a lot more depwarn fixes

test/adaptive/sde_linearadaptive_tests.jl

@@ -10,6 +10,7 @@ add_probs[2] = prob_sde_additivesystem
 
 
 for i in 1:2
+  global sol,sol2,err1
   bigprob = SDEProblem(probs[i].f,probs[i].g,big.(probs[i].u0),(big.(probs[i].tspan[1]),big.(probs[i].tspan[2])),noise=probs[i].noise)
   add_bigprob = SDEProblem(add_probs[i].f,add_probs[i].g,big.(add_probs[i].u0),(big.(add_probs[i].tspan[1]),big.(add_probs[i].tspan[2])),noise=add_probs[i].noise)
   ## SRIW1

test/commutative_tests.jl

@@ -11,7 +11,7 @@ A = full(Strang(2))
 B = Diagonal([σ_const for i in 1:2])
 
 function f_commute(du,u,p,t)
-  A_mul_B!(du,A,u)
+  mul!(du,A,u)
   du .+= 1.01u
 end
 function (::typeof(f_commute))(::Type{Val{:analytic}},u0,p,t,W)

test/iif_methods.jl

@@ -50,12 +50,12 @@ B = [σ_const 0
     0 σ_const]
 
 function f(du,u,p,t)
-  A_mul_B!(du,A,u)
+  mul!(du,A,u)
   du .+= 1.01u
 end
 function σ(du,u,p,t)
-  A_mul_B!(@view(du[:,1]),B,u)
-  A_mul_B!(@view(du[:,2]),B,u)
+  mul!(@view(du[:,1]),B,u)
+  mul!(@view(du[:,2]),B,u)
 end
 
 function (::typeof(f))(::Type{Val{:analytic}},u0,p,t,W)

test/mass_matrix_tests.jl

@@ -7,16 +7,16 @@ const mm_b = mm_A*ones(3)
 function mm_f(du,u,p,t)
       mul!(du,mm_A,u)
       tmp = t*mm_b
-      du .+= tmp
+      @. du += tmp
 end
 function mm_analytic(u0,p,t,W)
       @. 2ones(3)*exp(t) - t - 1
 end
 function mm_g(du,u,p,t)
-      du .= u + t
+      @. du = u + t
 end
 function g!(du,u,p,t)
-    du .= 0.0
+    @. du = 0.0
 end
 
 prob2 = SDEProblem(SDEFunction(mm_g,g!,analytic=mm_analytic),g!,ones(3),(0.0,1.0))
@@ -44,7 +44,7 @@ sol2 = solve(prob2, ImplicitEM(symplectic=true), dt = 0.01, adaptive = false)
 @test norm(sol .- sol2) ≈ 0 atol=1e-11
 
 function mm_f2(du,u,p,t)
-      A_mul_B!(du,mm_A,u)
+      mul!(du,mm_A,u)
 end
 function no_mm_f2(du,u,p,t)
       du .= u

test/multivariate_geometric.jl

@@ -7,11 +7,11 @@ B = [1/5 1/100
     1/100 1/5]
 
 function f(du,u,p,t)
-  A_mul_B!(du,A,u)
+  mul!(du,A,u)
 end
 function σ(du,u,p,t)
-  A_mul_B!(@view(du[:,1]),B,u)
-  A_mul_B!(@view(du[:,2]),B,u)
+  mul!(@view(du[:,1]),B,u)
+  mul!(@view(du[:,2]),B,u)
 end
 
 function (::typeof(f))(::Type{Val{:analytic}},u0,p,t,W)
@@ -39,12 +39,12 @@ B = [1/5 1/100
     1/100 1/5]
 
 function f(du,u,p,t)
-  A_mul_B!(du,A,u)
+  mul!(du,A,u)
   du .+= 1.01u
 end
 function σ(du,u,p,t)
-  A_mul_B!(@view(du[:,1]),B,u)
-  A_mul_B!(@view(du[:,2]),B,u)
+  mul!(@view(du[:,1]),B,u)
+  mul!(@view(du[:,2]),B,u)
 end
 
 function (::typeof(f))(::Type{Val{:analytic}},u0,p,t,W)

test/nondiagonal_tests.jl

@@ -14,7 +14,7 @@ function f_nondiag(u,p,t)
 end
 
 function f_nondiag(du,u,p,t)
-  A_mul_B!(du,A,u)
+  mul!(du,A,u)
   du .+= 1.01u
 end
 

test/static_array_tests.jl

@@ -3,14 +3,14 @@ using DiffEqBase, StochasticDiffEq
 
 f(du,u,p,t) = (du .= u)
 
-u0 = zeros(MVector{2,Float64}, 2) + 1
-u0[1] = ones(MVector{2,Float64}) + 1
+u0 = zeros(MVector{2,Float64}, 2) .+ 1
+u0[1] = ones(MVector{2,Float64}) .+ 1
 ode = SDEProblem(f,f, u0, (0.,1.))
 sol = solve(ode, EM(), dt=1.e-2)
 sol = solve(ode, SRIW1())
 
-u0 = zeros(SVector{2,Float64}, 2) + 1
-u0[1] = ones(SVector{2,Float64}) + 1
+u0 = zeros(SVector{2,Float64}, 2) .+ 1
+u0[1] = ones(SVector{2,Float64}) .+ 1
 ode = SDEProblem(f, f, u0, (0.,1.))
 sol = solve(ode, EM(), dt=1.e-2)
 sol = solve(ode, SRIW1(), dt=1.e-2)

test/tolerances_tests.jl

@@ -18,7 +18,7 @@ probs = [prob_sde_2Dlinear,
          SDEProblem(f,σ,prob_sde_2Dlinear.u0,prob_sde_2Dlinear.tspan)]
 =#
 
-let probs = [prob_sde_2Dlinear], algs = [SRI(),SRIW1(),SRA1(),SRA(),RKMil()]
+function tolerance_testing(probs,algs)
   for alg in algs, prob in probs
     dt = typeof(alg)<:StochasticDiffEqAdaptiveAlgorithm ? 0.0 : 0.1
     srand(100)
@@ -37,3 +37,7 @@ let probs = [prob_sde_2Dlinear], algs = [SRI(),SRIW1(),SRA1(),SRA(),RKMil()]
     @test sol.t == sol2.t && sol.u == sol2.u
   end
 end
+
+probs = [prob_sde_2Dlinear]
+algs = [SRI(),SRIW1(),SRA1(),SRA(),RKMil()]
+tolerance_testing(probs,algs)