refactor: format with v2

Author: AayushSabharwal

docs/src/API/variables.md

@@ -293,7 +293,7 @@ For systems that contain parameters with metadata like described above, have som
 In the example below, we define a system with tunable parameters and extract bounds vectors
 
 ```@example metadata
-@variables x(t)=0 u(t)=0 [input = true] y(t)=0 [output = true]
+@variables x(t)=0 u(t)=0 [input=true] y(t)=0 [output=true]
 @parameters T [tunable = true, bounds = (0, Inf)]
 @parameters k [tunable = true, bounds = (0, Inf)]
 eqs = [D(x) ~ (-x + k * u) / T # A first-order system with time constant T and gain k

docs/src/basics/Events.md

@@ -92,8 +92,8 @@ The basic purely symbolic continuous event interface to encode *one* continuous
 event is
 
 ```julia
-AbstractSystem(eqs, ...; continuous_events::Vector{Equation})
-AbstractSystem(eqs, ...; continuous_events::Pair{Vector{Equation}, Vector{Equation}})
+AbstractSystem(eqs, _...; continuous_events::Vector{Equation})
+AbstractSystem(eqs, _...; continuous_events::Pair{Vector{Equation}, Vector{Equation}})
 ```
 
 In the former, equations that evaluate to 0 will represent conditions that should
@@ -272,7 +272,7 @@ In addition to continuous events, discrete events are also supported. The
 general interface to represent a collection of discrete events is
 
 ```julia
-AbstractSystem(eqs, ...; discrete_events = [condition1 => affect1, condition2 => affect2])
+AbstractSystem(eqs, _...; discrete_events = [condition1 => affect1, condition2 => affect2])
 ```
 
 where conditions are symbolic expressions that should evaluate to `true` when an
@@ -497,7 +497,8 @@ so far we aren't using anything that's not possible with the implicit interface.
 You can also write
 
 ```julia
-[temp ~ furnace_off_threshold] => ModelingToolkit.ImperativeAffect(modified = (;
+[temp ~
+ furnace_off_threshold] => ModelingToolkit.ImperativeAffect(modified = (;
     furnace_on)) do x, o, i, c
     @set! x.furnace_on = false
 end

docs/src/basics/FAQ.md

@@ -63,7 +63,7 @@ The same principle applies to any parameter type that is not `Float64`.
 @parameters p1::Int # integer-valued
 @parameters p2::Bool # boolean-valued
 @parameters p3::MyCustomStructType # non-numeric
-@parameters p4::ComponentArray{...} # non-standard array
+@parameters p4::ComponentArray{_...} # non-standard array
 ```
 
 ## Getting the index for a symbol

docs/src/basics/MTKLanguage.md

@@ -381,7 +381,8 @@ Refer the following example for different ways to define symbolic arrays.
     @parameters begin
         p1[1:4]
         p2[1:N]
-        p3[1:N, 1:M] = 10,
+        p3[1:N,
+            1:M] = 10,
         [description = "A multi-dimensional array of arbitrary length with description"]
         (p4[1:N, 1:M] = 10),
         [description = "An alternate syntax for p3 to match the syntax of vanilla parameters macro"]

docs/src/basics/Validation.md

@@ -108,7 +108,7 @@ function ModelingToolkit.get_unit(op::typeof(dummycomplex), args)
 end
 
 sts = @variables a(t)=0 [unit = u"cm"]
-ps = @parameters s=-1 [unit = u"cm"] c=c [unit = u"cm"]
+ps = @parameters s=-1 [unit=u"cm"] c=c [unit=u"cm"]
 eqs = [D(a) ~ dummycomplex(c, s);]
 sys = System(
     eqs, t, [sts...;], [ps...;], name = :sys, checks = ~ModelingToolkit.CheckUnits)

docs/src/examples/sparse_jacobians.md

@@ -23,15 +23,20 @@ function brusselator_2d_loop(du, u, p, t)
     @inbounds for I in CartesianIndices((N, N))
         i, j = Tuple(I)
         x, y = xyd_brusselator[I[1]], xyd_brusselator[I[2]]
-        ip1, im1, jp1, jm1 = limit(i + 1, N), limit(i - 1, N), limit(j + 1, N),
+        ip1, im1, jp1,
+        jm1 = limit(i + 1, N), limit(i - 1, N), limit(j + 1, N),
         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, t)
-        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]
+        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, t)
+        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]
     end
 end
 p = (3.4, 1.0, 10.0, step(xyd_brusselator))

docs/src/examples/tearing_parallelism.md

@@ -15,7 +15,7 @@ using ModelingToolkit: t_nounits as t, D_nounits as D
 
 # Basic electric components
 @connector function Pin(; name)
-    @variables v(t)=1.0 i(t)=1.0 [connect = Flow]
+    @variables v(t)=1.0 i(t)=1.0 [connect=Flow]
     System(Equation[], t, [v, i], [], name = name)
 end
 
@@ -36,7 +36,7 @@ function ConstantVoltage(; name, V = 1.0)
 end
 
 @connector function HeatPort(; name)
-    @variables T(t)=293.15 Q_flow(t)=0.0 [connect = Flow]
+    @variables T(t)=293.15 Q_flow(t)=0.0 [connect=Flow]
     System(Equation[], t, [T, Q_flow], [], name = name)
 end
 

docs/src/tutorials/disturbance_modeling.md

@@ -188,7 +188,9 @@ disturbance_inputs = [ssys.d1, ssys.d2]
 P = ssys.system_model
 outputs = [P.inertia1.phi, P.inertia2.phi, P.inertia1.w, P.inertia2.w]
 
-(f_oop, f_ip), x_sym, p_sym, io_sys = ModelingToolkit.generate_control_function(
+(f_oop, f_ip), x_sym,
+p_sym,
+io_sys = ModelingToolkit.generate_control_function(
     model_with_disturbance, inputs, disturbance_inputs; disturbance_argument = true)
 
 g = ModelingToolkit.build_explicit_observed_function(

docs/src/tutorials/linear_analysis.md

@@ -39,7 +39,7 @@ This is signified by the name being the middle argument to `connect`.
 Of the above mentioned functions, all except for [`open_loop`](@ref) return the output of [`ModelingToolkit.linearize`](@ref), which is
 
 ```julia
-matrices, simplified_sys = linearize(...)
+matrices, simplified_sys = linearize(_...)
 # matrices = (; A, B, C, D)
 ```
 

ext/MTKCasADiDynamicOptExt.jl

@@ -67,7 +67,8 @@ function MTK.CasADiDynamicOptProblem(sys::System, op, tspan;
         dt = nothing,
         steps = nothing,
         guesses = Dict(), kwargs...)
-    prob, _ = MTK.process_DynamicOptProblem(
+    prob,
+    _ = MTK.process_DynamicOptProblem(
         CasADiDynamicOptProblem, CasADiModel, sys, op, tspan; dt, steps, guesses, kwargs...)
     prob
 end