Switch from JuliaFormatter to Runic.jl for code formatting

.github/workflows/FormatCheck.yml

@@ -1,13 +1,19 @@
-name: "Format Check"
+name: format-check
 
 on:
   push:
     branches:
+      - 'master'
       - 'main'
+      - 'release-'
     tags: '*'
   pull_request:
 
 jobs:
-  format-check:
-    name: "Format Check"
-    uses: "SciML/.github/.github/workflows/format-check.yml@v1"
+  runic:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: fredrikekre/runic-action@v1
+        with:
+          version: '1'

docs/make.jl

@@ -5,13 +5,17 @@ cp("./docs/Project.toml", "./docs/src/assets/Project.toml", force = true)
 
 include("pages.jl")
 
-makedocs(sitename = "ModelOrderReduction.jl",
+makedocs(
+    sitename = "ModelOrderReduction.jl",
     authors = "Bowen S. Zhu",
     modules = [ModelOrderReduction],
     clean = true, doctest = false, linkcheck = true,
     warnonly = [:missing_docs, :example_block],
-    format = Documenter.HTML(assets = ["assets/favicon.ico"],
-        canonical = "https://docs.sciml.ai/ModelOrderReduction/stable/"),
-    pages = pages)
+    format = Documenter.HTML(
+        assets = ["assets/favicon.ico"],
+        canonical = "https://docs.sciml.ai/ModelOrderReduction/stable/"
+    ),
+    pages = pages
+)
 
 deploydocs(repo = "github.com/SciML/ModelOrderReduction.jl.git")

src/DataReduction/POD.jl

@@ -2,7 +2,7 @@ using TSVD: tsvd
 using RandomizedLinAlg: rsvd
 
 function matricize(VoV::Vector{Vector{T}}) where {T}
-    reduce(hcat, VoV)
+    return reduce(hcat, VoV)
 end
 
 function _svd(data::Vector{Vector{T}}; kwargs...) where {T}
@@ -38,17 +38,19 @@ mutable struct POD <: AbstractDRProblem
     renergy::Any
     spectrum::Any
     # constructors
-    function POD(snaps;
+    function POD(
+            snaps;
             min_renergy = 1.0,
             min_nmodes::Int = 1,
-            max_nmodes::Int = length(snaps[1]))
+            max_nmodes::Int = length(snaps[1])
+        )
         nmodes = min_nmodes
         errorhandle(snaps, nmodes, min_renergy, min_nmodes, max_nmodes)
-        new(snaps, min_renergy, min_nmodes, max_nmodes, nmodes, missing, 1.0, missing)
+        return new(snaps, min_renergy, min_nmodes, max_nmodes, nmodes, missing, 1.0, missing)
     end
     function POD(snaps, nmodes::Int)
         errorhandle(snaps, nmodes, 0.0, nmodes, nmodes)
-        new(snaps, 0.0, nmodes, nmodes, nmodes, missing, 1.0, missing)
+        return new(snaps, 0.0, nmodes, nmodes, nmodes, missing, 1.0, missing)
     end
 end
 
@@ -66,11 +68,13 @@ end
 function reduce!(pod::POD, alg::SVD)
     u, s, v = _svd(pod.snapshots; alg.kwargs...)
     pod.nmodes,
-    pod.renergy = determine_truncation(s, pod.min_nmodes, pod.max_nmodes,
-        pod.min_renergy)
+        pod.renergy = determine_truncation(
+        s, pod.min_nmodes, pod.max_nmodes,
+        pod.min_renergy
+    )
     pod.rbasis = u[:, 1:(pod.nmodes)]
     pod.spectrum = s
-    nothing
+    return nothing
 end
 
 function reduce!(pod::POD, alg::TSVD)
@@ -79,7 +83,7 @@ function reduce!(pod::POD, alg::TSVD)
     pod.renergy = sum(s) / (sum(s) + (n_max - pod.nmodes) * s[end])
     pod.rbasis = u
     pod.spectrum = s
-    nothing
+    return nothing
 end
 
 function reduce!(pod::POD, alg::RSVD)
@@ -88,13 +92,15 @@ function reduce!(pod::POD, alg::RSVD)
     pod.renergy = sum(s) / (sum(s) + (n_max - pod.nmodes) * s[end])
     pod.rbasis = u
     pod.spectrum = s
-    nothing
+    return nothing
 end
 
