fix tests and most depwarns

Author: ChrisRackauckas

src/DiffEqDevTools.jl

@@ -17,13 +17,14 @@ import DiffEqBase: AbstractODEProblem, AbstractDDEProblem,
        ODERKTableau, AbstractTimeseriesSolution, ExplicitRKTableau,
        ImplicitRKTableau
 
+import LinearAlgebra: norm, I
 
 const TIMESERIES_ERRORS = Set([:l2,:l∞,:L2,:L∞])
 const DENSE_ERRORS = Set([:L2,:L∞])
 const WEAK_TIMESERIES_ERRORS = Set([:weak_l2,:weak_l∞])
 const WEAK_DENSE_ERRORS = Set([:weak_L2,:weak_L∞])
-const WEAK_ERRORS = union(Set([:weak_final]),WEAK_TIMESERIES_ERRORS,WEAK_DENSE_ERRORS)
-
+const WEAK_ERRORS = union(Set([:weak_final]),
+                          WEAK_TIMESERIES_ERRORS,WEAK_DENSE_ERRORS)
 
 parameterless_type(T::Type) = Base.typename(T).wrapper
 parameterless_type(x) = parameterless_type(typeof(x))
@@ -53,32 +54,48 @@ export constructEuler, constructKutta3, constructRK4, constructRK438Rule,
        constructLobattoIIIA4, constructLobattoIIIB2, constructLobattoIIIB4,
        constructLobattoIIIC2, constructLobattoIIIC4, constructLobattoIIICStar2,
        constructLobattoIIICStar4, constructLobattoIIID2, constructLobattoIIID4,
-       constructRadauIA3, constructRadauIA5, constructRadauIIA3, constructRadauIIA5,
+       constructRadauIA3, constructRadauIA5,
+       constructRadauIIA3, constructRadauIIA5,
        constructRalston, constructHeun, constructRKF5, constructBogakiShampine3,
        constructCashKarp, constructRKF8, constructDormandPrince8,
        constructMSRI1,constructFeagin10, constructFeagin12, constructFeagin14,
        constructDormandPrince8_64bit, constructRKF5, constructRungeFirst5,
-       constructCassity5, constructLawson5, constructLutherKonen5, constructLutherKonen52,
-       constructLutherKonen53, constructPapakostasPapaGeorgiou5, constructPapakostasPapaGeorgiou52,
+       constructCassity5, constructLawson5,
+       constructLutherKonen5, constructLutherKonen52,
+       constructLutherKonen53, constructPapakostasPapaGeorgiou5,
+       constructPapakostasPapaGeorgiou52,
        constructTsitouras5, constructBogakiShampine5, constructSharpSmart5,
-       constructButcher6, constructButcher7, constructDverk, constructClassicVerner6,
-       constructClassicVerner7, constructClassicVerner8, constructClassicVerner92,
-       constructVernerRobust7, constructEnrightVerner7, constructTanakaYamashitaStable7,
-       constructTanakaYamashitaEfficient7, constructSharpSmart7, constructSharpVerner7,
-       constructVernerEfficient7, constructCooperVerner8, constructCooperVerner82,
+       constructButcher6, constructButcher7,
+       constructDverk, constructClassicVerner6,
+       constructClassicVerner7, constructClassicVerner8,
+       constructClassicVerner92,
+       constructVernerRobust7, constructEnrightVerner7,
+       constructTanakaYamashitaStable7,
+       constructTanakaYamashitaEfficient7, constructSharpSmart7,
+       constructSharpVerner7,
+       constructVernerEfficient7, constructCooperVerner8,
+       constructCooperVerner82,
        constructTsitourasPapakostas8, constructdverk78, constructEnrightVerner8,
        constructCurtis8, constructVernerRobust9, constructVernerEfficient9,
-       constructSharp9, constructTsitouras9, constructTsitouras92,constructFeagin14Tableau,
-       constructFeagin12Tableau, constructOno12, constructCurtis10, constructOno10, constructFeagin10Tableau,
-       constructCurtis10, constructBaker10, constructHairer10, constructButcher63,
-       constructButcher6, constructButcher62, constructVerner6, constructDormandPrince6,
-       constructSharpVerner6, constructVerner9162, constructVerner916, constructVernerRobust6,
+       constructSharp9, constructTsitouras9,
+       constructTsitouras92,constructFeagin14Tableau,
+       constructFeagin12Tableau, constructOno12, constructCurtis10,
+       constructOno10, constructFeagin10Tableau,
+       constructCurtis10, constructBaker10,
+       constructHairer10, constructButcher63,
+       constructButcher6, constructButcher62,
+       constructVerner6, constructDormandPrince6,
+       constructSharpVerner6, constructVerner9162,
+       constructVerner916, constructVernerRobust6,
        constructVernerEfficient6, constructPapakostas6, constructLawson6,
        constructTsitourasPapakostas6, constructDormandLockyerMcCorriganPrince6,
-       constructTanakaKasugaYamashitaYazaki6D, constructTanakaKasugaYamashitaYazaki6C,
-       constructTanakaKasugaYamashitaYazaki6B, constructTanakaKasugaYamashitaYazaki6A,
+       constructTanakaKasugaYamashitaYazaki6D,
+       constructTanakaKasugaYamashitaYazaki6C,
+       constructTanakaKasugaYamashitaYazaki6B,
+       constructTanakaKasugaYamashitaYazaki6A,
        constructMikkawyEisa, constructChummund6, constructChummund62,
-       constructHuta62, constructHuta6, constructRKF4, constructVerner7, constructVerner8,
+       constructHuta62, constructHuta6, constructRKF4,
+       constructVerner7, constructVerner8,
        constructVerner9, constructVerner6, constructSSPRK22, constructSSPRK33,
        constructSSPRK43, constructSSPRK104
 

