Apply JuliaFormatter to fix code formatting

- Applied JuliaFormatter with SciML style guide
- Formatted 10 files

🤖 Generated by OrgMaintenanceScripts.jl

Author: SciML Bot

ext/SciMLBaseChainRulesCoreExt.jl

@@ -141,15 +141,17 @@ end
 # `back` explicitly while already in a reverse pass causing a nested gradient call. The mutable struct
 # causes accumulation anytime `getfield/property` is called, accumulating multiple times. This tries to treat
 # AbstractDEProblem as immutable for the purposes of reverse mode AD.
-function ChainRulesCore.rrule(::ChainRulesCore.RuleConfig{>:ChainRulesCore.HasReverseMode}, ::typeof(Base.getproperty), x::NonlinearProblem, f::Symbol)
+function ChainRulesCore.rrule(::ChainRulesCore.RuleConfig{>:ChainRulesCore.HasReverseMode},
+        ::typeof(Base.getproperty), x::NonlinearProblem, f::Symbol)
     val = getfield(x, f)
     function back(der)
         dx = if der === nothing
             ChainRulesCore.zero_tangent(x)
         else
             NamedTuple{(f,)}((der,))
         end
-        return (ChainRulesCore.NoTangent(), ChainRulesCore.ProjectTo(x)(dx), ChainRulesCore.NoTangent())
+        return (ChainRulesCore.NoTangent(), ChainRulesCore.ProjectTo(x)(dx),
+            ChainRulesCore.NoTangent())
     end
     return val, back
 end

src/problems/bvp_problems.jl

@@ -174,8 +174,10 @@ struct BVProblem{uType, tType, isinplace, nlls, P, F, LC, UC, PT, K} <:
             _nlls = _unwrap_val(nlls)
         end
 
-        return new{typeof(_u0), typeof(_tspan), iip, _nlls, typeof(p), typeof(f), typeof(lcons), typeof(ucons),
-            typeof(problem_type), typeof(kwargs)}(f, _u0, _tspan, p, lcons, ucons, problem_type, kwargs)
+        return new{typeof(_u0), typeof(_tspan), iip, _nlls, typeof(p),
+            typeof(f), typeof(lcons), typeof(ucons),
+            typeof(problem_type), typeof(kwargs)}(
+            f, _u0, _tspan, p, lcons, ucons, problem_type, kwargs)
     end
 
     function BVProblem{iip}(f, bc, u0, tspan, p = NullParameters(); kwargs...) where {iip}

src/problems/linear_problems.jl

@@ -47,7 +47,6 @@ function SymbolicIndexingInterface.observed(fn::SymbolicLinearInterface, sym)
     end
 end
 
-
 @doc doc"""
 
 Defines a linear system problem.
@@ -104,7 +103,8 @@ parameters. Any extra keyword arguments are passed on to the solvers.
   generated by a symbolic backend.
 * `kwargs`: The keyword arguments passed on to the solvers.
 """
-struct LinearProblem{uType, isinplace, F, bType, P, I <: Union{SymbolicLinearInterface, Nothing}, K} <:
+struct LinearProblem{
+    uType, isinplace, F, bType, P, I <: Union{SymbolicLinearInterface, Nothing}, K} <:
        AbstractLinearProblem{bType, isinplace}
     A::F
     b::bType
@@ -115,7 +115,8 @@ struct LinearProblem{uType, isinplace, F, bType, P, I <: Union{SymbolicLinearInt
     @add_kwonly function LinearProblem{iip}(A, b, p = NullParameters(); u0 = nothing,
             f = nothing, kwargs...) where {iip}
         warn_paramtype(p)
-        new{typeof(u0), iip, typeof(A), typeof(b), typeof(p), typeof(f), typeof(kwargs)}(A, b, u0, p,
+        new{typeof(u0), iip, typeof(A), typeof(b), typeof(p), typeof(f), typeof(kwargs)}(
+            A, b, u0, p,
             f, kwargs)
     end
 end
@@ -134,7 +135,9 @@ SymbolicIndexingInterface.symbolic_container(prob::LinearProblem) = prob.f
 SymbolicIndexingInterface.state_values(prob::LinearProblem) = prob.u0
 SymbolicIndexingInterface.parameter_values(prob::LinearProblem) = prob.p
 SymbolicIndexingInterface.is_time_dependent(::LinearProblem) = false
-function SymbolicIndexingInterface.set_parameter!(valp::LinearProblem{A, B, C, D, E, <:SymbolicLinearInterface}, val, idx) where {A, B, C, D, E}
+function SymbolicIndexingInterface.set_parameter!(
+        valp::LinearProblem{A, B, C, D, E, <:SymbolicLinearInterface},
+        val, idx) where {A, B, C, D, E}
     set_parameter!(parameter_values(valp), val, idx)
     valp.f.update_A!(valp.A, valp.p)
     valp.f.update_b!(valp.b, valp.p)