 function Base.show(io::IO, pod::POD)
     print(io, "POD \n")
     print(io, "Reduction Order = ", pod.nmodes, "\n")
-    print(io, "Snapshot size = (", size(pod.snapshots, 1), ",", size(pod.snapshots[1], 2),
-        ")\n")
-    print(io, "Relative Energy = ", pod.renergy, "\n")
+    print(
+        io, "Snapshot size = (", size(pod.snapshots, 1), ",", size(pod.snapshots[1], 2),
+        ")\n"
+    )
+    return print(io, "Relative Energy = ", pod.renergy, "\n")
 end

src/ErrorHandle.jl

@@ -1,16 +1,18 @@
 function errorhandle(data::AbstractMatrix, nmodes::Int, max_energy, min_nmodes, max_nmodes)
-    @assert size(data, 1)>1 "State vector is expected to be vector valued."
+    @assert size(data, 1) > 1 "State vector is expected to be vector valued."
     s = minimum(size(data))
-    @assert 0<nmodes<=s "Number of modes should be in {1,2,...,$s}."
-    @assert min_nmodes<=max_nmodes "Minimum number of modes must lie below maximum number of modes"
-    @assert 0.0<=max_energy<=1.0 "Maximum relative energy must be in [0,1]"
+    @assert 0 < nmodes <= s "Number of modes should be in {1,2,...,$s}."
+    @assert min_nmodes <= max_nmodes "Minimum number of modes must lie below maximum number of modes"
+    return @assert 0.0 <= max_energy <= 1.0 "Maximum relative energy must be in [0,1]"
 end
 
-function errorhandle(data::AbstractVector{T}, nmodes::Int, max_energy, min_nmodes,
-        max_nmodes) where {T <: AbstractVector}
-    @assert size(data[1], 1)>1 "State vector is expected to be vector valued."
+function errorhandle(
+        data::AbstractVector{T}, nmodes::Int, max_energy, min_nmodes,
+        max_nmodes
+    ) where {T <: AbstractVector}
+    @assert size(data[1], 1) > 1 "State vector is expected to be vector valued."
     s = min(size(data, 1), size(data[1], 1))
-    @assert 0<nmodes<=s "Number of modes should be in {1,2,...,$s}."
-    @assert min_nmodes<=max_nmodes "Minimum number of modes must lie below maximum number of modes"
-    @assert 0.0<=max_energy<=1.0 "Maximum relative energy must be in [0,1]"
+    @assert 0 < nmodes <= s "Number of modes should be in {1,2,...,$s}."
+    @assert min_nmodes <= max_nmodes "Minimum number of modes must lie below maximum number of modes"
+    return @assert 0.0 <= max_energy <= 1.0 "Maximum relative energy must be in [0,1]"
 end

src/ModelOrderReduction.jl

@@ -3,8 +3,8 @@ module ModelOrderReduction
 using DocStringExtensions: DocStringExtensions, FUNCTIONNAME, SIGNATURES, TYPEDSIGNATURES
 
 using ModelingToolkit: ModelingToolkit, @variables, Differential, Equation, Num, ODESystem,
-                       SymbolicUtils, Symbolics, arguments, build_function, complete,
-                       expand, substitute, tearing_substitution
+    SymbolicUtils, Symbolics, arguments, build_function, complete,
+    expand, substitute, tearing_substitution
 using LinearAlgebra: LinearAlgebra, /, \, mul!, qr, svd
 
 using Setfield: Setfield, @set!

src/Types.jl

@@ -7,20 +7,20 @@ abstract type AbstractSVD end
 struct SVD <: AbstractSVD
     kwargs::Any
     function SVD(; kwargs...)
-        new(kwargs)
+        return new(kwargs)
     end
 end
 
 struct TSVD <: AbstractSVD
     kwargs::Any
     function TSVD(; kwargs...)
-        new(kwargs)
+        return new(kwargs)
     end
 end
 
 struct RSVD <: AbstractSVD
     p::Int
     function RSVD(p::Int = 0)
-        new(p)
+        return new(p)
     end
 end

