run JuliaFormatter per contributing guidelines. I didn't touch any of these files except ext/MTKFMIExt.jl!

Author: EduardoMartinezLopez

docs/src/internals.md

@@ -6,11 +6,12 @@ This section documents the internal implementation details of ModelingToolkit. T
 
 ModelingToolkit's internal architecture consists of several key components:
 
-- **Structural Transformation**: Algorithms for transforming equation systems, including index reduction, tearing, and algebraic simplification
-- **Bipartite Graphs**: Graph representations used to analyze relationships between equations and variables
-- **System Structure**: Internal representations of system state and transformations
+  - **Structural Transformation**: Algorithms for transforming equation systems, including index reduction, tearing, and algebraic simplification
+  - **Bipartite Graphs**: Graph representations used to analyze relationships between equations and variables
+  - **System Structure**: Internal representations of system state and transformations
 
 These components work together to enable ModelingToolkit's symbolic manipulation and code generation capabilities.
 
 !!! warning
+    
     The functions and types documented in this section are internal implementation details. Users should not rely on these APIs as they may change or be removed without deprecation warnings.

docs/src/internals/bipartite_graph.md

@@ -1,6 +1,7 @@
 # Bipartite Graphs
 
 !!! warning "Internal API"
+    
     The functions documented on this page are internal implementation details of ModelingToolkit. They are not part of the public API and may change or be removed without notice in non-breaking releases. This documentation is provided to help contributors understand the codebase.
 
