Merge pull request #495 from LilithHafner/lh/format

Run JuliaFormatter.format()

Author: ChrisRackauckas

docs/make.jl

@@ -51,34 +51,34 @@ function create_tutorials(dirname, targetdir, excludes = [])
             Literate.markdown(ipath, targetdir)
             Literate.markdown(ipath, targetdir, execute = false, postprocess = mdpost)
             push!(tutorials,
-                  relpath(joinpath(targetdir, fname * ".md"), joinpath(@__DIR__, "src")))
+                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"))
+    joinpath(@__DIR__, "src/libs/datadrivendmd/examples"))
 sparse_tutorial = create_tutorials(joinpath(@__DIR__, "src/libs/datadrivensparse/"),
-                                   joinpath(@__DIR__, "src/libs/datadrivensparse/examples"))
+    joinpath(@__DIR__, "src/libs/datadrivensparse/examples"))
 sr_tutorial = create_tutorials(joinpath(@__DIR__, "src/libs/datadrivensr/"),
-                               joinpath(@__DIR__, "src/libs/datadrivensr/examples"))
+    joinpath(@__DIR__, "src/libs/datadrivensr/examples"))
 
 # Must be after tutorials is created
 include("pages.jl")
 
 # Create the docs
 makedocs(sitename = "DataDrivenDiffEq.jl",
-         authors = "Julius Martensen, Christopher Rackauckas, et al.",
-         modules = [DataDrivenDiffEq, DataDrivenDMD, DataDrivenSparse, DataDrivenSR],
-         clean = true, doctest = false, linkcheck = true,
-         warnonly = [:missing_docs, :cross_references],
-         linkcheck_ignore = ["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"],
-         format = Documenter.HTML(assets = ["assets/favicon.ico"],
-                                  canonical = "https://docs.sciml.ai/DataDrivenDiffEq/stable/"),
-         pages = pages)
+    authors = "Julius Martensen, Christopher Rackauckas, et al.",
+    modules = [DataDrivenDiffEq, DataDrivenDMD, DataDrivenSparse, DataDrivenSR],
+    clean = true, doctest = false, linkcheck = true,
+    warnonly = [:missing_docs, :cross_references],
+    linkcheck_ignore = ["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"],
+    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)
+    push_preview = true)

docs/pages.jl

@@ -6,13 +6,13 @@ pages = [
     "Solutions" => "solutions.md",
     "Utilities" => "utils.md",
     "DataDrivenDMD" => ["Background" => "libs/datadrivendmd/koopman.md",
-        "Examples" => koopman_tutorial,
+        "Examples" => koopman_tutorial
     ],
     "DataDrivenSparse" => ["Background" => "libs/datadrivensparse/sparse_regression.md",
-        "Examples" => sparse_tutorial,
+        "Examples" => sparse_tutorial
     ],
     "DataDrivenSR" => ["Background" => "libs/datadrivensr/symbolic_regression.md",
-        "Examples" => sr_tutorial,
+        "Examples" => sr_tutorial
     ],
-    "Citing" => "citations.md",
+    "Citing" => "citations.md"
 ]

docs/src/libs/datadrivensparse/example_02.jl

@@ -39,8 +39,8 @@ ts = sol.t;
 # in as a function `(u,p,t)->control` or an array of measurements.
 
 prob = ContinuousDataDrivenProblem(X, ts, GaussianKernel(),
-                                   U = (u, p, t) -> [exp(-((t - 5.0) / 5.0)^2)],
-                                   p = ones(2))
+    U = (u, p, t) -> [exp(-((t - 5.0) / 5.0)^2)],
+    p = ones(2))
 
 #md plot(prob, size = (600,600))
 
@@ -66,7 +66,7 @@ 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))
+    options = DataDrivenCommonOptions(data_processing = sampler, digits = 1))
 #src println(res) #hide
 
 # !!! info

docs/src/libs/datadrivensparse/example_03.jl

@@ -24,9 +24,9 @@ ode_problem = ODEProblem(michaelis_menten, u0, (0.0, 4.0));
 # 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...)
+    solve(remake(ode_problem, u0 = i * u0),
+        Tsit5(), saveat = 0.1, tspan = (0.0, 4.0))
+end...)
 
