Apply JuliaFormatter to fix code formatting

docs/src/tutorials/modelingtoolkit.md

@@ -8,6 +8,7 @@ by hand. Those exact features are also potentially useful for GPU computing, and
 tutorial showcases how to effectively use MTK with DiffEqGPU.jl.
 
 !!! warn
+
     This tutorial currently only works for ODEs defined by ModelingToolkit. More work
     will be required to support DAEs in full. This is work that is ongoing and expected
     to be completed by the summer of 2025.
@@ -29,16 +30,16 @@ eqs = [D(D(x)) ~ σ * (y - x),
     D(z) ~ x * y - β * z]
 
 @mtkbuild sys = ODESystem(eqs, t)
-u0 = SA[D(x) => 2f0,
-    x => 1f0,
-    y => 0f0,
-    z => 0f0]
+u0 = SA[D(x) => 2.0f0,
+    x => 1.0f0,
+    y => 0.0f0,
+    z => 0.0f0]
 
-p = SA[σ => 28f0,
-    ρ => 10f0,
-    β => 8f0 / 3f0]
+p = SA[σ => 28.0f0,
+    ρ => 10.0f0,
+    β => 8.0f0 / 3.0f0]
 
-tspan = (0f0, 100f0)
+tspan = (0.0f0, 100.0f0)
 prob = ODEProblem{false}(sys, u0, tspan, p)
 sol = solve(prob, Tsit5())
 ```
@@ -64,7 +65,7 @@ sym_setter = setsym_oop(sys, [σ, ρ, β])
 The return `sym_setter` is our optimized function, let's see it in action:
 
 ```@example mtk
-u0, p = sym_setter(prob,@SVector(rand(Float32,3)))
+u0, p = sym_setter(prob, @SVector(rand(Float32, 3)))
 ```
 
 Notice it takes in the vector of values for `[σ, ρ, β]` and spits out the new `u0, p`. So
@@ -73,7 +74,7 @@ we can build and solve an MTK generated ODE on the GPU using the following:
 ```@example mtk
 using DiffEqGPU, CUDA
 function prob_func2(prob, i, repeat)
-    u0, p = sym_setter(prob,@SVector(rand(Float32,3)))
+    u0, p = sym_setter(prob, @SVector(rand(Float32, 3)))
     remake(prob, u0 = u0, p = p)
 end
 

src/DiffEqGPU.jl

@@ -68,8 +68,6 @@ include("ensemblegpukernel/tableaus/verner_tableaus.jl")
 include("ensemblegpukernel/tableaus/rodas_tableaus.jl")
 include("ensemblegpukernel/tableaus/kvaerno_tableaus.jl")
 
-
-
 include("utils.jl")
 include("algorithms.jl")
 include("solve.jl")

src/ensemblegpuarray/problem_generation.jl

@@ -58,7 +58,6 @@ function generate_problem(prob::SciMLBase.AbstractODEProblem,
         _tgrad = nothing
     end
 
-
     f_func = ODEFunction(_f, Wfact = _Wfact!,
         Wfact_t = _Wfact!_t,
         #colorvec=colorvec,

src/solve.jl

@@ -273,7 +273,7 @@ function batch_solve_up_kernel(ensembleprob, probs, alg, ensemblealg, I, adaptiv
             _callback.continuous_callbacks)...)
 
     dev = ensemblealg.dev
-    probs = adapt(dev,adapt.((dev,), probs))
+    probs = adapt(dev, adapt.((dev,), probs))
 
     #Adaptive version only works with saveat
     if adaptive

test/ensemblegpuarray_sde.jl

@@ -50,9 +50,10 @@ NRate[2, 2] = 1
 u0 = ComplexF32[1.0; 0.0; 0.0; 0.0]
 tspan = (0.0f0, 10.0f0)
 p = (10.0f0, 28.0f0, 8 / 3.0f0)
-prob = SDEProblem(lorenz, multiplicative_noise, u0, tspan, p, noise_rate_prototype=NRate)
+prob = SDEProblem(lorenz, multiplicative_noise, u0, tspan, p, noise_rate_prototype = NRate)
 
-prob_func = (prob, i, repeat) -> remake(prob, p=p)
-monteprob = EnsembleProblem(prob, prob_func=prob_func)
+prob_func = (prob, i, repeat) -> remake(prob, p = p)
+monteprob = EnsembleProblem(prob, prob_func = prob_func)
 
-@test_throws "Incompatible problem detected. EnsembleGPUArray currently requires `prob.noise_rate_prototype === nothing`, i.e. only diagonal noise is currently supported. Track https://github.com/SciML/DiffEqGPU.jl/issues/331 for more information." sol = solve(monteprob, SRA1(), EnsembleCPUArray(), trajectories=10_000, saveat=1.0f0)
+@test_throws "Incompatible problem detected. EnsembleGPUArray currently requires `prob.noise_rate_prototype === nothing`, i.e. only diagonal noise is currently supported. Track https://github.com/SciML/DiffEqGPU.jl/issues/331 for more information." sol=solve(
+    monteprob, SRA1(), EnsembleCPUArray(), trajectories = 10_000, saveat = 1.0f0)