Merge pull request #145 from SciML/update_deps

Update for julia 1.5

Author: AlCap23

.travis.yml

@@ -5,8 +5,8 @@ os:
   - linux
 
 julia:
-  - 1.3
   - 1.4
+  - 1.5
   - nightly
 
 jobs:
@@ -15,7 +15,7 @@ jobs:
 
 env:
   - GROUP=Standard
-  #- GROUP=Integration
+  - GROUP=Integration
 
 notifications:
   email: false

Project.toml

@@ -21,18 +21,23 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Compat = "2.2, 3.0"
 DSP = "0.6"
 DataInterpolations = "3.1"
-DiffEqBase = "6.41"
+DiffEqBase = "6.45"
 FiniteDifferences = "0.9.6, 0.10"
-ModelingToolkit = "3.14"
+ModelingToolkit = "3.17"
 ProximalOperators = "0.11"
-QuadGK = "2.3.1"
+QuadGK = "2.4"
 StatsBase = "0.32.0, 0.33"
-julia = "1.3"
+julia = "^1.3.0"
 
 [extras]
+DiffEqFlux = "aae7a2af-3d4f-5e19-a356-7da93b79d9d0"
+DiffEqSensitivity = "41bf760c-e81c-5289-8e54-58b1f1f8abe2"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
+Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["OrdinaryDiffEq", "Test", "SafeTestsets"]
+test = ["DiffEqFlux", "DiffEqSensitivity", "Flux", "JLD2", "Optim", "OrdinaryDiffEq", "Test", "SafeTestsets"]

test/applications/partial_lotka_volterra.jl

@@ -49,10 +49,10 @@ function dudt_(u, p,t)
 end
 
 prob_nn = ODEProblem(dudt_,u0, tspan, p)
-s = concrete_solve(prob_nn, Tsit5(), u0, p, saveat = solution.t)
+s = solve(prob_nn, Tsit5(), u0 = u0, p = p, saveat = solution.t)
 
 function predict(θ)
-    Array(concrete_solve(prob_nn, Vern7(), u0, θ, saveat = solution.t,
+    Array(solve(prob_nn, Vern7(), u0 = u0, p = θ, saveat = solution.t,
                          abstol=1e-6, reltol=1e-6))
 end
 
@@ -119,19 +119,10 @@ opt = SR3()
 # Create the thresholds which should be used in the search process
 λ = exp10.(-10:0.05:-0.5)
 # Target function to choose the results from; x = L0 of coefficients and L2-Error of the model
-function eval_target(x)
-    y = similar(x)
-    if iszero(x[1])
-        y[1] = convert(eltype(x), Inf)
-    end
-    y[2] = x[2]
-    return y
-end
-
-alg = GoalProgramming(x->norm(x, 2), eval_target)
+g(x) = x[1] < 1 ? Inf : norm(x, 2)
 @info "Start SINDy regression with unknown threshold"
 # Test on uode derivative data
-Ψ = SINDy(X[:, 2:end], Y[:, 2:end], basis, λ,  opt = opt, maxiter = 10000, normalize = true, denoise = true, alg = alg) # Succeed
+Ψ = SINDy(X[:, 2:end], Y[:, 2:end], basis, λ, opt, g = g, maxiter = 10000, normalize = true, denoise = true) # Succeed
 p̂ = parameters(Ψ)
 @info "Build initial guess system"
 # The parameters are a bit off, so we reiterate another SINDy term to get closer to the ground truth
@@ -142,7 +133,7 @@ unknown_eq = ODEFunction(unknown_sys)
 b = Basis((u, p, t)->unknown_eq(u, ones(size(p̂)), t), u)
 # Test on uode derivative data
 @info "Refine the guess"
-Ψ = SINDy(X[:, 2:end], Y[:, 2:end],b, opt = SR3(0.1), maxiter = 1000) # Succeed
+Ψ = SINDy(X[:, 2:end], Y[:, 2:end],b, SR3(0.1), maxiter = 1000) # Succeed
 p̂ = parameters(Ψ)
 
 @info "Checking equations"
@@ -162,7 +153,6 @@ end
 unknown_sys = ODESystem(Ψ)
 unknown_eq = ODEFunction(unknown_sys)
 
-
 # Build a ODE for the estimated system
 function approx(du, u, p, t)
     # Add SINDy Term