Automatic JuliaFormatter.jl run

ext/TopOptMakieExt/TopOptMakieExt.jl

@@ -226,7 +226,7 @@ function TopOpt.visualize(
         # https://jkrumbiegel.github.io/MakieLayout.jl/v0.3/layoutables/#LScene-1
         # https://makie.juliaplots.org/stable/cameras.html#D-Camera
         # ax1 = layout[1, 1] = LScene(scene, camera = cam3d!, raw = false)
-        ax1 = LScene(fig[1, 1]; scenekw=(camera=cam3d!, raw=false)) # , height=750
+        ax1 = LScene(fig[1, 1]; scenekw=(camera=(cam3d!), raw=false)) # , height=750
     end
 
     # * support / load appearance / deformatione exaggeration control
@@ -417,7 +417,7 @@ function TopOpt.visualize(
         # ax1.aspect = AxisAspect(1)
         ax1.aspect = DataAspect()
     else
-        ax1 = LScene(fig[1, 1]; scenekw=(camera=cam3d!, raw=false)) #, height=750)
+        ax1 = LScene(fig[1, 1]; scenekw=(camera=(cam3d!), raw=false)) #, height=750)
     end
 
     # * linewidth scaling / support / load appearance / deformatione exaggeration control

src/CheqFilters/density_filter.jl

@@ -19,7 +19,7 @@ function DensityFilter(
     return DensityFilter(Val(filtering), solver, args...)
 end
 function DensityFilter(
-    ::Val{true}, solver::TS, rmin::T, ::Type{TI}=Int
+    ::Val{true}, solver::TS, rmin::T, (::Type{TI})=Int
 ) where {T,TI<:Integer,TS<:AbstractFEASolver}
     metadata = FilterMetadata(solver, rmin, TI)
     TM = typeof(metadata)
@@ -36,7 +36,7 @@ function DensityFilter(
 end
 
 function DensityFilter(
-    ::Val{false}, solver::TS, rmin::T, ::Type{TI}=Int
+    ::Val{false}, solver::TS, rmin::T, (::Type{TI})=Int
 ) where {T,TS<:AbstractFEASolver,TI<:Integer}
     metadata = FilterMetadata(T, TI)
     jacobian = zeros(T, 0, 0)

src/CheqFilters/sens_filter.jl

@@ -23,7 +23,7 @@ function SensFilter(::Val{filtering}, solver::AbstractFEASolver, args...) where
     return SensFilter(Val(filtering), solver, args...)
 end
 function SensFilter(
-    ::Val{true}, solver::TS, rmin::T, ::Type{TI}=Int
+    ::Val{true}, solver::TS, rmin::T, (::Type{TI})=Int
 ) where {T,TI<:Integer,TS<:AbstractFEASolver}
     metadata = FilterMetadata(solver, rmin, TI)
     TM = typeof(metadata)
@@ -47,7 +47,7 @@ function SensFilter(
 end
 
 function SensFilter(
-    ::Val{false}, solver::TS, rmin::T, ::Type{TI}=Int
+    ::Val{false}, solver::TS, rmin::T, (::Type{TI})=Int
 ) where {T,TS<:AbstractFEASolver,TI<:Integer}
     elementinfo = solver.elementinfo
     metadata = FilterMetadata(T, TI)

src/FEA/assembly_cg_displacement_solvers.jl

@@ -76,7 +76,7 @@ function PCGDisplacementSolver(
 end
 
 function (s::PCGDisplacementSolver{T})(
-    ::Type{Val{safe}}=Val{false};
+    (::Type{Val{safe}})=Val{false};
     assemble_f=true,
     rhs=assemble_f ? s.globalinfo.f : s.rhs,
     lhs=assemble_f ? s.u : s.lhs,

src/FEA/direct_displacement_solver.jl

@@ -46,8 +46,8 @@ function DirectDisplacementSolver(
     )
 end
 function (s::DirectDisplacementSolver{T})(
-    ::Type{Val{safe}}=Val{false},
-    ::Type{newT}=T;
+    (::Type{Val{safe}})=Val{false},
+    (::Type{newT})=T;
     assemble_f=true,
     reuse_fact=false,
     rhs=assemble_f ? s.globalinfo.f : s.rhs,

