Merge pull request #723 from LilithHafner/lh/format

Run JuliaFormatter.format()

docs/make.jl

@@ -1,6 +1,6 @@
 using Documenter, DiffEqBase, SciMLBase, OrdinaryDiffEq
 import ODEProblemLibrary,
-    SDEProblemLibrary, DDEProblemLibrary, DAEProblemLibrary, BVProblemLibrary
+       SDEProblemLibrary, DDEProblemLibrary, DAEProblemLibrary, BVProblemLibrary
 using Sundials, DASKR
 
 cp("./docs/Manifest.toml", "./docs/src/assets/Manifest.toml", force = true)
@@ -11,7 +11,8 @@ ENV["GKSwstype"] = "100"
 
 include("pages.jl")
 
-makedocs(modules = [
+makedocs(
+    modules = [
         DiffEqBase,
         SciMLBase,
         ODEProblemLibrary,
@@ -20,7 +21,7 @@ makedocs(modules = [
         DAEProblemLibrary,
         BVProblemLibrary,
         OrdinaryDiffEq,
-        Sundials, DASKR,
+        Sundials, DASKR
     ],
     linkcheck = true,
     linkcheck_ignore = ["https://www.izhikevich.org/publications/spikes.htm",
@@ -33,7 +34,7 @@ makedocs(modules = [
         "https://www.sciencedirect.com/science/article/abs/pii/S0375960109009591",
         "https://www.sciencedirect.com/science/article/abs/pii/0375960176901018",
         "https://www.worldscientific.com/doi/abs/10.1142/S0218127499001024",
-        "https://www.worldscientific.com/doi/abs/10.1142/S0218127499001383",
+        "https://www.worldscientific.com/doi/abs/10.1142/S0218127499001383"
     ],
     doctest = false, clean = true,
     warnonly = [:missing_docs],

docs/pages.jl

@@ -8,7 +8,8 @@ pages = Any["index.md",
         "tutorials/dae_example.md",
         "tutorials/jump_diffusion.md",
         "tutorials/bvp_example.md"],
-    "Examples" => Any["Beginner" => Any["examples/classical_physics.md",
+    "Examples" => Any[
+        "Beginner" => Any["examples/classical_physics.md",
             "examples/conditional_dosing.md",
             "examples/kepler_problem.md",
             "examples/outer_solar_system.md",

docs/src/examples/beeler_reuter.md

@@ -636,7 +636,8 @@ function (f::BeelerReuterGpu)(du, u, p, t)
     laplacian(f.Δv, u)
 
     # calculate the reaction portion
-    @cuda blocks=(ny ÷ L, nx ÷ L) threads=(L, L) update_du_gpu(f.d_du, f.d_u, f.d_XI, f.d_M,
+    @cuda blocks=(ny ÷ L, nx ÷ L) threads=(L, L) update_du_gpu(
+        f.d_du, f.d_u, f.d_XI, f.d_M,
         f.d_H, f.d_J, f.d_D, f.d_F,
         f.d_C)
 

docs/src/examples/outer_solar_system.md

@@ -25,16 +25,16 @@ M = [
     0.000285583733151,
     0.0000437273164546,
     0.0000517759138449,
-    1 / 1.3e8,
+    1 / 1.3e8
 ]
 planets = ["Sun", "Jupiter", "Saturn", "Uranus", "Neptune", "Pluto"]
 
 pos = [0.0 -3.5023653 9.0755314 8.310142 11.4707666 -15.5387357
-    0.0 -3.8169847 -3.0458353 -16.2901086 -25.7294829 -25.2225594
-    0.0 -1.5507963 -1.6483708 -7.2521278 -10.8169456 -3.1902382]
+       0.0 -3.8169847 -3.0458353 -16.2901086 -25.7294829 -25.2225594
+       0.0 -1.5507963 -1.6483708 -7.2521278 -10.8169456 -3.1902382]
 vel = [0.0 0.00565429 0.00168318 0.00354178 0.0028893 0.00276725
-    0.0 -0.0041249 0.00483525 0.00137102 0.00114527 -0.00170702
-    0.0 -0.00190589 0.00192462 0.00055029 0.00039677 -0.00136504]
+       0.0 -0.0041249 0.00483525 0.00137102 0.00114527 -0.00170702
+       0.0 -0.00190589 0.00192462 0.00055029 0.00039677 -0.00136504]
 tspan = (0.0, 200_000.0)
 ```
 
@@ -51,7 +51,8 @@ const N = 6
 u = collect(u)
 D = Differential(t)
 potential = -G *
-            ∑(i -> ∑(j -> (M[i] * M[j]) / √(∑(k -> (u[k, i] - u[k, j])^2, 1:3)), 1:(i - 1)),
+            ∑(
+    i -> ∑(j -> (M[i] * M[j]) / √(∑(k -> (u[k, i] - u[k, j])^2, 1:3)), 1:(i - 1)),
     2:N)
 ```
 