src/deim.jl

@@ -64,10 +64,12 @@ the ``\\rho_i``-th column of the identity matrix ``I_n\\in\\mathbb R^{n\\times n
 - `reduced_rhss`: the right-hand side of ROM.
 - `linear_projection_eqs`: the linear projection mapping ``\\mathbf y=V\\hat{\\mathbf y}``.
 """
-function deim(full_vars::AbstractVector, linear_coeffs::AbstractMatrix,
+function deim(
+        full_vars::AbstractVector, linear_coeffs::AbstractMatrix,
         constant_part::AbstractVector, nonlinear_part::AbstractVector,
         reduced_vars::AbstractVector, linear_projection_matrix::AbstractMatrix,
-        nonlinear_projection_matrix::AbstractMatrix; kwargs...)
+        nonlinear_projection_matrix::AbstractMatrix; kwargs...
+    )
     # rename variables for convenience
     y = full_vars
     A = linear_coeffs
@@ -91,7 +93,7 @@ function deim(full_vars::AbstractVector, linear_coeffs::AbstractMatrix,
     Â = V' * A * V
     ĝ = V' * g
     reduced_rhss = Â * ŷ + ĝ + F̂
-    reduced_rhss, linear_projection_eqs
+    return reduced_rhss, linear_projection_eqs
 end
 """
     $(FUNCTIONNAME)(
@@ -118,9 +120,11 @@ The POD basis used for DEIM interpolation is obtained from the snapshot matrix o
 nonlinear terms, which is computed by executing the runtime-generated function for
 nonlinear expressions.
 """
-function deim(sys::ODESystem, snapshot::AbstractMatrix, pod_dim::Integer;
+function deim(
+        sys::ODESystem, snapshot::AbstractMatrix, pod_dim::Integer;
         deim_dim::Integer = pod_dim, name::Symbol = Symbol(nameof(sys), :_deim),
-        kwargs...)::ODESystem
+        kwargs...
+    )::ODESystem
     sys = deepcopy(sys)
     @set! sys.name = name
 
@@ -166,15 +170,17 @@ function deim(sys::ODESystem, snapshot::AbstractMatrix, pod_dim::Integer;
     old_observed = ModelingToolkit.get_observed(sys)
     fullstates = [map(eq -> eq.lhs, old_observed); dvs; ModelingToolkit.get_unknowns(sys)]
     new_observed = [old_observed; linear_projection_eqs]
-    new_sorted_observed = ModelingToolkit.topsort_equations(new_observed, fullstates;
-        kwargs...)
+    new_sorted_observed = ModelingToolkit.topsort_equations(
+        new_observed, fullstates;
+        kwargs...
+    )
     @set! sys.observed = new_sorted_observed
 
     inv_dict = Dict(Symbolics.scalarize(ŷ .=> V' * dvs)) # reduced vars to original vars
     @set! sys.defaults = merge(ModelingToolkit.defaults(sys), inv_dict)
 
     # CRITICAL: Call complete() on the system before returning to ensure all subsystems,
-    # variables, and parameters are properly registered and namespaced. Without this, 
+    # variables, and parameters are properly registered and namespaced. Without this,
     # attempting to create an ODEProblem from the DEIM system will fail with errors about
     # missing initial conditions for variables that should exist in the system.
     # This is required due to changes in ModelingToolkit.jl's internal structure handling.

src/utils.jl

@@ -22,7 +22,7 @@ function get_deqs(sys::ODESystem)::Tuple{Vector{Equation}, Vector{Equation}}
             push!(others, eq)
         end
     end
-    deqs, others
+    return deqs, others
 end
 
 """
@@ -78,7 +78,7 @@ function separate_terms(exprs::AbstractVector, vars, iv)
         push!(linear_I, row_index)
         push!(linear_J, idxmap[term])
         push!(linear_V, value)
-        nothing
+        return nothing
     end
 
     other_terms = similar(exprs, Num) # create a vector of the same size
@@ -95,7 +95,7 @@ function separate_terms(exprs::AbstractVector, vars, iv)
         else
             other_terms[i] += expr
         end
-        nothing
+        return nothing
     end
 
     for (i, expr) in enumerate(exprs)
@@ -125,4 +125,4 @@ function separate_terms(exprs::AbstractVector, vars, iv)
     end
     linear = sparse(linear_I, linear_J, linear_V, length(exprs), length(vars))
     return linear, other_terms, nonlinear_terms
-end
+end

