:N]
+    global fcalls += 1
+    @SVector [handleleft(x,i) - 2x[i] + handleright(x,i) for i in 1:N]
 end
 ```
 
@@ -37,7 +37,7 @@ x = @SVector rand(N)
 FiniteDiff.finite_difference_jacobian(g,x)
 
 #=
-10×10 SArray{Tuple{10,10},Float64,2,100} with indices SOneTo(10)×SOneTo(10):
+10×10 SMatrix{10, 10, Float64, 100} with indices SOneTo(10)×SOneTo(10):
  -2.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0
   1.0  -2.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0
   0.0   1.0  -2.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0
@@ -65,7 +65,7 @@ FiniteDiff.finite_difference_jacobian!(output,f,x)
 output
 
 #=
-10×10 Array{Float64,2}:
+10×10 Matrix{Float64}:
  -2.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0
   1.0  -2.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0
   0.0   1.0  -2.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0
@@ -175,8 +175,8 @@ we get the analytical solution to the optimal matrix colors for our structured
 Jacobian. Now we can use this in our differencing routines:
 
 ```julia
-tridiagcache = FiniteDiff.JacobianCache(x,colorvec=colors,sparsity=tridiagjac)
-FiniteDiff.finite_difference_jacobian!(tridiagjac,f,x,tridiagcache)
+tridiagcache = FiniteDiff.JacobianCache(x, colorvec=colors, sparsity=tridiagjac)
+FiniteDiff.finite_difference_jacobian!(tridiagjac, f, x, tridiagcache)
 ```
 
 It'll use a special iteration scheme dependent on the matrix type to accelerate
@@ -189,14 +189,16 @@ differential equations, with a function like:
 
 ```julia
 function pde(out, x)
-	x = reshape(x, 100, 100)
-	out = reshape(out, 100, 100)
-	for i in 1:100
-		for j in 1:100
-			out[i, j] = x[i, j] + x[max(i -1, 1), j] + x[min(i+1, size(x, 1)), j] +  x[i, max(j-1, 1)]  + x[i, min(j+1, size(x, 2))]
-		end
-	end
-	return vec(out)
+    x = reshape(x, 100, 100)
+    out = reshape(out, 100, 100)
+    m = size(x, 1)
+    n = size(x, 2)
+    for i in 1:100
+        for j in 1:100
+            out[i, j] = x[i, j] + x[max(i-1, 1), j] + x[min(i+1, m), j] +  x[i, max(j-1, 1)]  + x[i, min(j+1, n)]
+        end
+    end
+    return vec(out)
 end
 x = rand(10000)
 ```
@@ -212,4 +214,4 @@ bbbcache = FiniteDiff.JacobianCache(x,colorvec=colorsbbb,sparsity=Jbbb)
 FiniteDiff.finite_difference_jacobian!(Jbbb, pde, x, bbbcache)
 ```
 
-And boom, a fast Jacobian filling algorithm on your special matrix.
+And boom, a fast Jacobian filling algorithm on your special matrix.

src/derivatives.jl

@@ -1,9 +1,10 @@
 """
     FiniteDiff.finite_difference_derivative(
-        f, x::T,
-        fdtype::Type{T1}=Val{:central},
-        returntype::Type{T2}=eltype(x),
-        f_x::Union{Nothing,T}=nothing)
+        f,
+        x          :: T,
+        fdtype     :: Type{T1}         = Val{:central},
+        returntype :: Type{T2}         = eltype(x),
+        f_x        :: Union{Nothing,T} = nothing)
 
 Single-point derivative of scalar->scalar maps.
 """
@@ -43,7 +44,7 @@ end
     FiniteDiff.DerivativeCache(
         x          :: AbstractArray{<:Number},
         fx         :: Union{Nothing,AbstractArray{<:Number}} = nothing,
-        epsilon    :: Union{Nothing,AbstractArray{<:Real}} = nothing,
+        epsilon    :: Union{Nothing,AbstractArray{<:Real}}   = nothing,
         fdtype     :: Type{T1} = Val{:central},
         returntype :: Type{T2} = eltype(x))
 
@@ -146,14 +147,14 @@ end
 
 """
     FiniteDiff.finite_difference_derivative!(
-        df::AbstractArray{<:Number},
+        df    :: AbstractArray{<:Number},
         f,
-        x::AbstractArray{<:Number},
-        cache::DerivativeCache{T1,T2,fdtype,returntype};
-        relstep=default_relstep(fdtype, eltype(x)),
-        absstep=relstep,
-        dir=true)
-    
+        x     :: AbstractArray{<:Number},
+        cache :: DerivativeCache{T1,T2,fdtype,returntype};
+        relstep = default_relstep(fdtype, eltype(x)),
+        absstep = relstep,
+        dir     = true)
+
 Compute the derivative `df` of a scalar-valued map `f` at a collection of points `x`.
 
