Merge pull request #1006 from ChrisRackauckas-Claude/runic-formatting

Switch from JuliaFormatter to Runic.jl for code formatting

Author: ChrisRackauckas

.JuliaFormatter.toml

@@ -1,13 +1,19 @@
-name: "Format Check"
+name: format-check
 
 on:
   push:
     branches:
       - 'master'
+      - 'main'
+      - 'release-'
     tags: '*'
   pull_request:
 
 jobs:
-  format-check:
-    name: "Format Check"
-    uses: "SciML/.github/.github/workflows/format-suggestions-on-pr.yml@v1"
+  runic:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: fredrikekre/runic-action@v1
+        with:
+          version: '1'

.github/workflows/FormatCheck.yml

@@ -9,13 +9,17 @@ using Plots
 
 include("pages.jl")
 
-makedocs(sitename = "NeuralPDE.jl",
+makedocs(
+    sitename = "NeuralPDE.jl",
     authors = "#",
     modules = [NeuralPDE],
     clean = true, doctest = false, linkcheck = true,
     warnonly = [:missing_docs],
-    format = Documenter.HTML(assets = ["assets/favicon.ico"],
-        canonical = "https://docs.sciml.ai/NeuralPDE/stable/"),
-    pages = pages)
+    format = Documenter.HTML(
+        assets = ["assets/favicon.ico"],
+        canonical = "https://docs.sciml.ai/NeuralPDE/stable/"
+    ),
+    pages = pages
+)
 
 deploydocs(repo = "github.com/SciML/NeuralPDE.jl.git"; push_preview = true)

docs/make.jl

@@ -1,11 +1,13 @@
-pages = ["index.md",
-    "ODE PINN Tutorials" => Any["Introduction to NeuralPDE for ODEs" => "tutorials/ode.md",
+pages = [
+    "index.md",
+    "ODE PINN Tutorials" => Any[
+        "Introduction to NeuralPDE for ODEs" => "tutorials/ode.md",
         "Bayesian PINNs for Coupled ODEs" => "tutorials/Lotka_Volterra_BPINNs.md",
         "PINNs DAEs" => "tutorials/dae.md",
         "Parameter Estimation with PINNs for ODEs" => "tutorials/ode_parameter_estimation.md",
         "Improved PINNs for Inverse problems in ODEs" => "tutorials/data_collocation_Inverse.md",
         "Physics informed Neural Operator ODEs" => "tutorials/pino_ode.md",
-        "Deep Galerkin Method" => "tutorials/dgm.md"        #"examples/nnrode_example.md", # currently incorrect
+        "Deep Galerkin Method" => "tutorials/dgm.md",        #"examples/nnrode_example.md", # currently incorrect
     ],
     "PDE PINN Tutorials" => Any[
         "Introduction to NeuralPDE for PDEs" => "tutorials/pdesystem.md",
@@ -17,23 +19,28 @@ pages = ["index.md",
         "Optimising Parameters (Solving Inverse Problems)" => "tutorials/param_estim.md",
         "Solving Integro Differential Equations" => "tutorials/integro_diff.md",
         "Transfer Learning with Neural Adapter" => "tutorials/neural_adapter.md",
-        "The Derivative Neural Network Approximation" => "tutorials/derivative_neural_network.md"],
-    "Extended Examples" => Any["examples/wave.md",
+        "The Derivative Neural Network Approximation" => "tutorials/derivative_neural_network.md",
+    ],
+    "Extended Examples" => Any[
+        "examples/wave.md",
         "examples/3rd.md",
         "examples/ks.md",
         "examples/heterogeneous.md",
         "examples/linear_parabolic.md",
         "examples/nonlinear_elliptic.md",
         "examples/nonlinear_hyperbolic.md",
-        "examples/complex.md"],
-    "Manual" => Any["manual/ode.md",
+        "examples/complex.md",
+    ],
+    "Manual" => Any[
+        "manual/ode.md",
         "manual/dae.md",
         "manual/pinns.md",
         "manual/bpinns.md",
         "manual/training_strategies.md",
         "manual/adaptive_losses.md",
         "manual/logging.md",
         "manual/neural_adapters.md",
-        "manual/pino_ode.md"],
-    "Developer Documentation" => Any["developer/debugging.md"]
+        "manual/pino_ode.md",
+    ],
+    "Developer Documentation" => Any["developer/debugging.md"],
 ]

