Merge pull request #2179 from SciML/myb/formatting

New formatting

Author: YingboMa

Project.toml

@@ -79,7 +79,7 @@ MacroTools = "0.5"
 NaNMath = "0.3, 1"
 RecursiveArrayTools = "2.3"
 Reexport = "0.2, 1"
-RuntimeGeneratedFunctions = "0.5.10"
+RuntimeGeneratedFunctions = "=0.5.9"
 SciMLBase = "1.76.1"
 Setfield = "0.7, 0.8, 1"
 SimpleNonlinearSolve = "0.1.0"

docs/make.jl

@@ -10,34 +10,34 @@ cp("./docs/Project.toml", "./docs/src/assets/Project.toml", force = true)
 include("pages.jl")
 
 mathengine = MathJax3(Dict(:loader => Dict("load" => ["[tex]/require", "[tex]/mathtools"]),
-                           :tex => Dict("inlineMath" => [["\$", "\$"], ["\\(", "\\)"]],
-                                        "packages" => [
-                                            "base",
-                                            "ams",
-                                            "autoload",
-                                            "mathtools",
-                                            "require",
-                                        ])))
+    :tex => Dict("inlineMath" => [["\$", "\$"], ["\\(", "\\)"]],
+        "packages" => [
+            "base",
+            "ams",
+            "autoload",
+            "mathtools",
+            "require",
+        ])))
 
 makedocs(sitename = "ModelingToolkit.jl",
-         authors = "Chris Rackauckas",
-         modules = [ModelingToolkit],
-         clean = true, doctest = false, linkcheck = true,
-         linkcheck_ignore = ["https://epubs.siam.org/doi/10.1137/0903023"],
-         strict = [
-             :doctest,
-             :linkcheck,
-             :parse_error,
-             :example_block,
-             # Other available options are
-             # :autodocs_block, :cross_references, :docs_block, :eval_block, :example_block, :footnote, :meta_block, :missing_docs, :setup_block
-         ],
-         format = Documenter.HTML(; analytics = "UA-90474609-3",
-                                  assets = ["assets/favicon.ico"],
-                                  mathengine,
-                                  canonical = "https://docs.sciml.ai/ModelingToolkit/stable/",
-                                  prettyurls = (get(ENV, "CI", nothing) == "true")),
-         pages = pages)
+    authors = "Chris Rackauckas",
+    modules = [ModelingToolkit],
+    clean = true, doctest = false, linkcheck = true,
+    linkcheck_ignore = ["https://epubs.siam.org/doi/10.1137/0903023"],
+    strict = [
+        :doctest,
+        :linkcheck,
+        :parse_error,
+        :example_block,
+        # Other available options are
+        # :autodocs_block, :cross_references, :docs_block, :eval_block, :example_block, :footnote, :meta_block, :missing_docs, :setup_block
+    ],
+    format = Documenter.HTML(; analytics = "UA-90474609-3",
+        assets = ["assets/favicon.ico"],
+        mathengine,
+        canonical = "https://docs.sciml.ai/ModelingToolkit/stable/",
+        prettyurls = (get(ENV, "CI", nothing) == "true")),
+    pages = pages)
 
 deploydocs(repo = "github.com/SciML/ModelingToolkit.jl.git";
-           push_preview = true)
+    push_preview = true)

