Format .jl files

deps/build.jl

@@ -0,0 +1 @@
+

docs/generate.jl

@@ -13,7 +13,7 @@ for example in readdir(EXAMPLE_DIR)
         Literate.markdown(input, GENERATED_DIR, postprocess = mdpost)
         Literate.notebook(input, GENERATED_DIR, execute = true)
     elseif any(endswith.(example, [".png", ".jpg", ".gif"]))
-        cp(joinpath(EXAMPLE_DIR, example), joinpath(GENERATED_DIR, example); force=true)
+        cp(joinpath(EXAMPLE_DIR, example), joinpath(GENERATED_DIR, example); force = true)
     else
         @warn "ignoring $example"
     end
@@ -22,4 +22,4 @@ end
 # remove any .vtu files in the generated dir (should not be deployed)
 cd(GENERATED_DIR) do
     foreach(file -> endswith(file, ".vtu") && rm(file), readdir())
-end
+end

docs/make.jl

@@ -7,30 +7,22 @@ using DocumenterCitations
 # Generate examples
 include("generate.jl")
 
-GENERATED_EXAMPLES = [joinpath("examples", f) for f in (
-    "simp.md",
-    "beso.md",
-    "geso.md",
-    "csimp.md",
-    "global_stress.md",
-    )]
+GENERATED_EXAMPLES = [
+    joinpath("examples", f) for
+    f in ("simp.md", "beso.md", "geso.md", "csimp.md", "global_stress.md")
+]
 
 bib = CitationBibliography(joinpath(@__DIR__, "biblio", "ref.bib"))
 makedocs(
     bib,
     sitename = "TopOpt.jl",
-    format = Documenter.HTML(
-        prettyurls = get(ENV, "CI", nothing) == "true"
-    ),
+    format = Documenter.HTML(prettyurls = get(ENV, "CI", nothing) == "true"),
     # doctest = false,
     pages = [
         "Home" => "index.md",
         "Examples" => GENERATED_EXAMPLES,
-        "API Reference" => [
-            "reference/TopOptProblems.md",
-            "reference/Algorithms.md",
-        ],
-        "Bibliography" => "bibliography.md"
+        "API Reference" => ["reference/TopOptProblems.md", "reference/Algorithms.md"],
+        "Bibliography" => "bibliography.md",
     ],
 )
 
@@ -40,8 +32,5 @@ makedocs(
 # end
 
 if get(ENV, "CI", nothing) == "true"
-    deploydocs(
-        repo = "github.com/JuliaTopOpt/TopOpt.jl.git",
-        push_preview=true,
-    )
+    deploydocs(repo = "github.com/JuliaTopOpt/TopOpt.jl.git", push_preview = true)
 end

docs/src/literate/beso.jl

@@ -47,4 +47,4 @@ result = beso(x0)
 #md #
 #md # ```julia
 #md # @__CODE__
-#md # ```
+#md # ```

docs/src/literate/csimp.jl

@@ -17,19 +17,14 @@ v = 0.3 # Poisson’s ratio
 f = 1.0 # downward force
 
 problems = Any[
-            PointLoadCantilever(Val{:Linear}, (60, 20, 20), (1.0, 1.0, 1.0), E, v, f), 
-            PointLoadCantilever(Val{:Linear}, (160, 40), (1.0, 1.0), E, v, f),
-            HalfMBB(Val{:Linear}, (60, 20), (1.0, 1.0), E, v, f), 
-            LBeam(Val{:Linear}, Float64, force = f),
-            TieBeam(Val{:Quadratic}, Float64)
-            ]
-problem_names = [
-    "3d cantilever beam",
-    "cantilever beam",
-    "half MBB beam",
-    "L-beam",
-    "tie-beam",
-    ]
+    PointLoadCantilever(Val{:Linear}, (60, 20, 20), (1.0, 1.0, 1.0), E, v, f),
+    PointLoadCantilever(Val{:Linear}, (160, 40), (1.0, 1.0), E, v, f),
+    HalfMBB(Val{:Linear}, (60, 20), (1.0, 1.0), E, v, f),
+    LBeam(Val{:Linear}, Float64, force = f),
+    TieBeam(Val{:Quadratic}, Float64),
+]
+problem_names =
+    ["3d cantilever beam", "cantilever beam", "half MBB beam", "L-beam", "tie-beam"]
 
 i = 2
 println(problem_names[i])
@@ -47,7 +42,7 @@ penalty = TopOpt.PowerPenalty(1.0)
 pcont = Continuation(penalty, steps = steps, xmin = xmin, pmax = 5.0)
 
