fix tests

SciML · Jul 25, 2018 · 068a508 · 068a508
1 parent 6401d0e
commit 068a508
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Showing 5 changed files with 80 additions and 55 deletions.
diff --git a/src/perform_step/iif.jl b/src/perform_step/iif.jl
@@ -7,7 +7,7 @@ mutable struct RHS_IIF1M_Scalar{F,CType,tType,P} <: Function
 end
 
 function (f::RHS_IIF1M_Scalar)(resid,u)
-  resid[1] .= u[1] - f.tmp - f.dt*f.f[2](u[1],f.p,f.t+f.dt)[1]
+  resid[1] = u[1] - f.tmp - f.dt*f.f.f2(u[1],f.p,f.t+f.dt)[1]
 end
 
 mutable struct RHS_IIF2M_Scalar{F,CType,tType,P} <: Function
@@ -19,7 +19,7 @@ mutable struct RHS_IIF2M_Scalar{F,CType,tType,P} <: Function
 end
 
 function (f::RHS_IIF2M_Scalar)(resid,u)
-  resid[1] = u[1] - f.tmp - 0.5f.dt*f.f[2](u[1],f.p,f.t+f.dt)[1]
+  resid[1] = u[1] - f.tmp - 0.5f.dt*f.f.f2(u[1],f.p,f.t+f.dt)[1]
 end
 
 @muladd function initialize!(integrator,cache::Union{IIF1MConstantCache,IIF2MConstantCache,IIF1MilConstantCache},f=integrator.f)
@@ -30,13 +30,13 @@ end
   @unpack t,dt,uprev,u,W,p = integrator
   @unpack uhold,rhs,nl_rhs = cache
   alg = unwrap_alg(integrator, true)
-  A = integrator.f[1](u,p,t)
+  A = integrator.f.f1(u,p,t)
   if typeof(cache) <: IIF1MilConstantCache
     error("Milstein correction does not work.")
   elseif typeof(cache) <: IIF1MConstantCache
-    tmp = expm(A*dt)*(uprev + integrator.g(uprev,p,t)*W.dW)
+    tmp = exp(A*dt)*(uprev + integrator.g(uprev,p,t)*W.dW)
   elseif typeof(cache) <: IIF2MConstantCache
-    tmp = expm(A*dt)*(uprev + 0.5dt*integrator.f[2](uprev,p,t) + integrator.g(uprev,p,t)*W.dW)
+    tmp = exp(A*dt)*(uprev + 0.5dt*integrator.f.f2(uprev,p,t) + integrator.g(uprev,p,t)*W.dW)
   end
 
   if integrator.iter > 1 && !integrator.u_modified
@@ -64,7 +64,7 @@ end
 function (f::RHS_IIF1)(resid,u)
   _du = get_du(f.dual_cache, eltype(u))
   du = reinterpret(eltype(u),_du)
-  f.f[2](du,reshape(u,f.sizeu),f.p,f.t+f.dt)
+  f.f.f2(du,reshape(u,f.sizeu),f.p,f.t+f.dt)
   @. resid = u - f.tmp - f.dt*du
 end
 
@@ -80,7 +80,7 @@ end
 function (f::RHS_IIF2)(resid,u)
   _du = get_du(f.dual_cache, eltype(u))
   du = reinterpret(eltype(u),_du)
-  f.f[2](du,reshape(u,f.sizeu),f.p,f.t+f.dt)
+  f.f.f2(du,reshape(u,f.sizeu),f.p,f.t+f.dt)
   @. resid = u - f.tmp - 0.5f.dt*du
 end
 
@@ -102,12 +102,12 @@ end
   rtmp3 .+= uprev
 
   if typeof(cache) <: IIF2MCache
-    integrator.f[2](rtmp1,uprev,p,t)
+    integrator.f.f2(rtmp1,uprev,p,t)
     @. rtmp3 = @muladd 0.5dt*rtmp1 + rtmp3
   end
 
-  A = integrator.f[1](rtmp1,uprev,p,t)
-  M = expm(A*dt)
+  A = integrator.f.f1(rtmp1,uprev,p,t)
+  M = exp(A*dt)
   mul!(tmp,M,rtmp3)
 
   if integrator.iter > 1 && !integrator.u_modified
@@ -134,8 +134,8 @@ end
   dW = W.dW; sqdt = integrator.sqdt
   f = integrator.f; g = integrator.g
 