 Cached.

src/gradients.jl

@@ -85,7 +85,7 @@ Non-Allocating Cache Constructor
 # Output 
 The output is a [`GradientCache`](@ref) struct.
 
-```julia
+```julia-repl
 julia> x = [1.0, 3.0]
 2-element Vector{Float64}:
  1.0
@@ -116,12 +116,12 @@ end
     FiniteDiff.finite_difference_gradient(
         f,
         x,
-        fdtype::Type{T1}=Val{:central},
-        returntype::Type{T2}=eltype(x),
-        inplace::Type{Val{T3}}=Val{true};
-        relstep=default_relstep(fdtype, eltype(x)),
-        absstep=relstep,
-        dir=true)
+        fdtype     :: Type{T1}      = Val{:central},
+        returntype :: Type{T2}      = eltype(x),
+        inplace    :: Type{Val{T3}} = Val{true};
+        relstep = default_relstep(fdtype, eltype(x)),
+        absstep = relstep,
+        dir     = true)
 
 Compute the gradient of function `f` at point `x` using finite differences.
 
@@ -235,13 +235,13 @@ end
 
 """
     FiniteDiff.finite_difference_gradient!(
-        df::AbstractArray{<:Number},
+        df    :: AbstractArray{<:Number},
         f,
-        x::AbstractArray{<:Number},
-        cache::GradientCache;
-        relstep=default_relstep(fdtype, eltype(x)),
-        absstep=relstep
-        dir=true)
+        x     :: AbstractArray{<:Number},
+        cache :: GradientCache;
+        relstep = default_relstep(fdtype, eltype(x)),
+        absstep = relstep
+        dir     = true)
 
 Gradients are either a vector->scalar map `f(x)`, or a scalar->vector map `f(fx,x)` if `inplace=Val{true}` and `fx=f(x)` if `inplace=Val{false}`.
 

src/hessians.jl

@@ -58,12 +58,9 @@ end
 
 """
     HessianCache(
-        xpp,
-        xpm,
-        xmp,
-        xmm,
-        fdtype::Type{T1}=Val{:hcentral},
-        inplace::Type{Val{T2}} = x isa StaticArray ? Val{true} : Val{false})
+        xpp, xpm, xmp, xmm,
+        fdtype  :: Type{T1}      = Val{:hcentral},
+        inplace :: Type{Val{T2}} = x isa StaticArray ? Val{true} : Val{false})
 
 Non-allocating cache constructor.
 """
@@ -78,8 +75,8 @@ end
 """
     HessianCache(
         x,
-        fdtype::Type{T1}=Val{:hcentral},
-        inplace::Type{Val{T2}} = x isa StaticArray ? Val{true} : Val{false})
+        fdtype  :: Type{T1}      = Val{:hcentral},
+        inplace :: Type{Val{T2}} = x isa StaticArray ? Val{true} : Val{false})
 
 Allocating cache constructor.
 """
@@ -94,11 +91,11 @@ end
 """
     finite_difference_hessian(
         f,
-        x::AbstractArray{<:Number},
-        fdtype::Type{T1}=Val{:hcentral},
-        inplace::Type{Val{T2}} = x isa StaticArray ? Val{true} : Val{false};
-        relstep=default_relstep(fdtype, eltype(x)),
-        absstep=relstep)
+        x       :: AbstractArray{<:Number},
+        fdtype  :: Type{T1}      = Val{:hcentral},
+        inplace :: Type{Val{T2}} = x isa StaticArray ? Val{true} : Val{false};
+        relstep = default_relstep(fdtype, eltype(x)),
+        absstep = relstep)
 
 Compute the Hessian matrix of scalar function `f` at point `x` using finite differences.
 
@@ -171,13 +168,13 @@ end
 
 """
     finite_difference_hessian!(
-        H::AbstractMatrix,
+        H          :: AbstractMatrix,
         f,
-        x::AbstractArray{<:Number},
-        fdtype     :: Type{T1}=Val{:hcentral},
+        x          :: AbstractArray{<:Number},
+        fdtype     :: Type{T1} = Val{:hcentral},
         inplace    :: Type{Val{T2}} = x isa StaticArray ? Val{true} : Val{false};
-        relstep=default_relstep(fdtype, eltype(x)),
-        absstep=relstep)
+        relstep = default_relstep(fdtype, eltype(x)),
+        absstep = relstep)
 
 Cache-less.
 """