src/Functions/trace.jl

@@ -42,7 +42,7 @@ struct TraceEstimationMean{TF,TV,TM} <: AbstractTraceEstimationMeanMethod
     sample_method::TM
 end
 function TraceEstimationMean(
-    F::SparseMatrixCSC, nv::Int, sample_once::Bool=true, sample_method=hutch_rand!
+    F::SparseMatrixCSC, nv::Int, sample_once::Bool=true, sample_method=(hutch_rand!)
 )
     V = zeros(eltype(F), size(F, 2), nv)
     sample_method(V)
@@ -56,7 +56,7 @@ struct TraceEstimationSVDMean{TUS,TV,TM} <: AbstractTraceEstimationMeanMethod
     sample_method::TM
 end
 function TraceEstimationSVDMean(
-    F::SparseMatrixCSC, nv::Int, sample_once::Bool=true, sample_method=hutch_rand!
+    F::SparseMatrixCSC, nv::Int, sample_once::Bool=true, sample_method=(hutch_rand!)
 )
     US = ExactSVDMean(F).US
     V = zeros(eltype(F), size(US, 2), nv)
@@ -65,7 +65,10 @@ function TraceEstimationSVDMean(
     return TraceEstimationSVDMean(US, size(F, 2), V, sample_once, sample_method)
 end
 function TraceEstimationSVDMean(
-    F::SparseMatrixCSC, V::AbstractMatrix, sample_once::Bool=true, sample_method=hutch_rand!
+    F::SparseMatrixCSC,
+    V::AbstractMatrix,
+    sample_once::Bool=true,
+    sample_method=(hutch_rand!),
 )
     US = ExactSVDMean(F).US
     sample_once = sample_method === hadamard! || sample_once
@@ -130,7 +133,7 @@ struct DiagonalEstimation{TF,TY,TQ,TV,Ttemp,TM} <: AbstractDiagonalEstimationMet
     sample_method::TM
 end
 function DiagonalEstimation(
-    F::SparseMatrixCSC, nv::Int, nE::Int, sample_once::Bool=true, sample_method=hadamard!
+    F::SparseMatrixCSC, nv::Int, nE::Int, sample_once::Bool=true, sample_method=(hadamard!)
 )
     V = zeros(eltype(F), size(F, 2), nv)
     sample_method(V)
@@ -145,7 +148,7 @@ function DiagonalEstimation(
     V::AbstractMatrix,
     nE::Int,
     sample_once::Bool=true,
-    sample_method=hadamard!,
+    sample_method=(hadamard!),
 )
     nv = size(V, 2)
     Y = zeros(eltype(F), size(F, 1), nv)

src/TopOptProblems/IO/INP/Parser/FeatureExtractors/extract_cells.jl

@@ -1,4 +1,4 @@
-function extract_cells(file, ::Type{TI}=Int) where {TI}
+function extract_cells(file, (::Type{TI})=Int) where {TI}
     line = readline(file)
 
     cell_idx_pattern = r"^(\d+)\s*,"

src/TopOptProblems/IO/INP/Parser/FeatureExtractors/extract_material.jl

@@ -1,4 +1,4 @@
-function extract_material(file, ::Type{TF}=Float64) where {TF}
+function extract_material(file, (::Type{TF})=Float64) where {TF}
     elastic_heading_pattern = r"\*ELASTIC"
     Emu_pattern = r"(-?\d+\.?\d*)\s*,\s*(-?\d+\.?\d*)"
     line = readline(file)

src/TopOptProblems/IO/INP/Parser/FeatureExtractors/extract_nodes.jl

@@ -1,4 +1,4 @@
-function extract_nodes(file, ::Type{TF}=Float64, ::Type{TI}=Int) where {TF,TI}
+function extract_nodes(file, (::Type{TF})=Float64, (::Type{TI})=Int) where {TF,TI}
     line = readline(file)
 
     pattern = r"(\d+)\s*,\s*(-?\d+\.?\d*(e[-\+]?\d*)?)\s*,\s*(-?\d+\.?\d*(e[-\+]?\d*)?)\s*(,\s*(-?\d+\.?\d*(e[-\+]?\d*)?))?"

src/TopOptProblems/elementinfo.jl

@@ -70,7 +70,7 @@ Constructs an instance of `ElementFEAInfo` from a stiffness problem `sp` using a
 The static matrices and vectors are more performant and GPU-compatible therefore they are used by default.
 """
 function ElementFEAInfo(
-    sp, quad_order=2, ::Type{Val{mat_type}}=Val{:Static}
+    sp, quad_order=2, (::Type{Val{mat_type}})=Val{:Static}
 ) where {mat_type}
     Kes, weights, dloads, cellvalues, facevalues = make_Kes_and_fes(
         sp, quad_order, Val{mat_type}
@@ -124,7 +124,7 @@ end
 