-  A = integrator.f[1](t,uprev,rtmp1)
-  M = expm(A*dt)
+  A = integrator.f.f1(t,uprev,rtmp1)
+  M = exp(A*dt)
 
   uidx = eachindex(u)
   integrator.g(rtmp2,uprev,p,t)
@@ -168,7 +168,7 @@ end
   end
 
   if typeof(cache) <: IIF2MCache
-    integrator.f[2](t,uprev,rtmp1)
+    integrator.f.f2(t,uprev,rtmp1)
     @. rtmp1 = @muladd 0.5dt*rtmp1 + uprev + rtmp3
     mul!(tmp,M,rtmp1)
   elseif !(typeof(cache) <: IIF1MilCache)

diff --git a/test/iif_methods.jl b/test/iif_methods.jl
@@ -1,26 +1,30 @@
 using DiffEqBase, StochasticDiffEq, DiffEqNoiseProcess,
-      Test, DiffEqDevTools
+      Test, DiffEqDevTools, Random, LinearAlgebra
 const μ = 1.01
 const σ_const = 0.87
 
 f(u,p,t) = μ * u + μ * u
 f1_μ(u,p,t) = μ
-(::typeof(f1_μ))(::Type{Val{:analytic}},u0,p,t,W) = u0.*exp.((2μ-(σ_const^2)/2)t+σ_const*W)
+f1_μ_analytic(u0,p,t,W) = u0.*exp.((2μ-(σ_const^2)/2)t+σ_const*W)
 f2(u,p,t) = μ * u
 σ(u,p,t) = σ_const*u
 no_noise(u,p,t) = 0.0
 f1_no_noise(u,p,t) = μ
-(::typeof(f1_no_noise))(::Type{Val{:analytic}},u0,p,t,W) = u0.*exp.(2μ*t)
+f1_no_noise_analytic(u0,p,t,W) = u0.*exp.(2μ*t)
 
-prob = SplitSDEProblem{false}(f1_μ,f2,σ,1/2,(0.0,1.0))
-no_noise_prob = SplictSDEProblem{false}(f1_no_noise,f2,no_noise,1/2,(0.0,1.0))
+ff1_μ = SplitSDEFunction(f1_μ,f2,σ,analytic=f1_μ_analytic)
+prob = SDEProblem(ff1_μ,σ,1/2,(0.0,1.0))
+ff1_no_noise = SplitSDEFunction(f1_no_noise,f2,no_noise,analytic=f1_no_noise_analytic)
+no_noise_prob = SDEProblem(ff1_no_noise,no_noise,1/2,(0.0,1.0))
 
 sol = solve(prob,IIF1M(),dt=1/10)
 
 prob2 = SDEProblem{false}(f,σ,1/2,(0.0,1.0),noise = NoiseWrapper(sol.W))
 
 sol2 = solve(prob2,EM(),dt=1/10)
 
+sol = solve(no_noise_prob,IIF1M(),dt=1/10)
+
 srand(100)
 dts = (1/2) .^ (7:-1:4) #14->7 good plot
 println("IIF scalar")
@@ -29,7 +33,7 @@ sim  = test_convergence(dts,prob,IIF1M(),numMonte=Int(1e2))
 sim  = test_convergence(dts,no_noise_prob,IIF1M(),numMonte=Int(2e1))
 @test abs(sim.𝒪est[:l2]-1.0) < 0.2 # closer to 1 at this part
 
-dts = (1/2) .^(7:-1:4) #14->7 good plot
+dts = (1/2) .^ (7:-1:4) #14->7 good plot
 println("IIF no noise scalar")
 srand(100)
 sim  = test_convergence(dts,prob,IIF2M(),numMonte=Int(1e2))
@@ -58,35 +62,37 @@ function σ(du,u,p,t)
   mul!(@view(du[:,2]),B,u)
 end
 
-function (::typeof(f))(::Type{Val{:analytic}},u0,p,t,W)
+function f_analytic(u0,p,t,W)
  tmp = (A+1.01I-(B^2))*t + B*sum(W)
  exp(tmp)*u0
 end
 
 f1_A(du,u,p,t) = A
-function (::typeof(f1_A))(::Type{Val{:analytic}},u0,p,t,W)
+function f1_A_analytic(u0,p,t,W)
  tmp = (A+1.01I-(B^2))*t + B*sum(W)
  exp(tmp)*u0
 end
 f2(du,u,p,t) = du .= μ .* u
 
