Apply JuliaFormatter to fix code formatting

- Applied JuliaFormatter with SciML style guide
- Formatted 76 files

🤖 Generated by OrgMaintenanceScripts.jl

Author: SciML Bot

docs/make.jl

@@ -51,7 +51,7 @@ makedocs(;
         "https://www.sciencedirect.com/science/article/abs/pii/S0045782523007156",
         "https://github.com/devernay/cminpack/blob/d1f5f5a273862ca1bbcf58394e4ac060d9e22c76/hybrd.c",
         "https://github.com/devernay/cminpack/blob/d1f5f5a273862ca1bbcf58394e4ac060d9e22c76/hybrj.c",
-        "https://github.com/devernay/cminpack/blob/d1f5f5a273862ca1bbcf58394e4ac060d9e22c76/lmder.c",
+        "https://github.com/devernay/cminpack/blob/d1f5f5a273862ca1bbcf58394e4ac060d9e22c76/lmder.c"
     ],
     checkdocs = :exports,
     warnonly = [:missing_docs],

docs/src/api/scipy.md

@@ -19,4 +19,4 @@ Note that using this package requires Python and SciPy to be available via Pytho
 NonlinearSolveSciPy.SciPyLeastSquares
 NonlinearSolveSciPy.SciPyRoot
 NonlinearSolveSciPy.SciPyRootScalar
-```
+```

docs/src/basics/diagnostics_api.md

@@ -70,7 +70,8 @@ cache.timer
 Let's try for some other solver:
 
 ```@example diagnostics_example
-cache = NLS.init(prob, NLS.DFSane(); show_trace = Val(true), trace_level = NLS.TraceMinimal(50));
+cache = NLS.init(
+    prob, NLS.DFSane(); show_trace = Val(true), trace_level = NLS.TraceMinimal(50));
 NLS.solve!(cache)
 cache.timer
 ```

docs/src/tutorials/large_systems.md

@@ -78,14 +78,19 @@ function brusselator_2d_loop(du, u, p)
     @inbounds for I in CartesianIndices((N, N))
         i, j = Tuple(I)
         x, y = xyd_brusselator[I[1]], xyd_brusselator[I[2]]
-        ip1, im1, jp1, jm1 = limit(i + 1, N), limit(i - 1, N), limit(j + 1, N),
+        ip1, im1, jp1,
+        jm1 = limit(i + 1, N), limit(i - 1, N), limit(j + 1, N),
         limit(j - 1, N)
-        du[i, j, 1] = alpha * (u[im1, j, 1] + u[ip1, j, 1] + u[i, jp1, 1] + u[i, jm1, 1] -
-                       4u[i, j, 1]) +
-                      B +
-                      u[i, j, 1]^2 * u[i, j, 2] - (A + 1) * u[i, j, 1] + brusselator_f(x, y)
-        du[i, j, 2] = alpha * (u[im1, j, 2] + u[ip1, j, 2] + u[i, jp1, 2] + u[i, jm1, 2] -
-                       4u[i, j, 2]) + A * u[i, j, 1] - u[i, j, 1]^2 * u[i, j, 2]
+        du[i,
+            j,
+            1] = alpha * (u[im1, j, 1] + u[ip1, j, 1] + u[i, jp1, 1] + u[i, jm1, 1] -
+                  4u[i, j, 1]) +
+                 B +
+                 u[i, j, 1]^2 * u[i, j, 2] - (A + 1) * u[i, j, 1] + brusselator_f(x, y)
+        du[i,
+            j,
+            2] = alpha * (u[im1, j, 2] + u[ip1, j, 2] + u[i, jp1, 2] + u[i, jm1, 2] -
+                  4u[i, j, 2]) + A * u[i, j, 1] - u[i, j, 1]^2 * u[i, j, 2]
     end
 end
 p = (3.4, 1.0, 10.0, step(xyd_brusselator))
@@ -144,7 +149,8 @@ nothing # hide
 import SparseConnectivityTracer
 
 prob_brusselator_2d_autosparse = NLS.NonlinearProblem(
-    NLS.NonlinearFunction(brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()),
+    NLS.NonlinearFunction(
+        brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()),
     u0, p; abstol = 1e-10, reltol = 1e-10
 )
 
@@ -185,7 +191,8 @@ import ADTypes
 
 f! = (du, u) -> brusselator_2d_loop(du, u, p)
 du0 = similar(u0)
-jac_sparsity = ADTypes.jacobian_sparsity(f!, du0, u0, SparseConnectivityTracer.TracerSparsityDetector())
+jac_sparsity = ADTypes.jacobian_sparsity(
+    f!, du0, u0, SparseConnectivityTracer.TracerSparsityDetector())
 ```
 
 Notice that Julia gives a nice print out of the sparsity pattern. That's neat, and would be
@@ -206,7 +213,8 @@ Now let's see how the version with sparsity compares to the version without:
 ```@example ill_conditioned_nlprob
 BenchmarkTools.@btime NLS.solve(prob_brusselator_2d, NLS.NewtonRaphson());
 BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse, NLS.NewtonRaphson());
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse, NLS.NewtonRaphson(linsolve = LS.KLUFactorization()));
+BenchmarkTools.@btime NLS.solve(
+    prob_brusselator_2d_sparse, NLS.NewtonRaphson(linsolve = LS.KLUFactorization()));
 nothing # hide
 ```
 
@@ -222,7 +230,8 @@ Krylov method. To swap the linear solver out, we use the `linsolve` command and
 GMRES linear solver.
 