 ModelingToolkit uses bipartite graphs to represent relationships between equations and variables in systems. These functions provide tools for working with and analyzing these graphs.
@@ -77,4 +78,4 @@ asdigraph
 ```@docs
 SRC
 DST
-```
+```

docs/src/internals/structural_transformation.md

@@ -1,6 +1,7 @@
 # Structural Transformation
 
 !!! warning "Internal API"
+    
     The functions documented on this page are internal implementation details of ModelingToolkit. They are not part of the public API and may change or be removed without notice in non-breaking releases. This documentation is provided to help contributors understand the codebase.
 
 These functions are used for structural analysis and transformation of equation systems, including index reduction, tearing, and other algebraic manipulations used in the simplification process.
@@ -84,4 +85,4 @@ InducedCondensationGraph
 MatchedCondensationGraph
 Unassigned
 unassigned
-```
+```

ext/MTKFMIExt.jl

@@ -373,13 +373,11 @@ function parseFMIVariableName(name::AbstractString)
             safe_name = name
         end
 
-
         idx = findfirst(',', safe_name)
         if idx === nothing
             name = @view name[5:(end - 1)]
             der = 1
         else
-            
             der = parse(Int, @view name[(idx + 1):(end - 1)])
             name = @view name[5:(idx - 1)]
         end

src/systems/diffeqs/basic_transformations.jl

@@ -207,14 +207,14 @@ sol = solve(prob, radau5(), abstol = 1e-10, reltol = 1e-10)
 """
 function fractional_to_ordinary(
         eqs, variables, alphas, epsilon, T;
-        initials = 0, additional_eqs = [], iv = only(@independent_variables t), matrix=false
+        initials = 0, additional_eqs = [], iv = only(@independent_variables t), matrix = false
 )
     D = Differential(iv)
     i = 0
     all_eqs = Equation[]
     all_def = Pair[]
-    
-    function fto_helper(sub_eq, sub_var, α; initial=0)
+
+    function fto_helper(sub_eq, sub_var, α; initial = 0)
         alpha_0 = α
 
         if (α > 1)
@@ -250,28 +250,30 @@ function fractional_to_ordinary(
         if matrix
             new_z = Symbol(:ʐ, :_, i)
             i += 1
-            γs = diagm([γ_i(index) for index in M:N-1])
-            cs = [c_i(index) for index in M:N-1]
+            γs = diagm([γ_i(index) for index in M:(N - 1)])
+            cs = [c_i(index) for index in M:(N - 1)]
 
             if (α < 1)
-                new_z = only(@variables $new_z(iv)[1:N-M])
+                new_z = only(@variables $new_z(iv)[1:(N - M)])
                 new_eq = D(new_z) ~ -γs*new_z .+ sub_eq
                 rhs = dot(cs, new_z) + initial
                 push!(def, new_z=>zeros(N-M))
             else
-                new_z = only(@variables $new_z(iv)[1:N-M, 1:m])
-                new_eq = D(new_z) ~ -γs*new_z + hcat(fill(sub_eq, N-M, 1), collect(new_z[:, 1:m-1]*diagm(1:m-1)))
-                rhs = coeff*sum(cs[i]*new_z[i, m] for i in 1:N-M)
+                new_z = only(@variables $new_z(iv)[1:(N - M), 1:m])
+                new_eq = D(new_z) ~
+                         -γs*new_z +
+                         hcat(fill(sub_eq, N-M, 1), collect(new_z[:, 1:(m - 1)]*diagm(1:(m - 1))))
+                rhs = coeff*sum(cs[i]*new_z[i, m] for i in 1:(N - M))
                 for (index, value) in enumerate(initial)
                     rhs += value * iv^(index - 1) / gamma(index)
                 end
                 push!(def, new_z=>zeros(N-M, m))
             end
             push!(new_eqs, new_eq)
         else
-            if (α < 1)  
+            if (α < 1)
                 rhs = initial
-                for index in range(M, N-1; step=1)
+                for index in range(M, N-1; step = 1)
                     new_z = Symbol(:ʐ, :_, i)
                     i += 1
                     new_z = ModelingToolkit.unwrap(only(@variables $new_z(iv)))
@@ -285,12 +287,12 @@ function fractional_to_ordinary(
                 for (index, value) in enumerate(initial)
                     rhs += value * iv^(index - 1) / gamma(index)
                 end
-                for index in range(M, N-1; step=1)
+                for index in range(M, N-1; step = 1)
                     new_z = Symbol(:ʐ, :_, i)
                     i += 1