src/benchmark.jl

@@ -231,7 +231,7 @@ function WorkPrecisionSet(prob::Union{AbstractODEProblem,AbstractDDEProblem,
 end
 
 @def error_calculation begin
-  if !has_analytic(prob.f)
+  if !DiffEqBase.has_analytic(prob.f)
     t = prob.tspan[1]:test_dt:prob.tspan[2]
     brownian_values = cumsum([[zeros(size(prob.u0))];[sqrt(test_dt)*randn(size(prob.u0)) for i in 1:length(t)-1]])
     brownian_values2 = cumsum([[zeros(size(prob.u0))];[sqrt(test_dt)*randn(size(prob.u0)) for i in 1:length(t)-1]])
@@ -275,7 +275,7 @@ end
             timeseries_errors=timeseries_errors,
             dense_errors = dense_errors)
     end
-    has_analytic(prob.f) ? err_sol = sol : err_sol = appxtrue(sol,true_sol)
+    DiffEqBase.has_analytic(prob.f) ? err_sol = sol : err_sol = appxtrue(sol,true_sol)
     tmp_solutions[i,j,k] = err_sol
   end
 end
@@ -285,13 +285,13 @@ function WorkPrecisionSet(prob::AbstractRODEProblem,abstols,reltols,setups,test_
                           print_names=false,names=nothing,appxsol_setup=nothing,
                           error_estimate=:final,parallel_type = :none,kwargs...)
 
-  timeseries_errors = has_analytic(prob.f) && error_estimate ∈ TIMESERIES_ERRORS
+  timeseries_errors = DiffEqBase.has_analytic(prob.f) && error_estimate ∈ TIMESERIES_ERRORS
   weak_timeseries_errors = error_estimate ∈ WEAK_TIMESERIES_ERRORS
   weak_dense_errors = error_estimate ∈ WEAK_DENSE_ERRORS
-  dense_errors = has_analytic(prob.f) && error_estimate ∈ DENSE_ERRORS
+  dense_errors = DiffEqBase.has_analytic(prob.f) && error_estimate ∈ DENSE_ERRORS
   N = length(setups); M = length(abstols)
-  times = Array{Float64}(M,N)
-  tmp_solutions = Array{Any}(numruns_error,M,N)
+  times = Array{Float64}(undef,M,N)
+  tmp_solutions = Array{Any}(undef,numruns_error,M,N)
   if names == nothing
     names = [string(parameterless_type(setups[i][:alg])) for i=1:length(setups)]
   end
@@ -310,7 +310,7 @@ function WorkPrecisionSet(prob::AbstractRODEProblem,abstols,reltols,setups,test_
   analytical_solution_ends = [tmp_solutions[i,1,1].u_analytic[end] for i in 1:numruns_error]
   sample_error = 1.96std(norm.(analytical_solution_ends))/sqrt(numruns_error)
   _solutions_k = [[MonteCarloSolution(tmp_solutions[:,j,k],0.0,true) for j in 1:M] for k in 1:N]
-  solutions = [[calculate_monte_errors(sim;weak_timeseries_errors=weak_timeseries_errors,weak_dense_errors=weak_dense_errors) for sim in sol_k] for sol_k in _solutions_k]
+  solutions = [[DiffEqBase.calculate_monte_errors(sim;weak_timeseries_errors=weak_timeseries_errors,weak_dense_errors=weak_dense_errors) for sim in sol_k] for sol_k in _solutions_k]
   if error_estimate ∈ WEAK_ERRORS
     errors = [[solutions[j][i].weak_errors[error_estimate] for i in 1:M] for j in 1:N]
   else
@@ -364,7 +364,7 @@ function WorkPrecisionSet(prob::AbstractRODEProblem,abstols,reltols,setups,test_
 end
 