 ```@example ill_conditioned_nlprob
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d, NLS.NewtonRaphson(linsolve = LS.KrylovJL_GMRES()));
+BenchmarkTools.@btime NLS.solve(
+    prob_brusselator_2d, NLS.NewtonRaphson(linsolve = LS.KrylovJL_GMRES()));
 nothing # hide
 ```
 
@@ -254,7 +263,8 @@ import IncompleteLU
 incompletelu(W, p = nothing) = IncompleteLU.ilu(W, τ = 50.0), LinearAlgebra.I
 
 BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse,
-    NLS.NewtonRaphson(linsolve = LS.KrylovJL_GMRES(precs = incompletelu), concrete_jac = true)
+    NLS.NewtonRaphson(
+        linsolve = LS.KrylovJL_GMRES(precs = incompletelu), concrete_jac = true)
 );
 nothing # hide
 ```
@@ -279,7 +289,9 @@ which is more automatic. The setup is very similar to before:
 import AlgebraicMultigrid
 
 function algebraicmultigrid(W, p = nothing)
-    return AlgebraicMultigrid.aspreconditioner(AlgebraicMultigrid.ruge_stuben(convert(AbstractMatrix, W))), LinearAlgebra.I
+    return AlgebraicMultigrid.aspreconditioner(AlgebraicMultigrid.ruge_stuben(convert(
+        AbstractMatrix, W))),
+    LinearAlgebra.I
 end
 
 BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse,
@@ -322,11 +334,13 @@ import DifferentiationInterface
 import SparseConnectivityTracer
 
 prob_brusselator_2d_exact_tracer = NLS.NonlinearProblem(
-    NLS.NonlinearFunction(brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()),
+    NLS.NonlinearFunction(
+        brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()),
     u0, p; abstol = 1e-10, reltol = 1e-10)
 prob_brusselator_2d_approx_di = NLS.NonlinearProblem(
     NLS.NonlinearFunction(brusselator_2d_loop;
-        sparsity = DifferentiationInterface.DenseSparsityDetector(ADTypes.AutoForwardDiff(); atol = 1e-4)),
+        sparsity = DifferentiationInterface.DenseSparsityDetector(
+            ADTypes.AutoForwardDiff(); atol = 1e-4)),
     u0, p; abstol = 1e-10, reltol = 1e-10)
 
 BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_exact_tracer, NLS.NewtonRaphson());

docs/src/tutorials/nonlinear_solve_gpus.md

@@ -31,7 +31,7 @@ using the first form.
 In this tutorial we will highlight both use cases in separate parts.
 
 !!! note
-
+    
     If you're looking for GPU-accelerated neural networks inside of nonlinear solvers,
     check out [DeepEquilibriumNetworks.jl](https://docs.sciml.ai/DeepEquilibriumNetworks/stable/).
 
@@ -59,7 +59,7 @@ f(u, p) = u .* u .- p
 u0 = CUDA.cu(ones(1000))
 p = CUDA.cu(collect(1:1000))
 prob = NLS.NonlinearProblem(f, u0, p)
-sol = NLS.solve(prob, NLS.NewtonRaphson(), abstol=1f-4)
+sol = NLS.solve(prob, NLS.NewtonRaphson(), abstol = 1.0f-4)
 ```
 
 Notice a few things here. One, nothing is different except the input array types. But
@@ -95,7 +95,8 @@ import AMDGPU # For if you have an AMD GPU
 import Metal # For if you have a Mac M-series device and want to use the built-in GPU
 import OneAPI # For if you have an Intel GPU
 
-@KernelAbstractions.kernel function parallel_nonlinearsolve_kernel!(result, @Const(prob), @Const(alg))
+KernelAbstractions.@kernel function parallel_nonlinearsolve_kernel!(
+        result, @Const(prob), @Const(alg))
     i = @index(Global)
     prob_i = SciMLBase.remake(prob; p = prob.p[i])
     sol = NLS.solve(prob_i, alg)
@@ -109,7 +110,7 @@ is saying, "for the ith call, get the i'th parameter set and solve with these pa
 The ith result is then this solution".
 
 !!! note
-
+    
     Because kernel code needs to be able to be compiled to a GPU kernel, it has very strict
     specifications of what's allowed because GPU cores are not as flexible as CPU cores.
     In general, this means that you need to avoid any runtime operations in kernel code,
@@ -140,16 +141,16 @@ Now let's build a nonlinear system to test it on.
     out2 = sqrt(p[2]) * (x[3] - x[4])
     out3 = (x[2] - p[3] * x[3])^2
     out4 = sqrt(p[4]) * (x[1] - x[4]) * (x[1] - x[4])
-    StaticArrays.SA[out1,out2,out3,out4]
+    StaticArrays.SA[out1, out2, out3, out4]
 end
 
 p = StaticArrays.@SVector [StaticArrays.@SVector(rand(Float32, 4)) for _ in 1:1024]
-u0 = StaticArrays.SA[1f0, 2f0, 3f0, 4f0]
+u0 = StaticArrays.SA[1.0f0, 2.0f0, 3.0f0, 4.0f0]
 prob = SciMLBase.ImmutableNonlinearProblem{false}(p2_f, u0, p)
 ```
 