-prob = SDEProblem((f1_A,f2),σ,u0,(0.0,1.0),noise_rate_prototype=rand(2,2))
+ff1_A = SplitSDEFunction(f1_A,f2,σ,analytic=f1_A_analytic)
+prob = SDEProblem(ff1_A,σ,u0,(0.0,1.0),noise_rate_prototype=rand(2,2))
 
 f1_no_noise(du,u,p,t) = A
 f2(du,u,p,t) = (du .= μ .* u)
 function σ22(du,u,p,t)
   du .= 0
 end
-function (::typeof(f1_no_noise))(::Type{Val{:analytic}},u0,p,t,W)
+function f1_no_noise_analytic(u0,p,t,W)
  tmp = (A+1.01I)*t
  exp(tmp)*u0
 end
-prob_no_noise = SDEProblem((f1_no_noise,f2),σ22,u0,(0.0,1.0),noise_rate_prototype=rand(2,2))
+ff1_A = SplitSDEFunction(f1_no_noise,f2,σ22,analytic=f1_no_noise_analytic)
+prob_no_noise = SDEProblem(ff1_A,σ22,u0,(0.0,1.0),noise_rate_prototype=rand(2,2))
 
 
 sol = solve(prob,IIF1M(),dt=1/10)
 
-dts = 1./2.^(8:-1:4) #14->7 good plot
+dts = (1/2) .^ (8:-1:4) #14->7 good plot
 
 srand(250)
 println("IIF")

diff --git a/test/runtests.jl b/test/runtests.jl
@@ -48,7 +48,7 @@ is_APPVEYOR = ( Sys.iswindows() && haskey(ENV,"APPVEYOR") )
     @time @testset "Non-diagonal SDE Tests" begin include("nondiagonal_tests.jl") end
     @time @testset "Rossler Order Tests" begin include("sde/sde_rosslerorder_tests.jl") end
     @time @testset "Convergence Tests" begin include("sde/sde_convergence_tests.jl") end
-    #@time @testset "Split Tests" begin include("split_tests.jl") end
+    @time @testset "Split Tests" begin include("split_tests.jl") end
     @time @testset "Stratonovich Convergence Tests" begin include("stratonovich_convergence_tests.jl") end
     @time @testset "IIF Convergence Tests" begin include("iif_methods.jl") end
     LONGER_TESTS && @time @testset "Weak Convergence Tests" begin include("weak_convergence.jl") end

diff --git a/test/split_tests.jl b/test/split_tests.jl
@@ -4,7 +4,7 @@ f(u,p,t) = (1.01) * u
 f1(u,p,t) = (1.01)/2 * u
 f2(u,p,t) = (1.01)/2 * u
 σ(u,p,t) = 0.87u
-#(::typeof(f))(::Type{Val{:analytic}},u0,p,t,W) = u0.*exp.(0.63155t+0.87W)
+f_split_analytic(u0,p,t,W) = u0.*exp.(0.63155t+0.87W)
 
 prob = SplitSDEProblem{false}(f1,f2,σ,1/2,(0.0,1.0))
 sol = solve(prob,SplitEM(),dt=1/10,save_noise=true)
@@ -15,7 +15,9 @@ sol2 = solve(prob,EM(),dt=1/10)
 @test sol[:] ≈ sol2[:]
 
 u0 = rand(4)
-prob = SplitSDEProblem{false}(f1,f2,σ,u0,(0.0,1.0))
+
+ff_split = SplitSDEFunction(f1,f2,σ,analytic=f_split_analytic)
+prob = SDEProblem(ff_split,σ,u0,(0.0,1.0))
 
 sol = solve(prob,SplitEM(),dt=1/10,save_noise=true)
 
@@ -34,13 +36,15 @@ sol2 = solve(prob,EM(),dt=1/10)
 ff1 = (u,p,t) -> β./sqrt.(1+t) - u./(2*(1+t))
 ff2 = (u,p,t) -> 0.0
 σ2 = (u,p,t) -> α*β./sqrt.(1+t)
-prob = SplitSDEProblem(ff1,ff2,σ2,1.,(0.0,1.0))
-(::typeof(prob.f))(::Type{Val{:analytic}},u0,p,t,W) = u0./sqrt.(1+t) + β*(t+α*W)./sqrt.(1+t)
+ff1_analytic(u0,p,t,W) = @. u0/sqrt(1+t) + β*(t+α*W)/sqrt(1+t)
+f_ff1 = SplitSDEFunction(ff1,ff2,σ2,analytic=ff1_analytic)
+prob = SDEProblem(f_ff1,σ2,1.,(0.0,1.0))
+
 
 sol = solve(prob,EM(),dt=1/10)
 sol2 = solve(prob,SKenCarp(),dt=1/10)
 
