Merge pull request #823 from JuliaReach/schillic/ode

Replace DifferentialEquations by OrdinaryDiffEq

docs/Project.toml

@@ -1,5 +1,4 @@
 [deps]
-DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 DisplayAs = "0b91fe84-8a4c-11e9-3e1d-67c38462b6d6"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 ExponentialUtilities = "d4d017d3-3776-5f7e-afef-a10c40355c18"
@@ -9,12 +8,12 @@ LaTeXStrings = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
 LazySets = "b4f0291d-fe17-52bc-9479-3d1a343d9043"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 MathematicalSystems = "d14a8603-c872-5ed3-9ece-53e0e82e39da"
+OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 ReachabilityBase = "379f33d0-9447-4353-bd03-d664070e549f"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 
 [compat]
-DifferentialEquations = "7"
 DisplayAs = "0.1"
 Documenter = "1"
 ExponentialUtilities = "1"
@@ -24,6 +23,7 @@ LaTeXStrings = "1"
 LazySets = "2"
 Literate = "2"
 MathematicalSystems = "0.11 - 0.13"
+OrdinaryDiffEq = "6"
 Plots = "1"
 ReachabilityBase = "0.2.3"
 Symbolics = "5"

docs/src/man/faq.md

@@ -123,20 +123,20 @@ DisplayAs.Text(DisplayAs.PNG(fig))  # hide
 
 It is often necessary to plot a "bunch" of trajectories starting from a set of initial conditions.
 The [parallel ensemble simulations](https://diffeq.sciml.ai/stable/features/ensemble/) capabilities
-from the Julia [DifferentialEquations.jl](https://github.com/SciML/DifferentialEquations.jl) suite
+from the Julia [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) suite
 can be used to numerically simulate a given number of trajectories and it counts with state-of-the-art
 algorithms for stiff and non-stiff ODEs as well as many other advanced features,
 such as distributed computing, multi-threading and GPU support.
 See [EnsembleAlgorithms](https://diffeq.sciml.ai/stable/features/ensemble/#EnsembleAlgorithms) for details.
 
 As a simple example consider the scalar ODE ``x'(t) = 1.01x(t)`` with initial condition on the
 interval ``x(0) ∈ [0, 0.5]``. To solve it using ensemble simulations, pass the `ensemble=true`
-keyword argument to the solve function (if the library `DifferentialEquations` was not loaded in
+keyword argument to the solve function (if the library `OrdinaryDiffEq` was not loaded in
 your current session, an error is triggered). The number of trajectories can be specified
 with the `trajectories` keyword argument.
 
 ```@example
-using ReachabilityAnalysis, DifferentialEquations
+using ReachabilityAnalysis, OrdinaryDiffEq
 
 # formulate initial-value problem
 prob = @ivp(x' = 1.01x, x(0) ∈ 0 .. 0.5)

docs/src/tutorials/linear_methods/introduction.md

@@ -72,7 +72,7 @@ plot!(trange, 2.0 * exp.(-trange), vars=(0, 1), c=:magenta, lab="")
 ```
 
 The user-facing interface is designed to be intuitive and interactive, and it is
-inspired by of other Julia packages such as DifferentialEquations.jl.
+inspired by of other Julia packages such as [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl).
 Moreover, the library internals are written in a modular and composable way,
 such that advanced users are able to modify and changed easily, or to compose with
 other algorithms, different steps of the solution process.

docs/src/tutorials/taylor_methods/introduction.md

@@ -18,10 +18,10 @@ x'(t) = -x(t) ~ \sin(t),\qquad t ≥ 0.
 ```
 
 Standard integration schemes fail to produce helpful solutions if the initial state is an interval. We illustrate this point
-by solving the given differential equation with the `Tsit5` algorithm from [DifferentialEquations.jl](https://diffeq.sciml.ai/stable/) suite.
+by solving the given differential equation with the `Tsit5` algorithm from [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) suite.
 