src/scimlfunctions.jl

@@ -2318,7 +2318,8 @@ For more details on this argument, see the ODEFunction documentation.
 
 The fields of the BVPFunction type directly match the names of the inputs.
 """
-struct BVPFunction{iip, specialize, twopoint, F, BF, C, EC, IC, TMM, Ta, Tt, TJ, BCTJ, JVP, VJP,
+struct BVPFunction{
+    iip, specialize, twopoint, F, BF, C, EC, IC, TMM, Ta, Tt, TJ, BCTJ, JVP, VJP,
     JP, BCJP, BCRP, SP, TW, TWt, TPJ, O, TCV, BCTCV,
     SYS, ID} <: AbstractBVPFunction{iip, twopoint}
     f::F
@@ -2663,7 +2664,6 @@ end
     end
 end
 
-
 (f::SDDEFunction)(args...) = f.f(args...)
 (f::SplitSDEFunction)(u, p, t) = f.f1(u, p, t) + f.f2(u, p, t)
 
@@ -4462,7 +4462,8 @@ function BVPFunction{iip, specialize, twopoint}(f, bc;
     paramjaciip = paramjac !== nothing ? isinplace(paramjac, 4, "paramjac", iip) : iip
     costiip = cost !== nothing ? isinplace(cost, 2, "cost", iip) : iip
     equalityiip = equality !== nothing ? isinplace(equality, 3, "equality", iip) : iip
-    inequalityiip = inequality !== nothing ? isinplace(inequality, 3, "inequality", iip) : iip
+    inequalityiip = inequality !== nothing ? isinplace(inequality, 3, "inequality", iip) :
+                    iip
 
     nonconforming = (bciip, jaciip, tgradiip, jvpiip, vjpiip, Wfactiip, Wfact_tiip,
         paramjaciip, costiip, equalityiip, inequalityiip) .!= iip

src/solve.jl

@@ -43,7 +43,8 @@ end
 
 """
 ```julia
-solve(prob::OptimizationProblem, alg::AbstractOptimizationAlgorithm, args...; kwargs...)::OptimizationSolution
+solve(prob::OptimizationProblem, alg::AbstractOptimizationAlgorithm,
+    args...; kwargs...)::OptimizationSolution
 ```
 
 For information about the returned solution object, refer to the documentation for [OptimizationSolution](@ref)
@@ -152,7 +153,8 @@ function _check_opt_alg(prob::OptimizationProblem, alg; kwargs...)
     requiresconstraints(alg) && isnothing(prob.f.cons) &&
         throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) requires constraints, pass them with the `cons` kwarg in `OptimizationFunction`."))
     # Check that if constraints are present and the algorithm supports constraints, both lcons and ucons are provided
-    allowsconstraints(alg) && !isnothing(prob.f.cons) && (isnothing(prob.lcons) || isnothing(prob.ucons)) &&
+    allowsconstraints(alg) && !isnothing(prob.f.cons) &&
+        (isnothing(prob.lcons) || isnothing(prob.ucons)) &&
         throw(ArgumentError("Constrained optimization problem requires both `lcons` and `ucons` to be provided to OptimizationProblem. " *
                             "Example: OptimizationProblem(optf, u0, p; lcons=[-Inf], ucons=[0.0])"))
     !allowscallback(alg) && haskey(kwargs, :callback) &&