 @def sample_errors begin
-  if !has_analytic(prob.f)
+  if !DiffEqBase.has_analytic(prob.f)
     true_sol = solve(prob,appxsol_setup[:alg];kwargs...,appxsol_setup...,
                      save_everystep=false)
     analytical_solution_ends[i] = norm(true_sol.u[end])

src/convergence.jl

@@ -62,7 +62,7 @@ function analyticless_test_convergence(dts::AbstractArray,
                           timeseries_errors=save_everystep,adaptive=false,
                           weak_timeseries_errors=false,weak_dense_errors=false,kwargs...)
   _solutions = []
-  tmp_solutions = Array{Any}(numMonte,length(dts))
+  tmp_solutions = Array{Any}(undef,numMonte,length(dts))
   @progress for j in 1:numMonte
     t = prob.tspan[1]:test_dt:prob.tspan[2]
     brownian_values = cumsum([[zeros(size(prob.u0))];[sqrt(test_dt)*randn(size(prob.u0)) for i in 1:length(t)-1]])
@@ -136,5 +136,6 @@ Base.length(sim::ConvergenceSimulation) = sim.N
 Base.endof( sim::ConvergenceSimulation) = length(sim)
 Base.getindex(sim::ConvergenceSimulation,i::Int) = sim.solutions[i]
 Base.getindex(sim::ConvergenceSimulation,i::Int,I::Int...) = sim.solutions[i][I]
-
+Base.lastindex(sim::ConvergenceSimulation) = lastindex(sim.solutions)
+Base.firstindex(sim::ConvergenceSimulation) = firstindex(sim.solutions)
 Base.length(sim::ConvergenceSetup) = sim.probs

test/analyticless_stochastic_wp.jl

@@ -1,7 +1,9 @@
 using StochasticDiffEq, DiffEqDevTools, DiffEqProblemLibrary, Test
+using DiffEqProblemLibrary.SDEProblemLibrary: importsdeproblems; importsdeproblems()
+import DiffEqProblemLibrary.SDEProblemLibrary: prob_sde_additivesystem
 
 prob = prob_sde_additivesystem
-prob = SDEProblem(prob.f,prob.g,prob.u0,(0.0,1.0))
+prob = SDEProblem(prob.f,prob.g,prob.u0,(0.0,1.0),prob.p)
 
 reltols = 1.0./10.0.^(1:5)
 abstols = reltols#[0.0 for i in eachindex(reltols)]
@@ -22,7 +24,7 @@ se = get_sample_errors(prob,numruns=[5;10;25;50])
 
 println("Now weak error")
 
-prob2 = SDEProblem((du,u,p,t)->prob.f(du,u,p,t),prob.g,prob.u0,(0.0,0.1))
+prob2 = SDEProblem((du,u,p,t)->prob.f(du,u,p,t),prob.g,prob.u0,(0.0,0.1),prob.p)
 test_dt = 1/10^5
 appxsol_setup = Dict(:alg=>SRIW1(),:abstol=>1e-5,:reltol=>1e-5)
 wp = WorkPrecisionSet(prob2,abstols,reltols,setups,test_dt;

test/benchmark_tests.jl

@@ -65,17 +65,7 @@ wp_set[end]
 @test (minimum(diff(wp_set[2].errors).==0)) # The errors for a fixed timestep method should be constant
 
 
-# 2D Linear ODE
-function f_2dlin(du,u,p,t)
-  for i in 1:length(u)
-    du[i] = 1.01*u[i]
-  end
-end
-function (::typeof(f_2dlin))(::Type{Val{:analytic}},u₀,p,t)
-  u₀*exp(1.01*t)
-end
-tspan = (0.0,10.0)
-prob = ODEProblem(f_2dlin,rand(100,100),tspan)
+prob = prob_ode_2Dlinear
 
 abstols = 1 ./10 .^(3:7)
 reltols = 1 ./10 .^(0:4)