 ```@example nonlinear_univariate
-using DifferentialEquations, IntervalArithmetic
+using OrdinaryDiffEq, IntervalArithmetic
 
 # initial condition
 x₀ = [-1 .. 1]

src/Continuous/solve.jl

@@ -67,7 +67,7 @@ function solve(ivp::IVP{<:AbstractContinuousSystem_}, args...; kwargs...)
     got_ensemble = get(kwargs, :ensemble, false)
     dict = Dict{Symbol,Any}(:ensemble => nothing)
     if got_ensemble
-        @requires DifferentialEquations
+        @requires OrdinaryDiffEq
         ensemble_sol = _solve_ensemble(ivp, args...; kwargs...)
         dict[:ensemble] = ensemble_sol
     end

src/Hybrid/solve.jl

@@ -170,7 +170,7 @@ function solve(ivp::IVP{<:AbstractHybridSystem}, args...;
 
     got_ensemble = get(kwargs, :ensemble, false)
     if got_ensemble
-        @requires DifferentialEquations
+        @requires OrdinaryDiffEq
         ensemble_sol = _solve_ensemble(ivp, args...; kwargs...)
         dict = Dict{Symbol,Any}(:ensemble => ensemble_sol)
         sol = ReachSolution(HFout, cpost, dict)

src/Initialization/init.jl

@@ -113,7 +113,7 @@ using Requires
 
 function __init__()
     # numerical differential equations suite
-    @require DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa" include("init_DifferentialEquations.jl")
+    @require OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed" include("init_OrdinaryDiffEq.jl")
 
     # sparse dynamic representation of multivariate polynomials
     @require DynamicPolynomials = "7c1d4256-1411-5781-91ec-d7bc3513ac07" include("init_DynamicPolynomials.jl")

src/Initialization/init_DifferentialEquations.jl → src/Initialization/init_OrdinaryDiffEq.jl

@@ -1,4 +1,4 @@
-import .DifferentialEquations as DE
+import .OrdinaryDiffEq as ODE
 import Random
 
 const DEFAULT_TRAJECTORIES = 10
@@ -10,8 +10,8 @@ const DEFAULT_TRAJECTORIES = 10
 # =====================================
 
 function _solve_ensemble(ivp::InitialValueProblem, args...;
-                         trajectories_alg=DE.Tsit5(),
-                         ensemble_alg=DE.EnsembleThreads(),
+                         trajectories_alg=ODE.Tsit5(),
+                         ensemble_alg=ODE.EnsembleThreads(),
                          inplace=true,
                          initial_states=nothing,
                          kwargs...)
@@ -39,8 +39,8 @@ function _solve_ensemble(ivp::InitialValueProblem, args...;
     end
 
     # formulate ensemble ODE problem
-    ensemble_prob = DE.ODEProblem(field, first(initial_states), tspan)
-    _prob_func(prob, i, repeat) = DE.remake(prob; u0=initial_states[i])
+    ensemble_prob = ODE.ODEProblem(field, first(initial_states), tspan)
+    _prob_func(prob, i, repeat) = ODE.remake(prob; u0=initial_states[i])
 
     # choose tolerances
     reltol = get(kwargs, :reltol, 1e-3)
@@ -49,14 +49,14 @@ function _solve_ensemble(ivp::InitialValueProblem, args...;
     callback = get(kwargs, :callback, nothing)
     dtmax = get(kwargs, :dtmax, Inf)
 
-    ensemble_prob = DE.EnsembleProblem(ensemble_prob; prob_func=_prob_func)
+    ensemble_prob = ODE.EnsembleProblem(ensemble_prob; prob_func=_prob_func)
 
     if isnothing(callback)
-        result = DE.solve(ensemble_prob, trajectories_alg, ensemble_alg;
+        result = ODE.solve(ensemble_prob, trajectories_alg, ensemble_alg;
                           trajectories=trajectories, reltol=reltol,
                           abstol=abstol, dtmax=dtmax)
     else
-        result = DE.solve(ensemble_prob, trajectories_alg, ensemble_alg;
+        result = ODE.solve(ensemble_prob, trajectories_alg, ensemble_alg;
                           trajectories=trajectories, reltol=reltol,
                           abstol=abstol, dtmax=dtmax, callback=callback)
     end
@@ -90,7 +90,7 @@ function _solve_ensemble(ivp::InitialValueProblem{<:AbstractHybridSystem},
     t0_g = tstart(tspan)
     T = tend(tspan)
 