-dts = 1./2.^(10:-1:2) #14->7 good plot
+dts = (1/2) .^(10:-1:2) #14->7 good plot
 sim10 = test_convergence(dts,prob,SKenCarp(),numMonte=Int(1e1))
 @test abs(sim10.𝒪est[:final]-2) < 0.3
 
@@ -51,8 +55,8 @@ sim10 = test_convergence(dts,prob,SKenCarp(),numMonte=Int(1e1))
 ff1 = (u,p,t) -> 0.0
 ff2 = (u,p,t) -> β./sqrt.(1+t) - u./(2*(1+t))
 σ2 = (u,p,t) -> α*β./sqrt.(1+t)
-prob = SplitSDEProblem(ff1,ff2,σ2,1.,(0.0,1.0))
-(::typeof(prob.f))(::Type{Val{:analytic}},u0,p,t,W) = u0./sqrt.(1+t) + β*(t+α*W)./sqrt.(1+t)
+f_ff1 = SplitSDEFunction(ff1,ff2,σ2,analytic=ff1_analytic)
+prob = SplitSDEProblem(f_ff1,σ2,1.,(0.0,1.0))
 
 sol = solve(prob,EM(),dt=1/10)
 sol2 = solve(prob,SKenCarp(),dt=1/10,seed=1)
@@ -68,8 +72,8 @@ sim10 = test_convergence(dts,prob,SKenCarp(),numMonte=Int(1e1))
 ff1 = (u,p,t) -> β./sqrt.(1+t)
 ff2 = (u,p,t) -> - u./(2*(1+t))
 σ2 = (u,p,t) -> α*β./sqrt.(1+t)
-prob = SplitSDEProblem(ff1,ff2,σ2,1.,(0.0,1.0))
-(::typeof(prob.f))(::Type{Val{:analytic}},u0,p,t,W) = u0./sqrt.(1+t) + β*(t+α*W)./sqrt.(1+t)
+f_ff1 = SplitSDEFunction(ff1,ff2,σ2,analytic=ff1_analytic)
+prob = SplitSDEProblem(f_ff1,σ2,1.,(0.0,1.0))
 
 sol = solve(prob,EM(),dt=1/10)
 sol2 = solve(prob,SKenCarp(),dt=1/10)
@@ -87,8 +91,8 @@ sim10 = test_convergence(dts,prob,SKenCarp(),numMonte=Int(1e1))
 ff1 = (du,u,p,t) -> du .= β./sqrt.(1+t) - u./(2*(1+t))
 ff2 = (du,u,p,t) -> du .= 0.0
 σ2 = (du,u,p,t) -> du .= α*β./sqrt.(1+t)
-prob = SplitSDEProblem(ff1,ff2,σ2,[1.],(0.0,1.0))
-(::typeof(prob.f))(::Type{Val{:analytic}},u0,p,t,W) = u0./sqrt.(1+t) + β*(t+α*W)./sqrt.(1+t)
+f_ff1 = SplitSDEFunction(ff1,ff2,σ2,analytic=ff1_analytic)
+prob = SplitSDEProblem(f_ff1,σ2,[1.],(0.0,1.0))
 
 sol = solve(prob,EM(),dt=1/10)
 sol2 = solve(prob,SKenCarp(),dt=1/10)
@@ -104,8 +108,8 @@ sim10 = test_convergence(dts,prob,SKenCarp(),numMonte=Int(1e1))
 ff1 = (du,u,p,t) -> du .= 0.0
 ff2 = (du,u,p,t) -> du .= β./sqrt.(1+t) - u./(2*(1+t))
 σ2 = (du,u,p,t) -> du .= α*β./sqrt.(1+t)
-prob = SplitSDEProblem(ff1,ff2,σ2,[1.],(0.0,1.0))
-(::typeof(prob.f))(::Type{Val{:analytic}},u0,p,t,W) = u0./sqrt.(1+t) + β*(t+α*W)./sqrt.(1+t)
+f_ff1 = SplitSDEFunction(ff1,ff2,σ2,analytic=ff1_analytic)
+prob = SplitSDEProblem(f_ff1,σ2,[1.],(0.0,1.0))
 
