Merge pull request #11 from JuliaDiffEq/myb/v0.7

0.7 compatibility

Author: ChrisRackauckas

src/DiffEqDevTools.jl

@@ -5,14 +5,17 @@ module DiffEqDevTools
 using DiffEqBase, RecipesBase, RecursiveArrayTools, DiffEqPDEBase,
       DiffEqMonteCarlo, DiffEqNoiseProcess, Juno
 
+using LinearAlgebra
+
 using Statistics
 
 import Base: length
 
 import DiffEqBase: AbstractODEProblem, AbstractDDEProblem,
        AbstractODESolution, AbstractRODEProblem, AbstractSDEProblem,
        AbstractDAEProblem, @def, ConvergenceSetup, DEAlgorithm,
-       ODERKTableau, AbstractTimeseriesSolution, ExplicitRKTableau
+       ODERKTableau, AbstractTimeseriesSolution, ExplicitRKTableau,
+       ImplicitRKTableau
 
 
 const TIMESERIES_ERRORS = Set([:l2,:l∞,:L2,:L∞])

src/benchmark.jl

@@ -1,6 +1,6 @@
 ## Shootouts
 
-type Shootout
+mutable struct Shootout
   setups::Vector{Dict{Symbol,Any}}
   times#::Vector{Float64}
   errors#::Vector{uType}
@@ -13,7 +13,7 @@ type Shootout
   winner::String
 end
 
-type ShootoutSet
+mutable struct ShootoutSet
   shootouts::Vector{Shootout}
   probs#::Vector{DEProblem}
   probaux#::Vector{Dict{Symbol,Any}}
@@ -115,7 +115,7 @@ Base.show(io::IO, set::ShootoutSet) = print(io,"ShootoutSet of $(set.N) shootout
 
 ## WorkPrecisions
 
-type WorkPrecision
+mutable struct WorkPrecision
   prob
   abstols
   reltols
@@ -125,7 +125,7 @@ type WorkPrecision
   N::Int
 end
 
-type WorkPrecisionSet
+mutable struct WorkPrecisionSet
   wps::Vector{WorkPrecision}
   N::Int
   abstols

src/convergence.jl

@@ -1,4 +1,4 @@
-type ConvergenceSimulation{SolType}
+mutable struct ConvergenceSimulation{SolType}
   solutions::Array{SolType}
   errors
   N

src/tableau_info.jl

@@ -1,4 +1,4 @@
-using NLsolve
+using NLsolve, LinearAlgebra
 """
 `Base.length(tab::ODERKTableau)`
 
