Aqua + typos CI

.github/workflows/SpellCheck.yml

@@ -0,0 +1,13 @@
+name: Spell Check
+
+on: [pull_request]
+
+jobs:
+  typos-check:
+    name: Spell Check with Typos
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Actions Repository
+        uses: actions/checkout@v3
+      - name: Check spelling
+        uses: crate-ci/[email protected] 

.typos.toml

@@ -0,0 +1 @@
+[default.extend-words]

Project.toml

@@ -12,7 +12,6 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
-ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 QuadGK = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
@@ -24,29 +23,35 @@ SymbolicUtils = "d1185830-fcd6-423d-90d6-eec64667417b"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 
 [compat]
+Aqua = "0.8"
 CommonSolve = "0.2"
 DataInterpolations = "4"
 DiffEqBase = "6"
 DocStringExtensions = "0.7, 0.8, 0.9"
+LinearAlgebra = "1"
 MLUtils = "0.3, 0.4"
 ModelingToolkit = "7, 8"
 Parameters = "0.12"
-ProgressMeter = "1.6"
+Pkg = "1"
 QuadGK = "2.4"
+Random = "1"
 RecipesBase = "1"
 Reexport = "1.0"
 Setfield = "1"
+SafeTestsets = "0.1"
 Statistics = "1"
 StatsBase = "0.32.0, 0.33, 0.34"
 SymbolicUtils = "1"
 Symbolics = "5"
+Test = "1"
 julia = "1.6"
 
 [extras]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "Random", "SafeTestsets", "Pkg"]
+test = ["Aqua", "Test", "Random", "SafeTestsets", "Pkg"]

docs/Project.toml

@@ -12,9 +12,11 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 [compat]
 DataDrivenDiffEq = "1"
 Documenter = "1"
+LinearAlgebra = "1"
 Literate = "2.14"
 ModelingToolkit = "8"
 OrdinaryDiffEq = "6"
 Plots = "1"
+Random = "1"
 StableRNGs = "1"
 StatsBase = "0.33, 0.34"

docs/src/libs/datadrivendmd/example_02.jl

@@ -20,7 +20,7 @@ sys = ODEProblem(f, u0, tspan)
 sol = solve(sys, Tsit5(), saveat = 0.05);
 
 # We could use the `DESolution` to define our problem, but here we want to use the data for didactic purposes.
-# For a [`ContinuousDataDrivenProblem`](@ref DataDrivenProblem), we need either the state trajectory and the timepoints or the state trajectory and its derivate.
+# For a [`ContinuousDataDrivenProblem`](@ref DataDrivenProblem), we need either the state trajectory and the timepoints or the state trajectory and its derivative.
 
 X = Array(sol)
 t = sol.t

docs/src/libs/datadrivendmd/example_04.jl

@@ -1,4 +1,4 @@
-# # [Nonlinear Time Continuous System](@id nonlinear_continuos)
+# # [Nonlinear Time Continuous System](@id nonlinear_continuous)
 #
 # Similarly, we can use the [Extended Dynamic Mode Decomposition](https://link.springer.com/article/10.1007/s00332-015-9258-5) via a nonlinear [`Basis`](@ref) of observables. Here, we will look at a rather [famous example](https://arxiv.org/pdf/1510.03007.pdf) with a finite dimensional solution.
 

lib/DataDrivenDMD/src/type.jl

@@ -101,7 +101,7 @@ Return the approximation of the discrete Koopman operator stored in `k`.
 """
 operator(k::Koopman{<:Any, <:Any, <:Any, true}) = __get_K(k)
 function operator(k::Koopman{<:Any, <:Any, <:Any, false})
-    throw(AssertionError("Koopman is continouos."))
+    throw(AssertionError("Koopman is continuous."))
 end
 
 """_

lib/DataDrivenLux/src/algorithms/rewards.jl

@@ -19,7 +19,7 @@ end
 """
 $(TYPEDEF)
 
-Scales the losses in such a way that the minimum loss is the most influencial reward.
+Scales the losses in such a way that the minimum loss is the most influential reward.
 """
 struct AbsoluteReward{risk} <: AbstractRewardScale{risk} end
 

lib/DataDrivenLux/src/caches/candidate.jl

@@ -224,7 +224,7 @@ get_nodes(c::Candidate) = ChainRulesCore.@ignore_derivatives get_nodes(c.outgoin
 function convert_to_basis(candidate::Candidate, ps = candidate.ps,
                           options = DataDrivenCommonOptions())
     @unpack basis, model = candidate.model
-    @unpack eval_expresssion = options
+    @unpack eval_expression = options
     p_best = get_parameters(candidate)
 
