Fix the failing github action tests (#297)

* Dummy change to get a PR going and trigger the github actions

* Remove many deprecation warnings from the tests

* Update the docs such that we don't recommend indexing with `sol[t]`

* Fix a bug in the classic solver init

* Fix another overlooked old array indexing

README.md

@@ -6,8 +6,6 @@
 [![Coverage](https://codecov.io/gh/nathanaelbosch/ProbNumDiffEq.jl/branch/main/graph/badge.svg?token=eufIemCGXn)](https://codecov.io/gh/nathanaelbosch/ProbNumDiffEq.jl)
 [![Benchmarks](http://img.shields.io/badge/benchmarks-docs-blueviolet.svg)](https://nathanaelbosch.github.io/ProbNumDiffEq.jl/dev/benchmarks/multi-language-wrappers/)
 
-<!-- [![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle) -->
-<!-- [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac) -->
 ![Banner](./examples/banner.svg?raw=true)
 
 __ProbNumDiffEq.jl__ provides _probabilistic numerical_ ODE solvers to the

docs/src/tutorials/getting_started.md

@@ -128,18 +128,12 @@ plot!(sol.t, zero(errors), ribbon=3error_estimates, label="error estimate",
 
 The solution object returned by ProbNumDiffEq.jl mostly behaves just like any other `ODESolution` in DifferentialEquations.jl --
 with some added uncertainties and related functionality on top.
-The `ProbabilisticODESolution` can be indexed
+The `ProbabilisticODESolution` can be indexed with
 
 ```@repl 1
-sol[1]
-sol[end]
-```
-
-and has fields `sol.t` and `sol.u` which store the time points and mean estimates:
-
-```@repl 1
-sol.t[end]
+sol.u[1]
 sol.u[end]
+sol.t[end]
 ```
 
 

src/initialization/classicsolverinit.jl

@@ -13,7 +13,7 @@ function initial_update!(integ, cache, ::ClassicSolverInit)
     is_secondorder = integ.f isa DynamicalODEFunction
     _u = is_secondorder ? view(u.x[2], :) : view(u, :)
     init_condition_on!(x, Proj(0), _u, cache)
-    is_secondorder ? u.x[1] : f(du, u, p, t)
+    is_secondorder ? f.f1(du, u.x[1], u.x[2], p, t) : f(du, u, p, t)
     integ.stats.nf += 1
     init_condition_on!(x, Proj(1), view(du, :), cache)
 

src/perform_step.jl

@@ -188,8 +188,8 @@ function compute_scaled_error_estimate!(integ, cache)
         DiffEqBase.calculate_residuals!(
             err_tmp,
             err_est_unscaled,
-            integ.u[1, :],
-            integ.uprev[1, :],
+            integ.u.x[1],
+            integ.uprev.x[1],
             integ.opts.abstol,
             integ.opts.reltol,
             integ.opts.internalnorm,

test/callbacks.jl

@@ -57,9 +57,9 @@ end
     @test_nowarn solve(prob, EK1(order=3), callback=ManifoldUpdate(E))
     sol2 = solve(prob, EK1(order=3), callback=ManifoldUpdate(E))
 
-    @test E(sol1[end]) .^ 2 > E(sol2[end]) .^ 2
+    @test E(sol1.u[end]) .^ 2 > E(sol2.u[end]) .^ 2
 
-    err1 = sol1[end] .- appxsol[end]
-    err2 = sol2[end] .- appxsol[end]
+    err1 = sol1.u[end] .- appxsol.u[end]
+    err2 = sol2.u[end] .- appxsol.u[end]
     @test all(err1 .^ 2 > err2 .^ 2)
 end

test/exponential_integrators.jl

@@ -18,13 +18,13 @@ using Test
     solexp = solve(prob, ExpEK(L=p, order=3))
     solros = solve(prob, RosenbrockExpEK(order=3))
 
-    err0 = norm(uend - sol0[end])
+    err0 = norm(uend - sol0.u[end])
     @test err0 < 1e-7
-    err1 = norm(uend - sol1[end])
+    err1 = norm(uend - sol1.u[end])
     @test err1 < 1e-9
-    errexp = norm(uend - solexp[end])
+    errexp = norm(uend - solexp.u[end])
     @test errexp < 1e-10
-    errros = norm(uend - solros[end])
+    errros = norm(uend - solros.u[end])
     @test errros < 1e-13
 
     @test errros < errexp < err1 < err0

test/ioup.jl

@@ -14,19 +14,19 @@ prob = ODEProblem(f, u0, tspan)
 ref = solve(prob, Vern9(), abstol=1e-9, reltol=1e-9);
 
 sol_iwp = solve(prob, EK1());
-err_iwp = norm(ref[end] - sol_iwp[end])
+err_iwp = norm(ref.u[end] - sol_iwp.u[end])
 @test err_iwp < 1e-5
 
