pre-commit adjustments

Author: chmerdon

docs/make.jl

@@ -31,7 +31,7 @@ makedocs(
         ],
         "Scripts" => [
             "poisson_script.md",
-        ]
+        ],
     ],
 )
 

docs/src/poisson_script.md

@@ -66,4 +66,4 @@ Results are automatically stored using [DrWatson.jl](https://github.com/JuliaDyn
 data/[problem]/[domain]/order=[order]_maxdofs=[maxdofs]_decay=[decay]_mean=[mean]_θ=([θ_spatial],[θ_stochastic])_tail=[tail_extension]
 ```
 
-If the equilibration estimator is used, `_eq` is appended to the filename.
+If the equilibration estimator is used, `_eq` is appended to the filename.

scripts/poisson_simple.jl

@@ -22,18 +22,19 @@ function main(;
         decay = 2.0,    # decay factor for the random coefficient
         mean = problem == PoissonProblemPrimal ? 1.0 : 0.0, # mean value of coefficient
         domain = "square",  # domain, e.g., "square" or "lshape"
-        initial_modes = [[0], [1,0], [0,1], [2,0], [0,0,1]],   # initial multi-indices for stochastic basis
+        initial_modes = [[0], [1, 0], [0, 1], [2, 0], [0, 0, 1]],   # initial multi-indices for stochastic basis
         f! = (result, qpinfo) -> (result[1] = 1),       # right-hand side function
         use_iterative_solver = true,    # use iterative solver ? (otherwise direct)
-        Plotter = nothing)
+        Plotter = nothing
+    )
 
     ## prepare stochastic coefficient
     τ = (problem <: PoissonProblemPrimal) ? 0.9 : 1.0
     if problem <: PoissonProblemPrimal
         @assert mean >= 1 "coefficient needs to be at least 1 to ensure ellipticity"
     end
     C = StochasticCoefficientCosinus(; τ = τ, decay = decay, mean = mean)
-    
+
     ## prepare grid
     xgrid = if domain == "square"
         uniform_refine(grid_unitsquare(Triangle2D), nrefs)
@@ -44,24 +45,24 @@ function main(;
     end
 
     ## prepare stochastic basis
-    multi_indices = Array{Array{Int,1},1}(initial_modes)
+    multi_indices = Array{Array{Int, 1}, 1}(initial_modes)
     prepare_multi_indices!(multi_indices)
     M = maximum(length.(multi_indices))
     OBType = problem <: PoissonProblemPrimal ? LegendrePolynomials : HermitePolynomials
     ansatz_deg = maximum([maximum(multi_indices[k]) for k in 1:length(multi_indices)]) + 4
-    TensorBasis = TensorizedBasis(OBType, M, ansatz_deg, 2*ansatz_deg, 2*ansatz_deg, multi_indices = multi_indices)
+    TensorBasis = TensorizedBasis(OBType, M, ansatz_deg, 2 * ansatz_deg, 2 * ansatz_deg, multi_indices = multi_indices)
 
     ## prepare FE spaces
     if problem <: LogTransformedPoissonProblemDual
-        FEType = [HDIVRTk{2,order}, order == 0 ? L2P0{1} : H1Pk{1,2,order}]
+        FEType = [HDIVRTk{2, order}, order == 0 ? L2P0{1} : H1Pk{1, 2, order}]
         FES = [FESpace{FEType[1]}(xgrid), FESpace{FEType[2]}(xgrid; broken = true)]
         unames = ["p", "u"]
     else
-        FEType = H1Pk{1,2,order}
+        FEType = H1Pk{1, 2, order}
         FES = FESpace{FEType}(xgrid)
         unames = ["u"]
     end
-    
+
     ## create solution vector
     sol = SGFEVector(FES, TensorBasis; active_modes = 1:length(multi_indices), unames = unames)
 
@@ -85,4 +86,4 @@ function main(;
     return sol
 end
 
-end # module
+end # module