not depwarns from problem library

Author: ChrisRackauckas

src/DiffEqProblemLibrary.jl

@@ -3,28 +3,43 @@ __precompile__(true)
 module DiffEqProblemLibrary
 
 module ODEProblemLibrary
-importodeproblems() =
-@isdefined(prob_ode_linear) || include(joinpath(@__DIR__, "ode/ode_premade_problems.jl"))
+function importodeproblems()
+  @isdefined(prob_ode_linear) ||
+  include(joinpath(@__DIR__, "ode/ode_premade_problems.jl"));
+  nothing
+end
 end # module
 
 module DAEProblemLibrary
-importdaeproblems() =
-@isdefined(prob_dae_resrob) || include(joinpath(@__DIR__, "dae_premade_problems.jl"))
+function importdaeproblems()
+  @isdefined(prob_dae_resrob) ||
+  include(joinpath(@__DIR__, "dae_premade_problems.jl"));
+  nothing
+end
 end # module
 
 module DDEProblemLibrary
-importddeproblems() =
-@isdefined(prob_dde_1delay) || include(joinpath(@__DIR__, "dde_premade_problems.jl"))
+function importddeproblems()
+  @isdefined(prob_dde_1delay) ||
+  include(joinpath(@__DIR__, "dde_premade_problems.jl"));
+  nothing
+end
 end # module
 
 module SDEProblemLibrary
-importsdeproblems() =
-@isdefined(prob_sde_wave) || include(joinpath(@__DIR__, "sde_premade_problems.jl"))
+function importsdeproblems()
+  @isdefined(prob_sde_wave) ||
+  include(joinpath(@__DIR__, "sde_premade_problems.jl"));
+  nothing
+end
 end # module
 
 module JumpProblemLibrary
-importjumpproblems() =
-@isdefined(prob_jump_dnarepressor) || include(joinpath(@__DIR__, "jump_premade_problems.jl"))
+function importjumpproblems()
+  @isdefined(prob_jump_dnarepressor) ||
+  include(joinpath(@__DIR__, "jump_premade_problems.jl"));
+  nothing
+end
 end # module
 
 end # module

src/ode/filament_prob.jl

@@ -217,8 +217,9 @@ end
 
 function filament_prob(::SolverDiffEq; N=20, Cm=32, ω=200, time_end=1.)
     f = FilamentCache(N, Solver=SolverDiffEq, Cm=Cm, ω=ω)
+    jac = (J, r, p, t) -> f(Val{:jac}, J, r, p, t)
     r0 = initialize!(:StraightX, f)
     stiffness_matrix!(f)
-    prob = ODEProblem(f, r0, (0., time_end))
+    prob = ODEProblem(ODEFunction(f,jac=jac), r0, (0., time_end))
 end
 prob_ode_filament = filament_prob(SolverDiffEq())

src/ode/ode_linear_prob.jl

@@ -1,6 +1,6 @@
 # Linear ODE
-linear = (u,p,t) -> (1.01*u)
-(f::typeof(linear))(::Type{Val{:analytic}},u0,p,t) = u0*exp(1.01*t)
+linear = (u,p,t) -> (p*u)
+linear_analytic = (u0,p,t) -> u0*exp(p*t)
 """
 Linear ODE
 
