Fix documentation building

.gitignore

@@ -5,5 +5,6 @@ Manifest.toml
 .DS_Store
 /docs/build/*
 /docs/src/libs/*/examples
+/docs/src/assets/Project.toml
 hall_of_fame.*
-/tmp/*
+/tmp/*

Project.toml

@@ -35,6 +35,7 @@ ProgressMeter = "1.6"
 QuadGK = "2.4"
 RecipesBase = "1"
 Reexport = "1.0"
+OrdinaryDiffEqTsit5 = "1"
 Setfield = "1"
 Statistics = "1"
 StatsBase = "0.32.0, 0.33, 0.34"
@@ -43,10 +44,11 @@ Symbolics = "5.30.1, 6"
 julia = "1.10"
 
 [extras]
+OrdinaryDiffEqTsit5 = "b1df2697-797e-41e3-8120-5422d3b24e4a"
 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 = ["Test", "Random", "SafeTestsets", "Pkg", "OrdinaryDiffEqTsit5"]

docs/make.jl

@@ -12,6 +12,8 @@ dev_subpkg("DataDrivenDMD")
 dev_subpkg("DataDrivenSparse")
 dev_subpkg("DataDrivenSR")
 
+Pkg.precompile()
+
 using Documenter
 using DataDrivenDiffEq
 using DataDrivenDMD
@@ -21,8 +23,8 @@ using DataDrivenSR
 using StatsBase
 using Literate
 
-cp("./docs/Manifest.toml", "./docs/src/assets/Manifest.toml", force = true)
-cp("./docs/Project.toml", "./docs/src/assets/Project.toml", force = true)
+cp(joinpath(@__DIR__, "Manifest.toml"), joinpath(@__DIR__, "src", "assets", "Manifest.toml"), force = true)
+cp(joinpath(@__DIR__, "Project.toml"), joinpath(@__DIR__, "src", "assets", "Project.toml"), force = true)
 
 ENV["GKSwstype"] = "100"
 
@@ -73,9 +75,12 @@ makedocs(sitename = "DataDrivenDiffEq.jl",
     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",
+    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"],
+        "https://www.pnas.org/doi/10.1073/pnas.1517384113",
+        "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)

docs/src/citations.md

@@ -15,16 +15,19 @@ If you are using `DataDrivenDiffEq.jl` for research, please cite
 }
 ```
 
-If you are using the [SymbolicRegression.jl](https://docs.sciml.ai/SymbolicRegression/stable/) API, please cite
+If you are using the [SymbolicRegression.jl](https://ai.damtp.cam.ac.uk/symbolicregression/dev/) API, please cite
 