docs/pages.jl

@@ -2,34 +2,34 @@ pages = [
     "Home" => "index.md",
     "tutorials/ode_modeling.md",
     "Tutorials" => Any["tutorials/acausal_components.md",
-                       "tutorials/nonlinear.md",
-                       "tutorials/optimization.md",
-                       "tutorials/modelingtoolkitize.md",
-                       "tutorials/stochastic_diffeq.md",
-                       "tutorials/parameter_identifiability.md"],
+        "tutorials/nonlinear.md",
+        "tutorials/optimization.md",
+        "tutorials/modelingtoolkitize.md",
+        "tutorials/stochastic_diffeq.md",
+        "tutorials/parameter_identifiability.md"],
     "Examples" => Any["Basic Examples" => Any["examples/higher_order.md",
-                                              "examples/spring_mass.md",
-                                              "examples/modelingtoolkitize_index_reduction.md",
-                                              "examples/parsing.md"],
-                      "Advanced Examples" => Any["examples/tearing_parallelism.md",
-                                                 "examples/sparse_jacobians.md",
-                                                 "examples/perturbation.md"]],
+            "examples/spring_mass.md",
+            "examples/modelingtoolkitize_index_reduction.md",
+            "examples/parsing.md"],
+        "Advanced Examples" => Any["examples/tearing_parallelism.md",
+            "examples/sparse_jacobians.md",
+            "examples/perturbation.md"]],
     "Basics" => Any["basics/AbstractSystem.md",
-                    "basics/ContextualVariables.md",
-                    "basics/Variable_metadata.md",
-                    "basics/Composition.md",
-                    "basics/Events.md",
-                    "basics/Linearization.md",
-                    "basics/Validation.md",
-                    "basics/DependencyGraphs.md",
-                    "basics/FAQ.md"],
+        "basics/ContextualVariables.md",
+        "basics/Variable_metadata.md",
+        "basics/Composition.md",
+        "basics/Events.md",
+        "basics/Linearization.md",
+        "basics/Validation.md",
+        "basics/DependencyGraphs.md",
+        "basics/FAQ.md"],
     "System Types" => Any["systems/ODESystem.md",
-                          "systems/SDESystem.md",
-                          "systems/JumpSystem.md",
-                          "systems/NonlinearSystem.md",
-                          "systems/OptimizationSystem.md",
-                          "systems/DiscreteSystem.md",
-                          "systems/PDESystem.md"],
+        "systems/SDESystem.md",
+        "systems/JumpSystem.md",
+        "systems/NonlinearSystem.md",
+        "systems/OptimizationSystem.md",
+        "systems/DiscreteSystem.md",
+        "systems/PDESystem.md"],
     "comparison.md",
     "internals.md",
 ]

docs/src/basics/Composition.md

@@ -22,9 +22,9 @@ function decay(; name)
     @variables x(t) f(t)
     D = Differential(t)
     ODESystem([
-                  D(x) ~ -a * x + f,
-              ];
-              name = name)
+            D(x) ~ -a * x + f,
+        ];
+        name = name)
 end
 
 @named decay1 = decay()
@@ -33,7 +33,7 @@ end
 @parameters t
 D = Differential(t)
 connected = compose(ODESystem([decay2.f ~ decay1.x
-                               D(decay1.f) ~ 0], t; name = :connected), decay1, decay2)
+            D(decay1.f) ~ 0], t; name = :connected), decay1, decay2)
 
 equations(connected)
 
@@ -52,10 +52,10 @@ Now we can solve the system:
 
 ```@example composition
 x0 = [decay1.x => 1.0
-      decay1.f => 0.0
-      decay2.x => 1.0]
+    decay1.f => 0.0
+    decay2.x => 1.0]
 p = [decay1.a => 0.1
-     decay2.a => 0.2]
+    decay2.a => 0.2]
 
 using DifferentialEquations
 prob = ODEProblem(simplified_sys, x0, (0.0, 100.0), p)
@@ -96,7 +96,7 @@ in their namespaced form. For example:
 
 ```julia
 u0 = [x => 2.0
-      subsys.x => 2.0]
+    subsys.x => 2.0]
 ```
 
 Note that any default values within the given subcomponent will be
@@ -124,11 +124,11 @@ With symbolic parameters, it is possible to set the default value of a parameter
 ```julia
 # ...
 sys = ODESystem(
-                # ...
-                # directly in the defaults argument
-                defaults = Pair{Num, Any}[x => u,
-                                          y => σ,
-                                          z => u - 0.1])
+# ...
+# directly in the defaults argument
+    defaults = Pair{Num, Any}[x => u,
+    y => σ,
+    z => u - 0.1])
 # by assigning to the parameter
 sys.y = u * 1.1
 ```
@@ -138,11 +138,11 @@ In a hierarchical system, variables of the subsystem get namespaced by the name
 ```julia
 @parameters t a b c d e f
 p = [a #a is a local variable
-     ParentScope(b) # b is a variable that belongs to one level up in the hierarchy
-     ParentScope(ParentScope(c))# ParentScope can be nested
-     DelayParentScope(d) # skips one level before applying ParentScope
-     DelayParentScope(e, 2) # second argument allows skipping N levels
-     GlobalScope(f)]
+    ParentScope(b) # b is a variable that belongs to one level up in the hierarchy
+    ParentScope(ParentScope(c))# ParentScope can be nested
+    DelayParentScope(d) # skips one level before applying ParentScope
+    DelayParentScope(e, 2) # second argument allows skipping N levels
+    GlobalScope(f)]
 