test/downstream/comprehensive_indexing.jl

@@ -43,7 +43,8 @@ begin
     osys = complete(System(diff_eqs, t; observed = obs, name = :osys))
     ssys = complete(System(
         diff_eqs, t, [X, Y], [kp, kd, k1, k2]; noise_eqs, observed = obs, name = :ssys))
-    jsys = complete(JumpSystem(jumps, t, [X, Y], [kp, kd, k1, k2]; observed = obs, name = :jsys))
+    jsys = complete(JumpSystem(
+        jumps, t, [X, Y], [kp, kd, k1, k2]; observed = obs, name = :jsys))
     nsys = complete(System(alg_eqs; observed = obs, name = :nsys))
     optsys = complete(OptimizationSystem(
         loss, [X, Y], [kp, kd, k1, k2]; observed = obs, name = :optsys))

test/downstream/modelingtoolkit_remake.jl

@@ -157,8 +157,12 @@ end
 @testset "DiscreteProblem" begin
     k = ShiftIndex(t)
     @mtkcompile discsys = System(
-        [x ~ x(k - 1) * ρ + y(k - 2), y ~ y(k - 1) * σ - z(k - 2), z ~ z(k - 1) * β + x(k - 2)],
-        t; defaults = [x => 1.0, y => 1.0, z => 1.0, x(k-1) => 0.0, y(k-1) => 0.0, z(k-1) => 0.0])
+        [
+            x ~ x(k - 1) * ρ + y(k - 2), y ~ y(k - 1) * σ - z(k - 2), z ~
+                                                                      z(k - 1) * β +
+                                                                      x(k - 2)],
+        t; defaults = [
+            x => 1.0, y => 1.0, z => 1.0, x(k-1) => 0.0, y(k-1) => 0.0, z(k-1) => 0.0])
     prob = DiscreteProblem(discsys, p, (0, 10))
     prob[x(k-1)] = 1.0
     prob[y(k-1)] = prob[z(k-1)] = 0.0
@@ -168,13 +172,16 @@ end
 
     baseType = Base.typename(typeof(prob)).wrapper
     ugetter = getsym(prob, [x(k-1), y(k-1), z(k-1)])
-    prob2 = @inferred baseType remake(prob; u0 = [x(k-1) => 2.0, y(k-1) => 3.0, z(k-1) => 4.0])
+    prob2 = @inferred baseType remake(prob; u0 = [
+        x(k-1) => 2.0, y(k-1) => 3.0, z(k-1) => 4.0])
     @test ugetter(prob2) == [2.0, 3.0, 4.0]
-    prob2 = @inferred baseType remake(prob; u0 = [sys.x(k-1) => 2.0, sys.y(k-1) => 3.0, sys.z(k-1) => 4.0])
+    prob2 = @inferred baseType remake(prob; u0 = [
+        sys.x(k-1) => 2.0, sys.y(k-1) => 3.0, sys.z(k-1) => 4.0])
     @test ugetter(prob2) == [2.0, 3.0, 4.0]
     prob2 = @inferred baseType remake(prob; u0 = [:xₜ₋₁ => 2.0, :yₜ₋₁ => 3.0, :zₜ₋₁ => 4.0])
     @test ugetter(prob2) == [2.0, 3.0, 4.0]
-    prob2 = @inferred baseType remake(prob; u0 = [x(k-1) => 2.0, sys.y(k-1) => 3.0, :zₜ₋₁ => 4.0])
+    prob2 = @inferred baseType remake(prob; u0 = [
+        x(k-1) => 2.0, sys.y(k-1) => 3.0, :zₜ₋₁ => 4.0])
     @test ugetter(prob2) == [2.0, 3.0, 4.0]
 
     prob2 = @inferred baseType remake(prob; u0 = [x(k-1) => 12.0])
@@ -232,7 +239,8 @@ end
     @test ugetter(prob2) ≈ [15.0, 0.0, 0.0]
 
