Enable formatting with join_lines_based_on_source=false

.JuliaFormatter.toml

@@ -2,3 +2,4 @@ style = "sciml"
 format_markdown = true
 annotate_untyped_fields_with_any = false
 format_docstrings = true
+join_lines_based_on_source = false

docs/make.jl

@@ -1,23 +1,27 @@
 using Documenter, DocumenterCitations
-using NonlinearSolve,
-      SimpleNonlinearSolve, Sundials, SteadyStateDiffEq, SciMLBase, DiffEqBase
+using NonlinearSolve, SimpleNonlinearSolve, Sundials, SteadyStateDiffEq, SciMLBase,
+      DiffEqBase
 
-cp(joinpath(@__DIR__, "Manifest.toml"), joinpath(@__DIR__, "src/assets/Manifest.toml"),
-    force = true)
-cp(joinpath(@__DIR__, "Project.toml"), joinpath(@__DIR__, "src/assets/Project.toml"),
-    force = true)
+cp(joinpath(@__DIR__, "Manifest.toml"),
+    joinpath(@__DIR__, "src/assets/Manifest.toml"), force = true)
+cp(joinpath(@__DIR__, "Project.toml"),
+    joinpath(@__DIR__, "src/assets/Project.toml"), force = true)
 
 include("pages.jl")
 
 bib = CitationBibliography(joinpath(@__DIR__, "src", "refs.bib"))
 
 makedocs(; sitename = "NonlinearSolve.jl",
     authors = "Chris Rackauckas",
-    modules = [NonlinearSolve, SimpleNonlinearSolve, SteadyStateDiffEq, Sundials,
-        DiffEqBase, SciMLBase],
-    clean = true, doctest = false, linkcheck = true,
+    modules = [NonlinearSolve, SimpleNonlinearSolve,
+        SteadyStateDiffEq, Sundials, DiffEqBase, SciMLBase],
+    clean = true,
+    doctest = false,
+    linkcheck = true,
     linkcheck_ignore = ["https://twitter.com/ChrisRackauckas/status/1544743542094020615"],
-    checkdocs = :exports, warnonly = [:missing_docs], plugins = [bib],
+    checkdocs = :exports,
+    warnonly = [:missing_docs],
+    plugins = [bib],
     format = Documenter.HTML(assets = ["assets/favicon.ico", "assets/citations.css"],
         canonical = "https://docs.sciml.ai/NonlinearSolve/stable/"),
     pages)

docs/pages.jl

@@ -1,47 +1,26 @@
 # Put in a separate page so it can be used by SciMLDocs.jl
 
-pages = [
-    "index.md",
+pages = ["index.md",
     "Getting Started with Nonlinear Rootfinding in Julia" => "tutorials/getting_started.md",
-    "Tutorials" => Any["tutorials/code_optimization.md",
-        "tutorials/large_systems.md",
-        "tutorials/modelingtoolkit.md",
-        "tutorials/small_compile.md",
-        "tutorials/iterator_interface.md",
-        "tutorials/optimizing_parameterized_ode.md"],
-    "Basics" => Any["basics/nonlinear_problem.md",
-        "basics/nonlinear_functions.md",
-        "basics/solve.md",
-        "basics/nonlinear_solution.md",
-        "basics/autodiff.md",
-        "basics/termination_condition.md",
-        "basics/diagnostics_api.md",
-        "basics/sparsity_detection.md",
-        "basics/faq.md"],
+    "Tutorials" => Any["tutorials/code_optimization.md", "tutorials/large_systems.md",
+        "tutorials/modelingtoolkit.md", "tutorials/small_compile.md",
+        "tutorials/iterator_interface.md", "tutorials/optimizing_parameterized_ode.md"],
+    "Basics" => Any["basics/nonlinear_problem.md", "basics/nonlinear_functions.md",
+        "basics/solve.md", "basics/nonlinear_solution.md", "basics/autodiff.md",
+        "basics/termination_condition.md", "basics/diagnostics_api.md",
+        "basics/sparsity_detection.md", "basics/faq.md"],
     "Solver Summaries and Recommendations" => Any["solvers/nonlinear_system_solvers.md",
-        "solvers/bracketing_solvers.md",
-        "solvers/steady_state_solvers.md",
-        "solvers/nonlinear_least_squares_solvers.md",
-        "solvers/fixed_point_solvers.md"],
-    "Native Functionalities" => Any["native/solvers.md",
-        "native/simplenonlinearsolve.md",
-        "native/steadystatediffeq.md",
-        "native/descent.md",
-        "native/globalization.md",
-        "native/diagnostics.md"],
-    "Wrapped Solver APIs" => Any["api/fastlevenbergmarquardt.md",
-        "api/fixedpointacceleration.md",
-        "api/leastsquaresoptim.md",
-        "api/minpack.md",
-        "api/nlsolve.md",
-        "api/siamfanlequations.md",
-        "api/speedmapping.md",
-        "api/sundials.md"],
-    "Development Documentation" => ["devdocs/internal_interfaces.md",
-        "devdocs/linear_solve.md",
-        "devdocs/jacobian.md",
-        "devdocs/operators.md",
-        "devdocs/algorithm_helpers.md"],
+        "solvers/bracketing_solvers.md", "solvers/steady_state_solvers.md",
+        "solvers/nonlinear_least_squares_solvers.md", "solvers/fixed_point_solvers.md"],
+    "Native Functionalities" => Any["native/solvers.md", "native/simplenonlinearsolve.md",
+        "native/steadystatediffeq.md", "native/descent.md",
+        "native/globalization.md", "native/diagnostics.md"],
+    "Wrapped Solver APIs" => Any[
+        "api/fastlevenbergmarquardt.md", "api/fixedpointacceleration.md",
+        "api/leastsquaresoptim.md", "api/minpack.md", "api/nlsolve.md",
+        "api/siamfanlequations.md", "api/speedmapping.md", "api/sundials.md"],
+    "Development Documentation" => [
+        "devdocs/internal_interfaces.md", "devdocs/linear_solve.md",
+        "devdocs/jacobian.md", "devdocs/operators.md", "devdocs/algorithm_helpers.md"],
     "Release Notes" => "release_notes.md",
-    "References" => "references.md"
-]
+    "References" => "references.md"]

