Switch from JuliaFormatter to Runic.jl for code formatting

.github/workflows/FormatCheck.yml

@@ -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-check.yml@v1"
+  runic:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: fredrikekre/runic-action@v1
+        with:
+          version: '1'

docs/make.jl

@@ -5,7 +5,7 @@ using Pkg
 
 function dev_subpkg(subpkg)
     subpkg_path = abspath(joinpath(dirname(@__FILE__), "..", "lib", subpkg))
-    Pkg.develop(PackageSpec(path = subpkg_path))
+    return Pkg.develop(PackageSpec(path = subpkg_path))
 end
 
 dev_subpkg("DataDrivenDMD")
@@ -50,25 +50,34 @@ function create_tutorials(dirname, targetdir, excludes = [])
             mdpost(str) = replace(str, "@__CODE__" => code)
             Literate.markdown(ipath, targetdir)
             Literate.markdown(ipath, targetdir, execute = false, postprocess = mdpost)
-            push!(tutorials,
-                relpath(joinpath(targetdir, fname * ".md"), joinpath(@__DIR__, "src")))
+            push!(
+                tutorials,
+                relpath(joinpath(targetdir, fname * ".md"), joinpath(@__DIR__, "src"))
+            )
         end
     end
     return tutorials
 end
 
-koopman_tutorial = create_tutorials(joinpath(@__DIR__, "src/libs/datadrivendmd/"),
-    joinpath(@__DIR__, "src/libs/datadrivendmd/examples"))
-sparse_tutorial = create_tutorials(joinpath(@__DIR__, "src/libs/datadrivensparse/"),
-    joinpath(@__DIR__, "src/libs/datadrivensparse/examples"))
-sr_tutorial = create_tutorials(joinpath(@__DIR__, "src/libs/datadrivensr/"),
-    joinpath(@__DIR__, "src/libs/datadrivensr/examples"))
+koopman_tutorial = create_tutorials(
+    joinpath(@__DIR__, "src/libs/datadrivendmd/"),
+    joinpath(@__DIR__, "src/libs/datadrivendmd/examples")
+)
+sparse_tutorial = create_tutorials(
+    joinpath(@__DIR__, "src/libs/datadrivensparse/"),
+    joinpath(@__DIR__, "src/libs/datadrivensparse/examples")
+)
+sr_tutorial = create_tutorials(
+    joinpath(@__DIR__, "src/libs/datadrivensr/"),
+    joinpath(@__DIR__, "src/libs/datadrivensr/examples")
+)
 
 # Must be after tutorials is created
 include("pages.jl")
 
 # Create the docs
-makedocs(sitename = "DataDrivenDiffEq.jl",
+makedocs(
+    sitename = "DataDrivenDiffEq.jl",
     authors = "Julius Martensen, Christopher Rackauckas, et al.",
     modules = [DataDrivenDiffEq, DataDrivenDMD, DataDrivenSparse, DataDrivenSR],
     clean = true, doctest = false, linkcheck = true,
@@ -77,11 +86,16 @@ makedocs(sitename = "DataDrivenDiffEq.jl",
         "http://cwrowley.princeton.edu/papers/Hemati-2017a.pdf",
         "https://royalsocietypublishing.org/doi/10.1098/rspa.2020.0279",
         "https://www.pnas.org/doi/10.1073/pnas.1517384113",
-        "https://link.springer.com/article/10.1007/s00332-015-9258-5"
+        "https://link.springer.com/article/10.1007/s00332-015-9258-5",
     ],
-    format = Documenter.HTML(assets = ["assets/favicon.ico"],
-        canonical = "https://docs.sciml.ai/DataDrivenDiffEq/stable/"),
-    pages = pages)
+    format = Documenter.HTML(
+        assets = ["assets/favicon.ico"],
+        canonical = "https://docs.sciml.ai/DataDrivenDiffEq/stable/"
+    ),
+    pages = pages
+)
 