 A_noisy = A + 1e-3 * randn(MersenneTwister(42), 2, 2)
 
 @testset "Adaptive steps" begin
     sol_ioup_noisy = solve(prob, EK1(prior=IOUP(3, A_noisy)))
-    err_ioup_noisy = norm(ref[end] - sol_ioup_noisy[end])
+    err_ioup_noisy = norm(ref.u[end] - sol_ioup_noisy.u[end])
     @test sol_ioup_noisy.stats.nf < sol_iwp.stats.nf
     @test err_ioup_noisy < 2e-5
 
     sol_ioup = solve(prob, EK1(prior=IOUP(3, A)))
-    err_ioup = norm(ref[end] - sol_ioup[end])
+    err_ioup = norm(ref.u[end] - sol_ioup.u[end])
     @test sol_ioup.stats.nf < sol_ioup_noisy.stats.nf
     @test err_ioup < 5e-10
 end
@@ -38,7 +38,7 @@ end
                 prior=IOUP(order, A_noisy),
                 diffusionmodel=FixedDiffusion(),
             ), adaptive=false, dt=1e-1)
-        err = norm(ref[end] - sol[end])
+        err = norm(ref.u[end] - sol.u[end])
         @test err < last_error
         last_error = err
     end
@@ -48,7 +48,7 @@ end
     @testset "$(typeof(r))" for r in (-α, [-α, -α], [-α 0; 0 -α], -α * I(2))
         sol = solve(prob, EK1(prior=IOUP(3, r)))
 
-        err = norm(ref[end] - sol[end])
+        err = norm(ref.u[end] - sol.u[end])
         @test err < 6e-6
     end
 end

test/mass_matrix.jl

@@ -12,7 +12,7 @@ using Test
 
     @testset "Correct EK1" begin
         sol = solve(prob, EK1(order=3))
-        @test sol[end] ≈ ref[end] rtol = 1e-10
+        @test sol.u[end] ≈ ref.u[end] rtol = 1e-10
     end
 
     @testset "Kronecker working" begin
@@ -38,7 +38,7 @@ using Test
         s0 = ek0()
 
         ref = solve(prob, RadauIIA5(), abstol=1e-9, reltol=1e-6)
-        @test s0[end] ≈ ref[end] rtol = 1e-7
+        @test s0.u[end] ≈ ref.u[end] rtol = 1e-7
 
         @test s1.pu.Σ[1] isa PSDMatrix{<:Number,<:Matrix}
         @test s0.pu.Σ[1] isa PSDMatrix{<:Number,<:ProbNumDiffEq.IsometricKroneckerProduct}
@@ -68,14 +68,14 @@ end
 
     ref = solve(prob, RadauIIA5())
     sol = solve(prob, EK1(order=3))
-    @test sol[end] ≈ ref[end] rtol = 1e-8
+    @test sol.u[end] ≈ ref.u[end] rtol = 1e-8
 
     sol = solve(prob, EK1(order=3, initialization=ForwardDiffInit(3)))
-    @test sol[end] ≈ ref[end] rtol = 1e-8
+    @test sol.u[end] ≈ ref.u[end] rtol = 1e-8
 
     sol = solve(prob, EK1(order=3, initialization=ClassicSolverInit(RadauIIA5())))
-    @test sol[end] ≈ ref[end] rtol = 1e-8
+    @test sol.u[end] ≈ ref.u[end] rtol = 1e-8
 
     sol = solve(prob, EK1(order=3, initialization=SimpleInit()))
-    @test sol[end] ≈ ref[end] rtol = 1e-8
+    @test sol.u[end] ≈ ref.u[end] rtol = 1e-8
 end

test/smoothing.jl

@@ -33,8 +33,8 @@ end
     sol_smooth = solve(prob, EK0(order=q, smooth=true), adaptive=false, dt=dt)
 
     @test sol_nonsmooth.t ≈ sol_smooth.t
-    @test sol_nonsmooth[end] == sol_smooth[end]
-    @test sol_nonsmooth[end-1] != sol_smooth[end-1]
+    @test sol_nonsmooth.u[end] == sol_smooth.u[end]
+    @test sol_nonsmooth.u[end-1] != sol_smooth.u[end-1]
 
     plot(sol_smooth, label="smooth")
     plot!(sol_nonsmooth, label="nonsmooth")

test/stiff_problem.jl

@@ -6,5 +6,5 @@ using ODEProblemLibrary: prob_ode_vanderpol_stiff
 prob = prob_ode_vanderpol_stiff
 appxsol = solve(prob, RadauIIA5())
 sol = solve(prob, EK1(order=3))
-@test appxsol[end] ≈ sol[end] rtol = 5e-3
+@test appxsol.u[end] ≈ sol.u[end] rtol = 5e-3
 @test appxsol(0.5) ≈ sol(0.5).μ rtol = 5e-3