@@ -15,7 +15,7 @@ Calculates the stability function from the tableau at `z`. Stable if <1.
 r(z) = \\frac{\\det(I-zA+zeb^T)}{\\det(I-zA)}
 ```
 """
-stability_region(z,tab::ODERKTableau) = det(eye(tab.stages)- z*tab.A + z*ones(tab.stages)*tab.α')/det(eye(tab.stages)-z*tab.A)
+stability_region(z,tab::ODERKTableau) = det(Matrix{Float64}(I,tab.stages,tab.stages)- z*tab.A + z*ones(tab.stages)*tab.α')/det(Matrix{Float64}(I,tab.stages,tab.stages)-z*tab.A)
 
 """
 `stability_region(tab::ODERKTableau; initial_guess=-3.0)`

src/test_solution.jl

@@ -2,7 +2,7 @@
 TestSolution
 
 """
-type TestSolution{T,N,hasinterp,tType,uType,iType} <: AbstractTimeseriesSolution{T,N}
+mutable struct TestSolution{T,N,hasinterp,tType,uType,iType} <: AbstractTimeseriesSolution{T,N}
   t::tType
   u::uType
   interp::iType
@@ -13,15 +13,15 @@ end
 function TestSolution(t,u)
   T = eltype(eltype(u))
   N = length((size(u[1])..., length(u)))
-  TestSolution{T,N,false,typeof(t),typeof(u),Void}(t,u,nothing,false,:Success)
+  TestSolution{T,N,false,typeof(t),typeof(u),Nothing}(t,u,nothing,false,:Success)
 end
 function TestSolution(t,u,interp)
   T = eltype(eltype(u))
   N = length((size(u[1])..., length(u)))
   TestSolution{T,N,true,typeof(t),typeof(u),typeof(interp)}(t,u,interp,true,:Success)
 end
-TestSolution(interp::DESolution) = TestSolution{Void,0,true,Void,Void,typeof(interp)}(nothing,nothing,interp,true,:Success)
-hasinterp{T,N,hi,tType,uType,iType}(::TestSolution{T,N,hi,tType,uType,iType}) = hi
+TestSolution(interp::DESolution) = TestSolution{Nothing,0,true,Nothing,Nothing,typeof(interp)}(nothing,nothing,interp,true,:Success)
+hasinterp(::TestSolution{T,N,hi,tType,uType,iType}) where {T,N,hi,tType,uType,iType} = hi
 """
 `appxtrue(sol::AbstractODESolution,sol2::TestSolution)`
 
@@ -41,7 +41,7 @@ function appxtrue(sol::AbstractODESolution,sol2::TestSolution)
     timeseries_analytic = _sol(sol.t)
     errors[:l∞] = maximum(vecvecapply((x)->abs.(x),sol-timeseries_analytic))
     errors[:l2] = sqrt(recursive_mean(vecvecapply((x)->float(x).^2,sol-timeseries_analytic)))
-    densetimes = collect(linspace(sol.t[1],sol.t[end],100))
+    densetimes = collect(range(sol.t[1],stop=sol.t[end],length=100))
     interp_u = sol(densetimes)
     interp_analytic = _sol(densetimes)
     interp_errors = Dict(:L∞=>maximum(vecvecapply((x)->abs.(x),interp_u-interp_analytic)),
@@ -54,7 +54,7 @@ function appxtrue(sol::AbstractODESolution,sol2::TestSolution)
       errors[:l2] = sqrt(recursive_mean(vecvecapply((x)->float(x).^2,sol-timeseries_analytic)))
     end
   end
-  build_solution(sol,timeseries_analytic,errors)
+  DiffEqBase.build_solution(sol,timeseries_analytic,errors)
 end
 
 """
@@ -71,7 +71,7 @@ function appxtrue(sol::AbstractODESolution,sol2::AbstractODESolution;timeseries_
     errors[:l∞] = maximum(vecvecapply((x)->abs.(x),sol-timeseries_analytic))
     errors[:l2] = sqrt(recursive_mean(vecvecapply((x)->float(x).^2,sol-timeseries_analytic)))
     if dense_errors
-      densetimes = collect(linspace(sol.t[1],sol.t[end],100))
+      densetimes = collect(range(sol.t[1],stop=sol.t[end],length=100))
       interp_u = sol(densetimes)
       interp_analytic = sol2(densetimes)
       interp_errors = Dict(:L∞=>maximum(vecvecapply((x)->abs.(x),interp_u-interp_analytic)),