 level0 = ODESystem(Equation[], t, [], p; name = :level0)
 level1 = ODESystem(Equation[], t, [], []; name = :level1) ∘ level0
@@ -208,18 +208,18 @@ N = S + I + R
 @named reqn = ODESystem([D(R) ~ γ * I])
 
 sir = compose(ODESystem([
-                            S ~ ieqn.S,
-                            I ~ seqn.I,
-                            R ~ ieqn.R,
-                            ieqn.S ~ seqn.S,
-                            seqn.I ~ ieqn.I,
-                            seqn.R ~ reqn.R,
-                            ieqn.R ~ reqn.R,
-                            reqn.I ~ ieqn.I], t, [S, I, R], [β, γ],
-                        defaults = [seqn.β => β
-                                    ieqn.β => β
-                                    ieqn.γ => γ
-                                    reqn.γ => γ], name = :sir), seqn, ieqn, reqn)
+            S ~ ieqn.S,
+            I ~ seqn.I,
+            R ~ ieqn.R,
+            ieqn.S ~ seqn.S,
+            seqn.I ~ ieqn.I,
+            seqn.R ~ reqn.R,
+            ieqn.R ~ reqn.R,
+            reqn.I ~ ieqn.I], t, [S, I, R], [β, γ],
+        defaults = [seqn.β => β
+            ieqn.β => β
+            ieqn.γ => γ
+            reqn.γ => γ], name = :sir), seqn, ieqn, reqn)
 ```
 
 Note that the states are forwarded by an equality relationship, while
@@ -241,7 +241,7 @@ u0 = [seqn.S => 990.0,
     reqn.R => 0.0]
 
 p = [β => 0.5
-     γ => 0.25]
+    γ => 0.25]
 
 tspan = (0.0, 40.0)
 prob = ODEProblem(sireqn_simple, u0, tspan, p, jac = true)

docs/src/basics/Events.md

@@ -69,7 +69,7 @@ function UnitMassWithFriction(k; name)
     @variables t x(t)=0 v(t)=0
     D = Differential(t)
     eqs = [D(x) ~ v
-           D(v) ~ sin(t) - k * sign(v)]
+        D(v) ~ sin(t) - k * sign(v)]
     ODESystem(eqs, t; continuous_events = [v ~ 0], name) # when v = 0 there is a discontinuity
 end
 @named m = UnitMassWithFriction(0.7)
@@ -94,7 +94,7 @@ root_eqs = [x ~ 0]  # the event happens at the ground x(t) = 0
 affect = [v ~ -v] # the effect is that the velocity changes sign
 
 @named ball = ODESystem([D(x) ~ v
-                         D(v) ~ -9.8], t; continuous_events = root_eqs => affect) # equation => affect
+        D(v) ~ -9.8], t; continuous_events = root_eqs => affect) # equation => affect
 
 ball = structural_simplify(ball)
 
@@ -114,14 +114,14 @@ Multiple events? No problem! This example models a bouncing ball in 2D that is e
 D = Differential(t)
 
 continuous_events = [[x ~ 0] => [vx ~ -vx]
-                     [y ~ -1.5, y ~ 1.5] => [vy ~ -vy]]
+    [y ~ -1.5, y ~ 1.5] => [vy ~ -vy]]
 
 @named ball = ODESystem([
-                            D(x) ~ vx,
-                            D(y) ~ vy,
-                            D(vx) ~ -9.8 - 0.1vx, # gravity + some small air resistance
-                            D(vy) ~ -0.1vy,
-                        ], t; continuous_events)
+        D(x) ~ vx,
+        D(y) ~ vy,
+        D(vx) ~ -9.8 - 0.1vx, # gravity + some small air resistance
+        D(vy) ~ -0.1vy,
+    ], t; continuous_events)
 
 ball = structural_simplify(ball)
 
@@ -189,7 +189,7 @@ affect interface:
 sts = @variables x(t), v(t)
 par = @parameters g = 9.8
 bb_eqs = [D(x) ~ v
-          D(v) ~ -g]
+    D(v) ~ -g]
 
 function bb_affect!(integ, u, p, ctx)
     integ.u[u.v] = -integ.u[u.v]
@@ -198,7 +198,7 @@ end
 reflect = [x ~ 0] => (bb_affect!, [v], [], nothing)
 
 @named bb_model = ODESystem(bb_eqs, t, sts, par,
-                            continuous_events = reflect)
+    continuous_events = reflect)
 
 bb_sys = structural_simplify(bb_model)
 u0 = [v => 0.0, x => 1.0]
@@ -288,7 +288,7 @@ killing = (t == tkill) => [α ~ 0.0]
 tspan = (0.0, 30.0)
 p = [α => 100.0, tinject => 10.0, M => 50, tkill => 20.0]
 @named osys = ODESystem(eqs, t, [N], [α, M, tinject, tkill];
-                        discrete_events = [injection, killing])
+    discrete_events = [injection, killing])
 oprob = ODEProblem(osys, u0, tspan, p)
 sol = solve(oprob, Tsit5(); tstops = [10.0, 20.0])
 plot(sol)
@@ -316,7 +316,7 @@ killing = [20.0] => [α ~ 0.0]
 
 p = [α => 100.0, M => 50]
 @named osys = ODESystem(eqs, t, [N], [α, M];
-                        discrete_events = [injection, killing])
+    discrete_events = [injection, killing])
 oprob = ODEProblem(osys, u0, tspan, p)
 sol = solve(oprob, Tsit5())
 plot(sol)

docs/src/basics/Linearization.md

@@ -26,8 +26,8 @@ using ModelingToolkit
 D = Differential(t)
 
 eqs = [u ~ kp * (r - y) # P controller
-       D(x) ~ -x + u    # First-order plant
-       y ~ x]           # Output equation
+    D(x) ~ -x + u    # First-order plant
+    y ~ x]           # Output equation
 
 @named sys = ODESystem(eqs, t)
 matrices, simplified_sys = linearize(sys, [r], [y]) # Linearize from r to y

docs/src/basics/Validation.md

@@ -12,10 +12,12 @@ using ModelingToolkit, Unitful
 
 @variables(t, [unit = u"s"], x(t), [unit = u"m"], g(t), w(t), [unit = "Hz"])
 
-@variables(begin t, [unit = u"s"],
-                 x(t), [unit = u"m"],
-                 g(t),
-                 w(t), [unit = "Hz"] end)
+@variables(begin
+    t, [unit = u"s"],
+    x(t), [unit = u"m"],
+    g(t),
+    w(t), [unit = "Hz"]
+end)
 
 # Simultaneously set default value (use plain numbers, not quantities)
 @variables x=10 [unit = u"m"]

docs/src/basics/Variable_metadata.md

@@ -113,7 +113,7 @@ Dₜ = Differential(t)
 @parameters T [tunable = true, bounds = (0, Inf)]
 @parameters k [tunable = true, bounds = (0, Inf)]
 eqs = [Dₜ(x) ~ (-x + k * u) / T # A first-order system with time constant T and gain k
-       y ~ x]
+    y ~ x]
 sys = ODESystem(eqs, t, name = :tunable_first_order)
 ```
 

docs/src/examples/parsing.md

@@ -7,8 +7,8 @@ symbolic forms from that? For example, say we had the following system we wanted
 
 ```@example parsing
 ex = [:(y ~ x)
-      :(y ~ -2x + 3 / z)
-      :(z ~ 2)]
+    :(y ~ -2x + 3 / z)
+    :(z ~ 2)]
 ```
 
 We can use the function `parse_expr_to_symbolic` from Symbolics.jl to generate the symbolic

docs/src/examples/sparse_jacobians.md

@@ -24,7 +24,7 @@ function brusselator_2d_loop(du, u, p, t)
         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),
-                             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] +