docs/pages.jl

@@ -3,15 +3,19 @@ module NeuralPDETensorBoardLoggerExt
 using NeuralPDE: NeuralPDE
 using TensorBoardLogger: TBLogger, log_value
 
-function NeuralPDE.logvector(logger::TBLogger, vector::AbstractVector{<:Real},
-        name::AbstractString, step::Integer)
-    foreach(enumerate(vector)) do (j, v)
+function NeuralPDE.logvector(
+        logger::TBLogger, vector::AbstractVector{<:Real},
+        name::AbstractString, step::Integer
+    )
+    return foreach(enumerate(vector)) do (j, v)
         log_value(logger, "$(name)/$(j)", v; step)
     end
 end
 
-function NeuralPDE.logscalar(logger::TBLogger, scalar::Real, name::AbstractString,
-        step::Integer)
+function NeuralPDE.logscalar(
+        logger::TBLogger, scalar::Real, name::AbstractString,
+        step::Integer
+    )
     log_value(logger, "$(name)", scalar; step)
     return nothing
 end

ext/NeuralPDETensorBoardLoggerExt.jl

@@ -101,19 +101,25 @@ Kevin Linka, Amelie Schäfer, Xuhui Meng, Zongren Zou, George Em Karniadakis, El
     verbose::Bool
 end
 
-function BNNODE(chain, kernel = HMC; strategy = nothing, draw_samples = 1000,
+function BNNODE(
+        chain, kernel = HMC; strategy = nothing, draw_samples = 1000,
         priorsNNw = (0.0, 2.0), param = nothing, l2std = [0.05], phystd = [0.05],
         phynewstd = (ode_params) -> [0.05], dataset = [], physdt = 1 / 20.0,
         MCMCkwargs = (n_leapfrog = 30,), nchains = 1, init_params = nothing,
-        Adaptorkwargs = (Adaptor = StanHMCAdaptor,
-            Metric = DiagEuclideanMetric, targetacceptancerate = 0.8),
+        Adaptorkwargs = (
+            Adaptor = StanHMCAdaptor,
+            Metric = DiagEuclideanMetric, targetacceptancerate = 0.8,
+        ),
         Integratorkwargs = (Integrator = Leapfrog,),
         numensemble = floor(Int, draw_samples / 3),
-        estim_collocate = false, autodiff = false, progress = false, verbose = false)
+        estim_collocate = false, autodiff = false, progress = false, verbose = false
+    )
     chain isa AbstractLuxLayer || (chain = FromFluxAdaptor()(chain))
-    return BNNODE(chain, kernel, strategy, draw_samples, priorsNNw, param, l2std, phystd,
+    return BNNODE(
+        chain, kernel, strategy, draw_samples, priorsNNw, param, l2std, phystd,
         phynewstd, dataset, physdt, MCMCkwargs, nchains, init_params, Adaptorkwargs,
-        Integratorkwargs, numensemble, estim_collocate, autodiff, progress, verbose)
+        Integratorkwargs, numensemble, estim_collocate, autodiff, progress, verbose
+    )
 end
 
 """
@@ -155,33 +161,38 @@ contains fields related to that).
     timepoints
 end
 
-function SciMLBase.__solve(prob::SciMLBase.ODEProblem, alg::BNNODE, args...; dt = nothing,
+function SciMLBase.__solve(
+        prob::SciMLBase.ODEProblem, alg::BNNODE, args...; dt = nothing,
         timeseries_errors = true, save_everystep = true, adaptive = false,
         abstol = 1.0f-6, reltol = 1.0f-3, verbose = false, saveat = 1 / 50.0,
-        maxiters = nothing)
+        maxiters = nothing
+    )
     (; chain, param, strategy, draw_samples, numensemble, verbose) = alg
 
