reapply formatter

.JuliaFormatter.toml

@@ -1,2 +1,3 @@
 style = "sciml"
-format_markdown = true
+format_markdown = true
+format_docstrings = true

src/named_array_partition.jl

@@ -2,9 +2,9 @@
     NamedArrayPartition(; kwargs...)
     NamedArrayPartition(x::NamedTuple) 
 
-Similar to an `ArrayPartition` but the individual arrays can be accessed via the 
-constructor-specified names. However, unlike `ArrayPartition`, each individual array 
-must have the same element type. 
+Similar to an `ArrayPartition` but the individual arrays can be accessed via the
+constructor-specified names. However, unlike `ArrayPartition`, each individual array
+must have the same element type.
 """
 struct NamedArrayPartition{T, A <: ArrayPartition{T}, NT <: NamedTuple} <: AbstractVector{T}
     array_partition::A

src/utils.jl

@@ -1,6 +1,6 @@
 """
 ```julia
-recursivecopy(a::Union{AbstractArray{T, N}, AbstractVectorOfArray{T,N}})
+recursivecopy(a::Union{AbstractArray{T, N}, AbstractVectorOfArray{T, N}})
 ```
 
 A recursive `copy` function. Acts like a `deepcopy` on arrays of arrays, but

src/vector_of_array.jl

@@ -29,10 +29,12 @@ returns a vector of the series for each component, that is, `A[i,:]` for each `i
 A plot recipe is provided, which plots the `A[i,:]` series.
 
 There is also support for `VectorOfArray` with constructed from multi-dimensional arrays
+
 ```julia
 VectorOfArray(u::AbstractArray{AT}) where {T, N, AT <: AbstractArray{T, N}}
 ```
-where `IndexStyle(typeof(u)) isa IndexLinear`. 
+
+where `IndexStyle(typeof(u)) isa IndexLinear`.
 """
 mutable struct VectorOfArray{T, N, A} <: AbstractVectorOfArray{T, N, A}
     u::A # A <: AbstractVector{<: AbstractArray{T, N - 1}}

test/downstream/symbol_indexing.jl

@@ -7,7 +7,7 @@ include("../testutils.jl")
 @parameters τ
 @variables RHS(t)
 @mtkbuild fol_separate = ODESystem([RHS ~ (1 - x) / τ,
-    D(x) ~ RHS],t)
+        D(x) ~ RHS], t)
 
 prob = ODEProblem(fol_separate, [x => 0.0], (0.0, 10.0), [τ => 3.0])
 sol = solve(prob, Tsit5())
@@ -41,7 +41,7 @@ test_tables_interface(sol_new, [:timestamp, Symbol("x(t)")], hcat(sol_new[t], so
 @variables y(t)
 @parameters α β γ δ
 @mtkbuild lv = ODESystem([D(x) ~ α * x - β * x * y,
-    D(y) ~ δ * x * y - γ * x * y],t)
+        D(y) ~ δ * x * y - γ * x * y], t)
 
 prob = ODEProblem(lv, [x => 1.0, y => 1.0], (0.0, 10.0),
     [α => 1.5, β => 1.0, γ => 3.0, δ => 1.0])