Formatted failing files using JuliaFormatter

Author: matthew-kapp

Project.toml

@@ -7,6 +7,7 @@ version = "2.19.0"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
 IfElse = "615f187c-cbe4-4ef1-ba3b-2fcf58d6d173"
+JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 PreallocationTools = "d236fae5-4411-538c-8e31-a6e3d9e00b46"
@@ -18,12 +19,13 @@ ChainRulesCore = "1.24"
 ControlSystemsBase = "1.4"
 DataFrames = "1.7"
 DataInterpolations = "6"
-ForwardDiff = "0.10"
 DiffEqBase = "6.152"
+ForwardDiff = "0.10"
 IfElse = "0.1"
+JuliaFormatter = "1.0.62"
 LinearAlgebra = "1.10"
-Optimization = "4"
 ModelingToolkit = "9.47"
+Optimization = "4"
 OrdinaryDiffEq = "6.87"
 OrdinaryDiffEqDefault = "1.1"
 PreallocationTools = "0.4.23"

docs/src/tutorials/input_component.md

@@ -38,8 +38,7 @@ function MassSpringDamper(; name)
     @variables f(t)=0 x(t)=0 dx(t)=0 ddx(t)=0
     @parameters m=10 k=1000 d=1
 
-    eqs = [
-           f ~ input.u
+    eqs = [f ~ input.u
            ddx * 10 ~ k * x + d * dx + f
            D(x) ~ dx
            D(dx) ~ ddx]
@@ -52,10 +51,8 @@ function MassSpringDamperSystem(data, time; name)
     @named clk = ContinuousClock()
     @named model = MassSpringDamper()
 
-    eqs = [
-        connect(src.input, clk.output)
-        connect(src.output, model.input)
-    ]
+    eqs = [connect(src.input, clk.output)
+           connect(src.output, model.input)]
 
     ODESystem(eqs, t, [], []; name, systems = [src, clk, model])
 end
@@ -100,11 +97,10 @@ using Plots
 
 function MassSpringDamper(; name)
     @named input = RealInput()
-    vars = @variables f(t) x(t)=0 dx(t) [guess=0] ddx(t)
+    vars = @variables f(t) x(t)=0 dx(t) [guess = 0] ddx(t)
     pars = @parameters m=10 k=1000 d=1
 
-    eqs = [
-           f ~ input.u
+    eqs = [f ~ input.u
            ddx * 10 ~ k * x + d * dx + f
            D(x) ~ dx
            D(dx) ~ ddx]
@@ -117,10 +113,8 @@ function MassSpringDamperSystem(data, time; name)
     @named clk = ContinuousClock()
     @named model = MassSpringDamper()
 
-    eqs = [
-        connect(model.input, src.output)
-        connect(src.input, clk.output)
-    ]
+    eqs = [connect(model.input, src.output)
+           connect(src.input, clk.output)]
 