 sol = solve(prob,EM(),dt=1/10)
 sol2 = solve(prob,SKenCarp(),dt=1/10)
@@ -121,8 +125,8 @@ sim10 = test_convergence(dts,prob,SKenCarp(),numMonte=Int(1e1))
 ff1 = (du,u,p,t) -> du .= β./sqrt.(1+t)
 ff2 = (du,u,p,t) -> du .= - u./(2*(1+t))
 σ2 = (du,u,p,t) -> du .= α*β./sqrt.(1+t)
-prob = SplitSDEProblem(ff1,ff2,σ2,[1.],(0.0,1.0))
-(::typeof(prob.f))(::Type{Val{:analytic}},u0,p,t,W) = u0./sqrt.(1+t) + β*(t+α*W)./sqrt.(1+t)
+f_ff1 = SplitSDEFunction(ff1,ff2,σ2,analytic=ff1_analytic)
+prob = SplitSDEProblem(f_ff1,σ2,[1.],(0.0,1.0))
 
 sol = solve(prob,EM(),dt=1/10)
 sol2 = solve(prob,SKenCarp(),dt=1/10)

diff --git a/test/weak_convergence.jl b/test/weak_convergence.jl
@@ -1,3 +1,4 @@
+using Distributed
 @everywhere using StochasticDiffEq, DiffEqDevTools, Test
 @everywhere using DiffEqProblemLibrary.SDEProblemLibrary: importsdeproblems
 @everywhere importsdeproblems()
@@ -6,65 +7,79 @@ srand(100)
 dts = 1./2.^(10:-1:2) #14->7 good plot
 
 prob = prob_sde_linear
-sim  = test_convergence(dts,prob,EM(),numMonte=Int(1e4),weak_timeseries_errors=true,weak_dense_errors=true)
+sim  = test_convergence(dts,prob,EM(),numMonte=Int(1e4),
+                        weak_timeseries_errors=true,weak_dense_errors=true)
 @test abs(sim.𝒪est[:weak_final]-1) < 0.3
 @test abs(sim.𝒪est[:weak_l2]-1) < 0.3
 @test abs(sim.𝒪est[:weak_l∞]-1) < 0.3
 @test abs(sim.𝒪est[:weak_L2]-1) < 0.3
 @test abs(sim.𝒪est[:weak_L∞]-1) < 0.3
-sim2 = test_convergence(dts,prob,RKMil(),numMonte=Int(1e4),weak_timeseries_errors=true,dense_errors=true)
+sim2 = test_convergence(dts,prob,RKMil(),numMonte=Int(1e4),
+                        weak_timeseries_errors=true,dense_errors=true)
 @test abs(sim2.𝒪est[:weak_final]-1) < 0.3
 @test abs(sim2.𝒪est[:weak_l2]-1) < 0.3
 @test abs(sim2.𝒪est[:weak_l∞]-1) < 0.3
-sim3 = test_convergence(dts,prob,SRI(),numMonte=Int(1e4),weak_timeseries_errors=true)
+sim3 = test_convergence(dts,prob,SRI(),numMonte=Int(1e4),
+                        weak_timeseries_errors=true)
 @test abs(sim3.𝒪est[:weak_final]-2) < 0.3
 @test abs(sim3.𝒪est[:weak_l2]-2) < 0.3
 @test abs(sim3.𝒪est[:weak_l∞]-2) < 0.3
-sim4 = test_convergence(dts,prob,SRIW1(),numMonte=Int(1e4),weak_timeseries_errors=true)
+sim4 = test_convergence(dts,prob,SRIW1(),numMonte=Int(1e4),
+                        weak_timeseries_errors=true)
 @test abs(sim4.𝒪est[:weak_final]-2) < 0.3
 @test abs(sim4.𝒪est[:weak_l2]-2) < 0.3
 @test abs(sim4.𝒪est[:weak_l∞]-2) < 0.3
 