 !!! note
-
+    
     Because the custom kernel is going to need to embed the the code for our nonlinear
     problem into the kernel, it also must be written to be GPU compatible.
     In general, this means that you need to avoid any runtime operations in kernel code,
@@ -176,4 +177,5 @@ vectorized_solve(prob, NLS.SimpleNewtonRaphson(); backend = Metal.MetalBackend()
 ```
 
 !!! warn
+    
     The GPU-based calls will only work on your machine if you have a compatible GPU!

docs/src/tutorials/optimizing_parameterized_ode.md

@@ -47,13 +47,15 @@ end
 
 p_init = zeros(4)
 
-nlls_prob = NLS.NonlinearLeastSquaresProblem(loss_function, p_init, vec(reduce(hcat, sol.u)))
+nlls_prob = NLS.NonlinearLeastSquaresProblem(
+    loss_function, p_init, vec(reduce(hcat, sol.u)))
 ```
 
 Now, we can use any NLLS solver to solve this problem.
 
 ```@example parameterized_ode
-res = NLS.solve(nlls_prob, NLS.LevenbergMarquardt(); maxiters = 1000, show_trace = Val(true),
+res = NLS.solve(
+    nlls_prob, NLS.LevenbergMarquardt(); maxiters = 1000, show_trace = Val(true),
     trace_level = NLS.TraceWithJacobianConditionNumber(25))
 nothing # hide
 ```

docs/src/tutorials/snes_ex2.md

@@ -38,7 +38,8 @@ details.
 
 ```@example snes_ex2
 nlfunc_dense = NLS.NonlinearFunction(form_residual!)
-nlfunc_sparse = NLS.NonlinearFunction(form_residual!; sparsity = SparseConnectivityTracer.TracerSparsityDetector())
+nlfunc_sparse = NLS.NonlinearFunction(
+    form_residual!; sparsity = SparseConnectivityTracer.TracerSparsityDetector())
 
 nlprob_dense = NLS.NonlinearProblem(nlfunc_dense, u0)
 nlprob_sparse = NLS.NonlinearProblem(nlfunc_sparse, u0)

ext/NonlinearSolveFastLevenbergMarquardtExt.jl

@@ -21,7 +21,8 @@ function SciMLBase.__solve(
         termination_condition, alg
     )
 
-    f_wrapped, u, resid = NonlinearSolveBase.construct_extension_function_wrapper(
+    f_wrapped, u,
+    resid = NonlinearSolveBase.construct_extension_function_wrapper(
         prob; alias_u0, can_handle_oop = Val(prob.u0 isa SArray)
     )
     f = if prob.u0 isa SArray
@@ -49,7 +50,8 @@ function SciMLBase.__solve(
     )
 
     if prob.u0 isa SArray
-        res, fx, info, iter, nfev, njev = FastLM.lmsolve(
+        res, fx, info, iter, nfev,
+        njev = FastLM.lmsolve(
             f, jac_fn, prob.u0; solver_kwargs...
         )
         LM, solver = nothing, nothing
@@ -68,7 +70,8 @@ function SciMLBase.__solve(
 
         LM = FastLM.LMWorkspace(u, resid, J)
 
-        res, fx, info, iter, nfev, njev, LM, solver = FastLM.lmsolve!(
+        res, fx, info, iter, nfev, njev,
+        LM, solver = FastLM.lmsolve!(
             f, jac_fn, LM; solver, solver_kwargs...
         )
     end

ext/NonlinearSolveFixedPointAccelerationExt.jl

@@ -15,7 +15,8 @@ function SciMLBase.__solve(
         termination_condition, alg
     )
 
-    f, u0, resid = NonlinearSolveBase.construct_extension_function_wrapper(
+    f, u0,
+    resid = NonlinearSolveBase.construct_extension_function_wrapper(
         prob; alias_u0, make_fixed_point = Val(true), force_oop = Val(true)
     )
 

ext/NonlinearSolveMINPACKExt.jl

@@ -17,7 +17,8 @@ function SciMLBase.__solve(
         termination_condition, alg
     )
 
-    f_wrapped!, u0, resid = NonlinearSolveBase.construct_extension_function_wrapper(
+    f_wrapped!, u0,
+    resid = NonlinearSolveBase.construct_extension_function_wrapper(
         prob; alias_u0
     )
     resid_size = size(resid)