-    termination_action = (integrator) -> DE.terminate!(integrator)
+    termination_action = (integrator) -> ODE.terminate!(integrator)
     use_discrete_callback = get(kwargs, :use_discrete_callback, false)
     dtmax = get(kwargs, :dtmax, Inf)
     if use_discrete_callback && isinf(dtmax)
@@ -118,11 +118,11 @@ function _solve_ensemble(ivp::InitialValueProblem{<:AbstractHybridSystem},
             if use_discrete_callback
                 # use a discrete callback function based on membership
                 condition = (u, t, integrator) -> u ∉ I⁻
-                callback = DE.DiscreteCallback(condition, termination_action)
+                callback = ODE.DiscreteCallback(condition, termination_action)
             else
                 # use a continuous callback function based on membership
                 condition = (u, t, integrator) -> u ∉ I⁻ ? -1 : 1
-                callback = DE.ContinuousCallback(condition, termination_action)
+                callback = ODE.ContinuousCallback(condition, termination_action)
             end
             trajectory = _solve_ensemble(ivp_loc, args...; initial_states=[x0],
                                          callback=callback, tspan=t0 .. T,

test/Project.toml

@@ -1,12 +1,13 @@
 [deps]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 CDDLib = "3391f64e-dcde-5f30-b752-e11513730f60"
-DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Expokit = "a1e7a1ef-7a5d-5822-a38c-be74e1bb89f4"
 Flowstar = "a8054ddd-9dca-4d20-8ffe-ae96ec1541f1"
 IntervalArithmetic = "d1acc4aa-44c8-5952-acd4-ba5d80a2a253"
 LazySets = "b4f0291d-fe17-52bc-9479-3d1a343d9043"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Optim = "429524aa-4258-5aef-a3af-852621145aeb"
+OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Polyhedra = "67491407-f73d-577b-9b50-8179a7c68029"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
@@ -18,11 +19,12 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 [compat]
 Aqua = "0.8"
 CDDLib = "0.5 - 0.9"
-DifferentialEquations = "6.17, 7"
 Expokit = "0.2"
 Flowstar = "0.2.4"
 IntervalArithmetic = "0.16 - 0.20, =0.20.9"  # new versions require updates and are incompatible with dependencies
 LazySets = "2.3"
+Optim = "0.15 - 0.22, 1"
+OrdinaryDiffEq = "6"
 Polyhedra = "0.5 - 0.7"
 StaticArrays = "0.12, 1"
 Suppressor = "0.2"

test/continuous/traces.jl

@@ -1,18 +1,18 @@
-import DifferentialEquations
+import OrdinaryDiffEq
 using ReachabilityAnalysis: _solve_ensemble, ReachSolution
 
-@testset "Simulation traces using DifferentialEquations.jl solvers" begin
+@testset "Simulation traces using OrdinaryDiffEq.jl solvers" begin
     prob = @ivp(x' = 1.0x, x(0) ∈ [1 / 2])
 
     # `solve` extends CommonSolve.jl
-    sol = DifferentialEquations.solve(prob; tspan=(0.0, 1.0))
+    sol = OrdinaryDiffEq.solve(prob; tspan=(0.0, 1.0))
     @test sol isa ReachSolution
 
-    # ensemble problems API 
+    # ensemble problems API
     sol = _solve_ensemble(prob; tspan=(0.0, 1.0))
-    @test sol isa DifferentialEquations.EnsembleSolution
+    @test sol isa OrdinaryDiffEq.EnsembleSolution
     sol = _solve_ensemble(prob; T=1.0)
-    @test sol isa DifferentialEquations.EnsembleSolution
+    @test sol isa OrdinaryDiffEq.EnsembleSolution
 
     # inplace (default)
     sol = _solve_ensemble(prob; T=1.0, inplace=true)

test/hybrid/hybrid.jl

@@ -1,5 +1,6 @@
 using ReachabilityAnalysis: _distribute, WaitingList, StateInLocation, state, location,
                             TimeInterval, DeterministicSwitching, NonDeterministicSwitching
+import Optim
 
 @testset "Hybrid utility functions" begin
     prob, _ = bouncing_ball()