 prob = prob_sde_2Dlinear
-sim  = test_convergence(dts,prob,EM(),numMonte=Int(1e4),weak_timeseries_errors=true)
+sim  = test_convergence(dts,prob,EM(),numMonte=Int(1e4),
+                        weak_timeseries_errors=true)
 @test abs(sim.𝒪est[:weak_final]-1) < 0.3
 @test abs(sim.𝒪est[:weak_l2]-1) < 0.3
 @test abs(sim.𝒪est[:weak_l∞]-1) < 0.3
-sim2 = test_convergence(dts,prob,RKMil(),numMonte=Int(1e4),weak_timeseries_errors=true)
+sim2 = test_convergence(dts,prob,RKMil(),numMonte=Int(1e4),
+                        weak_timeseries_errors=true)
 @test abs(sim2.𝒪est[:weak_final]-1) < 0.3
 @test abs(sim2.𝒪est[:weak_l2]-1) < 0.3
 @test abs(sim2.𝒪est[:weak_l∞]-1) < 0.3
-sim3 = test_convergence(dts,prob,SRI(),numMonte=Int(1e4),weak_timeseries_errors=true)
+sim3 = test_convergence(dts,prob,SRI(),numMonte=Int(1e4),
+                        weak_timeseries_errors=true)
 @test abs(sim3.𝒪est[:weak_final]-2) < 0.3
 @test abs(sim3.𝒪est[:weak_l2]-2) < 0.3
 @test abs(sim3.𝒪est[:weak_l∞]-2) < 0.3
-sim4 = test_convergence(dts,prob,SRIW1(),numMonte=Int(1e4),weak_timeseries_errors=true)
+sim4 = test_convergence(dts,prob,SRIW1(),numMonte=Int(1e4),
+                        weak_timeseries_errors=true)
 @test abs(sim4.𝒪est[:weak_final]-2) < 0.3
 @test abs(sim4.𝒪est[:weak_l2]-2) < 0.3
 @test abs(sim4.𝒪est[:weak_l∞]-2) < 0.35
 
 prob = prob_sde_additive
-sim  = test_convergence(dts,prob,EM(),numMonte=Int(1e4),weak_timeseries_errors=true)
+sim  = test_convergence(dts,prob,EM(),numMonte=Int(1e4),
+                        weak_timeseries_errors=true)
 @test abs(sim.𝒪est[:weak_final]-1) < 0.3
 @test abs(sim.𝒪est[:weak_l2]-1) < 0.3
 @test abs(sim.𝒪est[:weak_l∞]-1) < 0.3
-sim2 = test_convergence(dts,prob,RKMil(),numMonte=Int(1e4),weak_timeseries_errors=true)
+sim2 = test_convergence(dts,prob,RKMil(),numMonte=Int(1e4),
+                        weak_timeseries_errors=true)
 @test abs(sim2.𝒪est[:weak_final]-1) < 0.3
 @test abs(sim2.𝒪est[:weak_l2]-1) < 0.3
 @test abs(sim2.𝒪est[:weak_l∞]-1) < 0.3
-sim3 = test_convergence(dts,prob,SRI(),numMonte=Int(1e4),weak_timeseries_errors=true)
+sim3 = test_convergence(dts,prob,SRI(),numMonte=Int(1e4),
+                        weak_timeseries_errors=true)
 @test abs(sim3.𝒪est[:weak_final]-2) < 0.3
 @test abs(sim3.𝒪est[:weak_l2]-2) < 0.3
 @test abs(sim3.𝒪est[:weak_l∞]-2) < 0.3
-sim4 = test_convergence(dts,prob,SRIW1(),numMonte=Int(1e4),weak_timeseries_errors=true)
+sim4 = test_convergence(dts,prob,SRIW1(),numMonte=Int(1e4),
+                        weak_timeseries_errors=true)
 @test abs(sim4.𝒪est[:weak_final]-2) < 0.3
 @test abs(sim4.𝒪est[:weak_l2]-2) < 0.3
 @test abs(sim4.𝒪est[:weak_l∞]-2) < 0.35
-sim5 = test_convergence(dts,prob,SRA(),numMonte=Int(1e4),weak_timeseries_errors=true)
+sim5 = test_convergence(dts,prob,SRA(),numMonte=Int(1e4),
+                        weak_timeseries_errors=true)
 @test abs(sim5.𝒪est[:weak_final]-2) < 0.3
 @test abs(sim5.𝒪est[:weak_l2]-2) < 0.3
 @test abs(sim5.𝒪est[:weak_l∞]-2) < 0.3
-sim6 = test_convergence(dts,prob,SRA1(),numMonte=Int(1e4),weak_timeseries_errors=true)
+sim6 = test_convergence(dts,prob,SRA1(),numMonte=Int(1e4),
+                        weak_timeseries_errors=true)
 @test abs(sim6.𝒪est[:weak_final]-2) < 0.3
 @test abs(sim6.𝒪est[:weak_l2]-2) < 0.3
 @test abs(sim6.𝒪est[:weak_l∞]-2) < 0.3