-deploydocs(repo = "github.com/SciML/DataDrivenDiffEq.jl.git";
-    push_preview = true)
+deploydocs(
+    repo = "github.com/SciML/DataDrivenDiffEq.jl.git";
+    push_preview = true
+)

docs/pages.jl

@@ -5,14 +5,17 @@ pages = [
     "Basis" => "basis.md",
     "Solutions" => "solutions.md",
     "Utilities" => "utils.md",
-    "DataDrivenDMD" => ["Background" => "libs/datadrivendmd/koopman.md",
-        "Examples" => koopman_tutorial
+    "DataDrivenDMD" => [
+        "Background" => "libs/datadrivendmd/koopman.md",
+        "Examples" => koopman_tutorial,
     ],
-    "DataDrivenSparse" => ["Background" => "libs/datadrivensparse/sparse_regression.md",
-        "Examples" => sparse_tutorial
+    "DataDrivenSparse" => [
+        "Background" => "libs/datadrivensparse/sparse_regression.md",
+        "Examples" => sparse_tutorial,
     ],
-    "DataDrivenSR" => ["Background" => "libs/datadrivensr/symbolic_regression.md",
-        "Examples" => sr_tutorial
+    "DataDrivenSR" => [
+        "Background" => "libs/datadrivensr/symbolic_regression.md",
+        "Examples" => sr_tutorial,
     ],
-    "Citing" => "citations.md"
+    "Citing" => "citations.md",
 ]

docs/src/libs/datadrivendmd/example_01.jl

@@ -47,6 +47,6 @@ get_basis(res)
 #md # ```
 
 ## Test the result #src
-@test rss(res) <= 1e-3 #src
+@test rss(res) <= 1.0e-3 #src
 @test r2(res) >= 0.99 #src
 @test dof(res) == 3 #src

docs/src/libs/datadrivendmd/example_04.jl

@@ -10,7 +10,7 @@ using DataDrivenDMD
 
 function slow_manifold(du, u, p, t)
     du[1] = p[1] * u[1]
-    du[2] = p[2] * (u[2] - u[1]^2)
+    return du[2] = p[2] * (u[2] - u[1]^2)
 end
 
 u0 = [3.0; -2.0]

docs/src/libs/datadrivendmd/example_05.jl

@@ -41,7 +41,7 @@ end
 # System dynamics
 function controlled_system!(du, u, p, t)
     ctrl = control_signal(t)
-    du .= A_true * u .+ B_true .* ctrl
+    return du .= A_true * u .+ B_true .* ctrl
 end
 
 # Generate "experimental" data

docs/src/libs/datadrivensparse/example_01.jl

@@ -1,6 +1,6 @@
 # # [Getting Started](@id getting_started)
 #
-# The workflow for [DataDrivenDiffEq.jl](https://github.com/SciML/DataDrivenDiffEq.jl) is similar to other [SciML](https://sciml.ai/) packages. 
+# The workflow for [DataDrivenDiffEq.jl](https://github.com/SciML/DataDrivenDiffEq.jl) is similar to other [SciML](https://sciml.ai/) packages.
 # You start by defining a [`DataDrivenProblem`](@ref) and then dispatch on the [`solve`](@ref solve) command to return a [`DataDrivenSolution`](@ref).
 
 # Here is an outline of the required elements and choices:
@@ -13,7 +13,7 @@ using ModelingToolkit
 using LinearAlgebra
 using DataDrivenSparse
 
-# Generate a test problem 
+# Generate a test problem
 
 f(u) = u .^ 2 .+ 2.0u .- 1.0
 X = randn(1, 100);

docs/src/libs/datadrivensparse/example_02.jl

@@ -38,9 +38,11 @@ ts = sol.t;
 # Using a [collocation method](@ref collocation), it automatically provides the derivative and smoothes the trajectory. Control signals can be passed
 # in as a function `(u,p,t)->control` or an array of measurements.
 
-prob = ContinuousDataDrivenProblem(X, ts, GaussianKernel(),
+prob = ContinuousDataDrivenProblem(
+    X, ts, GaussianKernel(),
     U = (u, p, t) -> [exp(-((t - 5.0) / 5.0)^2)],
-    p = ones(2))
+    p = ones(2)
+)
 
 #md plot(prob, size = (600,600))
 
@@ -65,8 +67,10 @@ println(basis) # hide
 sampler = DataProcessing(split = 0.8, shuffle = true, batchsize = 30, rng = rng)
 λs = exp10.(-10:0.1:0)
 opt = STLSQ(λs)
-res = solve(prob, basis, opt,
-    options = DataDrivenCommonOptions(data_processing = sampler, digits = 1))
+res = solve(
+    prob, basis, opt,
+    options = DataDrivenCommonOptions(data_processing = sampler, digits = 1)
+)
 #src println(res) #hide
 