@@ -14,13 +14,12 @@ with initial condition ``u0=1/2``, ``α=1.01``, and solution
 u(t) = u0e^{αt}
 ```
 
-with Float64s
+with Float64s. The parameter is ``α``
 """
-prob_ode_linear = ODEProblem(linear,1/2,(0.0,1.0))
+prob_ode_linear = ODEProblem(
+                  ODEFunction(linear,analytic=linear_analytic),
+                  1/2,(0.0,1.0),1.01)
 
-const linear_bigα = parse(BigFloat,"1.01")
-f_linearbig = (u,p,t) -> (linear_bigα*u)
-(f::typeof(f_linearbig))(::Type{Val{:analytic}},u0,p,t) = u0*exp(linear_bigα*t)
 """
 Linear ODE
 
@@ -36,14 +35,12 @@ u(t) = u0e^{αt}
 
 with BigFloats
 """
-prob_ode_bigfloatlinear = ODEProblem(f_linearbig,parse(BigFloat,"0.5"),(0.0,1.0))
-
-f_2dlinear = (du,u,p,t) -> begin
-  for i in 1:length(u)
-    du[i] = 1.01*u[i]
-  end
-end
-(f::typeof(f_2dlinear))(::Type{Val{:analytic}},u0,p,t) = u0*exp.(linear_bigα*t)
+prob_ode_bigfloatlinear = ODEProblem(
+                          ODEFunction(linear,analytic=linear_analytic),
+                          big(0.5),(0.0,1.0),big(1.01))
+
+f_2dlinear = (du,u,p,t) -> (@. du = p*u)
+f_2dlinear_analytic = (u0,p,t) -> @. u0*exp(p*t)
 """
 4x2 version of the Linear ODE
 
@@ -59,7 +56,9 @@ u(t) = u0e^{αt}
 
 with Float64s
 """
-prob_ode_2Dlinear = ODEProblem(f_2dlinear,rand(4,2),(0.0,1.0))
+prob_ode_2Dlinear = ODEProblem(
+                    ODEFunction(f_2dlinear,analytic=f_2dlinear_analytic),
+                    rand(4,2),(0.0,1.0),1.01)
 
 """
 100x100 version of the Linear ODE
@@ -76,14 +75,10 @@ u(t) = u0e^{αt}
 
 with Float64s
 """
-prob_ode_large2Dlinear = ODEProblem(f_2dlinear,rand(100,100),(0.0,1.0))
-
-f_2dlinearbig = (du,u,p,t) -> begin
-  for i in 1:length(u)
-    du[i] = linear_bigα*u[i]
-  end
-end
-(f::typeof(f_2dlinearbig))(::Type{Val{:analytic}},u0,p,t) = u0*exp.(linear_bigα*t)
+prob_ode_large2Dlinear = ODEProblem(
+                    ODEFunction(f_2dlinear,analytic=f_2dlinear_analytic),
+                    rand(100,100),(0.0,1.0),1.01)
+
 """
 4x2 version of the Linear ODE
 
@@ -99,9 +94,11 @@ u(t) = u0e^{αt}
 
 with BigFloats
 """
-prob_ode_bigfloat2Dlinear = ODEProblem(f_2dlinearbig,map(BigFloat,rand(4,2)).*ones(4,2)/2,(0.0,1.0))
-f_2dlinear_notinplace = (u,p,t) -> 1.01*u
-(f::typeof(f_2dlinear_notinplace))(::Type{Val{:analytic}},u0,p,t) = u0*exp.(1.01*t)
+prob_ode_bigfloat2Dlinear = ODEProblem(
+                    ODEFunction(f_2dlinear,analytic=f_2dlinear_analytic),
+                    BigFloat.(rand(4,2)).*ones(4,2)/2,(0.0,1.0),big(1.01))
+
+f_2dlinear_notinplace = (u,p,t) -> p*u
 """
 4x2 version of the Linear ODE
 
@@ -117,4 +114,6 @@ u(t) = u0e^{αt}
 
 on Float64. Purposefully not in-place as a test.
 """
-prob_ode_2Dlinear_notinplace = ODEProblem(f_2dlinear_notinplace,rand(4,2),(0.0,1.0))
+prob_ode_2Dlinear_notinplace = ODEProblem(
+                    ODEFunction(f_2dlinear_notinplace,analytic=f_2dlinear_analytic),
+                    rand(4,2),(0.0,1.0),1.01)

src/ode/ode_simple_nonlinear_prob.jl

@@ -247,9 +247,9 @@ const mm_A = rand(4,4)
 mm_linear = function (du,u,p,t)
   mul!(du,mm_A,u)
 end
-const MM_linear =full(Diagonal(0.5ones(4)))
-(::typeof(mm_linear))(::Type{Val{:analytic}},u0,p,t) = exp(inv(MM_linear)*mm_A*t)*u0
-prob_ode_mm_linear = ODEProblem(mm_linear,rand(4),(0.0,1.0),mass_matrix=MM_linear)
+const MM_linear =Matrix(Diagonal(0.5ones(4)))
+mm_f = ODEFunction(mm_linear,analytic = (u0,p,t) -> exp(inv(MM_linear)*mm_A*t)*u0)
+prob_ode_mm_linear = ODEProblem(mm_f,rand(4),(0.0,1.0),mass_matrix=MM_linear)
 
 """
 [Hires Problem](http://nbviewer.jupyter.org/github/JuliaDiffEq/DiffEqBenchmarks.jl/blob/master/StiffODE/Hires.ipynb) (Stiff)

src/ode/pollution_prob.jl

@@ -74,7 +74,7 @@ function pollution(dy,y,p,t)
  dy[20] = -r25+r24
 end
 
-function pollution(::Type{Val{:jac}},J,y,p,t)
+function pollution_jac(J,y,p,t)
   J .= 0.0
   J[1,1]   = -k1-k10*y[11]-k14*y[6]-k23*y[4]-k24*y[19]
   J[1,11]  = -k10*y[1]+k9*y[2]
@@ -232,4 +232,5 @@ y0 = (0, 0.2, 0, 0.04, 0, 0, 0.1, 0.3, 0.01, 0, 0, 0, 0 ,0, 0, 0, 0.007, 0, 0, 0
 
 https://archimede.dm.uniba.it/~testset/report/pollu.pdf
 """
-prob_ode_pollution = ODEProblem(pollution,u0,(0.0,60.0))
+prob_ode_pollution = ODEProblem(ODEFunction(pollution,jac=pollution_jac),
+                                u0,(0.0,60.0))