     ODESystem(eqs, t; name, systems = [src, clk, model])
 end
@@ -143,13 +137,15 @@ plot(sol)
 ```
 
 If we want to run a new data set, this requires only remaking the problem and solving again
+
 ```@example parametrized_interpolation
 prob2 = remake(prob, p = [sys.src.data => ones(length(df.data))])
 sol2 = solve(prob2)
 plot(sol2)
 ```
 
 !!! note
+    
     Note that when changing the data, the length of the new data must be the same as the length of the original data.
 
 ## Custom Component with External Data
@@ -224,7 +220,7 @@ using ModelingToolkitStandardLibrary.Blocks
 using OrdinaryDiffEq
 
 function System(; name)
-    src = SampledData(Float64, name=:src)
+    src = SampledData(Float64, name = :src)
 
     vars = @variables f(t)=0 x(t)=0 dx(t)=0 ddx(t)=0
     pars = @parameters m=10 k=1000 d=1
@@ -238,22 +234,22 @@ function System(; name)
 end
 
 @named system = System()
-sys = structural_simplify(system, split=false)
+sys = structural_simplify(system, split = false)
 s = complete(system)
 
 dt = 4e-4
 time = 0:dt:0.1
 data1 = sin.(2 * pi * time * 100)
 data2 = cos.(2 * pi * time * 50)
 
-prob = ODEProblem(sys, [], (0, time[end]); split=false, tofloat = false, use_union=true)
+prob = ODEProblem(sys, [], (0, time[end]); split = false, tofloat = false, use_union = true)
 defs = ModelingToolkit.defaults(sys)
 
 function get_prob(data)
     defs[s.src.buffer] = Parameter(data, dt)
     # ensure p is a uniform type of Vector{Parameter{Float64}} (converting from Vector{Any})
     p = Parameter.(ModelingToolkit.varmap_to_vars(defs, parameters(sys); tofloat = false))
-    remake(prob; p, build_initializeprob=false)
+    remake(prob; p, build_initializeprob = false)
 end
 
 prob1 = get_prob(data1)

src/Blocks/Blocks.jl

@@ -10,7 +10,8 @@ using ModelingToolkit: getdefault, t_nounits as t, D_nounits as D
 export RealInput, RealInputArray, RealOutput, RealOutputArray, SISO
 include("utils.jl")
 
-export Gain, Sum, MatrixGain, Feedback, Add, Add3, Product, Division, Power, Modulo, UnaryMinus, Floor, Ceil
+export Gain, Sum, MatrixGain, Feedback, Add, Add3, Product, Division, Power, Modulo,
+       UnaryMinus, Floor, Ceil
 export Abs, Sign, Sqrt, Sin, Cos, Tan, Asin, Acos, Atan, Atan2, Sinh, Cosh, Tanh, Exp
 export Log, Log10
 include("math.jl")

src/Blocks/math.jl

@@ -226,7 +226,7 @@ Output the exponential with base as the first input and exponent as second input
         output = RealOutput()
     end
     @equations begin
-        output.u ~ base.u ^ exponent.u
+        output.u ~ base.u^exponent.u
     end
 end
 

src/Thermal/utils.jl

@@ -1,4 +1,5 @@
-@connector function HeatPort(; T = nothing, T_guess = 273.15 + 20, Q_flow = nothing, Q_flow_guess = 0.0, name)
+@connector function HeatPort(;
+        T = nothing, T_guess = 273.15 + 20, Q_flow = nothing, Q_flow_guess = 0.0, name)
     pars = @parameters begin
         T_guess = T_guess
         Q_flow_guess = Q_flow_guess

test/Blocks/math.jl

@@ -180,7 +180,7 @@ end
 
 @testset "Modulo" begin
     @named c1 = Ramp(height = 2, duration = 1, offset = 1, start_time = 0, smooth = false)
-    @named c2 = Constant(; k = 1) 
+    @named c2 = Constant(; k = 1)
     @named modl = Modulo(;)
     @named model = ODESystem(
         [
@@ -194,7 +194,7 @@ end
     sol = solve(prob, Rodas4())