     # ahmc_bayesian_pinn_ode needs param=[] for easier vcat operation for full vector of parameters
     param = param === nothing ? [] : param
     strategy = strategy === nothing ? GridTraining : strategy
 
-    @assert alg.draw_samples≥0 "Number of samples to be drawn has to be >=0."
+    @assert alg.draw_samples ≥ 0 "Number of samples to be drawn has to be >=0."
 
     mcmcchain, samples,
-    statistics = ahmc_bayesian_pinn_ode(
+        statistics = ahmc_bayesian_pinn_ode(
         prob, chain; strategy, alg.dataset, alg.draw_samples, alg.init_params,
         alg.physdt, alg.l2std, alg.phystd, alg.phynewstd,
         alg.priorsNNw, param, alg.nchains, alg.autodiff,
         Kernel = alg.kernel, alg.Adaptorkwargs, alg.Integratorkwargs,
-        alg.MCMCkwargs, alg.progress, alg.verbose, alg.estim_collocate)
+        alg.MCMCkwargs, alg.progress, alg.verbose, alg.estim_collocate
+    )
 
     fullsolution = BPINNstats(mcmcchain, samples, statistics)
     ninv = length(param)
     t = collect(eltype(saveat), prob.tspan[1]:saveat:prob.tspan[2])
 
     θinit, st = LuxCore.setup(Random.default_rng(), chain)
-    θ = [vector_to_parameters(samples[i][1:(end - ninv)], θinit)
-         for i in (draw_samples - numensemble):draw_samples]
+    θ = [
+        vector_to_parameters(samples[i][1:(end - ninv)], θinit)
+            for i in (draw_samples - numensemble):draw_samples
+    ]
 
     luxar = [chain(t', θ[i], st)[1] for i in 1:numensemble]
     # only need for size
@@ -205,27 +216,31 @@ function SciMLBase.__solve(prob::SciMLBase.ODEProblem, alg::BNNODE, args...; dt
         for r in 1:numoutput
             ensem_r = hcat(output_matrices[r]...)'
             ensemblecurve_r = prob.u0[r] .+
-                              [Particles(ensem_r[:, i]) for i in 1:length(t)] .*
-                              (t .- prob.tspan[1])
+                [Particles(ensem_r[:, i]) for i in 1:length(t)] .*
+                (t .- prob.tspan[1])
             push!(ensemblecurves, ensemblecurve_r)
         end
 
     else
         ensemblecurve = prob.u0 .+
-                        [Particles(reduce(vcat, luxar)[:, i]) for i in 1:length(t)] .*
-                        (t .- prob.tspan[1])
+            [Particles(reduce(vcat, luxar)[:, i]) for i in 1:length(t)] .*
+            (t .- prob.tspan[1])
         push!(ensemblecurves, ensemblecurve)
     end
 
     nnparams = length(θinit)
-    estimnnparams = [Particles(reduce(hcat, samples[(end - numensemble):end])[i, :])
-                     for i in 1:nnparams]
+    estimnnparams = [
+        Particles(reduce(hcat, samples[(end - numensemble):end])[i, :])
+            for i in 1:nnparams
+    ]
 
     if ninv == 0
         estimated_params = [nothing]
     else
-        estimated_params = [Particles(reduce(hcat, samples[(end - numensemble):end])[i, :])
-                            for i in (nnparams + 1):(nnparams + ninv)]
+        estimated_params = [
+            Particles(reduce(hcat, samples[(end - numensemble):end])[i, :])
+                for i in (nnparams + 1):(nnparams + ninv)
+        ]
     end
 
     return BPINNsolution(fullsolution, ensemblecurves, estimnnparams, estimated_params, t)