 #md plot(prob)
 

docs/src/libs/datadrivensparse/example_04.jl

@@ -20,7 +20,9 @@ using Test #src
     δ = 0.5
 end
 
-@variables begin x[1:2](t) = [20.0; 12.0] end
+@variables begin
+    x[1:2](t) = [20.0; 12.0]
+end
 
 x = collect(x)
 D = Differential(t)
@@ -52,7 +54,7 @@ basis = Basis(eqs, x, independent_variable = t, implicits = D.(x))
 
 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))
+    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

@@ -36,7 +36,7 @@ end
 t = solution.t
 
 ddprob = ContinuousDataDrivenProblem(X, t, DX = DX[3:4, :],
-                                     U = (u, p, t) -> [-0.2 + 0.5 * sin(6 * t)])
+    U = (u, p, t) -> [-0.2 + 0.5 * sin(6 * t)])
 
 #md plot(ddprob)
 

docs/src/libs/datadrivensr/example_01.jl

@@ -37,9 +37,9 @@ prob = ContinuousDataDrivenProblem(X, t, U = U)
 # We will stick to simple operations, use a `L1DistLoss`, and limit the verbosity of the algorithm.
 
 eqsearch_options = SymbolicRegression.Options(binary_operators = [+, *],
-                                              loss = L1DistLoss(),
-                                              verbosity = -1, progress = false, npop = 30,
-                                              timeout_in_seconds = 60.0)
+    loss = L1DistLoss(),
+    verbosity = -1, progress = false, npop = 30,
+    timeout_in_seconds = 60.0)
 
 alg = EQSearch(eq_options = eqsearch_options)
 

docs/src/libs/datadrivensr/example_02.jl

@@ -36,9 +36,9 @@ u = collect(u)
 basis = Basis([polynomial_basis(u, 2); sin.(u)], u)
 
 eqsearch_options = SymbolicRegression.Options(binary_operators = [+, *],
-                                              loss = L1DistLoss(),
-                                              verbosity = -1, progress = false, npop = 30,
-                                              timeout_in_seconds = 60.0)
+    loss = L1DistLoss(),
+    verbosity = -1, progress = false, npop = 30,
+    timeout_in_seconds = 60.0)
 
 alg = EQSearch(eq_options = eqsearch_options)
 

lib/DataDrivenDMD/src/algorithms.jl

@@ -20,14 +20,16 @@ function truncated_svd(A::AbstractMatrix{T}, truncation::Int) where {T <: Number
 end
 
 # General method with inputs
-function (x::AbstractKoopmanAlgorithm)(X::AbstractArray, Y::AbstractArray, U::AbstractArray,
-                                       B::AbstractArray)
+function (x::AbstractKoopmanAlgorithm)(
+        X::AbstractArray, Y::AbstractArray, U::AbstractArray,
+        B::AbstractArray)
     K, _ = x(X, Y - B * U)
     return (K, B)
 end
 
-function (x::AbstractKoopmanAlgorithm)(X::AbstractArray, Y::AbstractArray, U::AbstractArray,
-                                       ::Nothing)
+function (x::AbstractKoopmanAlgorithm)(
+        X::AbstractArray, Y::AbstractArray, U::AbstractArray,
+        ::Nothing)
     return x(X, Y, U)
 end
 
@@ -113,7 +115,7 @@ end
 
 # DMDc
 function (x::DMDSVD{T})(X::AbstractArray, Y::AbstractArray,
-                        U::AbstractArray) where {T <: Real}
+        U::AbstractArray) where {T <: Real}
     isempty(U) && return x(X, Y)
     nx, m = size(X)
     nu, m = size(U)
@@ -176,7 +178,7 @@ function (x::TOTALDMD)(X::AbstractArray, Y::AbstractArray, U::AbstractArray)
 end
 
 function (x::TOTALDMD)(X::AbstractArray, Y::AbstractArray, U::AbstractArray,
-                       B::AbstractArray)
+        B::AbstractArray)
     _, _, Q = truncated_svd([X; Y], x.truncation)
     K, _ = x.alg(X * Q, (Y - B * U) * Q)
     return (K, B)

lib/DataDrivenDMD/src/result.jl

@@ -25,7 +25,7 @@ struct KoopmanResult{K, B, C, Q, P, T} <: AbstractDataDrivenResult
     retcode::DDReturnCode
 
     function KoopmanResult(k_::K, b::B, c::C, q::Q, p::P, X::AbstractMatrix{T},
-                           Y::AbstractMatrix{T}, U::AbstractMatrix) where {K, B, C, Q, P, T}
+            Y::AbstractMatrix{T}, U::AbstractMatrix) where {K, B, C, Q, P, T}
         k = Matrix(k_)
         rss = isempty(b) ? sum(abs2, Y .- c * k * X) : sum(abs2, Y .- c * (k * X .+ b * U))
         dof = sum(!iszero, k)