weak errors in wp work

Author: ChrisRackauckas

src/DiffEqDevTools.jl

@@ -9,8 +9,10 @@ import Base: length
 
 const TIMESERIES_ERRORS = Set([:l2,:l∞,:L2,:L∞])
 const DENSE_ERRORS = Set([:L2,:L∞])
-const WEAK_TIMESERIES_ERRORS = Set([:weak_final])
-const WEAK_DENSE_ERRORS = Set([:weak_L2])
+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)
+
 
 parameterless_type(T::Type) = Base.typename(T).wrapper
 parameterless_type(x) = parameterless_type(typeof(x))

src/benchmark.jl

@@ -287,7 +287,11 @@ function WorkPrecisionSet(prob,abstols,reltols,setups,test_dt=nothing;
   tmp_solutions
   _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]
-  errors = [[solutions[j][i].error_means[error_estimate] for i in 1:M] for j in 1:N]
+  if error_estimate ∈ WEAK_ERRORS
+    errors = [[solutions[j][i].weak_errors[error_estimate] for i in 1:M] for j in 1:N]
+  else
+    errors = [[solutions[j][i].error_means[error_estimate] for i in 1:M] for j in 1:N]
+  end
 
   # Now time it
   for k in 1:N

test/analyticless_stochastic_wp.jl

@@ -22,4 +22,4 @@ test_dt = 2/10^5
 appxsol_setup = Dict(:alg=>SRIW1(),:abstol=>1e-7,:reltol=>1e-7)
 wp = WorkPrecisionSet(prob,abstols,reltols,setups,test_dt;
                                      appxsol_setup = appxsol_setup,
-                                     numruns=5,names=names,error_estimate=:l2)
+                                     numruns=5,names=names,error_estimate=:weak_final)