     # Test u0 dependent on p and p dependent on u0
-    prob2 = @inferred baseType remake(prob; u0 = [x(k-1) => 0.5σ + 1], p = [β => 0.5x(k-1) + 1])
+    prob2 = @inferred baseType remake(prob; u0 = [x(k-1) => 0.5σ + 1], p = [β => 0.5x(k-1) +
+                                                                                 1])
     @test ugetter(prob2) ≈ [15.0, 0.0, 0.0]
     @test_broken pgetter(prob2) ≈ [28.0, 8.5, 10.0]
     prob2 = @inferred baseType remake(

test/downstream/solution_interface.jl

@@ -9,19 +9,19 @@ using Plots: Plots, plot
 @testset "Basic indexing" begin
     @parameters a b c d
     @variables s1(t) s2(t)
-    
+
     eqs = [D(s1) ~ a * s1 / (1 + s1 + s2) - b * s1,
         D(s2) ~ +c * s2 / (1 + s1 + s2) - d * s2]
-    
+
     @mtkcompile population_model = System(eqs, t)
-    
+
     # Tests on ODEProblem.
     u0 = [s1 => 2.0, s2 => 1.0]
     p = [a => 2.0, b => 1.0, c => 1.0, d => 1.0]
     tspan = (0.0, 1000000.0)
     oprob = ODEProblem(population_model, [u0; p], tspan)
     sol = solve(oprob, Rodas4())
-    
+
     @test sol[s1] == sol[population_model.s1] == sol[:s1]
     @test sol[s2] == sol[population_model.s2] == sol[:s2]
     @test sol[s1][end] ≈ 1.0
@@ -57,93 +57,94 @@ using Plots: Plots, plot
     noisy_population_model = complete(noisy_population_model)
     sprob = SDEProblem(noisy_population_model, [u0; p], (0.0, 100.0))
     sol = solve(sprob, ImplicitEM())
-    
+
     @test sol[s1] == sol[noisy_population_model.s1] == sol[:s1]
     @test sol[s2] == sol[noisy_population_model.s2] == sol[:s2]
     @test_throws Exception sol[a]
     @test_throws Exception sol[noisy_population_model.a]
     @test_throws Exception sol[:a]
     @test_nowarn sol(0.5, idxs = noisy_population_model.s1)
     ### Tests on layered model (some things should not work). ###
-    
+
     @parameters σ ρ β
     @variables x(t) y(t) z(t)
-    
+
     eqs = [D(x) ~ σ * (y - x),
         D(y) ~ x * (ρ - z) - y,
         D(z) ~ x * y - β * z]
-    
+
     @named lorenz1 = System(eqs, t)
     @named lorenz2 = System(eqs, t)
-    
+
     @parameters γ
     @variables a(t) α(t)
     connections = [0 ~ lorenz1.x + lorenz2.y + a * γ,
         α ~ 2lorenz1.x + a * γ]
     @mtkcompile sys = System(connections, t, [a, α], [γ], systems = [lorenz1, lorenz2])
-    
+
     u0 = [lorenz1.x => 1.0,
         lorenz1.y => 0.0,
         lorenz1.z => 0.0,
         lorenz2.x => 0.0,
         lorenz2.y => 1.0,
         lorenz2.z => 0.0]
-    
+
     p = [lorenz1.σ => 10.0,
         lorenz1.ρ => 28.0,
         lorenz1.β => 8 / 3,
         lorenz2.σ => 10.0,
         lorenz2.ρ => 28.0,
         lorenz2.β => 8 / 3,
         γ => 2.0]
-    
+
     tspan = (0.0, 100.0)
     prob = ODEProblem(sys, [u0; p], tspan)
     sol = solve(prob, Rodas4())
-    
+
     @test_throws ArgumentError sol[x]
-    @test in(sol[lorenz1.x], [getindex.(sol.u, i) for i in 1:length(unknowns(sol.prob.f.sys))])
+    @test in(sol[lorenz1.x], [getindex.(sol.u, i)
+                              for i in 1:length(unknowns(sol.prob.f.sys))])
     @test_throws KeyError sol[:x]
-    
+
     ### Non-symbolic indexing tests
     @test sol[:, 1] isa AbstractVector
     @test sol[:, 1:2] isa AbstractDiffEqArray
     @test sol[:, [1, 2]] isa AbstractDiffEqArray
-    
+
     sol1 = sol(0.0:1.0:10.0)
     @test sol1.u isa Vector
     @test first(sol1.u) isa Vector
     @test length(sol1.u) == 11
     @test length(sol1.t) == 11
-    
+
     sol2 = sol(0.1)
     @test sol2 isa Vector
     @test length(sol2) == length(unknowns(sys))
     @test first(sol2) isa Real
-    
+
     sol3 = sol(0.0:1.0:10.0, idxs = [lorenz1.x, lorenz2.x])
-    
+
     sol7 = sol(0.0:1.0:10.0, idxs = [2, 1])
     @test sol7.u isa Vector
     @test first(sol7.u) isa Vector
     @test length(sol7.u) == 11
     @test length(sol7.t) == 11
     @test collect(sol7[t]) ≈ sol3.t
     @test collect(sol7[t, 1:5]) ≈ sol3.t[1:5]
-    
+
     sol8 = sol(0.1, idxs = [2, 1])
     @test sol8 isa Vector
     @test length(sol8) == 2
     @test first(sol8) isa Real
-    
+
     sol9 = sol(0.0:1.0:10.0, idxs = 2)
     @test sol9.u isa Vector
     @test first(sol9.u) isa Real
     @test length(sol9.u) == 11
     @test length(sol9.t) == 11
     @test collect(sol9[t]) ≈ sol3.t
     @test collect(sol9[t, 1:5]) ≈ sol3.t[1:5]
-    
+
     sol10 = sol(0.1, idxs = 2)
     @test sol10 isa Real
 end
@@ -194,7 +195,9 @@ end
 
