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ChrisRackauckasChrisRackauckas
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Move to SDEProblemLibrary
1 parent 017820a commit 8916196

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-48
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‎Project.toml

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@@ -58,14 +58,14 @@ julia = "1.6"
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DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
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DiffEqDevTools = "f3b72e0c-5b89-59e1-b016-84e28bfd966d"
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DiffEqOperators = "9fdde737-9c7f-55bf-ade8-46b3f136cc48"
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DiffEqProblemLibrary = "a077e3f3-b75c-5d7f-a0c6-6bc4c8ec64a9"
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LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
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ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
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Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
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SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
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SDEProblemLibrary = "c72e72a9-a271-4b2b-8966-303ed956772e"
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SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
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Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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[targets]
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test = ["DiffEqCallbacks", "DiffEqDevTools", "DiffEqOperators", "DiffEqProblemLibrary", "LinearSolve", "ModelingToolkit", "Pkg", "SafeTestsets", "SparseArrays", "Statistics", "Test"]
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test = ["DiffEqCallbacks", "DiffEqDevTools", "DiffEqOperators", "SDEProblemLibrary", "LinearSolve", "ModelingToolkit", "Pkg", "SafeTestsets", "SparseArrays", "Statistics", "Test"]

‎test/adaptive/sde_autostepsize_test.jl

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@@ -1,6 +1,6 @@
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using StochasticDiffEq, Random
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using DiffEqProblemLibrary.SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using DiffEqProblemLibrary.SDEProblemLibrary: prob_sde_2Dlinear
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using SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using SDEProblemLibrary: prob_sde_2Dlinear
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Random.seed!(100)
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#Let the solver determine the initial stepsize for you!

‎test/adaptive/sde_linearadaptive_tests.jl

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using StochasticDiffEq, Test, Random
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using DiffEqProblemLibrary.SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using DiffEqProblemLibrary.SDEProblemLibrary: prob_sde_linear, prob_sde_2Dlinear, prob_sde_additive, prob_sde_additivesystem
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using SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using SDEProblemLibrary: prob_sde_linear, prob_sde_2Dlinear, prob_sde_additive, prob_sde_additivesystem
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probs = Vector{SDEProblem}(undef, 2)
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add_probs = Vector{SDEProblem}(undef, 2)
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u0 = [1.0, 1.0]
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prob = SDEProblem(f, g, u0, (0.0, 10.0))
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sol = solve(prob, SKenCarp())
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sol = solve(prob, SKenCarp())

‎test/adaptive/sde_lorenzattractor_tests.jl

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using StochasticDiffEq, Random
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using DiffEqProblemLibrary.SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using DiffEqProblemLibrary.SDEProblemLibrary: prob_sde_lorenz
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using SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using SDEProblemLibrary: prob_sde_lorenz
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Random.seed!(100)
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prob = prob_sde_lorenz
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‎test/adaptive/sde_twodimlinearadaptive_tests.jl

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using StochasticDiffEq, Test, Random
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using DiffEqProblemLibrary.SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using DiffEqProblemLibrary.SDEProblemLibrary: prob_sde_2Dlinear
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using SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using SDEProblemLibrary: prob_sde_2Dlinear
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Random.seed!(100)
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prob = prob_sde_2Dlinear
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‎test/composite_algorithm_test.jl

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using StochasticDiffEq, Test, Random
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using DiffEqProblemLibrary.SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using DiffEqProblemLibrary.SDEProblemLibrary: prob_sde_linear, prob_sde_2Dlinear
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using SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using SDEProblemLibrary: prob_sde_linear, prob_sde_2Dlinear
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prob = prob_sde_2Dlinear
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choice_function(integrator) = (Int(integrator.t<0.5) + 1)
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alg_double = StochasticCompositeAlgorithm((SRIW1(),SRIW1()),choice_function)

‎test/oval2_single.jl

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using StochasticDiffEq, Random
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using DiffEqProblemLibrary.SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using DiffEqProblemLibrary.SDEProblemLibrary: oval2ModelExample
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using SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using SDEProblemLibrary: oval2ModelExample
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Random.seed!(200)
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prob = oval2ModelExample(largeFluctuations=true,useBigs=false)
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quick_prob = deepcopy(prob)

‎test/oval2_test.jl

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using DiffEqProblemLibrary.SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using DiffEqProblemLibrary.SDEProblemLibrary: prob_sde_lorenz, oval2ModelExample
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using SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using SDEProblemLibrary: prob_sde_lorenz, oval2ModelExample
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oval2ModelExample()
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‎test/reversal_tests.jl

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using StochasticDiffEq, DiffEqNoiseProcess, Test, Random
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using Random
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using DiffEqProblemLibrary.SDEProblemLibrary
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using SDEProblemLibrary
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# load problems
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SDEProblemLibrary.importsdeproblems()
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# automatically construct SDE transformation for Ito reversal

‎test/saveat_tests.jl

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using StochasticDiffEq, Test, Random
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using DiffEqProblemLibrary.SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using DiffEqProblemLibrary.SDEProblemLibrary: prob_sde_linear, prob_sde_2Dlinear
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using SDEProblemLibrary: importsdeproblems; importsdeproblems()
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using SDEProblemLibrary: prob_sde_linear, prob_sde_2Dlinear
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Random.seed!(100)
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prob = prob_sde_linear
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f(u,p,t) = 2u

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