 Constructs an empty instance of `GlobalFEAInfo` where the field `K` is an empty sparse matrix of element type `T` and the field `f` is an empty dense vector of element type `T`.
 """
-GlobalFEAInfo(::Type{T}=Float64) where {T} = GlobalFEAInfo{T}()
+GlobalFEAInfo((::Type{T})=Float64) where {T} = GlobalFEAInfo{T}()
 function GlobalFEAInfo{T}() where {T}
     return GlobalFEAInfo(sparse(zeros(T, 0, 0)), zeros(T, 0), cholesky(one(T)), qr(one(T)))
 end

src/TopOptProblems/grids.jl

@@ -511,15 +511,17 @@ function _QuadraticLGrid(
     )
 end
 
-function TieBeamGrid(::Type{Val{CellType}}, ::Type{T}=Float64, refine=1) where {T,CellType}
+function TieBeamGrid(
+    ::Type{Val{CellType}}, (::Type{T})=Float64, refine=1
+) where {T,CellType}
     if CellType === :Linear
         return _LinearTieBeamGrid(T, refine)
     else
         return _QuadraticTieBeamGrid(T, refine)
     end
 end
 
-function _LinearTieBeamGrid(::Type{T}=Float64, refine=1) where {T}
+function _LinearTieBeamGrid((::Type{T})=Float64, refine=1) where {T}
     nodes = Node{2,T}[]
     cells = Quadrilateral[]
     boundary = Tuple{Int,Int}[]
@@ -642,7 +644,7 @@ function _LinearTieBeamGrid(::Type{T}=Float64, refine=1) where {T}
     return Grid(cells, nodes; facesets=facesets, boundary_matrix=boundary_matrix)
 end
 
-function _QuadraticTieBeamGrid(::Type{T}=Float64, refine=1) where {T}
+function _QuadraticTieBeamGrid((::Type{T})=Float64, refine=1) where {T}
     nodes = Node{2,T}[]
     cells = QuadraticQuadrilateral[]
     boundary = Tuple{Int,Int}[]

src/TopOptProblems/problem_types.jl

@@ -463,7 +463,7 @@ problem = LBeam(Val{celltype}, E = E, ν = ν, force = force)
 """
 function LBeam(
     ::Type{Val{CellType}},
-    ::Type{T}=Float64;
+    (::Type{T})=Float64;
     length=100,
     height=100,
     upperslab=50,
@@ -637,7 +637,7 @@ end
 - `CellType`: can be either `:Linear` or `:Quadratic` to determine the order of the geometric and field basis functions and element type. Only isoparametric elements are supported for now.
 """
 function TieBeam(
-    ::Type{Val{CellType}}, ::Type{T}=Float64, refine=1, force=T(1); E=T(1), ν=T(0.3)
+    ::Type{Val{CellType}}, (::Type{T})=Float64, refine=1, force=T(1); E=T(1), ν=T(0.3)
 ) where {T,CellType}
     grid = TieBeamGrid(Val{CellType}, T, refine)
     dh = DofHandler(grid)

src/TrussTopOptProblems/elementinfo.jl

@@ -12,7 +12,7 @@ Constructs an instance of `ElementFEAInfo` from a stiffness **truss** problem `s
 The static matrices and vectors are more performant and GPU-compatible therefore they are used by default.
 """
 function ElementFEAInfo(
-    sp::TrussProblem, quad_order=1, ::Type{Val{mat_type}}=Val{:Static}
+    sp::TrussProblem, quad_order=1, (::Type{Val{mat_type}})=Val{:Static}
 ) where {mat_type}
     # weights: self-weight element load vectors, all zeros now
     Kes, weights, cellvalues, facevalues = make_Kes_and_fes(sp, quad_order, Val{mat_type})

src/Utilities/utils.jl

@@ -98,7 +98,7 @@ function Base.setindex!(ra::RaggedArray, v, i, j)
     return ra.values[r[i]] = v
 end
 
-function find_varind(black, white, ::Type{TI}=Int) where {TI}
+function find_varind(black, white, (::Type{TI})=Int) where {TI}
     nel = length(black)
     nel == length(white) || throw("Black and white vectors should be of the same length")
     varind = zeros(TI, nel)