 # NOTE: non-convexity + computational error lead to different solutions that satisfy the KKT tolerance
-mma_options = options = MMAOptions(maxiter=1000)
+mma_options = options = MMAOptions(maxiter = 1000)
 maxtol = 0.01 # maximum tolerance
 mintol = 0.0001 # minimum tolerance
 b = log(mintol / maxtol) / steps
@@ -58,9 +53,9 @@ mma_options_gen = TopOpt.MMAOptionsGen(
     ftol_gen = ExponentialContinuation(a, b, 0.0, steps + 1, mintol),
 )
 csimp_options = TopOpt.CSIMPOptions(
-    steps = steps, 
-    options_gen = mma_options_gen, 
-    p_gen = pcont, 
+    steps = steps,
+    options_gen = mma_options_gen,
+    p_gen = pcont,
     reuse = reuse,
 )
 
@@ -82,14 +77,12 @@ constr = x -> volfrac(filter(x)) - V
 
 # ### Define subproblem optimizer
 x0 = fill(V, length(solver.vars))
-optimizer = Optimizer(
-    obj, constr, x0, MMA87(),
-    options = mma_options, convcriteria = convcriteria,
-)
+optimizer =
+    Optimizer(obj, constr, x0, MMA87(), options = mma_options, convcriteria = convcriteria)
 
 # ### Define continuation SIMP optimizer
 simp = SIMP(optimizer, solver, penalty.p)
-cont_simp = ContinuationSIMP(simp, steps, csimp_options) 
+cont_simp = ContinuationSIMP(simp, steps, csimp_options)
 
 # ### Solve
 result = cont_simp(x0)
@@ -116,4 +109,4 @@ result = cont_simp(x0)
 #md #
 #md # ```julia
 #md # @__CODE__
-#md # ```
+#md # ```

docs/src/literate/geso.jl

@@ -47,4 +47,4 @@ result = geso(x0)
 #md #
 #md # ```julia
 #md # @__CODE__
-#md # ```
+#md # ```

docs/src/literate/global_stress.jl

@@ -18,15 +18,10 @@ f = 1.0 # downward force
 rmin = 3.0 # filter radius
 
 problems = Any[
-    PointLoadCantilever(Val{:Linear}, (60, 20), (1.0, 1.0), E, v, f), 
-    HalfMBB(Val{:Linear}, (60, 20), (1.0, 1.0), E, v, f), 
-]
-problem_names = [
-    "Cantilever beam",
-    "Half MBB beam",
-    "L-beam",
-    "Tie-beam",
+    PointLoadCantilever(Val{:Linear}, (60, 20), (1.0, 1.0), E, v, f),
+    HalfMBB(Val{:Linear}, (60, 20), (1.0, 1.0), E, v, f),
 ]
+problem_names = ["Cantilever beam", "Half MBB beam", "L-beam", "Tie-beam"]
 
 i = 1
 println(problem_names[i])
@@ -40,9 +35,7 @@ convcriteria = Nonconvex.KKTCriteria()
 penalty = TopOpt.PowerPenalty(1.0)
 
 # ### Define a finite element solver
-solver = FEASolver(
-    Direct, problem, xmin = xmin, penalty = penalty,
-)
+solver = FEASolver(Direct, problem, xmin = xmin, penalty = penalty)
 
 # ### Define **stress** objective
 # Notice that gradient is derived automatically by automatic differentiation (Zygote.jl)!
@@ -56,16 +49,12 @@ volfrac = TopOpt.Volume(problem, solver)
 
 obj = x -> volfrac(filter(x))
 constr = x -> norm(stress(filter(x)), 5) - 1.0
-options = MMAOptions(
-    maxiter=2000, tol = Nonconvex.Tolerance(kkt = 1e-4),
-)
+options = MMAOptions(maxiter = 2000, tol = Nonconvex.Tolerance(kkt = 1e-4))
 
 # ### Define subproblem optimizer
 x0 = fill(1.0, length(solver.vars))
-optimizer = Optimizer(
-    obj, constr, x0, MMA87(),
-    options = options, convcriteria = convcriteria,
-)
+optimizer =
+    Optimizer(obj, constr, x0, MMA87(), options = options, convcriteria = convcriteria)
 
 # ### Define continuation SIMP optimizer
 simp = SIMP(optimizer, solver, 3.0)
@@ -92,4 +81,4 @@ result = simp(x0)
 #md #
 #md # ```julia
 #md # @__CODE__
-#md # ```
+#md # ```

docs/src/literate/local_stress.jl

@@ -15,14 +15,12 @@ xmin = 0.0001 # minimum density
 steps = 40 # maximum number of penalty steps, delta_p0 = 0.1
 