     @test isequal(unbound_inputs(sys), [])
     @test sol.retcode == Success
-    @test sol[modl.remainder.u] ≈ mod.(2 * sol.t,1)
+    @test sol[modl.remainder.u] ≈ mod.(2 * sol.t, 1)
 end
 
 @testset "UnaryMinus" begin
@@ -213,7 +213,7 @@ end
     sol = solve(prob, Rodas4())
     @test isequal(unbound_inputs(sys), [])
     @test sol.retcode == Success
-    @test sol[minu.output.u] ≈ - sin.(2 * pi * sol.t)
+    @test sol[minu.output.u] ≈ -sin.(2 * pi * sol.t)
 end
 
 @testset "Floor" begin

test/Blocks/test_analysis_points.jl

@@ -266,7 +266,8 @@ Sinner = sminreal(ss(get_sensitivity(sys_inner, :u)[1]...))
 
 Souter = sminreal(ss(get_sensitivity(sys_outer, sys_outer.sys_inner.u)[1]...))
 
-Sinner2 = sminreal(ss(get_sensitivity(sys_outer, sys_outer.sys_inner.u, loop_openings = [:y2])[1]...))
+Sinner2 = sminreal(ss(get_sensitivity(
+    sys_outer, sys_outer.sys_inner.u, loop_openings = [:y2])[1]...))
 
 @test Sinner.nx == 1
 @test Sinner == Sinner2
@@ -332,7 +333,8 @@ eqs = [connect(r.output, F.input)
        connect(F.output, sys_inner.add.input1)]
 sys_outer = ODESystem(eqs, t, systems = [F, sys_inner, r], name = :outer)
 
-matrices, _ = get_sensitivity(sys_outer, [sys_outer.inner.plant_input, sys_outer.inner.plant_output])
+matrices, _ = get_sensitivity(
+    sys_outer, [sys_outer.inner.plant_input, sys_outer.inner.plant_output])
 
 Ps = tf(1, [1, 1]) |> ss
 Cs = tf(1) |> ss
@@ -346,7 +348,8 @@ So = CS.feedback(1, Ps * Cs)
 @test tf(G[1, 2]) ≈ tf(-CS.feedback(Cs, Ps))
 @test tf(G[2, 1]) ≈ tf(CS.feedback(Ps, Cs))
 
-matrices, _ = get_comp_sensitivity(sys_outer, [sys_outer.inner.plant_input, sys_outer.inner.plant_output])
+matrices, _ = get_comp_sensitivity(
+    sys_outer, [sys_outer.inner.plant_input, sys_outer.inner.plant_output])
 
 G = CS.ss(matrices...) |> sminreal
 Ti = CS.feedback(Cs * Ps)
@@ -369,13 +372,15 @@ L = CS.ss(matrices...) |> sminreal
 @test tf(L[1, 1]) ≈ -tf(Ps * Cs)
 
 # Calling looptransfer like below is not the intended way, but we can work out what it should return if we did so it remains a valid test
-matrices, _ = get_looptransfer(sys_outer, [sys_outer.inner.plant_input, sys_outer.inner.plant_output])
+matrices, _ = get_looptransfer(
+    sys_outer, [sys_outer.inner.plant_input, sys_outer.inner.plant_output])
 L = CS.ss(matrices...) |> sminreal
 @test tf(L[1, 1]) ≈ tf(0)
 @test tf(L[2, 2]) ≈ tf(0)
 @test sminreal(L[1, 2]) ≈ ss(-1)
 @test tf(L[2, 1]) ≈ tf(Ps)
 
-matrices, _ = linearize(sys_outer, [sys_outer.inner.plant_input], [sys_outer.inner.plant_output])
+matrices, _ = linearize(
+    sys_outer, [sys_outer.inner.plant_input], [sys_outer.inner.plant_output])
 G = CS.ss(matrices...) |> sminreal
 @test tf(G) ≈ tf(CS.feedback(Ps, Cs))

test/Thermal/piston.jl

@@ -42,6 +42,7 @@ using ModelingToolkitStandardLibrary.Blocks
     # The initial value doesn't add up to absolute zero, while the rest do. To avoid
     # tolerance on the latter, the test is split in two parts.
     @test sol[piston.gas.Q_flow][1] + sol[piston.coolant.Q_flow][1]≈0 atol=1e-6
-    @test sol[piston.gas.Q_flow][2:end] + sol[piston.coolant.Q_flow][2:end] ≈
-          zeros(length(sol) - 1) atol = 1e-6
+    @test sol[piston.gas.Q_flow][2:end] +
+          sol[piston.coolant.Q_flow][2:end]≈
+    zeros(length(sol) - 1) atol=1e-6
 end