Merge pull request #3860 from SciML/revert-juliaformatter

[ci-skip] Revert JuliaFormatter PR #3846

Author: ChrisRackauckas

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/perturbation.md

@@ -100,7 +100,7 @@ plot(sol, idxs = substitute(x_series, ϵ => 0.1); label = "Perturbative (ϵ=0.1)
 
 x_exact(t, ϵ) = exp(-ϵ * t) * sin(√(1 - ϵ^2) * t) / √(1 - ϵ^2)
 @assert isapprox(
-    sol(π / 2; idxs = substitute(x_series, ϵ => 0.1)), x_exact(π / 2, 0.1); atol = 1e-2) # compare around 1st peak # hide
+    sol(π/2; idxs = substitute(x_series, ϵ => 0.1)), x_exact(π/2, 0.1); atol = 1e-2) # compare around 1st peak # hide
 plot!(sol.t, x_exact.(sol.t, 0.1); label = "Exact (ϵ=0.1)")
 ```
 

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
 

ext/MTKCasADiDynamicOptExt.jl

@@ -57,7 +57,7 @@ function (M::MXLinearInterpolation)(τ)
     (i > length(M.t) || i < 1) && error("Cannot extrapolate past the tspan.")
     colons = ntuple(_ -> (:), length(size(M.u)) - 1)
     if i < length(M.t)
-        M.u[colons..., i] + Δ * (M.u[colons..., i + 1] - M.u[colons..., i])
+        M.u[colons..., i] + Δ*(M.u[colons..., i + 1] - M.u[colons..., i])
     else
         M.u[colons..., i]
     end
@@ -131,10 +131,10 @@ function MTK.lowered_integral(model::CasADiModel, expr, lo, hi)
     for (i, t) in enumerate(model.U.t)
         if lo < t < hi
             Δt = min(dt, t - lo)
-            total += (0.5 * Δt * (expr[i] + expr[i - 1]))
+            total += (0.5*Δt*(expr[i] + expr[i - 1]))
         elseif t >= hi && (t - dt < hi)
             Δt = hi - t + dt
-            total += (0.5 * Δt * (expr[i] + expr[i - 1]))
+            total += (0.5*Δt*(expr[i] + expr[i - 1]))
         end
     end
     model.tₛ * total
@@ -219,7 +219,7 @@ end
 function MTK.get_V_values(model::CasADiModel)
     value_getter = MTK.successful_solve(model) ? CasADi.debug_value : CasADi.value
     (nu, nt) = size(model.V.u)
-    if nu * nt != 0
+    if nu*nt != 0
         V_vals = value_getter(model.solver_opti, model.V.u)
         size(V_vals, 2) == 1 && (V_vals = V_vals')
         V_vals = [[V_vals[i, j] for i in 1:nu] for j in 1:nt]

ext/MTKInfiniteOptExt.jl

@@ -49,7 +49,7 @@ end
 
 MTK.generate_internal_model(m::Type{InfiniteOptModel}) = InfiniteModel()
 function MTK.generate_time_variable!(m::InfiniteModel, tspan, tsteps)
-    @infinite_parameter(m, t in [tspan[1], tspan[2]], num_supports=length(tsteps))
+    @infinite_parameter(m, t in [tspan[1], tspan[2]], num_supports = length(tsteps))
 end
 function MTK.generate_state_variable!(m::InfiniteModel, u0::Vector, ns, ts)
     @variable(m, U[i = 1:ns], Infinite(m[:t]), start=u0[i])
@@ -59,7 +59,7 @@ function MTK.generate_input_variable!(m::InfiniteModel, c0, nc, ts)
 end
 
 function MTK.generate_timescale!(m::InfiniteModel, guess, is_free_t)
-    @variable(m, tₛ≥0, start=guess)
+    @variable(m, tₛ ≥ 0, start = guess)
     if !is_free_t
         fix(tₛ, 1, force = true)
         set_start_value(tₛ, 1)
@@ -69,11 +69,11 @@ end
 
 function MTK.add_constraint!(m::InfiniteOptModel, expr::Union{Equation, Inequality})
     if expr isa Equation
-        @constraint(m.model, expr.lhs - expr.rhs==0)
+        @constraint(m.model, expr.lhs - expr.rhs == 0)
     elseif expr.relational_op === Symbolics.geq
-        @constraint(m.model, expr.lhs - expr.rhs≥0)
+        @constraint(m.model, expr.lhs - expr.rhs ≥ 0)
     else
-        @constraint(m.model, expr.lhs - expr.rhs≤0)
+        @constraint(m.model, expr.lhs - expr.rhs ≤ 0)
     end
 end
 MTK.set_objective!(m::InfiniteOptModel, expr) = @objective(m.model, Min, expr)

ext/MTKPyomoDynamicOptExt.jl

@@ -142,17 +142,17 @@ end
 function MTK.lowered_integral(m::PyomoDynamicOptModel, arg, lo, hi)
     @unpack model, model_sym, t_sym, dummy_sym = m
     total = 0
-    dt = Pyomo.pyconvert(Float64, (model.t.at(-1) - model.t.at(1)) / (model.steps - 1))
+    dt = Pyomo.pyconvert(Float64, (model.t.at(-1) - model.t.at(1))/(model.steps - 1))
     f = Symbolics.build_function(arg, model_sym, t_sym, expression = false)
     for (i, t) in enumerate(model.t)
         if Bool(lo < t) && Bool(t < hi)
-            t_p = model.t.at(i - 1)
+            t_p = model.t.at(i-1)
             Δt = min(t - lo, t - t_p)
-            total += 0.5 * Δt * (f(model, t) + f(model, t_p))
+            total += 0.5*Δt*(f(model, t) + f(model, t_p))
         elseif Bool(t >= hi) && Bool(t - dt < hi)
-            t_p = model.t.at(i - 1)
+            t_p = model.t.at(i-1)
             Δt = hi - t + dt
-            total += 0.5 * Δt * (f(model, t) + f(model, t_p))
+            total += 0.5*Δt*(f(model, t) + f(model, t_p))
         end
     end
     PyomoVar(model.tₛ * total)