test/DataReduction.jl

@@ -5,15 +5,15 @@ function lorenz_prob()
     function lorenz!(du, u, p, t)
         du[1] = p[1] * (u[2] - u[1])
         du[2] = u[1] * (p[2] - u[3]) - u[2]
-        du[3] = u[1] * u[2] - p[3] * u[3]
+        return du[3] = u[1] * u[2] - p[3] * u[3]
     end
 
     u0 = [1, 0, 0]
     p = [10, 28, 8 / 3]
     tspan = (0, 100)
     prob = ODEProblem(lorenz!, u0, tspan, p)
     sol = solve(prob, Tsit5())
-    sol
+    return sol
 end
 
 @testset "POD-Utils" begin

test/deim.jl

@@ -13,14 +13,20 @@ const γ = 2.0
 const c = 0.05
 f(v) = v * (v - 0.1) * (1.0 - v)
 i₀(t) = 50000.0t^3 * exp(-15.0t)
-eqs = [ε * Dt(v(x, t)) ~ ε^2 * Dxx(v(x, t)) + f(v(x, t)) - w(x, t) + c,
-    Dt(w(x, t)) ~ b * v(x, t) - γ * w(x, t) + c]
-bcs = [v(x, 0.0) ~ 0.0,
+eqs = [
+    ε * Dt(v(x, t)) ~ ε^2 * Dxx(v(x, t)) + f(v(x, t)) - w(x, t) + c,
+    Dt(w(x, t)) ~ b * v(x, t) - γ * w(x, t) + c,
+]
+bcs = [
+    v(x, 0.0) ~ 0.0,
     w(x, 0) ~ 0.0,
     Dx(v(0, t)) ~ -i₀(t),
-    Dx(v(L, t)) ~ 0.0]
-domains = [x ∈ (0.0, L),
-    t ∈ (0.0, 14.0)]
+    Dx(v(L, t)) ~ 0.0,
+]
+domains = [
+    x ∈ (0.0, L),
+    t ∈ (0.0, 14.0),
+]
 ivs = [x, t]
 dvs = [v(x, t), w(x, t)]
 pde_sys = PDESystem(eqs, bcs, domains, ivs, dvs; name = :FN)

test/qa.jl

@@ -3,8 +3,10 @@ using ModelOrderReduction, Aqua
     #Aqua.find_persistent_tasks_deps(ModelOrderReduction)
     Aqua.test_ambiguities(ModelOrderReduction, recursive = false)
     Aqua.test_deps_compat(ModelOrderReduction)
-    Aqua.test_piracies(ModelOrderReduction,
-        treat_as_own = [])
+    Aqua.test_piracies(
+        ModelOrderReduction,
+        treat_as_own = []
+    )
     Aqua.test_project_extras(ModelOrderReduction)
     Aqua.test_stale_deps(ModelOrderReduction)
     Aqua.test_unbound_args(ModelOrderReduction)

test/utils.jl

@@ -8,12 +8,12 @@ using ModelingToolkit
         vars = [x, y, z]
         exprs = [x, 2y, 3z, 4w]
         A, c, n = ModelOrderReduction.separate_terms(exprs, vars, t)
-        
+
         # Test dimensions are correct
         @test size(A) == (length(exprs), length(vars))
         @test length(c) == length(exprs)
         @test length(n) == length(exprs)
-        
+
         # Test that function doesn't error and returns expected types
     end
 
@@ -32,10 +32,12 @@ using ModelingToolkit
 
     @testset "separate_terms nonunique vars" begin
         vars = [x, y, y]
-        exprs = [3.0x + 4.5y + 6.0,
-                 2.0z + 3.4w + 7.0 + sin(x),
-                 9.8 + x * (1.0 - y),
-                 5.6y + 1.3z^2]
+        exprs = [
+            3.0x + 4.5y + 6.0,
+            2.0z + 3.4w + 7.0 + sin(x),
+            9.8 + x * (1.0 - y),
+            5.6y + 1.3z^2,
+        ]
         @test_throws ArgumentError ModelOrderReduction.separate_terms(exprs, vars, t)
     end
-end
+end