@@ -85,7 +85,7 @@ function appxtrue(sol::AbstractODESolution,sol2::AbstractODESolution;timeseries_
       errors[:l2] = sqrt(recursive_mean(vecvecapply((x)->float(x).^2,sol-timeseries_analytic)))
     end
   end
-  build_solution(sol,timeseries_analytic,errors)
+  DiffEqBase.build_solution(sol,timeseries_analytic,errors)
 end
 
 function appxtrue(sim::MonteCarloSolution,appx_setup;kwargs...)

test/analyticless_convergence_tests.jl

@@ -1,4 +1,4 @@
-using OrdinaryDiffEq, ParameterizedFunctions, Base.Test
+using OrdinaryDiffEq, ParameterizedFunctions, Test, Random
 
 f = @ode_def LotkaVolterra begin
   dx = 1.5x - x*y

test/analyticless_stochastic_wp.jl

@@ -1,4 +1,4 @@
-using StochasticDiffEq, DiffEqDevTools, DiffEqProblemLibrary, Base.Test
+using StochasticDiffEq, DiffEqDevTools, DiffEqProblemLibrary, Test
 
 prob = prob_sde_additivesystem
 prob = SDEProblem(prob.f,prob.g,prob.u0,(0.0,1.0))

test/benchmark_tests.jl

@@ -1,18 +1,20 @@
-using OrdinaryDiffEq, DelayDiffEq, DiffEqDevTools, DiffEqProblemLibrary, DiffEqBase, Base.Test
+using OrdinaryDiffEq, DelayDiffEq, DiffEqDevTools, DiffEqBase, Test
+using DiffEqProblemLibrary.ODEProblemLibrary: importodeproblems; importodeproblems()
+import DiffEqProblemLibrary.ODEProblemLibrary: prob_ode_2Dlinear, prob_ode_linear
 
 ## Setup Tests
 
 prob = prob_ode_linear
 
-probs = Vector{ODEProblem}(2)
+probs = Vector{ODEProblem}(undef, 2)
 probs[1] = prob
 probs[2] = prob_ode_2Dlinear
 
 tspan = [0,1]
-tspans = Vector{Vector{Int64}}(2)
+tspans = Vector{Vector{Int64}}(undef, 2)
 tspans[1] = tspan; tspans[2] = tspan
-abstols = 1./10.^(3:10)
-reltols = 1./10.^(3:10)
+abstols = 1. /10 .^(3:10)
+reltols = 1 ./10 .^(3:10)
 
 
 setups = [Dict(:alg=>RK4());Dict(:alg=>Euler());Dict(:alg=>BS3());
@@ -72,8 +74,8 @@ end
 tspan = (0.0,10.0)
 prob = ODEProblem(f_2dlin,rand(100,100),tspan)
 
-abstols = 1./10.^(3:7)
-reltols = 1./10.^(0:4)
+abstols = 1 ./10 .^(3:7)
+reltols = 1 ./10 .^(0:4)
 
 setups = [Dict(:alg=>DP5())
           Dict(:alg=>Tsit5())]
@@ -86,8 +88,8 @@ end
 
 prob = ODEProblem(lotka,[1.0;1.0],(0.0,10.0))
 
-abstols = 1./10.^(6:9)
-reltols = 1./10.^(3:6)
+abstols = 1 ./10 .^(6:9)
+reltols = 1 ./10 .^(3:6)
 sol = solve(prob,Vern7(),abstol=1/10^14,reltol=1/10^14)
 test_sol = TestSolution(sol)
 
@@ -100,8 +102,8 @@ wp = WorkPrecisionSet(prob,abstols,reltols,setups;appxsol=test_sol,save_everyste
 # DDE problem
 prob = prob_dde_1delay
 
-abstols = 1./10.^(7:10)
-reltols = 1./10.^(4:7)
+abstols = 1 ./10 .^(7:10)
+reltols = 1 ./10 .^(4:7)
 sol = solve(prob, MethodOfSteps(Vern9(), max_fixedpoint_iters=1000); reltol=1e-8, abstol=1e-8)
 test_sol = TestSolution(sol)
 

test/ode_appxtrue_tests.jl

@@ -1,8 +1,10 @@
-using OrdinaryDiffEq, DiffEqBase, DiffEqProblemLibrary, DiffEqDevTools, Base.Test
+using OrdinaryDiffEq, DiffEqBase, DiffEqDevTools, Test
+using DiffEqProblemLibrary.ODEProblemLibrary: importodeproblems; importodeproblems()
+import DiffEqProblemLibrary.ODEProblemLibrary: prob_ode_linear
 
 f = (u,p,t) -> u
-prob = ODEProblem(f,1/2,(0.0,1.0))
-(::typeof(f))(::Type{Val{:analytic}},u0,p,t) = u0*exp(t)
+fun = ODEFunction(f; analytic=(u0,p,t)->u0*exp(t))
+prob = ODEProblem(fun,1/2,(0.0,1.0))
 
 sol =solve(prob,Euler();dt=1//2^(4),dense_errors=true)
 sol2 =solve(prob,Vern9();dt=1//2^(10),abstol=1e-14,reltol=1e-14)