Apply JuliaFormatter to fix code formatting

- Applied JuliaFormatter with SciML style guide
- Formatted 50 files

🤖 Generated by OrgMaintenanceScripts.jl

Author: SciML Bot

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

@@ -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/MTKLanguage.md

@@ -382,7 +382,7 @@ Refer the following example for different ways to define symbolic arrays.
         p1[1:4]
         p2[1:N]
         p3[1:N,
-        1:M] = 10,
+            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

@@ -27,16 +27,16 @@ function brusselator_2d_loop(du, u, p, t)
         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)
+            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]
+            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
 

src/bipartite_graph.jl

@@ -635,6 +635,7 @@ function Graphs.incidence_matrix(g::BipartiteGraph, val = true)
     I = Int[]
     J = Int[]
     for i in 𝑠vertices(g), n in 𝑠neighbors(g, i)
+
         push!(I, i)
         push!(J, n)
     end

src/deprecations.jl

@@ -72,6 +72,7 @@ for T in [:ODEProblem, :DDEProblem, :SDEProblem, :SDDEProblem, :DAEProblem,
     end
 
     for pType in [SciMLBase.NullParameters, Nothing], uType in [Any, Nothing]
+
         @eval function SciMLBase.$T(sys::System, u0::$uType, tspan, p::$pType; kw...)
             ctor = string($T)
             pT = string($(QuoteNode(pType)))
@@ -152,6 +153,7 @@ for T in [:NonlinearProblem, :NonlinearLeastSquaresProblem,
         end
     end
     for pType in [SciMLBase.NullParameters, Nothing], uType in [Any, Nothing]
+
         @eval function SciMLBase.$T(sys::System, u0::$uType, p::$pType; kw...)
             ctor = string($T)
             pT = string($(QuoteNode(pType)))

src/inputoutput.jl

@@ -120,7 +120,7 @@ function same_or_inner_namespace(u, var)
     nu == nv ||           # namespaces are the same
         startswith(nv, nu) || # or nv starts with nu, i.e., nv is an inner namespace to nu
         occursin(NAMESPACE_SEPARATOR, string(getname(var))) &&
-            !occursin(NAMESPACE_SEPARATOR, string(getname(u))) # or u is top level but var is internal
+        !occursin(NAMESPACE_SEPARATOR, string(getname(u))) # or u is top level but var is internal
 end
 
 function inner_namespace(u, var)
@@ -129,7 +129,7 @@ function inner_namespace(u, var)
     nu == nv && return false
     startswith(nv, nu) || # or nv starts with nu, i.e., nv is an inner namespace to nu
         occursin(NAMESPACE_SEPARATOR, string(getname(var))) &&
-            !occursin(NAMESPACE_SEPARATOR, string(getname(u))) # or u is top level but var is internal
+        !occursin(NAMESPACE_SEPARATOR, string(getname(u))) # or u is top level but var is internal
 end
 
 """

src/structural_transformation/bareiss.jl

@@ -131,6 +131,7 @@ end
 
 function bareiss_update!(zero!, M::StridedMatrix, k, swapto, pivot, prev_pivot)
     @inbounds for i in (k + 1):size(M, 2), j in (k + 1):size(M, 1)
+
         M[j, i] = exactdiv(M[j, i] * pivot - M[j, k] * M[k, i], prev_pivot)
     end
     zero!(M, (k + 1):size(M, 1), k)
@@ -269,6 +270,7 @@ function reduce_echelon!(A::AbstractMatrix{T}, rank, d,
     end
     @label out
     @inbounds for i in (rank + 1):m, j in 1:n
+
         A[i, j] = zero(T)
     end
     isreduced && return A