docs/src/extras/timestepping.md

@@ -189,7 +189,7 @@ struct CustomController <: StochasticDiffEq.AbstractController
 end
 
 function StochasticDiffEq.stepsize_controller!(integrator::StochasticDiffEq.SDEIntegrator,
-    controller::CustomController, alg)
+        controller::CustomController, alg)
     integrator.q11 = DiffEqBase.value(DiffEqBase.fastpow(integrator.EEst, controller.beta1))
     integrator.q = DiffEqBase.value(integrator.q11 /
                                     DiffEqBase.fastpow(integrator.qold, controller.beta2))
@@ -199,15 +199,16 @@ function StochasticDiffEq.stepsize_controller!(integrator::StochasticDiffEq.SDEI
     nothing
 end
 
-function StochasticDiffEq.step_accept_controller!(integrator::StochasticDiffEq.SDEIntegrator,
-    controller::CustomController, alg)
+function StochasticDiffEq.step_accept_controller!(
+        integrator::StochasticDiffEq.SDEIntegrator,
+        controller::CustomController, alg)
     integrator.dtnew = DiffEqBase.value(integrator.dt / integrator.q) *
                        oneunit(integrator.dt)
     nothing
 end
 
 function step_reject_controller!(integrator::StochasticDiffEq.SDEIntegrator,
-    controller::CustomController, alg)
+        controller::CustomController, alg)
     integrator.dtnew = integrator.dt / min(inv(integrator.opts.qmin),
         integrator.q11 / integrator.opts.gamma)
 end

docs/src/getting_started.md

@@ -403,9 +403,9 @@ We can define a matrix of linear ODEs as follows:
 using DifferentialEquations
 using Plots
 A = [1.0 0 0 -5
-    4 -2 4 -3
-    -4 0 0 1
-    5 -2 2 3]
+     4 -2 4 -3
+     -4 0 0 1
+     5 -2 2 3]
 u0 = rand(4, 2)
 tspan = (0.0, 1.0)
 f(u, p, t) = A * u
@@ -438,9 +438,9 @@ above, with the only change being the type for the initial condition and constan
 ```@example ODE4
 using StaticArrays
 A = @SMatrix [1.0 0.0 0.0 -5.0
-    4.0 -2.0 4.0 -3.0
-    -4.0 0.0 0.0 1.0
-    5.0 -2.0 2.0 3.0]
+              4.0 -2.0 4.0 -3.0
+              -4.0 0.0 0.0 1.0
+              5.0 -2.0 2.0 3.0]
 u0 = @SMatrix rand(4, 2)
 tspan = (0.0, 1.0)
 f2(u, p, t) = A * u
@@ -504,4 +504,4 @@ Many more are defined in the relevant sections of the docs. Please explore the r
 of the documentation, including tutorials for getting started with other types
 of equations. In addition, to get help, please either
 [file an issue at the main repository](https://github.com/SciML/DifferentialEquations.jl)
-or [come have an informal discussion at the Julia Zulip chatroom](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged).
+or [come have an informal discussion at the Julia Zulip chatroom](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged).

docs/src/solvers/bvp_solve.md

@@ -30,4 +30,4 @@ off via the keyword argument `adaptive = false`.
 ODEInterface.jl can be used seamlessly with BoundaryValueDiffEq.jl, after we define our model using `BVProblem` or `TwoPointBVProblem`, we can directly call the solvers from ODEInterface.jl.
 
   - `BVPM2` - FORTRAN code for solving two-point boundary value problems. `BVPM2` is only compatible with `TwoPointBVProblem`.
-  - `BVPSOL` - FORTRAN77 code which solves highly nonlinear two point boundary value problems using a local linear solver (condensing algorithm) or a global sparse linear solver for the solution of the arising linear subproblems, by Peter Deuflhard, Georg Bader, Lutz Weimann. `BVPSOL` should be used with `TwoPointBVProblem` and initial guess.
+  - `BVPSOL` - FORTRAN77 code which solves highly nonlinear two point boundary value problems using a local linear solver (condensing algorithm) or a global sparse linear solver for the solution of the arising linear subproblems, by Peter Deuflhard, Georg Bader, Lutz Weimann. `BVPSOL` should be used with `TwoPointBVProblem` and initial guess.

docs/src/solvers/dae_solve.md

@@ -92,6 +92,7 @@ extra options for the solvers, see the ODE solver page.
   - `ROS34PW3` - A 4th order strongly A-stable (Rinf~0.63) Rosenbrock-W method.
 
 !!! note
+
     `Rosenbrock23` and `Rosenbrock32` have a stiff-aware interpolation but this interpolation is not safe for the algebraic variables.
     Thus use the interpolation (and therefore `saveat`) with caution if the default Hermite interpolation is used.
 

docs/src/tutorials/advanced_ode_example.md

@@ -39,13 +39,16 @@ f(x, y, t) = \begin{cases}
 0 & \quad \text{else}
 \end{cases}, \mathrm{and}
 ```
+
 $\nabla^2 = \frac{\partial^2}{\partial x^2} + \frac{\partial^2}{\partial y^2}$ is the two dimensional Laplacian operator. The above equations are to be solved for a time interval $t \in [0, 11.5]$ subject to the initial conditions
+
 ```math
 \begin{align}
 U(x, y, 0) &= 22\cdot (y(1-y))^{3/2} \\
 V(x, y, 0) &= 27\cdot (x(1-x))^{3/2}
 \end{align},
 ```
+
 and the periodic boundary conditions
 
 ```math
@@ -336,7 +339,8 @@ function. We will use [ModelingToolkit.jl](https://mtk.sciml.ai/dev/)'s
 
