WIP: Calibrate and ensemble endpoints

This is relying on some PRs like SciML/SciMLBase.jl#467

I need to figure out who to talk to in order to know what the inputs look like.

src/SimulationService.jl

@@ -471,16 +471,59 @@ end
 
 #-----------------------------------------------------------------------------# calibrate
 struct Calibrate <: Operation
- # TODO
+ sys::ODESystem
+ timespan::Tuple{Float64, Float64}
+ priors::Any # ???
+ data::Any # ???
+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)
+
+ # what the data should be like
+ # o.data
+ tsave1 = collect(10.0:10.0:100.0)
+ sol_data1 = solve(prob, saveat = tsave1)
+ tsave2 = collect(10.0:13.5:100.0)
+ sol_data2 = solve(prob, saveat = tsave2)
+ data_with_t = [x => (tsave1, sol_data1[x]), z => (tsave2, sol_data2[z])]
+
+ p_posterior = bayesian_datafit(prob, o.priors, data_with_t)
+
+ df = DataFrame(last.(p_posterior), :auto)
+ rename!(df, Symbol.(first.(p_posterior)))
+
+ df
 end
-Calibrate(o::OperationRequest) = error("TODO")
-solve(o::Calibrate; callback) = error("TODO")
 
 #-----------------------------------------------------------------------------# ensemble
 struct Ensemble <: Operation
- # TODO
+ sys::Vector{ODESystem}
+ priors::Vector{Pair{Num,Any}} # Any = Distribution
+ train_datas::Any
+ ensem_datas::Any
+ t_forecast::Vector{Float64}
+ quantiles::Vector{Float64}
 end
+
 Ensemble(o::OperationRequest) = error("TODO")
-solve(o::Ensemble; callback) = error("TODO")
+
+function solve(o::Ensemble; callback)
+ probs = [ODEProblem(s, [], o.timespan) for s in sys]
+ ps = [[β => Uniform(0.01, 10.0), γ => Uniform(0.01, 10.0)] for i in 1:3]
+ datas = [data_train,data_train,data_train]
+ enprobs = bayesian_ensemble(probs, ps, datas)
+ ensem_weights = ensemble_weights(sol, data_ensem)
+
+ forecast_probs = [remake(enprobs.prob[i]; tspan = (t_train[1],t_forecast[end])) for i in 1:length(enprobs.prob)]
+ fit_enprob = EnsembleProblem(forecast_probs)
+ sol = solve(fit_enprob; saveat = o.t_forecast);
+
+ # Requires https://github.com/SciML/SciMLBase.jl/pull/467
+ # weighted_ensem = WeightedEnsembleSolution(sol, ensem_weights; quantiles = o.quantiles)
+ # DataFrame(weighted_ensem)
+end 
 
 end # module