                     γ = γ_i(index)
                     base = sub_eq
-                    for k in range(1, m; step=1)
+                    for k in range(1, m; step = 1)
                         new_z = Symbol(:ʐ, :_, index-M, :_, k)
                         new_z = ModelingToolkit.unwrap(only(@variables $new_z(iv)))
                         new_eq = D(new_z) ~ base - γ*new_z
@@ -307,12 +309,12 @@ function fractional_to_ordinary(
     end
 
     for (eq, cur_var, alpha, init) in zip(eqs, variables, alphas, initials)
-        (new_eqs, def) = fto_helper(eq, cur_var, alpha; initial=init)
+        (new_eqs, def) = fto_helper(eq, cur_var, alpha; initial = init)
         append!(all_eqs, new_eqs)
         append!(all_def, def)
     end
     append!(all_eqs, additional_eqs)
-    @named sys = System(all_eqs, iv; defaults=all_def)
+    @named sys = System(all_eqs, iv; defaults = all_def)
     return mtkcompile(sys)
 end
 
@@ -338,7 +340,7 @@ sol = solve(prob, radau5(), abstol = 1e-5, reltol = 1e-5)
 """
 function linear_fractional_to_ordinary(
         degrees, coeffs, rhs, epsilon, T;
-        initials = 0, symbol = :x, iv = only(@independent_variables t), matrix=false
+        initials = 0, symbol = :x, iv = only(@independent_variables t), matrix = false
 )
     previous = Symbol(symbol, :_, 0)
     previous = ModelingToolkit.unwrap(only(@variables $previous(iv)))
@@ -371,17 +373,17 @@ function linear_fractional_to_ordinary(
         if matrix
             new_z = Symbol(:ʐ, :_, i)
             i += 1
-            γs = diagm([γ_i(index) for index in M:N-1])
-            cs = [c_i(index) for index in M:N-1]
+            γs = diagm([γ_i(index) for index in M:(N - 1)])
+            cs = [c_i(index) for index in M:(N - 1)]
 
-            new_z = only(@variables $new_z(iv)[1:N-M])
+            new_z = only(@variables $new_z(iv)[1:(N - M)])
             new_eq = D(new_z) ~ -γs*new_z .+ sub_eq
             sum = dot(cs, new_z)
             push!(def, new_z=>zeros(N-M))
             push!(new_eqs, new_eq)
         else
             sum = 0
-            for index in range(M, N-1; step=1)
+            for index in range(M, N-1; step = 1)
                 new_z = Symbol(:ʐ, :_, i)
                 i += 1
                 new_z = ModelingToolkit.unwrap(only(@variables $new_z(iv)))
@@ -394,7 +396,7 @@ function linear_fractional_to_ordinary(
         return (new_eqs, def, sum)
     end
 
-    for i in range(1, ceil(Int, degrees[1]); step=1)
+    for i in range(1, ceil(Int, degrees[1]); step = 1)
         new_x = Symbol(symbol, :_, i)
         new_x = ModelingToolkit.unwrap(only(@variables $new_x(iv)))
         push!(all_eqs, D(previous) ~ new_x)
@@ -417,7 +419,7 @@ function linear_fractional_to_ordinary(
         end
     end
     push!(all_eqs, 0 ~ new_rhs)
-    @named sys = System(all_eqs, iv; defaults=all_def)
+    @named sys = System(all_eqs, iv; defaults = all_def)
     return mtkcompile(sys)
 end
 

test/components.jl

@@ -352,7 +352,8 @@ end
     @named comp1 = System(Equation[], t; systems = [input])
     @named output = RealOutput()
     @named comp2 = System(Equation[], t; systems = [output])
-    @named sys = System([connect(comp2.output.u, comp1.input.u)], t; systems = [comp1, comp2])
+    @named sys = System([connect(comp2.output.u, comp1.input.u)], t; systems = [
+        comp1, comp2])
     eq = only(equations(expand_connections(sys)))
     # as opposed to `output.u ~ input.u`
     @test isequal(eq, comp1.input.u ~ comp2.output.u)

test/fractional_to_ordinary.jl

@@ -6,8 +6,8 @@ using Test
 @independent_variables t
 @variables x(t)
 D = Differential(t)
-tspan = (0., 1.)
-timepoint = [0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.]
+tspan = (0.0, 1.0)
+timepoint = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
 
 function expect(t, α)
     return (3/2*t^(α/2) - t^4)^2
@@ -19,23 +19,23 @@ eqs += (gamma(9)*t^(8 - α)/gamma(9 - α)) + (3/2*t^(α/2)-t^4)^3 - x^(3/2)
 sys = fractional_to_ordinary(eqs, x, α, 10^-7, 1)
 
 prob = ODEProblem(sys, [], tspan)
-sol = solve(prob, radau5(), saveat=timepoint, abstol = 1e-10, reltol = 1e-10)
+sol = solve(prob, radau5(), saveat = timepoint, abstol = 1e-10, reltol = 1e-10)
 
 for time in 0:0.1:1
-    @test isapprox(expect(time, α), sol(time, idxs=x), atol=1e-7)