docs/src/basics/diagnostics_api.md

@@ -33,9 +33,7 @@ using ModelingToolkit, NonlinearSolve
 @parameters σ ρ β
 
 # Define a nonlinear system
-eqs = [0 ~ σ * (y - x),
-    0 ~ x * (ρ - z) - y,
-    0 ~ x * y - β * z]
+eqs = [0 ~ σ * (y - x), 0 ~ x * (ρ - z) - y, 0 ~ x * y - β * z]
 @named ns = NonlinearSystem(eqs, [x, y, z], [σ, ρ, β])
 
 u0 = [x => 1.0, y => 0.0, z => 0.0]

docs/src/basics/faq.md

@@ -16,17 +16,16 @@ myfun(x, lv) = x * sin(x) - lv
 function f(out, levels, u0)
     for i in 1:N
         out[i] = solve(
-            IntervalNonlinearProblem{false}(IntervalNonlinearFunction{false}(myfun),
-                u0, levels[i]),
+            IntervalNonlinearProblem{false}(
+                IntervalNonlinearFunction{false}(myfun), u0, levels[i]),
             Falsi()).u
     end
 end
 
 function f2(out, levels, u0)
     for i in 1:N
         out[i] = solve(
-            NonlinearProblem{false}(NonlinearFunction{false}(myfun),
-                u0, levels[i]),
+            NonlinearProblem{false}(NonlinearFunction{false}(myfun), u0, levels[i]),
             SimpleNewtonRaphson()).u
     end
 end
@@ -75,8 +74,8 @@ be a Dual number. This causes the error. To fix it:
  1. Specify the `autodiff` to be `AutoFiniteDiff`
 
 ```@example dual_error_faq
-sol = solve(prob_oop, LevenbergMarquardt(; autodiff = AutoFiniteDiff()); maxiters = 10000,
-    abstol = 1e-8)
+sol = solve(prob_oop, LevenbergMarquardt(; autodiff = AutoFiniteDiff());
+    maxiters = 10000, abstol = 1e-8)
 ```
 
 This worked but, Finite Differencing is not the recommended approach in any scenario.

docs/src/basics/sparsity_detection.md

@@ -23,12 +23,10 @@ Now you can help the solver further by providing the color vector. This can be d
 
 ```julia
 prob = NonlinearProblem(
-    NonlinearFunction(nlfunc; sparsity = jac_prototype,
-        colorvec = colorvec), x0)
+    NonlinearFunction(nlfunc; sparsity = jac_prototype, colorvec = colorvec), x0)
 # OR
 prob = NonlinearProblem(
-    NonlinearFunction(nlfunc; jac_prototype = jac_prototype,
-        colorvec = colorvec), x0)
+    NonlinearFunction(nlfunc; jac_prototype = jac_prototype, colorvec = colorvec), x0)
 ```
 