         @mtkcompile sys = System([D(x) ~ x + p * y, 1 ~ sin(y) + cos(x)], t)
         xidx = variable_index(sys, x)
-        prob = DAEProblem(sys, [D(x) => x + p * y, D(y) => 1 / sqrt(1 - (1 - cos(x))^2), x => 1.0, y => asin(1 - cos(x)), p => 2.0],
+        prob = DAEProblem(sys,
+            [D(x) => x + p * y, D(y) => 1 / sqrt(1 - (1 - cos(x))^2),
+                x => 1.0, y => asin(1 - cos(x)), p => 2.0],
             (0.0, 1.0); build_initializeprob = false)
         dae_sol = solve(prob, DFBDF(); save_idxs = [x])
 
@@ -225,10 +228,11 @@ end
     @testset "ODE with callbacks" begin
         @variables x(t) y(t)
         @parameters p q(t) r(t) s(t) u(t)
-        evs = [
-            ModelingToolkit.SymbolicDiscreteCallback(0.1, [q ~ Pre(q) + 1, s ~ Pre(s) - 1]; discrete_parameters = [q, s], iv = t)
-            ModelingToolkit.SymbolicDiscreteCallback(0.1, [r ~ 2Pre(r), u ~ Pre(u) / 2]; discrete_parameters = [r, u], iv = t)
-        ]
+        evs = [ModelingToolkit.SymbolicDiscreteCallback(
+                   0.1, [q ~ Pre(q) + 1, s ~ Pre(s) - 1];
+                   discrete_parameters = [q, s], iv = t)
+               ModelingToolkit.SymbolicDiscreteCallback(0.1, [r ~ 2Pre(r), u ~ Pre(u) / 2];
+                   discrete_parameters = [r, u], iv = t)]
         @mtkcompile sys = System([D(x) ~ x + p * y, D(y) ~ 2p + x], t, [x, y],
             [p, q, r, s, u], discrete_events = evs)
         @test length(unknowns(sys)) == 2
@@ -243,7 +247,9 @@ end
         sidx = parameter_index(sys, s)
         uidx = parameter_index(sys, u)
 