 ```bibtex
-@software{pysr,
-  author       = {Miles Cranmer},
-  title        = {PySR: Fast \& Parallelized Symbolic Regression in Python/Julia},
-  month        = sep,
-  year         = 2020,
-  publisher    = {Zenodo},
-  doi          = {10.5281/zenodo.4041459},
-  url          = {http://doi.org/10.5281/zenodo.4041459}
+@misc{cranmerInterpretableMachineLearning2023,
+    title = {Interpretable {Machine} {Learning} for {Science} with {PySR} and {SymbolicRegression}.jl},
+    url = {http://arxiv.org/abs/2305.01582},
+    doi = {10.48550/arXiv.2305.01582},
+    urldate = {2023-07-17},
+    publisher = {arXiv},
+    author = {Cranmer, Miles},
+    month = may,
+    year = {2023},
+    note = {arXiv:2305.01582 [astro-ph, physics:physics]},
+    keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Computer Science - Symbolic Computation, Physics - Data Analysis, Statistics and Probability},
 }
 ```

docs/src/index.md

@@ -94,7 +94,7 @@ Pkg.add("DataDrivenSR")
 ## Contributing
 
   - Please refer to the
-    [SciML ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://github.com/SciML/ColPrac/blob/master/README.md)
+    [SciML ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://github.com/SciML/ColPrac)
     for guidance on PRs, issues, and other matters relating to contributing to SciML.
 
   - See the [SciML Style Guide](https://github.com/SciML/SciMLStyle) for common coding practices and other style decisions.

docs/src/libs/datadrivendmd/example_04.jl

@@ -1,6 +1,6 @@
 # # [Nonlinear Time Continuous System](@id nonlinear_continuos)
 #
-# 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.
+# 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) with a finite dimensional solution.
 
 using DataDrivenDiffEq
 using LinearAlgebra

docs/src/libs/datadrivensparse/example_03.jl

@@ -1,7 +1,7 @@
 # # [Implicit Nonlinear Dynamics : Michaelis-Menten](@id michaelis_menten)
 #
 # What if you want to estimate an implicitly defined system of the form ``f(u_t, u, p, t) = 0``?
-# The solution : Implicit Sparse Identification. This method was originally described in [this paper](http://ieeexplore.ieee.org/document/7809160/), and currently there exist [robust algorithms](https://royalsocietypublishing.org/doi/10.1098/rspa.2020.0279) to identify these systems.
+# The solution : Implicit Sparse Identification. This method was originally described in [this paper](https://ieeexplore.ieee.org/document/7809160/), and currently there exist [robust algorithms](https://royalsocietypublishing.org/doi/10.1098/rspa.2020.0279) to identify these systems.
 # We will focus on [Michaelis-Menten Kinetics](https://en.wikipedia.org/wiki/Michaelis%E2%80%93Menten_kinetics). As before, we will define the [`DataDrivenProblem`](@ref) and the [`Basis`](@ref) containing possible candidate functions for our [sparse regression](@ref sparse_algorithms).
 # Let's generate some data! We will use two experiments starting from different initial conditions.
 

docs/src/libs/datadrivensparse/example_04.jl

@@ -5,45 +5,41 @@
 
 using DataDrivenDiffEq
 using ModelingToolkit
+using ModelingToolkit: t_nounits as t, D_nounits as D
 using OrdinaryDiffEq
 using LinearAlgebra
 using DataDrivenSparse
 #md using Plots
 #md gr()
 using Test #src
 
-@parameters begin
-    t
-    α = 1.0
-    β = 1.3
-    γ = 2.0
-    δ = 0.5
+@mtkmodel Autoregulation begin
+    @parameters begin
+        α = 1.0
+        β = 1.3
+        γ = 2.0
+        δ = 0.5
+    end
+    @variables begin
+        (x(t))[1:2] = [20.0; 12.0]
+    end
+    @equations begin
+        D(x[1]) ~ α / (1 + x[2]) - β * x[1]
+        D(x[2]) ~ γ / (1 + x[1]) - δ * x[2]
+    end
 end
 
-@variables begin
-    x[1:2](t) = [20.0; 12.0]
-end
-
-x = collect(x)
-D = Differential(t)
-
-eqs = [D(x[1]) ~ α / (1 + x[2]) - β * x[1];
-       D(x[2]) ~ γ / (1 + x[1]) - δ * x[2]]
-
-sys = ODESystem(eqs, t, x, [α, β, γ, δ], name = :Autoregulation)
-
-x0 = [x[1] => 20.0; x[2] => 12.0]
-
+@mtkbuild sys = Autoregulation()
 tspan = (0.0, 5.0)
-
-de_problem = ODEProblem{true, SciMLBase.NoSpecialize}(sys, x0, tspan)
+de_problem = ODEProblem{true, SciMLBase.NoSpecialize}(sys, [], tspan, [])
 de_solution = solve(de_problem, Tsit5(), saveat = 0.005);
 #md plot(de_solution)
 
 # As always, we start by defining a [DataDrivenProblem](@ref problem) and a sufficient basis for sparse regression.
 
 dd_prob = DataDrivenProblem(de_solution)
 
+x = sys.x
 eqs = [polynomial_basis(x, 4); D.(x); x .* D(x[1]); x .* D(x[2])]
 
 basis = Basis(eqs, x, independent_variable = t, implicits = D.(x))

docs/src/libs/datadrivensr/example_01.jl

@@ -5,7 +5,7 @@
 #    Hence, the performance might differ and depends strongly on the hyperparameters of the optimization.
 #    This example might not recover the groundtruth, but is showing off the use within `DataDrivenDiffEq.jl`.
 #
-# DataDrivenDiffEq offers an interface to [`SymbolicRegression.jl`](https://docs.sciml.ai/SymbolicRegression/stable/) to infer more complex functions.
+# DataDrivenDiffEq offers an interface to [`SymbolicRegression.jl`](https://ai.damtp.cam.ac.uk/symbolicregression/dev/) to infer more complex functions.
 
 using DataDrivenDiffEq
 using LinearAlgebra

lib/DataDrivenDMD/src/algorithms.jl

@@ -189,7 +189,7 @@ $(TYPEDEF)
 
 
 Approximates the Koopman operator `K` via the forward-backward DMD.
-It is assumed that `K = sqrt(K₁*inv(K₂))`, where `K₁` is the approximation via forward and `K₂` via [DMDSVD](@ref). Based on [this paper](https://arxiv.org/pdf/1507.02264.pdf).
+It is assumed that `K = sqrt(K₁*inv(K₂))`, where `K₁` is the approximation via forward and `K₂` via [DMDSVD](@ref). Based on [this paper](https://arxiv.org/pdf/1507.02264).
 
 If `truncation` ∈ (0, 1) is given, the singular value decomposition is reduced to include only
 entries bigger than `truncation*maximum(Σ)`. If `truncation` is an integer, the reduced SVD up to `truncation` is used for computation.

lib/DataDrivenSparse/src/algorithms/STLSQ.jl

@@ -2,7 +2,7 @@
 $(TYPEDEF)
 `STLSQ` is taken from the [original paper on SINDY](https://www.pnas.org/doi/10.1073/pnas.1517384113) and implements a
 sequentially thresholded least squares iteration. `λ` is the threshold of the iteration.
-It is based upon [this Matlab implementation](https://github.com/eurika-kaiser/SINDY-MPC/blob/master/utils/sparsifyDynamics.m).
+It is based upon [this Matlab implementation](https://github.com/eurika-kaiser/SINDY-MPC/blob/e1dfd9908b2b56af303ee9fb30a133aced4fd757/utils/sparsifyDynamics.m).
 It solves the following problem
 ```math
 \\argmin_{x} \\frac{1}{2} \\| Ax-b\\|_2 + \\rho \\|x\\|_2