 # !!! info

docs/src/libs/datadrivensparse/example_03.jl

@@ -13,7 +13,7 @@ using DataDrivenSparse
 using Test #src
 
 function michaelis_menten(u, p, t)
-    [0.6 - 1.5u[1] / (0.3 + u[1])]
+    return [0.6 - 1.5u[1] / (0.3 + u[1])]
 end
 
 u0 = [0.5]
@@ -23,10 +23,14 @@ ode_problem = ODEProblem(michaelis_menten, u0, (0.0, 4.0));
 # Since we have multiple trajectories at hand, we define a [`DataDrivenDataset`](@ref), which collects multiple problems but handles them as a unit
 # for the processing.
 
-prob = DataDrivenDataset(map(1:2) do i
-    solve(remake(ode_problem, u0 = i * u0),
-        Tsit5(), saveat = 0.1, tspan = (0.0, 4.0))
-end...)
+prob = DataDrivenDataset(
+    map(1:2) do i
+        solve(
+            remake(ode_problem, u0 = i * u0),
+            Tsit5(), saveat = 0.1, tspan = (0.0, 4.0)
+        )
+    end...
+)
 
 #md plot(prob)
 
@@ -44,7 +48,7 @@ basis = Basis([h; h .* (D(u[1]))], u, implicits = D.(u), iv = t)
 # Next, we define the [`ImplicitOptimizer`](@ref) and `solve` the problem. It wraps a standard optimizer, by default [`STLSQ`](@ref), and performs
 # implicit sparse regression upon the selected basis.
 
-opt = ImplicitOptimizer(1e-1:1e-1:5e-1)
+opt = ImplicitOptimizer(1.0e-1:1.0e-1:5.0e-1)
 res = solve(prob, basis, opt)
 #md println(res) #hide
 

docs/src/libs/datadrivensparse/example_04.jl

@@ -49,8 +49,10 @@ basis = Basis(eqs, x, independent_variable = t, implicits = D.(x))
 # Next to varying over different sparsity penalties, we also want to batch our data using [`DataProcessing`](@ref).
 
 sampler = DataProcessing(split = 0.8, shuffle = true, batchsize = 30)
-res = solve(dd_prob, basis, ImplicitOptimizer(STLSQ(1e-2:1e-2:1.0)),
-    options = DataDrivenCommonOptions(data_processing = sampler, digits = 2))
+res = solve(
+    dd_prob, basis, ImplicitOptimizer(STLSQ(1.0e-2:1.0e-2:1.0)),
+    options = DataDrivenCommonOptions(data_processing = sampler, digits = 2)
+)
 #md println(res) #hide
 
 # And have a look at the resulting plot

docs/src/libs/datadrivensparse/example_05.jl

@@ -17,7 +17,7 @@ function cart_pole(u, p, t)
     du[1] = u[3]
     du[2] = u[4]
     du[3] = -(19.62 * sin(u[1]) + sin(u[1]) * cos(u[1]) * u[3]^2 + F * cos(u[1])) /
-            (2 - cos(u[1])^2)
+        (2 - cos(u[1])^2)
     du[4] = -(sin(u[1]) * u[3]^2 + 9.81 * sin(u[1]) * cos(u[1]) + F) / (2 - cos(u[1])^2)
     return du
 end
@@ -35,15 +35,17 @@ for (i, xi) in enumerate(eachcol(X))
 end
 t = solution.t
 
-ddprob = ContinuousDataDrivenProblem(X, t, DX = DX[3:4, :],
-    U = (u, p, t) -> [-0.2 + 0.5 * sin(6 * t)])
+ddprob = ContinuousDataDrivenProblem(
+    X, t, DX = DX[3:4, :],
+    U = (u, p, t) -> [-0.2 + 0.5 * sin(6 * t)]
+)
 
 #md plot(ddprob)
 
-# Note that we just included the third and forth time derivative, assuming that we already know that the velocity `x[3:4]` is equal to the time 
+# Note that we just included the third and forth time derivative, assuming that we already know that the velocity `x[3:4]` is equal to the time
 # derivative of the position `x[1:2]`.
-# Next, we define a sufficient [`Basis`](@ref). Again, we need to include `implicits` in the definition of 
-# our candidate functions and inform the [`Basis`](@ref) of it.  
+# Next, we define a sufficient [`Basis`](@ref). Again, we need to include `implicits` in the definition of
+# our candidate functions and inform the [`Basis`](@ref) of it.
 