-        prob = ODEProblem(sys, [x => 1.0, y => 1.0, p => 0.5, q => 0.0, r => 1.0, s => 10.0, u => 4096.0], (0.0, 5.0))
+        prob = ODEProblem(
+            sys, [x => 1.0, y => 1.0, p => 0.5, q => 0.0, r => 1.0, s => 10.0, u => 4096.0],
+            (0.0, 5.0))
 
         @test SciMLBase.SavedSubsystem(sys, prob.p, [x, y, q, r, s, u]) === nothing
 
@@ -268,13 +274,16 @@ end
     @testset "SavedSubsystemWithFallback" begin
         @variables x(t) y(t)
         @parameters p q(t) r(t) s(t) u(t)
-        evs = [
-            ModelingToolkit.SymbolicDiscreteCallback(0.1, [q ~ Pre(q) + 1, s ~ Pre(s) - 1]; discrete_parameters = [q, s], iv = t)
-            ModelingToolkit.SymbolicDiscreteCallback(0.1, [r ~ 2Pre(r), u ~ Pre(u) / 2]; discrete_parameters = [r, u], iv = t)
-        ]
+        evs = [ModelingToolkit.SymbolicDiscreteCallback(
+                   0.1, [q ~ Pre(q) + 1, s ~ Pre(s) - 1];
+                   discrete_parameters = [q, s], iv = t)
+               ModelingToolkit.SymbolicDiscreteCallback(0.1, [r ~ 2Pre(r), u ~ Pre(u) / 2];
+                   discrete_parameters = [r, u], iv = t)]
         @mtkcompile sys = System([D(x) ~ x + p * y, D(y) ~ 2p + x^2], t, [x, y],
             [p, q, r, s, u], discrete_events = evs)
-        prob = ODEProblem(sys, [x => 1.0, y => 1.0, p => 0.5, q => 0.0, r => 1.0, s => 10.0, u => 4096.0], (0.0, 5.0))
+        prob = ODEProblem(
+            sys, [x => 1.0, y => 1.0, p => 0.5, q => 0.0, r => 1.0, s => 10.0, u => 4096.0],
+            (0.0, 5.0))
         ss = SciMLBase.SavedSubsystem(sys, prob.p, [x, q, s, r])
         @test SciMLBase.get_saved_state_idxs(ss) == [variable_index(sys, x)]
         sswf = SciMLBase.SavedSubsystemWithFallback(ss, sys)
@@ -314,13 +323,16 @@ end
     @testset "get_save_idxs_and_saved_subsystem" begin
         @variables x(t) y(t)
         @parameters p q(t) r(t) s(t) u(t)
-        evs = [
-            ModelingToolkit.SymbolicDiscreteCallback(0.1, [q ~ Pre(q) + 1, s ~ Pre(s) - 1]; discrete_parameters = [q, s], iv = t)
-            ModelingToolkit.SymbolicDiscreteCallback(0.1, [r ~ 2Pre(r), u ~ Pre(u) / 2]; discrete_parameters = [r, u], iv = t)
-        ]
+        evs = [ModelingToolkit.SymbolicDiscreteCallback(
+                   0.1, [q ~ Pre(q) + 1, s ~ Pre(s) - 1];
+                   discrete_parameters = [q, s], iv = t)
+               ModelingToolkit.SymbolicDiscreteCallback(0.1, [r ~ 2Pre(r), u ~ Pre(u) / 2];
+                   discrete_parameters = [r, u], iv = t)]
         @mtkcompile sys = System([D(x) ~ x + p * y, D(y) ~ 2p + x^2], t, [x, y],
             [p, q, r, s, u], discrete_events = evs)
-        prob = ODEProblem(sys, [x => 1.0, y => 1.0, p => 0.5, q => 0.0, r => 1.0, s => 10.0, u => 4096.0], (0.0, 5.0))
+        prob = ODEProblem(
+            sys, [x => 1.0, y => 1.0, p => 0.5, q => 0.0, r => 1.0, s => 10.0, u => 4096.0],
+            (0.0, 5.0))
 
         _idxs, _ss = @inferred SciMLBase.get_save_idxs_and_saved_subsystem(prob, nothing)
         @test _idxs === _ss === nothing