construct outputs

src/SimulationService.jl

@@ -480,7 +480,8 @@ end
 Calibrate(o::OperationRequest) = Calibrate(o.sys, o.timespan, o.priors, o.data)
 
 function solve(o::Calibrate; callback)
- prob = ODEProblem(o.sys, [], o.timespan, saveat=1)
+ prob = ODEProblem(o.sys, [], o.timespan)
+ names = [states(o.sys);getproperty.(observed(o.sys), :lhs)]
 
  # what the data should be like
  # o.data
@@ -492,10 +493,16 @@ function solve(o::Calibrate; callback)
 
  p_posterior = bayesian_datafit(prob, o.priors, data_with_t)
 
+ probs = [remake(prob, p = Pair.(first.(p_posterior), getindex.(p_posterior.(fit), i))) for i in 1:length(p_posterior[1][2])]
+ enprob = EnsembleProblem(probs)
+ ensol = solve(enprob, saveat = 1)
+ soldata = DataFrame([sol.t;Matrix(sol[names])'])
+ rename!(soldata, names)
+
  df = DataFrame(last.(p_posterior), :auto)
  rename!(df, Symbol.(first.(p_posterior)))
 
- df
+ df, soldata
 end
 
 #-----------------------------------------------------------------------------# ensemble
@@ -521,9 +528,12 @@ function solve(o::Ensemble; callback)
  fit_enprob = EnsembleProblem(forecast_probs)
  sol = solve(fit_enprob; saveat = o.t_forecast);
 
+ soldata = DataFrame([sol.t;Matrix(sol[names])'])
+
  # Requires https://github.com/SciML/SciMLBase.jl/pull/467
  # weighted_ensem = WeightedEnsembleSolution(sol, ensem_weights; quantiles = o.quantiles)
- # DataFrame(weighted_ensem)
+ # df = DataFrame(weighted_ensem)
+ # df, soldata
 end 
 
 end # module