 @parameters t
 @variables u[1:4] du[1:2] x[1:1]
@@ -70,8 +72,10 @@ basis = Basis(implicits, u, implicits = du, controls = x, iv = t);
 
 # We solve the problem by varying over a sufficient set of thresholds for the associated optimizer.
 
-λ = [1e-4; 5e-4; 1e-3; 2e-3; 3e-3; 4e-3; 5e-3; 6e-3; 7e-3; 8e-3; 9e-3; 1e-2; 2e-2; 3e-2;
-     4e-2; 5e-2]
+λ = [
+    1.0e-4; 5.0e-4; 1.0e-3; 2.0e-3; 3.0e-3; 4.0e-3; 5.0e-3; 6.0e-3; 7.0e-3; 8.0e-3; 9.0e-3; 1.0e-2; 2.0e-2; 3.0e-2;
+    4.0e-2; 5.0e-2
+]
 
 opt = ImplicitOptimizer(λ)
 res = solve(ddprob, basis, opt)
@@ -81,8 +85,8 @@ res = solve(ddprob, basis, opt)
 system = get_basis(res)
 #md println(system) # hide
 
-# We have recovered the correct equations of motion! 
-# Another visual check using the problem and the result yields 
+# We have recovered the correct equations of motion!
+# Another visual check using the problem and the result yields
 
 #md plot(
 #md     plot(ddprob), plot(res), layout = (1,2)

docs/src/libs/datadrivensr/example_01.jl

@@ -36,10 +36,12 @@ prob = ContinuousDataDrivenProblem(X, t, U = U)
 # To solve our problem, we will use [`EQSearch`](@ref), which provides a wrapper for the [symbolic regression interface](https://ai.damtp.cam.ac.uk/symbolicregression/stable/api/#Options).
 # We will stick to simple operations, use a `L1DistLoss`, and limit the verbosity of the algorithm.
 
-eqsearch_options = SymbolicRegression.Options(binary_operators = [+, *],
+eqsearch_options = SymbolicRegression.Options(
+    binary_operators = [+, *],
     loss = L1DistLoss(),
     verbosity = -1, progress = false, npop = 30,
-    timeout_in_seconds = 60.0)
+    timeout_in_seconds = 60.0
+)
 
 alg = EQSearch(eq_options = eqsearch_options)
 
@@ -68,5 +70,5 @@ system = get_basis(res)
 #md # ```
 
 ## Test #src
-@test rss(res) .<= 5e-1 #src
+@test rss(res) .<= 5.0e-1 #src
 @test r2(res) >= 0.95 #src

docs/src/libs/datadrivensr/example_02.jl

@@ -11,7 +11,7 @@ using DataDrivenSR
 
 function pendulum!(du, u, p, t)
     du[1] = u[2]
-    du[2] = -9.81 * sin(u[1])
+    return du[2] = -9.81 * sin(u[1])
 end
 
 u0 = [0.1, π / 2]
@@ -35,10 +35,12 @@ u = collect(u)
 
 basis = Basis([polynomial_basis(u, 2); sin.(u)], u)
 
-eqsearch_options = SymbolicRegression.Options(binary_operators = [+, *],
+eqsearch_options = SymbolicRegression.Options(
+    binary_operators = [+, *],
     loss = L1DistLoss(),
     verbosity = -1, progress = false, npop = 30,
-    timeout_in_seconds = 60.0)
+    timeout_in_seconds = 60.0
+)
 
 alg = EQSearch(eq_options = eqsearch_options)
 
@@ -63,5 +65,5 @@ system = get_basis(res)
 #md # ```
 
 ## Test #src
-@test rss(res) .<= 5e-2 #src
+@test rss(res) .<= 5.0e-2 #src
 @test r2(res) >= 0.95 #src

lib/DataDrivenDMD/src/DataDrivenDMD.jl

@@ -20,7 +20,7 @@ using LinearAlgebra
 
 abstract type AbstractKoopmanAlgorithm <: AbstractDataDrivenAlgorithm end
 
-# Results 
+# Results
 include("./result.jl")
 export KoopmanResult
 export get_operator, get_inputmap, get_outputmap, get_trainerror, get_testerror