docs: remove DifferentialEquations.jl from docs environment

SciML · Jan 6, 2025 · 520daf4 · 520daf4
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diff --git a/docs/Project.toml b/docs/Project.toml
@@ -3,7 +3,6 @@ BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 BifurcationKit = "0f109fa4-8a5d-4b75-95aa-f515264e7665"
 ControlSystemsBase = "aaaaaaaa-a6ca-5380-bf3e-84a91bcd477e"
 DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
-DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DynamicQuantities = "06fc5a27-2a28-4c7c-a15d-362465fb6821"
@@ -28,7 +27,6 @@ Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 BenchmarkTools = "1.3"
 BifurcationKit = "0.4"
 DataInterpolations = "6.5"
-DifferentialEquations = "7.6"
 Distributions = "0.25"
 Documenter = "1"
 DynamicQuantities = "^0.11.2, 0.12, 1"

diff --git a/docs/src/basics/Composition.md b/docs/src/basics/Composition.md
@@ -56,7 +56,7 @@ x0 = [decay1.x => 1.0
 p = [decay1.a => 0.1
      decay2.a => 0.2]
 
-using DifferentialEquations
+using OrdinaryDiffEq
 prob = ODEProblem(simplified_sys, x0, (0.0, 100.0), p)
 sol = solve(prob, Tsit5())
 sol[decay2.f]

diff --git a/docs/src/examples/perturbation.md b/docs/src/examples/perturbation.md
@@ -49,7 +49,7 @@ These are the ODEs we want to solve. Now construct an `ODESystem`, which automat
 To solve the `ODESystem`, we generate an `ODEProblem` with initial conditions $x(0) = 0$, and $ẋ(0) = 1$, and solve it:
 
 ```@example perturbation
-using DifferentialEquations
+using OrdinaryDiffEq
 u0 = Dict([unknowns(sys) .=> 0.0; D(y[0]) => 1.0]) # nonzero initial velocity
 prob = ODEProblem(sys, u0, (0.0, 3.0))
 sol = solve(prob)

diff --git a/docs/src/examples/sparse_jacobians.md b/docs/src/examples/sparse_jacobians.md
@@ -11,7 +11,7 @@ First, let's start out with an implementation of the 2-dimensional Brusselator
 partial differential equation discretized using finite differences:
 
 ```@example sparsejac
-using DifferentialEquations, ModelingToolkit
+using OrdinaryDiffEq, ModelingToolkit
 
 const N = 32
 const xyd_brusselator = range(0, stop = 1, length = N)

diff --git a/docs/src/examples/spring_mass.md b/docs/src/examples/spring_mass.md
@@ -5,7 +5,7 @@ In this tutorial, we will build a simple component-based model of a spring-mass
 ## Copy-Paste Example
 
 ```@example component
-using ModelingToolkit, Plots, DifferentialEquations, LinearAlgebra
+using ModelingToolkit, Plots, OrdinaryDiffEq, LinearAlgebra
 using ModelingToolkit: t_nounits as t, D_nounits as D
 using Symbolics: scalarize
 

diff --git a/docs/src/tutorials/acausal_components.md b/docs/src/tutorials/acausal_components.md
@@ -20,7 +20,7 @@ equalities before solving. Let's see this in action.
 ## Copy-Paste Example
 
 ```@example acausal
-using ModelingToolkit, Plots, DifferentialEquations
+using ModelingToolkit, Plots, OrdinaryDiffEq
 using ModelingToolkit: t_nounits as t, D_nounits as D
 
 @connector Pin begin

diff --git a/docs/src/tutorials/modelingtoolkitize.md b/docs/src/tutorials/modelingtoolkitize.md
@@ -33,10 +33,10 @@ to improve a simulation code before it's passed to the solver.
 ## Example Usage: Generating an Analytical Jacobian Expression for an ODE Code
 
 Take, for example, the Robertson ODE
-defined as an `ODEProblem` for DifferentialEquations.jl:
+defined as an `ODEProblem` for OrdinaryDiffEq.jl:
 
 ```@example mtkize
-using DifferentialEquations, ModelingToolkit
+using OrdinaryDiffEq, ModelingToolkit
 function rober(du, u, p, t)
     y₁, y₂, y₃ = u
     k₁, k₂, k₃ = p

diff --git a/docs/src/tutorials/ode_modeling.md b/docs/src/tutorials/ode_modeling.md
@@ -34,7 +34,7 @@ using ModelingToolkit: t_nounits as t, D_nounits as D
     end
 end
 
-using DifferentialEquations: solve
+using OrdinaryDiffEq
 @mtkbuild fol = FOL()
 prob = ODEProblem(fol, [], (0.0, 10.0), [])
 sol = solve(prob)
@@ -84,10 +84,10 @@ Note that equations in MTK use the tilde character (`~`) as equality sign.
 
 `@mtkbuild` creates an instance of `FOL` named as `fol`.
 
-After construction of the ODE, you can solve it using [DifferentialEquations.jl](https://docs.sciml.ai/DiffEqDocs/stable/):
+After construction of the ODE, you can solve it using [OrdinaryDiffEq.jl](https://docs.sciml.ai/DiffEqDocs/stable/):
 
 ```@example ode2
-using DifferentialEquations
+using OrdinaryDiffEq
 using Plots
 
 prob = ODEProblem(fol, [], (0.0, 10.0), [])

diff --git a/docs/src/tutorials/programmatically_generating.md b/docs/src/tutorials/programmatically_generating.md
@@ -49,7 +49,7 @@ eqs = [D(x) ~ (h - x) / τ] # create an array of equations
 # Note: Complete models cannot be subsystems of other models!
 fol = structural_simplify(model)
 prob = ODEProblem(fol, [], (0.0, 10.0), [])
-using DifferentialEquations: solve
+using OrdinaryDiffEq
 sol = solve(prob)
 
 using Plots