     p_new = map(enumerate(ModelingToolkit.parameters(basis))) do (i, ps)
@@ -243,5 +243,5 @@ function convert_to_basis(candidate::Candidate, ps = candidate.ps,
           controls = controls(basis), observed = observed(basis),
           implicits = implicit_variables(basis),
           name = gensym(:Basis),
-          eval_expression = eval_expresssion)
+          eval_expression = eval_expression)
 end

lib/DataDrivenLux/src/solve.jl

@@ -15,7 +15,7 @@ function CommonSolve.solve!(prob::InternalDataDrivenProblem{A}) where {
                                                                        AbstractDAGSRAlgorithm
                                                                        }
     @unpack alg, basis, testdata, traindata, control_idx, options, problem, kwargs = prob
-    @unpack maxiters, progress, eval_expresssion, abstol = options
+    @unpack maxiters, progress, eval_expression, abstol = options
 
     cache = init_cache(alg, basis, problem)
     p = ProgressMeter.Progress(maxiters, dt = 0.1, enabled = progress)

lib/DataDrivenLux/test/candidate.jl

@@ -30,7 +30,7 @@ using StableRNGs
                                                    options = Optim.Options())
 end
 
-@testset "Candidate with parametes" begin
+@testset "Candidate with parameters" begin
     fs = (exp,)
     arities = (1,)
     dag = LayeredDAG(1, 1, 1, arities, fs, skip = true)

lib/DataDrivenSR/src/DataDrivenSR.jl

@@ -131,7 +131,7 @@ end
 function convert_to_basis(paretofrontier, prob)
     @unpack alg, basis, problem, options = prob
     @unpack eq_options = alg
-    @unpack maxiters, eval_expresssion, generate_symbolic_parameters, digits, roundingmode = options
+    @unpack maxiters, eval_expression, generate_symbolic_parameters, digits, roundingmode = options
 
     eqs_ = map(paretofrontier) do dom
         node_to_symbolic(dom[end].tree, eq_options)
@@ -175,7 +175,7 @@ function convert_to_basis(paretofrontier, prob)
           controls = controls(basis), observed = observed(basis),
           implicits = implicit_variables(basis),
           name = gensym(:Basis),
-          eval_expression = eval_expresssion)
+          eval_expression = eval_expression)
 end
 
 # apply the algorithm on each dataset
@@ -207,7 +207,7 @@ function CommonSolve.solve!(ps::InternalDataDrivenProblem{EQSearch})
     @unpack alg, basis, testdata, traindata, kwargs = ps
     @unpack weights, numprocs, procs, addprocs_function, parallelism, runtests, eq_options = alg
     @unpack traindata, testdata, basis, options = ps
-    @unpack maxiters, eval_expresssion, generate_symbolic_parameters, digits, roundingmode, selector = options
+    @unpack maxiters, eval_expression, generate_symbolic_parameters, digits, roundingmode, selector = options
     @unpack problem = ps
 
     results = map(traindata) do (X, Y)

lib/DataDrivenSparse/src/commonsolve.jl

@@ -69,7 +69,7 @@ function __sparse_regression(ps::InternalDataDrivenProblem{<:ImplicitOptimizer},
     opt_iterations = []
 
     foreach(enumerate(eachcol(candidate_matrix))) do (i, idx)
-        # We enforce that one of the implicit variables is necessary for sucess
+        # We enforce that one of the implicit variables is necessary for success
         coeff, thresholds, iters = alg(X[idx, :], Y, options = options,
                                        necessary_idx = implicit_idx[idx, i])
         opt_coefficients[i:i, idx] .= coeff

src/DataDrivenDiffEq.jl

@@ -44,7 +44,7 @@ using RecipesBase
 @enum DDProbType begin
     Direct = 1 # Direct problem without further information
     Discrete = 2 # Time discrete problem
-    Continuous = 3 # Time continous problem
+    Continuous = 3 # Time continuous problem
 end
 
 # We want to export the ReturnCodes

src/basis/build_function.jl

@@ -61,7 +61,7 @@ function (f::DataDrivenFunction{false, true})(u::AbstractVector, p::P, t::Number
     _apply_function(f, __EMPTY_VECTOR, u, p, t, c)
 end
 
-# With implict, without controls
+# With implicit, without controls
 function (f::DataDrivenFunction{true, false})(du::AbstractVector, u::AbstractVector, p::P,
                                               t::Number) where {
                                                                 P <:
@@ -100,7 +100,7 @@ function (f::DataDrivenFunction{false, true})(res::AbstractVector, u::AbstractVe
     _apply_function!(f, res, __EMPTY_VECTOR, u, p, t, c)
 end
 
