Apply JuliaFormatter to fix code formatting

docs/src/tutorials/linear.md

@@ -139,7 +139,7 @@ Let's check these are the same:
 
 ```@example linsys1
 v = rand(5)
-mfopA*v - A*v
+mfopA * v - A * v
 ```
 
 Notice `mfopA` does this without having to have `A` because it just uses the equivalent `Afunc!` instead. Now, even though

ext/LinearSolveEnzymeExt.jl

@@ -227,7 +227,7 @@ function EnzymeRules.reverse(config, func::Const{typeof(LinearSolve.solve!)},
         end
     else
         dlinsolves = linsolve.dval
-        if any(x->(iszero(x.A) || iszero(x.b)) && !iszero(x.u), linsolve.dval)
+        if any(x -> (iszero(x.A) || iszero(x.b)) && !iszero(x.u), linsolve.dval)
             error("Adjoint case currently not handled. Instead of using `solve!(cache); s1 = copy(cache.u) ...`, use `sol = solve!(cache); s1 = copy(sol.u)`.")
         end
     end

src/KLU/klu.jl

@@ -14,7 +14,8 @@ import Base: (\), size, getproperty, setproperty!, propertynames, show,
              copy, eachindex, view, sortperm, unsafe_load, zeros, convert, eltype,
              length, parent, stride, finalizer, Complex, complex, imag, real, map!,
              summary, println, oneunit, sizeof, isdefined, setfield!, getfield,
-             OutOfMemoryError, ArgumentError, OverflowError, ErrorException, DimensionMismatch
+             OutOfMemoryError, ArgumentError, OverflowError, ErrorException,
+             DimensionMismatch
 
 # Convert from 1-based to 0-based indices
 function decrement!(A::AbstractArray{T}) where {T <: Integer}

src/generic_lufact.jl

@@ -55,7 +55,7 @@
                 # Update the rest
                 for j in (k + 1):n
                     for i in (k + 1):m
-                        A[i, j] -= A[i, k]*A[k, j]
+                        A[i, j] -= A[i, k] * A[k, j]
                     end
                 end
             end
@@ -119,7 +119,7 @@ elseif VERSION < v"1.13"
                 # Update the rest
                 for j in (k + 1):n
                     for i in (k + 1):m
-                        A[i, j] -= A[i, k]*A[k, j]
+                        A[i, j] -= A[i, k] * A[k, j]
                     end
                 end
             end

src/simplegmres.jl

@@ -308,7 +308,7 @@ function SciMLBase.solve!(cache::SimpleGMRESCache{false}, lincache::LinearCache)
             # [cₖ  sₖ] [ r̄ₖ.ₖ ] = [rₖ.ₖ]
             # [s̄ₖ -cₖ] [hₖ₊₁.ₖ]   [ 0  ]
             (c[inner_iter], s[inner_iter],
-                R[nr + inner_iter]) = _sym_givens(
+            R[nr + inner_iter]) = _sym_givens(
                 R[nr + inner_iter],
                 Hbis)
 

test/adjoint.jl

@@ -31,7 +31,7 @@ A = rand(n, n);
 b1 = rand(n);
 
 _ff = (x,
-    y) -> f(x,
+y) -> f(x,
     y;
     alg = LinearSolve.DefaultLinearSolver(LinearSolve.DefaultAlgorithmChoice.LUFactorization))
 _ff(copy(A), copy(b1))

test/default_algs.jl

@@ -170,4 +170,4 @@ sol = solve(prob,
 @test sol.u == zeros(4)
 
 sol = solve(prob)
-@test sol.u ≈ svd(A)\b
+@test sol.u ≈ svd(A) \ b

test/hypretests.jl

@@ -87,7 +87,7 @@ function test_interface(alg; kw...)
 
         # Solve prob directly (without cache)
         y = solve(prob, alg; cache_kwargs..., Pl = HYPRE.BoomerAMG)
-        @test A*to_array(y.u)≈b atol=atol rtol=rtol
+        @test A * to_array(y.u)≈b atol=atol rtol=rtol
         @test y.iters > 0
         @test y.resid < rtol
 
@@ -99,7 +99,7 @@ function test_interface(alg; kw...)
         cache = y.cache
         @test cache.isfresh == cache.cacheval.isfresh_A ==
               cache.cacheval.isfresh_b == cache.cacheval.isfresh_u == false
-        @test A*to_array(y.u)≈b atol=atol rtol=rtol
+        @test A * to_array(y.u)≈b atol=atol rtol=rtol
 
         # Update A
         cache.A = A
@@ -109,7 +109,7 @@ function test_interface(alg; kw...)
         cache = y.cache
         @test cache.isfresh == cache.cacheval.isfresh_A ==
               cache.cacheval.isfresh_b == cache.cacheval.isfresh_u == false
-        @test A*to_array(y.u)≈b atol=atol rtol=rtol
+        @test A * to_array(y.u)≈b atol=atol rtol=rtol
 
         # Update b
         b2 = 2 * to_array(b)
@@ -123,7 +123,7 @@ function test_interface(alg; kw...)
         cache = y.cache
         @test cache.isfresh == cache.cacheval.isfresh_A ==
               cache.cacheval.isfresh_b == cache.cacheval.isfresh_u == false
-        @test A*to_array(y.u)≈to_array(b2) atol=atol rtol=rtol
+        @test A * to_array(y.u)≈to_array(b2) atol=atol rtol=rtol
     end
     return
 end

test/nopre/enzyme.jl

@@ -33,14 +33,14 @@ b1 = rand(n);
 db1 = zeros(n);
 
 _ff = (x,
-    y) -> f(x,
+y) -> f(x,
     y;
     alg = LinearSolve.DefaultLinearSolver(LinearSolve.DefaultAlgorithmChoice.LUFactorization))
 _ff(copy(A), copy(b1))
 
 Enzyme.autodiff(Reverse,
     (x,
-        y) -> f(x,
+    y) -> f(x,
         y;
         alg = LinearSolve.DefaultLinearSolver(LinearSolve.DefaultAlgorithmChoice.LUFactorization)),
     Duplicated(copy(A), dA),
@@ -174,7 +174,7 @@ db2 = zeros(n);
 Enzyme.autodiff(set_runtime_activity(Reverse), f3, Duplicated(copy(A), dA),
     Duplicated(copy(b1), db1), Duplicated(copy(b2), db2))
 
-@test dA ≈ dA2 atol=5e-5
+@test dA≈dA2 atol=5e-5
 @test db1 ≈ db12
 @test db2 ≈ db22
 

test/retcodes.jl

@@ -80,7 +80,7 @@ staticarrayalgs = (
 )
 @testset "StaticArray Success" begin
     A = Float64[1 2 3; 4 3.5 1.7; 5.2 1.8 9.7]
-    A = A*A'
+    A = A * A'
     b = Float64[2, 5, 8]
     prob1 = LinearProblem(SMatrix{3, 3}(A), SVector{3}(b))
     sol = solve(prob1)