 ```@example stiff1
 using ModelingToolkit
-prob_ode_brusselator_2d_mtk = ODEProblem(modelingtoolkitize(prob_ode_brusselator_2d_sparse),
+prob_ode_brusselator_2d_mtk = ODEProblem(
+    modelingtoolkitize(prob_ode_brusselator_2d_sparse),
     [], (0.0, 11.5), jac = true, sparse = true);
 # @btime solve(prob_ode_brusselator_2d_mtk,CVODE_BDF(linear_solver=:KLU),save_everystep=false); # compiles very slowly
 nothing # hide

docs/src/tutorials/bvp_example.md

@@ -91,7 +91,7 @@ function bc2b!(resid_b, u_b, p) # u_b is at the ending of the time span
     resid_b[1] = u_b[1] - pi / 2 # the solution at the end of the time span should be pi/2
 end
 bvp2 = TwoPointBVProblem(simplependulum!, (bc2a!, bc2b!), [pi / 2, pi / 2], tspan;
-                         bcresid_prototype = (zeros(1), zeros(1)))
+    bcresid_prototype = (zeros(1), zeros(1)))
 sol2 = solve(bvp2, MIRK4(), dt = 0.05)
 plot(sol2)
 ```
@@ -101,4 +101,3 @@ for the final time point. `bcresid_prototype` is a prototype array which is pass
 `resid_a` and `resid_b`. In this case, we have one residual term for the start and one for the final time point,
 and thus we have `bcresid_prototype = (zeros(1), zeros(1))`. If we wanted to only have boundary conditions at the
 final time, we could instead have done `bcresid_prototype = (zeros(0), zeros(2))`.
-

docs/src/tutorials/dae_example.md

@@ -56,8 +56,8 @@ function rober(du, u, p, t)
     nothing
 end
 M = [1.0 0 0
-    0 1.0 0
-    0 0 0]
+     0 1.0 0
+     0 0 0]
 f = ODEFunction(rober, mass_matrix = M)
 prob_mm = ODEProblem(f, [1.0, 0.0, 0.0], (0.0, 1e5), (0.04, 3e7, 1e4))
 sol = solve(prob_mm, Rodas5(), reltol = 1e-8, abstol = 1e-8)

docs/src/tutorials/sde_example.md

@@ -45,7 +45,7 @@ plot(sol)
 ### Using Higher Order Methods
 
 One unique feature of DifferentialEquations.jl is that higher-order methods for
-stochastic differential equations are included. To illustrate it, let us compare the 
+stochastic differential equations are included. To illustrate it, let us compare the
 accuracy of the `EM()` method and a higher-order method `SRIW1()` with the analytical solution.
 This is a good way to judge the accuracy of a given algorithm, and is also useful
 to test convergence of new methods being developed. To setup our problem, we define

docs/src/types/sdae_types.md

@@ -23,8 +23,8 @@ DAE.
 
 ```julia
 const mm_A = [-2.0 1 4
-    4 -2 1
-    0 0 0]
+              4 -2 1
+              0 0 0]
 function f!(du, u, p, t)
     du[1] = u[1]
     du[2] = u[2]