-# With implict, without controls
+# With implicit, without controls
 function (f::DataDrivenFunction{true, false})(res::AbstractVector, du::AbstractVector,
                                               u::AbstractVector, p::P,
                                               t::Number) where {

src/basis/type.jl

@@ -289,7 +289,7 @@ function (b::Basis{false, true})(u::AbstractVector,
     f(u, p, t, c)
 end
 
-# With implict, without controls
+# With implicit, without controls
 function (b::Basis{true, false})(du::AbstractVector, u::AbstractVector,
                                  p::P = parameters(b),
                                  t::Number = get_iv(b)) where {

src/commonsolve.jl

@@ -93,6 +93,6 @@ function CommonSolve.init(prob::AbstractDataDrivenProblem, basis::AbstractBasis,
 end
 
 function CommonSolve.solve!(::InternalDataDrivenProblem{ZeroDataDrivenAlgorithm})
-    @warn "No sufficient algorithm choosen! Return ErrorDataDrivenResult!"
+    @warn "No sufficient algorithm chosen! Return ErrorDataDrivenResult!"
     return ErrorDataDrivenResult()
 end

src/problem/type.jl

@@ -58,7 +58,7 @@ Three construction methods are available:
 
 + `DirectDataDrivenProblem` for direct mappings
 + `DiscreteDataDrivenProblem` for time discrete systems
-+ `ContinousDataDrivenProblem` for systems continuous in time
++ `ContinuousDataDrivenProblem` for systems continuous in time
 
 where all are aliases for constructing a problem.
 
@@ -123,7 +123,7 @@ function DataDrivenProblem(probType, X, t, DX, Y, U, p; name = gensym(:DDProblem
         if (isempty(DX) && !isempty(Y))
             probType = DDProbType(1) # Direct problem
         elseif !isempty(DX)
-            probType = DDProbType(3) # Continouos
+            probType = DDProbType(3) # Continuous
         end
     end
 
@@ -206,7 +206,7 @@ function DiscreteDataDrivenProblem(X::AbstractMatrix, t::AbstractVector, U::Func
     return DataDrivenProblem(X; t = t, U = U, probtype = DDProbType(2), kwargs...)
 end
 
-## Continouos Constructors
+## Continuous Constructors
 """
 A time continuous `DataDrivenProblem` useable for problems of the form `f(x,p,t,u) ↦ dx/dt`.
 

src/utils/build_basis.jl

@@ -114,7 +114,7 @@ function _implicit_build_eqs(basis, eqs, p, prob)
 end
 
 function __construct_basis(X, b, prob, options)
-    @unpack eval_expresssion, generate_symbolic_parameters, digits, roundingmode = options
+    @unpack eval_expression, generate_symbolic_parameters, digits, roundingmode = options
 
     p = parameters(prob)
 
@@ -162,7 +162,7 @@ function __construct_basis(X, b, prob, options)
           controls = controls(b), observed = observed(b),
           implicits = implicits,
           name = gensym(:Basis),
-          eval_expression = eval_expresssion)
+          eval_expression = eval_expression)
 end
 
 function unit_basis(prob::DataDrivenProblem)

src/utils/common_options.jl

@@ -48,7 +48,7 @@ Denoising happens before normalization!
     """Enables the use of symbolic parameters for the result. If `false`, the numerical value is used."""
     generate_symbolic_parameters::Bool = true
     """Evaluate the expression, see [`Symbolics.build_function`](https://symbolics.juliasymbolics.org/stable/manual/build_function/)"""
-    eval_expresssion::Bool = false
+    eval_expression::Bool = false
     """Additional kwargs"""
     kwargs::K = (;)
 end

test/qa.jl

@@ -0,0 +1,11 @@
+using DataDrivenDiffEq, Aqua
+@testset "Aqua" begin
+    Aqua.find_persistent_tasks_deps(DataDrivenDiffEq)
+    Aqua.test_ambiguities(DataDrivenDiffEq, recursive = false, broken = true)
+    Aqua.test_deps_compat(DataDrivenDiffEq)
+    Aqua.test_piracies(DataDrivenDiffEq, broken = true)
+    Aqua.test_project_extras(DataDrivenDiffEq)
+    Aqua.test_stale_deps(DataDrivenDiffEq)
+    Aqua.test_unbound_args(DataDrivenDiffEq, broken = true)
+    Aqua.test_undefined_exports(DataDrivenDiffEq, broken = true)
+end

test/runtests.jl

@@ -17,6 +17,7 @@ function activate_subpkg_env(subpkg)
 end
 
 @time begin if GROUP == "All" || GROUP == "Core" || GROUP == "Downstream"
+    @safetestset "Quality Assurance" begin include("qa.jl") end
     @safetestset "Basis" begin include("./basis/basis.jl") end
     @safetestset "Implicit Basis" begin include("./basis/implicit_basis.jl") end
     @safetestset "Basis generators" begin include("./basis/generators.jl") end