 If the `colorvec` is not provided, then it is computed on demand.
@@ -46,12 +44,11 @@ If you don't have a Sparse Jacobian Prototype, but you know the which sparsity d
 algorithm you want to use, then you can create your problem as follows:
 
 ```julia
-prob = NonlinearProblem(NonlinearFunction(nlfunc; sparsity = SymbolicsSparsityDetection()),
-    x0)  # Remember to have Symbolics.jl loaded
+prob = NonlinearProblem(
+    NonlinearFunction(nlfunc; sparsity = SymbolicsSparsityDetection()), x0)  # Remember to have Symbolics.jl loaded
 # OR
 prob = NonlinearProblem(
-    NonlinearFunction(nlfunc; sparsity = ApproximateJacobianSparsity()),
-    x0)
+    NonlinearFunction(nlfunc; sparsity = ApproximateJacobianSparsity()), x0)
 ```
 
 These Detection Algorithms are from [SparseDiffTools.jl](https://github.com/JuliaDiff/SparseDiffTools.jl),

docs/src/index.md

@@ -84,9 +84,15 @@ using TOML
 using Markdown
 version = TOML.parse(read("../../Project.toml", String))["version"]
 name = TOML.parse(read("../../Project.toml", String))["name"]
-link_manifest = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
+link_manifest = "https://github.com/SciML/" *
+                name *
+                ".jl/tree/gh-pages/v" *
+                version *
                 "/assets/Manifest.toml"
-link_project = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
+link_project = "https://github.com/SciML/" *
+               name *
+               ".jl/tree/gh-pages/v" *
+               version *
                "/assets/Project.toml"
 Markdown.parse("""You can also download the
 [manifest]($link_manifest)

docs/src/tutorials/getting_started.md

@@ -180,8 +180,8 @@ be skipped for out of place problems):
 
 ```@example 1
 u0 = [0.0, 0.0]
-prob = NonlinearLeastSquaresProblem(NonlinearFunction(nlls!, resid_prototype = zeros(3)),
-    u0)
+prob = NonlinearLeastSquaresProblem(
+    NonlinearFunction(nlls!, resid_prototype = zeros(3)), u0)
 
 solve(prob)
 ```

docs/src/tutorials/large_systems.md

@@ -78,11 +78,10 @@ function brusselator_2d_loop(du, u, p)
         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)
+                      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]
+                       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))
@@ -100,8 +99,8 @@ function init_brusselator_2d(xyd)
 end
 
 u0 = init_brusselator_2d(xyd_brusselator)
-prob_brusselator_2d = NonlinearProblem(brusselator_2d_loop, u0, p; abstol = 1e-10,
-    reltol = 1e-10)
+prob_brusselator_2d = NonlinearProblem(
+    brusselator_2d_loop, u0, p; abstol = 1e-10, reltol = 1e-10)
 ```
 
 ## Choosing Jacobian Types
@@ -140,11 +139,11 @@ using BenchmarkTools # for @btime
 @btime solve(prob_brusselator_2d,
     NewtonRaphson(; autodiff = AutoSparseForwardDiff(; chunksize = 32)));
 @btime solve(prob_brusselator_2d,
-    NewtonRaphson(; autodiff = AutoSparseForwardDiff(; chunksize = 32),
-        linsolve = KLUFactorization()));
+    NewtonRaphson(;
+        autodiff = AutoSparseForwardDiff(; chunksize = 32), linsolve = KLUFactorization()));
 @btime solve(prob_brusselator_2d,
-    NewtonRaphson(; autodiff = AutoSparseForwardDiff(; chunksize = 32),
-        linsolve = KrylovJL_GMRES()));
+    NewtonRaphson(;
+        autodiff = AutoSparseForwardDiff(; chunksize = 32), linsolve = KrylovJL_GMRES()));
 nothing # hide
 ```
 
@@ -164,8 +163,8 @@ kick out a sparse matrix with our pattern, that we can turn into our `jac_protot
 ```@example ill_conditioned_nlprob
 using Symbolics
 du0 = copy(u0)
-jac_sparsity = Symbolics.jacobian_sparsity((du, u) -> brusselator_2d_loop(du, u, p),
-    du0, u0)
+jac_sparsity = Symbolics.jacobian_sparsity(
+    (du, u) -> brusselator_2d_loop(du, u, p), du0, u0)
 ```
 