 # ### Continuation SIMP
-x0 = fill(1.0, 160*40) # initial design
+x0 = fill(1.0, 160 * 40) # initial design
 x = copy(x0)
 for p in [1.0, 2.0, 3.0]
     global penalty, stress, filter, result, stress, x
     penalty = TopOpt.PowerPenalty(p)
-    solver = FEASolver(
-        Direct, problem, xmin = xmin, penalty = penalty,
-    )
+    solver = FEASolver(Direct, problem, xmin = xmin, penalty = penalty)
     stress = TopOpt.MicroVonMisesStress(solver)
     filter = DensityFilter(solver, rmin = rmin)
     volfrac = TopOpt.Volume(problem, solver)
@@ -31,17 +29,11 @@ for p in [1.0, 2.0, 3.0]
     thr = 10 # stress threshold
     constr = x -> begin
         s = stress(filter(x))
-        vcat(
-            (s .- thr) / 100,
-            logsumexp(s) - log(length(s)) - thr,
-        )
+        vcat((s .- thr) / 100, logsumexp(s) - log(length(s)) - thr)
     end
     alg = PercivalAlg()
     options = PercivalOptions()
-    optimizer = Optimizer(
-        obj, constr, x, alg,
-        options = options,
-    )
+    optimizer = Optimizer(obj, constr, x, alg, options = options)
     simp = SIMP(optimizer, solver, p)
     result = simp(x)
     x = result.topology

docs/src/literate/simp.jl

@@ -16,7 +16,7 @@ E = 1.0 # Young’s modulus
 v = 0.3 # Poisson’s ratio
 f = 1.0; # downward force
 
-nels = (30, 10, 10) 
+nels = (30, 10, 10)
 problem = PointLoadCantilever(Val{:Linear}, nels, (1.0, 1.0, 1.0), E, v, f);
 
 # See also the detailed API of `PointLoadCantilever`:
@@ -31,9 +31,7 @@ rmin = 2.0; # density filter radius
 
 # ### Define a finite element solver
 penalty = TopOpt.PowerPenalty(3.0)
-solver = FEASolver(
-    Direct, problem, xmin = xmin, penalty = penalty,
-)
+solver = FEASolver(Direct, problem, xmin = xmin, penalty = penalty)
 
 # ### Define compliance objective
 comp = TopOpt.Compliance(problem, solver)
@@ -47,15 +45,15 @@ constr = x -> volfrac(filter(x)) - V
 # You can enable the iteration printouts with `Nonconvex.show_residuals[] = true`
 
 # ### Define subproblem optimizer
-mma_options = options = MMAOptions(
-    maxiter = 3000, tol = Nonconvex.Tolerance(x = 1e-3, f = 1e-3, kkt = 0.001),
-)
+mma_options =
+    options = MMAOptions(
+        maxiter = 3000,
+        tol = Nonconvex.Tolerance(x = 1e-3, f = 1e-3, kkt = 0.001),
+    )
 convcriteria = Nonconvex.KKTCriteria()
 x0 = fill(V, length(solver.vars))
-optimizer = Optimizer(
-    obj, constr, x0, MMA87(),
-    options = mma_options, convcriteria = convcriteria,
-)
+optimizer =
+    Optimizer(obj, constr, x0, MMA87(), options = mma_options, convcriteria = convcriteria)
 
 # ### Define SIMP optimizer
 simp = SIMP(optimizer, solver, penalty.p);
@@ -93,4 +91,4 @@ result = simp(x0);
 #md #
 #md # ```julia
 #md # @__CODE__
-#md # ```
+#md # ```

src/Algorithms/Algorithms.jl

@@ -8,17 +8,17 @@ using Parameters: @unpack, @pack!
 using ..Utilities, Ferrite
 using LinearAlgebra, Zygote
 
-export  Optimizer,
-        SIMP,
-        ExponentialContinuation,
-        ContinuationSIMP,
-        AdaptiveSIMP,
-        MMAOptionsGen,
-        CSIMPOptions,
-        BESO,
-        GESO,
-        Continuation,
-        PowerContinuation
+export Optimizer,
+    SIMP,
+    ExponentialContinuation,
+    ContinuationSIMP,
+    AdaptiveSIMP,
+    MMAOptionsGen,
+    CSIMPOptions,
+    BESO,
+    GESO,
+    Continuation,
+    PowerContinuation
 
 const to = TimerOutput()