+    @test isapprox(expect(time, α), sol(time, idxs = x), atol = 1e-7)
     time += 0.1
 end
 
 α = 0.3
 eqs = (9*gamma(1 + α)/4) - (3*t^(4 - α/2)*gamma(5 + α/2)/gamma(5 - α/2))
 eqs += (gamma(9)*t^(8 - α)/gamma(9 - α)) + (3/2*t^(α/2)-t^4)^3 - x^(3/2)
-sys = fractional_to_ordinary(eqs, x, α, 10^-7, 1; matrix=true)
+sys = fractional_to_ordinary(eqs, x, α, 10^-7, 1; matrix = true)
 
 prob = ODEProblem(sys, [], tspan)
-sol = solve(prob, radau5(), saveat=timepoint, abstol = 1e-10, reltol = 1e-10)
+sol = solve(prob, radau5(), saveat = timepoint, abstol = 1e-10, reltol = 1e-10)
 
 for time in 0:0.1:1
-    @test isapprox(expect(time, α), sol(time, idxs=x), atol=1e-7)
+    @test isapprox(expect(time, α), sol(time, idxs = x), atol = 1e-7)
 end
 
 α = 0.9
@@ -44,43 +44,46 @@ eqs += (gamma(9)*t^(8 - α)/gamma(9 - α)) + (3/2*t^(α/2)-t^4)^3 - x^(3/2)
 sys = fractional_to_ordinary(eqs, x, α, 10^-7, 1)
 
 prob = ODEProblem(sys, [], tspan)
-sol = solve(prob, radau5(), saveat=timepoint, abstol = 1e-10, reltol = 1e-10)
+sol = solve(prob, radau5(), saveat = timepoint, abstol = 1e-10, reltol = 1e-10)
 
 for time in 0:0.1:1
-    @test isapprox(expect(time, α), sol(time, idxs=x), atol=1e-7)
+    @test isapprox(expect(time, α), sol(time, idxs = x), atol = 1e-7)
 end
 
 # Testing for example 2 of Section 7
 @independent_variables t
 @variables x(t) y(t)
 D = Differential(t)
-tspan = (0., 220.)
+tspan = (0.0, 220.0)
 
-sys = fractional_to_ordinary([1 - 4*x + x^2 * y, 3*x - x^2 * y], [x, y], [1.3, 0.8], 10^-8, 220; initials=[[1.2, 1], 2.8], matrix=true)
+sys = fractional_to_ordinary([1 - 4*x + x^2 * y, 3*x - x^2 * y], [x, y], [1.3, 0.8],
+    10^-8, 220; initials = [[1.2, 1], 2.8], matrix = true)
 prob = ODEProblem(sys, [], tspan)
 sol = solve(prob, radau5(), abstol = 1e-8, reltol = 1e-8)
 
-@test isapprox(1.0097684171, sol(220, idxs=x), atol=1e-5)
-@test isapprox(2.1581264031, sol(220, idxs=y), atol=1e-5)
+@test isapprox(1.0097684171, sol(220, idxs = x), atol = 1e-5)
+@test isapprox(2.1581264031, sol(220, idxs = y), atol = 1e-5)
 
 #Testing for example 3 of Section 7
 @independent_variables t
 @variables x_0(t)
 D = Differential(t)
-tspan = (0., 5000.)
+tspan = (0.0, 5000.0)
 
 function expect(t)
     return sqrt(2) * sin(t + pi/4)
 end
 
-sys = linear_fractional_to_ordinary([3, 2.5, 2, 1, .5, 0], [1, 1, 1, 4, 1, 4], 6*cos(t), 10^-5, 5000; initials=[1, 1, -1])
+sys = linear_fractional_to_ordinary([3, 2.5, 2, 1, 0.5, 0], [1, 1, 1, 4, 1, 4],
+    6*cos(t), 10^-5, 5000; initials = [1, 1, -1])
 prob = ODEProblem(sys, [], tspan)
 sol = solve(prob, radau5(), abstol = 1e-5, reltol = 1e-5)
 
-@test isapprox(expect(5000), sol(5000, idxs=x_0), atol=1e-5)
+@test isapprox(expect(5000), sol(5000, idxs = x_0), atol = 1e-5)
 
-msys = linear_fractional_to_ordinary([3, 2.5, 2, 1, .5, 0], [1, 1, 1, 4, 1, 4], 6*cos(t), 10^-5, 5000; initials=[1, 1, -1], matrix=true)
+msys = linear_fractional_to_ordinary([3, 2.5, 2, 1, 0.5, 0], [1, 1, 1, 4, 1, 4], 6*cos(t),
+    10^-5, 5000; initials = [1, 1, -1], matrix = true)
 mprob = ODEProblem(sys, [], tspan)
 msol = solve(prob, radau5(), abstol = 1e-5, reltol = 1e-5)
 
-@test isapprox(expect(5000), msol(5000, idxs=x_0), atol=1e-5)
+@test isapprox(expect(5000), msol(5000, idxs = x_0), atol = 1e-5)

test/model_parsing.jl

@@ -1065,16 +1065,16 @@ end
             MyBool => false
             NewInt => 1
         end
-        
+
         @parameters begin
             k = 1.0
         end
-        
+
         @variables begin
             x(t)
             y(t)
         end
-        
+
         @equations begin
             D(x) ~ -k * x
             y ~ x

test/parameter_dependencies.jl

@@ -174,7 +174,7 @@ end
 end
 
 @testset "Change Tunables" begin
-    @variables θ(t)=π/6 ω(t)=0.
+    @variables θ(t)=π/6 ω(t)=0.0
     @parameters g=9.81 L=1.0 b=0.1 errp=1
     eqs = [
         D(θ) ~ ω,

test/runtests.jl

@@ -103,7 +103,7 @@ end
             @safetestset "Fractional Differential Equations Tests" include("fractional_to_ordinary.jl")
         end
     end
-    
+
     if GROUP == "All" || GROUP == "SymbolicIndexingInterface"
         @safetestset "SymbolicIndexingInterface test" include("symbolic_indexing_interface.jl")
         @safetestset "SciML Problem Input Test" include("sciml_problem_inputs.jl")