 Notice that Julia gives a nice print out of the sparsity pattern. That's neat, and would be
@@ -275,8 +274,8 @@ function algebraicmultigrid(W, du, u, p, t, newW, Plprev, Prprev, solverdata)
 end
 
 @btime solve(prob_brusselator_2d_sparse,
-    NewtonRaphson(linsolve = KrylovJL_GMRES(), precs = algebraicmultigrid,
-        concrete_jac = true));
+    NewtonRaphson(
+        linsolve = KrylovJL_GMRES(), precs = algebraicmultigrid, concrete_jac = true));
 nothing # hide
 ```
 
@@ -286,8 +285,8 @@ or with a Jacobi smoother:
 function algebraicmultigrid2(W, du, u, p, t, newW, Plprev, Prprev, solverdata)
     if newW === nothing || newW
         A = convert(AbstractMatrix, W)
-        Pl = AlgebraicMultigrid.aspreconditioner(AlgebraicMultigrid.ruge_stuben(A,
-            presmoother = AlgebraicMultigrid.Jacobi(rand(size(A, 1))),
+        Pl = AlgebraicMultigrid.aspreconditioner(AlgebraicMultigrid.ruge_stuben(
+            A, presmoother = AlgebraicMultigrid.Jacobi(rand(size(A, 1))),
             postsmoother = AlgebraicMultigrid.Jacobi(rand(size(A, 1)))))
     else
         Pl = Plprev
@@ -296,8 +295,8 @@ function algebraicmultigrid2(W, du, u, p, t, newW, Plprev, Prprev, solverdata)
 end
 
 @btime solve(prob_brusselator_2d_sparse,
-    NewtonRaphson(linsolve = KrylovJL_GMRES(), precs = algebraicmultigrid2,
-        concrete_jac = true));
+    NewtonRaphson(
+        linsolve = KrylovJL_GMRES(), precs = algebraicmultigrid2, concrete_jac = true));
 nothing # hide
 ```
 
@@ -309,12 +308,10 @@ sparsity detection. Let's compare the two by setting the sparsity detection algo
 
 ```@example ill_conditioned_nlprob
 prob_brusselator_2d_exact = NonlinearProblem(
-    NonlinearFunction(brusselator_2d_loop;
-        sparsity = SymbolicsSparsityDetection()),
+    NonlinearFunction(brusselator_2d_loop; sparsity = SymbolicsSparsityDetection()),
     u0, p; abstol = 1e-10, reltol = 1e-10)
 prob_brusselator_2d_approx = NonlinearProblem(
-    NonlinearFunction(brusselator_2d_loop;
-        sparsity = ApproximateJacobianSparsity()),
+    NonlinearFunction(brusselator_2d_loop; sparsity = ApproximateJacobianSparsity()),
     u0, p; abstol = 1e-10, reltol = 1e-10)
 
 @btime solve(prob_brusselator_2d_exact, NewtonRaphson());

docs/src/tutorials/modelingtoolkit.md

@@ -12,14 +12,10 @@ using ModelingToolkit, NonlinearSolve
 @parameters σ ρ β
 
 # Define a nonlinear system
-eqs = [0 ~ σ * (y - x),
-    0 ~ x * (ρ - z) - y,
-    0 ~ x * y - β * z]
+eqs = [0 ~ σ * (y - x), 0 ~ x * (ρ - z) - y, 0 ~ x * y - β * z]
 @named ns = NonlinearSystem(eqs, [x, y, z], [σ, ρ, β])
 
-u0 = [x => 1.0,
-    y => 0.0,
-    z => 0.0]
+u0 = [x => 1.0, y => 0.0, z => 0.0]
 
 ps = [σ => 10.0
       ρ => 26.0
@@ -60,13 +56,8 @@ solved more simply. Let's take a look at a quick system:
 
 ```@example mtk
 @variables u1 u2 u3 u4 u5
-eqs = [
-    0 ~ u1 - sin(u5),
-    0 ~ u2 - cos(u1),
-    0 ~ u3 - hypot(u1, u2),
-    0 ~ u4 - hypot(u2, u3),
-    0 ~ u5 - hypot(u4, u1)
-]
+eqs = [0 ~ u1 - sin(u5), 0 ~ u2 - cos(u1), 0 ~ u3 - hypot(u1, u2),
+    0 ~ u4 - hypot(u2, u3), 0 ~ u5 - hypot(u4, u1)]
 @named sys = NonlinearSystem(eqs, [u1, u2, u3, u4, u5], [])
 ```