src/problem/type.jl

@@ -152,10 +152,11 @@ function DataDrivenProblem(X::AbstractMatrix;
         DX::AbstractMatrix = Array{eltype(X)}(undef, 0, 0),
         Y::AbstractMatrix = Array{eltype(X)}(undef, 0, 0),
         U::F = Array{eltype(X)}(undef, 0, 0),
-        p::AbstractVector = Array{eltype(X)}(undef, 0),
+        p::Union{AbstractVector, MTKParameters} = Array{eltype(X)}(undef, 0),
         probtype = nothing,
         kwargs...) where {F <: Union{AbstractMatrix, Function}}
-    return DataDrivenProblem(probtype, X, t, DX, Y, U, p; kwargs...)
+    _p = isa(p, MTKParameters) ? p.tunable : p
+    return DataDrivenProblem(probtype, X, t, DX, Y, U, _p; kwargs...)
 end
 
 function Base.summary(io::IO, x::DataDrivenProblem{N, C, P}) where {N, C, P}
@@ -313,7 +314,7 @@ function ModelingToolkit.get_dvs(p::ABSTRACT_DIRECT_PROB, i = :, j = :)
 end
 
 function ModelingToolkit.get_dvs(p::ABSTRACT_DISCRETE_PROB, i = :, j = :)
-    ModelingToolkit.states(p, i, j)
+    states(p, i, j)
 end
 
 function ModelingToolkit.observed(p::AbstractDataDrivenProblem, i = :, j = :)
@@ -327,7 +328,7 @@ function ModelingToolkit.controls(p::AbstractDataDrivenProblem, i = :, j = :)
 end
 
 function Base.getindex(p::AbstractDataDrivenProblem, i = :, j = :)
-    return (ModelingToolkit.states(p, i, j),
+    return (states(p, i, j),
         ModelingToolkit.parameters(p),
         ModelingToolkit.independent_variable(p, j),
         ModelingToolkit.controls(p, i, j))

src/utils/collocation.jl

@@ -142,7 +142,7 @@ $(SIGNATURES)
 
 Unified interface for collocation techniques. The input can either be
 a `CollocationKernel` (see list below) or a wrapped `InterpolationMethod` from
-[DataInterpolations.jl](https://github.com/PumasAI/DataInterpolations.jl).
+[DataInterpolations.jl](https://github.com/SciML/DataInterpolations.jl).
 
 Computes a non-parametrically smoothed estimate of `u'` and `u`
 given the `data`, where each column is a snapshot of the timeseries at
@@ -156,7 +156,7 @@ u′,u,t = collocate_data(data,tpoints,interp)
 ```
 
 # Collocation Kernels
-See [this paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2631937/) for more information.
+See [this paper](https://pmc.ncbi.nlm.nih.gov/articles/PMC2631937/) for more information.
 + EpanechnikovKernel
 + UniformKernel
 + TriangularKernel
@@ -170,7 +170,7 @@ See [this paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2631937/) for more
 + SilvermanKernel
 
 # Interpolation Methods
-See [DataInterpolations.jl](https://github.com/PumasAI/DataInterpolations.jl) for more information.
+See [DataInterpolations.jl](https://github.com/SciML/DataInterpolations.jl) for more information.
 + ConstantInterpolation
 + LinearInterpolation
 + QuadraticInterpolation

test/problem/problem.jl

@@ -162,3 +162,31 @@ end
         prob2 = (X = X, t = t, Y = Y))
     @test_throws ArgumentError ContinuousDataset(wrong_data)
 end
+
+@testset "DataDrivenProblem from ODEProblem solution" begin
+    using OrdinaryDiffEqTsit5
+    using ModelingToolkit: t_nounits as time, D_nounits as D
+
+    @mtkmodel Autoregulation begin
+        @parameters begin
+            α = 1.0
+            β = 1.3
+            γ = 2.0
+            δ = 0.5
+        end
+        @variables begin
+            (x(time))[1:2] = [20.0; 12.0]
+        end
+        @equations begin
+            D(x[1]) ~ α / (1 + x[2]) - β * x[1]
+            D(x[2]) ~ γ / (1 + x[1]) - δ * x[2]
+        end
+    end
+
+    @mtkbuild sys = Autoregulation()
+    tspan = (0.0, 5.0)
+    de_problem = ODEProblem{true, SciMLBase.NoSpecialize}(sys, [], tspan, [])
+    de_solution = solve(de_problem, Tsit5(), saveat = 0.005)
+    prob = DataDrivenProblem(de_solution)
+    @test is_valid(prob)
+end