Hi Kristina,
You’re correct that the returned MarginalModel only stores the data from the last run of the Monte Carlo simulation. There is not currently a way to return the information you want from FUND’s default compute_sc function, although we would like to add this ability soon in a new version.
For now, I would recommend writing your own version of the compute_sc function where you can pass in as an argument a modified version of the SimulationDefinition that specifies what variables you would like the simulation to save for you. The function could look like this:
using Mimi, MimiFUND
function _my_compute_sc(m::Model=get_model();
simdef = nothing,
return_si = false,
gas::Symbol = :CO2,
year::Union{Int, Nothing} = nothing,
eta::Float64 = 1.45,
prtp::Float64 = 0.015,
equity_weights::Bool = false,
equity_weights_normalization_region::Int = 0,
last_year::Int = 3000,
pulse_size::Float64 = 1e7,
return_mm::Bool = false,
n::Union{Int, Nothing} = nothing,
trials_output_filename::Union{String, Nothing} = nothing,
seed::Union{Int, Nothing} = nothing
)
year === nothing ? error("Must specify an emission year. Try `compute_sc(year=2020)`.") : nothing
!(last_year in 1950:3000) ? error("Invlaid value for `last_year`: $last_year. `last_year` must be within the model's time index 1950:3000.") : nothing
!(year in 1950:last_year) ? error("Invalid value for `year`: $year. `year` must be within the model's time index 1950:$last_year.") : nothing
mm = MimiFUND.get_marginal_model(m; year = year, gas = gas, pulse_size = pulse_size)
ntimesteps = MimiFUND.getindexfromyear(last_year)
if n === nothing
# Run the "best guess" social cost calculation
run(mm; ntimesteps = ntimesteps)
sc = MimiFUND._compute_sc_from_mm(mm, year = year, gas = gas, ntimesteps = ntimesteps, equity_weights = equity_weights, eta = eta, prtp = prtp, equity_weights_normalization_region=equity_weights_normalization_region)
elseif n < 1
error("Invalid n = $n. Number of trials must be a positive integer.")
else
# Run a Monte Carlo simulation
if simdef === nothing
simdef = MimiFUND.getmcs()
end
payload = (Array{Float64, 1}(undef, n), year, gas, ntimesteps, equity_weights, equity_weights_normalization_region, eta, prtp) # first item is an array to hold SC values calculated in each trial
Mimi.set_payload!(simdef, payload)
seed !== nothing ? Random.seed!(seed) : nothing
si = run(simdef, mm, n, ntimesteps = ntimesteps, post_trial_func = MimiFUND._fund_sc_post_trial, trials_output_filename = trials_output_filename)
sc = Mimi.payload(si)[1]
end
if return_mm
return (sc = sc, mm = mm)
elseif return_si
return(sc = sc, si = si)
else
return sc
end
end
And then your code for using this function could look like:
m = MimiFUND.get_model()
run(m)
simdef = MimiFUND.getmcs()
Mimi.addSave!(simdef, :climatech4cycle, :acch4)
result_ens = _my_compute_sc(m, simdef = simdef, return_si = true, year = 2020, gas = :CH4, eta = 0.0, prtp = 0.03, n = 100, seed = 1234)
si = result_ens.si
# If using Mimi 1.0, do the following to access the results:
getdataframe(si, :climatech4cycle, :acch4)
# In earlier versions of Mimi, use this instead:
si[:climatech4cycle, :acch4]
I wrote this quickly, so please let me know if this doesn’t work out for you and are still stuck on anything!